A strategic classification support system for brownfield redevelopment

Brownfield redevelopment (BR) is an ongoing issue for governments, communities, and consultants around the world. It is also an increasingly popular research topic in several academic fields. Strategic decision support that is now available for BR is surveyed and assessed. Then a dominance-based rough-set approach is developed and used to classify cities facing BR issues according to the level of two characteristics, BR effectiveness and BR future needs. The data for the classification are based on the widely available results of a survey of US cities. The unique features of the method are its reduced requirement for preference information, its ability to handle missing information effectively, and the easily understood linguistic decision rules that it generates, based on a training classification provided by experts. The resulting classification should be a valuable aid to cities and governments as they plan their BR projects and budgets.     

DECODE: a new method for discovering clusters of different densities in spatial data

When clusters with different densities and noise lie in a spatial point set, the major obstacle to classifying these data is the determination of the thresholds for classification, which may form a series of bins for allocating each point to different clusters. Much of the previous work has adopted a model-based approach, but is either incapable of estimating the thresholds in an automatic way, or limited to only two point processes, i.e. noise and clusters with the same density. In this paper, we present a new density-based cluster method (DECODE), in which a spatial data set is presumed to consist of different point processes and clusters with different densities belong to different point processes. DECODE is based upon a reversible jump Markov Chain Monte Carlo (MCMC) strategy and divided into three steps. The first step is to map each point in the data to its mth nearest distance, which is referred to as the distance between a point and its mth nearest neighbor. In the second step, classification thresholds are determined via a reversible jump MCMC strategy. In the third step, clusters are formed by spatially connecting the points whose mth nearest distances fall into a particular bin defined by the thresholds. Four experiments, including two simulated data sets and two seismic data sets, are used to evaluate the algorithm. Results on simulated data show that our approach is capable of discovering the clusters automatically. Results on seismic data suggest that the clustered earthquakes, identified by DECODE, either imply the epicenters of forthcoming strong earthquakes or indicate the areas with the most intensive seismicity, this is consistent with the tectonic states and estimated stress distribution in the associated areas. The comparison between DECODE and other state-of-the-art methods, such as DBSCAN, OPTICS and Wavelet Cluster, illustrates the contribution of our approach: although DECODE can be computationally expensive, it is capable of identifying the number of point processes and simultaneously estimating the classification thresholds with little prior knowledge.     

An efficient multiobjective differential evolution algorithm for engineering design

Solving engineering design and resources optimization via multiobjective evolutionary algorithms (MOEAs) has attracted much attention in the last few years. In this paper, an efficient multiobjective differential evolution algorithm is presented for engineering design. Our proposed approach adopts the orthogonal design method with quantization technique to generate the initial archive and evolutionary population. An archive (or secondary population) is employed to keep the nondominated solutions found and it is updated by a new relaxed form of Pareto dominance, called Pareto-adaptive ϵ-dominance (paϵ-dominance), at each generation. In addition, in order to guarantee to be the best performance produced, we propose a new hybrid selection mechanism to allow the archive solutions to take part in the generating process. To handle the constraints, a new constraint-handling method is employed, which does not need any parameters to be tuned for constraint handling. The proposed approach is tested on seven benchmark constrained problems to illustrate the capabilities of the algorithm in handling mathematically complex problems. Furthermore, four well-studied engineering design optimization problems are solved to illustrate the efficiency and applicability of the algorithm for multiobjective design optimization. Compared with Nondominated Sorting Genetic Algorithm II, one of the best MOEAs available at present, the results demonstrate that our approach is found to be statistically competitive. Moreover, the proposed approach is very efficient and is capable of yielding a wide spread of solutions with good coverage and convergence to true Pareto-optimal fronts.     

P2P网具有QoS保障的语义Web服务匹配方法

在P2P网络中,有大量的web服务,如何准确高效的发现零散分布于世界各地的语义Web服务呢？文章从P2P网络的特点出发,分析了现有的一些研究和方法,在扩展UDDI的基础上,结合目前语义Web服务匹配的多层匹配思想,提出了P2P网络中支持QoS的语义Web服务匹配和发现的有效方法。该方法采用一个四层次的匹配筛选算法,对QoS各层的匹配算法进行了深入讨论,并以相似函数来度量服务提供方与服务请求方的Web服务相似程度。     

基于DSP的柑橘分级系统硬件设计

文章介绍了基于DSP的柑橘分级系统的硬件方案所使用的技术.成像系统将采集的图像信息通过视频编码、解码后输入DSP进行预处理,并提取特征值,与标称值进行比较,从而确定每个柑橘的质量级别,实现分级.     

A class of classification and regression methods by multiobjective programming

An extensive review for the recent developments of multiple criteria linear programming data mining models is provided in this paper. These researches, which include classification and regression methods, are introduced in a systematic way. Some applications of these methods to real-world problems are also involved in this paper. This paper is a summary and reference of multiple criteria linear programming methods that might be helpful for researchers and applications in data mining.     

Stability analysis of neutral systems with mixed delays

The stability of neutral systems with mixed delays is studied in this paper. A new discretized Lyapunov functional method is proposed. The method in this paper is less conservative than the existing ones and the results are very close to the analytical results. In addition, this method allows the coefficient matrix of the neutral term to have time-varying uncertainties.     

Stability analysis of neutral systems with distributed delays

The stability of neutral systems with distributed delays is investigated in this paper. A modified Lyapunov-Krasovskii functional is constructed to study this class of systems. The proposed stability criterion is discrete-, distributed- and neutral-delay-dependent. In addition, by this method one can study the case when the coefficient matrix of the neutral delay term is time-varying uncertain. The reduced conservatism is illustrated in a numerical example.     

Adaptive robust posture control of a parallel manipulator driven by pneumatic muscles

Rather severe parametric uncertainties and uncertain nonlinearities exist in the dynamic modeling of a parallel manipulator driven by pneumatic muscles. Those uncertainties not only come from the time-varying friction forces and the static force modeling errors of pneumatic muscles but also from the inherent complex nonlinearities and unknown disturbances of the parallel manipulator. In this paper, a discontinuous projection-based adaptive robust control strategy is adopted to compensate for both the parametric uncertainties and uncertain nonlinearities of a three-pneumatic-muscles-driven parallel manipulator to achieve precise posture trajectory tracking control. The resulting controller effectively handles the effects of various parameter variations and the hard-to-model nonlinearities such as the friction forces of the pneumatic muscles. Simulation and experimental results are obtained to illustrate the effectiveness of the proposed adaptive robust controller.