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In the DC power system, arc fault caused by insulation defects and loose joints is easy to cause fire, explosion, and other safety accidents, but it is not easy to extinguish and detect. Therefore, it is of great significance to study the electrical characteristics of the DC arc and propose effective detection methods. Detection of DC arc faults in photovoltaic systems has attracted widespread attention from scholars at home and abroad. In this paper, several experimental conditions that may affect arc characteristics are analyzed. By using the photovoltaic strings and the arc generator, a lot of field experiments are carried out to obtain the current data during arc fault in an actual photovoltaic system under different conditions of current, cable lengths, and arc gaps. According to the experimental data under different conditions, several characteristic variables from both the time domain and the frequency domain are extracted to identify the arc fault. In this paper, three machine learning methods in the field of artificial intelligence: Random Forest, Support Vector Machine, and Bayes Classifier, are used to learn the experimental samples. Through training, a model is obtained in each method to distinguish between normal current and arc fault current. Then, the three classifier models are validated by hundreds of sets of experimental data under normal and arc conditions, and the classifier with the highest accuracy is found. According to the results, the proposed method can accurately identify arc fault conditions and normal working conditions. Among them, the random forest algorithm has the highest recognition accuracy.

Congestion detection has become a hot issue in Networks-on-Chip. Congestion-aware routing algorithms are designed to avoid congestion in the network; however, most of them achieve small performance benefits while drastically increasing the cost if the mesh size becomes larger. Using a different approach, we utilize the network itself to detect the congestion. In this paper, a congestion detecting mechanism is proposed, which is capable of locating where the congestion is in the network within several cycles. Then, the mechanism is applied to routing algorithm selection and task scheduling. The most suitable routing algorithm is selected according to the detection result. If the congestion status changes, the mechanism can also detect the change and judge whether the routing algorithm needs to be changed. Moreover, from the detection result, we can know the effect of task scheduling and judge whether it needs to be changed. Experimental results show that with the proposed detecting mechanism, the suitable routing algorithm is able to be successfully selected according to the congestion status and the better choice of task scheduling can be made. Consequently, the performance of NoC with the proposed congestion detection mechanism increases.