The USV (unmanned surface vehicle) is playing an important role in many tasks such as marine environmental observation and maritime security, for the advantages of high autonomy and mobility. Detecting the targets on the surface of the water with high precision ensures the subsequent task implementation. However, the changes from the lights and the surface environment influence the performance of the target detecting method in a long-term task with USV. Therefore, this paper proposed a novel target detection method by fusing DenseNet in YOLOV3 to improve the stability of detection to decrease the feature loss, while the target feature is transmitted in the layers of a deep neural network. All the image data used to train and test the proposed method were obtained in the real ocean environment with a USV in the South China Sea during a one month sea trial in November 2019. The experiment results demonstrate the performance of the proposed method is more suitable for the changed weather conditions though comparing with the existing methods, and the real-time performance is available in practical ocean tasks for USV.

This article concludes the industrial development of China since the puppet Manchurian period and the influence of technical education within this development. Japanese imperialism gradually established various colonial schools at all levels by formulating a series of colonial educational policies, and firmly controlling the educational rights in Northeast China. Furthermore, it argues that amongst all of the aspects of educational development during the puppet Manchurian period, the introduction and development of the apprenticeship system has most greatly impacted industries in Northeast China. From the perspective of dynamic changes, during the Ninth Five-Year Plan (1996~2000) and the Tenth Five-Year Plan (2001~2005), the relative advantages of industrialization of the three northeastern provinces continued to be lost and even turned into relative disadvantages. The guidance of public policies is an important aspect of this industrial decrease, especially the deviation from the implementation of industrial support policies and the lack of vocational educational support.

With the integration of distributed generators (DGs) and multiple types of loads, it is increasingly difficult to cope with the uncertainties of DGs, which brings challenges to the secure operation of distribution networks (DNs). Source and load cannot obtain the power balance locally, which may cause operational problems such as line power overload and node voltage violation. However, the methods for quantitatively analyzing the operating status of DNs have not yet formed a unified standard. For which reason, the concept of operating margin is proposed in this study, to represent the distance from the operating point to the boundary. In this study, to generally evaluate the operating status of DNs, the concept of node power margin is proposed first. Then the evaluation indexes including line power margin (LPM) and node voltage margin (NVM) are introduced. The quantitative calculation method for system operating margin (SOM) is further elaborated, which is supposed to be the evaluation indexes on system level. After that, the analysis process of evaluation methods for SOM is discussed. Finally, case studies are performed on the modified IEEE 33-node system to evaluate the operating margin of DNs and verify the enhancement brought by the energy storage system (ESS) and soft open point (SOP) through the flexible adjustment of power flow. The simulation results show that the distance between the DN and the security boundary is effectively quantified through the proposed method, the value can be easily calculated, and the change can be clearly reflected.