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Online Tracking Simulation System of a 660 MW Ultra-Supercritical Circulating Fluidized Bed Boiler
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作者 WANG Xiaosheng YANG Chen ZHANG Zonglong 《Journal of Thermal Science》 SCIE EI CAS CSCD 2023年第5期1819-1831,共13页
In this paper,an online tracking simulation system for the 660 MW ultra-supercritical circulating fluidized bed(USCFB)boiler is established,and a tracking simulation test is conducted for the cold start-up process of ... In this paper,an online tracking simulation system for the 660 MW ultra-supercritical circulating fluidized bed(USCFB)boiler is established,and a tracking simulation test is conducted for the cold start-up process of the boiler.The system comprises two parts:the USCFB boiler model and a tracking mechanism based on sliding mode control algorithm.The USCFB boiler model includes a water-steam system,an air-flue gas system,a material supply system,and an ash circulation system.The online tracking simulation system receives the same control signal as the plant and runs synchronously in digital space.The tracking mechanism updates model parameters to eliminate deviations between simulation values and measured values.The SMC-based multi-input,multi-output algorithm is based on a state-space model,providing two distinct advantages.Firstly,it enables more efficient elimination of deviations;secondly,it exhibits robustness against uncertainties associated with simulation model behavior and measurement noise.Finally,this paper conducts tracking simulation research on the cold start-up process of the boiler. 展开更多
关键词 online tracking simulation system USCFB boiler sliding mode control cold start-up
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基于改进YOLOv3的街道行人检测与跟踪方法 被引量:12
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作者 武明虎 黄咏曦 王娟 《科学技术与工程》 北大核心 2021年第17期7230-7236,共7页
针对室外街道的行人检测与跟踪,提出一种改进YOLOv3与简单在线实时跟踪(simple online and real-time tracking,SORT)算法相结合的检测及跟踪方法。首先,引入距离和比例交并比(distance and proportional-IOU,DPIOU)损失,将原有的损失... 针对室外街道的行人检测与跟踪,提出一种改进YOLOv3与简单在线实时跟踪(simple online and real-time tracking,SORT)算法相结合的检测及跟踪方法。首先,引入距离和比例交并比(distance and proportional-IOU,DPIOU)损失,将原有的损失函数中的均方误差(mean square error,MSE)部分进行变化,从而得到更精确的检测框;其次,将网络结构中的RestNet进行优化,改变下采样区域,增加池化层,进而减少特征信息的丢失;最后将检测结果输入SORT算法进行建模和匹配。实验结果表明,在室外街道的场景下,改进的算法与YOLOv3相比较,损失值收敛更快,平均准确率高出4.85%,跟踪准确率上升3.4%,同时,模型的速度有所提高,最快可达14.39 FPS。 展开更多
关键词 行人检测 目标跟踪 YOLOv3 简单在线实时跟踪(simple online and real-time tracking SORT)算法
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