Probabilistic reachability and safety for controlled discrete time stochastic hybrid systems

In this work, probabilistic reachability over a finite horizon is investigated for a class of discrete time stochastic hybrid systems with control inputs. A suitable embedding of the reachability problem in a stochastic control framework reveals that it is amenable to two complementary interpretations, leading to dual algorithms for reachability computations. In particular, the set of initial conditions providing a certain probabilistic guarantee that the system will keep evolving within a desired ‘safe’ region of the state space is characterized in terms of a value function, and ‘maximally safe’ Markov policies are determined via dynamic programming. These results are of interest not only for safety analysis and design, but also for solving those regulation and stabilization problems that can be reinterpreted as safety problems. The temperature regulation problem presented in the paper as a case study is one such case.     

Regularized Linear Fuzzy Clustering and Probabilistic PCA Mixture Models

Fuzzy$c$-means (FCM)-type fuzzy clustering approaches are closely related to Gaussian mixture models (GMMs) and EM-like algorithms have been used in FCM clustering with regularized objective functions. Especially, FCM with regularization by Kullback–Leibler information (KLFCM) is a fuzzy counterpart of GMMs. In this paper, we propose to apply probabilistic principal component analysis (PCA) mixture models to linear clustering following a discussion on the relationship between local PCA and linear fuzzy clustering. Although the proposed method is a kind of the constrained model of KLFCM, the algorithm includes the fuzzy$c$-varieties (FCV) algorithm as a special case, and the algorithm can be regarded as a modified FCV algorithm with regularization by K–L information. Numerical experiments demonstrate that the proposed clustering algorithm is more flexible than the maximum likelihood approaches and is useful for capturing local substructures properly.     

A study of Gaussian mixture models of color and texture features for image classification and segmentation

The aims of this paper are two-fold: to define Gaussian mixture models (GMMs) of colored texture on several feature spaces and to compare the performance of these models in various classification tasks, both with each other and with other models popular in the literature. We construct GMMs over a variety of different color and texture feature spaces, with a view to the retrieval of textured color images from databases. We compare supervised classification results for different choices of color and texture features using the Vistex database, and explore the best set of features and the best GMM configuration for this task. In addition we introduce several methods for combining the ‘color’ and ‘structure’ information in order to improve the classification performances. We then apply the resulting models to the classification of texture databases and to the classification of man-made and natural areas in aerial images. We compare the GMM model with other models in the literature, and show an overall improvement in performance.     

自适应对称自回归模型的压缩图像内插方法

目的 大多数图像内插方法只考虑低分辨率图像的下采样降质过程,忽略编码噪声的影响。提出一种新的自适应对称自回归模型的压缩图像内插方法。方法 假设局部图像相似的图像块具有相同的图像内插模型。方法分为训练和重建两个阶段。在训练阶段,首先对训练图像采用主成分分析提取图像块的局部梯度主方向,根据方向进行一次分类,分别建立各个方向的对称自回归模型和训练集;其次对每个方向的训练集,根据图像基元特征,利用K均值聚类方法进行二次分类;最后对每个二次分类训练子集,选择其所属方向类的模型,使用有约束的最小二乘法估计对应于该子集的模型系数。在重建阶段,首先根据测试图像块的局部梯度主方向,确定方向类别,再计算测试块基元特征和该方向类中所有聚类中心的欧氏距离,选择具有最小欧氏距离的聚类中心的自回归模型用于内插。结果 采用8种不同的测试图像在JPEG的2种量化方式条件下进行测试,与7种典型的图像内插相比,结果表明本文方法能够有效地克服编码噪声的影响,峰值信噪比(PSNR)和结构相似度(SSIM)均优于其他方法。结论 本文方法具有较低的复杂度,可以适用于图像通信中增强图像的分辨率。     

Modelling and solving English Peg Solitaire

Peg Solitaire is a well known puzzle, which can prove difficult despite its simple rules. Pegs are arranged on a board such that at least one ‘hole’ remains. By making draughts/checkers-like moves, pegs are gradually removed until no further moves are possible or some goal configuration is achieved. This paper considers the English variant, consisting of a board in a cross shape with 33 holes. Modelling Peg Solitaire via constraint or integer programming techniques presents a considerable challenge and is examined in detail. The merits of the resulting models are discussed and they are compared empirically. The sequential nature of the puzzle naturally conforms to a planning problem, hence we also present an experimental comparison with several leading AI planning systems. Other variants of the puzzle, such as ‘Fool's Solitaire’ and ‘Long-hop’ Solitaire are also considered.     

Categorizing commercial products for customer oriented online retailing

Online retailing can offer several benefits. In order to realize these benefits, a commercial web site needs to ameliorate its deficiencies due to the absence of actual physical products. Although the use and popularity of the Internet continues to increase, it is still unclear how online retailers can establish effective online retailing strategies to capitalize on their benefits. For this clarification, this paper proposes a framework to categorize products according to consumers’ involvement and information quality fitness. Involvement refers to the degree of psychological identification or emotional ties consumers have with a particular product. Information quality fitness refers to the extent to which a product can fit with shopper’s requirements for online information. Thirty-six commercial products are empirically tested. It is found that products can be grouped into four categories such as ‘complex,’ ‘intelligent,’ ‘light,’ and ‘simple.’ This categorization can help understand product characteristics when they are sold via the Internet. Our finding implies that online retailers need to formulate their retailing strategies in view of these product categories.     

Boundedness analysis of finitely recursive processes. II.Sequential and hybrid processes

Part I of this paper has shown that a subclass of the finitely recursive processes (FRP), composed without the sequential composition operator (SCO), is bounded. In this sequel it is first shown that boundedness is not guaranteed but decidable for the subclass of FRP built using the SCO, but without the parallel composition operator (PCO). To overcome the limitation in modeling using the SCO only, some bounded hybrid subclasses have been proposed where both the SCO and PCO can be used, although in a restricted fashion. Finally, the proposed hybrid framework is used to model a job-shop, and its potential in the specification and design of supervisory control systems is demonstrated     

A customer-oriented organisational diagnostic model based on data mining of customer-complaint databases

The purpose of this paper is to develop a customer-oriented organisational diagnostic model, ‘PARA’ model, based on data mining of customer-complaint databases. The proposed ‘PARA’ model, which is designed to diagnose and correct service failures, takes its name from the initial letters of the four analytical stages of the model: (i) ‘primary diagnosis’; (ii) ‘advanced diagnosis’; (iii) ‘review’; and (iv) ‘action’. In the primary-diagnosis stage, the customer-complaint database is comprehensively analysed to identify themes and categories of complaints. In the advanced-diagnosis stage, a data-mining technique is employed to investigate the relationship between the categories of customer complaints and the deficiencies of the service system. In the review stage, the identified weaknesses of the service system are reviewed and awareness of these weaknesses is enhanced among the organisation’s employees. In the action stage, a strategy of action plans for improvement is developed. An empirical case study is conducted to demonstrate the practical efficacy of the ‘PARA’ model. The paper concludes by summarising the advantages of the proposed model and the implications for future research.     

Design with part behaviors: behavior model, representation and applications

A part is primarily characterized by its ‘function(s)’. The function of the part is achieved through its ‘interactions’ with other parts in an assembly under a set of operating conditions. These ‘interactions’ constitute the ‘behavior’ of the part. The ‘part behavior’ is achieved through a set of spatial and design functional relationships between the interacting surfaces of the parts. The set of spatial and design functional relationships for a part constitutes the Part Function Model (PFM) of that part. In this paper, the nature and role of part ‘functions’ and ‘behaviors’ have been studied in the context of a product design system. The paper addresses the following issues: (i) relationship between function, behavior, and geometry of a part; (ii) importance of ‘part behavior’ over ‘part function’, and development of a ‘part behavior’ model; (iii) methodology for transforming the part behaviors into the PFM model of the part; (iv) product model framework for storing the PFM model information with an Object Oriented Programming (OOP) based CAD system (Concentra's Concept Modeller); (v) importance of the PFM model within the product development process; and (vi) application of the PFM model for generating various product specifications of the part. The prototype implementation of a Functional Design System for transforming part behaviors into different types of part specifications has also been presented in this paper.     

Mixtures of robust probabilistic principal component analyzers

Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is specifically designed to extract the local principal orientations in the data. An important issue with this generative model is its sensitivity to data lying off the low-dimensional manifold. In order to address this problem, the mixtures of robust probabilistic principal component analyzers are introduced. They take care of atypical points by means of a long tail distribution, the Student-t. It is shown that the resulting mixture model is an extension of the mixture of Gaussians, suitable for both robust clustering and dimensionality reduction. Finally, we briefly discuss how to construct a robust version of the closely related mixture of factor analyzers.     

Physical discomfort and psychosocial job stress among male and female operators at telecommunication call centers in Taiwan

The prevalence of job stress, distributions of major job stressors, and associations between perceived job stress levels and multiple physical discomforts are assessed via a cross-sectional study of 1023 male and female operators at telecommunication call centers in Taiwan. Cases of discomfort are identified via questionnaire surveys requiring respondents to self-reported symptoms of discomfort. Information is obtained on demographics, health status, perceived job stress levels, major job stressors and psychosocial job characteristics. Multivariate logistic regression models are developed to predict physical discomfort in nine body areas. ‘Eye strain’, ‘hoarse or painful throat’ and ‘musculoskeletal discomfort’ are the most pronounced and prevalent complaints after prolonged work time at call centers. Female operators had higher prevalence of physical discomfort than male operators for all body areas. ‘Encountering difficult customers’ ranked as the most important job stress factor among both male and female operators. Working in a call center for more than 4 years is strongly associated with discomfort in all body areas (odds ratio ranges from 1.65 to 2.15). Analysis of risk factors vs. physical discomfort reveals that operators who perceive higher job stress have significantly increased risk of several health complaints, including eye strain, tinnitus, hoarse or painful throat, chronic cough with phlegm, chest tightness, irritable stomach or peptic ulcers, frequent urination and musculoskeletal discomfort.     

Optimal implementations of UPGMA and other common clustering algorithms

In this work we consider hierarchical clustering algorithms, such as UPGMA, which follow the closest-pair joining scheme. We survey optimal O(n²)-time implementations of such algorithms which use a ‘locally closest’ joining scheme, and specify conditions under which this relaxed joining scheme is equivalent to the original one (i.e. ‘globally closest’).     

Characterizing electrical signals evoked by acupuncture through complex network mapping: a new perspective on acupuncture

The electrical signals are obtained in spinal dorsal root after different manipulations of acupuncture (MA) being taken at the ‘Zusanli’ point of the experiment rats. After combining the analysis of the data generated from neuronal network model and that evoked by acupuncture, it is found that features of neuronal chaotic rate time series induced by periodic stimuli can be characterized by complex network approach. The features of signals evoked by MA ‘nb’ ‘nx’ (twisting) and MA ‘tb’ ‘tx’ (lifting and thrusting) are shown to be different according to the topologies of the mapped networks. This study provides us a new perspective on the analysis of acupuncture and may give potential helps on clinical treatment.     

Drawing curves onto a cloud of points for point-based modelling

Point-based geometric models are gaining popularity in both the computer graphics and CAD fields. A related design/modelling problem is the focus of the reported research: drawing curves onto digital surfaces represented by clouds of points. The problem is analyzed and solved, and a set of ‘design tools’ are proposed which allow the user/designer to efficiently perform ‘product development’ (alternative name: ‘detail design’) tasks which require efficient processing of a ‘digital surface’. The primary tool is a robust and efficient point projection algorithm combined with a smoothing technique for producing smooth ‘digital curves’ lying onto the cloud surface. The new design tools are tested on a real-life industrial example with very satisfactory results, which are thoroughly presented in the paper.     

Provisions for naive users of model management systems

Model management systems (MMS) have typically focused on model formulation or problem representation, and thus have often been conceived as tools for experienced model builders. In this paper, attention is drawn to the roles of naive users of MMS. Then we present a prototype MMS which was designed and developed with this rationale. MMS II, a frame-based prototype MMS, is a tool equipped with facilities for direct retrieval of models, guided retrieval, model modification and integration of models retrieved from different model bases. Discussed here are the original features designed to aid naive users, i.e. non-professional model builders. These include navigational guidance through a model base, if-sought attachments for explanation and suggestions, providing a critique via shadow processing, approximation following failure to retrieve a suitable model, suggestions of models to modify, and model integration via specification of model links.     

DBCAMM: A novel density based clustering algorithm via using the Mahalanobis metric

In this paper we propose a new density based clustering algorithm via using the Mahalanobis metric. This is motivated by the current state-of-the-art density clustering algorithm DBSCAN and some fuzzy clustering algorithms. There are two novelties for the proposed algorithm: One is to adopt the Mahalanobis metric as distance measurement instead of the Euclidean distance in DBSCAN and the other is its effective merging approach for leaders and followers defined in this paper. This Mahalanobis metric is closely associated with dataset distribution. In order to overcome the unique density issue in DBSCAN, we propose an approach to merge the sub-clusters by using the local sub-cluster density information. Eventually we show how to automatically and efficiently extract not only ‘traditional’ clustering information, such as representative points, but also the intrinsic clustering structure. Extensive experiments on some synthetic datasets show the validity of the proposed algorithm. Further the segmentation results on some typical images by using the proposed algorithm and DBSCAN are presented in this paper and they are shown that the proposed algorithm can produce much better visual results in image segmentation.     

Clustering with block mixture models

Basing cluster analysis on mixture models has become a classical and powerful approach. Until now, this approach, which allows to explain some classic clustering criteria such as the well-known k-means criteria and to propose general criteria, has been developed to classify a set of objects measured on a set of variables. But, for this kind of data, if most clustering procedures are designated to construct an optimal partition of objects or, sometimes, of variables, there exist others methods, named block clustering methods, which consider simultaneously the two sets and organize the data into homogeneous blocks.In this work, a new mixture model called block mixture model is proposed to take into account this situation. This model allows to embed simultaneous clustering of objects and variables in a mixture approach. We first consider this probabilistic model in a general context and we develop a new algorithm of simultaneous partitioning based on the CEM algorithm. Then, we focus on the case of binary data and we show that our approach allows us to extend a block clustering method, which had been proposed in this case. Simplicity, fast convergence and the possibility to process large data sets are the major advantages of the proposed approach.     

Embedding visual cognition in 3D reconstruction from multi-view engineering drawings

In this paper we discuss how to reconstruct 3D models from multi-view engineering draws by employing human engineers’ approaches. Human's ‘divide and conquer’ interpretation strategy in visual cognition is simulated, and successfully carried out on the basis of spatial division of 3D object space. At first, a volume-oriented method is utilized to decompose the 3D object space in a set of 3D ‘cell-boxes’ whose three-view bounding rectangles will isolate its related sub-projections from input projections views, and in every cell-box a cell primitive is implied. Then, a 3D model of each cell primitive is ‘locally’ generated from its ‘sub-projection’ views by wire-frame oriented algorithms. The final interpretation result of the overall projection-views is a ‘Union’ of these cell primitives. To deal with the ambiguity, a visual reasoning engine is implemented on the basis of principles from Gestalt psychology. It will be activated to pick out the most reasonable interpretation, when ambiguities are generated during the reconstruction process. The section views are also incorporated in getting rid of the ambiguity. Moreover, we design a natural and convenient interaction way to encourage the user to be involved in the process while interpreting complex projections. The key steps of this human-like reconstruction approach are presented in detail.     

Kernel mixture model for probability density estimation in Bayesian classifiers

Estimating reliable class-conditional probability is the prerequisite to implement Bayesian classifiers, and how to estimate the probability density functions (PDFs) is also a fundamental problem for other probabilistic induction algorithms. The finite mixture model (FMM) is able to represent arbitrary complex PDFs by using a mixture of mutimodal distributions, but it assumes that the component mixtures follows a given distribution, which may not be satisfied for real world data. This paper presents a non-parametric kernel mixture model (KMM) based probability density estimation approach, in which the data sample of a class is assumed to be drawn by several unknown independent hidden subclasses. Unlike traditional FMM schemes, we simply use the k-means clustering algorithm to partition the data sample into several independent components, and the regional density diversities of components are combined using the Bayes theorem. On the basis of the proposed kernel mixture model, we present a three-step Bayesian classifier, which includes partitioning, structure learning, and PDF estimation. Experimental results show that KMM is able to improve the quality of estimated PDFs of conventional kernel density estimation (KDE) method, and also show that KMM-based Bayesian classifiers outperforms existing Gaussian, GMM, and KDE-based Bayesian classifiers.     

A computational model of time for stiff hybrid systems applied to control synthesis

Computation has quickly become of paramount importance in the design of engineered systems, both to support their features as well as their design. Tool support for high-level modeling formalisms has endowed design specifications with executable semantics. Such specifications typically include not only discrete-time and discrete-event behavior, but also continuous-time behavior that is stiff from a numerical integration perspective. The resulting stiff hybrid dynamic systems necessitate variable-step solvers to simulate the continuous-time behavior as well as solver algorithms for the simulation of discrete-time and discrete-event behavior. The combined solvers rely on complex computer code which makes it difficult to directly solve design tasks with the executable specifications. To further leverage the executable specifications in design, this work aims to formalize the semantics of stiff hybrid dynamic systems at a declarative level by removing implementation detail and only retaining ‘what’ the computer code does and not ‘how’ it does it. A stream-based approach is adopted to formalize variable-step solver semantics and to establish a computational model of time that supports discrete-time and discrete-event behavior. The corresponding declarative formalization is amenable to computational methods and it is shown how model checking can automatically generate, or synthesize, a feedforward control strategy for a stiff hybrid dynamic system. Specifically, a stamper in a surface mount device is controlled to maintain a low acceleration of the stamped component for a prescribed minimum duration of time.