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In this paper, the second reclosure strategies of 220 kV and above transmission lines in Northeast China power grids are studied. By analyzing the historical fault information, reclosing actions and manual reclosing results, the necessity of the second automatic reclosure is demonstrated. The factors affecting the performance of the reclosing of transmission lines are investigated. Further, based on the sequential action process of the secondary reclosing, a complete secondary automatic reclosing strategy, including the reclosing schemes, time delay settings and acting logics, are proposed. The simulation results show that the proposed secondary automatic reclosing strategy has good adaptabilities to 220 kV and 500 kV transmission lines.

An application of differential evolution for parameter optimization in the low dropout regulator (LDO) is presented in this paper. The parameters optimization by manual work for the analog integrated circuit, such as LDO, is laborious and time-consuming, and it is uncertain to find the relatively good result. In this paper, the differential evolution is used to optimize the parameters and find the relatively good performance of LDO. In order to improve the convergence speed and optimization effect, a new constraint solution and a fast weight-based non-dominated sorting method are proposed. Simulation results show that the gain-bandwidth product,load regulation and line regulation are improved by 206.5%, 58.1% and 87.6%, respectively, compared with the manual solution.

Facing uncertain and dynamic demands over different days, logistics companies need to adopt adaptive solutions for the last-mile delivery. Meanwhile, one of the major barriers for implementing vehicle routing problem (VRP) algorithms in real world dispatching systems is the stability of delivery driver’s routes and schedules. To establish a theoretically sound and practically useful solution framework, this paper aims to optimize robust and consistent space-time paths for the multi-day VRP by providing daily schedules with limited variations from the master schedule. A multi-commodity network flow-based optimization model is proposed to minimize generalized transportation costs and the daily deviation of day-dependent space-time paths among demand analysis zones. Lagrange relaxation (LR) methods and alternating direction method of multipliers (ADMM) are used to handle complex side constraints. Experiments on illustrative networks and Beijing delivery network are developed to demonstrate the effectiveness of the proposed method.

Now days, the concentration of PM2.5 is mostly predicted by BP-neural network. But the BP-neural network is sensitive to the initial value, and it is easy to fall into the local minimum value. Artificial Bee Colony Algorithm (ABC) is adopted in this paper for solving the problems and optimizing the BP-neural network, using the idea and method of that bees are looking for high-quality food efficiently, to find the best value of weights and thresholds of network. The optimized neural network not only possess generalized mapping ability of neural network, but also possess global iteration and local search ability like Artificial Bee Colony Algorithm. Through comparative experiments, it is proved that the PM2.5 prediction model based on the neural network optimized by Artificial Bee Colony Algorithm effectively improves the accuracy of PM2.5 prediction.

This paper proposes a novel non-stationary multi-ring channel model for massive multiple-input multiple-output (MIMO) systems. As the dimension of the antenna arrays comes to be huge in massive MIMO systems, farfield assumption used in conventional MIMO channel models is not considered in the proposed model. Thus, the spherical wavefront assumption and a birth-death process to model non-stationary properties on both time and array axes are adopted to the proposed model. In addition, the multi-ring geometry relationships which are relative to the delays of multipath components (MPCs) are assumed to mimic the distribution of the clusters in massive MIMO channels. The numerical results, such as received power levels over the array, antenna correlation and eigenvalue distributions show that the proposed channel model captures specific characteristics of the massive MIMO channel comparing with the measurements.

Generally, a service provider only gets negative assessments from his/her customers due to bad services. However, this is not always the case. Recently, a new type of e-commerce swindle called “malicious rating attack” has disordered the evaluation system of Taobao, which offers the largest virtual platform in mainland. Different from earlier fraud cases aiming shoppers, this time “profitable” owners of online stores fall victims to the tricky cheat. For fear that negative assessments from malicious raters may lead to customer drain, most poor retailers can do nothing but be blackmailed. Therefore the paper tries to design a new assessment mechanism to prevent online retailers from such swindles and to normalize the operation of online business. In the modified evaluation system, online shoppers' credits, which can be measured by the length of register time and the number of transactions, is included. Since most malicious raters change their accounts frequently in case of detected by network surveillance, they cannot be customers of high credibility. And thus, to launch an attack in the evaluation system considering the weight of credit will be extremely hard and costly. As the sensitivity analysis in the paper turns out that it will call for more than 60 times the size of the former gang to realize the same attack effect. It is believed that such obstacles for malicious raters to making profit will eventually bring an end to the “treat or trick” game.

This paper describes a new design principle of three-axis electronic compass that is extraordinarily suitable for Virtual Auditory Space, owing to the outstanding performances on miniaturization, anti-interference capability and low power consumption. The miniaturization characteristics are mainly obtained by using three-axis Honeywell magneto resistive sensor HMC5843 with digital interface. Low-power consumption chip MSP430F149 is selected as the processor. A compensating algorithm is used to reduce the hard magnetic interference and soft magnetic interference.

In this paper, an innovative application of an existing nondestructive inspection, i.e, time-of-flight mode of scanning acoustic microscopy (SAM), was proposed for the measurement of through silicon via (TSV) depth. A feasibility study was performed on a dummy wafer with various sizes of TSVs to evaluate this method. From the image reconstruction of B-scan SAM, the depth of TSVs can be clearly identified. In addition, cross-section inspections were performed to verify this nondestructive inspection method. It was found that, for TSV sizes larger than φ30 μm, the depth measured by the ultrasonic scanning method could be very close to the results of cross-section inspection (less than 5% error). The current results are considered very encouraging and the proposed nondestructive inspection method should have very good potential for the high throughput and low cost in-line measurement of TSV depth during the TSV forming process.