Robust Calibration for Localization in Clustered Wireless Sensor Networks

This paper presents a robust calibration procedure for clustered wireless sensor networks. Accurate calibration of between-node distances is one crucial step in localizing sensor nodes in an ad-hoc sensor network. The calibration problem is formulated as a parameter estimation problem using a linear calibration model. For reducing or eliminating the unwanted influence of measurement corruptions or outliers on parameter estimation, which may be caused by sensor or communication failures, a robust regression estimator such as the least-trimmed squares (LTS) estimator is a natural choice. Despite the availability of the FAST-LTS routine in several statistical packages (e.g., R, S-PLUS, SAS), applying it to the sensor network calibration is not a simple task. To use the FAST-LTS, one needs to input a trimming parameter, which is a function of the sensor redundancy in a network. Computing the redundancy degree and subsequently solving the LTS estimation both turn out to be computationally demanding. Our research aims at utilizing some cluster structure in a network configuration in order to do robust estimation more efficiently. We present two algorithms that compute the exact value and a lower bound of the redundancy degree, respectively, and an algorithm that computes the LTS estimation. Two examples are presented to illustrate how the proposed methods help alleviate the computational demands associated with robust estimation and thus facilitate robust calibration in a sensor network.     

Skew detection in document images based on rectangular active contour

The digitalization processes of documents produce frequently images with small rotation angles. The skew angles in document images degrade the performance of optical character recognition (OCR) tools. Therefore, skew detection of document images plays an important role in automatic document analysis systems. In this paper, we propose a Rectangular Active Contour Model (RAC Model) for content region detection and skew angle calculation by imposing a rectangular shape constraint on the zero-level set in Chan–Vese Model (C-V Model) according to the rectangular feature of content regions in document images. Our algorithm differs from other skew detection methods in that it does not rely on local image features. Instead, it uses global image features and shape constraint to obtain a strong robustness in detecting skew angles of document images. We experimented on different types of document images. Comparing the results with other skew detection algorithms, our algorithm is more accurate in detecting the skews of the complex document images with different fonts, tables, illustrations, and layouts. We do not need to pre-process the original image, even if it is noisy, and at the same time the rectangular content region of a document image is also detected.     

Correlation of direct piezoelectric effect on EAPap under ambient factors

Direct piezoelectricity of electro-active papers(EAPap)is analysed in this paper. The test setups for direct effect are designed and determined. Different ambient factors impacting the piezoelectricity of EAPap, such as temperature, humidity, and strain rate, are applied and analyzed. Strong piezoelectricity of EAPap is found on the basis of the test results and in comparison with polyvinylidene fluoride(PVDF)and lead zirconate titanate(PZT)-5H. The maximum piezoelectric constant is achieved to be 504 pC/N. The reason of strong piezoelectricity of EAPap is discussed in this paper. The potential of EAPap as a biomimetic actuator and sensor is also investigated.     

A collaborative platform for complex product design with an extended HLA integration architecture

How to integrate the heterogeneous, autonomous models in a distributed environment, running on Internet, integration based on multilayer federation architecture and simulation resources dynamic reuse are the major difficulties for complex product collaborative design and simulation. An extended HLA-based collaborative simulation platform for multidisciplinary collaborative design is proposed. The architecture of platform is first given. The idea and realization of four key technologies, the dynamic creation of simulation federation based on resource management federation (RMF), HLA enabled template, collaborative simulation algorithm based on stage-data synchronization and TH_RTI, an extensible HLA runtime infrastructure (RTI), which is a web enabled RTI and can be used at Internet, are emphasized. Finally, an industry case is given. The demonstration of these prototype systems shows that extended HLA integration architecture is effective infrastructure for multidisciplinary collaborative modeling and simulation (M&S).     

一种双环智能太阳能监控系统的设计与实现

针对目前平板式太阳能集中供热系统在农村应用较少的现状,采用智能控制技术开发了一种新型的太阳能控制系统;系统提出双环的控制结构,主环采用VC++操作串口实现数据的操纵与处理,副环的设计采用基于GSM和J2ME的无线多通道的实时数据采集与处理控制;对与建筑集成的太阳能供热系统进行控制,连续测试系统的运行状况;应用结果表明,该控制系统运行稳定可靠,满足了实时信号的处理要求;最后通过多项工程经济技术评价指标评价太阳能供热系统,事实证明具有一定的实用性和推广价值.     

卫星天线自动对星控制系统研究与设计

卫星天线手动对星方式存在精度不高、速度慢、操作复杂等缺点,针对这一现状,文章提出了一种二维卫星天线自动对星控制系统方案;系统以ATMega1280为控制核心,通过GSP、电子罗盘和绝对式编码器获取天线姿态信息,从而控制伺服电机来调整天线姿态,达到对星的目的;另外还实现了多组卫星数据存储、手动调节、误差补偿和参数实时显示等功能;实验表明,该系统工作稳定、精度高、响应速度快、操作方便,在军事和民用卫星通讯中均有较高的实用价值.     

2HL055恒流二极管参数测试

2HL055是贵州博越公司生产的恒流二极管,该类器件恒定电流大,极限电压低,可以直接驱动负载,但器件形成实验室成品后,没有进行过参数测试,其基本参数范围不能确定。文章拟通过试验,进一步测试2HL055器件的基本参数,并确定部分参数的测试方法。     

一种基于核密度估计的模拟电路故障诊断方法

为解决模拟电路中含有噪声等异常信息给支持向量机的最优分类面建立带来的困难,提出了一种基于核密度估计方法的模拟电路故障诊断新方法。首先提取电路的时域信号统计参数作为故障特征,然后运用核密度估计方法构造模糊隶属度函数,将该隶属度函数应用到模糊支持向量机上进行故障诊断。通过训练模糊支持向量机获得故障诊断模型,实现对电路单故障和多故障的诊断分类,能有效消除特征中噪声和野点的影响。将该方法应用于CSTV滤波电路进行仿真实验,结果表明该方法能突出不同故障的特性并正确有效地诊断出多故障类型,综合诊断正确率达到95%,为模拟电路故障诊断提供了新的技术途径。     

Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes

Support vector machines (SVM) are an emerging data classification technique with many diverse applications. The feature subset selection, along with the parameter setting in the SVM training procedure significantly influences the classification accuracy. In this paper, the asymptotic behaviors of support vector machines are fused with genetic algorithm (GA) and the feature chromosomes are generated, which thereby directs the search of genetic algorithm to the straight line of optimal generalization error in the superparameter space. On this basis, a new approach based on genetic algorithm with feature chromosomes, termed GA with feature chromosomes, is proposed to simultaneously optimize the feature subset and the parameters for SVM.To evaluate the proposed approach, the experiment adopts several real world datasets from the UCI database and from the Benchmark database. Compared with the GA without feature chromosomes, the grid search, and other approaches, the proposed approach not only has higher classification accuracy and smaller feature subsets, but also has fewer processing time.     

An improved adaptive genetic algorithm based on hormone modulation mechanism for job-shop scheduling problem

An improved adaptive genetic algorithm (IAGA) for solving the minimum makespan problem of job-shop scheduling problem (JSP) is presented. Though the traditional genetic algorithm (GA) exhibits implicit parallelism and can retain useful redundant information about what is learned from previous searches by its representation in individuals in the population, yet GA may lose solutions and substructures due to the disruptive effects of genetic operators and is not easy to regulate GA’s convergence. The proposed IAGA is inspired from hormone modulation mechanism, and then the adaptive crossover probability and adaptive mutation probability are designed. The proposed IAGA is characterized by simplifying operations, high search precision, overcoming premature phenomenon and slow evolution. The proposed method by employing operation-based encoding is effectively applied to solve a dynamic job-shop scheduling problem (DJSP) and a complicated contrastive experiment of JSP in manufacturing system. Meanwhile, in order to ensure to create a feasible solution, a new method for crossover operation is adopted, named, partheno-genetic operation (PGO). The computational results validate the effectiveness of the proposed IAGA, which can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing genetic algorithms reported recently in the literature. By employing IAGA, machines can be used more efficiently, which means that tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently.