With the development of machine learning and image recognition technology, the detector system tends to be standardized and intelligent. However, large numbers of distributed radiation detectors connected to the power grid will bring huge uncertainty to the operation of the power grid and even cause certain interference. The monitoring system of the distributed radiation detectors can control the running status of the distributed photoelectric detection system in real-time and guarantee the safe and stable operation of the detector system. This article proposes an improved genetic detector system to avoid “blind spots” in the radiation detector monitoring based on the characteristics of photovoltaic (PV) arrays, which are considered as individual pixels, and then the reliability of the monitoring is ensured by the monitoring coverage of these pixels by the detector nodes. The performance of the radiation detector monitoring is restored by activating those spare nodes with sufficient energy to replace those that fail, ensuring that the distributed detection system can be monitored in a timely and efficient manner at all times. The simulation results confirm the reasonable validity of the algorithm.
Embedding has emerged as an important approach to prediction, inference, data mining and information retrieval based on knowledge bases and various embedding models have been presented. Most of these models are "typeless," namely, treating a knowledge base solely as a collection of instances without considering the types of the entities therein. In this paper, we investigate the use of entity type information for knowledge base embedding. We present a framework that augments a generic "typeless" embedding model to a typed one. The framework interprets an entity type as a constraint on the set of all entities and let these type constraints induce isomorphically a set of subsets in the embedding space. Additional cost functions are then introduced to model the fitness between these constraints and the embedding of entities and relations. A concrete example scheme of the framework is proposed. We demonstrate experimentally that this framework offers improved embedding performance over the typeless models and other typed models.
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