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of detection</title> <meta name="description" content="Search results for: probability of detection"> <meta name="keywords" content="probability of detection"> <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 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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="probability of detection"> <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> 4601</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: probability of detection</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4601</span> Saliency Detection Using a Background Probability Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Junling%20Li">Junling Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Fang%20Meng"> Fang Meng</a>, <a href="https://publications.waset.org/abstracts/search?q=Yichun%20Zhang"> Yichun Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image saliency detection has been long studied, while several challenging problems are still unsolved, such as detecting saliency inaccurately in complex scenes or suppressing salient objects in the image borders. In this paper, we propose a new saliency detection algorithm in order to solving these problems. We represent the image as a graph with superixels as nodes. By considering appearance similarity between the boundary and the background, the proposed method chooses non-saliency boundary nodes as background priors to construct the background probability model. The probability that each node belongs to the model is computed, which measures its similarity with backgrounds. Thus we can calculate saliency by the transformed probability as a metric. We compare our algorithm with ten-state-of-the-art salient detection methods on the public database. Experimental results show that our simple and effective approach can attack those challenging problems that had been baffling in image saliency detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=visual%20saliency" title="visual saliency">visual saliency</a>, <a href="https://publications.waset.org/abstracts/search?q=background%20probability" title=" background probability"> background probability</a>, <a href="https://publications.waset.org/abstracts/search?q=boundary%20knowledge" title=" boundary knowledge"> boundary knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=background%20priors" title=" background priors"> background priors</a> </p> <a href="https://publications.waset.org/abstracts/13729/saliency-detection-using-a-background-probability-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13729.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">429</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">4600</span> Performance of Nakagami Fading Channel over Energy Detection Based Spectrum Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Ranjeeth">M. Ranjeeth</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Anuradha"> S. Anuradha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spectrum sensing is the main feature of cognitive radio technology. Spectrum sensing gives an idea of detecting the presence of the primary users in a licensed spectrum. In this paper we compare the theoretical results of detection probability of different fading environments like Rayleigh, Rician, Nakagami-m fading channels with the simulation results using energy detection based spectrum sensing. The numerical results are plotted as P_f Vs P_d for different SNR values, fading parameters. It is observed that Nakagami fading channel performance is better than other fading channels by using energy detection in spectrum sensing. A MATLAB simulation test bench has been implemented to know the performance of energy detection in different fading channel environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spectrum%20sensing" title="spectrum sensing">spectrum sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20detection" title=" energy detection"> energy detection</a>, <a href="https://publications.waset.org/abstracts/search?q=fading%20channels" title=" fading channels"> fading channels</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20of%20detection" title=" probability of detection"> probability of detection</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20of%20false%20alarm" title=" probability of false alarm"> probability of false alarm</a> </p> <a href="https://publications.waset.org/abstracts/15800/performance-of-nakagami-fading-channel-over-energy-detection-based-spectrum-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15800.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">532</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">4599</span> Preliminary Results on a Maximum Mean Discrepancy Approach for Seizure Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boumediene%20Hamzi">Boumediene Hamzi</a>, <a href="https://publications.waset.org/abstracts/search?q=Turky%20N.%20AlOtaiby"> Turky N. AlOtaiby</a>, <a href="https://publications.waset.org/abstracts/search?q=Saleh%20AlShebeili"> Saleh AlShebeili</a>, <a href="https://publications.waset.org/abstracts/search?q=Arwa%20AlAnqary"> Arwa AlAnqary</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that are computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=kernel%20methods" title="kernel methods">kernel methods</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20mean%20discrepancy" title=" maximum mean discrepancy"> maximum mean discrepancy</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure%20detection" title=" seizure detection"> seizure detection</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/83558/preliminary-results-on-a-maximum-mean-discrepancy-approach-for-seizure-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83558.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">238</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">4598</span> Gaussian Probability Density for Forest Fire Detection Using Satellite Imagery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Benkraouda">S. Benkraouda</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Djelloul-Khedda"> Z. Djelloul-Khedda</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Yagoubi"> B. Yagoubi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> we present a method for early detection of forest fires from a thermal infrared satellite image, using the image matrix of the probability of belonging. The principle of the method is to compare a theoretical mathematical model to an experimental model. We considered that each line of the image matrix, as an embodiment of a non-stationary random process. Since the distribution of pixels in the satellite image is statistically dependent, we divided these lines into small stationary and ergodic intervals to characterize the image by an adequate mathematical model. A standard deviation was chosen to generate random variables, so each interval behaves naturally like white Gaussian noise. The latter has been selected as the mathematical model that represents a set of very majority pixels, which we can be considered as the image background. Before modeling the image, we made a few pretreatments, then the parameters of the theoretical Gaussian model were extracted from the modeled image, these settings will be used to calculate the probability of each interval of the modeled image to belong to the theoretical Gaussian model. The high intensities pixels are regarded as foreign elements to it, so they will have a low probability, and the pixels that belong to the background image will have a high probability. Finally, we did present the reverse of the matrix of probabilities of these intervals for a better fire detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forest%20fire" title="forest fire">forest fire</a>, <a href="https://publications.waset.org/abstracts/search?q=forest%20fire%20detection" title=" forest fire detection"> forest fire detection</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20image" title=" satellite image"> satellite image</a>, <a href="https://publications.waset.org/abstracts/search?q=normal%20distribution" title=" normal distribution"> normal distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=theoretical%20gaussian%20model" title=" theoretical gaussian model"> theoretical gaussian model</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20infrared%20matrix%20image" title=" thermal infrared matrix image"> thermal infrared matrix image</a> </p> <a href="https://publications.waset.org/abstracts/118320/gaussian-probability-density-for-forest-fire-detection-using-satellite-imagery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118320.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">4597</span> Enhancement of Primary User Detection in Cognitive Radio by Scattering Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Moawad">A. Moawad</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20C.%20Yao"> K. C. Yao</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Mansour"> A. Mansour</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Gautier"> R. Gautier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The detecting of an occupied frequency band is a major issue in cognitive radio systems. The detection process becomes difficult if the signal occupying the band of interest has faded amplitude due to multipath effects. These effects make it hard for an occupying user to be detected. This work mitigates the missed-detection problem in the context of cognitive radio in frequency-selective fading channel by proposing blind channel estimation method that is based on scattering transform. By initially applying conventional energy detection, the missed-detection probability is evaluated, and if it is greater than or equal to 50%, channel estimation is applied on the received signal followed by channel equalization to reduce the channel effects. In the proposed channel estimator, we modify the Morlet wavelet by using its first derivative for better frequency resolution. A mathematical description of the modified function and its frequency resolution is formulated in this work. The improved frequency resolution is required to follow the spectral variation of the channel. The channel estimation error is evaluated in the mean-square sense for different channel settings, and energy detection is applied to the equalized received signal. The simulation results show improvement in reducing the missed-detection probability as compared to the detection based on principal component analysis. This improvement is achieved at the expense of increased estimator complexity, which depends on the number of wavelet filters as related to the channel taps. Also, the detection performance shows an improvement in detection probability for low signal-to-noise scenarios over principal component analysis- based energy detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=channel%20estimation" title="channel estimation">channel estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title=" cognitive radio"> cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=scattering%20transform" title=" scattering transform"> scattering transform</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrum%20sensing" title=" spectrum sensing"> spectrum sensing</a> </p> <a href="https://publications.waset.org/abstracts/79688/enhancement-of-primary-user-detection-in-cognitive-radio-by-scattering-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79688.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">196</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">4596</span> Ship Detection Requirements Analysis for Different Sea States: Validation on Real SAR Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaime%20Mart%C3%ADn-de-Nicol%C3%A1s">Jaime Martín-de-Nicolás</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Mata-Moya"> David Mata-Moya</a>, <a href="https://publications.waset.org/abstracts/search?q=Nerea%20del-Rey-Maestre"> Nerea del-Rey-Maestre</a>, <a href="https://publications.waset.org/abstracts/search?q=Pedro%20G%C3%B3mez-del-Hoyo"> Pedro Gómez-del-Hoyo</a>, <a href="https://publications.waset.org/abstracts/search?q=Mar%C3%ADa-Pilar%20Jarabo-Amores"> María-Pilar Jarabo-Amores</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ship detection is nowadays quite an important issue in tasks related to sea traffic control, fishery management and ship search and rescue. Although it has traditionally been carried out by patrol ships or aircrafts, coverage and weather conditions and sea state can become a problem. Synthetic aperture radars can surpass these coverage limitations and work under any climatological condition. A fast CFAR ship detector based on a robust statistical modeling of sea clutter with respect to sea states in SAR images is used. In this paper, the minimum SNR required to obtain a given detection probability with a given false alarm rate for any sea state is determined. A Gaussian target model using real SAR data is considered. Results show that SNR does not depend heavily on the class considered. Provided there is some variation in the backscattering of targets in SAR imagery, the detection probability is limited and a post-processing stage based on morphology would be suitable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SAR" title="SAR">SAR</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20gamma%20distribution" title=" generalized gamma distribution"> generalized gamma distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=detection%20curves" title=" detection curves"> detection curves</a>, <a href="https://publications.waset.org/abstracts/search?q=radar%20detection" title=" radar detection"> radar detection</a> </p> <a href="https://publications.waset.org/abstracts/52278/ship-detection-requirements-analysis-for-different-sea-states-validation-on-real-sar-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52278.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">452</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">4595</span> Probability-Based Damage Detection of Structures Using Kriging Surrogates and Enhanced Ideal Gas Molecular Movement Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Ghasemi">M. R. Ghasemi</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Ghiasi"> R. Ghiasi</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Varaee"> H. Varaee </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Surrogate model has received increasing attention for use in detecting damage of structures based on vibration modal parameters. However, uncertainties existing in the measured vibration data may lead to false or unreliable output result from such model. In this study, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The kriging technique allows one to genuinely quantify the surrogate error, therefore it is chosen as metamodeling technique. Enhanced version of ideal gas molecular movement (EIGMM) algorithm is used as main algorithm for model updating. The developed approach is applied to detect simulated damage in numerical models of 72-bar space truss and 120-bar dome truss. The simulation results show the proposed method can perform well in probability-based damage detection of structures with less computational effort compared to direct finite element model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=probability-based%20damage%20detection%20%28PBDD%29" title="probability-based damage detection (PBDD)">probability-based damage detection (PBDD)</a>, <a href="https://publications.waset.org/abstracts/search?q=Kriging" title=" Kriging"> Kriging</a>, <a href="https://publications.waset.org/abstracts/search?q=surrogate%20modeling" title=" surrogate modeling"> surrogate modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20quantification" title=" uncertainty quantification"> uncertainty quantification</a>, <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=enhanced%20ideal%20gas%20molecular%20movement%20%28EIGMM%29" title=" enhanced ideal gas molecular movement (EIGMM)"> enhanced ideal gas molecular movement (EIGMM)</a> </p> <a href="https://publications.waset.org/abstracts/56392/probability-based-damage-detection-of-structures-using-kriging-surrogates-and-enhanced-ideal-gas-molecular-movement-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56392.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">239</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">4594</span> Error Probability of Multi-User Detection Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Komal%20Babbar">Komal Babbar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multiuser Detection is the intelligent estimation/demodulation of transmitted bits in the presence of Multiple Access Interference. The authors have presented the Bit-error rate (BER) achieved by linear multi-user detectors: Matched filter (which treats the MAI as AWGN), Decorrelating and MMSE. In this work, authors investigate the bit error probability analysis for Matched filter, decorrelating, and MMSE. This problem arises in several practical CDMA applications where the receiver may not have full knowledge of the number of active users and their signature sequences. In particular, the behavior of MAI at the output of the Multi-user detectors (MUD) is examined under various asymptotic conditions including large signal to noise ratio; large near-far ratios; and a large number of users. In the last section Authors also shows Matlab Simulation results for Multiuser detection techniques i.e., Matched filter, Decorrelating, MMSE for 2 users and 10 users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=code%20division%20multiple%20access" title="code division multiple access">code division multiple access</a>, <a href="https://publications.waset.org/abstracts/search?q=decorrelating" title=" decorrelating"> decorrelating</a>, <a href="https://publications.waset.org/abstracts/search?q=matched%20filter" title=" matched filter"> matched filter</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20mean%20square%20detection%20%28MMSE%29%20detection" title=" minimum mean square detection (MMSE) detection"> minimum mean square detection (MMSE) detection</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20access%20interference%20%28MAI%29" title=" multiple access interference (MAI)"> multiple access interference (MAI)</a>, <a href="https://publications.waset.org/abstracts/search?q=multiuser%20detection%20%28MUD%29" title=" multiuser detection (MUD)"> multiuser detection (MUD)</a> </p> <a href="https://publications.waset.org/abstracts/26329/error-probability-of-multi-user-detection-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26329.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">527</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">4593</span> Adaptive CFAR Analysis for Non-Gaussian Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bouchemha%20Amel">Bouchemha Amel</a>, <a href="https://publications.waset.org/abstracts/search?q=Chachoui%20Takieddine"> Chachoui Takieddine</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Maalem"> H. Maalem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automatic detection of targets in a modern communication system RADAR is based primarily on the concept of adaptive CFAR detector. To have an effective detection, we must minimize the influence of disturbances due to the clutter. The detection algorithm adapts the CFAR detection threshold which is proportional to the average power of the clutter, maintaining a constant probability of false alarm. In this article, we analyze the performance of two variants of adaptive algorithms CA-CFAR and OS-CFAR and we compare the thresholds of these detectors in the marine environment (no-Gaussian) with a Weibull distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CFAR" title="CFAR">CFAR</a>, <a href="https://publications.waset.org/abstracts/search?q=threshold" title=" threshold"> threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=clutter" title=" clutter"> clutter</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution" title=" distribution"> distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Weibull" title=" Weibull"> Weibull</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a> </p> <a href="https://publications.waset.org/abstracts/21359/adaptive-cfar-analysis-for-non-gaussian-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21359.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">588</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">4592</span> Low Probability of Intercept (LPI) Signal Detection and Analysis Using Choi-Williams Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20S.%20S.%20Kumar">V. S. S. Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Ramya"> V. Ramya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the modern electronic warfare, the signal scenario is changing at a rapid pace with the introduction of Low Probability of Intercept (LPI) radars. In the modern battlefield, radar system faces serious threats from passive intercept receivers such as Electronic Attack (EA) and Anti-Radiation Missiles (ARMs). To perform necessary target detection and tracking and simultaneously hide themselves from enemy attack, radar systems should be LPI. These LPI radars use a variety of complex signal modulation schemes together with pulse compression with the aid of advancement in signal processing capabilities of the radar such that the radar performs target detection and tracking while simultaneously hiding enemy from attack such as EA etc., thus posing a major challenge to the ES/ELINT receivers. Today an increasing number of LPI radars are being introduced into the modern platforms and weapon systems so these LPI radars created a requirement for the armed forces to develop new techniques, strategies and equipment to counter them. This paper presents various modulation techniques used in generation of LPI signals and development of Time Frequency Algorithms to analyse those signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anti-radiation%20missiles" title="anti-radiation missiles">anti-radiation missiles</a>, <a href="https://publications.waset.org/abstracts/search?q=cross%20terms" title=" cross terms"> cross terms</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20attack" title=" electronic attack"> electronic attack</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20intelligence" title=" electronic intelligence"> electronic intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20warfare" title=" electronic warfare"> electronic warfare</a>, <a href="https://publications.waset.org/abstracts/search?q=intercept%20receiver" title=" intercept receiver"> intercept receiver</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20probability%20of%20intercept" title=" low probability of intercept"> low probability of intercept</a> </p> <a href="https://publications.waset.org/abstracts/4226/low-probability-of-intercept-lpi-signal-detection-and-analysis-using-choi-williams-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4226.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">471</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">4591</span> Probability-Based Damage Detection of Structures Using Model Updating with Enhanced Ideal Gas Molecular Movement Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Ghasemi">M. R. Ghasemi</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Ghiasi"> R. Ghiasi</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Varaee"> H. Varaee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Model updating method has received increasing attention in damage detection structures based on measured modal parameters. Therefore, a probability-based damage detection (PBDD) procedure based on a model updating procedure is presented in this paper, in which a one-stage model-based damage identification technique based on the dynamic features of a structure is investigated. The presented framework uses a finite element updating method with a Monte Carlo simulation that considers the uncertainty caused by measurement noise. Enhanced ideal gas molecular movement (EIGMM) is used as the main algorithm for model updating. Ideal gas molecular movement (IGMM) is a multiagent algorithm based on the ideal gas molecular movement. Ideal gas molecules disperse rapidly in different directions and cover all the space inside. This is embedded in the high speed of molecules, collisions between them and with the surrounding barriers. In IGMM algorithm to accomplish the optimal solutions, the initial population of gas molecules is randomly generated and the governing equations related to the velocity of gas molecules and collisions between those are utilized. In this paper, an enhanced version of IGMM, which removes unchanged variables after specified iterations, is developed. The proposed method is implemented on two numerical examples in the field of structural damage detection. The results show that the proposed method can perform well and competitive in PBDD of structures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=enhanced%20ideal%20gas%20molecular%20movement%20%28EIGMM%29" title="enhanced ideal gas molecular movement (EIGMM)">enhanced ideal gas molecular movement (EIGMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=ideal%20gas%20molecular%20movement%20%28IGMM%29" title=" ideal gas molecular movement (IGMM)"> ideal gas molecular movement (IGMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20updating%20method" title=" model updating method"> model updating method</a>, <a href="https://publications.waset.org/abstracts/search?q=probability-based%20damage%20detection%20%28PBDD%29" title=" probability-based damage detection (PBDD)"> probability-based damage detection (PBDD)</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20quantification" title=" uncertainty quantification"> uncertainty quantification</a> </p> <a href="https://publications.waset.org/abstracts/55805/probability-based-damage-detection-of-structures-using-model-updating-with-enhanced-ideal-gas-molecular-movement-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55805.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">277</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">4590</span> Cooperative Spectrum Sensing Using Hybrid IWO/PSO Algorithm in Cognitive Radio Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deepa%20Das">Deepa Das</a>, <a href="https://publications.waset.org/abstracts/search?q=Susmita%20Das"> Susmita Das</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cognitive Radio (CR) is an emerging technology to combat the spectrum scarcity issues. This is achieved by consistently sensing the spectrum, and detecting the under-utilized frequency bands without causing undue interference to the primary user (PU). In soft decision fusion (SDF) based cooperative spectrum sensing, various evolutionary algorithms have been discussed, which optimize the weight coefficient vector for maximizing the detection performance. In this paper, we propose the hybrid invasive weed optimization and particle swarm optimization (IWO/PSO) algorithm as a fast and global optimization method, which improves the detection probability with a lesser sensing time. Then, the efficiency of this algorithm is compared with the standard invasive weed optimization (IWO), particle swarm optimization (PSO), genetic algorithm (GA) and other conventional SDF based methods on the basis of convergence and detection probability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title="cognitive radio">cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrum%20sensing" title=" spectrum sensing"> spectrum sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20decision%20fusion" title=" soft decision fusion"> soft decision fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=GA" title=" GA"> GA</a>, <a href="https://publications.waset.org/abstracts/search?q=PSO" title=" PSO"> PSO</a>, <a href="https://publications.waset.org/abstracts/search?q=IWO" title=" IWO"> IWO</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20IWO%2FPSO" title=" hybrid IWO/PSO"> hybrid IWO/PSO</a> </p> <a href="https://publications.waset.org/abstracts/9362/cooperative-spectrum-sensing-using-hybrid-iwopso-algorithm-in-cognitive-radio-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9362.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">467</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">4589</span> Lane Detection Using Labeling Based RANSAC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yeongyu%20Choi">Yeongyu Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ju%20H.%20Park"> Ju H. Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Ho-Youl%20Jung"> Ho-Youl Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose labeling based RANSAC algorithm for lane detection. Advanced driver assistance systems (ADAS) have been widely researched to avoid unexpected accidents. Lane detection is a necessary system to assist keeping lane and lane departure prevention. The proposed vision based lane detection method applies Canny edge detection, inverse perspective mapping (IPM), K-means algorithm, mathematical morphology operations and 8 connected-component labeling. Next, random samples are selected from each labeling region for RANSAC. The sampling method selects the points of lane with a high probability. Finally, lane parameters of straight line or curve equations are estimated. Through the simulations tested on video recorded at daytime and nighttime, we show that the proposed method has better performance than the existing RANSAC algorithm in various environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Canny%20edge%20detection" title="Canny edge detection">Canny edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means%20algorithm" title=" k-means algorithm"> k-means algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=RANSAC" title=" RANSAC"> RANSAC</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20perspective%20mapping" title=" inverse perspective mapping"> inverse perspective mapping</a> </p> <a href="https://publications.waset.org/abstracts/92894/lane-detection-using-labeling-based-ransac-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92894.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">243</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">4588</span> Quantum Mechanics Approach for Ruin Probability</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmet%20Kaya">Ahmet Kaya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Incoming cash flows and outgoing claims play an important role to determine how is companies’ profit or loss. In this matter, ruin probability provides to describe vulnerability of the companies against ruin. Quantum mechanism is one of the significant approaches to model ruin probability as stochastically. Using the Hamiltonian method, we have performed formalisation of quantum mechanics < x|e-ᵗᴴ|x' > and obtained the transition probability of 2x2 and 3x3 matrix as traditional and eigenvector basis where A is a ruin operator and H|x' > is a Schroedinger equation. This operator A and Schroedinger equation are defined by a Hamiltonian matrix H. As a result, probability of not to be in ruin can be simulated and calculated as stochastically. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ruin%20probability" title="ruin probability">ruin probability</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20mechanics" title=" quantum mechanics"> quantum mechanics</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamiltonian%20technique" title=" Hamiltonian technique"> Hamiltonian technique</a>, <a href="https://publications.waset.org/abstracts/search?q=operator%20approach" title=" operator approach"> operator approach</a> </p> <a href="https://publications.waset.org/abstracts/53562/quantum-mechanics-approach-for-ruin-probability" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53562.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">340</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">4587</span> Energy Detection Based Sensing and Primary User Traffic Classification for Cognitive Radio</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Urvee%20B.%20Trivedi">Urvee B. Trivedi</a>, <a href="https://publications.waset.org/abstracts/search?q=U.%20D.%20Dalal"> U. D. Dalal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As wireless communication services grow quickly; the seriousness of spectrum utilization has been on the rise gradually. An emerging technology, cognitive radio has come out to solve today’s spectrum scarcity problem. To support the spectrum reuse functionality, secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance. Once sensing is done, different prediction rules apply to classify the traffic pattern of primary user. Primary user follows two types of traffic patterns: periodic and stochastic ON-OFF patterns. A cognitive radio can learn the patterns in different channels over time. Two types of classification methods are discussed in this paper, by considering edge detection and by using autocorrelation function. Edge detection method has a high accuracy but it cannot tolerate sensing errors. Autocorrelation-based classification is applicable in the real environment as it can tolerate some amount of sensing errors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio%20%28CR%29" title="cognitive radio (CR)">cognitive radio (CR)</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20of%20detection%20%28PD%29" title=" probability of detection (PD)"> probability of detection (PD)</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20of%20false%20alarm%20%28PF%29" title=" probability of false alarm (PF)"> probability of false alarm (PF)</a>, <a href="https://publications.waset.org/abstracts/search?q=primary%20user%20%28PU%29" title=" primary user (PU)"> primary user (PU)</a>, <a href="https://publications.waset.org/abstracts/search?q=secondary%20user%20%28SU%29" title=" secondary user (SU)"> secondary user (SU)</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20Fourier%20transform%20%28FFT%29" title=" fast Fourier transform (FFT)"> fast Fourier transform (FFT)</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20to%20noise%20ratio%20%28SNR%29" title=" signal to noise ratio (SNR)"> signal to noise ratio (SNR)</a> </p> <a href="https://publications.waset.org/abstracts/45944/energy-detection-based-sensing-and-primary-user-traffic-classification-for-cognitive-radio" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45944.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">345</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">4586</span> Detection of Resistive Faults in Medium Voltage Overhead Feeders</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mubarak%20Suliman">Mubarak Suliman</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Hassan"> Mohamed Hassan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detection of downed conductors occurring with high fault resistance (reaching kilo-ohms) has always been a challenge, especially in countries like Saudi Arabia, on which earth resistivity is very high in general (reaching more than 1000 Ω-meter). The new approaches for the detection of resistive and high impedance faults are based on the analysis of the fault current waveform. These methods are still under research and development, and they are currently lacking security and dependability. The other approach is communication-based solutions which depends on voltage measurement at the end of overhead line branches and communicate the measured signals to substation feeder relay or a central control center. However, such a detection method is costly and depends on the availability of communication medium and infrastructure. The main objective of this research is to utilize the available standard protection schemes to increase the probability of detection of downed conductors occurring with a low magnitude of fault currents and at the same time avoiding unwanted tripping in healthy conditions and feeders. By specifying the operating region of the faulty feeder, use of tripping curve for discrimination between faulty and healthy feeders, and with proper selection of core balance current transformer (CBCT) and voltage transformers with fewer measurement errors, it is possible to set the pick-up of sensitive earth fault current to minimum values of few amps (i.e., Pick-up Settings = 3 A or 4 A, …) for the detection of earth faults with fault resistance more than (1 - 2 kΩ) for 13.8kV overhead network and more than (3-4) kΩ fault resistance in 33kV overhead network. By implementation of the outcomes of this study, the probability of detection of downed conductors is increased by the utilization of existing schemes (i.e., Directional Sensitive Earth Fault Protection). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sensitive%20earth%20fault" title="sensitive earth fault">sensitive earth fault</a>, <a href="https://publications.waset.org/abstracts/search?q=zero%20sequence%20current" title=" zero sequence current"> zero sequence current</a>, <a href="https://publications.waset.org/abstracts/search?q=grounded%20system" title=" grounded system"> grounded system</a>, <a href="https://publications.waset.org/abstracts/search?q=resistive%20fault%20detection" title=" resistive fault detection"> resistive fault detection</a>, <a href="https://publications.waset.org/abstracts/search?q=healthy%20feeder" title=" healthy feeder"> healthy feeder</a> </p> <a href="https://publications.waset.org/abstracts/135913/detection-of-resistive-faults-in-medium-voltage-overhead-feeders" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135913.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">115</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">4585</span> Rational Probabilistic Method for Calculating Thermal Cracking Risk of Mass Concrete Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naoyuki%20Sugihashi">Naoyuki Sugihashi</a>, <a href="https://publications.waset.org/abstracts/search?q=Toshiharu%20Kishi"> Toshiharu Kishi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The probability of occurrence of thermal cracks in mass concrete in Japan is evaluated by the cracking probability diagram that represents the relationship between the thermal cracking index and the probability of occurrence of cracks in the actual structure. In this paper, we propose a method to directly calculate the cracking probability, following a probabilistic theory by modeling the variance of tensile stress and tensile strength. In this method, the relationship between the variance of tensile stress and tensile strength, the thermal cracking index, and the cracking probability are formulated and presented. In addition, standard deviation of tensile stress and tensile strength was identified, and the method of calculating cracking probability in a general construction controlled environment was also demonstrated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thermal%20crack%20control" title="thermal crack control">thermal crack control</a>, <a href="https://publications.waset.org/abstracts/search?q=mass%20concrete" title=" mass concrete"> mass concrete</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20cracking%20probability" title=" thermal cracking probability"> thermal cracking probability</a>, <a href="https://publications.waset.org/abstracts/search?q=durability%20of%20concrete" title=" durability of concrete"> durability of concrete</a>, <a href="https://publications.waset.org/abstracts/search?q=calculating%20method%20of%20cracking%20probability" title=" calculating method of cracking probability"> calculating method of cracking probability</a> </p> <a href="https://publications.waset.org/abstracts/74943/rational-probabilistic-method-for-calculating-thermal-cracking-risk-of-mass-concrete-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74943.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">346</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">4584</span> Research on ARQ Transmission Technique in Mars Detection Telecommunications System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhongfei%20Cai">Zhongfei Cai</a>, <a href="https://publications.waset.org/abstracts/search?q=Hui%20He"> Hui He</a>, <a href="https://publications.waset.org/abstracts/search?q=Changsheng%20Li"> Changsheng Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper studied in the automatic repeat request (ARQ) transmission technique in Mars detection telecommunications system. An ARQ method applied to proximity-1 space link protocol was proposed by this paper. In order to ensure the efficiency of data reliable transmission, this ARQ method combined these different ARQ maneuvers characteristics. Considering the Mars detection communication environments, this paper analyzed the characteristics of the saturation throughput rate, packet dropping probability, average delay and energy efficiency with different ARQ algorithms. Combined thus results with the theories of ARQ transmission technique, an ARQ transmission project in Mars detection telecommunications system was established. The simulation results showed that this algorithm had excellent saturation throughput rate and energy efficiency with low complexity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARQ" title="ARQ">ARQ</a>, <a href="https://publications.waset.org/abstracts/search?q=mars" title=" mars"> mars</a>, <a href="https://publications.waset.org/abstracts/search?q=CCSDS" title=" CCSDS"> CCSDS</a>, <a href="https://publications.waset.org/abstracts/search?q=proximity-1" title=" proximity-1"> proximity-1</a>, <a href="https://publications.waset.org/abstracts/search?q=deepspace" title=" deepspace"> deepspace</a> </p> <a href="https://publications.waset.org/abstracts/31557/research-on-arq-transmission-technique-in-mars-detection-telecommunications-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31557.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">340</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">4583</span> The Modeling and Effectiveness Evaluation for Vessel Evasion to Acoustic Homing Torpedo</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Minghui">Li Minghui</a>, <a href="https://publications.waset.org/abstracts/search?q=Min%20Shaorong"> Min Shaorong</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhang%20Jun"> Zhang Jun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims for studying the operational efficiency of surface warship’s motorized evasion to acoustic homing torpedo. It orderly developed trajectory model, self-guide detection model, vessel evasion model, as well as anti-torpedo error model in three-dimensional space to make up for the deficiency of precious researches analyzing two-dimensionally confrontational models. Then, making use of the Monte Carlo method, it carried out the simulation for the confrontation process of evasion in the environment of MATLAB. At last, it quantitatively analyzed the main factors which determine vessel’s survival probability. The results show that evasion relative bearing and speed will affect vessel’s survival probability significantly. Thus, choosing appropriate evasion relative bearing and speed according to alarming range and alarming relative bearing for torpedo, improving alarming range and positioning accuracy and reducing the response time against torpedo will improve the vessel’s survival probability significantly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acoustic%20homing%20torpedo" title="acoustic homing torpedo">acoustic homing torpedo</a>, <a href="https://publications.waset.org/abstracts/search?q=vessel%20evasion" title=" vessel evasion"> vessel evasion</a>, <a href="https://publications.waset.org/abstracts/search?q=monte%20carlo%20method" title=" monte carlo method"> monte carlo method</a>, <a href="https://publications.waset.org/abstracts/search?q=torpedo%20defense" title=" torpedo defense"> torpedo defense</a>, <a href="https://publications.waset.org/abstracts/search?q=vessel%27s%20survival%20probability" title=" vessel's survival probability"> vessel's survival probability</a> </p> <a href="https://publications.waset.org/abstracts/5331/the-modeling-and-effectiveness-evaluation-for-vessel-evasion-to-acoustic-homing-torpedo" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5331.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">455</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">4582</span> Applied Complement of Probability and Information Entropy for Prediction in Student Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kennedy%20Efosa%20Ehimwenma">Kennedy Efosa Ehimwenma</a>, <a href="https://publications.waset.org/abstracts/search?q=Sujatha%20Krishnamoorthy"> Sujatha Krishnamoorthy</a>, <a href="https://publications.waset.org/abstracts/search?q=Safiya%20Al%E2%80%91Sharji"> Safiya Al‑Sharji</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The probability computation of events is in the interval of [0, 1], which are values that are determined by the number of outcomes of events in a sample space S. The probability Pr(A) that an event A will never occur is 0. The probability Pr(B) that event B will certainly occur is 1. This makes both events A and B a certainty. Furthermore, the sum of probabilities Pr(E₁) + Pr(E₂) + … + Pr(Eₙ) of a finite set of events in a given sample space S equals 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. This paper first discusses Bayes, the complement of probability, and the difference of probability for occurrences of learning-events before applying them in the prediction of learning objects in student learning. Given the sum of 1; to make a recommendation for student learning, this paper proposes that the difference of argMaxPr(S) and the probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates i) the probability of skill-set events that have occurred that would lead to higher-level learning; ii) the probability of the events that have not occurred that requires subject-matter relearning; iii) accuracy of the decision tree in the prediction of student performance into class labels and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complement%20of%20probability" title="complement of probability">complement of probability</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayes%E2%80%99%20rule" title=" Bayes’ rule"> Bayes’ rule</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-assessments" title=" pre-assessments"> pre-assessments</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20education" title=" computational education"> computational education</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20theory" title=" information theory"> information theory</a> </p> <a href="https://publications.waset.org/abstracts/135595/applied-complement-of-probability-and-information-entropy-for-prediction-in-student-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135595.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">161</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">4581</span> Quantum Mechanism Approach for Non-Ruin Probability and Comparison of Path Integral Method and Stochastic Simulations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmet%20Kaya">Ahmet Kaya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Quantum mechanism is one of the most important approaches to calculating non-ruin probability. We apply standard Dirac notation to model given Hamiltonians. By using the traditional method and eigenvector basis, non-ruin probability is found for several examples. Also, non-ruin probability is calculated for two different Hamiltonian by using the tensor product. Finally, the path integral method is applied to the examples and comparison is made for stochastic simulations and path integral calculation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quantum%20physics" title="quantum physics">quantum physics</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamiltonian%20system" title=" Hamiltonian system"> Hamiltonian system</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20integral" title=" path integral"> path integral</a>, <a href="https://publications.waset.org/abstracts/search?q=tensor%20product" title=" tensor product"> tensor product</a>, <a href="https://publications.waset.org/abstracts/search?q=ruin%20probability" title=" ruin probability"> ruin probability</a> </p> <a href="https://publications.waset.org/abstracts/56920/quantum-mechanism-approach-for-non-ruin-probability-and-comparison-of-path-integral-method-and-stochastic-simulations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56920.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">4580</span> Deep Learning-Based Automated Structure Deterioration Detection for Building Structures: A Technological Advancement for Ensuring Structural Integrity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kavita%20Bodke">Kavita Bodke</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Structural health monitoring (SHM) is experiencing growth, necessitating the development of distinct methodologies to address its expanding scope effectively. In this study, we developed automatic structure damage identification, which incorporates three unique types of a building’s structural integrity. The first pertains to the presence of fractures within the structure, the second relates to the issue of dampness within the structure, and the third involves corrosion inside the structure. This study employs image classification techniques to discern between intact and impaired structures within structural data. The aim of this research is to find automatic damage detection with the probability of each damage class being present in one image. Based on this probability, we know which class has a higher probability or is more affected than the other classes. Utilizing photographs captured by a mobile camera serves as the input for an image classification system. Image classification was employed in our study to perform multi-class and multi-label classification. The objective was to categorize structural data based on the presence of cracks, moisture, and corrosion. In the context of multi-class image classification, our study employed three distinct methodologies: Random Forest, Multilayer Perceptron, and CNN. For the task of multi-label image classification, the models employed were Rasnet, Xceptionet, and Inception. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SHM" title="SHM">SHM</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</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=multi-class%20classification" title=" multi-class classification"> multi-class classification</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-label%20classification" title=" multi-label classification"> multi-label classification</a> </p> <a href="https://publications.waset.org/abstracts/187844/deep-learning-based-automated-structure-deterioration-detection-for-building-structures-a-technological-advancement-for-ensuring-structural-integrity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187844.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">36</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">4579</span> Starlink Satellite Collision Probability Simulation Based on Simplified Geometry Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Toby%20Li">Toby Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Julian%20Zhu"> Julian Zhu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a model based on a simplified geometry is introduced to give a very conservative collision probability prediction for the Starlink satellite in its most densely clustered region. Under the model in this paper, the probability of collision for Starlink satellite where it clustered most densely is found to be 8.484 ∗ 10^−4. It is found that the predicted collision probability increased nonlinearly with the increased safety distance set. This simple model provides evidence that the continuous development of maneuver avoidance systems is necessary for the future of the orbital safety of satellites under the harsher Lower Earth Orbit environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Starlink" title="Starlink">Starlink</a>, <a href="https://publications.waset.org/abstracts/search?q=collision%20probability" title=" collision probability"> collision probability</a>, <a href="https://publications.waset.org/abstracts/search?q=debris" title=" debris"> debris</a>, <a href="https://publications.waset.org/abstracts/search?q=geometry%20model" title=" geometry model"> geometry model</a> </p> <a href="https://publications.waset.org/abstracts/171068/starlink-satellite-collision-probability-simulation-based-on-simplified-geometry-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171068.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">82</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">4578</span> Riesz Mixture Model for Brain Tumor Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mouna%20Zitouni">Mouna Zitouni</a>, <a href="https://publications.waset.org/abstracts/search?q=Mariem%20Tounsi">Mariem Tounsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research introduces an application of the Riesz mixture model for medical image segmentation for accurate diagnosis and treatment of brain tumors. We propose a pixel classification technique based on the Riesz distribution, derived from an extended Bartlett decomposition. To our knowledge, this is the first study addressing this approach. The Expectation-Maximization algorithm is implemented for parameter estimation. A comparative analysis, using both synthetic and real brain images, demonstrates the superiority of the Riesz model over a recent method based on the Wishart distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EM%20algorithm" title="EM algorithm">EM algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=Riesz%20probability%20distribution" title=" Riesz probability distribution"> Riesz probability distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Wishart%20probability%20distribution" title=" Wishart probability distribution"> Wishart probability distribution</a> </p> <a href="https://publications.waset.org/abstracts/192126/riesz-mixture-model-for-brain-tumor-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192126.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">17</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">4577</span> Profit-Based Artificial Neural Network (ANN) Trained by Migrating Birds Optimization: A Case Study in Credit Card Fraud Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashkan%20Zakaryazad">Ashkan Zakaryazad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ekrem%20Duman"> Ekrem Duman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently. <p class="card-text"><strong>Keywords:</strong> <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=profit-based%20neural%20network" title=" profit-based neural network"> profit-based neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=sum%20of%20squared%20errors%20%28SSE%29" title=" sum of squared errors (SSE)"> sum of squared errors (SSE)</a>, <a href="https://publications.waset.org/abstracts/search?q=MBO" title=" MBO"> MBO</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20descent" title=" gradient descent"> gradient descent</a> </p> <a href="https://publications.waset.org/abstracts/31637/profit-based-artificial-neural-network-ann-trained-by-migrating-birds-optimization-a-case-study-in-credit-card-fraud-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31637.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">475</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">4576</span> Parameter Estimation for Contact Tracing in Graph-Based Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Augustine%20Okolie">Augustine Okolie</a>, <a href="https://publications.waset.org/abstracts/search?q=Johannes%20M%C3%BCller"> Johannes Müller</a>, <a href="https://publications.waset.org/abstracts/search?q=Mirjam%20Kretzchmar"> Mirjam Kretzchmar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We adopt a maximum-likelihood framework to estimate parameters of a stochastic susceptible-infected-recovered (SIR) model with contact tracing on a rooted random tree. Given the number of detectees per index case, our estimator allows to determine the degree distribution of the random tree as well as the tracing probability. Since we do not discover all infectees via contact tracing, this estimation is non-trivial. To keep things simple and stable, we develop an approximation suited for realistic situations (contract tracing probability small, or the probability for the detection of index cases small). In this approximation, the only epidemiological parameter entering the estimator is the basic reproduction number R0. The estimator is tested in a simulation study and applied to covid-19 contact tracing data from India. The simulation study underlines the efficiency of the method. For the empirical covid-19 data, we are able to compare different degree distributions and perform a sensitivity analysis. We find that particularly a power-law and a negative binomial degree distribution meet the data well and that the tracing probability is rather large. The sensitivity analysis shows no strong dependency on the reproduction number. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stochastic%20SIR%20model%20on%20graph" title="stochastic SIR model on graph">stochastic SIR model on graph</a>, <a href="https://publications.waset.org/abstracts/search?q=contact%20tracing" title=" contact tracing"> contact tracing</a>, <a href="https://publications.waset.org/abstracts/search?q=branching%20process" title=" branching process"> branching process</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20inference" title=" parameter inference"> parameter inference</a> </p> <a href="https://publications.waset.org/abstracts/167983/parameter-estimation-for-contact-tracing-in-graph-based-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167983.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">77</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">4575</span> An Approaching Index to Evaluate a forward Collision Probability</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuan-Lin%20Chen">Yuan-Lin Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an approaching forward collision probability index (AFCPI) for alerting and assisting driver in keeping safety distance to avoid the forward collision accident in highway driving. The time to collision (TTC) and time headway (TH) are used to evaluate the TTC forward collision probability index (TFCPI) and the TH forward collision probability index (HFCPI), respectively. The Mamdani fuzzy inference algorithm is presented combining TFCPI and HFCPI to calculate the approaching collision probability index of the vehicle. The AFCPI is easier to understand for the driver who did not even have any professional knowledge in vehicle professional field. At the same time, the driver’s behavior is taken into account for suiting each driver. For the approaching index, the value 0 is indicating the 0% probability of forward collision, and the values 0.5 and 1 are indicating the 50% and 100% probabilities of forward collision, respectively. The AFCPI is useful and easy-to-understand for alerting driver to avoid the forward collision accidents when driving in highway. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approaching%20index" title="approaching index">approaching index</a>, <a href="https://publications.waset.org/abstracts/search?q=forward%20collision%20probability" title=" forward collision probability"> forward collision probability</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20to%20collision" title=" time to collision"> time to collision</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20headway" title=" time headway"> time headway</a> </p> <a href="https://publications.waset.org/abstracts/74855/an-approaching-index-to-evaluate-a-forward-collision-probability" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74855.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">293</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">4574</span> Modern Spectrum Sensing Techniques for Cognitive Radio Networks: Practical Implementation and Performance Evaluation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Antoni%20Ivanov">Antoni Ivanov</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikolay%20Dandanov"> Nikolay Dandanov</a>, <a href="https://publications.waset.org/abstracts/search?q=Nicole%20Christoff"> Nicole Christoff</a>, <a href="https://publications.waset.org/abstracts/search?q=Vladimir%20Poulkov"> Vladimir Poulkov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spectrum underutilization has made cognitive radio a promising technology both for current and future telecommunications. This is due to the ability to exploit the unused spectrum in the bands dedicated to other wireless communication systems, and thus, increase their occupancy. The essential function, which allows the cognitive radio device to perceive the occupancy of the spectrum, is spectrum sensing. In this paper, the performance of modern adaptations of the four most widely used spectrum sensing techniques namely, energy detection (ED), cyclostationary feature detection (CSFD), matched filter (MF) and eigenvalues-based detection (EBD) is compared. The implementation has been accomplished through the PlutoSDR hardware platform and the GNU Radio software package in very low Signal-to-Noise Ratio (SNR) conditions. The optimal detection performance of the examined methods in a realistic implementation-oriented model is found for the common relevant parameters (number of observed samples, sensing time and required probability of false alarm). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title="cognitive radio">cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20spectrum%20access" title=" dynamic spectrum access"> dynamic spectrum access</a>, <a href="https://publications.waset.org/abstracts/search?q=GNU%20Radio" title=" GNU Radio"> GNU Radio</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrum%20sensing" title=" spectrum sensing"> spectrum sensing</a> </p> <a href="https://publications.waset.org/abstracts/81419/modern-spectrum-sensing-techniques-for-cognitive-radio-networks-practical-implementation-and-performance-evaluation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81419.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">245</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">4573</span> Determination of the Best Fit Probability Distribution for Annual Rainfall in Karkheh River at Iran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karim%20Hamidi%20Machekposhti">Karim Hamidi Machekposhti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Sedghi"> Hossein Sedghi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study was designed to find the best-fit probability distribution of annual rainfall based on 50 years sample (1966-2015) in the Karkheh river basin at Iran using six probability distributions: Normal, 2-Parameter Log Normal, 3-Parameter Log Normal, Pearson Type 3, Log Pearson Type 3 and Gumbel distribution. The best fit probability distribution was selected using Stormwater Management and Design Aid (SMADA) software and based on the Residual Sum of Squares (R.S.S) between observed and estimated values Based on the R.S.S values of fit tests, the Log Pearson Type 3 and then Pearson Type 3 distributions were found to be the best-fit probability distribution at the Jelogir Majin and Pole Zal rainfall gauging station. The annual values of expected rainfall were calculated using the best fit probability distributions and can be used by hydrologists and design engineers in future research at studied region and other region in the world. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Log%20Pearson%20Type%203" title="Log Pearson Type 3">Log Pearson Type 3</a>, <a href="https://publications.waset.org/abstracts/search?q=SMADA" title=" SMADA"> SMADA</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall" title=" rainfall"> rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=Karkheh%20River" title=" Karkheh River"> Karkheh River</a> </p> <a href="https://publications.waset.org/abstracts/97806/determination-of-the-best-fit-probability-distribution-for-annual-rainfall-in-karkheh-river-at-iran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97806.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">191</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">4572</span> A Case Study on the Numerical-Probability Approach for Deep Excavation Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Komeil%20Valipourian">Komeil Valipourian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Urban advances and the growing need for developing infrastructures has increased the importance of deep excavations. In this study, after the introducing probability analysis as an important issue, an attempt has been made to apply it for the deep excavation project of Bangkok’s Metro as a case study. For this, the numerical probability model has been developed based on the Finite Difference Method and Monte Carlo sampling approach. The results indicate that disregarding the issue of probability in this project will result in an inappropriate design of the retaining structure. Therefore, probabilistic redesign of the support is proposed and carried out as one of the applications of probability analysis. A 50% reduction in the flexural strength of the structure increases the failure probability just by 8% in the allowable range and helps improve economic conditions, while maintaining mechanical efficiency. With regard to the lack of efficient design in most deep excavations, by considering geometrical and geotechnical variability, an attempt was made to develop an optimum practical design standard for deep excavations based on failure probability. On this basis, a practical relationship is presented for estimating the maximum allowable horizontal displacement, which can help improve design conditions without developing the probability analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=numerical%20probability%20modeling" title="numerical probability modeling">numerical probability modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20excavation" title=" deep excavation"> deep excavation</a>, <a href="https://publications.waset.org/abstracts/search?q=allowable%20maximum%20displacement" title=" allowable maximum displacement"> allowable maximum displacement</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20difference%20method%20%28FDM%29" title=" finite difference method (FDM)"> finite difference method (FDM)</a> </p> <a href="https://publications.waset.org/abstracts/128141/a-case-study-on-the-numerical-probability-approach-for-deep-excavation-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128141.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">127</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=probability%20of%20detection&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=probability%20of%20detection&page=3">3</a></li> <li class="page-item"><a class="page-link" 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