Vehicle license plate tilt correction based on the straight line fitting method and minimizing variance of coordinates of projection points

Tilt correction is a very crucial and inevitable task in the automatic recognition of the vehicle license plate (VLP). In this paper, according to the least square fitting with perpendicular offsets (LSFPO), the VLP region is fitted to a straight line. After the line slope is obtained, rotation angle of the VLP is estimated. Then the whole image is rotated for tilt correction in horizontal direction by this angle. Tilt correction in vertical direction by minimizing the variance of coordinates of the projection points is proposed. Character segmentation is performed after horizontal correction and character points are projected along the vertical direction after shear transform. Despite the success of VLP detection approaches in the past decades, a few of them can effectively locate license plate (LP), even when vehicle bodies and LPs have similar color. A common drawback of color-based VLP detection is the failure to detect the boundaries or border of LPs. In this paper, we propose a modified recursive labeling algorithm for solving this problem and detecting candidate regions. According to different colored LP, these candidate regions may include LP regions. Geometrical properties of the LP such as area, bounding box and aspect-ratio are then used for classification. Various LP images were used with a variety of conditions to test the proposed method and results are presented to prove its effectiveness.     

基于无线传感器网络的煤矿瓦斯监测系统的设计

为了满足煤矿瓦斯监测的需要,开发了一种基于无线传感器网络的智能化瓦斯监测系统。该系统采用数字瓦斯传感器实时检测瓦斯,避免了普通传感器需进行A/D转换的复杂性,提高了测量精度;采用无线传感器网络,克服了有线传感器网络的局限性,避免了其它无线通信技术高功耗的缺点。文章详细介绍了系统的硬件和软件设计,并给出了测试结果。     

一种附加直流电源漏电保护器的研制

分析了煤矿井下中性点不接地供电系统对地绝缘电阻的基本要求,给出了附加直流电源检测对地绝缘电阻的原理与保护方法;在此基础上介绍了一种附加直流电源漏电保护器的软、硬件设计方案。实验和实际运行结果表明,该附加直流电源漏电保护器误差较小,能够满足煤矿井下电网的漏电保护要求。     

Stabilization for nonlinear systems via a limited capacity communication channel with data packet dropout

This paper addresses the stabilization problem for a class of nonlinear systems. It is assumed that the controller can only receive the transmitted sequence of finite coded signals via a limited digital communication channel. Both state and output feedback coder-decoder-controller procedures are proposed. Stabilization conditions involving the size of coding alphabet, the sampling period, system state growth rate and data packet dropout rate are obtained. Finally, an example is given to illustrate the design procedures and effectiveness of the proposed results.     

Energy-efficient adaptive sensor scheduling for target tracking in wireless sensor networks

Sensor scheduling is essential to collaborative target tracking in wireless sensor networks (WSNs). In the existing works for target tracking in WSNs, such as the information-driven sensor query (IDSQ), the tasking sensors are scheduled to maximize the information gain while minimizing the resource cost based on the uniform sampling intervals, ignoring the changing of the target dynamics and the specific desirable tracking goals. This paper proposes a novel energyefficient adaptive sensor scheduling approach that jointly selects tasking sensors and determines their associated sampling intervals according to the predicted tracking accuracy and tracking energy cost. At each time step, the sensors are scheduled in alternative tracking mode, namely, the fast tracking mode with smallest sampling interval or the tracking maintenance mode with larger sampling interval, according to a specified tracking error threshold. The approach employs an extended Kalman filter (EKF)-based estimation technique to predict the tracking accuracy and adopts an energy consumption model to predict the energy cost. Simulation results demonstrate that, compared to a non-adaptive sensor scheduling approach, the proposed approach can save energy cost significantly without degrading the tracking accuracy.     

Compensation of hysteresis in piezoelectric actuator with iterative learning control

This paper presents the application of iterative learning control (ILC) to compensate hysteresis in a piezoelectric actuator. The proposed controller is a hybrid of proportional-integral-differential (PID) control, whose main function is for trajectory tracking, and a chatter-based ILC, whose main function is for hysteresis compensation. Stability analysis of the proposed ILC is presented, with the PID included in the dynamic of the piezoelectric actuator. The performance of the proposed controller is analysed through simulation and verified with experiment with a piezoelectric actuator.     

Multi-goal Q-learning of cooperative teams

This paper studies a multi-goal Q-learning algorithm of cooperative teams. Member of the cooperative teams is simulated by an agent. In the virtual cooperative team, agents adapt its knowledge according to cooperative principles. The multi-goal Q-learning algorithm is approached to the multiple learning goals. In the virtual team, agents learn what knowledge to adopt and how much to learn (choosing learning radius). The learning radius is interpreted in Section 3.1. Five basic experiments are manipulated proving the validity of the multi-goal Q-learning algorithm. It is found that the learning algorithm causes agents to converge to optimal actions, based on agents’ continually updated cognitive maps of how actions influence learning goals. It is also proved that the learning algorithm is beneficial to the multiple goals. Furthermore, the paper analyzes how sensitive the learning performance is affected by the parameter values of the learning algorithm.     

Adaptive product tracking in RFID-enabled large-scale supply chain

The Radio Frequency Identification (RFID) technology is gradually being adopted and deployed for product flow management in the supply chain. In order to track RFID-tagged products efficiently in the RFID-enabled, large-scale supply chain, this paper first presents the design of a product tracking system that can collaborate with the EPC Network, a suite of network services for RFID data management in the supply chain. Next, we explain a product monitoring procedure that is performed by comparing the actual path of a product with its planned path. Finally, we develop an adaptive product search algorithm based on a reinforcement learning technique to efficiently locate a product deviated from its planned path. Experiment results are provided to show the performance of the search algorithm.     

Classification for predicting offender affiliation with murder victims

The National Incident-Based Reporting System (NIBRS) is used by law enforcement to record a detailed picture of crime incidents, including data on offenses, victims and suspected arrestees. Such incident data lends itself to the use of data mining to uncover hidden patterns that can provide meaningful insights to law enforcement and policy makers. In this paper we analyze all homicide data recorded over one year in the NIBRS database, and use classification to predict the relationships between murder victims and the offenders. We evaluate different ways for formulating classification problems for this prediction and compare four classification methods: decision tree, random forest, support vector machine and neural network. Our results show that by setting up binary classification problems to discriminate each type of victim–offender relationship versus all others good classification accuracy can be obtained, especially by the support vector machine method and the random forest approach. Furthermore, our results show that interesting structural insight can be obtain by performing attribute selection and by using transparent decision tree models.     

A secure dynamic ID based remote user authentication scheme for multi-server environment using smart cards

Recently, Hsiang et al. pointed out that Liao-Wang’s dynamic ID based remote user authentication scheme for multi-server environment is vulnerable to insider attack, masquerade attack, server spoofing attack, registration center attack and is not easily reparable. Besides, Liao-Wang’s scheme cannot achieve mutual authentication. For this, Hsiang et al. proposed an improved scheme to overcome these weaknesses and claimed that their scheme is efficient, secure, and suitable for the practical application environment. However, we observe that Hsiang et al.’s scheme is still vulnerable to a masquerade attack, server spoofing attack, and is not easily reparable. Furthermore, it cannot provide mutual authentication. Therefore, in this paper we propose an improved scheme to solve these weaknesses.