基于MATLAB软件开发的自动控制原理课程虚拟实验平台,由6个典型实验给出平台的具体实现内容及实现过程.采用MATLAB/GUI构建产机交互界面设计,利用MATLAB中的产机交互工具GUIDE生成图形化窗口,并在图形窗口中设置各种功能控件,通过编辑M文件实现各操作功能、图形显示和结果分析等功能.理论计算验证平台实验结果满足实验要求,实现了直观、易懂、节省资源及可扩展性强的开发效果.

This paper presents the analysis of a cantilevered piezoelectric energy harvester (PEH) under the random vibration excitation of the car body. A two degree-of-freedom (2-DOF) quarter car model is established. The Matlab/Simulink simulation software is used to analyze the dynamic response of the car body under the random road excitation. The transient analysis of the PEH is carried out using ANSYS software to obtain the time-dependent voltage, current and power outputs of the PEH. The root mean square (RMS) power is used to evaluate the electricity generation of the PEH. The effect of the load resistance, the car speed, and the road surface roughness on RMS power are studied. The results show that it exists the optimal load resistance to achieve the maximum RMS power output for the vibration-based PEH. When the road surface roughness is constant, the increase of the car speed can induce the increase of the RMS power of the PEH. When the car speed is constant, higher RMS power outputs of the PEH can be achieved by increasing road surface roughness coefficient.

对一种悬臂梁结构车载压电俘能器发电性能进行了研究.建立了两自由度1/4车辆垂向振动模型,使用仿真软件MATLAB/Simulink分析了路面随机激励下的车体振动响应,并以此作为激励源作用于压电俘能器上,使用有限元法分析了俘能器基频、外接负载、车速、路面不平度对俘能器输出电压、电流和功率的影响关系.结果表明,当俘能器基频与车辆系统固有频率相匹配时,俘能器可以获得更高的输出电压.增加负载阻抗值,俘能器输出电压增加、输出电流减小,存在一个最优负载阻抗(约为200 kΩ)使俘能器输出功率达到最大.负载阻值相同时,提高车速和增加路面不平度系数均可以使俘能器获得更高的输出功率.当车辆以10 m/s速度行驶在B级路面上时,压电俘能器最大输出功率可到达0.5 mW.

The quality of coverage achieved by mobile sensors depends on the velocity, mobility pattern, number of mobile sensors deployed and the dynamics of the phenomenon being sensed. The gains attained by mobile sensors over static sensors and the optimal motion strategies for mobile sensors are not well understood. In this paper we consider the following event capture problem: The events of interest arrive at certain points in the sensor field and disappear according to known arrival and departure time distributions. An event is said to be captured if it is sensed by one of the mobile sensors before it fades away. We analyze how the quality of coverage scales with velocity and number of mobile sensors. We present algorithm for the motion planning problem: for sensors with fixed speed, what is the minimum number of sensors required to satisfy a bound on the event loss probability? For the minimum sensor problem, when the robots are restricted to move along a line our algorithm return the number of sensors used is within a factor of two of the optimal solution. Simulation experiments show that the Superiority of the proposed algorithm.

As a generalization of one-dimensional minimum-error threshold segmentation method was widely used in image segmentation.In this paper,two-dimensional minimum-error threshold algorithm and its fast algorithm(here referring to the original method) are improved,and threshold acquirement formula of the original minimum-error threshold segmentation method was deformed.By using look-up table method,acquirement of two-dimensional threshold is transformed into that of two one-dimensional threshold,so the calculation is simplified and time complexity is reduced from O(L2) to O(L).Experimental results show this decomposition algorithm outperforms original algorithm.Without losing the robustness to noise and needing more time and space,it can get the same segmentation threshold as the original method.

As the classical image segmentation algorithm, Ostu threshold algorithm has been widely used in image processing. The using of the algorithm which was developed on the basis of two-dimensional, high-dimensional threshold algorithm was restricted because of its complexity. But they consider both the gray value of a pixel and the average gray value of its neighborhood, thus is more robust to noise. By constructing look-up table recursively, its fast algorithm reduces its complexity from four-dimensional to two-dimensional. Algorithm based on the decomposition threshold selection, using threshold of two one-dimensional Ostu algorithms to replace it of two-dimensional Ostu, the method is the same as two-dimensional Ostu algorithm in segmentation threshold, and the algorithm's time complexity can be reduced to one-dimensional. In the paper, the algorithm was improved, one-dimensional threshold average decomposition for two-dimensional Ostu algorithm was proposed. It can be confirmed by a large of number experiments, The algorithm not only maintained advantages of less time and smaller space of calculating the threshold but also better segmentation results can be found from experiments.

As a classical image segmentation method, Ostu threshold algorithm had been applied widely in image processing. This paper had a comparison of the following two-dimensional Ostu thresholding method. One-dimensional Ostu method considered only grayscale information of the pixel, 2D Ostu algorithm considered both the gray value of a pixel and the average gray value of its neighborhood, thus is more robust to noise. But because calculating of the two-dimensional Ostu threshold method demands a long time, so restricted its use, by constructing look-up tables recursively, its fast algorithm reduces its complexity from O(L4) to O(L2), based on the decomposition of 2D Ostu adaptive algorithm. When the hypothesis of original 2D Ostu algorithm holds, the method can get the same segmentation threshold as the original two-dimensional method, while the computational complexity is reduced further. In the paper, the algorithm is improved on the basis of the original one, one-dimensional threshold average decomposition of two-dimensional Ostu algorithm is proposed. The algorithm not only maintained advantages of less time and smaller space of calculating the threshold of 2D Ostu adaptive algorithm, of the threshold shorter and space advantages of a smaller, but also better segmentation results can be found to get from experiment.