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Search results for: Shanliang Yao

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class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/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="Shanliang Yao"> <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> 2</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Shanliang Yao</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Study on Optimal Control Strategy of PM2.5 in Wuhan, China</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Qiuling%20Xie">Qiuling Xie</a>, <a href="https://publications.waset.org/search?q=Shanliang%20Zhu"> Shanliang Zhu</a>, <a href="https://publications.waset.org/search?q=Zongdi%20Sun"> Zongdi Sun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper, we analyzed the correlation relationship among PM2.5 from other five Air Quality Indices (AQIs) based on the grey relational degree, and built a multivariate nonlinear regression equation model of PM2.5 and the five monitoring indexes. For the optimal control problem of PM2.5, we took the partial large Cauchy distribution of membership equation as satisfaction function. We established a nonlinear programming model with the goal of maximum performance to price ratio. And the optimal control scheme is given.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Grey%20relational%20degree" title="Grey relational degree">Grey relational degree</a>, <a href="https://publications.waset.org/search?q=multiple%20linear%20regression" title=" multiple linear regression"> multiple linear regression</a>, <a href="https://publications.waset.org/search?q=membership%20function" title=" membership function"> membership function</a>, <a href="https://publications.waset.org/search?q=nonlinear%20programming." title=" nonlinear programming."> nonlinear programming.</a> </p> <a href="https://publications.waset.org/10005440/study-on-optimal-control-strategy-of-pm25-in-wuhan-china" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10005440/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10005440/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10005440/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10005440/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10005440/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10005440/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10005440/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10005440/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10005440/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10005440/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10005440.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">1408</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> RV-YOLOX: Object Detection on Inland Waterways Based on Optimized YOLOX through Fusion of Vision and 3+1D Millimeter Wave Radar</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Zixian%20Zhang">Zixian Zhang</a>, <a href="https://publications.waset.org/search?q=Shanliang%20Yao"> Shanliang Yao</a>, <a href="https://publications.waset.org/search?q=Zile%20Huang"> Zile Huang</a>, <a href="https://publications.waset.org/search?q=Zhaodong%20Wu"> Zhaodong Wu</a>, <a href="https://publications.waset.org/search?q=Xiaohui%20Zhu"> Xiaohui Zhu</a>, <a href="https://publications.waset.org/search?q=Yong%20Yue"> Yong Yue</a>, <a href="https://publications.waset.org/search?q=Jieming%20Ma"> Jieming Ma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Unmanned Surface Vehicles (USVs) hold significant value for their capacity to undertake hazardous and labor-intensive operations over aquatic environments. Object detection tasks are significant in these applications. Nonetheless, the efficacy of USVs in object detection is impeded by several intrinsic challenges, including the intricate dispersal of obstacles, reflections emanating from coastal structures, and the presence of fog over water surfaces, among others. To address these problems, this paper provides a fusion method for USVs to effectively detect objects in the inland surface environment, utilizing vision sensors and 3+1D Millimeter-wave radar. The MMW radar is a complementary tool to vision sensors, offering reliable environmental data. This approach involves the conversion of the radar鈥檚 3D point cloud into a 2D radar pseudo-image, thereby standardizing the format for radar and vision data by leveraging a point transformer. Furthermore, this paper proposes the development of a multi-source object detection network, named RV-YOLOX, which leverages radar-vision integration specifically tailored for inland waterway environments. The performance is evaluated on our self-recording waterways dataset. Compared with the YOLOX network, our fusion network significantly improves detection accuracy, especially for objects with bad light conditions.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Inland%20waterways" title="Inland waterways">Inland waterways</a>, <a href="https://publications.waset.org/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/search?q=YOLO" title=" YOLO"> YOLO</a>, <a href="https://publications.waset.org/search?q=sensor%0D%0Afusion" title=" sensor fusion"> sensor fusion</a>, <a href="https://publications.waset.org/search?q=self-attention" title=" self-attention"> self-attention</a>, <a href="https://publications.waset.org/search?q=deep%20learning." title=" deep learning."> deep learning.</a> </p> <a 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