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Early Access articles are new content made available in advance of the final electronic or print versions and result from IEEE's Preprint or Rapid Post processes. Preprint articles are peer-reviewed but not fully edited. Rapid Post articles are peer-reviewed and edited but not paginated. Both these types of Early Access articles are fully citable from the moment they appear in IEEE Xplore.

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Displaying Results 1 - 25 of 61
  • Semantic Retrieval of Trademarks Based on Conceptual Similarity

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (3955 KB)  

    Trademarks are signs of high reputational value. Thus, they require protection. This paper studies conceptual similarities between trademarks, which occurs when two or more trademarks evoke identical or analogous semantic content. This paper advances the state-of-the-art by proposing a computational approach based on semantics that can be used to compare trademarks for conceptual similarity. A trademark retrieval algorithm is developed that employs natural language processing techniques and an external knowledge source in the form of a lexical ontology. The search and indexing technique developed uses similarity distance, which is derived using Tversky's theory of similarity. The proposed retrieval algorithm is validated using two resources: a trademark database of 1400 disputed cases and a database of 378,943 company names. The accuracy of the algorithm is estimated using measures from two different domains: the R-precision score, which is commonly used in information retrieval and human judgment/collective human opinion, which is used in human-machine systems. View full abstract»

    Open Access
  • Adaptive Visual Tracking Control for Manipulator With Actuator Fuzzy Dead-Zone Constraint and Unmodeled Dynamic

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2784 KB)  

    This paper focuses on a problem of adaptive visual tracking control for an uncalibrated image-based visual servoing manipulator system with actuator fuzzy dead-zone constrain and unknown dynamic. Without a prior knowledge of the system, fuzzy logic systems are employed to approximate the unmodeled nonlinear manipulator dynamics and external disturbances. By using the recursive Newton-Euler method, the total number of fuzzy rules can be reduced significantly as compared with the traditional fuzzy logic system. By defuzzifying the fuzzy slope k of the fuzzy dead-zone model to a deterministic value k, a novel fuzzy adaptive controller is constructed to eliminate the harmful effect of fuzzy dead-zone constrain. Lyapunov functions are presented for stability analysis of visual feedback control problem with unknown dynamics and actuator fuzzy dead-zone constrains. Experimental results are carried out to test the visual tracking performance of the proposed controller and the boundedness of the closed-loop system. View full abstract»

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  • Evaluation of Probability Transformations of Belief Functions for Decision Making

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1880 KB)  

    The transformation of belief function into probability is one of the most important and common ways for decision making under the framework of evidence theory. In this paper, we focus on the evaluation of such probability transformations (PTs), which are crucial for their proper applications and the design of new ones. Shannon entropy or probabilistic information content (PIC) measure is traditionally used in evaluating PTs. The transformation having the lowest entropy or highest PIC is considered as the best one. This standpoint is questioned in this paper by comparing a PT based on uncertainty minimization with other available PTs. It shows experimentally that entropy or PIC is not comprehensive to evaluate a PT. To make a comprehensive evaluation, some new approaches are proposed by the joint use of PIC and the distance of evidence according to the value- and rank-based fusion. A pattern classification application oriented evaluation approach for PTs is also proposed. Some desired properties for PTs are also discussed. Experimental results and related analysis are provided to show the rationality of the new evaluation approaches. View full abstract»

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  • Observer-Based Adaptive Fuzzy Control for a Class of Nonlinear Delayed Systems

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (775 KB)  

    This paper considers the problem of observer-based adaptive fuzzy control for a class of nonlinear time-delay systems in nonstrict-feedback form, which includes the nonlinear strict-feedback systems as a special case. An adaptive fuzzy output feedback backstepping approach is first proposed for nonlinear systems in nonstrict-feedback form. Fuzzy logic systems are used to approximate the unknown nonlinear functions. Adaptive technique and backstepping are utilized to construct a controller. The proposed adaptive fuzzy output feedback controller guarantees that all the signals in the adaptive closed-loop system are semi-globally uniformly ultimately bounded. Simulation results are provided to demonstrate the effectiveness of the presented approach. View full abstract»

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  • Lexicographic Multiobjective Integer Programming for Optimal and Structurally Minimal Petri Net Supervisors of Automated Manufacturing Systems

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1168 KB)  

    Based on Petri net (PN) models of automated manufacturing systems, this paper proposes a deadlock prevention method to obtain a maximally permissive (optimal) supervisor while minimizing its structure. The optimal supervisor can be achieved by forbidding all first-met bad markings (FBMs) and permitting all legal markings in a PN model. An FBM obtained via a single transition's firing at a legal marking is a deadlock or marking that inevitably evolves into a deadlock. A lexicographic multiobjective integer programming problem with multiple objectives to be achieved sequentially is formulated to design such an optimal and structurally minimal supervisor. As a nonlinear function, the quantity of its directed arcs is minimized. A conversion method is proposed to convert the nonlinear model into a linear one. With the premise that each place in the supervisor is associated with a nonnegative place invariant, the controlled net holds all legal markings of the net model, and the supervisor has the minimal structure. Finally, some examples are used to illustrate the application of the proposed approach. View full abstract»

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  • Evaluating Driving Styles by Normalizing Driving Behavior Based on Personalized Driver Modeling

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1125 KB)  

    Driving style evaluation is important for vehicle calibrations and intelligent transportation. In this paper, we propose to quantitatively evaluate driving styles by normalizing driving behavior based on personalized driver modeling. First, a personalized driver model is established for each driver to be evaluated by using the neural network, e.g., the radial basis function, and real-world vehicle test data, with respect to vehicle and road situations. Second, the established driver model is employed to perform the simulated standard driving cycle test for driving behavior normalization, where the desired speed profile is adopted from the standard driving cycle test, e.g., federal test procedure-75. Third, based on the energy spectral density analysis on normalized behavior, an aggressiveness index is proposed to quantitatively evaluate driving styles. Finally, this index is applied to detect abnormal driving behavior. Simulations are conducted to verify the effectiveness of the proposed scheme. View full abstract»

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  • Learning to Adjust and Refine Gait Patterns for a Biped Robot

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2060 KB)  

    In this paper, a reinforced learning method for biped walking is proposed, where the robot learns to appropriately modulate an observed walking pattern. The biped robot was equipped with two Q-learning mechanisms. First, the robot learns a policy to adjust a defective walking pattern, gait-by-gait, into a more stable one. To avoid the complexity of adjusting too many joints of a humanoid robot and to speed up the learning process, the dimensionality of the action space was reduced. In turn, the other learning mechanism trained the robot to walk in a refined pattern, allowing it to walk faster without the loss of other required criteria, such as walking straight. This approach was implemented with both a simulated robot model and an actual biped robot. The results from the simulations and experiments show that successful walking policies were obtained. The learning system works quickly enough so that the robot was able to continually adapt to the terrain as it walked. View full abstract»

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  • Can You Trust Online Ratings? A Mutual Reinforcement Model for Trustworthy Online Rating Systems

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2226 KB)  

    The average of customer ratings on a product, which we call a reputation, is one of the key factors in online purchasing decisions. There is, however, no guarantee of the trustworthiness of a reputation since it can be manipulated rather easily. In this paper, we define false reputation as the problem of a reputation being manipulated by unfair ratings and design a general framework that provides trustworthy reputations. For this purpose, we propose TRUE-REPUTATION, an algorithm that iteratively adjusts a reputation based on the confidence of customer ratings. We also show the effectiveness of TRUE-REPUTATION through extensive experiments in comparisons to state-of-the-art approaches. View full abstract»

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  • Design and Analysis of Multimodel-Based Anomaly Intrusion Detection Systems in Industrial Process Automation

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2393 KB)  

    Industrial process automation is undergoing an increased use of information communication technologies due to high flexibility interoperability and easy administration. But it also induces new security risks to existing and future systems. Intrusion detection is a key technology for security protection. However, traditional intrusion detection systems for the IT domain are not entirely suitable for industrial process automation. In this paper, multiple models are constructed by comprehensively analyzing the multidomain knowledge of field control layers in industrial process automation, with consideration of two aspects: physics and information. And then, a novel multimodel-based anomaly intrusion detection system with embedded intelligence and resilient coordination for the field control system in industrial process automation is designed. In the system, an anomaly detection based on multimodel is proposed, and the corresponding intelligent detection algorithms are designed. Furthermore, to overcome the disadvantages of anomaly detection, a classifier based on an intelligent hidden Markov model, is designed to differentiate the actual attacks from faults. Finally, based on a combination simulation platform using optimized performance network engineering tool, the detection accuracy and the real-time performance of the proposed intrusion detection system are analyzed in detail. Experimental results clearly demonstrate that the proposed system has good performance in terms of high precision and good real-time capability. View full abstract»

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  • Stochastic Decision Making for Adaptive Crowdsourcing in Medical Big-Data Platforms

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (587 KB)  

    This paper proposes two novel algorithms for adaptive crowdsourcing in 60-GHz medical imaging big-data platforms, namely, a max-weight scheduling algorithm for medical cloud platforms and a stochastic decision-making algorithm for distributed power-and-latency-aware dynamic buffer management in medical devices. In the first algorithm, medical cloud platforms perform a joint queue-backlog and rate-aware scheduling decisions for matching deployed access points (APs) and medical users where APs are eventually connected to medical clouds. In the second algorithm, each scheduled medical device computes the amounts of power allocation to upload its own medical data to medical big-data clouds with stochastic decision making considering joint energy-efficiency and buffer stability optimization. Through extensive simulations, the proposed algorithms are shown to achieve the desired results. View full abstract»

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  • An Estimation of Distribution Algorithm-Based Memetic Algorithm for the Distributed Assembly Permutation Flow-Shop Scheduling Problem

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1953 KB)  

    In this paper, an estimation of distribution algorithm (EDA)-based memetic algorithm (MA) is proposed for solving the distributed assembly permutation flow-shop scheduling problem (DAPFSP) with the objective to minimize the maximum completion time. A novel bi-vector-based method is proposed to represent a solution for the DAPFSP. In the searching phase of the EDA-based MA (EDAMA), the EDA-based exploration and the local-search-based exploitation are incorporated within the MA framework. For the EDA-based exploration phase, a probability model is built to describe the probability distribution of superior solutions. Besides, a novel selective-enhancing sampling mechanism is proposed for generating new solutions by sampling the probability model. For the local-search-based exploitation phase, the critical path of the DAPFSP is analyzed to avoid invalid searching operators. Based on the analysis, a critical-path-based local search strategy is proposed to further improve the potential solutions obtained in the EDA-based searching phase. Moreover, the effect of parameter setting is investigated based on the Taguchi method of design-of-experiment. Suitable parameter values are suggested for instances with different scales. Finally, numerical simulations based on 1710 benchmark instances are carried out. The experimental results and comparisons with existing algorithms show the effectiveness of the EDAMA in solving the DAPFSP. In addition, the best-known solutions of 181 instances are updated by the EDAMA. View full abstract»

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  • Intelligent Line Segment Perception With Cortex-Like Mechanisms

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3543 KB)  

    This paper proposes a novel general framework for line segment perception, which is motivated by a biological visual cortex, and requires no parameter tuning. In this framework, we design a model to approximate receptive fields of simple cells. More importantly, the structure of biological orientation columns is imitated by organizing artificial complex and hypercomplex cells with the same orientation into independent arrays. Besides, an interaction mechanism is implemented by a set of self-organization rules. Enlightened by the visual topological theory, the outputs of these artificial cells are integrated to generate line segments that can describe nonlocal structural information of images. Each line segment is evaluated quantitatively by its significance. The computation complexity is also analyzed. The proposed method is tested and compared to state-of-the-art algorithms on real images with complex scenes and strong noises. The experiments demonstrate that our method outperforms the existing methods in the balance between conciseness and completeness. View full abstract»

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  • A New Framework for Rapid Wireless Tracking Verifications Based on Optimized Trajectories in Received Signal Strength Measurements

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1811 KB)  

    Secure physical regions ({e.g.,} border areas, nuclear zones, or military facilities) are often patrolled by networked robotic vehicles that require the capability to rapidly verify the advertised location of a potential intruder based on received signal strengths. In this paper, we develop novel algorithms by which mobile robots can coordinate their motions in order to minimize the time required to verify the advertised location for given accuracy bounds. Our specific contributions on this paper are threefold. Firstly, we develop a framework that uses a combination of the particle filters (for position estimation) and the Cramér-Rao lower bounds (for threshold of validation) to drive the motion models for rapid verification of the reported position. We believe our approach is the first in the literature that is accurate, easy to compute, and feasible for practical implementation. Secondly, we propose a centralized coordinated motion algorithm that is optimal at each sampling time. This provides a lower bound on detection time that can be used as a benchmark for practical considerations. Thirdly, we present a practical heuristic approach that allows for distributed protocol based on the concept of the gradient vectors. Subsequently, we also advocate a sub-optimal approach, derived from our heuristic approach, which provides a good trade-off between performance and computational resources. Our results are important for the development of secure access control schemes to prevent unauthorized access of communication networks from malicious users. View full abstract»

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  • On the Complexity of Deciding Soundness of Acyclic Workflow Nets

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (417 KB)  

    This paper focuses on the complexity of the (weak) soundness problem of acyclic workflow (WF) nets, and two main results are established: 1) soundness of 1-bounded acyclic WF nets is co-NP-complete and 2) weak soundness of 3-bounded acyclic asymmetric-choice WF nets is co-NP-complete. View full abstract»

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  • A Bilevel Model for Project Scheduling in a Fuzzy Random Environment

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2342 KB)  

    In this paper, a new bilevel model with multiple decision-makers is proposed for a project scheduling problem (PSP) which considers the interests of both the project owner and contractor. In this model, the project owner is considered to be the leader and the contractor, the follower. The project owner has two objectives: 1) to maximize profit and 2) minimize makespan, while the contractor's objective is to minimize cost only. A fuzzy random simulation-based bilevel global-local-neighbor particle swarm optimization technique is proposed to solve the multiple decision-maker PSP (MDPSP). A case study based on the Wanjiakouzi Hydropower Station is used to demonstrate an application of the developed model and illustrate the scope of the MDPSP. View full abstract»

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  • Trust Evaluation via Large-Scale Complex Service-Oriented Online Social Networks

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1116 KB)  

    Service-oriented online social networks (SOSNs) are emerging ubiquitous platforms for numerous services where service consumers require the selection of trustworthy service providers who are unknown to them before invoking services with the aid of other intermediate participants. Under this circumstance, evaluation of the trust level of the service provider along the social trust paths from the service consumer to the service provider is required. To this end, selection of the optimal social trust path (OSTP) that can yield the most trustworthy evaluation result is a prerequisite. While existing single-trust-value methods can provide good but simple information to service consumers, more trust information, such as social intimacy degree between participants and role impact factor of intermediate participants, should be considered to represent the trust level of a service provider more comprehensively. When more trust information is considered, OSTP selection will become an NP-complete problem. In this paper, we propose path integral Monte Carlo quantum annealing (PIMCQA)-based OSTP (PIMCQA_OSTP) selection algorithm for complex SOSNs. PIMCQA_OSTP serves as the very first quantum inspired OSTP selection algorithm in complex SOSNs. Due to that quantum mechanics work with wave functions that can sample different regions of phase space equally well, and quantum systems can tunnel through classically impenetrable potential barriers between energy valleys, PIMCQA_OSTP shows its outstanding search ability and outperforms existing methods. Results of experiments on a real dataset of online social networks verify that PIMCQA_OSTP is a promising tool and is especially fit for complex SOSNs. View full abstract»

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  • An Improved Polynomial Neural Network Classifier Using Real-Coded Genetic Algorithm

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1957 KB)  

    In this paper, a novel approach is proposed to improve the classification performance of a polynomial neural network (PNN). In this approach, the partial descriptions (PDs) are generated at the first layer based on all possible combinations of two features of the training input patterns of a dataset. The set of PDs from the first layer, the set of all input features, and a bias constitute the chromosome of the real-coded genetic algorithm (RCGA). A system of equations is solved to determine the values of the real coefficients of each chromosome of the RCGA for the training dataset with the mean classification accuracy (CA) as the fitness value of each chromosome. To adjust these values for unknown testing patterns, the RCGA is iterated in the usual manner using simple selection, crossover, mutation, and elitist selection. The method is tested extensively with the University of California, Irvine benchmark datasets by utilizing tenfold cross validation of each dataset, and the performance is compared with various well-known state-of-the-art techniques. The results obtained from the proposed method in terms of CA are superior and outperform other known methods on various datasets. View full abstract»

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  • Model-Based System Specification With Tesperanto: Readable Text From Formal Graphics

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3735 KB)  

    Technical reports and papers may be represented by a fundamental model, which can take the form of a block diagram, a state-machine, a flow diagram, or alternatively some ad hoc chart. This basic scheme can convey better the true value of otherwise verbose and potentially encumbered narrative-based specifications. We present a model-based methodology for authoring technical documents. The underlying idea is to first formalize the system to be specified using a conceptual model, and then automatically generate from the tested and verified model a humanly-readable text in a subset of English we call Tesperanto. This technical documents' authoring methodology is carried out in an integrated bimodal text-graphics document authoring environment. The methodology was evaluated with the International Organization for Standardization standards and a medical robotics case study. The evaluation resulted in tangible improvements in the quality and consistency of international standards. Further, it can serve to document complex dynamics among agents, such as interaction between an operation room technician robot and the surgeon, suggesting that it could be applied to represent and bring value to other types of technical documents. View full abstract»

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  • Learning Automata-Based Adaptive Petri Net and Its Application to Priority Assignment in Queuing Systems With Unknown Parameters

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (988 KB)  

    In this paper, an adaptive Petri net (PN), capable of adaptation to environmental changes, is introduced by the fusion of learning automata and PN. In this new model, called learning automata-based adaptive PN (APN-LA), learning automata are used to resolve the conflicts among the transitions. In the proposed APN-LA model, transitions are portioned into several sets of conflicting transitions and each set of conflicting transitions is equipped with a learning automaton which is responsible for controlling the conflicts among transitions in the corresponding transition set. We also generalize the proposed APN-LA to ASPN-LA which is a fusion between LA and stochastic PN (SPN). An application of the proposed ASPN-LA to priority assignment in queuing systems with unknown parameters is also presented. View full abstract»

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  • Analysis and Improvement of Multiproduct Bernoulli Serial Lines: Theory and Application

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1847 KB)  

    This paper is devoted to the performance analysis and continuous improvement of multiproduct manufacturing systems. First, a Bernoulli model of multiproduct serial line with unreliable machines and finite buffers is introduced. In such a model, each machine is capable of processing multiple product types, and each buffer is shared for all products. Closed formulas have been derived to evaluate the production rate of the line with one or two machines, and recursive procedures are used to analyze longer lines. Numerical studies indicate that such a method has a high precision in performance evaluation. The system-theoretic properties, such as asymptotic property, monotonicity, and reversibility, have been investigated. Second, in order to improve system performance, bottleneck (BN) analysis has been carried out to identify the machine and product whose improvement will lead to the largest improvement in the system production rate. Various types of BNs have been defined, and the BN indicators, based on the data collected on the factory floor, are proposed to identify the BNs without the complicated calculations of the production rate and its sensitivities. Numerical experiments have justified the practical usefulness of such indicators for BN identification in multiproduct manufacturing systems. Finally, a case study is introduced to illustrate the applicability of the model and the method. View full abstract»

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  • Formalization and Verification of Group Behavior Interactions

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1401 KB)  

    Group behavior interactions, such as multirobot teamwork and group communications in social networks, are widely seen in both natural, social, and artificial behavior-related applications. Behavior interactions in a group are often associated with varying coupling relationships, for instance, conjunction or disjunction. Such coupling relationships challenge existing behavior representation methods, because they involve multiple behaviors from different actors, constraints on the interactions, and behavior evolution. In addition, the quality of behavior interactions are not checked through verification techniques. In this paper, we propose an ontology-based behavior modeling and checking system (OntoB for short) to explicitly represent and verify complex behavior relationships, aggregations, and constraints. The OntoB system provides both a visual behavior model and an abstract behavior tuple to capture behavioral elements, as well as building blocks. It formalizes various intra-coupled interactions (behaviors conducted by the same actor) via transition systems (TSs), and inter-coupled behavior aggregations (behaviors conducted by different actors) from temporal, inferential, and party-based perspectives. OntoB converts a behavior-oriented application into a TS and temporal logic formulas for further verification and refinement. We demonstrate and evaluate the effectiveness of the OntoB in modeling multirobot behaviors and their interactions in the Robocup soccer competition game. We show, that the OntoB system can effectively model complex behavior interactions, verify and refine the modeling of complex group behavior interactions in a sound manner. View full abstract»

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  • Deriving All Minimal Hitting Sets Based on Join Relation

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1465 KB)  

    Deriving all minimal hitting sets (MHSes) for a family of conflict sets is a classical problem in model-based diagnosis. A technique for distributed MHSes based on the join relation of elements is proposed. Then, a strategy for deriving all distributed MHSes is presented. If the family of sets is decomposed into a number of equivalence classes based on the join relation, then parallel computation of MHSes for each distribution can be applied. Moreover, an incremental, distributed approach is introduced. When a new conflict set is added, only related distributed MHSes are chosen to incrementally update the final result. From a theoretical point of view, the complexity of the distributed algorithm is O(2num/k), while the complexity of the corresponding centralized algorithm is O(2num), with k and num being the number of equivalence classes and the number of basic elements in all the conflict sets, respectively. Furthermore, compared with the corresponding centralized approach, a large number of set-containment checks are avoided by the incremental, distributed approach. Experimental results, including both numerous artificial examples and typical International Symposium on Circuits and Systems-85 benchmark circuit conflict set examples, offer evidence that, compared with centralized methods, the efficiency for deriving all MHSes in a distributed (incremental) way is considerably improved. View full abstract»

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  • A Novel TRUST-TECH Guided Branch-and-Bound Method for Nonlinear Integer Programming

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1827 KB)  

    Nonlinear integer programming has not reached the same level of maturity as linear programming, and is still difficult to solve, especially for large-scale systems. Branch-and-bound (B&B) and its variants are widely used methods for integer programming, and numerical solutions obtained by them still can be far away from the global optimum. In this paper, we propose a novel approach to guide the deterministic/heuristic methods and the commercial solvers for nonlinear integer programming, and aim at improving the solution quality by taking advantage of transformation under stability-retraining equilibrium characterization (TRUST-TECH) method. Moreover, we examine the effectiveness by developing and simulating TRUST-TECH guided B&B and TRUST-TECH guided commercial solver(s), and compare their performance with that of the original methods/solvers (e.g., GAMS (General Algebraic Modeling System)/ BARON, GAMS/SCIP, and LINDO (Linear, INteractive, Discrete Optimizer)/MINLP) and also with that of recently-reported evolutionary-algorithm (EA)-based methods. Simulation results provide evidence that, the solution quality is substantially improved, and the global-optimal solutions are usually obtained after the application of TRUST-TECH. The proposed approach can be immediately utilized to guide other EA-based methods and commercial solvers which incorporate intelligent searching components. View full abstract»

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  • A Transaction and QoS-Aware Service Selection Approach Based on Genetic Algorithm

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1087 KB)  

    As there are various risks of failure in its execution, a composite web service (CWS) requires a transactional mechanism to guarantee its reliable execution. Though the existing service selection methods have considered that its transactional properties may affect its quality of service (QoS) such as its execution time, some of these methods can just give the locally optimal transactional CWS while others can give globally optimal CWS only under a given fixed transactional workflow. This paper addresses the issue of selecting and composing web services via a genetic algorithm (GA) and gives a transaction and QoS-aware selection approach. First, it introduces transactional properties of a single web service and CWS and the transactional rules used to compose them. Next, it conducts the performance analysis of basic workflow patterns such as sequential, parallel, selectable, and loop patterns and develops an algorithm to compute the execution time of a complex CWS. Then, it presents a GA-based approach, which takes into account the execution time, price, transactional property, stability, and penalty-factor, to achieve globally optimal service selection. Finally, this paper reports experimental results that compare the proposed approach with the exhaustive search algorithm, transactional-QoS-driven selection algorithm, and transactional service selection algorithm. The experimental results show that the proposed algorithm is efficient and effective and can give a globally optimal transactional CWS. View full abstract»

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  • Optimal Design of Hybrid Redundant Systems With Delayed Failure-Driven Standby Mode Transfer

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (928 KB)  

    Standby redundancy is a design technique that has been widely adopted to enhance system reliability and achieve fault tolerance in many critical applications. In this paper, we consider a 1-out-of-N: G hybrid standby redundant system with unrepairable elements being subject to delayed failure-driven standby mode transfers. Specifically, in the considered system, all the standby elements are initially in a warm standby mode (WSM) but can be transferred to a hot standby mode (HSM) so as to be ready to replace the online operating element when it fails. The WSM to HSM transfer is performed with a fixed time delay after no element resides in HSM either due to the element failure or because the element leaves the HSM to replace the failed online element for operation. A new iterative numerical algorithm is first proposed for evaluating reliability and expected mission cost (relevant to elements' standby expense, operation expense, as well as mode transfer expense) of the considered hybrid standby system. The algorithm has no restriction on element time-to-failure distribution types. Based on the proposed evaluation algorithm, we further formulate and solve a new optimization problem that finds the optimal delay and optimal sequence of standby elements with the objective to minimize expected mission cost while satisfying a certain level of mission reliability constraint. Examples are presented to demonstrate applications of the proposed methodology. View full abstract»

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Aims & Scope

The scope of the IEEE Transactions on Systems, Man, and Cybernetics: Systems includes the fields of systems engineering.

 

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Editor-in-Chief
C. L. Philip Chen
The University of Macau