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In recent years, the establishment of a substantial amount of academic groups on scientific social network has brought new opportunities for the collaboration among researchers. In this situation, conducting paper recommendation to these academic groups is of terrific necessity in that it can further facilitate group activities. However, when producing group recommendation, existing methods fail to make full use of the abundant group information, from which a great deal of valuable information can be inferred to facilitate the recommendation performance. In addition, those methods tend to assign an equal weight to each group member when aggregating their recommendations, which is unreasonable in practice. Although some improvements have been made to remedy this problem by assigning different weights to group members, they fail to take into account the reliabilities of group members. Therefore, a group-oriented paper recommendation method based on probabilistic matrix factorization and evidential reasoning (GPMF_ER) is proposed in this article to tackle these problems. More specifically, the group and paper content information are integrated into the probabilistic matrix factorization model to enhance the accuracy of individual recommendation. Afterward, evidential reasoning rule is introduced in the aggregation step to consider both the weights and reliabilities of group members. Extensive experiments have been conducted on the real world CiteULike dataset and the results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. In this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refining system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. Finally, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption. [ABSTRACT FROM AUTHOR]

The work presented in this paper concerns a switching-based control formulation for multi-intersection and multiphase traffic light systems. A macroscopic traffic flow modeling approach is first presented, which is instrumental to the development of a model-based and switching-based optimization method for traffic signal operation, in the framework of adaptive dynamic programming (ADP). The main advantage of the switching-based formulation is its capability to determine both “when”’ to switch and “which” mode to switch on without the need to use the cycle-based average flow approximation typical of state-of-the-art formulations. In addition, the framework can handle different cycle times across intersections without the need for synchronization constraints and, moreover, minimum dwell-time constraints can be directly enforced to comply with minimum green/red times in each phase. The simulation experiments on a multi-intersection and multiphase traffic light systems are presented to show the effectiveness of the method. [ABSTRACT FROM AUTHOR]

With the rapid development and widespread utilization of advanced communication, sensing, and computation technologies, increasing attention has been paid to develop new techniques of control and filtering for distributed networked systems. Note that the utilization of communication networks can improve efficiency, flexibility, and scalability in designing networked controllers and filters. However, communication networks usually suffer from communication resource constraints with detrimental consequences, such as long latency, increased packet dropout, reduced throughput, and so on. In order to address the challenges caused by limited communication resources, an event-triggered communication paradigm is an effective and promising technique that can significantly reduce computation/communication utilization while maintaining the desired estimation and the control performance. As a result, a variety of event-triggered control and filtering techniques for distributed networked systems have been proposed. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

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The takeoff point in this paper is a random variable X for which large positive values are desired. When its probability distribution is highly skewed, the possibility of a long fat-tailed distribution can lead to an expected value, \mu =\mathbb E[ X] , which is unduly optimistic. For the reverse case when small values of X are desired, the ideas in this paper are applied to - X . This issue of over-optimism in the expected value is particularly important when a mission-critical random variable is involved. For example, when considering earthquake intensity or flood levels, reporting an understated expected value to a technically unsophisticated general public would be considered by many as highly undesirable. The Conservative Expected Value (CEV) of X , denoted by \mathbb CEV ( X ) is a new definition provided in this paper. It is a metric which we argue is particularly useful when risk aversion must be highly emphasized. At the same time, while being conservative, the CEV is also aimed at avoiding reflection of excessive pessimism. When, then \mathbb CEV ( X ), while being conservative, is defined in such a way so as not to be unduly pessimistic. In classical analysis, enhancement of a calculation often includes the variance \sigma ^ 2 \,\, = var( X ). However, when large X -values are desired, this may further distort one’s overview of the risk at hand; e.g., if X is profit, values above the mean should not be penalized. With these one-sidedness considerations in mind, we work with a new reward-risk pair, the CEV and the so-called Conservative Semi-Variance (CSV) of X , denoted by \mathbb CSV ( X ). Whereas the CEV definition is entirely new, the CSV definition is motivated by ideas in the area of finance. This paper also illustrates calculation of the CEV and CSV pair for a number of classical probability distributions and includes description of a number of properties of these metrics which suggest that this new theory is mathematically rich. Finally, we demonstrate the potential for application of the theory via two numerical examples. [ABSTRACT FROM AUTHOR]

The approach of feedback iteration in control theory is considered for solving the problem of control allocation algorithm for the case of three moments in this paper. The core of our novel idea is to introduce a closed-loop feedback iteration for obtaining an available solution to control allocation, which is referred to as control theoretic control allocation (CTCA). Two types of CTCA are investigated in this paper: linear case and nonlinear case, where, in the former, the control Lyapunov function (Lyapunov optimizing) and conjugate gradient are introduced, and in the latter, a nonlinear feedback theory is utilized. Thus, the control Lyapunov function and Lyapunov optimizing control allocation (CLF/LOCA), conjugate gradient control allocation (CGCA), and nonlinear CTCA (NCTCA) are proposed in this paper. The performance of each algorithm is evaluated for computational efficiency and accuracy by use of linear or nonlinear aircraft models, respectively. The simulation results show that the proposed CTCA algorithm will terminate in finite steps. [ABSTRACT FROM AUTHOR]

This paper provides new insight into the theoretical principle and the practical applicability of the ranked nodes method (RNM) that is used to construct conditional probability tables (CPTs) for Bayesian networks (BNs) by expert elicitation. RNM is designed for specific types of discrete random variables called ranked nodes that are common in real-world applications of BNs. Despite its active use in recent years, there remains ambiguity about the exact theoretical basis of RNM which can hamper its effective employment. In addition, there are a lack of studies about the general ability of CPTs generated with RNM to represent probabilistic relationships in real-world applications. In this paper, it is shown how the generation of probabilities with RNM is underpinned by a regression model of continuous random variables. Then, it is experimentally determined that in typical applications of RNM, one can generate in a matter of seconds CPTs whose elements reflect well probabilities given by the underlying regression model. Another experiment discovers that CPTs generated with RNM provide a good average fit to a large portion of various real-world CPTs investigated. This confirms the usefulness of RNM in practical applications. The results of the experiment also indicate that choices made by the user of RNM can considerably impact the ability of a generated CPT to represent a given probabilistic relationship. This paper then provides practical advice on the efficient use of RNM with regard to the user-controlled features explored in the experiment. [ABSTRACT FROM AUTHOR]

This paper extends a traditional supply chain network design problem through two perspectives, i.e., the multiperiod decision environment and the environmental concerns. A planning horizon contains multiple periods. Different periods have different demands of products and hence should have different supply chain decisions, which mainly include facilities’ locations, the environment protection level in facilities, as well as the transportation flows of products in the supply chain. For each facility in two consecutive periods, the changes of the location and environment protection level related decisions may incur adjustment cost. A bi-objective mixed-integer programming model is proposed to minimize both the total cost and the CO2 emissions of the supply chain during the planning horizon with multiple periods. A solution approach is proposed to solve the model. Numerical experiments are conducted to validate the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

The increasing application of wheeled mobile robots (WMR) in many fields has brought new challenges on its control and teleoperation, two of which are induced by contact slippage phenomenon between wheel and terrain as well as time delays in the master-slave communication channel. In the WMR bilateral tele-driving system, in this paper, the linear velocity of the slave mobile robot follows the position command from the haptic master robot while the slippage-induced velocity error is fed back as a haptic force felt by the human operator. To cope with the slippage-induced nonpassivity and constant time delays, this paper proposes three methods to design the WMR bilateral teleoperation system’s controller. An experiment system is set up with Phantom Premium 1.5A haptic device as the master robot and a simulation platform of WMR as the slave robot. Experiments with the proposed methods demonstrate that they can result in a stable WMR bilateral tele-driving system under wheel’s slippage and constant time-delays. [ABSTRACT FROM AUTHOR]

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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

The article presents information on the topics such as modeling, simulation, and analysis of social systems covered by the journal IEEE Transactions on Computational Social Systems and the names of the websites where the papers can be submitted.

Single-link flexible-joint manipulator (FJM) is a kind of time-varying nonlinear system with underactuated characteristics. This paper takes the uncertain single-link FJM (USLFJM) as the object, and presents a novel robust control approach based on equivalent input disturbance (EID) method for motion control (i.e., position control and trajectory tracking control) of the USLFJM. Based on the uncertain dynamic model and desired target of this system, an error system is constructed. The error system is considered as a linear system with a nonlinear virtual disturbance. The motion control objective of the USLFJM can be realized by globally asymptotically stabilizing this linear system and compensating the influence of the nonlinear virtual disturbance. Then, an EID-based control system is designed to realize this control objective. Only position measurements are utilized in this paper, and the stability of the control system is proved. The simulation results are presented to illustrate the validity and the robustness of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper investigates the output consensus control problem of uncertain second-order nonlinear multiagent systems with unknown nonlinear dead zone, where the communication networks can be described as a directed graph with a fixed topology. The control input in each follower is assumed to be subject to unknown nonlinear dead zone. By the function approximation capability of neural networks (NNs), a distributed adaptive NN-based controller is designed for each follower in the graph such that all the outputs of the followers can asymptotically synchronize to the output of the leader with synchronization errors being semiglobally uniformly ultimately bounded. Based on Lyapunov stability theory and algebraic graph theory, analysis is conducted on stability and parameter convergence of the proposed algorithm. Finally, an example is provided to validate the theoretical results. [ABSTRACT FROM PUBLISHER]

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Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers. [ABSTRACT FROM PUBLISHER]

Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers. [ABSTRACT FROM PUBLISHER]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers. [ABSTRACT FROM AUTHOR]

A fundamental challenge for system engineers is to capture a problem with an effective model or framework and then facilitate transferring the information of that captured problem to practical systems engineering tools and methods. The early problem definition phase requires an application of systems thinking with adequate modeling tools and methods. Then, the later problem definition phase and early system architecting phase requires transferring the captured problem to systems engineering tools and methods through emerging techniques such as model-based systems engineering (MBSE) using SysML (MBSE is the practice of using a modeling tools to capture systems engineering diagrams). This paper presents a method for capturing a problem through systemigrams and the Boardman soft systems methodology and then directly translating the systemigrams into SysML diagrams. With MBSE increasing in usage, this method could provide a time savings opportunity during model development along with the possibility of lowering information distortion or loss that can occur during transformation of systems thinking to systems engineering activities. This paper includes a case study which demonstrates how the proposed approach was applied on a problem being considered by the U.S. Army—Contingency Basing for Small Combat Units. Finally, this paper will provide the conclusion on the development of the method and describe future research directions that can allow systems thinking and MBSE to function in a congruent methodology. [ABSTRACT FROM AUTHOR]

In group decision making, it is inevitable that the individual decision maker’s subjectivity is involved, which causes difficulty in reaching a group decision. One of the difficulties is to aggregate a small set of expert opinions with the individual subjectivity or uncertainty modeled with probability theory. This difficult problem is called probability distribution function aggregation (DFA). This paper presents a simple and efficient approach to the DFA problem. The main idea of the proposed approach is that the DFA problem is modeled as a nonlinear function of a set of probability distribution functions, and then a linear feedback iteration scheme is proposed to solve the nonlinear function, leading to a group judgment or decision. Illustration of this new approach is given by a well-known DFA example which was solved with the Delphi method. The DFA problem is a part of the group decision problem. Therefore, the proposed algorithm is also useful to the decision making problem in general. Another contribution of the this paper is the proposed notation of systematically representing the imprecise group decision problem with the classification of imprecise information into three classes, namely incomplete information, vague information, and uncertain information. The particular DFA problem dealt with in this paper is then characterized with this general notation. [ABSTRACT FROM AUTHOR]

A group of geographically dispersed and logically collaborated emergency organizations are involved when emergency occurs. Two challenging issues are a reasonable emergency resource allocation mechanism and an efficient resource conflict checking and resolution control mechanism to ensure the conflict-free execution of global cross-organization emergency response processes. To address them, this paper proposes an approach to support emergency resource management including both intraorganization private resource management and cross-organization public resource management. The former has been fully discussed in our previous work using E-net. To cope with the latter, this paper presents a novel type of Petri net that is extended with both time and resource factors to model cross-organization emergency response processes. Then, according to the resource requirement analysis, their worst, delayed, and best cases are obtained. Next, emergency resource conflict checking and four conflict resolution strategies are proposed to resolve the detected resource conflicts for the delayed execution case. This paper shows how to use different resolution strategies to construct the conflict-free model by designing corresponding resolution controllers. Finally, their performance is evaluated by a fire emergency response example. [ABSTRACT FROM AUTHOR]

Today, identity verification is required in many common activities, and it is arguably true that most people would like to be authenticated in the easiest and most transparent way, without having to remember a personal identification number. To this regard, this paper presents a multibiometric system based on the observation that the instinctive gesture of responding to a phone call can be used to capture two different biometrics, namely ear and arm gesture, which are complementary due to their, respectively, physical and behavioral nature. We conducted a comprehensive set of experiments aimed at assessing the contribution of each of the two biometrics as well as the advantage in their fusion to the system’s overall performance. Experiments also provide objective measurement of both saliency and correlation of data captured by each sensor involved (accelerometer, gyroscope, and camera) according to various features extraction, features matching, and data-fusion techniques. The reports provide evidences about the potential of the proposed system and method for user authentication “in-the-wild,” whilst its eventual usage for person identification is also investigated. All of the experiments have been carried out on a specifically built, publicly available ear–arm database, including multibiometric captures of more than 100 subjects performed during different sessions, that represents an additional contribution of this paper. [ABSTRACT FROM AUTHOR]

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This paper presents the theoretical keystone for a decentralization of model-based diagnosis by proving the equivalence between decentralized and centralized residual generation. The proof is based on structural analysis and graph-theoretical concepts. The second contribution of this paper is the design of a decentralized fault-focused residual generation scheme advantageously implementing a strategy of fault isolation on request. Algorithms are tested on the attitude determination and control system of a low Earth orbit satellite. [ABSTRACT FROM PUBLISHER]

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]

This paper aims to build a simple and low-cost system by using images to detect human breath in a real-time fashion to estimate the peak of the inspiratory phase of a breath so as to define a proper triggering timing for X-ray shooting. In fact, it is very difficult to detect very small breathing motion on images. In this paper, well-known techniques are employed to obtain useful features from the chest area for the Lucas–Kanade algorithm. Various levels of the Pyramidal Lucas–Kanade are then adapted to track possible small motions of those features. The proposed approach can successfully detect the inspiratory–expiratory motions and the peak time of inspiratory phase can be predicted within an acceptable interval of error time. From the experiments conducted, the breath motion can be successfully observed in two different environment situations (dim-lighting and lighting conditions). It can be found that the tracked features are quite robust and stable without losing the quantity over a long period of testing time. Thus, the proposed approach can effectively be used to define a proper triggering timing for X-ray shooting. Besides, in our experiments, even though the target is 6 m away, the breath detection is still successful. In other words, the proposed approach can also be used for surveillance or healthcare environments. [ABSTRACT FROM AUTHOR]

In the system and control community, the sliding mode control technology has been known as a tool which is able to handle system uncertainties and external disturbances. Simplicity in implementation and good stability performance are the other interesting features of the sliding modes. However, traditional sliding mode controllers exhibit two weaknesses, as follows: 1) generally, they have asymptotic stability in sliding mode dynamics which results in a slow speed system and 2) the so-called chattering phenomenon exists, which produces undesirable high frequency oscillations in the control signal. In this paper, the former problem is solved using a twofold terminal sliding manifold displaying desired transient and steady state behavior. The latter issue is responded to by a twofold sliding mode technology which leads to a continuous sliding control rule instead of a discontinuous switching control. The effects of uncertain terms as well as external perturbations with unknown bounds are fully compensated using updated parameters. The values of the updated parameters approach fixed values as the system trajectories converge to the equilibrium states. The analytical results of this paper are theoretically proved using the Lyapunov technique and the finite-time control strategy. Comparative computer simulations on a robotic manipulator confirm the fast convergence attribute and robust performance of the introduced adaptive robust nonsingular terminal sliding control algorithm. [ABSTRACT FROM AUTHOR]

Tidal power is an emerging technology with great potential to provide a sustainable means of renewable energy in many areas worldwide. However, the nature of the underwater environment provides challenges. Submerged machinery cannot be easily accessed for inspections, and turbines must be brought to the surface for maintenance. This is an expensive process and results in prolonged periods of downtime where no power can be supplied to the grid. Condition monitoring systems, capable of accurately and remotely assessing the health state of machinery while in operation, can therefore be of great value to this industry. This paper presents an approach for condition monitoring of a tidal turbine’s gearbox from monitoring data with low sample rates. Models of normal behavior were trained using weighted least squares regression, where prediction errors are used to identify changes in response. This paper then examines how prediction errors from a number of different cases (including changes in control scheme and simulated gearbox faults) can be interpreted by operators to classify anomalous behavior. [ABSTRACT FROM AUTHOR]

Nowadays, the increased amount of users’ devices produce huge volumes of data that should be efficiently managed by modern applications. Streams are adopted to deliver data that, usually, are stored into a number of partitions. Splitting the data offers a lot of advantages as applications can process them in parallel, thus, they increase the speed of processing. Progressive analytics are also adopted to deliver partial responses, during processing, thus, saving time in the execution of applications. Data exploration and analytics queries are very significant for future applications. Usually, such queries demand for an ordered set of objects as a response and require intelligent predictive schemes to deliver the responses on top of the partial results retrieved by the distributed data partitions. A finite set of query processors are adopted to produce these partial results. Processors are placed in front of each partition and report progressive analytics to a central entity. In this paper, we envision the query controller (QC) as the central entity that collects progressive analytics and return the final response to users/applications. The QC receives partial ordered sets of objects and aggregates them to derive the final outcome. We focus on a QC that applies time-optimized techniques and aggregation operators to deliver every response, i.e., ordered sets, over streams of partial ordered subsets. We perform a comprehensive performance assessment with synthetic data and report on the performance of the QC. Our experimental evaluation reveals the pros and cons of the proposed model and a comparison assessment places this paper in the respective literature. [ABSTRACT FROM AUTHOR]

This paper describes a Markov decision process approach to a robust spacecraft mission control policy that maximizes the expected value of science reward assuming a circular orbit. The control policy that governs mission steps can be computed off-board or onboard depending upon the availability of communication bandwidth and on-board computational resources. This paper considers a sample science mission, where the spacecraft collects data from celestial objects viewable only within a certain orbit true anomaly window. Science data collection requires the spacecraft to slew its instrument(s) toward each target, and continue pointing in the direction of the target while the spacecraft traverses its orbit. Robustness and stochastic optimization of scientific reward, is achieved at the cost of computational complexity. Approximate dynamic programming (ADP) is exploited to reduce the computational time and effort to manageable levels and to treat larger problem sizes. The proposed ADP algorithm partitions the state-space based on true anomaly regions, enabling grouping of adjacent science targets. Results of a simulation case study demonstrate that our proposed ADP approach performs quite well for reasonable ranges of key problem parameters. [ABSTRACT FROM AUTHOR]

In this paper, feasibility study along with conceptual design of missile-borne synthetic aperture radar (SAR) has been accomplished, which can effectively improve the detection capability and the blow precision of a missile. First, SAR echo signal modeling, geometry configuration for missile-borne SAR scenario, and an appropriate image formation algorithm are presented. Then, a system block diagram along with complete discussions about different parts and system design considerations of the SAR seeker are explained. Finally, simulation of the whole system along with raw data generation considering the imaging geometry of the missile-borne SAR seeker has been performed. Experimental results show that the proposed system is capable of imaging and targeting in the complicated geometry of missile-borne SAR. [ABSTRACT FROM AUTHOR]

Operational decisions made by manufacturers and retailers are often affected by the power relationships of both vertical and horizontal competitions in a supply chain. Although many studies have been carried to assess how the power structure impacts supply chains, most of them only focus on the power dynamics in the vertical competition or horizontal competition. This paper systematically examines the impacts of bidimensional power structure on a supply chain. Considering a triadic setting that is made of two rival retailers and a common manufacturer, we compare the equilibriums for the supply chain and individual firms with five different possible power structures. The analysis results show that the bidimensional power dynamics have a significant impact. Furthermore, the impacts of the bidimensional power structure are also dependent on the nature of customer demand. This paper provides important management implications that are supportive for companies to make strategic and operational decisions. [ABSTRACT FROM AUTHOR]

In this paper, based on the Takagi–Sugeno fuzzy model approach, a novel output feedback control strategy is developed for a class of continuous-time nonlinear time delay systems via modified repetitive control scheme. The main objective of this paper is to design a fuzzy rule-based modified repetitive controller such that the system output can track any given periodic reference signal even in the presence of periodic disturbances. For this purpose, by employing fuzzy-dependent Lyapunov–Krasovskii functional together with the free-weighting matrix technique, a new set of sufficient conditions is presented in terms of linear matrix inequalities to guarantee the robust stability of the resulting closed-loop system. Based on these conditions and using internal model principle, it is shown that the system output tracks the given reference input within the desired error. Finally, two application-oriented examples are presented to validate the obtained theoretical results in which the proposed control design scheme gives better performance over the existing control scheme. [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 considers the sample-data-based consensus problem of multiagent systems with time-varying delay and packet losses. To distinguish the time delays caused by network-induced time-delay and packet losses, the switched system is utilized. A lower gain controller is designed based on the solution of a parametric algebraic Riccati equation. The Lyapunov stability theory is utilized to obtain the limitations on the frequency and the duration of packet losses which guarantees the consensus of multiagent systems. On the other hand, we theoretically prove that refined time-delay function can reduce the conservatism. A simulation example is given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The emergence of Internet of Things (IoT) and new manufacturing paradigms have brought greater complexity of massive datasets. Radio frequency identification (RFID), as one of the key IoT technologies, has been used to collect real-time production data to support the manufacturing decision-making in smart factories. The adoption of these technologies results in a large amount of data collection. To extract useful information from this data, this paper utilizes a big data approach to figure out useful insights from RFID-enabled data regarding possible bottlenecks or inefficiencies on the shop floor so as to improve the quality management. Time and quality are the main metrics measured in this paper, where the longest process times, part accuracy percentage, and failure rate are determined for each of the workers (UserIDs) and process types (ProcCodes). Key findings and observations are significant to make advanced decisions in the smart factory by making full use of the RFID captured data. Note to Practitioners— This paper was motivated by a real-life company which has used RFID technology for over ten years. Great myriad of data has been captured from frontline production shop floors where machines, workers, materials, and manufacturing jobs were logically associated. Processing time and quality data are studied in this paper so as to investigate the production performance in the RFID-enabled smart factory. [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]

Decision support is a vital function in electronic commerce (e-commerce). The purpose of this paper is to construct a review-based decision support model for items comparison in e-commerce. The proposed model uses probability multivalued neutrosophic linguistic numbers (PMVNLNs) to characterize online reviews. It overcomes the limitation of existing models by considering neutral information and hesitancy in text reviews. The fuzzy characterization of reviews (i.e., PMVNLN) can reflect similarities and differences in positive (negative) information. In addition, the model considers consumers’ bounded rational behaviors by combining the regret theory with an outranking method. We empirically compare the proposed model with models in http://PConline.com and four existing models with data from http://PConline.com. The performance of these models in terms of accuracy is measured by the total relative difference metric. Results indicate the good performance of the proposed model. Our model is a promising option for e-commerce to provide consumers with good decision support service. [ABSTRACT FROM AUTHOR]

This paper investigates the output feedback $ {\mathcal {H}_{\infty }}$ control problem for a class of continuous-time nonlinear systems with both output and input quantization. The nonlinear plant is represented by a Takagi–Sugeno fuzzy model. The measurement output and the control input signals will be quantized by dynamic quantizers. The attention of this paper is focused on the design of the output feedback controller and quantizers such that the fuzzy closed-loop system is asymptotically stable and also achieves a prescribed $ {\mathcal {H}_{\infty }}$ noise attenuation level with respect to the effect of quantization. In the presence of quantization, two novel $ {\mathcal {H}_{\infty }}$ performance analysis criteria are presented for the considered fuzzy systems based on the descriptor representation approach. Then, sufficient conditions for the existence of the feedback $ {\mathcal {H}_{\infty }}$ controller gain and the quantizers’ dynamic parameters are expressed in terms of linear matrix inequalities. An adjusting scheme on quantizers’ parameters is proposed for the fuzzy system. Simulation examples are provided to show the effectiveness of the proposed design method. [ABSTRACT FROM AUTHOR]

In this paper, we propose an acceleration-level pseudo-dynamic visual servoing structure for the nonholonomic mobile robots, based on which we design two different adaptive controllers—backstepping and dynamic surface control (DSC) in the presence of unknown depth information. Different from existing kinematic controllers, which directly regard linear and angular velocities as control inputs, this paper designs acceleration control that is integrated to easily obtain smooth velocity signals to be accurately executed by the robot. Two controllers are designed and analyzed with Lyapunov techniques: 1) a backstepping controller yielding asymptotical stability and 2) a dynamic surface controller ensuring system errors to be ultimately uniformly bounded. The unknown depth is handled by designing an adaptive parameter estimation law in both methods. Finally, a comparison between backstepping and DSC is given based on the experimental results and the design procedures. [ABSTRACT FROM AUTHOR]

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This paper reconsiders the iterative learning control (ILC) problem for variable trial lengths via compensating output data by using an auxiliary predictive model when the controlled process does not reach the desired trial length. Moreover, this paper aims to propose a general and data-driven ILC method without requiring any explicit mechanistic model information. Specifically, an iterative difference with state transition expression is performed at first over the desired trial length in iteration domain to build an auxiliary predictive model for the iterative input–output dynamics of the linear discrete-time system. Then, an auxiliary predictive compensation-based ILC (APC-ILC) method is presented by defining an expanded output variable in which the predictive output is incorporated to compensate the unavailable output data due to the shorter operation length. The learning gain is iteration-time-varying and is updated using real-time data to adapt to system changes. Furthermore, the proposed learning control law contains additional input information to further improve the control performance. Theoretical analysis and simulations further verify the effectiveness of the proposed APC-ILC. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]