Algorithm for partially decentralized processing of a posteriori information in an integrated detection system

The integration of detectors can lead to an improvement in their performance. In this case, heterogeneous (radar, optoelectronic, radiotechnical) detectors can be combined. The task of integrating radar detectors is relevant for multi-positional radar stations. The construction of integrated identification systems can also be viewed as the task of combining detectors of their objects. There are three main options for combining detectors: centralized, partially decentralized, and fully decentralized. Centralized detection implements aggregation at the primary processing level, which provides potential characteristics, but it is difficult to implement such a combination in practice. Partially decentralized detection is implemented more simply, when preliminary detection decisions are made in the integrated detectors, which are then jointly processed and a final decision is formed. In joint processing of preliminary decisions, the probabilities of correct detection and false alarm of complexed detectors are usually used. The article presents an algorithm for partially decentralized combining of detectors using posterior probabilities of correct detection and false alarm. According to the results of computer simulation of the algorithm, characteristics were obtained that indicate an increase in the quality of detection relative to the best of the combined detectors. The use of the usual probabilities of correct detection and false alarm when combining does not guarantee such an increase – for certain characteristics of the detectors being combined, the effect of combining can be negative: the detection quality after combining will be lower relative to the best of the combined detectors.

Anomaly detection for electricity consumption in cloud computing: framework, methods, applications, and challenges

Driven by industrial development and the rising population, the upward trend of electricity consumption is not going to curb. While the electricity suppliers make every endeavor to satisfy the needs of consumers, they are facing the plight of indirect losses caused by technical or non-technical factors. Technical losses are usually induced by short circuits, power outage, or grid failures. The non-technical losses result from humans’ improper behaviors, e.g., electricity burglars. Due to the restrictions of the detection methods, the detection rate in the traditional power grid is lousy. To provide better electricity service for the customers and minimize the losses for the providers, a leap in the power grid is occurring, which is referred to as the smart grid. The smart grid is envisioned to increase the detection accuracy to an acceptable level by utilizing modern technologies, such as cloud computing. With the aim of obtaining achievements of anomaly detection for electricity consumption with cloud computing, we firstly introduce the basic definition of anomaly detection for electricity consumption. Next, we conduct the surveys on the proposed framework of anomaly detection for electricity consumption and propose a new framework with cloud computing. This is followed by centralized and decentralized detection methods. Then, the applications of centralized and decentralized detection methods for the anomaly electricity consumption are listed. Finally, the open challenges of the accuracy of detection and anomaly detection for electricity consumption with edge computing are discussed.