Systematic convergence of nonlinear stochastic estimators on the Special Orthogonal Group SO(3)

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group with the tracking error of the normalized Euclidean distance meeting predefined transient and steady‐state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from low‐cost inertial measurement units. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form.     

Occurrence and diversity of Listeria spp. in seafood processing plant environments

Listeria contamination in processing plant environments is a major issue for the seafood industry worldwide; faster and more reliable results are therefore desired for early detection and monitoring of environmental Listeria spp. This study aimed to gain a better understanding of the prevalence and diversity of Listeria spp., and to evaluate a rapid detection method, the 3M Molecular Detection Assay (MDA) Listeria, for its ability to detect Listeria spp. in environmental samples from seafood processing plants. Duplicate environmental sponge samples (n = 444) were collected from 152 different sites within three seafood processing plants, and analyzed for Listeria spp. by the MDA method (after 26 and 48 h of enrichment) and the U.S. Food and Drug Administration Bacteriological Analytical Manual method. Overall, detection of Listeria spp. by the two methods did not differ significantly (p > 0.05); 11 (4.9%) and 13 (5.9%) samples were positive for Listeria spp. by the MDA and FDA-BAM method, respectively. The sensitivity of the MDS was 87.0% (95% CI: 77.4–96.6%), specificity was 97.6% (95% CI: 95.5–99.7%), accuracy was 95.3%, and the positive predictive value was 89.4% (95% CI: 80.5–98.2%). Classification of 19 Listeria isolates by partial SigB sequencing analysis identified three allelic types. Twelve of these isolates were ATs 58 and 60 which were classified as Listeria monocytogenes lineage I and serotypes 1/2b, 3b, 4b, 4d, 4e, by multiplex-PCR serotyping. Six Listeria isolates were classified as Listeria innocua (AT31). Our data show that the 3M Molecular Detection Assay Listeria provides rapid and reliable results for detection and monitoring of Listeria spp., which are important for seafood processing plants. Effective Listeria monitoring programs will allow for improved development of Listeria control measures in order to minimize cross-contamination in finished products.     

Learning fuzzy controllers in mobile robotics with embedded preprocessing

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.     

现场总线在冶炼烟气制酸系统中的应用实践

分析了冶炼烟气制酸工艺的特点，介绍了由PROFIBUS-DP现场总线构建的冶炼厂烟气制酸监控系统，详细介绍了其硬件配置及相应的软件设计。     

Frequency Insertion Strategy for Channel Assignment Problem

This paper presents a new heuristic method for quickly finding a good feasible solution to the channel assignment problem (CAP). Like many other greedy-type heuristics for CAP, the proposed method also assigns a frequency to a call, one at a time. Hence, the method requires computational time that increases only linear to the number of calls. However, what distinguishes the method from others is that it starts with a narrow enough frequency band so as to provoke violations of constraints that we need to comply with in order to avoid radio interference. Each violation is then resolved by inserting frequencies at the most appropriate positions so that the band of frequencies expands minimally. An extensive computational experiment using a set of randomly generated problems as well as the Philadelphia benchmark instances shows that the proposed method perform statistically better than existing methods of its kind and even yields optimum solutions to most of Philadelphia benchmark instances among which two cases are reported for the first time ever, in this paper. Won-Young Shin was born in Busan, Korea in 1978. He received B.S. in industrial engineering from Pohang University of Science and Technology (POSTECH) in 2001 and M.S in operation research and applied statistics from POSTECH in 2003. Since 2003 he has been a researcher of Agency for Defense Development (ADD) in Korea. He is interested in optimization of communication system and applied statistics. Soo Y. Chang is an associate professor in the Department of Industrial Engineering at Pohang University of Science and Technology (POSTECH), Pohang, Korea. He teaches linear programming, discrete optimization, network flows and operations research courses. His research interests include mathematical programming and scheduling. He has published in several journals including Discrete Applied Mathematics, Computers and Mathematics with Application, IIE Transactions, International Journal of Production Research, and so on. He is a member of Korean IIE, and ORMSS. Jaewook Lee is an assistant professor in the Department of Industrial Engineering at Pohang University of Science and Technology (POSTECH), Pohang, Korea. He received the B.S. degree in mathematics with honors from Seoul National University, and the Ph.D. degree from Cornell University in applied mathematics in 1993 and 1999, respectively. He is currently an assistant professor in the department of industrial engineering at the Pohang University of Science and Technology (POSTECH). His research interests include nonlinear systems, neural networks, nonlinear optimization, and their applications to data mining and financial engineering. Chi-Hyuck Jun was born in Seoul, Korea in 1954. He received B.S. in mineral and petroleum engineering from Seoul National University in 1977, M.S. in industrial engineering from Korea Advanced Institute of Science and Technology in 1979 and Ph.D. in operations research from University of California, Berkeley, in 1986. Since 1987 he has been with the department of industrial engineering, Pohang University of Science and Technology (POSTECH) and he is now a professor and the department head. He is interested in performance analysis of communication and production systems. He has published in several journals including IIE Transactions, IEEE Transactions, Queueing Systems and Chemometrics and Intelligent Laboratory Systems. He is a member of IEEE, INFORMS and ASQ.     

企业质量管理体系运行的思考

质量管理永远是企业生命的源泉,质量管理体系是一个复杂的系统工程,企业只有不断完善质量体系,进行质量管理创新,保持质量管理体系的长期有效的进行,才能在市场竞争中保持领先。     

运动会新闻发布系统的设计与实现

针对运动会比赛期间随时需要方便、快捷、高效地更新大量新闻的需求 ,研制和开发一套运动会新闻发布系统。利用微软最新推出的asp .net动态网页开发工具 ,采用web分布式三层设计模型设计实现了一套操作简单、功能完善、具有通用性的运动会新闻发布系统     

马钢二烧结配料计算机控制系统

阐明了马钢二烧结配科计算机控制系统的目的、控制形式、控制原理；根据烧结配料控制对象的特点对信号采样和控制算法进行了探讨；提供了系统的主要硬件结构，软件功能和程序框图。     

指纹考勤管理软件的研究与实现

针对企、事业单位原有的考勤系统，如打卡钟、磁卡、IC卡等考勤方式存在代打卡、卡丢失等不足和缺陷的实际，结合指纹识别技术，建立一套考勤、薪资、人事管理于一体的软件系统。给出了系统的体系结构和功能实现，能使企业考勤管理更加简便快捷、客观公正。并提出了进一步的发展方向和展望。     

Handover and Uplink Power Control Performance in the 3.84 Mcps TDD mode of UTRA Network

In this article we consider the performance of the 3.84 Mcpstime-division duplex (TDD) mode of UTRA (Universal TerrestrialRadio Access) network. We emphasize two of the radio resourcemanagement algorithms, handover and uplink power control, whoserole in the overall system performance is studied extensively.First, a handover algorithm used in WCDMA (Wideband Code DivisionMultiple Access) standard is considered in a TDD-mode operation.This gives rise to a careful setting of different handoverparameters, and the evaluation of the effects to the systemperformance. Secondly, the specified uplink power controlalgorithm is considered. Since it is based on several user-mademeasurements which may involve both random and systematic errors acareful study about the suitability of the power control scheme iscarried out.