A Novel Density-Based Spatial Clustering of Application with Noise Method for Data Clustering

The traditional methods are easy to generate a large number of fake samples or data loss when classifying unbalanced data.Therefore,this paper proposes a novel DBSCAN(density-based spatial clustering of application with noise)for data clustering.The density-based DBSCAN clustering decomposition algorithm is applied to most classes of unbalanced data sets,which reduces the advantage of most class samples without data loss.The algorithm uses different distance measurements for disordered and ordered classification data,and assigns corresponding weights with average entropy.The experimental results show that the new algorithm has better clustering effect than other advanced clustering algorithms on both artificial and real data sets.

泡沫镍负载Co-MoC@N-CNS/CNT作为自支撑电极用于全水分解（英文）

在众多的过渡金属催化剂中,碳化钼因具有类贵金属电子结构、高电子导电性、宽pH适用范围,优异的催化活性和稳定性等优点受到科研工作者的广泛关注.密度泛函理论计算(DFT)得出的“火山图”表明碳化钼具有较强的氢吸附能,而Co具有较弱的氢吸附能.已有报道指出将Co与碳化钼化学耦合能够促使其展现相对适中的氢吸附能,从而提高材料的本征催化活性.此外,碳化钼很少被认为是一种氧析出反应催化剂,而Co则被广泛认为是高效且稳定的碱性水分解析氧催化剂.基于此,我们提出化学耦合Co与碳化钼能够同时提升材料的析氧催化性能.值得一提的是,自支撑材料可直接用作工作电极以避免粘结剂(覆盖活性位,阻碍催化过程中传质的发生,增加电子转移阻抗)的使用.然而,目前关于自支撑碳化钼催化剂的研究鲜有报道.本文采用简单的水热法制备了泡沫镍负载钴掺杂碳化钼耦合的碳纳米片和碳纳米管(Co-MoC@N-CNS/CNT)自支撑电极.通过X射线粉末衍射(XRD)、扫描电镜(SEM)、透射电镜(TEM)、X射线和紫外光电子能谱(XPS、UPS)等表征手段对产物形貌结构进行了表征,利用电化学工作站对其水分解催化性能进行了研究.通过调控前驱体制备时的钴源浓度得到了一系列不同Co掺杂量的样品.UPS测试表明,Co成功掺杂到MoC晶体结构中能够显著增加其费米能级附近的电子密度,从而优化其HER动力学.与纯相的MoC和Co单质相比,掺杂后样品的催化活性得到显著提升.其优异的电化学活性可归结为如下几点:(1)2D纳米片和1D纳米线交错形成的3D纳米结构可有效暴露更多的活性位点和促进电子转移;(2)Co掺杂能够优化MoC晶体结构费米能级附近的电子结构,从而提升其HER动力学;(3)N-CNS/CNT不仅能够保护Co和MoC免于碱性腐蚀,还能促进纳米颗粒和碳基质间快速的电子传递.上述结果表明,通过合理设计碳化钼的组成和纳米结构可获得具有高活性和稳定性的双功能催化剂,为高性能催化剂的开发和利用开辟了新的途径.

Pedestrian Re-recognition Based on Hybrid Network

With the rapid development of related computer vision algorithms,the large-scale use of video surveillance systems has not only improved traffic safety,but also promoted the development of intelligent high-speed.However,due to the complexity of the application scene,especially in the face of complex scene occlusion factors,the noise generated by the occlusion inevitably leads to the loss of the feature information of the identified person or object,which poses a great challenge to the existing pedestrian re-recognition algorithms.Therefore,this paper proposes a novel pedestrian re-recognition based on hybrid network.Feature extraction is carried out on four cooperative branches:local branch,global branch,global contrast pool branch and associated branch,and powerful diversity pedestrian feature expression ability is obtained.The network in this paper can be applied to different backbone networks.Through experimental comparison,the proposed algorithm has certain advantages compared with the latest methods,and the ablation experimental analysis further proves the effectiveness of the proposed network structure.

Improved Long Short-term Memory Network for Gesture Recognition

Surface EMG contains a lot of physiological information reflecting the intention of human movement.Gesture recognition by surface EMG has been widely concerned in the field of human-computer interaction and rehabilitation.At present,most studies on gesture recognition based on surface EMG signal are obtained by discrete separation method,ignoring continuous natural motion.A gesture recognition method of surface EMG based on improved long short-term memory network is proposed.sEMG sensors are rationally arranged according to physiological structure and muscle function.In this paper,the finger curvature is used to describe the gesture state,and the gesture at every moment can be represented by the set of different finger curvature,so as to realize continuous gesture recognition.Finally,the proposed gesture recognition model is tested on Ninapro(a large gesture recognition database).The results show that the proposed method can effectively improve the representation mining ability of surface EMG signal,and provide reference for deep learning modeling of human gesture recognition.

Review for Anomaly Detection in Video Surveillance System Based on Deep Learning

In this paper,abnormal target detection and location in video surveillance system are studied.In recent years,with the rapid development of network information technology,video surveillance technology has been widely used,artificial anomaly detection methods have no way to meet the effective growth of video surveillance data,with 3D technology,face recognition technology,etc.,also promote the development of the field of computer vision,for the rapid analysis of a large number of video data to provide effective support.At present,abnormal target detection methods in video surveillance system mainly include the following two methods:One is to extract two-dimensional data features from video surveillance data,and effectively express video targets according to the extracted features.The information expressed mainly includes time information and spatial information.The second is to directly learn 3D space-time features for the module with motion information to detect the location of the abnormal target.Finally,the paper summarizes the full text and looks forward to the future development direction of video anomaly detection from three aspects:data set,method and evaluation index.