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Search results for: Kai Liua
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<form method="get" 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="Kai Liua"> <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: Kai Liua</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Improvements in Double Q-Learning for Anomalous Radiation Source Searching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bo-Bin%20Xiaoa">Bo-Bin Xiaoa</a>, <a href="https://publications.waset.org/abstracts/search?q=Chia-Yi%20Liua"> Chia-Yi Liua</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the task of searching for anomalous radiation sources, personnel holding radiation detectors to search for radiation sources may be exposed to unnecessary radiation risk, and automated search using machines becomes a required project. The research uses various sophisticated algorithms, which are double Q learning, dueling network, and NoisyNet, of deep reinforcement learning to search for radiation sources. The simulation environment, which is a 10*10 grid and one shielding wall setting in it, improves the development of the AI model by training 1 million episodes. In each episode of training, the radiation source position, the radiation source intensity, agent position, shielding wall position, and shielding wall length are all set randomly. The three algorithms are applied to run AI model training in four environments where the training shielding wall is a full-shielding wall, a lead wall, a concrete wall, and a lead wall or a concrete wall appearing randomly. The 12 best performance AI models are selected by observing the reward value during the training period and are evaluated by comparing these AI models with the gradient search algorithm. The results show that the performance of the AI model, no matter which one algorithm, is far better than the gradient search algorithm. In addition, the simulation environment becomes more complex, the AI model which applied Double DQN combined Dueling and NosiyNet algorithm performs better. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=double%20Q%20learning" title="double Q learning">double Q learning</a>, <a href="https://publications.waset.org/abstracts/search?q=dueling%20network" title="dueling network">dueling network</a>, <a href="https://publications.waset.org/abstracts/search?q=NoisyNet" title="NoisyNet">NoisyNet</a>, <a href="https://publications.waset.org/abstracts/search?q=source%20searching" title="source searching">source searching</a> </p> <a href="https://publications.waset.org/abstracts/144663/improvements-in-double-q-learning-for-anomalous-radiation-source-searching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144663.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">113</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Assessing the Effects of Land Use Spatial Structure on Urban Heat Island Using New Launched Remote Sensing in Shenzhen, China</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kai%20Liua">Kai Liua</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongbo%20Sua"> Hongbo Sua</a>, <a href="https://publications.waset.org/abstracts/search?q=Weimin%20Wangb"> Weimin Wangb</a>, <a href="https://publications.waset.org/abstracts/search?q=Hong%20Liangb"> Hong Liangb </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Urban heat island (UHI) has attracted attention around the world since they profoundly affect human life and climatological. Better understanding the effects of landscape pattern on UHI is crucial for improving the ecological security and sustainability of cities. This study aims to investigate how landscape composition and configuration would affect UHI in Shenzhen, China, based on the analysis of land surface temperature (LST) in relation landscape metrics, mainly with the aid of three new satellite sensors launched by China. HJ-1B satellite system was utilized to estimate surface temperature and comprehensively explore the urban thermal spatial pattern. The landscape metrics of the high spatial resolution remote sensing satellites (GF-1 and ZY-3) were compared and analyzed to validate the performance of the new launched satellite sensors. Results show that the mean LST is correlated with main landscape metrics involving class-based metrics and landscape-based metrics, suggesting that the landscape composition and the spatial configuration both influence UHI. These relationships also reveal that urban green has a significant effect in mitigating UHI in Shenzhen due to its homogeneous spatial distribution and large spatial extent. Overall, our study not only confirm the applicability and effectiveness of the HJ-1B, GF-1 and ZY-3 satellite system for studying UHI but also reveal the impacts of the urban spatial structure on UHI, which is meaningful for the planning and management of the urban environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=urban%20heat%20island" title="urban heat island">urban heat island</a>, <a href="https://publications.waset.org/abstracts/search?q=Shenzhen" title=" Shenzhen"> Shenzhen</a>, <a href="https://publications.waset.org/abstracts/search?q=new%20remote%20sensing%20sensor" title=" new remote sensing sensor"> new remote sensing sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing%20satellites" title=" remote sensing satellites"> remote sensing satellites</a> </p> <a href="https://publications.waset.org/abstracts/15982/assessing-the-effects-of-land-use-spatial-structure-on-urban-heat-island-using-new-launched-remote-sensing-in-shenzhen-china" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15982.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">406</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" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational 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