Search

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.sysarc.2006.10.012 Byline: Madhura Purnaprajna, Marek Reformat, Witold Pedrycz Keywords: Hardware-software partitioning; Genetic algorithms; Reconfiguration Abstract: A scheme for time and power efficient embedded system design, using hardware and software components, is presented. Our objective is to reduce the execution time and the power consumed by the system, leading to the simultaneous multi-objective minimization of time and power. The goal of suitably partitioning the system into hardware and software components is achieved using Genetic Algorithms (GA). Multiple tests were conducted to confirm the consistency of the results obtained and the versatile nature of the objective functions. An enhanced resource constrained scheduling algorithm is used to determine the system performance. To emulate the characteristics of practical systems, the influence of inter-processor communication is examined. The suitability of introducing a reconfigurable hardware resource over pre-configured hardware is explored for the same objectives. The distinct difference in the task to resource mapping with the variation in design objective is studied. Further, the procedure to allocate optimal number of resources based on the design objective is proposed. The implementation is constrained for power and time individually, with GA being used to arrive at the resource count to suit the objective. The results obtained are compared by varying the time and power constraints. The test environment is developed using randomly generated task graphs. Exhaustive sets of tests are performed on the set design objectives to validate the proposed solution. Author Affiliation: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada T6G2V4 Article History: Received 7 September 2004; Revised 24 August 2005; Accepted 3 October 2006

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2004.06.018 Byline: Witold Pedrycz (a)(b), Giancarlo Succi (c) Keywords: Software quality; Hyperbox geometry of classifiers; Software measures; Genetic algorithms; Fuzzy clustering; Fuzzy C-Means (FCM) Abstract: Hyperbox classifiers are one of the most appealing and intuitively transparent classification schemes. As the name itself stipulates, these classifiers are based on a collection of hyperboxes--generic and highly interpretable geometric descriptors of data belonging to a given class. The hyperboxes translate into conditional statements (rules) of the form "if feature.sub.1 is in [a, b] and feature.sub.2 is in [d, f] and ... and feature.sub.n is in [w, z] then class I" where the intervals ([a, b],...,[w, z]) are the respective edges of the hyperbox. The proposed design process of hyperboxes comprises of two main phases. In the first phase, a collection of "seeds" of the hyperboxes is formed through data clustering (realized by means of the Fuzzy C-Means algorithm, FCM). In the second phase, the hyperboxes are "grown" (expanded) by applying mechanisms of genetic optimization (and genetic algorithm, in particular). We reveal how the underlying geometry of the hyperboxes supports an immediate interpretation of software data concerning software maintenance and dealing with rules describing a number of changes made to software modules and their linkages with various software measures (such as size of code, McCabe cyclomatic complexity, number of comments, number of characters, etc.). Author Affiliation: (a) Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada T6G 2G7 (b) Systems Research Institute of Polish Academy of Sciences, Warsaw, Poland (c) Department of Computer Science, University of Bozen, I-39100 Bozen, Italy Article History: Received 4 July 2003; Revised 9 June 2004; Accepted 12 June 2004

Information granules form an abstract and efficient characterization of large volumes of numeric data. Fuzzy clustering is a commonly encountered information granulation approach. A reconstruction (degranulation) is about decoding information granules into numeric data. In this study, to enhance quality of reconstruction, we augment the generic data reconstruction approach by introducing a transformation mapping of the originally produced partition matrix and setting up an adjustment mechanism modifying a localization of the prototypes. We engage several population-based search algorithms to optimize interaction matrices and prototypes. A series of experimental results dealing with both synthetic and publicly available data sets are reported to show the enhancement of the data reconstruction performance provided by the proposed method. [ABSTRACT FROM AUTHOR]

For any two one-dimensional time series of equal or non-equal length, we propose a new method to determine their shape distance. Each of the original time series is represented by a sequence of linear segments which are produced by l 1 trend filtering. As the dimensionality of this representation ranges between time series, dynamic time warping (DTW) method is used to calculate the distance between time series. In contrast to the standard dynamic time warping method, here the element of the new distance matrix concerns the distance between two linear segments instead of two elements of the original time series. More specifically, the distance between the two linear segments is calculated as the area of a triangle which is formed by the two linear segments after their translation and connection. In brief, the new measure can be regarded as the dynamic time warping distance computed in a piecewise linear space. Furthermore, we show that new distance measure quantitatively reflects the shape's difference between two one-dimensional time series. The simulation experiments presented in this paper illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Highlights: [•] Introduction of the principle of justifiable granularity. [•] Comprehensive algorithmic framework supporting the design of information granules. [•] Discussed are two essential requirements of information granularity. [•] Provided is a series of experimental studies. [ABSTRACT FROM AUTHOR]

Abstract: In this study, the use of conditional Fuzzy C-Means (CFCM) aimed at estimation of desired states of an unknown digital communication channel is considered for blind channel equalization. In the proposed CFCM, a collection of estimated centers is treated as a set of pre-defined desired channel states, and used to extract channel output states. By considering the combinations of the extracted channel output states, all possible sets of desired channel states are constructed. The set of desired states characterized by the maximal value of the Bayesian fitness function is subsequently selected for the next clustering epoch. This modification of CFCM makes it possible to search for the optimal desired channel states of an unknown channel. Finally, given the desired channel states, the Bayesian equalizer is implemented to reconstruct transmitted symbols. In a series of simulations, binary signals are generated at random with Gaussian noise, and both linear and nonlinear channels are evaluated. The experimental studies demonstrate that the performance (being expressed in terms of accuracy and speed) of the proposed CFCM is superior to the performance of the existing method exploiting the “conventional” Fuzzy C-Means (FCM). [ABSTRACT FROM AUTHOR]

Abstract: Finding a global optimum of an unknown system has attracted a great deal of interest in many engineering problems. In this settings, meta-heuristics are very common as efficient approaches for solving complex real-world problems in global continuous optimization problems (GCOPs) as they can approximate solutions without considering mathematical constraints such as differentiability. In this study, we propose a method based on tabu search (TS) and Nelder–Mead (NM) search strategy in application to GCOPs. To increase the robustness of the proposed method, we add a new phase, referred to as partitioning phase, before diversification which is realized by the TS. The TS is improved and then followed by the NM search strategy. The partitioning phase aims at distributing initial random solutions in the search space. By doing this, we increase the robustness of the method. The TS has an interesting ability of covering a wide solution space by promoting the search far away from the current solution and consecutively decreasing the possibility of trapping in local minima. The neighbour search-strategy of the TS is improved to accelerate the speed of finding the near optimum solution. Instead of just generating random neighbours around the current solution, we generate some neighbours in the direction of the previous move as well as some neighbours in the previous best crown. When certain criteria are reached for the diversification of the search space, the NM search strategy is carried out with the focus on the intensification of the solution found in the diversification phase. We assess the performance of the algorithm for a range of standard test functions available in the literature and compare the obtained results with those available in the literature. There are two main advantages of the proposed method; first, it can apply to any GCOP without considering any constraints and secondly, it shows better performance (in terms of function evaluation, success rate, and average error) for the functions with less than four input variables and relatively small or medium input domains. In other cases, the method still has acceptable perfomance and produces the results that are comparable with the results produced by other methods. [Copyright &y& Elsevier]

Abstract: The underlying objective of this study is to show how fuzzy sets (and information granules in general) and grammatical inference play an interdependent role in information granularization and knowledge-based problem characterization. The bottom-up organization of the material starts with a concept and selected techniques of data compactification which involves information granulation and gives rise to higher-order constructs (type-2 fuzzy sets). The detailed algorithmic investigations are provided. In the sequel, we focus on Computing with Words (CW), which in this context is treated as a general paradigm of processing information granules. We elaborate on a role of randomization and offer a detailed example illustrating the essence of the granular constructs along with the grammatical aspects of the processing. [Copyright &y& Elsevier]

This study is devoted to the generalization of information granules by forming higher order, namely, order-2 information granules. Information granules are semantically meaningful entities, which play a central role in knowledge representation and system modeling in the framework of Granular Computing. The encountered information granules could exhibit significant heterogeneity because of the diversified formal formalisms. To facilitate an effective generalization of heterogeneous granular data when using clustering algorithms, an efficient scheme has been proposed to form a unified representation of various types of granular data by using Possibility–necessity measures. Once the clustering process has been completed in the possibility–necessity feature space, the higher order information granules come as results of decoding by involving the possibility–necessity metrics and fuzzy relational calculus. The extent to which the higher order information granules are supported by the granular data present at a lower level of hierarchy is quantified in terms of the membership degrees obtained in the clustering process. Experimental studies concerning a series of publicly available datasets coming from UCI and KEEL machine learning repositories are carried out in this study. • A novel encoding/decoding method for representing heterogeneous granular data. • Clustering algorithm can be applied to cluster encoded heterogeneous granular data. • Supported by fuzzy relational calculus theory and Possibility-necessity measures. [ABSTRACT FROM AUTHOR]

Abstract: Two optimizations for decomposition problem of fuzzy relation (image) are proposed. The first optimization is a fast decomposition method of fuzzy relation based on the properties of max and min operations and the simultaneous updating of the prototype. The second optimization corresponds to an improvement of a cost function, in order to obtain a good quality of the solution of the decomposition problem. [Copyright &y& Elsevier]

In this paper, a new approach to estimate scaling factors of the fuzzy PID controller is presented. The performance of the fuzzy PID controller is sensitive to the variety of scaling factors. The design procedure dwells on the use of evolutionary computing (more specifically, a genetic algorithm) and estimation algorithm. The tuning of the scaling factors of the fuzzy PID controller is essential to the entire optimization process. And then we estimate scaling factors of the fuzzy PID controller by means of three types of estimation algorithms such as HCM (Hard C-Means) clustering-based regression polynomial, neuro-fuzzy networks, and regression polynomials. Numerical studies are presented in detail along with a detailed comparative analysis. [Copyright &y& Elsevier]

In this paper, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multi-layer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The development of the ‘conventional’ FPNNs uses an extended Group Method of Data Handling. The network exploits a fixed fuzzy inference type in each FPN of the FPNN as well as considers a fixed number of input nodes at FPNs (or nodes) located in each layer. Here, the proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least-square method-based learning. The performance of the networks is quantified through experimentation involving two time series dataset already used in fuzzy modeling. The results demonstrate their superiority over the existing fuzzy and neural models. [Copyright &y& Elsevier]

Data available for training a neural network may be deficient not only in quantity of data but entire independent variables with their data may be missing such as is often the situation for software engineering data. This may cause the relation based on the available data to exhibit the property of one-to-many (o-m) valuedness or almost o-m valuedness. Multiplayer perceptrons or feed-forward network however are generally trained to represent functions or m-o mappings. The solution consists of adding another input to the standard feed-forward network and of modifying the training algorithm to allow for determination of this input for which no training data is available. If the values for the additional input are restricted to a discrete set then they may be perceived as cluster identifiers and the training method may be perceived as another form of clustering or segmentation of the input. If the missing input variable is assumed to have a finite number of values the method proposed here may be compared to mixture of experts and mixture density networks except that the proposed method is more direct solution.The modified feed-forward network and training method has been successfully applied to several examples. [Copyright &y& Elsevier]

This paper investigates decision making with normalized probabilistic linguistic preference relations (NPLPRs). Consistency analysis is indispensable for deriving the reasonable ranking. After recalling previous research, we find that all previous concepts cannot fully define consistent NPLPRs. As a fundamental topic of decision making with preference relations, it is necessary to further study the consistency of NPLPRs. For this purpose, an interactive algorithm for deriving disjunctive probabilistic additive linguistic preference relations (DP-ALPRs) is provided, by which an additive consistency concept for NPLPRs is defined. When NPLPRs are unacceptably consistent, models for obtaining acceptably additively consistent NPLPRs are built. Considering the situation where only incomplete NPLPRs are obtained, a disjunctive probability and additive consistency based interactive algorithm for ascertaining missing judgments is provided. Meanwhile, we discuss group decision making (GDM) with NPLPRs and offer a distance measure based formula to determine the weights of the decision makers. In addition, the method defines a consensus index and builds models for improving the consensus level. Under the additive consistency and consensus discussions, an interactive algorithm for GDM with NPLPRs is proposed. Finally, the new method is applied to select green raw material suppliers to illustrate the application and compared with several previous ones. [ABSTRACT FROM AUTHOR]

Label distribution learning (LDL) has garnered increased attention in recent studies on label ambiguity. However, collecting complete annotations for LDL tasks is often time-consuming and labor-intensive compared to traditional learning paradigms. Therefore, designing effective incomplete LDL algorithms is crucial to broaden LDL's application scope. In this paper, we propose a novel LDL algorithm, called Incom plete L abel D istribution L earning via L abel C orrelation D ecomposition (IncomLDL-LCD), which simultaneously learns label distributions and recovers missing description degrees of labels through label correlations. Specifically, we decompose the label correlation into sparse local label correlation and low-rank global label correlation using a soft-thresholding operator and a singular value thresholding operator, respectively. The former is utilized to capture the related label subsets necessary for reconstructing each possible label, while the latter focuses on extracting the coarse-grained semantic concepts from all labels and exploring the groupings of labels. Additionally, we develop an alternating solution with the accelerated proximal gradient descent method for optimization. Extensive experiments on 16 real-world data sets with varying degrees of missing annotations validate that our algorithm effectively handles incomplete LDL tasks and outperforms state-of-the-art algorithms. • Local and global label correlations are integrated into a unified framework. • IncomLDL-LCD learns label distributions and recovers missing description degrees of labels. • IncomLDL-LCD employs an alternating solution with accelerated proximal gradient descent method. [ABSTRACT FROM AUTHOR]

Abstract: In this study, polynomial-based radial basis function neural networks are proposed as one of the functional components of the overall face recognition system. The system consists of the preprocessing and recognition module. The design methodology and resulting procedure of the proposed P-RBF NNs are presented. The structure helps construct a solution to high-dimensional pattern recognition problems. In data preprocessing part, principal component analysis (PCA) is generally used in face recognition. It is useful in reducing the dimensionality of the feature space. However, because it is concerned with the overall face image, it cannot guarantee the same classification rate when changing viewpoints. To compensate for these limitations, linear discriminant analysis (LDA) is used to enhance the separation between different classes. In this paper, we elaborate on the PCA-LDA algorithm and design an optimal P-RBF NNs for the recognition module. The proposed P-RBF NNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part realized in terms of fuzzy “if–then” rules. In the condition part of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means (FCM) algorithm. In the conclusion part of rules, the connection weight is realized through three types of polynomials such as constant, linear, and quadratic. The coefficients of the P-RBF NNs model are obtained by fuzzy inference method forming the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum, fuzzification coefficient, and the feature selection mechanism) of the networks are optimized by means of differential evolution (DE). The experimental results completed on benchmark face datasets – the AT&T, and Yale datasets demonstrate the effectiveness and efficiency of PCA-LDA combined algorithm compared with other algorithms such as PCA, LPP, 2D-PCA and 2D-LPP. A real time face recognition system realized in this way is also presented. [Copyright &y& Elsevier]

In today's world of connectivity, various domains use different multi-sensor Internet of Things (IoT) and Intelligent Systems (IS) applications. These applications involve extensive interactions between thousands or millions of components in open environments, which challenges verifying their reliability and efficiency. This paper introduces the first investigation in verifying IoT applications and intelligent systems in multi-source data settings with multi-agent commitment protocols in uncertain or inconsistent environments. Specifically, we present original and efficient solutions for modeling and verifying these systems with conditional and unconditional commitment protocols under uncertain or inconsistent settings. We introduce 4v-CTL c and 4v-CTL c c , multi-valued logics of commitment for reasoning about inconsistency over these systems that expand 3v-CTL c and 3v-CTL c c for reasoning about uncertainty. Moreover, we introduce new reduction algorithms for reducing our multi-valued model checking problems to the two-valued ones. To implement these algorithms, we develop two new automatic verification tools. The first tool translates the multi-valued logics to CTL and automatically interacts with the NuSMV model checker. The second tool translates these logics to the two-valued versions, CTL c and CTL c c , and automatically interacts with the MCMAS+ model checker. We apply our verification approaches to a Smart Home, a Smart Hospital and a Smart Mortgage system with multi-source data as case studies using sets of properties, including safety, liveness and reachability. The experimental results obtained by the proposed multi-valued model checking techniques proved these techniques' high efficiency and applicability to modeling and verifying intelligent and autonomous multi-source data systems. • New multi-valued logics to reason about inconsistency in multi-source data settings. • Modeling IoT and IS applications using different multi-valued commitment logics. • New reduction-based model checking algorithms for multi-source data applications. • Implementing two new model checking tools: MCMAS-interactor and NuSMV-interactor. [ABSTRACT FROM AUTHOR]

• Take morphology and shape features into account for granularity representation. • Using interval information granules to achieve system modelling. • A granular model is proposed and used in the field of anomaly detection. • Evaluation indexes are established to quantify the performance. • Greatly reduce the data volume and improve the detection performance. Since time series are characterized by a substantial volume of data, high levels of noise and the correlation between data in the time series attributes, it becomes challenging to mine crucial information from the series and apply it to anomaly detection. In this study, inspired by the concept of information granularity being applied to the process of system modelling, a granular Markov model is proposed for time series anomaly detection. Anomalies are generally caused by the changes in amplitude and shape; in this study we take both the original time series data and their amplitude change data into consideration. First, we utilize an interval information granularity representation based on the principle of justifiable granularity to represent the original time series data in an abstract manner to arrive at the corresponding representation results-- that is, interval information granules. Then, based on the results of the interval information granularity representation and the Fuzzy C-Means (FCM) clustering algorithm, a granular Markov model is developed to produce anomaly scores to quantify possible anomalies. Compared with state-of-the-art methods, experimental studies completed for a large number of datasets demonstrate that the proposed method can significantly improve the anomaly detection process with higher data anomaly resolution. The obtained results are consistent across all datasets. [ABSTRACT FROM AUTHOR]

• Apply dynamic utility calculation framework to mine closed high utility itemsets. • Dramatically reduce the cost of database scans by adopting the P-set structure. • Propose a new CHUIM algorithm, named iEFIM-Closed. • Conduct experiments to measure the performance of the proposed algorithm. The problem of discovering high-utility itemsets (HUIs) in transaction databases, which is an extension of Frequent Itemset Mining, is a commonly encountered mining task. Researchers have proposed algorithms to efficiently mine highly profitable itemsets in customer transaction databases, in which the unit profits of items are fixed. However, this assumption does not reflect the true nature of the utility measure of items in real-life transaction databases, which might vary over time. Moreover, since this important characteristic is ignored by all the current HUI mining algorithms, they are either not applicable to this type of database, or they generate inaccurate results. In addition, the HUI mining algorithms' traditional limitation is that they produce a huge number of HUIs for users. In this paper, we define the problem of mining a lossless, concise and compact representation of HUIs, called closed HUIs (CHUIs), in dynamic unit profit databases. Based on newly defined of utility measure, a novel algorithm, called iEFIM-Closed, is introduced. This relies on this new utility measure, a novel compact database format to reduce the cost of database scans and increase the efficiency of the mining process. Experimental evaluations show that iEFIM-Closed significantly outperforms state-of-the-art algorithms for mining CHUIs on sparse databases with dynamic profit, and has competitive performance in dense databases in terms of runtime, the cost of database scans and scalability. [ABSTRACT FROM AUTHOR]

• Our method for mining contrast sets (CSs) is 10–77 times faster than the baselines. • Our method eliminates up to thousands of redundant CSs. • Our method achieves 1–16% improvements in classification accuracy over the baselines. • Our method is an efficient way to analyze the length of stay in an emergency department. Many applications of intelligent systems involve understanding a group of contrastively different outcome (e.g., all survivors of a deadly cancer, a top performing team in a large corporation). The intelligent system needs to identify attributes (features) which best describe or explain the group versus its alternatives. In data mining, this problem is studied under the framework of contrast set mining (CSM). Although CSM is not new, the era of big data has produced new computational and statistical challenges. In particular, existing algorithms fail (1) to perform efficiently in terms of runtime on large-scale datasets and (2) to accommodate simultaneous inference on an overwhelming array of features which are often repetitive and collinear. In this paper, we develop a CSM algorithm which addresses both challenges. The computational challenge is addressed with a tree structure and two theorems while the statistical challenge is addressed with the application of false discovery rate for multiple testing. The computational and statistical advantages of the proposed algorithm over three state-of-the-art algorithms are demonstrated with comprehensive experiments. In addition, we also show the effectiveness of our proposed method in an intelligence-system application involving hospital process redesign. The proposed method not only improves the performance of machine learning systems, but also generates succinct and insightful patterns directly relevant to clinical decision-making. [ABSTRACT FROM AUTHOR]