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layer</title> <meta name="description" content="Search results for: deep layer"> <meta name="keywords" content="deep layer"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="deep layer"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 4449</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: deep layer</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4449</span> Forecasting the Temperature at a Weather Station Using Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Debneil%20Saha%20Roy">Debneil Saha Roy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast horizon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short%20term%20memory" title=" long short term memory"> long short term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20perceptron" title=" multi-layer perceptron"> multi-layer perceptron</a> </p> <a href="https://publications.waset.org/abstracts/124787/forecasting-the-temperature-at-a-weather-station-using-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124787.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4448</span> A Deep Learning Model with Greedy Layer-Wise Pretraining Approach for Optimal Syngas Production by Dry Reforming of Methane</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Zarabian">Maryam Zarabian</a>, <a href="https://publications.waset.org/abstracts/search?q=Hector%20Guzman"> Hector Guzman</a>, <a href="https://publications.waset.org/abstracts/search?q=Pedro%20Pereira-Almao"> Pedro Pereira-Almao</a>, <a href="https://publications.waset.org/abstracts/search?q=Abraham%20Fapojuwo"> Abraham Fapojuwo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=dry%20reforming%20of%20methane" title=" dry reforming of methane"> dry reforming of methane</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=greedy%20layer-wise%20pretraining" title=" greedy layer-wise pretraining"> greedy layer-wise pretraining</a> </p> <a href="https://publications.waset.org/abstracts/163075/a-deep-learning-model-with-greedy-layer-wise-pretraining-approach-for-optimal-syngas-production-by-dry-reforming-of-methane" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163075.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">86</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4447</span> Facial Emotion Recognition Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashutosh%20Mishra">Ashutosh Mishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikhil%20Goyal"> Nikhil Goyal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A 3D facial emotion recognition model based on deep learning is proposed in this paper. Two convolution layers and a pooling layer are employed in the deep learning architecture. After the convolution process, the pooling is finished. The probabilities for various classes of human faces are calculated using the sigmoid activation function. To verify the efficiency of deep learning-based systems, a set of faces. The Kaggle dataset is used to verify the accuracy of a deep learning-based face recognition model. The model's accuracy is about 65 percent, which is lower than that of other facial expression recognition techniques. Despite significant gains in representation precision due to the nonlinearity of profound image representations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=facial%20recognition" title="facial recognition">facial recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20intelligence" title=" computational intelligence"> computational intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=depth%20map" title=" depth map"> depth map</a> </p> <a href="https://publications.waset.org/abstracts/139253/facial-emotion-recognition-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139253.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">231</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4446</span> Restricted Boltzmann Machines and Deep Belief Nets for Market Basket Analysis: Statistical Performance and Managerial Implications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Hruschka">H. Hruschka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the first comparison of the performance of the restricted Boltzmann machine and the deep belief net on binary market basket data relative to binary factor analysis and the two best-known topic models, namely Dirichlet allocation and the correlated topic model. This comparison shows that the restricted Boltzmann machine and the deep belief net are superior to both binary factor analysis and topic models. Managerial implications that differ between the investigated models are treated as well. The restricted Boltzmann machine is defined as joint Boltzmann distribution of hidden variables and observed variables (purchases). It comprises one layer of observed variables and one layer of hidden variables. Note that variables of the same layer are not connected. The comparison also includes deep belief nets with three layers. The first layer is a restricted Boltzmann machine based on category purchases. Hidden variables of the first layer are used as input variables by the second-layer restricted Boltzmann machine which then generates second-layer hidden variables. Finally, in the third layer hidden variables are related to purchases. A public data set is analyzed which contains one month of real-world point-of-sale transactions in a typical local grocery outlet. It consists of 9,835 market baskets referring to 169 product categories. This data set is randomly split into two halves. One half is used for estimation, the other serves as holdout data. Each model is evaluated by the log likelihood for the holdout data. Performance of the topic models is disappointing as the holdout log likelihood of the correlated topic model – which is better than Dirichlet allocation - is lower by more than 25,000 compared to the best binary factor analysis model. On the other hand, binary factor analysis on its own is clearly surpassed by both the restricted Boltzmann machine and the deep belief net whose holdout log likelihoods are higher by more than 23,000. Overall, the deep belief net performs best. We also interpret hidden variables discovered by binary factor analysis, the restricted Boltzmann machine and the deep belief net. Hidden variables characterized by the product categories to which they are related differ strongly between these three models. To derive managerial implications we assess the effect of promoting each category on total basket size, i.e., the number of purchased product categories, due to each category's interdependence with all the other categories. The investigated models lead to very different implications as they disagree about which categories are associated with higher basket size increases due to a promotion. Of course, recommendations based on better performing models should be preferred. The impressive performance advantages of the restricted Boltzmann machine and the deep belief net suggest continuing research by appropriate extensions. To include predictors, especially marketing variables such as price, seems to be an obvious next step. It might also be feasible to take a more detailed perspective by considering purchases of brands instead of purchases of product categories. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20factor%20analysis" title="binary factor analysis">binary factor analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20belief%20net" title=" deep belief net"> deep belief net</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20basket%20analysis" title=" market basket analysis"> market basket analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=restricted%20Boltzmann%20machine" title=" restricted Boltzmann machine"> restricted Boltzmann machine</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20models" title=" topic models"> topic models</a> </p> <a href="https://publications.waset.org/abstracts/84200/restricted-boltzmann-machines-and-deep-belief-nets-for-market-basket-analysis-statistical-performance-and-managerial-implications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84200.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">199</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4445</span> Effect of Deep Mixing Columns and Geogrid on Embankment Settlement on the Soft Soil</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Abolhasan%20Naeini">Seyed Abolhasan Naeini</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeideh%20Mohammadi"> Saeideh Mohammadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Embankment settlement on soft clays has always been problematic due to the high compaction and low shear strength of the soil. Deep soil mixing and geosynthetics are two soil improvement methods in such fields. Here, a numerical study is conducted on the embankment performance on the soft ground improved by deep soil mixing columns and geosynthetics based on the data of a real project. For this purpose, the finite element method is used in the Plaxis 2D software. The Soft Soil Creep model considers the creep phenomenon in the soft clay layer while the Mohr-Columb model simulates other soil layers. Results are verified using the data of an experimental embankment built on deep mixing columns. The effect of depth and diameter of deep mixing columns and the stiffness of geogrid on the vertical and horizontal movements of embankment on clay subsoil will be investigated in the following. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PLAXIS%202D" title="PLAXIS 2D">PLAXIS 2D</a>, <a href="https://publications.waset.org/abstracts/search?q=embankment%20settlement" title=" embankment settlement"> embankment settlement</a>, <a href="https://publications.waset.org/abstracts/search?q=horizontal%20movement" title=" horizontal movement"> horizontal movement</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20soil%20mixing%20column" title=" deep soil mixing column"> deep soil mixing column</a>, <a href="https://publications.waset.org/abstracts/search?q=geogrid" title=" geogrid"> geogrid</a> </p> <a href="https://publications.waset.org/abstracts/129452/effect-of-deep-mixing-columns-and-geogrid-on-embankment-settlement-on-the-soft-soil" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129452.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">173</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4444</span> Lightweight Hybrid Convolutional and Recurrent Neural Networks for Wearable Sensor Based Human Activity Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sonia%20Perez-Gamboa">Sonia Perez-Gamboa</a>, <a href="https://publications.waset.org/abstracts/search?q=Qingquan%20Sun"> Qingquan Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Zhang"> Yan Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Non-intrusive sensor-based human activity recognition (HAR) is utilized in a spectrum of applications, including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short term memory (LSTM) recurrent neural networks (RNNs) provide a way to achieve HAR accurately and effectively. In this paper, we design a multi-layer hybrid architecture with CNN and LSTM and explore a variety of multi-layer combinations. Based on the exploration, we present a lightweight, hybrid, and multi-layer model, which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model, which can achieve a 94.7% activity recognition rate on a benchmark human activity dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between recognition rate and training time consumption. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20activity%20recognition" title=" human activity recognition"> human activity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=inertial%20sensor" title=" inertial sensor"> inertial sensor</a> </p> <a href="https://publications.waset.org/abstracts/131782/lightweight-hybrid-convolutional-and-recurrent-neural-networks-for-wearable-sensor-based-human-activity-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131782.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4443</span> Cellular Traffic Prediction through Multi-Layer Hybrid Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Supriya%20H.%20S.">Supriya H. S.</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandrakala%20B.%20M."> Chandrakala B. M.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep learning based models have been recently successful adoption for network traffic prediction. However, training a deep learning model for various prediction tasks is considered one of the critical tasks due to various reasons. This research work develops Multi-Layer Hybrid Network (MLHN) for network traffic prediction and analysis; MLHN comprises the three distinctive networks for handling the different inputs for custom feature extraction. Furthermore, an optimized and efficient parameter-tuning algorithm is introduced to enhance parameter learning. MLHN is evaluated considering the “Big Data Challenge” dataset considering the Mean Absolute Error, Root Mean Square Error and R^2as metrics; furthermore, MLHN efficiency is proved through comparison with a state-of-art approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MLHN" title="MLHN">MLHN</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20traffic%20prediction" title=" network traffic prediction"> network traffic prediction</a> </p> <a href="https://publications.waset.org/abstracts/154887/cellular-traffic-prediction-through-multi-layer-hybrid-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154887.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">89</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4442</span> Phytoplankton Community Structure in the Moroccan Coast of the Mediterranean Sea: Case Study of Saiidia, Three Forks Cape</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Idmoussi">H. Idmoussi</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Somoue"> L. Somoue</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20Ettahiri"> O. Ettahiri</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Makaoui"> A. Makaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Charib"> S. Charib</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Agouzouk"> A. Agouzouk</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ben%20Mhamed"> A. Ben Mhamed</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Hilmi"> K. Hilmi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Errhif"> A. Errhif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study on the composition, abundance, and distribution of phytoplankton was conducted along the Moroccan coast of the Mediterranean Sea (Saiidia - Three Forks Cape) in April 2018. Samples were collected at thirteen stations using Niskin bottles within two layers (surface and deep layers). The identification and enumeration of phytoplankton were carried out according to the Utermöhl method (1958). A total number of 54 phytoplankton species were identified over the entire survey area. Thirty-six species could be found both in the surface and the deep layers while eleven species were observed only in the surface layer and seven in the deep layer. The phytoplankton throughout the study area was dominated by diatoms represented mainly by Nitzschia sp., Pseudonitzschia sp., Chaetoceros sp., Cylindrotheca closterium, Leptocylindrus minimus, Leptocylindrus danicus, Dactyliosolen fragilissimus. Dinoflagellates were dominated by Gymnodinium sp., Scrippsiella sp., Gyrodinium spirale, Noctulica sp, Prorocentrum micans. Euglenophyceae, Silicoflagellates and Raphidophyceae were present in low numbers. Most of the phytoplankton were concentrated in the surface layer, particularly towards the Three Forks Cape (25200 cells·l⁻¹). Shannon species diversity (ranging from 2 and 4 Bits) and evenness index (broadly > 0.7) suggested that phytoplankton community is generally diversified and structured in the studied area. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=abundance" title="abundance">abundance</a>, <a href="https://publications.waset.org/abstracts/search?q=diversity" title=" diversity"> diversity</a>, <a href="https://publications.waset.org/abstracts/search?q=Mediterranean%20Sea" title=" Mediterranean Sea"> Mediterranean Sea</a>, <a href="https://publications.waset.org/abstracts/search?q=phytoplankton" title=" phytoplankton"> phytoplankton</a> </p> <a href="https://publications.waset.org/abstracts/100510/phytoplankton-community-structure-in-the-moroccan-coast-of-the-mediterranean-sea-case-study-of-saiidia-three-forks-cape" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100510.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">158</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4441</span> Multi-Level Attentional Network for Aspect-Based Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xinyuan%20Liu">Xinyuan Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaojun%20Jing"> Xiaojun Jing</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuan%20He"> Yuan He</a>, <a href="https://publications.waset.org/abstracts/search?q=Junsheng%20Mu"> Junsheng Mu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aspect-based Sentiment Analysis (ABSA) has attracted much attention due to its capacity to determine the sentiment polarity of the certain aspect in a sentence. In previous works, great significance of the interaction between aspect and sentence has been exhibited in ABSA. In consequence, a Multi-Level Attentional Networks (MLAN) is proposed. MLAN consists of four parts: Embedding Layer, Encoding Layer, Multi-Level Attentional (MLA) Layers and Final Prediction Layer. Among these parts, MLA Layers including Aspect Level Attentional (ALA) Layer and Interactive Attentional (ILA) Layer is the innovation of MLAN, whose function is to focus on the important information and obtain multiple levels’ attentional weighted representation of aspect and sentence. In the experiments, MLAN is compared with classical TD-LSTM, MemNet, RAM, ATAE-LSTM, IAN, AOA, LCR-Rot and AEN-GloVe on SemEval 2014 Dataset. The experimental results show that MLAN outperforms those state-of-the-art models greatly. And in case study, the works of ALA Layer and ILA Layer have been proven to be effective and interpretable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=aspect-based%20sentiment%20analysis" title=" aspect-based sentiment analysis"> aspect-based sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=attention" title=" attention"> attention</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a href="https://publications.waset.org/abstracts/124988/multi-level-attentional-network-for-aspect-based-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124988.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">138</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4440</span> Deep Groundwater Potential and Chemical Analysis Based on Well Logging Analysis at Kapuk-Cengkareng, West Jakarta, DKI Jakarta, Indonesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Josua%20Sihotang">Josua Sihotang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Jakarta Capital Special Region is the province that densely populated with rapidly growing infrastructure but less attention for the environmental condition. This makes some social problem happened like lack of clean water supply. Shallow groundwater and river water condition that has contaminated make the layer of deep water carrier (aquifer) should be done. This research aims to provide the people insight about deep groundwater potential and to determine the depth, location, and quality where the aquifer can be found in Jakarta’s area, particularly Kapuk-Cengkareng’s people. This research was conducted by geophysical method namely Well Logging Analysis. Well Logging is the geophysical method to know the subsurface lithology with the physical characteristic. The observation in this research area was conducted with several well devices that is Spontaneous Potential Log (SP Log), Resistivity Log, and Gamma Ray Log (GR Log). The first devices well is SP log which is work by comprising the electrical potential difference between the electrodes on the surface with the electrodes that is contained in the borehole and rock formations. The second is Resistivity Log, used to determine both the hydrocarbon and water zone based on their porosity and permeability properties. The last is GR Log, work by identifying radioactivity levels of rocks which is containing elements of thorium, uranium, or potassium. The observation result is curve-shaped which describes the type of lithological coating in subsurface. The result from the research can be interpreted that there are four of the deep groundwater layer zone with different quality. The good groundwater layer can be found in layers with good porosity and permeability. By analyzing the curves, it can be known that most of the layers which were found in this wellbore are clay stone with low resistivity and high gamma radiation. The resistivity value of the clay stone layers is about 2-4 ohm-meter with 65-80 Cps gamma radiation. There are several layers with high resistivity value and low gamma radiation (sand stone) that can be potential for being an aquifer. This is reinforced by the sand layer with a right-leaning SP log curve proving that this layer is permeable. These layers have 4-9 ohm-meter resistivity value with 40-65 Cps gamma radiation. These are mostly found as fresh water aquifer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aquifer" title="aquifer">aquifer</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20groundwater%20potential" title=" deep groundwater potential"> deep groundwater potential</a>, <a href="https://publications.waset.org/abstracts/search?q=well%20devices" title=" well devices"> well devices</a>, <a href="https://publications.waset.org/abstracts/search?q=well%20logging%20analysis" title=" well logging analysis"> well logging analysis</a> </p> <a href="https://publications.waset.org/abstracts/77330/deep-groundwater-potential-and-chemical-analysis-based-on-well-logging-analysis-at-kapuk-cengkareng-west-jakarta-dki-jakarta-indonesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77330.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">252</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4439</span> Hydrogeological Study of Shallow and Deep Aquifers in Balaju-Boratar Area, Kathmandu, Central Nepal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hitendra%20Raj%20Joshi">Hitendra Raj Joshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Bipin%20Lamichhane"> Bipin Lamichhane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Groundwater is the main source of water for the industries of Balaju Industrial District (BID) and the denizens of Balaju-Boratar area. The quantity of groundwater is in a fatal condition in the area than earlier days. Water levels in shallow wells have highly lowered and deep wells are not providing an adequate amount of water as before because of higher extraction rate than the recharge rate. The main recharge zone of the shallow aquifer lies at the foot of Nagarjuna mountain, where recent colluvial debris are accumulated. Urbanization in the area is the main reason for decreasing water table. Recharge source for the deep aquifer in the region is aquiclude leakage. Sand layer above the Kalimati clay is the shallow aquifer zone, which is limited only in Balaju and eastern part of the Boratar, while the layer below the Kalimati clay spreading around Gongabu, Machhapohari, and Balaju area is considered as a potential area of deep aquifer. Over extraction of groundwater without considering water balance in the aquifers may dry out the source and can initiate the land subsidence problem. Hence, all the responsible of the industries in BID area and the denizens of Balaju-Boratar area should be encouraged to practice artificial groundwater recharge. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aquiclude%20leakage" title="aquiclude leakage">aquiclude leakage</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalimati%20clay" title=" Kalimati clay"> Kalimati clay</a>, <a href="https://publications.waset.org/abstracts/search?q=groundwater%20recharge" title=" groundwater recharge"> groundwater recharge</a> </p> <a href="https://publications.waset.org/abstracts/2321/hydrogeological-study-of-shallow-and-deep-aquifers-in-balaju-boratar-area-kathmandu-central-nepal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2321.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">506</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4438</span> Integrating Knowledge Distillation of Multiple Strategies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Min%20Jindong">Min Jindong</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Mingxia"> Wang Mingxia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the widespread use of artificial intelligence in life, computer vision, especially deep convolutional neural network models, has developed rapidly. With the increase of the complexity of the real visual target detection task and the improvement of the recognition accuracy, the target detection network model is also very large. The huge deep neural network model is not conducive to deployment on edge devices with limited resources, and the timeliness of network model inference is poor. In this paper, knowledge distillation is used to compress the huge and complex deep neural network model, and the knowledge contained in the complex network model is comprehensively transferred to another lightweight network model. Different from traditional knowledge distillation methods, we propose a novel knowledge distillation that incorporates multi-faceted features, called M-KD. In this paper, when training and optimizing the deep neural network model for target detection, the knowledge of the soft target output of the teacher network in knowledge distillation, the relationship between the layers of the teacher network and the feature attention map of the hidden layer of the teacher network are transferred to the student network as all knowledge. in the model. At the same time, we also introduce an intermediate transition layer, that is, an intermediate guidance layer, between the teacher network and the student network to make up for the huge difference between the teacher network and the student network. Finally, this paper adds an exploration module to the traditional knowledge distillation teacher-student network model. The student network model not only inherits the knowledge of the teacher network but also explores some new knowledge and characteristics. Comprehensive experiments in this paper using different distillation parameter configurations across multiple datasets and convolutional neural network models demonstrate that our proposed new network model achieves substantial improvements in speed and accuracy performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title="object detection">object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20distillation" title=" knowledge distillation"> knowledge distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20network" title=" convolutional network"> convolutional network</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20compression" title=" model compression"> model compression</a> </p> <a href="https://publications.waset.org/abstracts/148652/integrating-knowledge-distillation-of-multiple-strategies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148652.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">278</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4437</span> Convergence Analysis of Training Two-Hidden-Layer Partially Over-Parameterized ReLU Networks via Gradient Descent</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhifeng%20Kong">Zhifeng Kong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over-parameterized neural networks have attracted a great deal of attention in recent deep learning theory research, as they challenge the classic perspective of over-fitting when the model has excessive parameters and have gained empirical success in various settings. While a number of theoretical works have been presented to demystify properties of such models, the convergence properties of such models are still far from being thoroughly understood. In this work, we study the convergence properties of training two-hidden-layer partially over-parameterized fully connected networks with the Rectified Linear Unit activation via gradient descent. To our knowledge, this is the first theoretical work to understand convergence properties of deep over-parameterized networks without the equally-wide-hidden-layer assumption and other unrealistic assumptions. We provide a probabilistic lower bound of the widths of hidden layers and proved linear convergence rate of gradient descent. We also conducted experiments on synthetic and real-world datasets to validate our theory. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=over-parameterization" title="over-parameterization">over-parameterization</a>, <a href="https://publications.waset.org/abstracts/search?q=rectified%20linear%20units%20ReLU" title=" rectified linear units ReLU"> rectified linear units ReLU</a>, <a href="https://publications.waset.org/abstracts/search?q=convergence" title=" convergence"> convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20descent" title=" gradient descent"> gradient descent</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/118561/convergence-analysis-of-training-two-hidden-layer-partially-over-parameterized-relu-networks-via-gradient-descent" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118561.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4436</span> Deep-Learning Based Approach to Facial Emotion Recognition through Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nouha%20Khediri">Nouha Khediri</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Ben%20Ammar"> Mohammed Ben Ammar</a>, <a href="https://publications.waset.org/abstracts/search?q=Monji%20Kherallah"> Monji Kherallah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. Accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER, benefiting from deep learning, especially CNN and VGG16. First, the data is pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CNN" title="CNN">CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=deep-learning" title=" deep-learning"> deep-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20emotion%20recognition" title=" facial emotion recognition"> facial emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/150291/deep-learning-based-approach-to-facial-emotion-recognition-through-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150291.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">95</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4435</span> Numerical Evaluation of Deep Ground Settlement Induced by Groundwater Changes During Pumping and Recovery Test in Shanghai</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shuo%20Wang">Shuo Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The hydrogeological parameters of the engineering site and the hydraulic connection between the aquifers can be obtained by the pumping test. Through the recovery test, the characteristics of water level recovery and the law of surface subsidence recovery can be understood. The above two tests can provide the basis for subsequent engineering design. At present, the deformation of deep soil caused by pumping tests is often neglected. However, some studies have shown that the maximum settlement subject to groundwater drawdown is not necessarily on the surface but in the deep soil. In addition, the law of settlement recovery of each soil layer subject to water level recovery is not clear. If the deformation-sensitive structure is deep in the test site, safety accidents may occur. In this study, the pumping test and recovery test of a confined aquifer in Shanghai are introduced. The law of measured groundwater changes and surface subsidence are analyzed. In addition, the fluid-solid coupling model was established by ABAQUS based on the Biot consolidation theory. The models are verified by comparing the computed and measured results. Further, the variation law of water level and the deformation law of deep soil during pumping and recovery tests under different site conditions and different times and spaces are discussed through the above model. It is found that the maximum soil settlement caused by pumping in a confined aquifer is related to the permeability of the overlying aquitard and pumping time. There is a lag between soil deformation and groundwater changes, and the recovery rate of settlement deformation of each soil layer caused by the rise of water level is different. Finally, some possible research directions are proposed to provide new ideas for academic research in this field. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coupled%20hydro-mechanical%20analysis" title="coupled hydro-mechanical analysis">coupled hydro-mechanical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20ground%20settlement" title=" deep ground settlement"> deep ground settlement</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20simulation" title=" numerical simulation"> numerical simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=pumping%20test" title=" pumping test"> pumping test</a>, <a href="https://publications.waset.org/abstracts/search?q=recovery%20test" title=" recovery test"> recovery test</a> </p> <a href="https://publications.waset.org/abstracts/185178/numerical-evaluation-of-deep-ground-settlement-induced-by-groundwater-changes-during-pumping-and-recovery-test-in-shanghai" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185178.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">44</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4434</span> Electrochemical Layer by Layer Assembly</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mao%20Li">Mao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuguang%20Ma"> Yuguang Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Katsuhiko%20Ariga"> Katsuhiko Ariga</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The performance of functional materials is governed by their ability to interact with surrounding environments in a well-defined and controlled manner. Layer-by-Layer (LbL) assembly is one of the most widely used technologies for coating both planar and particulate substrates in a diverse range of fields, including optics, energy, catalysis, separations, and biomedicine. Herein, we introduce electrochemical-coupling layer-by-layer assembly as a novel fabrication methodology for preparing layered thin films. This assembly method not only determines the process properties (such as the time, scalability, and manual intervention) but also directly control the physicochemical properties of the films (such as the thickness, homogeneity, and inter- and intra-layer film organization), with both sets of properties linked to application-specific performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=layer%20by%20layer%20assembly" title="layer by layer assembly">layer by layer assembly</a>, <a href="https://publications.waset.org/abstracts/search?q=electropolymerization" title=" electropolymerization"> electropolymerization</a>, <a href="https://publications.waset.org/abstracts/search?q=carbazole" title=" carbazole"> carbazole</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20thin%20film" title=" optical thin film"> optical thin film</a>, <a href="https://publications.waset.org/abstracts/search?q=electronics" title=" electronics"> electronics</a> </p> <a href="https://publications.waset.org/abstracts/42525/electrochemical-layer-by-layer-assembly" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42525.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4433</span> A Fully Interpretable Deep Reinforcement Learning-Based Motion Control for Legged Robots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Haodong%20Huang">Haodong Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zida%20Zhao"> Zida Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Shilong%20Sun"> Shilong Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Chiyao%20Li"> Chiyao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Wenfu%20Xu"> Wenfu Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The control methods for legged robots based on deep reinforcement learning have seen widespread application; however, the inherent black-box nature of neural networks presents challenges in understanding the decision-making motives of the robots. To address this issue, we propose a fully interpretable deep reinforcement learning training method to elucidate the underlying principles of legged robot motion. We incorporate the dynamics of legged robots into the policy, where observations serve as inputs and actions as outputs of the dynamics model. By embedding the dynamics equations within the multi-layer perceptron (MLP) computation process and making the parameters trainable, we enhance interpretability. Additionally, Bayesian optimization is introduced to train these parameters. We validate the proposed fully interpretable motion control algorithm on a legged robot, opening new research avenues for motion control and learning algorithms for legged robots within the deep learning framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title="deep reinforcement learning">deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=interpretation" title=" interpretation"> interpretation</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20control" title=" motion control"> motion control</a>, <a href="https://publications.waset.org/abstracts/search?q=legged%20robots" title=" legged robots"> legged robots</a> </p> <a href="https://publications.waset.org/abstracts/189290/a-fully-interpretable-deep-reinforcement-learning-based-motion-control-for-legged-robots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189290.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">21</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4432</span> Understanding and Improving Neural Network Weight Initialization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diego%20Aguirre">Diego Aguirre</a>, <a href="https://publications.waset.org/abstracts/search?q=Olac%20Fuentes"> Olac Fuentes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a taxonomy of weight initialization schemes used in deep learning. We survey the most representative techniques in each class and compare them in terms of overhead cost, convergence rate, and applicability. We also introduce a new weight initialization scheme. In this technique, we perform an initial feedforward pass through the network using an initialization mini-batch. Using statistics obtained from this pass, we initialize the weights of the network, so the following properties are met: 1) weight matrices are orthogonal; 2) ReLU layers produce a predetermined number of non-zero activations; 3) the output produced by each internal layer has a unit variance; 4) weights in the last layer are chosen to minimize the error in the initial mini-batch. We evaluate our method on three popular architectures, and a faster converge rates are achieved on the MNIST, CIFAR-10/100, and ImageNet datasets when compared to state-of-the-art initialization techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title=" image classification"> image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=weight%20initialization" title=" weight initialization"> weight initialization</a> </p> <a href="https://publications.waset.org/abstracts/89735/understanding-and-improving-neural-network-weight-initialization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89735.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">135</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4431</span> Classification of Computer Generated Images from Photographic Images Using Convolutional Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chaitanya%20Chawla">Chaitanya Chawla</a>, <a href="https://publications.waset.org/abstracts/search?q=Divya%20Panwar"> Divya Panwar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurneesh%20Singh%20Anand"> Gurneesh Singh Anand</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20P.%20S%20Bhatia"> M. P. S Bhatia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20forensics" title="image forensics">image forensics</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20graphics" title=" computer graphics"> computer graphics</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a> </p> <a href="https://publications.waset.org/abstracts/95266/classification-of-computer-generated-images-from-photographic-images-using-convolutional-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95266.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">336</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4430</span> Investigation on Behavior of Fixed-Ended Reinforced Concrete Deep Beams </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Heyrani%20Birak">Y. Heyrani Birak</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Hizaji"> R. Hizaji</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Shahkarami"> J. Shahkarami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reinforced Concrete (RC) deep beams are special structural elements because of their geometry and behavior under loads. For example, assumption of strain- stress distribution is not linear in the cross section. These types of beams may have simple supports or fixed supports. A lot of research works have been conducted on simply supported deep beams, but little study has been done in the fixed-end RC deep beams behavior. Recently, using of fixed-ended deep beams has been widely increased in structures. In this study, the behavior of fixed-ended deep beams is investigated, and the important parameters in capacity of this type of beams are mentioned. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20beam" title="deep beam">deep beam</a>, <a href="https://publications.waset.org/abstracts/search?q=capacity" title=" capacity"> capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforced%20concrete" title=" reinforced concrete"> reinforced concrete</a>, <a href="https://publications.waset.org/abstracts/search?q=fixed-ended" title=" fixed-ended"> fixed-ended</a> </p> <a href="https://publications.waset.org/abstracts/57558/investigation-on-behavior-of-fixed-ended-reinforced-concrete-deep-beams" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57558.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">334</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4429</span> Failure Mechanism in Fixed-Ended Reinforced Concrete Deep Beams under Cyclic Load</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Aarabzadeh">A. Aarabzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Hizaji"> R. Hizaji</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reinforced Concrete (RC) deep beams are a special type of beams due to their geometry, boundary conditions, and behavior compared to ordinary shallow beams. For example, assumption of a linear strain-stress distribution in the cross section is not valid. Little study has been dedicated to fixed-end RC deep beams. Also, most experimental studies are carried out on simply supported deep beams. Regarding recent tendency for application of deep beams, possibility of using fixed-ended deep beams has been widely increased in structures. Therefore, it seems necessary to investigate the aforementioned structural element in more details. In addition to experimental investigation of a concrete deep beam under cyclic load, different failure mechanisms of fixed-ended deep beams under this type of loading have been evaluated in the present study. The results show that failure mechanisms of deep beams under cyclic loads are quite different from monotonic loads. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20beam" title="deep beam">deep beam</a>, <a href="https://publications.waset.org/abstracts/search?q=cyclic%20load" title=" cyclic load"> cyclic load</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforced%20concrete" title=" reinforced concrete"> reinforced concrete</a>, <a href="https://publications.waset.org/abstracts/search?q=fixed-ended" title=" fixed-ended"> fixed-ended</a> </p> <a href="https://publications.waset.org/abstracts/56504/failure-mechanism-in-fixed-ended-reinforced-concrete-deep-beams-under-cyclic-load" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56504.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">361</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4428</span> Polymer Advancement with Poly(High Internal Phase Emulsion) Poly(S/DVB) Modified via Layer-by-Layer for CO2 Adsorption</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saifon%20Chongthub">Saifon Chongthub</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this research is to synthesize adsorbent foam for CO2 adsorption. The polymer was prepared from poly High Internal Phase Emulsion (PolyHIPE) using styrene as monomer and divinylbenzene as comonomer. Its morphology was determined by Scanning Electron Microscopy (SEM). To further increased CO2 adsorption of the prepared polyHIPE, the layer by layer (LbL) technique was applied, which alternated polyelectrolyte injection between layers of Poly(styrenesulfonate) (PSS) and Poly(diallyldimetyl-ammonium chloride)(PDADMAC) as primary layer, and layers of PSS and polyetyleneimine (PEI) as secondary layer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high%20internal%20phase%20emulsion" title="high internal phase emulsion">high internal phase emulsion</a>, <a href="https://publications.waset.org/abstracts/search?q=polyHIPE" title=" polyHIPE"> polyHIPE</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20modification" title=" surface modification"> surface modification</a>, <a href="https://publications.waset.org/abstracts/search?q=layer%20by%20layer%20technique" title=" layer by layer technique"> layer by layer technique</a>, <a href="https://publications.waset.org/abstracts/search?q=CO2%20adsorption" title=" CO2 adsorption"> CO2 adsorption</a> </p> <a href="https://publications.waset.org/abstracts/2180/polymer-advancement-with-polyhigh-internal-phase-emulsion-polysdvb-modified-via-layer-by-layer-for-co2-adsorption" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2180.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">289</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4427</span> Low Resistivity Pay Identification in Carbonate Reservoirs of Yadavaran Oilfield</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Mardi">Mohammad Mardi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generally, the resistivity is high in oil layer and low in water layer. Yet there are intervals of oil-bearing zones showing low resistivity, high porosity, and low resistance. In the typical example, well A (depth: 4341.5-4372.0m), both Spectral Gamma Ray (SGR) and Corrected Gamma Ray (CGR) are relatively low; porosity varies from 12-22%. Above 4360 meters, the reservoir shows the conventional positive difference between deep and shallow resistivity with high resistance; below 4360m, the reservoir shows a negative difference with low resistance, especially at depths of 4362.4 meters and 4371 meters, deep resistivity is only 2Ω.m, and the CAST-V imaging map shows that there are low resistance substances contained in the pores or matrix in the reservoirs of this interval. The rock slice analysis data shows that the pyrite volume is 2-3% in the interval 4369.08m-4371.55m. A comprehensive analysis on the volume of shale (Vsh), porosity, invasion features of resistivity, mud logging, and mineral volume indicates that the possible causes for the negative difference between deep and shallow resistivities with relatively low resistance are erosional pores, caves, micritic texture and the presence of pyrite. Full-bore Drill Stem Test (DST) verified 4991.09 bbl/d in this interval. To identify and thoroughly characterize low resistivity intervals coring, Nuclear Magnetic Resonance (NMR) logging and further geological evaluation are needed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=low%20resistivity%20pay" title="low resistivity pay">low resistivity pay</a>, <a href="https://publications.waset.org/abstracts/search?q=carbonates%20petrophysics" title=" carbonates petrophysics"> carbonates petrophysics</a>, <a href="https://publications.waset.org/abstracts/search?q=microporosity" title=" microporosity"> microporosity</a>, <a href="https://publications.waset.org/abstracts/search?q=porosity" title=" porosity"> porosity</a> </p> <a href="https://publications.waset.org/abstracts/150564/low-resistivity-pay-identification-in-carbonate-reservoirs-of-yadavaran-oilfield" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150564.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4426</span> Hysteresis in Sustainable Two-layer Circular Tube under a Lateral Compression Load</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ami%20Nomura">Ami Nomura</a>, <a href="https://publications.waset.org/abstracts/search?q=Ken%20Imanishi"> Ken Imanishi</a>, <a href="https://publications.waset.org/abstracts/search?q=Etsuko%20Ueda"> Etsuko Ueda</a>, <a href="https://publications.waset.org/abstracts/search?q=Tadahiro%20Wada"> Tadahiro Wada</a>, <a href="https://publications.waset.org/abstracts/search?q=Shinichi%20Enoki"> Shinichi Enoki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, there have been a lot of earthquakes in Japan. It is necessary to promote seismic isolation devices for buildings. The devices have been hardly diffused in attached houses, because the devices are very expensive. We should develop a low-cost seismic isolation device for detached houses. We suggested a new seismic isolation device which uses a two-layer circular tube as a unit. If hysteresis is produced in the two-layer circular tube under lateral compression load, we think that the two-layer circular tube can have energy absorbing capacity. It is necessary to contact the outer layer and the inner layer to produce hysteresis. We have previously reported how the inner layer comes in contact with the outer layer from a perspective of analysis used mechanics of materials. We have clarified that the inner layer comes in contact with the outer layer under a lateral compression load. In this paper, we explored contact area between the outer layer and the inner layer under a lateral compression load by using FEA. We think that changing the inner layer’s thickness is effective in increase the contact area. In order to change the inner layer’s thickness, we changed the shape of the inner layer. As a result, the contact area changes depending on the inner layer’s thickness. Additionally, we experimented to check whether hysteresis occurs in fact. As a consequence, we can reveal hysteresis in the two-layer circular tube under the condition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=contact%20area" title="contact area">contact area</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20absorbing%20capacity" title=" energy absorbing capacity"> energy absorbing capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=hysteresis" title=" hysteresis"> hysteresis</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20isolation%20device" title=" seismic isolation device"> seismic isolation device</a> </p> <a href="https://publications.waset.org/abstracts/13041/hysteresis-in-sustainable-two-layer-circular-tube-under-a-lateral-compression-load" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13041.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">295</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4425</span> Study on Hysteresis in Sustainable Two-Layer Circular Tube under a Lateral Compression Load</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ami%20Nomura">Ami Nomura</a>, <a href="https://publications.waset.org/abstracts/search?q=Ken%20Imanishi"> Ken Imanishi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yukinori%20Taniguchi"> Yukinori Taniguchi</a>, <a href="https://publications.waset.org/abstracts/search?q=Etsuko%20Ueda"> Etsuko Ueda</a>, <a href="https://publications.waset.org/abstracts/search?q=Tadahiro%20Wada"> Tadahiro Wada</a>, <a href="https://publications.waset.org/abstracts/search?q=Shinichi%20Enoki"> Shinichi Enoki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, there have been a lot of earthquakes in Japan. It is necessary to promote seismic isolation devices for buildings. The devices have been hardly diffused in attached houses, because the devices are very expensive. We should develop a low-cost seismic isolation device for detached houses. We suggested a new seismic isolation device which uses a two-layer circular tube as a unit. If hysteresis is produced in the two-layer circular tube under lateral compression load, we think that the two-layer circular tube can have energy absorbing capacity. It is necessary to contact the outer layer and the inner layer to produce hysteresis. We have previously reported how the inner layer comes in contact with the outer layer from a perspective of analysis used mechanics of materials. We have clarified that the inner layer comes in contact with the outer layer under a lateral compression load. In this paper, we explored contact area between the outer layer and the inner layer under a lateral compression load by using FEA. We think that changing the inner layer’s thickness is effective in increase the contact area. In order to change the inner layer’s thickness, we changed the shape of the inner layer. As a result, the contact area changes depending on the inner layer’s thickness. Additionally, we experimented to check whether hysteresis occurs in fact. As a consequence, we can reveal hysteresis in the two-layer circular tube under the condition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=contact%20area" title="contact area">contact area</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20absorbing%20capacity" title=" energy absorbing capacity"> energy absorbing capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=hysteresis" title=" hysteresis"> hysteresis</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20isolation%20device" title=" seismic isolation device"> seismic isolation device</a> </p> <a href="https://publications.waset.org/abstracts/18191/study-on-hysteresis-in-sustainable-two-layer-circular-tube-under-a-lateral-compression-load" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18191.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">361</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4424</span> Health Trajectory Clustering Using Deep Belief Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farshid%20Hajati">Farshid Hajati</a>, <a href="https://publications.waset.org/abstracts/search?q=Federico%20Girosi"> Federico Girosi</a>, <a href="https://publications.waset.org/abstracts/search?q=Shima%20Ghassempour"> Shima Ghassempour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, ﬁnancial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=health%20trajectory" title="health trajectory">health trajectory</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=DBN" title=" DBN"> DBN</a> </p> <a href="https://publications.waset.org/abstracts/37834/health-trajectory-clustering-using-deep-belief-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37834.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">369</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4423</span> A Comprehensive Study of Camouflaged Object Detection Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalak%20Bin%20Khair">Khalak Bin Khair</a>, <a href="https://publications.waset.org/abstracts/search?q=Saqib%20Jahir"> Saqib Jahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Ibrahim"> Mohammed Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Fahad%20Bin"> Fahad Bin</a>, <a href="https://publications.waset.org/abstracts/search?q=Debajyoti%20Karmaker"> Debajyoti Karmaker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Object detection is a computer technology that deals with searching through digital images and videos for occurrences of semantic elements of a particular class. It is associated with image processing and computer vision. On top of object detection, we detect camouflage objects within an image using Deep Learning techniques. Deep learning may be a subset of machine learning that's essentially a three-layer neural network Over 6500 images that possess camouflage properties are gathered from various internet sources and divided into 4 categories to compare the result. Those images are labeled and then trained and tested using vgg16 architecture on the jupyter notebook using the TensorFlow platform. The architecture is further customized using Transfer Learning. Methods for transferring information from one or more of these source tasks to increase learning in a related target task are created through transfer learning. The purpose of this transfer of learning methodologies is to aid in the evolution of machine learning to the point where it is as efficient as human learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=TensorFlow" title=" TensorFlow"> TensorFlow</a>, <a href="https://publications.waset.org/abstracts/search?q=camouflage" title=" camouflage"> camouflage</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=architecture" title=" architecture"> architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=VGG16" title=" VGG16"> VGG16</a> </p> <a href="https://publications.waset.org/abstracts/152633/a-comprehensive-study-of-camouflaged-object-detection-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152633.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">149</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4422</span> Defect Detection for Nanofibrous Images with Deep Learning-Based Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaokai%20Liu">Gaokai Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automatic defect detection for nanomaterial images is widely required in industrial scenarios. Deep learning approaches are considered as the most effective solutions for the great majority of image-based tasks. In this paper, an edge guidance network for defect segmentation is proposed. First, the encoder path with multiple convolution and downsampling operations is applied to the acquisition of shared features. Then two decoder paths both are connected to the last convolution layer of the encoder and supervised by the edge and segmentation labels, respectively, to guide the whole training process. Meanwhile, the edge and encoder outputs from the same stage are concatenated to the segmentation corresponding part to further tune the segmentation result. Finally, the effectiveness of the proposed method is verified via the experiments on open nanofibrous datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=defect%20detection" title=" defect detection"> defect detection</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=nanomaterials" title=" nanomaterials"> nanomaterials</a> </p> <a href="https://publications.waset.org/abstracts/133093/defect-detection-for-nanofibrous-images-with-deep-learning-based-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133093.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">149</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4421</span> Development of Single Layer of WO3 on Large Spatial Resolution by Atomic Layer Deposition Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Zhuiykov">S. Zhuiykov</a>, <a href="https://publications.waset.org/abstracts/search?q=Zh.%20Hai"> Zh. Hai</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Xu"> H. Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Xue"> C. Xue</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Unique and distinctive properties could be obtained on such two-dimensional (2D) semiconductor as tungsten trioxide (WO<sub>3</sub>) when the reduction from multi-layer to one fundamental layer thickness takes place. This transition without damaging single-layer on a large spatial resolution remained elusive until the atomic layer deposition (ALD) technique was utilized. Here we report the ALD-enabled atomic-layer-precision development of a single layer WO<sub>3</sub> with thickness of 0.77±0.07 nm on a large spatial resolution by using (<sup>t</sup>BuN)<sub>2</sub>W(NMe<sub>2</sub>)<sub>2</sub> as tungsten precursor and H<sub>2</sub>O as oxygen precursor, without affecting the underlying SiO<sub>2</sub>/Si substrate. Versatility of ALD is in tuning recipe in order to achieve the complete WO<sub>3</sub> with desired number of WO<sub>3</sub> layers including monolayer. Governed by self-limiting surface reactions, the ALD-enabled approach is versatile, scalable and applicable for a broader range of 2D semiconductors and various device applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Atomic%20Layer%20Deposition%20%28ALD%29" title="Atomic Layer Deposition (ALD)">Atomic Layer Deposition (ALD)</a>, <a href="https://publications.waset.org/abstracts/search?q=tungsten%20oxide" title=" tungsten oxide"> tungsten oxide</a>, <a href="https://publications.waset.org/abstracts/search?q=WO%E2%82%83" title=" WO₃"> WO₃</a>, <a href="https://publications.waset.org/abstracts/search?q=two-dimensional%20semiconductors" title=" two-dimensional semiconductors"> two-dimensional semiconductors</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20fundamental%20layer" title=" single fundamental layer"> single fundamental layer</a> </p> <a href="https://publications.waset.org/abstracts/54206/development-of-single-layer-of-wo3-on-large-spatial-resolution-by-atomic-layer-deposition-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54206.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">242</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4420</span> Mechanical Properties of D2 Tool Steel Cryogenically Treated Using Controllable Cooling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Rabin">A. Rabin</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Mazor"> G. Mazor</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Ladizhenski"> I. Ladizhenski</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Shneck"> R. Shneck</a>, <a href="https://publications.waset.org/abstracts/search?q=Z."> Z.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The hardness and hardenability of AISI D2 cold work tool steel with conventional quenching (CQ), deep cryogenic quenching (DCQ) and rapid deep cryogenic quenching heat treatments caused by temporary porous coating based on magnesium sulfate was investigated. Each of the cooling processes was examined from the perspective of the full process efficiency, heat flux in the austenite-martensite transformation range followed by characterization of the temporary porous layer made of magnesium sulfate using confocal laser scanning microscopy (CLSM), surface and core hardness and hardenability using Vickr’s hardness technique. The results show that the cooling rate (CR) at the austenite-martensite transformation range have a high influence on the hardness of the studied steel. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AISI%20D2" title="AISI D2">AISI D2</a>, <a href="https://publications.waset.org/abstracts/search?q=controllable%20cooling" title=" controllable cooling"> controllable cooling</a>, <a href="https://publications.waset.org/abstracts/search?q=magnesium%20sulfate%20coating" title=" magnesium sulfate coating"> magnesium sulfate coating</a>, <a href="https://publications.waset.org/abstracts/search?q=rapid%20cryogenic%20heat%20treatment" title=" rapid cryogenic heat treatment"> rapid cryogenic heat treatment</a>, <a href="https://publications.waset.org/abstracts/search?q=temporary%20porous%20layer" title=" temporary porous layer"> temporary porous layer</a> </p> <a href="https://publications.waset.org/abstracts/153436/mechanical-properties-of-d2-tool-steel-cryogenically-treated-using-controllable-cooling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153436.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=deep%20layer&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=deep%20layer&page=3">3</a></li> <li class="page-item"><a class="page-link" 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