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hyperparameters tuning</title> <meta name="description" content="Search results for: hyperparameters tuning"> <meta name="keywords" content="hyperparameters tuning"> <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 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action="https://publications.waset.org/abstracts/search"> <div 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="hyperparameters tuning"> <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> 353</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: hyperparameters tuning</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">353</span> Drone Classification Using Classification Methods Using Conventional Model With Embedded Audio-Visual Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hrishi%20Rakshit">Hrishi Rakshit</a>, <a href="https://publications.waset.org/abstracts/search?q=Pooneh%20Bagheri%20Zadeh"> Pooneh Bagheri Zadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates the performance of drone classification methods using conventional DCNN with different hyperparameters, when additional drone audio data is embedded in the dataset for training and further classification. In this paper, first a custom dataset is created using different images of drones from University of South California (USC) datasets and Leeds Beckett university datasets with embedded drone audio signal. The three well-known DCNN architectures namely, Resnet50, Darknet53 and Shufflenet are employed over the created dataset tuning their hyperparameters such as, learning rates, maximum epochs, Mini Batch size with different optimizers. Precision-Recall curves and F1 Scores-Threshold curves are used to evaluate the performance of the named classification algorithms. Experimental results show that Resnet50 has the highest efficiency compared to other DCNN methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drone%20classifications" title="drone classifications">drone classifications</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20convolutional%20neural%20network" title=" deep convolutional neural network"> deep convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperparameters" title=" hyperparameters"> hyperparameters</a>, <a href="https://publications.waset.org/abstracts/search?q=drone%20audio%20signal" title=" drone audio signal"> drone audio signal</a> </p> <a href="https://publications.waset.org/abstracts/172929/drone-classification-using-classification-methods-using-conventional-model-with-embedded-audio-visual-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172929.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">104</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">352</span> Optimization of Machine Learning Regression Results: An Application on Health Expenditures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Songul%20Cinaroglu">Songul Cinaroglu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning regression methods are recommended as an alternative to classical regression methods in the existence of variables which are difficult to model. Data for health expenditure is typically non-normal and have a heavily skewed distribution. This study aims to compare machine learning regression methods by hyperparameter tuning to predict health expenditure per capita. A multiple regression model was conducted and performance results of Lasso Regression, Random Forest Regression and Support Vector Machine Regression recorded when different hyperparameters are assigned. Lambda (λ) value for Lasso Regression, number of trees for Random Forest Regression, epsilon (ε) value for Support Vector Regression was determined as hyperparameters. Study results performed by using 'k' fold cross validation changed from 5 to 50, indicate the difference between machine learning regression results in terms of R², RMSE and MAE values that are statistically significant (p < 0.001). Study results reveal that Random Forest Regression (R² ˃ 0.7500, RMSE ≤ 0.6000 ve MAE ≤ 0.4000) outperforms other machine learning regression methods. It is highly advisable to use machine learning regression methods for modelling health expenditures. <p class="card-text"><strong>Keywords:</strong> <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=lasso%20regression" title=" lasso regression"> lasso regression</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20regression" title=" random forest regression"> random forest regression</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression" title=" support vector regression"> support vector regression</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperparameter%20tuning" title=" hyperparameter tuning"> hyperparameter tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20expenditure" title=" health expenditure"> health expenditure</a> </p> <a href="https://publications.waset.org/abstracts/97629/optimization-of-machine-learning-regression-results-an-application-on-health-expenditures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97629.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">226</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">351</span> Gaits Stability Analysis for a Pneumatic Quadruped Robot Using Reinforcement Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soofiyan%20Atar">Soofiyan Atar</a>, <a href="https://publications.waset.org/abstracts/search?q=Adil%20Shaikh"> Adil Shaikh</a>, <a href="https://publications.waset.org/abstracts/search?q=Sahil%20Rajpurkar"> Sahil Rajpurkar</a>, <a href="https://publications.waset.org/abstracts/search?q=Pragnesh%20Bhalala"> Pragnesh Bhalala</a>, <a href="https://publications.waset.org/abstracts/search?q=Aniket%20Desai"> Aniket Desai</a>, <a href="https://publications.waset.org/abstracts/search?q=Irfan%20Siddavatam"> Irfan Siddavatam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep reinforcement learning (deep RL) algorithms leverage the symbolic power of complex controllers by automating it by mapping sensory inputs to low-level actions. Deep RL eliminates the complex robot dynamics with minimal engineering. Deep RL provides high-risk involvement by directly implementing it in real-world scenarios and also high sensitivity towards hyperparameters. Tuning of hyperparameters on a pneumatic quadruped robot becomes very expensive through trial-and-error learning. This paper presents an automated learning control for a pneumatic quadruped robot using sample efficient deep Q learning, enabling minimal tuning and very few trials to learn the neural network. Long training hours may degrade the pneumatic cylinder due to jerk actions originated through stochastic weights. We applied this method to the pneumatic quadruped robot, which resulted in a hopping gait. In our process, we eliminated the use of a simulator and acquired a stable gait. This approach evolves so that the resultant gait matures more sturdy towards any stochastic changes in the environment. We further show that our algorithm performed very well as compared to programmed gait using robot dynamics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model-based%20reinforcement%20learning" title="model-based reinforcement learning">model-based reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=gait%20stability" title=" gait stability"> gait stability</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=pneumatic%20quadruped" title=" pneumatic quadruped"> pneumatic quadruped</a> </p> <a href="https://publications.waset.org/abstracts/140524/gaits-stability-analysis-for-a-pneumatic-quadruped-robot-using-reinforcement-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140524.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">316</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">350</span> Exploring the Impact of Input Sequence Lengths on Long Short-Term Memory-Based Streamflow Prediction in Flashy Catchments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farzad%20Hosseini%20Hossein%20Abadi">Farzad Hosseini Hossein Abadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Cristina%20Prieto%20Sierra"> Cristina Prieto Sierra</a>, <a href="https://publications.waset.org/abstracts/search?q=Cesar%20%C3%81lvarez%20D%C3%ADaz"> Cesar Álvarez Díaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting streamflow accurately in flashy catchments prone to floods is a major research and operational challenge in hydrological modeling. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown to be promising in achieving accurate hydrological predictions at daily and hourly time scales. In this work, a multi-timescale LSTM (MTS-LSTM) network was applied to the context of regional hydrological predictions at an hourly time scale in flashy catchments. The case study includes 40 catchments allocated in the Basque Country, north of Spain. We explore the impact of hyperparameters on the performance of streamflow predictions given by regional deep learning models through systematic hyperparameter tuning - where optimal regional values for different catchments are identified. The results show that predictions are highly accurate, with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) metrics values as high as 0.98 and 0.97, respectively. A principal component analysis reveals that a hyperparameter related to the length of the input sequence contributes most significantly to the prediction performance. The findings suggest that input sequence lengths have a crucial impact on the model prediction performance. Moreover, employing catchment-scale analysis reveals distinct sequence lengths for individual basins, highlighting the necessity of customizing this hyperparameter based on each catchment’s characteristics. This aligns with well known “uniqueness of the place” paradigm. In prior research, tuning the length of the input sequence of LSTMs has received limited focus in the field of streamflow prediction. Initially it was set to 365 days to capture a full annual water cycle. Later, performing limited systematic hyper-tuning using grid search, revealed a modification to 270 days. However, despite the significance of this hyperparameter in hydrological predictions, usually studies have overlooked its tuning and fixed it to 365 days. This study, employing a simultaneous systematic hyperparameter tuning approach, emphasizes the critical role of input sequence length as an influential hyperparameter in configuring LSTMs for regional streamflow prediction. Proper tuning of this hyperparameter is essential for achieving accurate hourly predictions using deep learning models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LSTMs" title="LSTMs">LSTMs</a>, <a href="https://publications.waset.org/abstracts/search?q=streamflow" title=" streamflow"> streamflow</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperparameters" title=" hyperparameters"> hyperparameters</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrology" title=" hydrology"> hydrology</a> </p> <a href="https://publications.waset.org/abstracts/184629/exploring-the-impact-of-input-sequence-lengths-on-long-short-term-memory-based-streamflow-prediction-in-flashy-catchments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184629.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">69</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">349</span> A Tuning Method for Microwave Filter via Complex Neural Network and Improved Space Mapping</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shengbiao%20Wu">Shengbiao Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Weihua%20Cao"> Weihua Cao</a>, <a href="https://publications.waset.org/abstracts/search?q=Min%20Wu"> Min Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Can%20Liu"> Can Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an intelligent tuning method of microwave filter based on complex neural network and improved space mapping. The tuning process consists of two stages: the initial tuning and the fine tuning. At the beginning of the tuning, the return loss of the filter is transferred to the passband via the error of phase. During the fine tuning, the phase shift caused by the transmission line and the higher order mode is removed by the curve fitting. Then, an Cauchy method based on the admittance parameter (Y-parameter) is used to extract the coupling matrix. The influence of the resonant cavity loss is eliminated during the parameter extraction process. By using processed data pairs (the amount of screw variation and the variation of the coupling matrix), a tuning model is established by the complex neural network. In view of the improved space mapping algorithm, the mapping relationship between the actual model and the ideal model is established, and the amplitude and direction of the tuning is constantly updated. Finally, the tuning experiment of the eight order coaxial cavity filter shows that the proposed method has a good effect in tuning time and tuning precision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=microwave%20filter" title="microwave filter">microwave filter</a>, <a href="https://publications.waset.org/abstracts/search?q=scattering%20parameter" title=" scattering parameter"> scattering parameter</a>, <a href="https://publications.waset.org/abstracts/search?q=coupling%20matrix" title=" coupling matrix"> coupling matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20tuning" title=" intelligent tuning"> intelligent tuning</a> </p> <a href="https://publications.waset.org/abstracts/81373/a-tuning-method-for-microwave-filter-via-complex-neural-network-and-improved-space-mapping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81373.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">311</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">348</span> 70% Ultra-Wide Tuning CMOS VCO Based on Magnetic Energy Adjustment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tai-Hsing%20Lee">Tai-Hsing Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhe-Wei%20Lin"> Zhe-Wei Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper demonstrates an ultra-wide tuning VCO implemented by CMOS 0.18μm process technology. By employing the proposed technique of magnetic energy adjustment in the oscillator tank, our proposed VCO achieves a wide frequency tuning range of 69.46% from 0.9 GHz to 1.86 GHz. The phase noise at an operating frequency of 1.86 GHz is -110 dBc/Hz (Offset frequency=1MHz). Furthermore, it achieves an excellent FOMT of 190.03 dBc/Hz. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=VCO" title="VCO">VCO</a>, <a href="https://publications.waset.org/abstracts/search?q=Ultra-wide%20tuning" title=" Ultra-wide tuning"> Ultra-wide tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=Frequency%20tuning%20range" title=" Frequency tuning range"> Frequency tuning range</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20noise" title=" phase noise"> phase noise</a>, <a href="https://publications.waset.org/abstracts/search?q=Magnetic%20energy%20adjustment" title=" Magnetic energy adjustment"> Magnetic energy adjustment</a> </p> <a href="https://publications.waset.org/abstracts/190304/70-ultra-wide-tuning-cmos-vco-based-on-magnetic-energy-adjustment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190304.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">39</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">347</span> Improve Closed Loop Performance and Control Signal Using Evolutionary Algorithms Based PID Controller</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Shahbazian">Mehdi Shahbazian</a>, <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Aarabi"> Alireza Aarabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Hadiyan"> Mohsen Hadiyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Proportional-Integral-Derivative (PID) controllers are the most widely used controllers in industry because of its simplicity and robustness. Different values of PID parameters make different step response, so an increasing amount of literature is devoted to proper tuning of PID controllers. The problem merits further investigation as traditional tuning methods make large control signal that can damages the system but using evolutionary algorithms based tuning methods improve the control signal and closed loop performance. In this paper three tuning methods for PID controllers have been studied namely Ziegler and Nichols, which is traditional tuning method and evolutionary algorithms based tuning methods, that are, Genetic algorithm and particle swarm optimization. To examine the validity of PSO and GA tuning methods a comparative analysis of DC motor plant is studied. Simulation results reveal that evolutionary algorithms based tuning method have improved control signal amplitude and quality factors of the closed loop system such as rise time, integral absolute error (IAE) and maximum overshoot. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithm" title="evolutionary algorithm">evolutionary algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=PID%20controller" title=" PID controller"> PID controller</a> </p> <a href="https://publications.waset.org/abstracts/24261/improve-closed-loop-performance-and-control-signal-using-evolutionary-algorithms-based-pid-controller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24261.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">483</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">346</span> Parameter Tuning of Complex Systems Modeled in Agent Based Modeling and Simulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rabia%20Korkmaz%20Tan">Rabia Korkmaz Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=%C5%9Eebnem%20Bora"> Şebnem Bora</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The major problem encountered when modeling complex systems with agent-based modeling and simulation techniques is the existence of large parameter spaces. A complex system model cannot be expected to reflect the whole of the real system, but by specifying the most appropriate parameters, the actual system can be represented by the model under certain conditions. When the studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in agent based simulations, and these studies have focused on tuning parameters of a single model. In this study, an approach of parameter tuning is proposed by using metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colonies (ABC), Firefly (FA) algorithms. With this hybrid structured study, the parameter tuning problems of the models in the different fields were solved. The new approach offered was tested in two different models, and its achievements in different problems were compared. The simulations and the results reveal that this proposed study is better than the existing parameter tuning studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parameter%20tuning" title="parameter tuning">parameter tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=agent%20based%20modeling%20and%20simulation" title=" agent based modeling and simulation"> agent based modeling and simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic%20algorithms" title=" metaheuristic algorithms"> metaheuristic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=complex%20systems" title=" complex systems"> complex systems</a> </p> <a href="https://publications.waset.org/abstracts/77307/parameter-tuning-of-complex-systems-modeled-in-agent-based-modeling-and-simulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77307.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">226</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">345</span> Design and Simulation a Low Phase Noise CMOS LC VCO for IEEE802.11a WLAN Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hooman%20Kaabi">Hooman Kaabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Raziyeh%20Karkoub"> Raziyeh Karkoub</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work proposes a structure of AMOS-varactors. A 5GHz LC-VCO designed in TSMC 0.18μm CMOS to improve phase noise and tuning range performance. The tuning range is from 5.05GHZ to 5.88GHz.The phase noise is -154.9dBc/Hz at 1MHz offset from the carrier. It meets the requirements for IEEE 802.11a WLAN standard. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CMOS%20LC%20VCO" title="CMOS LC VCO">CMOS LC VCO</a>, <a href="https://publications.waset.org/abstracts/search?q=spiral%20inductor" title=" spiral inductor"> spiral inductor</a>, <a href="https://publications.waset.org/abstracts/search?q=varactor" title=" varactor"> varactor</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20noise" title=" phase noise"> phase noise</a>, <a href="https://publications.waset.org/abstracts/search?q=tuning%20range" title=" tuning range"> tuning range</a> </p> <a href="https://publications.waset.org/abstracts/25972/design-and-simulation-a-low-phase-noise-cmos-lc-vco-for-ieee80211a-wlan-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25972.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">536</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">344</span> Suitable Tuning Method Selection for PID Controller Used in Digital Excitation System of Brushless Synchronous Generator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deepak%20M.%20Sajnekar">Deepak M. Sajnekar</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20B.%20Deshpande"> S. B. Deshpande</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20M.%20Mohril"> R. M. Mohril</a> </p> <p class="card-text"><strong>Abstract:</strong></p> At present many rotary excitation control system are using analog type of Automatic Voltage Regulator which now started to replace with the digital automatic voltage regulator which is provided with PID controller and tuning of PID controller is a challenging task. The cases where digital excitation control system is used tuning of PID controller are still carried out by pole placement method. Tuning of PID controller used for static excitation control system is not challenging because it does not involve exciter time constant. This paper discusses two methods of tuning PID controller i.e. Pole placement method and pole zero cancellation method. GUI prepared for both the methods on the platform of MATLAB. Using this GUI, performance results and time required for tuning for both the methods are compared. Sensitivity of the methods is also presented with parameter variation like loop gain ‘K’ and exciter time constant ‘te’. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20excitation%20system" title="digital excitation system">digital excitation system</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20voltage%20regulator" title=" automatic voltage regulator"> automatic voltage regulator</a>, <a href="https://publications.waset.org/abstracts/search?q=pole%20placement%20method" title=" pole placement method"> pole placement method</a>, <a href="https://publications.waset.org/abstracts/search?q=pole%20zero%20cancellation%20method" title=" pole zero cancellation method"> pole zero cancellation method</a> </p> <a href="https://publications.waset.org/abstracts/12214/suitable-tuning-method-selection-for-pid-controller-used-in-digital-excitation-system-of-brushless-synchronous-generator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12214.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">678</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">343</span> Method for Tuning Level Control Loops Based on Internal Model Control and Closed Loop Step Test Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arnaud%20Nougues">Arnaud Nougues</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a two-stage methodology derived from internal model control (IMC) for tuning a proportional-integral-derivative (PID) controller for levels or other integrating processes in an industrial environment. Focus is the ease of use and implementation speed which are critical for an industrial application. Tuning can be done with minimum effort and without the need for time-consuming open-loop step tests on the plant. The first stage of the method applies to levels only: the vessel residence time is calculated from equipment dimensions and used to derive a set of preliminary proportional-integral (PI) settings with IMC. The second stage, re-tuning in closed-loop, applies to levels as well as other integrating processes: a tuning correction mechanism has been developed based on a series of closed-loop simulations with model errors. The tuning correction is done from a simple closed-loop step test and the application of a generic correlation between observed overshoot and integral time correction. A spin-off of the method is that an estimate of the vessel residence time (levels) or open-loop process gain (other integrating process) is obtained from the closed-loop data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=closed-loop%20model%20identification" title="closed-loop model identification">closed-loop model identification</a>, <a href="https://publications.waset.org/abstracts/search?q=IMC-PID%20tuning%20method" title=" IMC-PID tuning method"> IMC-PID tuning method</a>, <a href="https://publications.waset.org/abstracts/search?q=integrating%20process%20control" title=" integrating process control"> integrating process control</a>, <a href="https://publications.waset.org/abstracts/search?q=on-line%20PID%20tuning%20adaptation" title=" on-line PID tuning adaptation"> on-line PID tuning adaptation</a> </p> <a href="https://publications.waset.org/abstracts/133791/method-for-tuning-level-control-loops-based-on-internal-model-control-and-closed-loop-step-test-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133791.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">221</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">342</span> Developing a DNN Model for the Production of Biogas From a Hybrid BO-TPE System in an Anaerobic Wastewater Treatment Plant</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hadjer%20Sadoune">Hadjer Sadoune</a>, <a href="https://publications.waset.org/abstracts/search?q=Liza%20Lamini"> Liza Lamini</a>, <a href="https://publications.waset.org/abstracts/search?q=Scherazade%20Krim"> Scherazade Krim</a>, <a href="https://publications.waset.org/abstracts/search?q=Amel%20Djouadi"> Amel Djouadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Rachida%20Rihani"> Rachida Rihani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep neural networks are highly regarded for their accuracy in predicting intricate fermentation processes. Their ability to learn from a large amount of datasets through artificial intelligence makes them particularly effective models. The primary obstacle in improving the performance of these models is to carefully choose the suitable hyperparameters, including the neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and other relevant factors. This study predicts biogas production from real wastewater treatment plant data using a sophisticated approach: hybrid Bayesian optimization with a tree-structured Parzen estimator (BO-TPE) for an optimised deep neural network (DNN) model. The plant utilizes an Upflow Anaerobic Sludge Blanket (UASB) digester that treats industrial wastewater from soft drinks and breweries. The digester has a working volume of 1574 m3 and a total volume of 1914 m3. Its internal diameter and height were 19 and 7.14 m, respectively. The data preprocessing was conducted with meticulous attention to preserving data quality while avoiding data reduction. Three normalization techniques were applied to the pre-processed data (MinMaxScaler, RobustScaler and StandardScaler) and compared with the Non-Normalized data. The RobustScaler approach has strong predictive ability for estimating the volume of biogas produced. The highest predicted biogas volume was 2236.105 Nm³/d, with coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anaerobic%20digestion" title="anaerobic digestion">anaerobic digestion</a>, <a href="https://publications.waset.org/abstracts/search?q=biogas%20production" title=" biogas production"> biogas production</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20network" title=" deep neural network"> deep neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20bo-tpe" title=" hybrid bo-tpe"> hybrid bo-tpe</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperparameters%20tuning" title=" hyperparameters tuning"> hyperparameters tuning</a> </p> <a href="https://publications.waset.org/abstracts/185307/developing-a-dnn-model-for-the-production-of-biogas-from-a-hybrid-bo-tpe-system-in-an-anaerobic-wastewater-treatment-plant" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185307.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">38</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">341</span> Tuning of Fixed Wing Micro Aerial Vehicles Using Tethered Setup</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shoeb%20Ahmed%20Adeel">Shoeb Ahmed Adeel</a>, <a href="https://publications.waset.org/abstracts/search?q=Vivek%20Paul"> Vivek Paul</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Prajwal"> K. Prajwal</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Fenelon"> Michael Fenelon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Techniques have been used to tether and stabilize a multi-rotor MAV but carrying out the same process to a fixed wing MAV is a novel method which can be utilized in order to reduce damage occurring to the fixed wing MAVs while conducting flight test trials and PID tuning. A few sensors and on board controller is required to carry out this experiment in horizontal and vertical plane of the vehicle. Here we will be discussing issues such as sensitivity of the air vehicle, endurance and external load of the string acting on the vehicle. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MAV" title="MAV">MAV</a>, <a href="https://publications.waset.org/abstracts/search?q=PID%20tuning" title=" PID tuning"> PID tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=tethered%20flight" title=" tethered flight"> tethered flight</a>, <a href="https://publications.waset.org/abstracts/search?q=UAV" title=" UAV"> UAV</a> </p> <a href="https://publications.waset.org/abstracts/35297/tuning-of-fixed-wing-micro-aerial-vehicles-using-tethered-setup" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35297.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">635</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">340</span> Self-Tuning Robot Control Based on Subspace Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mathias%20Marquardt">Mathias Marquardt</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20D%C3%BCnow"> Peter Dünow</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandra%20Ba%C3%9Fler"> Sandra Baßler</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper describes the use of subspace based identification methods for auto tuning of a state space control system. The plant is an unstable but self balancing transport robot. Because of the unstable character of the process it has to be identified from closed loop input-output data. Based on the identified model a state space controller combined with an observer is calculated. The subspace identification algorithm and the controller design procedure is combined to a auto tuning method. The capability of the approach was verified in a simulation experiments under different process conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto%20tuning" title="auto tuning">auto tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=balanced%20robot" title=" balanced robot"> balanced robot</a>, <a href="https://publications.waset.org/abstracts/search?q=closed%20loop%20identification" title=" closed loop identification"> closed loop identification</a>, <a href="https://publications.waset.org/abstracts/search?q=subspace%20identification" title=" subspace identification"> subspace identification</a> </p> <a href="https://publications.waset.org/abstracts/49108/self-tuning-robot-control-based-on-subspace-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49108.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">380</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">339</span> Tuning of the Thermal Capacity of an Envelope for Peak Demand Reduction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Isha%20Rathore">Isha Rathore</a>, <a href="https://publications.waset.org/abstracts/search?q=Peeyush%20Jain"> Peeyush Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Elangovan%20Rajasekar"> Elangovan Rajasekar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The thermal capacity of the envelope impacts the cooling and heating demand of a building and modulates the peak electricity demand. This paper presents the thermal capacity tuning of a building envelope to minimize peak electricity demand for space cooling. We consider a 40 m² residential testbed located in Hyderabad, India (Composite Climate). An EnergyPlus model is validated using real-time data. A Parametric simulation framework for thermal capacity tuning is created using the Honeybee plugin. Diffusivity, Thickness, layer position, orientation and fenestration size of the exterior envelope are parametrized considering a five-layered wall system. A total of 1824 parametric runs are performed and the optimum wall configuration leading to minimum peak cooling demand is presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thermal%20capacity" title="thermal capacity">thermal capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=tuning" title=" tuning"> tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=peak%20demand%20reduction" title=" peak demand reduction"> peak demand reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=parametric%20analysis" title=" parametric analysis"> parametric analysis</a> </p> <a href="https://publications.waset.org/abstracts/143562/tuning-of-the-thermal-capacity-of-an-envelope-for-peak-demand-reduction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143562.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">184</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">338</span> Parameters Tuning of a PID Controller on a DC Motor Using Honey Bee and Genetic Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saeid%20Jalilzadeh">Saeid Jalilzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> PID controllers are widely used to control the industrial plants because of their robustness and simple structures. Tuning of the controller's parameters to get a desired response is difficult and time consuming. With the development of computer technology and artificial intelligence in automatic control field, all kinds of parameters tuning methods of PID controller have emerged in endlessly, which bring much energy for the study of PID controller, but many advanced tuning methods behave not so perfect as to be expected. Honey Bee algorithm (HBA) and genetic algorithm (GA) are extensively used for real parameter optimization in diverse fields of study. This paper describes an application of HBA and GA to the problem of designing a PID controller whose parameters comprise proportionality constant, integral constant and derivative constant. Presence of three parameters to optimize makes the task of designing a PID controller more challenging than conventional P, PI, and PD controllers design. The suitability of the proposed approach has been demonstrated through computer simulation using MATLAB/SIMULINK. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=controller" title="controller">controller</a>, <a href="https://publications.waset.org/abstracts/search?q=GA" title=" GA"> GA</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=PID" title=" PID"> PID</a>, <a href="https://publications.waset.org/abstracts/search?q=PSO" title=" PSO"> PSO</a> </p> <a href="https://publications.waset.org/abstracts/15526/parameters-tuning-of-a-pid-controller-on-a-dc-motor-using-honey-bee-and-genetic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15526.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">544</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">337</span> Optimum Tuning Capacitors for Wireless Charging of Electric Vehicles Considering Variation in Coil Distances</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Abdullah%20Arafat">Muhammad Abdullah Arafat</a>, <a href="https://publications.waset.org/abstracts/search?q=Nahrin%20Nowrose"> Nahrin Nowrose</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wireless charging of electric vehicles is becoming more and more attractive as large amount of power can now be transferred to a reasonable distance using magnetic resonance coupling method. However, proper tuning of the compensation network is required to achieve maximum power transmission. Due to the variation of coil distance from the nominal value as a result of change in tire condition, change in weight or uneven road condition, the tuning of the compensation network has become challenging. In this paper, a tuning method has been described to determine the optimum values of the compensation network in order to maximize the average output power. The simulation results show that 5.2 percent increase in average output power is obtained for 10 percent variation in coupling coefficient using the optimum values without the need of additional space and electro-mechanical components. The proposed method is applicable to both static and dynamic charging of electric vehicles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coupling%20coefficient" title="coupling coefficient">coupling coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20vehicles" title=" electric vehicles"> electric vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20coupling" title=" magnetic resonance coupling"> magnetic resonance coupling</a>, <a href="https://publications.waset.org/abstracts/search?q=tuning%20capacitor" title=" tuning capacitor"> tuning capacitor</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20power%20transfer" title=" wireless power transfer"> wireless power transfer</a> </p> <a href="https://publications.waset.org/abstracts/149064/optimum-tuning-capacitors-for-wireless-charging-of-electric-vehicles-considering-variation-in-coil-distances" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149064.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">195</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">336</span> Passive and Active Spatial Pendulum Tuned Mass Damper with Two Tuning Frequencies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=W.%20T.%20A.%20Mohammed">W. T. A. Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Eltaeb"> M. Eltaeb</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Kashani"> R. Kashani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The first bending modes of tall asymmetric structures in the two lateral X and Y-directions have two different natural frequencies. To add tuned damping to these bending modes, one needs to either a) use two pendulum-tuned mass dampers (PTMDs) with one tuning frequency, each PTMD targeting one of the bending modes, or b) use one PTMD with two tuning frequencies (one in each lateral directions). Option (a), being more massive, requiring more space, and being more expensive, is less attractive than option (b). Considering that the tuning frequency of a pendulum depends mainly on the pendulum length, one way of realizing option (b) is by constraining the swinging length of the pendulum in one direction but not in the other; such PTMD is dubbed passive Bi-PTMD. Alternatively, option (b) can be realized by actively setting the tuning frequencies of the PTMD in the two directions. In this work, accurate physical models of passive Bi-PTMD and active PTMD are developed and incorporated into the numerical model of a tall asymmetric structure. The model of PTMDs plus structure is used for a)synthesizing such PTMDs for particular applications and b)evaluating their damping effectiveness in mitigating the dynamic lateral responses of their target asymmetric structures, perturbed by wind load in X and Y-directions. Depending on how elaborate the control scheme is, the active PTMD can either be made to yield the same damping effectiveness as the passive Bi-PTMD of the same size or the passive Bi-TMD twice as massive as the active PTMD. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=active%20tuned%20mass%20damper" title="active tuned mass damper">active tuned mass damper</a>, <a href="https://publications.waset.org/abstracts/search?q=high-rise%20building" title=" high-rise building"> high-rise building</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-frequency%20tuning" title=" multi-frequency tuning"> multi-frequency tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration%20control" title=" vibration control"> vibration control</a> </p> <a href="https://publications.waset.org/abstracts/164535/passive-and-active-spatial-pendulum-tuned-mass-damper-with-two-tuning-frequencies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164535.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">105</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">335</span> Fractional-Order PI Controller Tuning Rules for Cascade Control System </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Truong%20Nguyen%20Luan%20Vu">Truong Nguyen Luan Vu</a>, <a href="https://publications.waset.org/abstracts/search?q=Le%20Hieu%20Giang"> Le Hieu Giang</a>, <a href="https://publications.waset.org/abstracts/search?q=Le%20Linh"> Le Linh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The fractional–order proportional integral (FOPI) controller tuning rules based on the fractional calculus for the cascade control system are systematically proposed in this paper. Accordingly, the ideal controller is obtained by using internal model control (IMC) approach for both the inner and outer loops, which gives the desired closed-loop responses. On the basis of the fractional calculus, the analytical tuning rules of FOPI controller for the inner loop can be established in the frequency domain. Besides, the outer loop is tuned by using any integer PI/PID controller tuning rules in the literature. The simulation study is considered for the stable process model and the results demonstrate the simplicity, flexibility, and effectiveness of the proposed method for the cascade control system in compared with the other methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bode%E2%80%99s%20ideal%20transfer%20function" title="Bode’s ideal transfer function">Bode’s ideal transfer function</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20calculus" title=" fractional calculus"> fractional calculus</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%E2%80%93order%20proportional%20integral%20%28FOPI%29%20controller" title=" fractional–order proportional integral (FOPI) controller"> fractional–order proportional integral (FOPI) controller</a>, <a href="https://publications.waset.org/abstracts/search?q=cascade%20control%20system" title=" cascade control system"> cascade control system</a> </p> <a href="https://publications.waset.org/abstracts/48740/fractional-order-pi-controller-tuning-rules-for-cascade-control-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48740.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">377</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">334</span> Auto-Tuning of CNC Parameters According to the Machining Mode Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jenq-Shyong%20Chen">Jenq-Shyong Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ben-Fong%20Yu"> Ben-Fong Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> CNC(computer numerical control) machining centers have been widely used for machining different metal components for various industries. For a specific CNC machine, its everyday job is assigned to cut different products with quite different attributes such as material type, workpiece weight, geometry, tooling, and cutting conditions. Theoretically, the dynamic characteristics of the CNC machine should be properly tuned match each machining job in order to get the optimal machining performance. However, most of the CNC machines are set with only a standard set of CNC parameters. In this study, we have developed an auto-tuning system which can automatically change the CNC parameters and in hence change the machine dynamic characteristics according to the selection of machining modes which are set by the mixed combination of three machine performance indexes: the HO (high surface quality) index, HP (high precision) index and HS (high speed) index. The acceleration, jerk, corner error tolerance, oscillation and dynamic bandwidth of machine’s feed axes have been changed according to the selection of the machine performance indexes. The proposed auto-tuning system of the CNC parameters has been implemented on a PC-based CNC controller and a three-axis machining center. The measured experimental result have shown the promising of our proposed auto-tuning system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto-tuning" title="auto-tuning">auto-tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=CNC%20parameters" title=" CNC parameters"> CNC parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=machining%20mode" title=" machining mode"> machining mode</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20speed" title=" high speed"> high speed</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20accuracy" title=" high accuracy"> high accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20surface%20quality" title=" high surface quality"> high surface quality</a> </p> <a href="https://publications.waset.org/abstracts/26213/auto-tuning-of-cnc-parameters-according-to-the-machining-mode-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26213.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">380</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">333</span> Real-Time Implementation of Self-Tuning Fuzzy-PID Controller for First Order Plus Dead Time System Base on Microcontroller STM32</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maitree%20Thamma">Maitree Thamma</a>, <a href="https://publications.waset.org/abstracts/search?q=Witchupong%20Wiboonjaroen"> Witchupong Wiboonjaroen</a>, <a href="https://publications.waset.org/abstracts/search?q=Thanat%20Suknuan"> Thanat Suknuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Karan%20Homchat"> Karan Homchat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> First order plus dead time (FOPDT) is a high dynamic system. Therefore, the controller must be intelligent. This paper presents the development and implementation of self-tuning Fuzzy-PID controller for controlling the FOPDT system. The water level process used represented FOPDT system and the mathematical model of the system was approximated by using System Identification toolbox in Matlab. The control programming and Fuzzy-PID algorithm used Matlab/Simulink and run on Microcontroller STM32. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=real-time%20control" title="real-time control">real-time control</a>, <a href="https://publications.waset.org/abstracts/search?q=self-tuning%20fuzzy-PID" title=" self-tuning fuzzy-PID"> self-tuning fuzzy-PID</a>, <a href="https://publications.waset.org/abstracts/search?q=FOPDT%20system" title=" FOPDT system"> FOPDT system</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20water%20lever%20process" title=" the water lever process"> the water lever process</a> </p> <a href="https://publications.waset.org/abstracts/62422/real-time-implementation-of-self-tuning-fuzzy-pid-controller-for-first-order-plus-dead-time-system-base-on-microcontroller-stm32" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62422.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">292</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">332</span> Efficient Tuning Parameter Selection by Cross-Validated Score in High Dimensional Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yoonsuh%20Jung">Yoonsuh Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As DNA microarray data contain relatively small sample size compared to the number of genes, high dimensional models are often employed. In high dimensional models, the selection of tuning parameter (or, penalty parameter) is often one of the crucial parts of the modeling. Cross-validation is one of the most common methods for the tuning parameter selection, which selects a parameter value with the smallest cross-validated score. However, selecting a single value as an "optimal" value for the parameter can be very unstable due to the sampling variation since the sample sizes of microarray data are often small. Our approach is to choose multiple candidates of tuning parameter first, then average the candidates with different weights depending on their performance. The additional step of estimating the weights and averaging the candidates rarely increase the computational cost, while it can considerably improve the traditional cross-validation. We show that the selected value from the suggested methods often lead to stable parameter selection as well as improved detection of significant genetic variables compared to the tradition cross-validation via real data and simulated data sets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cross%20validation" title="cross validation">cross validation</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20averaging" title=" parameter averaging"> parameter averaging</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20selection" title=" parameter selection"> parameter selection</a>, <a href="https://publications.waset.org/abstracts/search?q=regularization%20parameter%20search" title=" regularization parameter search"> regularization parameter search</a> </p> <a href="https://publications.waset.org/abstracts/36409/efficient-tuning-parameter-selection-by-cross-validated-score-in-high-dimensional-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36409.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">415</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">331</span> Optimizing Perennial Plants Image Classification by Fine-Tuning Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khairani%20Binti%20Supyan">Khairani Binti Supyan</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatimah%20Khalid"> Fatimah Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=Mas%20Rina%20Mustaffa"> Mas Rina Mustaffa</a>, <a href="https://publications.waset.org/abstracts/search?q=Azreen%20Bin%20Azman"> Azreen Bin Azman</a>, <a href="https://publications.waset.org/abstracts/search?q=Amirul%20Azuani%20Romle"> Amirul Azuani Romle</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Perennial plant classification plays a significant role in various agricultural and environmental applications, assisting in plant identification, disease detection, and biodiversity monitoring. Nevertheless, attaining high accuracy in perennial plant image classification remains challenging due to the complex variations in plant appearance, the diverse range of environmental conditions under which images are captured, and the inherent variability in image quality stemming from various factors such as lighting conditions, camera settings, and focus. This paper proposes an adaptation approach to optimize perennial plant image classification by fine-tuning the pre-trained DNNs model. This paper explores the efficacy of fine-tuning prevalent architectures, namely VGG16, ResNet50, and InceptionV3, leveraging transfer learning to tailor the models to the specific characteristics of perennial plant datasets. A subset of the MYLPHerbs dataset consisted of 6 perennial plant species of 13481 images under various environmental conditions that were used in the experiments. Different strategies for fine-tuning, including adjusting learning rates, training set sizes, data augmentation, and architectural modifications, were investigated. The experimental outcomes underscore the effectiveness of fine-tuning deep neural networks for perennial plant image classification, with ResNet50 showcasing the highest accuracy of 99.78%. Despite ResNet50's superior performance, both VGG16 and InceptionV3 achieved commendable accuracy of 99.67% and 99.37%, respectively. The overall outcomes reaffirm the robustness of the fine-tuning approach across different deep neural network architectures, offering insights into strategies for optimizing model performance in the domain of perennial plant image classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=perennial%20plants" title="perennial plants">perennial plants</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=deep%20neural%20networks" title=" deep neural networks"> deep neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=fine-tuning" title=" fine-tuning"> fine-tuning</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=VGG16" title=" VGG16"> VGG16</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet50" title=" ResNet50"> ResNet50</a>, <a href="https://publications.waset.org/abstracts/search?q=InceptionV3" title=" InceptionV3"> InceptionV3</a> </p> <a href="https://publications.waset.org/abstracts/182850/optimizing-perennial-plants-image-classification-by-fine-tuning-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182850.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">64</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">330</span> Power System Stability Enhancement Using Self Tuning Fuzzy PI Controller for TCSC</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salman%20Hameed">Salman Hameed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a self-tuning fuzzy PI controller (STFPIC) is proposed for thyristor controlled series capacitor (TCSC) to improve power system dynamic performance. In a STFPIC controller, the output scaling factor is adjusted on-line by an updating factor (α). The value of α is determined from a fuzzy rule-base defined on error (e) and change of error (Δe) of the controlled variable. The proposed self-tuning controller is designed using a very simple control rule-base and the most natural and unbiased membership functions (MFs) (symmetric triangles with equal base and 50% overlap with neighboring MFs). The comparative performances of the proposed STFPIC and the standard fuzzy PI controller (FPIC) have been investigated on a multi-machine power system (namely, 4 machine two area system) through detailed non-linear simulation studies using MATLAB/SIMULINK. From the simulation studies it has been found out that for damping oscillations, the performance of the proposed STFPIC is better than that obtained by the standard FPIC. Moreover, the proposed STFPIC as well as the FPIC have been found to be quite effective in damping oscillations over a wide range of operating conditions and are quite effective in enhancing the power carrying capability of the power system significantly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title="genetic algorithm">genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20system%20stability" title=" power system stability"> power system stability</a>, <a href="https://publications.waset.org/abstracts/search?q=self-tuning%20fuzzy%20controller" title=" self-tuning fuzzy controller"> self-tuning fuzzy controller</a>, <a href="https://publications.waset.org/abstracts/search?q=thyristor%20controlled%20series%20capacitor" title=" thyristor controlled series capacitor"> thyristor controlled series capacitor</a> </p> <a href="https://publications.waset.org/abstracts/7935/power-system-stability-enhancement-using-self-tuning-fuzzy-pi-controller-for-tcsc" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7935.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">423</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">329</span> Detecting Music Enjoyment Level Using Electroencephalogram Signals and Machine Learning Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raymond%20Feng">Raymond Feng</a>, <a href="https://publications.waset.org/abstracts/search?q=Shadi%20Ghiasi"> Shadi Ghiasi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An electroencephalogram (EEG) is a non-invasive technique that records electrical activity in the brain using scalp electrodes. Researchers have studied the use of EEG to detect emotions and moods by collecting signals from participants and analyzing how those signals correlate with their activities. In this study, researchers investigated the relationship between EEG signals and music enjoyment. Participants listened to music while data was collected. During the signal-processing phase, power spectral densities (PSDs) were computed from the signals, and dominant brainwave frequencies were extracted from the PSDs to form a comprehensive feature matrix. A machine learning approach was then taken to find correlations between the processed data and the music enjoyment level indicated by the participants. To improve on previous research, multiple machine learning models were employed, including K-Nearest Neighbors Classifier, Support Vector Classifier, and Decision Tree Classifier. Hyperparameters were used to fine-tune each model to further increase its performance. The experiments showed that a strong correlation exists, with the Decision Tree Classifier with hyperparameters yielding 85% accuracy. This study proves that EEG is a reliable means to detect music enjoyment and has future applications, including personalized music recommendation, mood adjustment, and mental health therapy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</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=mood" title=" mood"> mood</a>, <a href="https://publications.waset.org/abstracts/search?q=music%20enjoyment" title=" music enjoyment"> music enjoyment</a>, <a href="https://publications.waset.org/abstracts/search?q=physiological%20signals" title=" physiological signals"> physiological signals</a> </p> <a href="https://publications.waset.org/abstracts/182307/detecting-music-enjoyment-level-using-electroencephalogram-signals-and-machine-learning-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182307.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">61</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">328</span> A Tool Tuning Approximation Method: Exploration of the System Dynamics and Its Impact on Milling Stability When Amending Tool Stickout</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nikolai%20Bertelsen">Nikolai Bertelsen</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20A.%20Alphinas"> Robert A. Alphinas</a>, <a href="https://publications.waset.org/abstracts/search?q=Klaus%20B.%20Orskov"> Klaus B. Orskov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The shortest possible tool stickout has been the traditional go-to approach with expectations of increased stability and productivity. However, experimental studies at Danish Advanced Manufacturing Research Center (DAMRC) have proven that for some tool stickout lengths, there exist local productivity optimums when utilizing the Stability Lobe Diagrams for chatter avoidance. This contradicts with traditional logic and the best practices taught to machinists. This paper explores the vibrational characteristics and behaviour of a milling system over the tool stickout length. The experimental investigation has been conducted by tap testing multiple endmills where the tool stickout length has been varied. For each length, the modal parameters have been recorded and mapped to visualize behavioural tendencies. Furthermore, the paper explores the correlation between the modal parameters and the Stability Lobe Diagram to outline the influence and importance of each parameter in a multi-mode system. The insights are conceptualized into a tool tuning approximation solution. It builds on an almost linear change in the natural frequencies when amending tool stickout, which results in changed positions of the Chatter-free Stability Lobes. Furthermore, if the natural frequency of two modes become too close, it will onset of the dynamic absorber effect phenomenon. This phenomenon increases the critical stable depth of cut, allowing for a more stable milling process. Validation tests on the tool tuning approximation solution have shown varying success of the solution. This outlines the need for further research on the boundary conditions of the solution to understand at which conditions the tool tuning approximation solution is applicable. If the conditions get defined, the conceptualized tool tuning approximation solution outlines an approach for quick and roughly approximating tool stickouts with the potential for increased stiffness and optimized productivity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=milling" title="milling">milling</a>, <a href="https://publications.waset.org/abstracts/search?q=modal%20parameters" title=" modal parameters"> modal parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=stability%20lobes" title=" stability lobes"> stability lobes</a>, <a href="https://publications.waset.org/abstracts/search?q=tap%20testing" title=" tap testing"> tap testing</a>, <a href="https://publications.waset.org/abstracts/search?q=tool%20tuning" title=" tool tuning"> tool tuning</a> </p> <a href="https://publications.waset.org/abstracts/128047/a-tool-tuning-approximation-method-exploration-of-the-system-dynamics-and-its-impact-on-milling-stability-when-amending-tool-stickout" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128047.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">157</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">327</span> Self-Tuning Dead-Beat PD Controller for Pitch Angle Control of a Bench-Top Helicopter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Mansor">H. Mansor</a>, <a href="https://publications.waset.org/abstracts/search?q=S.B.%20Mohd-Noor"> S.B. Mohd-Noor</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20I.%20Othman"> N. I. Othman</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Tazali"> N. Tazali</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20I.%20Boby"> R. I. Boby</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an improved robust Proportional Derivative controller for a 3-Degree-of-Freedom (3-DOF) bench-top helicopter by using adaptive methodology. Bench-top helicopter is a laboratory scale helicopter used for experimental purposes which is widely used in teaching laboratory and research. Proportional Derivative controller has been developed for a 3-DOF bench-top helicopter by Quanser. Experiments showed that the transient response of designed PD controller has very large steady state error i.e., 50%, which is very serious. The objective of this research is to improve the performance of existing pitch angle control of PD controller on the bench-top helicopter by integration of PD controller with adaptive controller. Usually standard adaptive controller will produce zero steady state error; however response time to reach desired set point is large. Therefore, this paper proposed an adaptive with deadbeat algorithm to overcome the limitations. The output response that is fast, robust and updated online is expected. Performance comparisons have been performed between the proposed self-tuning deadbeat PD controller and standard PD controller. The efficiency of the self-tuning dead beat controller has been proven from the tests results in terms of faster settling time, zero steady state error and capability of the controller to be updated online. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20control" title="adaptive control">adaptive control</a>, <a href="https://publications.waset.org/abstracts/search?q=deadbeat%20control" title=" deadbeat control"> deadbeat control</a>, <a href="https://publications.waset.org/abstracts/search?q=bench-top%20helicopter" title=" bench-top helicopter"> bench-top helicopter</a>, <a href="https://publications.waset.org/abstracts/search?q=self-tuning%20control" title=" self-tuning control"> self-tuning control</a> </p> <a href="https://publications.waset.org/abstracts/10581/self-tuning-dead-beat-pd-controller-for-pitch-angle-control-of-a-bench-top-helicopter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10581.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">324</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">326</span> Predicting Options Prices Using Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Krishang%20Surapaneni">Krishang Surapaneni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42% <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=finance" title="finance">finance</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression%20model" title=" linear regression model"> linear regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20model" title=" machine learning model"> machine learning model</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20price" title=" stock price"> stock price</a> </p> <a href="https://publications.waset.org/abstracts/160197/predicting-options-prices-using-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160197.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">75</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">325</span> Grey Wolf Optimization Technique for Predictive Analysis of Products in E-Commerce: An Adaptive Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shital%20Suresh%20Borse">Shital Suresh Borse</a>, <a href="https://publications.waset.org/abstracts/search?q=Vijayalaxmi%20Kadroli"> Vijayalaxmi Kadroli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> E-commerce industries nowadays implement the latest AI, ML Techniques to improve their own performance and prediction accuracy. This helps to gain a huge profit from the online market. Ant Colony Optimization, Genetic algorithm, Particle Swarm Optimization, Neural Network & GWO help many e-commerce industries for up-gradation of their predictive performance. These algorithms are providing optimum results in various applications, such as stock price prediction, prediction of drug-target interaction & user ratings of similar products in e-commerce sites, etc. In this study, customer reviews will play an important role in prediction analysis. People showing much interest in buying a lot of services& products suggested by other customers. This ultimately increases net profit. In this work, a convolution neural network (CNN) is proposed which further is useful to optimize the prediction accuracy of an e-commerce website. This method shows that CNN is used to optimize hyperparameters of GWO algorithm using an appropriate coding scheme. Accurate model results are verified by comparing them to PSO results whose hyperparameters have been optimized by CNN in Amazon's customer review dataset. Here, experimental outcome proves that this proposed system using the GWO algorithm achieves superior execution in terms of accuracy, precision, recovery, etc. in prediction analysis compared to the existing systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction%20analysis" title="prediction analysis">prediction analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=e-commerce" title=" e-commerce"> e-commerce</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=grey%20wolf%20optimization" title=" grey wolf optimization"> grey wolf optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a> </p> <a href="https://publications.waset.org/abstracts/148039/grey-wolf-optimization-technique-for-predictive-analysis-of-products-in-e-commerce-an-adaptive-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148039.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">113</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">324</span> Experimental Study of Hyperparameter Tuning a Deep Learning Convolutional Recurrent Network for Text Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bharatendra%20Rai">Bharatendra Rai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The sequence of words in text data has long-term dependencies and is known to suffer from vanishing gradient problems when developing deep learning models. Although recurrent networks such as long short-term memory networks help to overcome this problem, achieving high text classification performance is a challenging problem. Convolutional recurrent networks that combine the advantages of long short-term memory networks and convolutional neural networks can be useful for text classification performance improvements. However, arriving at suitable hyperparameter values for convolutional recurrent networks is still a challenging task where fitting a model requires significant computing resources. This paper illustrates the advantages of using convolutional recurrent networks for text classification with the help of statistically planned computer experiments for hyperparameter tuning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory%20networks" title="long short-term memory networks">long short-term memory networks</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20recurrent%20networks" title=" convolutional recurrent networks"> convolutional recurrent networks</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title=" text classification"> text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperparameter%20tuning" title=" hyperparameter tuning"> hyperparameter tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=Tukey%20honest%20significant%20differences" title=" Tukey honest significant differences"> Tukey honest significant differences</a> </p> <a href="https://publications.waset.org/abstracts/169795/experimental-study-of-hyperparameter-tuning-a-deep-learning-convolutional-recurrent-network-for-text-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169795.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">129</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=hyperparameters%20tuning&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=hyperparameters%20tuning&page=3">3</a></li> 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