非线性大系统的分散线性化与分散控制

将非线性控制系统的精确线性化方法应用于非线性大系统,提出了非线性大系统 的分散线性化方法,并得到了非线性系统可分散线性化的充要条件.按照这个方法,可将难度 较大的一类非线性大系统分散控制器的设计转化为易于处理的线性大系统分散控制器的设 计.在得到该线性大系统的分散控制器后,可通过分散坐标变换的逆变换将线性大系统的控 制器变换为原非线性大系统的控制器.同时,控制器的分散性保持不变.该方法明显地降低了 该类非线性大系统分散控制器的设计难度.     

Open Embedded Control

Embedded control devices today usually allow parameter changes, and possibly activation of different pre-implemented algorithms. Full reprogramming using the complete source code is not allowed for safety, efficiency, and proprietary reasons. For these reasons, embedded regulators are quite rigid and closed concerning the control structure. In several applications, like industrial robots, there is a need to tailor the low level control to meet specific application demands. In order to meet the efficiency and safety demands, a way of building more generic and open regulators has been developed. The key idea is to use pieces of compiled executable code as functional operators, which in the simplest case may appear as ordinary control parameters. In an object oriented framework, this means that new methods can be added to controller objects after implementation of the basic control, even at run-time. The implementation was carried out in industrially well accepted languages such as C and C++. The dynamic binding at run-time differs from ordinary dynamic linking in that only a subset of the symbols can be used. This subset is defined by the fixed part of the system. The safety demands can therefore still be fulfilled. Encouraged by results from fully implemented test cases, we believe that extensive use of this concept will admit more open, still efficient, embedded systems.     

多传感器检测系统的自适应融合算法

通过分析分布式检测系统的工作原理，提出了一种新的基于并行检测系统的全局检测融合方法，该方法利用检测器局部判决结果与融合中心融合结果的误差对全局检测器下一步判决所采用的方法与参数进行修正，从而达到提高全局检测系统检测概率的目的．分析表明，该算法能有效提高系统的性能．     

小波分析在信号处理中的应用

在简述小波分析、小波识别原理的基础上，详细介绍了小波分析在信号处理中的应用，包括信号消噪、特征提取、奇异性检测等。计算机硬件技术的日趋发展，特别是DSP技术的发展，将为小波分析应用于工程中提供有力的保障。     

New MPPT algorithm for PV applications based on hybrid dynamical approach

This paper proposes a new Maximum Power Point Tracking (MPPT) algorithm for photovoltaic applications using the multicellular converter as a stage of power adaptation. The proposed MPPT technique has been designed using a hybrid dynamical approach to model the photovoltaic generator. The hybrid dynamical theory has been applied taking advantage of the particular topology of the multicellular converter. Then, a hybrid automata has been established to optimize the power production. The maximization of the produced solar energy is achieved by switching between the different operative modes of the hybrid automata, which is conditioned by some invariance and transition conditions. These conditions have been validated by simulation tests under different conditions of temperature and irradiance. Moreover, the performance of the proposed algorithm has been then evaluated by comparison with standard MPPT techniques numerically and by experimental tests under varying external working conditions. The results have shown the interesting features that the hybrid MPPT technique presents in terms of performance and simplicity for real time implementation.     

Incremental mining of weighted maximal frequent itemsets from dynamic databases

Frequent itemset mining allows us to find hidden, important information from large databases. Moreover, processing incremental databases in the itemset mining area has become more essential because a huge amount of data has been accumulated continually in a variety of application fields and users want to obtain mining results from such incremental data in more efficient ways. One of the major problems in incremental itemset mining is that the corresponding mining results can be very large-scale according to threshold settings and data volumes. In addition, it is considerably hard to analyze all of them and find meaningful information. Furthermore, not all of the mining results become actually important information. In this paper, to solve these problems, we propose an algorithm for mining weighted maximal frequent itemsets from incremental databases. By scanning a given incremental database only once, the proposed algorithm can not only conduct its mining operations suitable for the incremental environment but also extract a smaller number of important itemsets compared to previous approaches. The proposed method also has an effect on expert and intelligent systems since it can automatically provide more meaningful pattern results reflecting characteristics of given incremental databases and threshold settings, which can help users analyze the given data more easily. Our comprehensive experimental results show that the proposed algorithm is more efficient and scalable than previous state-of-the-art algorithms.     

Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system

The aim of this study is to introduce a comprehensive comparison of various energy management strategies of fuel cell/supercapacitor/battery storage systems. These strategies are utilized to manage the energy demand response of hybrid systems, in an optimal way, under highly fluctuating load condition. Two novel strategies based on salp swarm algorithm (SSA) and mine-blast optimization are proposed. The outcomes of these strategies are compared with commonly used strategies like fuzzy logic control, classical proportional integral control, the state machine, equivalent fuel consumption minimization, maximization, external energy maximization, and equivalent consumption minimization. Hydrogen fuel economy and overall efficiency are used for the comparison of these different strategies. Results demonstrate that the proposed SSA management strategy performed best compared with all other used strategies in terms of hydrogen fuel economy and overall efficiency. The minimum consumed hydrogen and maximum efficiency are found 19.4 gm and 85.61%, respectively.     

Partition-based fuzzy median filter based on adaptive resonance theory

This paper presents a novel partition-based fuzzy median filter for noise removal from corrupted digital images. The proposed filter is obtained as the weighted sum of the current pixel value and the output of the median filter, where the weight is set by using fuzzy rules concerning the state of the input signal sequence to indicate to what extent the pixel is considered to be noise. Based on the adaptive resonance theory, the authors developed a neural network model and created a new weight function where the neural network model is employed to partition the observation vector. In this framework, each observation vector is mapped to one of the M blocks that form the observation vector space. The least mean square (LMS) algorithm is applied to obtain the optimal weight for each block. Experiment results have confirmed the high performance of the proposed filter in efficiently removing impulsive noise and Gaussian noise.     

BI4BI: A continuous evaluation system for Business Intelligence systems

Business Intelligence (BI) allows supporting decision making by providing methods and tools to easily access and manage business information. Indeed, it is crucial to maintain BI systems and make them evolve in order to fit business and users’ needs. For this purpose, applying BI for BI is suggested. It helps to analyze the current BI environment and to make decisions about its use and evolution. Then, the main contribution of this paper consists in developing a novel system called BI4BI which is based on both BI systems’ data and BI users’ feedbacks.As a result, based on a user centered approach, an automated system for BI evaluation is developed helping its continuous evolution. To this end, system-based and user-based solutions provide two complementary data sources, required to effectively gather insights on the use of BI systems and the way they should evolve. A case study in a semiconductor manufacturer company demonstrates the impact of our solution on the evaluation and evolution of BI systems. Hence, thanks to the involvement of the company’s BI users during the solution design, a set of evaluation criteria responding to their objectives for evaluating the BI system are identified and validated. Furthermore, the results of this case study show that our proposed solution is better than existing ones, in terms of level of focus of its BI evaluation criteria, its level of automation, and its continuous processing.     

热连轧液压泵站控制系统设计与应用

根据热连轧精轧机组HAGC(液压自动厚度控制系统)液压泵站控制系统连续工作时间长、可靠性要求高、信号复杂的特点,以ABB公司AC800M型PLC为基础,对液压泵站的硬件构成、工作过程和程序结构进行设计.该液压泵站控制系统控制精度高、可靠性强,完全满足连轧机液压泵站控制要求,已经成功通过模拟调试,并将应用于生产实际.