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Compared with traditional models, semi-random epidemic network models may be more reasonable to describe the real dynamics of many epidemics. In this paper, we first investigate the optimal control problem (OCP) of semi-random epidemic networks. By using the Pontryagin’s minimum principle, we obtain the optimal control strategy aimed to minimize the total epidemic incidence and control cost. We then define a centrality index which can measure average control strength of the optimal control. Based on this index, the OCP is converted into a static OCP (SOCP), whose solution is utilized to design a nonidentical constant control (NCC). NCC is suboptimal as it is optimal on a subset of the whole control set, and is determined by only the network’s clustering coefficient and initial condition. We finally propose an effective targeted constant quarantine control by using this centrality index. The results uncover the relationship between the optimal control and the network’s topological structure, provide a convenient method to determine suboptimal control, and present a strategy for targeted constant control. This paper can help to design effective control strategies for more general epidemic networks in the real world. [ABSTRACT FROM AUTHOR]

A substantial body of literature on global design optimization techniques is based on an implicit assumption that a system under optimization is designed by a perfectly centralized design team with all team members committed to a global optimum solution. This assumption enables the system engineers to minimize all the objective functions simultaneously while controlling all design decisions at the same time. Recent research suggested that this inherent assumption may need to be revisited. This paper proposes a novel framework for design optimization using game theory, which represents a design process as a normal-form game and unifies two fundamental decisions of discipline designers, namely a system-global or a discipline-local optimum solution. This paper provides a general mathematical formulation of the framework for a design process with any number of discipline designers. The framework introduces the notion of the Nash front, which is a set of design solutions consisting of the Nash equilibria derived through the reallocation of design authority between discipline designers. This paper then tests the framework on a numerical optimization case study of a Stirling micro combined heat and power plant with two discipline designers. The result indicates that for the system capital cost and the system power output the difference between global Pareto-optimum designs and the Nash front designs on average across five different scenarios of design authority allocation was 22% and 16%, correspondingly. The application of the novel framework in different fields of engineering could help to build models that are more realistic and prevent impractical technical objectives. [ABSTRACT FROM AUTHOR]

Modern military operations in high-tech information warfare settings are dynamic processes involving various types of combat systems connected via multiple channels, which can be abstracted as a type of complex temporal combat network (TCN). The equipment contribution analysis in TCNs is of significant military value for optimizing operation process planning and improving network resilience in complex electromagnetic battlefields. This paper presents an integrated framework called capability oriented equipment contribution analysis (CECA) to analyze key equipment in TCNs. Specifically, a temporal network model is proposed to characterize TCNs by considering their dynamic nature and the heterogeneity capabilities of different types of functional entities during military operations. Based on this model, an operation capability contribution index is proposed to measure the contribution of each piece of equipment involved in executing operational tasks. Finally, the reliability and effectiveness of the CECA are demonstrated based on a TCN case study. This paper provides a detailed and precise quantitative analysis of the equipment contributions in TCNs, which yields useful insights for operation guidance and designing a more resilient combat system-of-systems. [ABSTRACT FROM AUTHOR]

This article is written in recognition of W. Bledsoe, who with Browning, introduced the N-tuple subspace classifier in 1959. This 1959 article was the first article to introduce subspace classifiers and the sum rule to combine the outputs of the classifiers. A mathematical notation is given to easily express in a precise and unambiguous way everything going on in the N-tuple subspace classifier. Extensions of the N-tuple method are discussed using a generalized product expression and we relate the generalization to graphical models. We discuss the sum rule, the product rule, and the plurality voting rule for combining the scores of the subspace classifiers. We selected a representative sample of papers that the 1959 N-tuple subspace classifier inspired. Some of the papers introduced specialized improvements. Many of the papers showed the value of the N-tuple subspace classifier in all kinds of applications and compared the results of one or more varieties of the N-tuple subspace classifier with other state-of-the-art classifiers. Their experiments showed that the N-tuple subspace classifier was competitive with the state-of-the-art classifiers and often had a higher accuracy. Finally, we highlight some papers that describe experiments of the N-tuple subspace classifier executing in a quantum computer. [ABSTRACT FROM AUTHOR]

This paper mainly investigates the problem of extended dissipative filter design for switched discrete-time systems where the variation of switching signals among subsystems is governed by the persistent dwell-time strategy. Considering that in practical applications, the congestion of the communication channels between systems and filters may cause data loss, therefore the packet dropouts are taken into account to make the issue under consideration more general. Meanwhile, it is generally known that the mode information of the system may sometimes not be accessed, so we consider the design of a unified filter including mode-dependent and mode-independent filters simultaneously. A white sequence that obeys the Bernoulli distribution is used to express the random switching of the filters. The main purpose of this paper is to find a suitable filter design method which ensures that the resulting error system is exponentially mean-square stable and extended dissipative. Sufficient conditions are established to make sure the solvability of the addressed problem. By using appropriate decoupling method, some conditions that could be readily solved are obtained. In order to explain the correctness and effectiveness of the proposed method, two illustrated examples are given in the final part of this paper. [ABSTRACT FROM AUTHOR]

This paper studies the dissipativity of singular time-delay systems. The objective is to derive a less conservative criterion with less computational demand. Throughout this paper, the augmented Lyapunov–Krasovskii functional (LKF) and the free-matrix-based integral inequality are used to realize less conservativeness. On the basis of these techniques, a less conservative criterion is first given. Then, by decomposing singular time-delay systems into differential equations and algebraic ones, state vectors of the systems are divided into two parts. To reduce redundant decision variables, each part of the state vectors is considered independently to construct a special augmented LKF with less decision variables. As a result, an improved criterion is deduced, and thus less conservativeness and less computational demand are achieved at the same time. Moreover, the presented state decomposition method provides a new research idea for singular time-delay systems not yet reported in the literatures. Finally, the above statements are demonstrated by a numerical example. [ABSTRACT FROM AUTHOR]

The development of efficient solutions for deadlock problem in large-scale automated manufacturing systems (AMSs) is an issue of increasing interest in the scientific community, largely because of the nonapplicability of most existing approaches as AMS grows in size. Furthermore, much of these approaches is focused on systems with either assembly operations or flexible routes, implying that generalizing these existing results to more complex systems is difficult. As a result, we initiate on the deadlock problem of AMSs embedding assembly operations in flexible routes. By modeling AMSs as Petri nets, an innovative distributed approach is developed in this paper, which can be realized in an online, look-ahead, and dynamic way without requiring external and extra information. Beyond the value of the presented results, this paper is also intended to develop more general results which will be adaptable to a broader class of AMSs. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed event-triggered (ET) ${H} _{\boldsymbol \infty }$ filtering problem for discrete-time T–S fuzzy systems over sensor networks. In order to mitigate the effect of channel noise on the filtering error systems, dynamic saturation levels are considered in this paper. Different from the existing linear distributed ET filters design results, new piecewise fuzzy distributed filters are established to address the challenges caused by the distributed ET mechanisms. By constructing piecewise differentiable Lyapunov functionals, sufficient conditions in terms of linear matrix inequalities are obtained to guarantee the exponential stability and desired performance of the filtering error system. Finally, two examples are employed to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the state estimation problem for singularly perturbed switched complex dynamic networks (CDNs) is addressed, in which the persistent dwell-time (DT) switching mechanism is employed to depict the switchings among parameters of each node. In the aforementioned switching mechanism, the concept of stage consisting of the persistent portion and the DT portion is introduced. Based on the singular perturbation theory, a two-time-scaling variables containing fast and slow states are taken into account on the CDNs simultaneously, which make the constructed networks more realistic. Furthermore, the main aim is to design a mode-dependent estimator to track the state information that cannot be directly obtained and further ensures the global uniform exponential stability of the investigated systems with a generalized dissipativity property. Some sufficient criteria on the existence of the desired mode-dependent state estimator are established by utilizing a modified matrix decoupling method. Finally, the availability of the estimator design procedures is verified by a simulation example. [ABSTRACT FROM AUTHOR]

We investigate the matrix function optimization under the orthonormal constraint on the matrix variable. By introducing an index-notation-arrangement-based chain rule (I-Chain rule), we obtain the gradient of the cost function and propose a revisited orthonormal-constraint-based projected gradient method to locate a minimum of an objective/cost function of matrix variables iteratively subject to orthonormal constraint. To guarantee the convergence the proposed method, existing schemes require the gradient can be represented by the multiplication of a symmetrical matrix and the matrix variable itself. This condition has been relaxed in this paper. New techniques are proposed to establish the convergence property of the iterative algorithm. Simulation results show the effectiveness of our framework. This paper allows more extensive applications of matrix function optimization problems in science and engineering. [ABSTRACT FROM AUTHOR]

Role assignment is an important and complex task in collaboration and management. Group role assignment (GRA) pursues the optimal team performance by assigning pertinent roles to agents in a team from the team’s viewpoint. There are many factors that should be considered when conducting role assignment. Budgets are such a factor. This paper presents a challenging role assignment problem in collaboration, called GRA with budget constraints (GRABC). This paper contributes a thorough investigation of the GRABC problem including: 1) the first formalization of the proposed problem; 2) a set of practical solutions to different forms of this problem by using the IBM ILOG CPLEX optimization package (CPLEX); 3) a set of necessary conditions for these problems to possess feasible solutions to improve the CPLEX solutions; and 4) a synthesized process to deal with GRABC problems. Contribution 2) actually clarifies a list of requirements in solving the GRABC problem. Simulations, experiments and a case study are used to verify the practicability and efficiency of the proposed solutions. [ABSTRACT FROM AUTHOR]

This paper presents a planning procedure that allows an anthropomorphic dual-arm robotic system to perform a manipulation task in a natural human-like way by using demonstrated human movements. The key idea of the proposal is to convert the demonstrated trajectories into attractive potential fields defined over the configuration space and then use an RRT*-based planning algorithm that minimizes a path-cost function designed to bias the tree growth toward the human-demonstrated configurations. This paper presents a description of the proposed approach as well as results from a conceptual and a real application example, the latter using a real anthropomorphic dual-arm robotic system. A path-quality measure, based on first-order synergies (correlations between joint velocities) obtained from real human movements, is also proposed and used for evaluation and comparison purposes. The obtained results show that the paths obtained with the proposed procedure are more human-like. [ABSTRACT FROM AUTHOR]

In this paper, we provide a novel framework to assess the vulnerability/robustness of a network with respect to pair-wise nodes’ connectivity. In particular, we consider attackers that aim, at the same time, at dealing the maximum possible damage to the network in terms of the residual connectivity after the attack and at keeping the cost of the attack (e.g., the number of attacked nodes) at a minimum. Differently from the previous literature, we consider the attacker perspective using a multiobjective formulation and, rather than making hypotheses on the mindset of the attacker in terms of a particular tradeoff between the objectives, we consider the entire Pareto front of nondominated solutions. Based on that, we define novel global and local robustness/vulnerability indicators and we show that such indices can be the base for the implementation of effective protection strategies. Specifically, we propose two different problem formulations and we assess their performances. We conclude this paper by analyzing, as case studies, the IEEE118 power network and the U.S. Airline Network as it was in 1997, comparing the proposed approach against centrality measures. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of quasi-synchronization (QS) for the periodic neural networks (NNs). In order to address more general NNs, the parameter and the period mismatches are both considered, that is, excluding parameters, periods of the target dynamic and the followers are also different. In addition, the constrainted target information is studied, where the logarithmic quantizer is used to overcome the limited communication capacity and Bernoulli processes are employed to model cases of the information loss. A new period is established based on the lowest common multiple period of the target dynamic and the followers to obtain an augmented synchronization error system (ASES). Further, a suboptimal iterative algorithm is proposed to cut down the QS range of the ASES and the corresponding controllers are designed. At last, the controller design method is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

This paper is concerned with the asynchronous fault detection and isolation (FDI) strategy in finite frequency domain for Markov jump systems (MJSs) with actuator failures under networked environment. A set of binary-valued Bernoulli distributed sequences is introduced to describe the transmission and the scheduling of the sensor nodes. Then, an MJS directing by two Markov process and with multiple stochastic parameters is derived to characterize the whole system dynamics. Subsequently, by means of the stochastic $\mathcal {H}_{-}$ index in finite frequency domain and the geometric mapping approach, an asynchronous FDI scheme is investigated, in which the filters complete the FDI mission with only partial information of the measurements. Meanwhile, each FDI filter is only sensitive to one possible actuator fault and decoupled from others. Then the existence of the desired filters is ensured by some novel sufficient conditions. Finally, an application example is presented to show the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the event-triggered filter design problem is investigated for a class of discrete-time repeated scalar nonlinear systems. An admissible filter is introduced and sufficient conditions to ensure the asymptotical stability of the controlled system with a given performance are established. The solution of the corresponding event-triggered filter problem is settled by the projection lemma, and the cone complementary linearization technique is introduced to derive the filter parameters. An example is provided to demonstrate the effectiveness and potential of the proposed new design method. [ABSTRACT FROM AUTHOR]

Optimal control system has been considered as a valuable technique to enhance the reliability and security of wastewater treatment process (WWTP). However, due to the complex environments, it is difficult to realize the optimal control with changeable operational conditions in WWTP. To overcome this problem, an intelligent optimal control system with flexible objective functions (IOCS-FOF) was proposed in this paper. The main merits of this proposed IOCS-FOF were shown as follows. First, an optimal control system scheme was developed, where different time-scale operational objectives were detailed to describe the operational features. Second, a flexible objective strategy was designed to determine suitable objective functions, which could adapt to complex environments of WWTP. Third, a novel dynamic multiobjective optimization algorithm for flexible objectives (FO-DMOA) was developed to obtain the optimal solutions of IOCS-FOF. Finally, the proposed IOCS-FOF was applied in the benchmark simulation model No. 1 (BSM1) to confirm its technique capability. The results demonstrate that this proposed method can improve the operation performance of WWTP. [ABSTRACT FROM AUTHOR]

In this paper, we consider single-machine group scheduling with nonperiodical maintenance and deteriorating effects. Nonperiodical maintenance, which has unfixed maintaining interval or the number of jobs in each group is unfixed, results in a variable number of groups. Deteriorating effects lead to longer processing times of which the deterioration index depends on job grouping. This problem is of significance in different production settings and is much more difficult than and general that other simpler single-machine group scheduling problems. Making use of historical processing times, we construct the actual processing time model for jobs. We prove that the problem under study is NP-hard. By transforming the optimization objective, properties are discovered and two batch-based heuristics are presented for small size problems. To further improve the effectiveness for large size problems, an iterated greedy algorithm is proposed being its main advantages simplicity and effectiveness. The proposed methods are evaluated over a large number of random instances with calibrated parameters and components. Comprehensive computational and statistical analyses demonstrate the superiority of the methods proposed over adapted existing approaches. [ABSTRACT FROM AUTHOR]

While yielding satisfactory segmentation results for images with low SNR and poor contrast, one-dimensional (1-D) and two-dimensional (2-D) Otsu’s thresholding methods have the downside of high computational complexity. So far, three-dimensional (3-D) Otsu method has been based on histogram, which has only probability distribution of pixels as an object of interest. Histogram-based segmentation methods do not consider the contextual information which is significant to enrich the quality of segmented image. In this paper, a context-based 3-D Otsu algorithm has been proposed that considers the pixel intensity values as well as spatial information along with same properties of histogram. The proposed method is evaluated comprehensively with respect to quality and a detailed analysis is presented to compare the results of histogram-based 1-D, 2-D, and 3-D Otsu and energy-based 1-D, 2-D, and 3-D Otsu method, respectively. Experimental outcomes demonstrate the superiority of energy-based 3-D Otsu algorithm compared to histogram-based methods in terms of improved performance metrics, including mean error (ME), mean square error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), and entropy. Experiments on standard daily life color images have been carried out to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations. [ABSTRACT FROM AUTHOR]

Rapid changes in production environments have motivated researchers and industrial manufacturers to coordinate the production and distribution in supply chain management. This paper aims to address the permutation flow shop scheduling problem with batch delivery to multiple customers. In this problem, products are first manufactured in a permutation flow shop, and subsequently delivered to multiple customers in batches. To optimize the tradeoff between customer service and distribution cost, the objective of this paper is to minimize the total cost of tardiness and batch delivery. To deal with such optimization problem, two simple heuristics and a novel meta-heuristic (GA-TVNS) are developed to determine integrated production and distribution schedules. GA-TVNS hybridizes genetic algorithm and variable neighborhood search (VNS) to provide better exploration and exploitation in the search space. Moreover, to improve the local search of VNS, two new learning-based neighborhood structures are designed based on the classical school learning process of teaching–learning-based optimization. Computation experiments on both small-sized and large-sized test problems indicate that GA-TVNS performs the best among all the compared scheduling algorithms. [ABSTRACT FROM AUTHOR]

Existing results for cooperative control of high-order agents mainly employ the Nussbaum-type function to cope with unknown control directions and mostly require an assumption that the control directions are identical and unknown. This paper proposes a class of algorithms with nonlinear PI functions to relax such an assumption and make them suitable for nonidentical unknown control directions. It is proven that if the distributed nonlinear PI functions are suitably selected, the proposed algorithms can achieve consensus for high-order agents under strongly connected topologies and switching topologies with a jointly strongly connected basis (JSCB). Furthermore, we extend the consensus results to the case of time-varying topologies described by $\delta $ -connected, continuous graphs. As a special case, the consensus of high-order agents under the directed graph having a spanning tree is also investigated. Finally, illustrative simulations are presented to indicate the efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the fault-tolerant control (FTC) issue for a class of multilayer interconnected nonlinear systems by using the inclusion principle. First, an overlapping decomposition method is proposed that expands the original interconnected system into a new one where each layer is not overlapped with each other. Second, for such an expanded system, the coupling effects among multiple layers are analyzed by using cyclic-small-gain theorem, and FTC schemes based on layer cooperation are further developed. Finally, the control designed for the expanded system is contracted back to the original interconnected system to achieve its FTC goal. An example of multiple pendulums is taken to illustrate the efficiency and applicability of the obtained theoretical results. [ABSTRACT FROM AUTHOR]

Multicriteria decision making (MCDM) problems are often encountered in complex system design. Most of them need to be evaluated with a large number of interactive and qualitative indices, which are difficult to be addressed effectively through the existing methods. In this paper, a novel fuzzy Choquet integral-based grey comprehensive evaluation (GCE) method, called fuzzy grey Choquet integral (FGCI), is proposed to evaluate MCDM problems with many interactive and qualitative indices. In this method, expert evaluation of qualitative indices is represented through fuzzy linguistic values. Fuzzy values are defuzzified and standardized to obtain the original evaluation matrix. The original values are replaced by the correlation coefficients, which, to a certain extent, eliminate the influence of experts’ subjective preference. An improved teaching-learning-based optimization algorithm is employed to identify $ {\lambda }$ -fuzzy-measures following the weights given by experts in order to enhance the consistency of weights. Then the correlation coefficients are aggregated through Choquet integral among ${\lambda }$ -fuzzy-measures, which can reflect interactions among indices. In addition, according to the characteristics of ${\lambda }$ -fuzzy-measures, the construction guidelines for a corresponding index system are given to overcome the limitations of FGCI. Finally, the performance of the proposed method is demonstrated via a practical example of green design evaluation and compared with the GCE method. The results validate its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

Joint tracking and classification (JTC) is gaining momentum in recent years. Many algorithms have been proposed. However, not enough attention has been paid to JTC evaluation, although it is important in practice. In this paper, we deal with evaluating the goodness and credibility of JTC. For the JTC goodness, tracking and classification so far have been largely evaluated separately without considering the interdependence of tracking and classification. We propose a joint measure—joint probability divergence (JPD)—to quantify tracking error, misclassification and their interdependence. The basic idea of JPD is to measure the closeness between the cumulative distribution functions of the perfect JTC and the JTC to be evaluated. The proposed method has a number of attractive properties. Some results from algorithms can be regarded as self-assessments. The credibility problem is concerned with whether these assessments are credible or how credible they are. We define the credibility problem for decision and propose an associated noncredibility index (NCI). We also propose a joint NCI (JNCI) to quantify the noncredibility of estimation and decision jointly. Four examples are presented to demonstrate how well the JPD and JNCI reflect the joint performance of tracking and classification. [ABSTRACT FROM AUTHOR]

Motivated by the practical needs of simulation-based optimization, this paper considers a problem for selecting the best feasible design from a finite set of alternatives, subject to stochastic constraints given in several secondary objectives. We propose an efficient ranking and selection procedure that maximizes the accuracy of the selection under a limited simulation budget. The proposed procedure sequentially updates the simulation data of designs with a heuristic policy that allocates further simulation replications according to the evaluation results of data based on a statistical hypothesis test. Compared to recent studies, such as OCBA-CO and SCORE, this procedure can be more efficient when the simulation model involves large stochastic noise because its heuristic policy considers the precision of the sample mean ignored by the previous studies. Several experimental results of benchmarks demonstrate its improved efficiency, and a case study on the design of military network system shows its effectiveness for practical problems. [ABSTRACT FROM AUTHOR]

Data clustering is one of the most fundamental techniques in exploratory data analysis. It is widely used for determining the underlying data structure, classifying natural data and compressing data in engineering, business management, social statistics, computer science, and medicine. Under the assumption that clusters are high density regions in the feature space separated by relatively low density neighbors, a novel approach is proposed for modeling any high dimensional clustering problem as a one-dimensional analysis of the probability distribution. First, a special path between two vertexes, namely eigenpath, is defined in this paper to represent their close connection. Second, we propose the connectedness index based on the eigenpath for quantitatively describing the connection between two vertexes. Third, the connectedness index is applied to the candidates of cluster centers and measures the connection between different candidates. Then an indicative curve can be drawn with the knowledge of connectedness index. This approach not only provides effective indicative curve for unknown data sets but also facilitates eliminating the curse of dimensionality partly as well as correctly recognizes arbitrary cluster forms and automatically excludes outliers. Extensive experiments showed the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

This article is a position paper about models and algorithms that are generally called “stream clustering.” Semantics and methods used in this field are often co-opted from static clustering, but they do not serve well for streaming data analysis. Most “state-of-the-art” methods, such as sequential k-means, Birch, CluStream, DenStream, etc., acknowledge that the data are seen but once in real streaming analysis (e.g., intrusion detection, voter fraud, etc.). Interpretation of their outputs generally overlooks the fact that when the data cannot be saved, batch clustering ideas, such as preclustering assessment, partitioning, and cluster validity are not relevant. But in the current literature, the data, or some subset of it, are often saved for hindsight evaluation (we call this fake stream clustering). Our position? Useful analysis of real streaming data is in its infancy. We do not argue that current approaches to streaming clustering are wrong: rather, we regard them as transitional methods which will eventually lead to a new and useful paradigm for this type of computation. We think that this class of models and algorithms are actually classifiers, but with a special added component, viz., continuously updated cluster footprints of the instream processing. We need to carefully define the objectives of streaming analysis, and then choose terminology and methods that suit this evolving paradigm. [ABSTRACT FROM AUTHOR]

The proper supply of end-of-life (EOL) items plays a critical role in the success of remanufacturing systems. In many real-world practices, a remanufacturer has multiple links with independent third party collectors, while these links should be simultaneously coordinated. However, most previous studies mainly focus on reverse supply chain (RSC) coordination in a one-to-one setting (single link). Moreover, the competition among third party collectors affects the acquisition of EOL products and this is an important issue. In this paper, an RSC consisting of a single remanufacturer and duopolistic competing third party collectors is explored and a new mechanism is proposed to achieve channel coordination across multiple links. To be specific, we consider the case when the collectors compete on the acquisition prices offered to the consumers and the remanufacturer decides on the pricing and environmental effort decisions with two considerations: 1) increasing return of EOL items and 2) boosting demand of remanufactured products. The optimal decisions of RSC members and performance of RSC are analyzed under different behaviors of competing collectors. Moreover, a multilateral two-part tariff contract is proposed to coordinate the decisions across multiple links. The results indicate that the proposed contract improves not only profits of the remanufacturer and competing collectors but also the collection quantity of EOL items and demand of remanufactured products, which helps improve the environment. [ABSTRACT FROM AUTHOR]

This paper considers two firms that compete on both advertising and pricing. Each firm decides whether to use mass advertising to cover the entire market or adopt targeted advertising to inform only the switching shoppers. We capture this problem through a dynamic game model in which each firm can endogenously choose its advertising strategy. Our results reveal a number of interesting findings. We show that the two firms would not choose mass advertising simultaneously but might adopt targeted advertising at the same time. Targeted advertising can hurt both firms even if it is more cost-efficient than mass advertising: when the marginal cost of advertising is moderate, the two firms may fall into the well-known trap of the prisoners’ dilemma. In another interesting finding, symmetric firms may employ asymmetric advertising strategies in equilibrium: when the advertising cost is sufficiently low, one firm chooses targeted advertising, while the other adopts mass advertising. In addition, while a fixed cost associated with acquiring the targeting capability weakens firms’ incentives to adopt targeted advertising, a large fixed cost might benefit firms in equilibrium. [ABSTRACT FROM AUTHOR]

In this paper, a continuous-time multiagent system is proposed for solving optimal resource allocation problems with local allocation feasible constraints. In the system, all the primal agents are divided into different groups. We use dual variables which describe the dual agents to represent the groups of the original agents. The groups of dual agents are used to communicate with others on behalf of the primal agents to reduce communication costs. That is to say, primal agents aim to seek their own optimal solutions by using local information. And dual agents represent primal agents to communicate with other agents in different groups by using the whole group information. The two kinds of agents cooperate to find the optimal solution of the problem. In this way, we only need to know the connections of dual agents to design the multiagent network, and do not need to consider the connections of the primal agents. So the communication cost and the amount of variables will be largely reduced especially for large-scale problem. Furthermore, it is proved that the multiagent system can reach consensus with respect to the dual variables. At the same time, the primal variables are convergent to the optimal solutions of the optimization problem under some certain assumptions on the communication network. For large-scale problem if we take the groups as areas, then the system is suitable for multiarea problem. Simulation results are presented to demonstrate the performance of the proposed multiagent system. [ABSTRACT FROM AUTHOR]

Cluster analysis is a data mining technique that has been widely used to exploit useful information in a great amount of data. Because of their evaluation mechanism based on an intracluster distance (ICD) function, traditional single-objective clustering algorithms are not appropriate for not-well-separated data. Specifically, they may easily result in the drop of the optimal solution accuracy on their late stages of search when dealing with the latter. To overcome the problem, in this paper a novel index reflecting the similarity of data within a cluster is presented and called intracluster cohesion (ICC). However, if a multiobjective method is used to cluster with ICD and ICC as the specified objectives, its clustering accuracy may depend on one’s experience. Motivated by these, we propose an accelerated two-stage particle swarm optimization (ATPSO) in which ${K}$ -means is utilized to accelerate particles’ convergence during the population initialization. Its clustering process consists of two stages. First, the main objective of minimizing ICD is to execute preliminary clustering; second, ICC is optimized to promote the clustering accuracy. Extensive experiments with the help of 17 open-source clustering sets in various geometric distributions are conducted. The results show that ATPSO outperforms PSO, ${K}$ -means PSO (KPSO), chaotic PSO (CPSO), and accelerated CPSO in terms of accuracy, and its efficiency is approximate to that of KPSO. Its convergence trend indicates that the adoption of the proposed ICC contributes to the clustering accuracy. Remarkably, compared with the Pareto-based multiobjective PSO, ATPSO can detect clusters more accurately and quickly through the proposed two-stage search. [ABSTRACT FROM AUTHOR]

A hybrid multirobot cluster tool is composed of both single and dual-arm robotic cluster tools. Since the behavior of different individual tools is different, it is very challenging to coordinate their activities in such a tool and to schedule it optimally. To find a one-wafer cyclic schedule to reach the shortest cycle time for a treelike hybrid multirobot cluster tool whose bottleneck tool is process-bound, this paper extends resource-oriented Petri nets to model it such that a schedule can be parameterized by its robots’ waiting time. Based on the model, this paper then establishes the conditions under which there is a one-wafer cyclic schedule such that the shortest cycle time can be obtained. An efficient algorithm is also given to test the existence of such a schedule and to find it if existing. At last, examples are used to illustrate the proposed approaches. [ABSTRACT FROM AUTHOR]

With the growing economic pressure, one lever for companies to be competitive is to efficiently monitor projects. To this goal, the different processes involved in the project must be carefully supervised and the project manager needs to be well-informed on their state and progress to take the best decisions thanks to accurate performances of indicators, process, and project. Another key factor of successful decision-making in engineering projects beyond taking into account information from the project manager on the project management (PM) is to consider information from the systems engineer on the product development. This paper proposes a process to monitor engineering projects relying on a set of project performance indicators that integrate the views of both project manager and systems engineer. This process includes three main activities: 1) defining project performance indicators related to the processes described by international guides and standards; 2) valuating and weighting these indicators by consulting project managers and systems engineers; and 3) constructing a hierarchical framework of indexes to support project monitoring. This proposal thus refers to the practices described in PM and systems engineering norms and improves them by involving project managers and systems engineers into decision-making, with the goal to take more coherent decisions. [ABSTRACT FROM AUTHOR]

Due to the rapid technology advancement and market changes, products are becoming outdated and subsequently discarded faster than ever before. Recovery, recycling, and remanufacturing of end-of-life (EOL) products are getting more attention. Disassembly is indispensable to recycle and remanufacture EOL products, and a disassembly line is an efficient way to perform it. A disassembly line balancing problem (DLBP) aims at streamlining the disassembly activities such that the total disassembly time consumed at each workstation is approximately the same and approaching the cycle time. However, the assignment of disassembly operations to workstations in a disassembly shop should ensure the recovery of valuable components and reduce undesirable impact on the environment as much as possible. In this paper, a novel heuristic technique combining multicriterion decision making (MCDM) and general variable neighborhood search (GVNS) is proposed to solve the DLBP. Based on the characteristics of the DLBP, an innovative MCDM method based on fuzzy set theory, grey relational analysis, and Choquet fuzzy integral is developed to evaluate the performance scores and determine the ranking of disassembly tasks. Subsequently, an improved GVNS algorithm is employed to further balance a disassembly line with three objectives, in which a new metric is formulated to integrate with the ranking from MCDM. The proposed method not only takes a comprehensive objective system into consideration but effectively generates a good enough tradeoff disassembly solution. Finally, the proposed approach is illustrated with an example and compared with two other heuristics to show its efficacy in solving the DLBP. [ABSTRACT FROM AUTHOR]

Availability of a common, shared benchmark to provide repeatable, quantifiable, and comparable results is an added value for any scientific community. International consortia provide benchmarks in a wide range of domains, being normally used by industry, vendors, and researchers for evaluating their software products. In this regard, a benchmark of untimed Petri net models was developed to be used in a yearly software competition driven by the Petri net community. However, to the best of our knowledge there is not a similar benchmark to evaluate solution techniques for Petri nets with timing extensions. In this paper, we propose an evaluation framework for the comparative analysis of generalized stochastic Petri nets (GSPNs) simulation techniques. Although we focus on simulation techniques, our framework provides a baseline for a comparative analysis of different GSPN solvers (e.g., simulators, numerical solvers, or other techniques). The evaluation framework encompasses a set of 50 GSPN models including test cases and case studies from the literature, and a set of evaluation guidelines for the comparative analysis. In order to show the applicability of the proposed framework, we carry out a comparative analysis of steady-state simulators implemented in three academic software tools, namely, GreatSPN, PeabraiN, and TimeNET. The results allow us to validate the trustfulness of these academic software tools, as well as to point out potential problems and algorithmic optimization opportunities. [ABSTRACT FROM AUTHOR]

At present, no efficient method is proposed for the liveness analysis of general unbounded Petri nets (UPNs) except some of their subclasses. Our previous work presents a non-Karp–Miller finite reachability tree, i.e., lean reachability tree (LRT) to represent their markings. It faithfully expresses and folds the reachability set of an unbounded net. It can totally avoid the efforts made by the existing modified Karp–Miller trees on the expression of potentially unbounded nodes and elimination of all fake markings. By exploiting it, this paper presents a method for comprehensively analyzing the properties of general UPNs. Particularly, we reveal the repeatability of deadlock with the unfolding of some unbounded leaves in LRT and present a sufficient and necessary condition of deadlock existence. Then, LRT and some partial trees generated from it, instead of entire reachability graphs, are utilized to analyze the liveness and reversibility of general UPNs rather than some special ones. The related theoretical results are proven. A unified algorithm based on LRT for analysis of boundedness, liveness, deadlock, and reversibility of general UPNs is developed for the first time. The results of a case study show that the presented method is effective for general UPNs. [ABSTRACT FROM AUTHOR]

This paper addresses a class of network-to-network control problems in the presence of heterogeneous topologies. To achieve the coordination of nodes in a controlled network, a networked controller with a heterogeneous topology is presented in an observer form based on the nearest neighbor rule. It is shown that the network-to-network control problem can be transformed into a coordination problem on a network subject to hybrid static and dynamic interactions. Furthermore, these hybrid interactions among nodes can be represented by appropriately constructing a dynamic graph, of which the connectivity provides a necessary and sufficient guarantee for all nodes to reach agreement on the average quantity of their initial conditions. Simulations are given to illustrate the effectiveness of our results obtained through the dynamic graph approach. [ABSTRACT FROM AUTHOR]

Robust ${H}_{{\infty }}$ guaranteed cost time-varying formation tracking problems for high-order multiagent systems with external disturbances and time-varying delays are considered. It is required that the followers’ states need to track the leader’s state and to actualize the desired time-varying formation simultaneously. First, this paper devises a linear time-varying formation tracking protocol considering the effects of the delays constituted by the neighboring information. Then, an integral linear quadratic cost function constructed by the control input constraint and the formation tracking error constraint is utilized to analyze the suboptimal robust ${H}_{{\infty }}$ guaranteed cost time-varying formation tracking performance. Third, sufficient conditions for actualizing the guaranteed cost time-varying formation tracking with ${H}_{{\infty }}$ disturbance attenuation performance are derived, and the conditions for the feasible time-varying formation tracking are raised. To obtain the gain matrix of the protocol, only four linear matrix inequalities are required to be solved. Finally, a numerical simulation example reveals the effectiveness of the acquired results, where five agents achieve the robust ${H}_{{\infty }}$ guaranteed cost time-varying formation tracking. [ABSTRACT FROM AUTHOR]

In this paper, the prescribed performance-based consensus problem for nonlinear multiagent systems with unknown control directions and switching networks is investigated. Different from the existing works, the unknown control directions and prescribed performance of consensus errors are considered simultaneously under the switching networks. In this case, the graph Laplacian matrix may be singular and the controllers are interacted. To handle this problem which cannot be dealt with directly using the existing methods, a distributed adaptive observer is introduced so that the controllers are decoupled and the difficulty caused by the unknown control directions can be overcome by adopting the existing Nussbaum functions and the unknown control directions of agents are allowed to be nonidentical. Meanwhile, by utilizing the designed controller, the consensus errors can be guaranteed with the prescribed performance. Finally, a numerical example verifies the validity of the proposed method. [ABSTRACT FROM AUTHOR]

As the mutation strategy and algorithmic parameters in differential evolution (DE) are sensitive to the problems being solved, a hot research topic is to adaptively control the strategy and parameters according to the requirements of the problem. In the literature, most adaptive DE use either historical experiences of the population or heuristic information of the individuals to promote adaptation. In this paper, we develop a novel variant of adaptive DE, utilizing both the historical experience and heuristic information for the adaptation. In this novel historical and heuristic DE (HHDE), each individual dynamically adjusts its mutation strategy and associated parameters not only by learning from previous successful experience of the whole population, but also according to heuristic information related with its own current state. These help the algorithm select a more suitable mutation strategy and determinate better parameters for each individual in different evolutionary stages. The performance of the proposed HHDE is extensively evaluated on 30 benchmark functions with different dimensions. Experimental results confirm the competitiveness of the proposed algorithm to a number of DE variants. [ABSTRACT FROM AUTHOR]

The asymptotic mean square stability and ${{\mathcal {H}}}_{{2}}$ performance of the 2-D hidden Bernoulli jump system is analyzed in this paper. This system, essentially, has a bi-jumping property, in other words, its jumps depend on two jumping parameters, one is the Bernoulli process, the other is dependent on the Bernoulli process via a conditional probability matrix, they together determine which subsystem is active and are coined as hidden Bernoulli model. By means of Lyapunov function method, a sufficient condition is derived, which reveals that the system is asymptotically mean square stable and has a certain ${\mathcal {H}}_{{2}}$ performance if a set of matrix inequalities are satisfied. Furthermore, the issues of asynchronous control and filtering are addressed for 2-D Bernoulli jump system based on the hidden Bernoulli model and the derived result. Finally, numerical simulations indicate that these proposed theories and methods are reliable and efficient. [ABSTRACT FROM AUTHOR]

This paper investigates the cooperative stabilization problem for a class of linear switched systems under asynchronous switching. Based on a novel class of switching signals which prevail over the traditional average dwell time scheme, a sufficient condition of global asymptotic stability is proposed for the considered system. Unlike the existing results, the provided condition permits the Lyapunov-like function to increase not only in the period of mode-identifying process but also in normal-working period with matched controller. Compensating the increment by an increased decrement, the proposed approach can guarantee the decrease of the Lyapunov-like function from a whole perspective. Moreover, the solvability condition is established as well to obtain the gains for corresponding controllers. Finally, a simulation example is provided to verify the validity of the presented method. [ABSTRACT FROM AUTHOR]

In this paper, an improved data-driven point-to-point iterative learning control is proposed for nonlinear repetitive systems where only the system outputs at the multiple intermediate prespecified points are considered. The entire finite time interval is divided into multiple time-subintervals according to the prespecified points. Then a new objective function is designed to generate optimal control inputs over a time-subinterval piecewisely. As a result, the control inputs are updated in a time-subinterval wise using additional input signals from the previous time-subintervals of the same iteration to help improving control performance. By removing the constraints on the unimportant intermediate points, the control system can be designed with additional freedom to achieve a better performance in tracking points of interest. Meanwhile, the proposed approach is data-driven and no process model is required for the control system design and analysis. Both a simulation with nonlinear batch reactor and an experiment with a permanent magnet linear motor servomechanism are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Palmprint processes a number of unique features for reliable personal recognition. However, different types of palmprint images contain different dominant features. Instead, only some features of the palmprint are visible in a palmprint image, whereas the other features may not be notable. For example, the low-resolution palmprint image has visible principal lines and wrinkles. By contrast, the high-resolution palmprint image contains clear ridge patterns and minutiae points. In addition, the three dimensional (3-D) palmprint image possesses curvatures of the palmprint surface. So far, there is no work to summarize the feature extraction of different types of palmprint images. In this paper, we have an aim to completely study the feature extraction and recognition of palmprint. We propose to use a unified framework to classify palmprint images into four categories: 1) the contact-based; 2) contactless; 3) high-resolution; and 4) 3-D palmprint images. Then, we analyze the motivations and theories of the representative extraction and matching methods for different types of palmprint images. Finally, we compare and test the state-of-the-art methods via the widely used palmprint databases, and point out some potential directions for future research. [ABSTRACT FROM AUTHOR]

We present three problems found in this paper “Policy equilibrium and generalized metarationalities for multiple decision-maker conflicts,” by Zeng et al., published in IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 37, no. 4, pp. 456–463, Jul. 2007. More specifically, we show by means of counterexamples that: 1) the definition proposed for n-decision maker (DM) conflicts does not coincide with the existing generalized metarationality defined by these same authors for conflicts with 2 DMs; 2) the equivalence between generalized metarationality for 2 rounds and symmetric metarational stability is false; and 3) generalized metarational equilibrium does not imply policy equilibrium. [ABSTRACT FROM AUTHOR]

The green design of electromechanical products is a pivotal link to manufacturing industry. The question on how to design green products must be answered by excellent designers using both advanced design methods and effective assessment techniques of design alternatives. Making an objective and precise assessment of green designs is of increasing importance to ensure sustainable development. This work proposes a framework based on the combination of analytical hierarchy process (AHP), gray correlation (GC), and technique for order performance by similarity to ideal solution (TOPSIS) to evaluate the performance of design alternatives. AHP is used to determine the weights of performance indices and a nonlinear programming model with constraints is proposed to obtain the integrated closeness index based on the similarity closeness index from GC and distance closeness index from TOPSIS. A case study, i.e., three kinds of refrigerators, is illustrated to verify the proposed method. By comparing with existing methods, i.e., AHP-TOPSIS and AHP-GC, the effectiveness of the proposed method has been confirmed. In addition, sensitivity analysis is also provided in order to assess the robustness of the proposed method. Also, the following implication can be obtained from our results: 1) chloro–fluoro–carbons (mg/ $\text{m}^{3}$ ) (C1), production noise (dB) (C6), and environmental cost (C16) have a large impact on refrigerator’s green design since these factors carry relatively larger weights. Results of the sensitivity analysis from different cases demonstrate that the best alternative may change when different weights are assigned to the evaluation criteria. This finding means the importance of establishing a qualified group of experts/designers in design evaluation and 2) the selection of design alternatives to produce a green product is critical to product development. The main contribution of this paper is the definition and development of an effective evaluation framework to guide managers to assess product design alternatives. The results show that it overcomes the one-sidedness of AHP-TOPSIS and AHP-GC, and makes the evaluation results more objective and realistic. It provides an accurate, effective, and systematic decision support tool for green performance evaluation of product design alternatives. [ABSTRACT FROM AUTHOR]

This paper proposes a group decision-making (GDM) method for integrating heterogeneous information. To avoid information loss, instead of transforming heterogeneous information into a single form, the proposed method integrates heterogeneous information using a weighted-power average operator. The consensus degree between the individual-decision matrix and the group-decision matrix is then calculated based on the deviation degree. In addition, the feedback mechanism with the iterative algorithm is used to adjust the individual decision matrix, which does not reach the consensus. Furthermore, a ranking formula with heterogeneous technique for order preference by similarity to an ideal solution is adopted to select the best alternative. A numerical example of supplier selection is introduced to validate the proposed model and compare it with other similar GDM models. The results illustrate that the proposed method cannot only avoid information loss, but also effectively integrate heterogeneous information in heterogeneous GDM environment. [ABSTRACT FROM AUTHOR]

Hosting multiple applications in a shared infrastructure of wireless sensor networks is a trend nowadays, and sharing sensory data for answering concurrent applications is a promising and energy-efficient strategy. To address this challenge, this paper proposes an energy-efficient query optimization mechanism for supporting multiple concurrent applications leveraging our two-tier cooperative caching mechanism. Specifically, query requests for concurrent applications are represented as binary strings, which are reduced to a single one for avoiding the reprocessing of shared subquery requests. This reduced query request is answered through our cooperative caching mechanism, where sensory data, which are highly possible to be reused for answering forthcoming query requests, are cached at the sink node (SN). Besides, the gray model GM(1, 1) is adopted for forecasting sensory data units which may be interested mostly by forthcoming query requests. These units of sensory data may be prefetched from the network and cached at the SN. Experimental evaluation shows that this approach can reduce the energy consumption significantly, and improve the network capacity to an extent, especially when the number of concurrent query requests is relatively large. [ABSTRACT FROM PUBLISHER]

A mixed-model two-sided assembly line is a manufacturing system designed for the production of large-sized products. In order to describe the actual condition, this paper presents a novel multiobjective programming model for balancing a mixed-model two-sided assembly line subject to multiple constraints, in which, additional constraints including zoning, synchronous, and positional constraints are considered besides the traditional constraints, e.g., the precedence constraint. Two objectives are simultaneously to be optimized, one is to minimize the combination of the weighted line efficiency and the weighted smoothness index, and the other is to minimize the weighted total relevant costs per unit of a product. A novel multiobjective hybrid imperialist competitive algorithm (MOHICA) is proposed to solve this problem. In the presented MOHICA, the sigma method is employed to quantify every individual, a novel merging method is introduced to reserve better individuals into the evolutionary population, and late acceptance hill-climbing (LAHC) algorithm is presented as a local search algorithm to achieve accurate balance between intensification and diversification. The experimental results on the selected benchmark instances and a practical case show that the proposed multiobjective algorithm outperforms nondominated sorting genetic algorithm (NSGA)-II, multiobjective improved teaching-learning-based optimization, and NSGA-III existing in the literature. [ABSTRACT FROM PUBLISHER]

Linguistic large-scale group decision making (LGDM) problems are more and more common nowadays. In such problems a large group of decision makers are involved in the decision process and elicit linguistic information that are usually assessed in different linguistic scales with diverse granularity because of decision makers’ distinct knowledge and background. To keep maximum information in initial stages of the linguistic LGDM problems, the use of multigranular linguistic distribution assessments seems a suitable choice, however, to manage such multigranular linguistic distribution assessments, it is necessary the development of a new linguistic computational approach. In this paper, it is proposed a novel computational model based on the use of extended linguistic hierarchies, which not only can be used to operate with multigranular linguistic distribution assessments but also can provide interpretable linguistic results to decision makers. Based on this new linguistic computational model, an approach to linguistic large-scale multiattribute group decision making is proposed and applied to a talent selection process in universities. [ABSTRACT FROM AUTHOR]