No abstract

In the background of high-power propulsion application on ships and existing uncertain electromagnetic and vibration features of multiphase fractional-slot concentrated-winding permanent magnet (PM) motor, the electromagnetic design and analysis methods of six-phase fractional-slot concentrated-winding PM motor are presented. Four different pole and slot combination schemes are sifted out, and air-gap magnetic field and electromagnetic force of these four motors are studied in contrast. On the basis of the above, a 20-kW prototype of six-phase fractional-slot concentrated-winding PM motor with 48 slots and 44 poles is designed and manufactured, and its electromagnetic and vibration performances are tested through experiments. The test results are in good agreement with calculations, which could guide the development of high-power ship propulsion.INDEX TERMS Fractional-slot, concentrated-winding, modal analysis, electromagnetic vibration, six-phase PM motor.

Collaborative target tracking is one of the most important applications of wireless sensor networks (WSNs), in which the network must rely on sensor scheduling to balance the tracking accuracy and energy consumption, due to the limited network resources for sensing, communication, and computation. With the recent development of energy acquisition technologies, the building of WSNs based on energy harvesting has become possible to overcome the limitation of battery energy in WSNs, where theoretically the lifetime of the network could be extended to infinite. However, energy-harvesting WSNs pose new technical challenges for collaborative target tracking on how to schedule sensors over the infinite horizon under the restriction on limited sensor energy harvesting capabilities. In this paper, we propose a novel adaptive dynamic programming (ADP)-based multi-sensor scheduling algorithm (ADP-MSS) for collaborative target tracking for energy-harvesting WSNs. ADP-MSS can schedule multiple sensors for each time step over an infinite horizon to achieve high tracking accuracy, based on the extended Kalman filter (EKF) for target state prediction and estimation. Theoretical analysis shows the optimality of ADP-MSS, and simulation results demonstrate its superior tracking accuracy compared with an ADP-based single-sensor scheduling scheme and a simulated-annealing based multi-sensor scheduling scheme.

This article researches on a traffic congestion status forecasting method to improve the real-time monitoring and controlling of air traffic in terminal areas. First, a traffic congestion status evaluation method was introduced based on a fuzzy Cmeans clustering algorithm, as well as several traffic congestion status evaluation metrics. And then, a traffic congestion status forecasting model was proposed based on support vector machine. Finally, a real case study from a terminal area in China was provided to test and verify the proposed evaluation method and forecasting model. The evaluation results show that traffic congestion status of the terminal area can be classified into five levels: free, smooth, slightly congested, moderately congested, and severely congested. The forecasting results show that the mean absolute error and the cluster accuracy are 0.041% and 92.2%, respectively, which indicate that the forecasting model is very effective and accurate. In addition, it is also found that the parameters of forecasting period and size of training set have some influence on forecasting results, and the optimal results can be found when the two parameters values are 15 and 3, respectively.

Battery energy storage technology is an important part of the industrial parks to ensure the stable power supply, and its rough charging and discharging mode is difficult to meet the application requirements of energy saving, emission reduction, cost reduction, and efficiency increase. As a classic method of deep reinforcement learning, the deep Q-network is widely used to solve the problem of user-side battery energy storage charging and discharging. In some scenarios, its performance has reached the level of human expert. However, the updating of storage priority in experience memory often lags behind updating of Q-network parameters. In response to the need for lean management of battery charging and discharging, this paper proposes an improved deep Q-network to update the priority of sequence samples and the training performance of deep neural network, which reduces the cost of charging and discharging action and energy consumption in the park. The proposed method considers factors such as real-time electricity price, battery status, and time. The energy consumption state, charging and discharging behavior, reward function, and neural network structure are designed to meet the flexible scheduling of charging and discharging strategies, and can finally realize the optimization of battery energy storage benefits. The proposed method can solve the problem of priority update lag, and improve the utilization efficiency and learning performance of the experience pool samples. The paper selects electricity price data from the United States and some regions of China for simulation experiments. Experimental results show that compared with the traditional algorithm, the proposed approach can achieve better performance in both electricity price systems, thereby greatly reducing the cost of battery energy storage and providing a stronger guarantee for the safe and stable operation of battery energy storage systems in industrial parks.