Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet.Despite several studies on...Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet.Despite several studies on the mining of unique load characteristics,few studies have extensively considered the high computational burden and sample training.Based on lowfrequency sampling data,a non-intrusive load monitoring algorithm utilizing the graph total variation(GTV)is proposed in this study.The algorithm can effectively depict the load state without the need for prior training.First,the combined Kmeans clustering algorithm and graph signals are used to build concise and accurate graph structures as load models.The GTV representing the internal structure of the graph signal is introduced as the optimization model and solved using the augmented Lagrangian iterative algorithm.The introduction of the difference operator reduces the computing cost and addresses the inaccurate reconstruction of the graph signal.With low-frequency sampling data,the algorithm only requires a little prior data and no training,thereby reducing the computing cost.Experiments conducted using the reference energy disaggregation dataset and almanac of minutely power dataset demonstrated the stable superiority of the algorithm and its low computational burden.展开更多
Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges i...Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges including crowd multi-scale variations and high network complexity,etc.To tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is proposed.Specifically,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual concatenation.In addition,a compound loss function is introduced for training themethod to improve global context information correlation.The proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public datasets.The accuracy is better than those of many advanced approaches,while the number of parameters is smaller.The experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.展开更多
以TiO2 P25纳米颗粒为原料,通过碱-水热法制备TiO2纳米带,再采用水热法成功制备出Bi2WO6/TiO2纳米带材料.利用X射线衍射仪(X-ray diffraction,XRD)、扫描电子显微镜(scanning electron microscope,SEM)以及透射电子显微镜(transmission ...以TiO2 P25纳米颗粒为原料,通过碱-水热法制备TiO2纳米带,再采用水热法成功制备出Bi2WO6/TiO2纳米带材料.利用X射线衍射仪(X-ray diffraction,XRD)、扫描电子显微镜(scanning electron microscope,SEM)以及透射电子显微镜(transmission electron microscope,TEM)分别对该材料的物相和形貌进行了分析,并研究了其在可见光下的光催化的性能和催化机理.研究结果表明:制备出的Bi2WO6纳米带通过分子间作用力成功负载在TiO2纳米带上.制备出的Bi2WO6/TiO2在可见光下具有优越的光催化性能,这归因于材料较好的光生电子-空穴分离效率以及材料特定的物理性质,不仅为光催化反应提供了丰富的活性位点,还有效促进了电子的轴向迁移率.展开更多
基金supported by National Natural Science Foundation of China(No.52107117)。
文摘Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet.Despite several studies on the mining of unique load characteristics,few studies have extensively considered the high computational burden and sample training.Based on lowfrequency sampling data,a non-intrusive load monitoring algorithm utilizing the graph total variation(GTV)is proposed in this study.The algorithm can effectively depict the load state without the need for prior training.First,the combined Kmeans clustering algorithm and graph signals are used to build concise and accurate graph structures as load models.The GTV representing the internal structure of the graph signal is introduced as the optimization model and solved using the augmented Lagrangian iterative algorithm.The introduction of the difference operator reduces the computing cost and addresses the inaccurate reconstruction of the graph signal.With low-frequency sampling data,the algorithm only requires a little prior data and no training,thereby reducing the computing cost.Experiments conducted using the reference energy disaggregation dataset and almanac of minutely power dataset demonstrated the stable superiority of the algorithm and its low computational burden.
基金Double First-Class Innovation Research Project for People’s Public Security University of China(2023SYL08).
文摘Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges including crowd multi-scale variations and high network complexity,etc.To tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is proposed.Specifically,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual concatenation.In addition,a compound loss function is introduced for training themethod to improve global context information correlation.The proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public datasets.The accuracy is better than those of many advanced approaches,while the number of parameters is smaller.The experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.
文摘以TiO2 P25纳米颗粒为原料,通过碱-水热法制备TiO2纳米带,再采用水热法成功制备出Bi2WO6/TiO2纳米带材料.利用X射线衍射仪(X-ray diffraction,XRD)、扫描电子显微镜(scanning electron microscope,SEM)以及透射电子显微镜(transmission electron microscope,TEM)分别对该材料的物相和形貌进行了分析,并研究了其在可见光下的光催化的性能和催化机理.研究结果表明:制备出的Bi2WO6纳米带通过分子间作用力成功负载在TiO2纳米带上.制备出的Bi2WO6/TiO2在可见光下具有优越的光催化性能,这归因于材料较好的光生电子-空穴分离效率以及材料特定的物理性质,不仅为光催化反应提供了丰富的活性位点,还有效促进了电子的轴向迁移率.
文摘利用水热法合成二硫化锡六方晶片,通过氧化聚合包裹聚苯胺,水热还原制备锡氧硫化合物@聚苯胺@还原氧化石墨烯(SnO_(x)S_(y)@PANI@rGO)复合材料.分别利用X射线衍射(X-ray diffraction,XRD)、傅里叶变换红外(Fourier transform infrared,FT-IR)光谱、扫描电子显微镜(scanning electron microscope,SEM)和透射电子显微镜(transmission electron microscope,TEM)对材料进行形貌和物相分析,结果表明:制备的六方形SnO_(x)S_(y)被PANI和rGO双层包覆.将复合材料作为锂离子电池的负极进行电化学性能研究,结果显示:由于多元复合材料中的聚苯胺和还原石墨烯增加了其导电性,缓冲了SnO_(x)S_(y)在充放电过程的体积膨胀,保持了结构稳定性,展现了优越的电性能.
