A model of sea surface temperature front detection based on a threshold interval

A model （Bayesian oceanic front detection, BOFD） of sea surface temperature （SST） front detection in satel- lite-derived SST images based on a threshold interval is presented, to be used in different applications such as climatic and environmental studies or fisheries. The model first computes the SST gradient by using a Sobel algorithm template. On the basis of the gradient value, the threshold interval is determined by a gradi- ent cumulative histogram. According to this threshold interval, front candidates can be acquired and prior probability and likelihood can be calculated. Whether or not the candidates are front points can be deter- mined by using the Bayesian decision theory. The model is evaluated on the Advanced Very High-Resolution Radiometer images of part of the Kuroshio front region. Results are compared with those obtained by using several SST front detection methods proposed in the literature. This comparison shows that the BOFD not only suppresses noise and small-scale fronts, but also retains continuous fronts.

Fraud Identification of Chinese Listed Companies——an Improvement Based on M-Score

To evaluate the applicability of the M-score model in the Chinese capital market,this research observed 190 financial fraud samples punished by the China Securities Regulatory Commission(CSRC)in the years from 2014 to 2018.The test results indicate that two types of errors are high,which means that the applicability of the M-score is unacceptable.Therefore,in this paper,a 9-index model is constructed by Wald's backward stepwise regression method,and the optimal threshold is set by the Beneish expected cost method(ECM).The accuracy of the modified M-score is significantly improved,especially the Type I error rate of is reduced from 70.37%to 19.75%.The receiver operating characteristic(ROC)curve test also proves the superior identification effect of the modified M-score applied in the Chinese market.Finally,variables such as current ratio,fixed asset index,and equity concentration in the modified model could represent the fraud characteristics of Chinese listed companies.

A Novel Single Phase Grounding Fault Protection Scheme Without Threshold Setting for Neutral Ineffectively Earthed Power Systems

The setting values of thresholds for fault feature parameters are critical in all kinds of protection schemes.When the detected feature parameter value exceeds the setting value,the protection will trip.However,the setting value based conventional protection schemes sometimes cannot satisfy the protection requirements of neutral ineffectively earthed power systems(NIEPS)due to wide variations in operating conditions and the complexities of fault cases.In this paper,a novel single phase grounding fault protection scheme without threshold setting is proposed.The fault detection is achieved based on operating states rather than setting values.A fuzzy c-means algorithm is used to divide the operating state of the protected feeder into non-fault states and fault states.The cluster center of each state is then obtained by classifying the historical feature samples of the protected feeder extracted under various operating conditions into their corresponding states in a constructed multi-dimensional fault feature space.The distances between the detected feature samples and the cluster centers of the non-fault and the fault states are calculated.If the distance to the fault state is shorter than that to the non-fault state,a fault is detected.Otherwise,the feeder is considered normal.A PSCAD/EMTDC simulator is used to simulate a 35 kV NIEPS under various operating conditions,non-linear loads,and complex fault cases.Results show that the proposed single phase grounding fault protection scheme without threshold setting can protect the system correctly under all kinds of faults.