一种安全自动装置的集群式高温老化异常监视方法

随着电力系统安全自动装置的大规模生产,大批量装置的高温老化环节的异常检测工序占用人力和工时越来越大,成本高昂。该文提出一种装置的集群式高温老化异常监视方法和软件,可以有效解决该问题。在制造中心实际应用该方法和软件,成效显著,大幅节省大批量装置高温老化流程环节的工时,并提高批量装置高温老化异常现象的正确检出率。

基于运维平台的监视源信号分析研究

本文研究了一种多雷达信号分析系统,用于对自动化系统单路航迹和融合航迹进行解码显示,从而快速定位异常雷达,给技术人员提供分析问题的重要依据。运维平台是技术人员记录异常信息的工作系统,可从中提取故障信息进行统计分析。两者结合对雷达信号统计分析提供了极大便利。

融合机器学习的云监控数据分析仿真

为进一步提升云监控平台数据分析的准确性和效率,在研究了集成云平台和分布式计算基础上,设计了融合机器学习的云监控数据分析平台。该平台包含核心结构和扩展结构2部分,分别用于对小量数据及大规模数据进行分析及分布式计算。核心架构可分为3层:智能搜索层、应用程序接口(API)层和云计算层。这3层相互作用,处理工作负载跟踪,并监视任何异常模式。扩展结构有利于用户在Hadoop文件系统中存储数据,且为用户提供了高可用性和可扩展的存储功能。对核心结构及扩展结构进行仿真分析。结果进一步验证了云监控数据分析平台的有效性及实用性。该平台为系统CPU及内存使用分析过程提供了一定依据。

Video-based urban expressway traffic measurement and performance monitoring

This paper presents an urban expressway video surveillance and monitoring system for traffic flow measurement and abnormal performance detection. The proposed flow detection module collects traffic flow statistics in real time by leveraging multi-vehicle tracking information. Based on these online statistics, road operating situations can be easily obtained. Using spatiotemporal trajectories, vehicle motion paths are encoded by hidden Markov models. With path division and parameter matching, abnormal performances containing extra low or high speed driving, illegal stopping and turning are detected in real scenes. The traffic surveillance approach is implemented and evaluated on a DM642 DSP-based embedded platform. Experimental results demonstrate that the proposed system is feasible for the detection of vehicle speed, vehicle counts and road efficiency, and it is effective for the monitoring of the aforementioned anomalies with low computational costs.

电网设备智能告警及辅助决策技术分析

随着电力负荷的增长,电网建设的规模也在不断扩大,需监视电网设备数量的不断增加给电网运行监控人员带来了较大的工作量。为提高电网设备的运行监视效率,可在调控主站系统采取电网设备智能告警及辅助决策技术,从而降低电网监控人员的工作量,也能及时发现电网设备存在的异常运行情况。对电网设备智能告警技术和辅助决策技术原理进行了分析,对于保障电网的安全稳定运行具有一定的意义。

Biomarkers for colitis-associated colorectal cancer

Patients with extensive ulcerative colitis(UC) of more than eight years duration have an increased risk of colorectal cancer. Molecular biomarkers for dysplasia and cancer could have a great clinical value in managing cancer risk in these UC patients. Using a wide range of molecular techniques- including cutting-edge OMICS technologies- recent studies have identified clinically relevant biomarker candidates from a variety of biosamples, including colonic biopsies, blood, stool, and urine. While the challenge remains to validate these candidate biomarkers in multi-center studies and with larger patient cohorts, it is certain that accurate biomarkers of colitis-associated neoplasia would improve clinical management of neoplastic risk in UC patients. This review highlights the ongoing avenues of research in biomarker development for colitis-associated colorectal cancer.

ADS-B Anomaly Data Detection Model Based on Deep Learning and Difference of Gaussian Approach

Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.

Abnormal activity detection for surveillance video synopsis

Video synopsis is an effective and innovative way to produce short video abstraction for huge video archives,while keeping the dynamic characteristic of activities in the original video.Abnormal activity,as the critical event,is always the main concern in video surveillance context.However,in traditional video synopsis,all the normal and abnormal activities are condensed together equally,which can make the synopsis video confused and worthless.In addition,the traditional video synopsis methods always neglect redundancy in the content domain.To solve the above-mentioned issues,a novel video synopsis method is proposed based on abnormal activity detection and key observation selection.In the proposed algorithm,activities are classified into normal and abnormal ones based on the sparse reconstruction cost from an atomically learned activity dictionary.And key observation selection using the minimum description length principle is conducted for eliminating content redundancy in normal activity.Experiments conducted in publicly available datasets demonstrate that the proposed approach can effectively generate satisfying synopsis videos.