Search results for: Wei-Hua Ho

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Ho</title> <meta name="description" content="Search results for: Wei-Hua Ho"> <meta name="keywords" content="Wei-Hua Ho"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Wei-Hua Ho"> <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> 5</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Wei-Hua Ho</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> A Study of Hamilton-Jacobi-Bellman Equation Systems Arising in Differential Game Models of Changing Society</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Weihua%20Ruan">Weihua Ruan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kuan-Chou%20Chen"> Kuan-Chou Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is concerned with a system of Hamilton-Jacobi-Bellman equations coupled with an autonomous dynamical system. The mathematical system arises in the differential game formulation of political economy models as an infinite-horizon continuous-time differential game with discounted instantaneous payoff rates and continuously and discretely varying state variables. The existence of a weak solution of the PDE system is proven and a computational scheme of approximate solution is developed for a class of such systems. A model of democratization is mathematically analyzed as an illustration of application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamilton-Jacobi-Bellman%20equations" title="Hamilton-Jacobi-Bellman equations">Hamilton-Jacobi-Bellman equations</a>, <a href="https://publications.waset.org/abstracts/search?q=infinite-horizon%20differential%20games" title=" infinite-horizon differential games"> infinite-horizon differential games</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20and%20discrete%20state%20variables" title=" continuous and discrete state variables"> continuous and discrete state variables</a>, <a href="https://publications.waset.org/abstracts/search?q=political-economy%20models" title=" political-economy models"> political-economy models</a> </p> <a href="https://publications.waset.org/abstracts/69675/a-study-of-hamilton-jacobi-bellman-equation-systems-arising-in-differential-game-models-of-changing-society" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69675.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">4</span> Microstructure of AlCrFeNiMn High Entropy Alloy and Its Corrosion Behavior in Supercritical CO₂ Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yang%20Wanhuan">Yang Wanhuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Zou%20Jichun"> Zou Jichun</a>, <a href="https://publications.waset.org/abstracts/search?q=LI%20Shen"> LI Shen</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhong%20Weihua"> Zhong Weihua</a>, <a href="https://publications.waset.org/abstracts/search?q=Yang%20Wen"> Yang Wen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> High entropy alloys (HEAs) have aroused significant concern in high-temperature supercritical carbon dioxide (S-CO2) environments due to their unique microstructures and outstanding properties. However, the anti-corrosion ability and mechanism of these HEAs in the S-CO₂ remain unclear. Herein, we developed a new AlCrFeNiMn (AM)-HEA with double phases by vacuum arc melting furnace. The corrosion behavior of AM-HEA in the S-CO₂ at 500 ℃ under 25 MPa for 400 hours was deciphered by multiple characterization techniques. The results show that the discrepancy of corrosion between the matrix and boundary was accounted for by their microstructure and components. The role and mechanism of Mn contents for their oxide scales in boundary zones were emphasized. More importantly, the nano-precipitated second phase and numerous boundaries for the outstanding anti-corrosion ability of the matrix were proposed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high%20entropy%20alloy" title="high entropy alloy">high entropy alloy</a>, <a href="https://publications.waset.org/abstracts/search?q=microstructure" title=" microstructure"> microstructure</a>, <a href="https://publications.waset.org/abstracts/search?q=corrosion" title=" corrosion"> corrosion</a>, <a href="https://publications.waset.org/abstracts/search?q=supercritical%20carbon%20oxide" title=" supercritical carbon oxide"> supercritical carbon oxide</a>, <a href="https://publications.waset.org/abstracts/search?q=AlCrFeNiMn" title=" AlCrFeNiMn"> AlCrFeNiMn</a> </p> <a href="https://publications.waset.org/abstracts/174155/microstructure-of-alcrfenimn-high-entropy-alloy-and-its-corrosion-behavior-in-supercritical-co2-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174155.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">146</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">3</span> An Improved C-Means Model for MRI Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ying%20Shen">Ying Shen</a>, <a href="https://publications.waset.org/abstracts/search?q=Weihua%20Zhu"> Weihua Zhu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Medical images are important to help identifying different diseases, for example, Magnetic resonance imaging (MRI) can be used to investigate the brain, spinal cord, bones, joints, breasts, blood vessels, and heart. Image segmentation, in medical image analysis, is usually the first step to find out some characteristics with similar color, intensity or texture so that the diagnosis could be further carried out based on these features. This paper introduces an improved C-means model to segment the MRI images. The model is based on information entropy to evaluate the segmentation results by achieving global optimization. Several contributions are significant. Firstly, Genetic Algorithm (GA) is used for achieving global optimization in this model where fuzzy C-means clustering algorithm (FCMA) is not capable of doing that. Secondly, the information entropy after segmentation is used for measuring the effectiveness of MRI image processing. Experimental results show the outperformance of the proposed model by comparing with traditional approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20image%20%28MRI%29" title="magnetic resonance image (MRI)">magnetic resonance image (MRI)</a>, <a href="https://publications.waset.org/abstracts/search?q=c-means%20model" title=" c-means model"> c-means model</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20entropy" title=" information entropy"> information entropy</a> </p> <a href="https://publications.waset.org/abstracts/79824/an-improved-c-means-model-for-mri-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79824.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">225</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">2</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">1</span> Application of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Multipoint Optimal Minimum Entropy Deconvolution in Railway Bearings Fault Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yao%20Cheng">Yao Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Weihua%20Zhang"> Weihua Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although the measured vibration signal contains rich information on machine health conditions, the white noise interferences and the discrete harmonic coming from blade, shaft and mash make the fault diagnosis of rolling element bearings difficult. In order to overcome the interferences of useless signals, a new fault diagnosis method combining Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) and Multipoint Optimal Minimum Entropy Deconvolution (MOMED) is proposed for the fault diagnosis of high-speed train bearings. Firstly, the CEEMDAN technique is applied to adaptively decompose the raw vibration signal into a series of finite intrinsic mode functions (IMFs) and a residue. Compared with Ensemble Empirical Mode Decomposition (EEMD), the CEEMDAN can provide an exact reconstruction of the original signal and a better spectral separation of the modes, which improves the accuracy of fault diagnosis. An effective sensitivity index based on the Pearson's correlation coefficients between IMFs and raw signal is adopted to select sensitive IMFs that contain bearing fault information. The composite signal of the sensitive IMFs is applied to further analysis of fault identification. Next, for propose of identifying the fault information precisely, the MOMED is utilized to enhance the periodic impulses in composite signal. As a non-iterative method, the MOMED has better deconvolution performance than the classical deconvolution methods such Minimum Entropy Deconvolution (MED) and Maximum Correlated Kurtosis Deconvolution (MCKD). Third, the envelope spectrum analysis is applied to detect the existence of bearing fault. The simulated bearing fault signals with white noise and discrete harmonic interferences are used to validate the effectiveness of the proposed method. Finally, the superiorities of the proposed method are further demonstrated by high-speed train bearing fault datasets measured from test rig. The analysis results indicate that the proposed method has strong practicability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bearing" title="bearing">bearing</a>, <a href="https://publications.waset.org/abstracts/search?q=complete%20ensemble%20empirical%20mode%20decomposition%20with%20adaptive%20noise" title=" complete ensemble empirical mode decomposition with adaptive noise"> complete ensemble empirical mode decomposition with adaptive noise</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=multipoint%20optimal%20minimum%20entropy%20deconvolution" title=" multipoint optimal minimum entropy deconvolution"> multipoint optimal minimum entropy deconvolution</a> </p> <a href="https://publications.waset.org/abstracts/80335/application-of-complete-ensemble-empirical-mode-decomposition-with-adaptive-noise-and-multipoint-optimal-minimum-entropy-deconvolution-in-railway-bearings-fault-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80335.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">374</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" 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Search results for: Wei-Hua Ho