Asymptotic stability and disturbance attenuation properties for a class of networked control systems

In this paper, stability and disturbance attenuation issues for a class of Networked Control Systems (NCSs) under uncertain access delay and packet dropout effects are considered. Our aim is to find conditions on the delay and packet dropout rate, under which the system stability and H∞ disturbance attenuation properties are preserved to a desired level. The basic idea in this paper is to formulate such Networked Control System as a discrete-time switched system. Then the NCSs’ stability and performance problems can be reduced to the corresponding problems for switched systems, which have been studied for decades and for which a number of results are available in the literature. The techniques in this paper are based on recent progress in the discrete-time switched systems and piecewise Lyapunov functions.     

Asymptotic stability and disturbance attenuation properties for a class of networked control systems

In this paper, stability and disturbance attenuation issues for a class of Networked Control Systems (NCSs) under uncertain access delay and packet dropout effects are considered. Our aim is to find conditions on the delay and packet dropout rate, under which the system stability and H∞ disturbance attenuation properties are preserved to a desired level. The basic idea in this paper is to formulate such Networked Control System as a discrete-time switched system. Then the NCSs’ stability and performance problems can be reduced to the corresponding problems for switched systems, which have been studied for decades and for which a number of results are available in the literature. The techniques in this paper are based on recent progress in the discrete-time switched systems and piecewise Lyapunov functions.     

Scaling CMOS devices through alternative structures

The conventional wisdom holds that CMOS devices cannot be scaled much further from where they are today because of several device physics limitations such as the large tunneling current in very thin gate dielectrics. It is shown that alternative device structures can allow CMOS transistors to scale by another 20 times. That is as large a factor of scaling as what the semiconductor industry accomplished in the past 25 years. There will be many opportunities and challenges in finding novel device structures and new processing techniques, and in understanding the physics of future devices.     

Real-time visual odometry estimation based on principal direction detection on ceiling vision

In this paper,we present a novel algorithm for odometry estimation based on ceiling vision.The main contribution of this algorithm is the introduction of principal direction detection that can greatly reduce error accumulation problem in most visual odometry estimation approaches.The principal direction is defned based on the fact that our ceiling is flled with artifcial vertical and horizontal lines which can be used as reference for the current robot s heading direction.The proposed approach can be operated in real-time and it performs well even with camera s disturbance.A moving low-cost RGB-D camera(Kinect),mounted on a robot,is used to continuously acquire point clouds.Iterative closest point(ICP) is the common way to estimate the current camera position by registering the currently captured point cloud to the previous one.However,its performance sufers from data association problem or it requires pre-alignment information.The performance of the proposed principal direction detection approach does not rely on data association knowledge.Using this method,two point clouds are properly pre-aligned.Hence,we can use ICP to fne-tune the transformation parameters and minimize registration error.Experimental results demonstrate the performance and stability of the proposed system under disturbance in real-time.Several indoor tests are carried out to show that the proposed visual odometry estimation method can help to signifcantly improve the accuracy of simultaneous localization and mapping(SLAM).     

An improved chaotic hybrid differential evolution for the short-term hydrothermal scheduling problem considering practical constraints

Short-term hydrothermal scheduling(STHTS) is a non-linear and complex optimization problem with a set of operational hydraulic and thermal constraints. Earlier, this problem has been addressed by several classical techniques; however, due to limitations such as non-linearity and non-convexity in cost curves, artificial intelligence tools based techniques are being used to solve the STHTS problem. In this paper an improved chaotic hybrid differential evolution(ICHDE) algorithm is proposed to find an optimal solution to this problem taking into account practical constraints. A self-adjusted parameter setting is obtained in differential evolution(DE) with the application of chaos theory, and a chaotic hybridized local search mechanism is embedded in DE to effectively prevent it from premature convergence. Furthermore, heuristic constraint handling techniques without any penalty factor setting are adopted to handle the complex hydraulic and thermal constraints. The superiority and effectiveness of the developed methodology are evaluated by its application in two illustrated hydrothermal test systems taken from the literature. The transmission line losses, prohibited discharge zones of hydel plants, and ramp rate limits of thermal plants are also taken into account. The simulation results reveal that the proposed technique is competent to produce an encouraging solution as compared with other recently established evolutionary approaches.     

Linear Time Baire Hierarchical Clustering for Enterprise Information Retrieval

The Baire or longest common prefix metric induces an ultrametric or tree topology. It has many interesting properties such as the following: the Baire distance, or metric, is also an ultrametric; associated with the tree topology is a hierarchically-structured, embedded set of clusters; the hierarchical clustering can be viewed in terms of density-based and grid-based structuring of the data. We are interested in using the hierarchical structuring of the data induced by the Baire metric for top-down search, in an information retrieval context. Enterprise search and retrieval requires exhaustivity of retrievals. Another requirement is that enterprise search supports situation awareness in order to implement different policies of access to, and use of, data. We show how situation awareness can be supported by the Baire metric, as used for structuring data in order to support enterprise search and retrieval.     

Visibility-Aware Direct Volume Rendering

Direct volume rendering (DVR) is a powerful visualization technique which allows users to effectively explore and study volumetric datasets.Different transparency settings can be flexibly assigned to different structures such that some valuable information can be revealed in direct volume rendered images (DVRIs).However,end-users often feel that some risks are always associated with DVR because they do not know whether any important information is missing from the transparent regions of DVRIs.In this paper,we investigate how to semi-automatically generate a set of DVRIs and also an animation which can reveal information missed in the original DVRIs and meanwhile satisfy some image quality criteria such as coherence.A complete framework is developed to tackle various problems related to the generation and quality evaluation of visibility-aware DVRIs and animations.Our technique can reduce the risk of using direct volume rendering and thus boost the confidence of users in volume rendering systems.     

CAutoCSD – Evolutionary Search and Optimisation Enabled Computer Automated Control System Design

This paper attempts to set a unified scene for various linear time-invariant (LTI) control system design schemes, by transforming the existing concept of "computer-aided control system design" (CACSD) to novel "computer-automated control system design" (CAutoCSD). The first step towards this goal is to accommodate, under practical constraints, various design objectives that are desirable in both time and frequency domains. Such performance-prioritised unification is aimed at relieving practising engineers from having to select a particular control scheme and from sacrificing certain performance goals resulting from pre-commitment to such schemes. With recent progress in evolutionary computing based extra-numeric, multi-criterion search and optimisation techniques, such unification of LTI control schemes becomes feasible, analytical and practical, and the resultant designs can be creative. The techniques developed are applied to, and illustrated by, three design problems. The unified approach automatically provides an integrator for zero-steady state error in velocity control of a DC motor, and meets multiple objectives in the design of an LTI controller for a non-minimum phase plant and offers a high-performance LTI controller network for a non-linear chemical process.     

Fault-tolerant Control Systems—An Introductory Overview

This paper presents an introductory overview on the development of fault-tolerant control systems. For this reason, the paper is written in a tutorial fashion to summarize some of the important results in this subject area deliberately without going into details in any of them. However, key references are provided from which interested readers can obtain more detailed information on a particular subject. It is necessary to mention that, throughout this paper, no efforts were made to provide an exhaustive coverage on the subject matter. In fact, it is far from it. The paper merely represents the view and experience of its author. It can very well be that some important issues or topics were left out unintentionally. If that is the case, the author sincerely apologizes in advance.After a brief account of fault-tolerant control systems, particularly on the original motivations, and the concept of redundancies, the paper reviews the development of fault-tolerant control systems with highlights to several important issues from a historical perspective. The general approaches to fault-tolerant control has been divided into passive, active, and hybrid approaches. The analysis techniques for active fault-tolerant control systems are also discussed. Practical applications of fault- tolerant control are highlighted from a practical and industrial perspective. Finally, some critical issues in this area are discussed as open problems for future research/development in this emerging field.     

PRSOM: a new visualization method by hybridizing multidimensional scaling and self-organizing map

Self-organizing map (SOM) is an approach of nonlinear dimension reduction and can be used for visualization. It only preserves topological structures of input data on the projected output space. The interneuron distances of SOM are not preserved from input space into output space such that the visualization of SOM can be degraded. Visualization-induced SOM (ViSOM) has been proposed to overcome this problem. However, ViSOM is derived from heuristic and no cost function is assigned to it. In this paper, a probabilistic regularized SOM (PRSOM) is proposed to give a better visualization effect. It is associated with a cost function and gives a principled rule for weight-updating. The advantages of both multidimensional scaling (MDS) and SOM are incorporated in PRSOM. Like MDS, The interneuron distances of PRSOM in input space resemble those in output space, which are predefined before training. Instead of the hard assignment by ViSOM, the soft assignment by PRSOM can be further utilized to enhance the visualization effect. Experimental results demonstrate the effectiveness of the proposed PRSOM method compared with other dimension reduction methods.     

Adaptive nonlinear manifolds and their applications to pattern recognition

Dimensionality reduction has long been associated with retinotopic mapping for understanding cortical maps. Multisensory information is processed, fused and mapped to an essentially 2-D cortex in an information preserving manner. Data processing and projection techniques inspired by this biological mechanism are playing an increasingly important role in pattern recognition, computational intelligence, data mining, information retrieval and image recognition. Dimensionality reduction involves reduction of features or volume of data and has become an essential step of information processing in many fields. The topic of manifold learning has recently attracted a great deal of attention, and a number of advanced techniques for extracting nonlinear manifolds and reducing data dimensions have been proposed from statistics, geometry theory and adaptive neural networks. This paper provides an overview of this challenging and emerging topic and discusses various recent methods such as self-organizing map (SOM), kernel PCA, principal manifold, isomap, local linear embedding, and Laplacian eigenmap. Many of them can be considered in a learning manifold framework. The paper further elaborates on the biologically inspired SOM model and its metric preserving variant ViSOM under the framework of adaptive manifold; and their applications in dimensionality reduction with face recognition are investigated. The experiments demonstrate that adaptive ViSOM-based methods produce markedly improved performance over the others due to their metric scaling and preserving properties along the nonlinear manifold.     

ViSOM - a novel method for multivariate data projection andstructure visualization

When used for visualization of high-dimensional data, the self-organizing map (SOM) requires a coloring scheme, such as the U-matrix, to mark the distances between neurons. Even so, the structures of the data clusters may not be apparent and their shapes are often distorted. In this paper, a visualization-induced SOM (ViSOM) is proposed to overcome these shortcomings. The algorithm constrains and regularizes the inter-neuron distance with a parameter that controls the resolution of the map. The mapping preserves the inter-point distances of the input data on the map as well as the topology. It produces a graded mesh in the data space such that the distances between mapped data points on the map resemble those in the original space, like in the Sammon mapping. However, unlike the Sammon mapping, the ViSOM can accommodate both training data and new arrivals and is much simpler in computational complexity. Several experimental results and comparisons with other methods are presented.     

基于ICA与SOM的不完整数据处理

介绍了数据挖掘中不完整数据的研究现状及ICA与SOM的特点,提出了基于ICA与SOM的不完整数据的处理模型IVS-IDH,研究了数据之间存在相关关系且为非高斯分布时不完整数据的处理方法,在SOM基础上取得了不完整数据集的可视化分析结果,从而克服了Wang S提出的不完整数据处理方法的不足。     

Local matrix adaptation in topographic neural maps

The self-organizing map (SOM) and neural gas (NG) and generalizations thereof such as the generative topographic map constitute popular algorithms to represent data by means of prototypes arranged on a (hopefully) topology representing map. Most standard methods rely on the Euclidean metric, hence the resulting clusters tend to have isotropic form and they cannot account for local distortions or correlations of data. For this reason, several proposals exist in the literature which extend prototype-based clustering towards more general models which, for example, incorporate local principal directions into the winner computation. This allows to represent data faithfully using less prototypes. In this contribution, we establish a link of models which rely on local principal components (PCA), matrix learning, and a formal cost function of NG and SOM which allows to show convergence of the algorithm. For this purpose, we consider an extension of prototype-based clustering algorithms such as NG and SOM towards a more general metric which is given by a full adaptive matrix such that ellipsoidal clusters are accounted for. The approach is derived from a natural extension of the standard cost functions of NG and SOM (in the form of Heskes). We obtain batch optimization learning rules for prototype and matrix adaptation based on these generalized cost functions and we show convergence of the algorithm. The batch optimization schemes can be interpreted as local principal component analysis (PCA) and the local eigenvectors correspond to the main axes of the ellipsoidal clusters. Thus, this approach provides a cost function associated to proposals in the literature which combine SOM or NG with local PCA models. We demonstrate the behavior of matrix NG and SOM in several benchmark examples and in an application to image compression.     

Improved multidimensional scaling analysis using neural networks with distance-error backpropagation

We show that neural networks, with a suitable error function for backpropagation, can be successfully used for metric multidimensional scaling (MDS) (i.e., dimensional reduction while trying to preserve the original distances between patterns) and are in fact able to outdo the standard algebraic approach to MDS, known as classical scaling.     

一种改进的高维数据可视化模型

可视化诱导自组织映射（ViSOM）是一种人工神经网络模型,已经被成功应用于高维数据的可视化分析。但是,标准的ViSOM方法不仅没有考虑数据之间的相关性,而且当输出网络结点太多时,需要消耗大量运算开销;输出网络结点太少,又难以分析数据的可视化结果。为克服ViSOM的这两个弱点,本文首先在ViSOM的基础上提出了一个改进的映射算法MViSOM,接着在独立成分分析（ICA）与MViSOM的基础上提出了一个改进的高维数据可视化模型IMViSOM。论文最后通过实验说明了IMViSOM模型在对群聚数据的可视化分类效果及运算速度方面都优于ViSOM方法,从而验证了IMViSOM模型的正确性与合理性。     

Visualizing non-metric similarities in multiple maps

Techniques for multidimensional scaling visualize objects as points in a low-dimensional metric map. As a result, the visualizations are subject to the fundamental limitations of metric spaces. These limitations prevent multidimensional scaling from faithfully representing non-metric similarity data such as word associations or event co-occurrences. In particular, multidimensional scaling cannot faithfully represent intransitive pairwise similarities in a visualization, and it cannot faithfully visualize “central” objects. In this paper, we present an extension of a recently proposed multidimensional scaling technique called t-SNE. The extension aims to address the problems of traditional multidimensional scaling techniques when these techniques are used to visualize non-metric similarities. The new technique, called multiple maps t-SNE, alleviates these problems by constructing a collection of maps that reveal complementary structure in the similarity data. We apply multiple maps t-SNE to a large data set of word association data and to a data set of NIPS co-authorships, demonstrating its ability to successfully visualize non-metric similarities.     

基于SOM聚类的垃圾处理方式可视化分析方法

针对全国各省份垃圾处理方式的数据,提出一种混合可视分析方法。为了从多角度分析数据,混合U矩阵、平行坐标以及Small-Multiple 3种可视化技术,设计并实现了3种可视化视图的交互联动。首先,对数据进行聚类处理,将各省份近年的垃圾处理方式划分类别,采用SOM神经网络聚类算法实现聚类。然后,针对SOM聚类结果,采用U矩阵的方式进行可视化,并采用平行坐标描述每个聚类结果的各个属性。为了分析数据的地理属性及时序属性,采用Small-Multiple可视化技术。最后,实现多视图联动、刷新技术等交互方式,帮助用户自行探索数据,实现多视图的交互展示与分析。实验表明,这种混合可视方式可达到较好的多属性交互可视化效果,能够帮助用户了解并分析我国垃圾处理方式的分布及趋势。     

Data validation and missing data reconstruction using self-organizing map for water treatment

Applications in the water treatment domain generally rely on complex sensors located at remote sites. The processing of the corresponding measurements for generating higher-level information such as optimization of coagulation dosing must therefore account for possible sensor failures and imperfect input data. In this paper, self-organizing map (SOM)-based methods are applied to multiparameter data validation and missing data reconstruction in a drinking water treatment. The SOM is a special kind of artificial neural networks that can be used for analysis and visualization of large high-dimensional data sets. It performs both in a nonlinear mapping from a high-dimensional data space to a low-dimensional space aiming to preserve the most important topological and metric relationships of the original data elements and, thus, inherently clusters the data. Combining the SOM results with those obtained by a fuzzy technique that uses marginal adequacy concept to identify the functional states (normal or abnormal), the SOM performances of validation and reconstruction process are tested successfully on the experimental data stemming from a coagulation process involved in drinking water treatment.