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To drive a paradigm shift towards a human-centric approach in remanufacturing systems and promote energy-efficient, carbon-reduction-friendly upgrades, this paper introduces a stochastic human–robot collaborative disassembly line balancing problem for real-world disassembly lines characterized by uncertain operation times. The developed mathematical model takes into account task priority relationships, task attributes, human–robot collaboration, and cycle time constraints, with the goal of optimizing workstation numbers, smoothing indices, and carbon emissions. Subsequently, we propose a hybrid evolutionary algorithm (HEA) to efficiently address this problem. The algorithm includes methods for generating initial solutions and decoding them based on random disassembly time. Additionally, an enhanced differential evolution operation is integrated into the non-dominated sorting genetic algorithm II (NSGA-II) framework to improve the algorithm's evolutionary and search capabilities. Validation experiments involving 21 benchmark cases demonstrate that HEA significantly outperforms seven other algorithms reported in the existing literature. Finally, the proposed algorithm is applied to a printer case comprising 55 components. The results underscore the algorithm's superior search capabilities compared to the innovative hybrid algorithm, genetic simulated annealing algorithm, as well as traditional NSGA-II and evolutionary algorithms, under both low and high randomness conditions. • The carbon emission evaluation index was developed for HRC-DLBP. • Incorporating product- and operator-induced uncertainty into HRC-DLBP. • Developed a multi-objective hybrid evolutionary algorithm for SHRC-DLBP. • Apply the proposed method to the discarded printer disassembly case. [ABSTRACT FROM AUTHOR]

Abstract Relevance-Based Language Models are a formal probabilistic approach for explicitly introducing the concept of relevance in the Statistical Language Modelling framework. Recently, they have been determined as a very effective way of computing recommendations. When combining this new recommendation approach with Posterior Probabilistic Clustering for computing neighbourhoods, the item ranking is further improved, radically surpassing rating prediction recommendation techniques. Nevertheless, in the current landscape where the number of recommendation scenarios reaching the big data scale is increasing day after day, high figures of effectiveness are not enough. In this paper, we address one urging and common need of recommendation systems which is algorithm scalability. Particularly, we adapted those highly effective algorithms to the functional MapReduce paradigm, that has been previously proved as an adequate tool for enabling recommenders scalability. We evaluated the performance of our approach under realistic circumstances, showing a good scalability behaviour on the number of nodes in the MapReduce cluster. Additionally, as a result of being able to execute our algorithms distributively, we can show measures in a much bigger collection supporting the results presented on the seminal paper. [ABSTRACT FROM AUTHOR]

Classical methods of medical image super-resolution (MISR) utilize open-loop architecture with implicit under-resolution (UR) unit and explicit super-resolution (SR) unit. The UR unit can always be given, assumed, or estimated, while the SR unit is elaborately designed according to various SR algorithms. The closed-loop feedback mechanism is widely employed in current MISR approaches and can efficiently improve their performance. The feedback mechanism may be divided into two categories: local feedback and global feedback. Therefore, this paper proposes a global feedback-based closed-cycle framework, circular MISR (CMISR), with unambiguous UR and advanced SR elements. Mathematical model and closed-loop equation of CMISR are built. Mathematical proof with Taylor-series approximation indicates that CMISR has zero recovery error in steady-state. In addition, CMISR holds plug-and-play characteristic that fuses model-based and learning-based approaches and can be established on any existing MISR algorithms. Five CMISR algorithms are respectively proposed based on the state-of-the-art open-loop MISR algorithms. Experimental results with three scale factors and on three open medical image datasets show that CMISR is superior to MISR in reconstruction performance and is particularly suited to medical images with strong edges or intense contrast. [ABSTRACT FROM AUTHOR]

This paper presents an offline optimization method designed for use with industrial robots in environments with static obstacles. It is particularly useful in industry where stability and predictability are crucial to meeting expected timelines in automated guided vehicle operations. The main methodological contribution of this work lies in the integral process used to define an effective fitness function that guides the optimization method in the search for optimal solutions. This cost function plays a critical role in the effectiveness of the trajectory tracking algorithm by quantifying path quality and allowing comparisons between solutions. The design of this fitness function poses challenges including accuracy, suitability, minimization of path length, and avoiding or reducing collisions. To achieve the optimization objectives and address some issues such as sensitivity to parameter scaling and the risk of premature convergence, different approaches can be used. This work proposes to incorporate constraints into the fitness function, adjust the optimization parameters to reflect the conditions of the problem, and design a fitness function based on prior knowledge and an accurate representation of the goals. The three relevant contributions for the planning and optimization of routes of automated guided vehicle in industrial environments are the following. Firstly, the development of a mathematical model of trajectories based on Frenet curves that considers the static occupancy map of the environment. Second, an optimization strategy to generate optimal safe paths. Finally, a fitness function that guides the optimization method towards optimal solutions considering the sensitivity to scaling and resolution of the parameters. This study presents an exhaustive analysis of the different fitness functions obtained, each one evaluated based on key metrics such as the length of the trajectory, the average and minimum distance to the occupancy map, and the number of collisions along the path. The results show that the obtained cost function successfully avoids collisions with the environment in all scenarios and consistently remains the fitness function with the largest average distance to obstacles, at least 50% higher than other functions used in this study. [ABSTRACT FROM AUTHOR]

Classical methods of medical image super-resolution (MISR) utilize open-loop architecture with implicit under-resolution (UR) unit and explicit super-resolution (SR) unit. The UR unit can always be given, assumed, or estimated, while the SR unit is elaborately designed according to various SR algorithms. The closed-loop feedback mechanism is widely employed in current MISR approaches and can efficiently improve their performance. The feedback mechanism may be divided into two categories: local feedback and global feedback. Therefore, this paper proposes a global feedback-based closed-cycle framework, circular MISR (CMISR), with unambiguous UR and advanced SR elements. Mathematical model and closed-loop equation of CMISR are built. Mathematical proof with Taylor-series approximation indicates that CMISR has zero recovery error in steady-state. In addition, CMISR holds plug-and-play characteristic that fuses model-based and learning-based approaches and can be established on any existing MISR algorithms. Five CMISR algorithms are respectively proposed based on the state-of-the-art open-loop MISR algorithms. Experimental results with three scale factors and on three open medical image datasets show that CMISR is superior to MISR in reconstruction performance and is particularly suited to medical images with strong edges or intense contrast. [ABSTRACT FROM AUTHOR]

Synergetic control theory is introduced into hydraulic generator excitation system (HGES) and hydraulic generator governing system (HGRS) in this paper. Synergetic excitation controller (SEC), synergetic governing controller (SGC) of HGU have been designed. In order to enhance the terminal voltage control and mechanical power tracking performances simultaneously, the integrated synergetic controller (ISC) is also proposed. ISC implements synergetic control of terminal voltage, rotor speed, mechanical input power and guide vane opening. Namely, the ISC is considering both of the excitation system and governing system of hydraulic generator unit (HGU), which can provide control function instead of SEC and SGC. In addition, the control rules of the aforementioned three controllers are deduced from the nonlinear mathematical analytic model of hydroelectric generator unit. At the end of this paper, comparative case studies between the proposed SGC, SEC, ISC and classic PID controller are presented. The results show that the proposed ISC improves the nonlinear HGU system performance with a more accurate precision and shorter settling time in different operating conditions. [ABSTRACT FROM AUTHOR]

The second-hand vessel market has quite different dynamics than the market of the new-building vessel, and highly complicated and conflicting criteria and many uncertainties affect the ship selection process. Therefore, it is required to use a robust mathematical model to solve these kinds of decision-making problems. For this purpose, this paper presents an extended version of WASPAS (Weighted Aggregated Sum Product ASsessment) techniques with the help of T2NN based on the Bonferroni function (T2NN WASPAS'B). The three main focal points of the proposed approach are (i) setting the influential criteria to select the appropriate Ro-Ro vessel in the second-hand vessel market; and (ii) presenting a flexible group decision-making approach, which is proper to real decision-making problems. (iii) detecting the interrelations among criteria and eliminating the negative impacts of undesirable and excessive values in input variables on the results. Practical use of the proposed approach is demonstrated to select the appropriate Ro-Ro vessel in the second-hand market. The analysis results show that the most effective and determinative factor is Trailer Lane length, and the most effective alternative is GREIFSWALD. Besides, the consistency and validity of the obtained results have been verified with the help of a stability and robustness check. The results prove that the proposed novel T2NN WASPAS'B model is robust, powerful, and reliable for making rational and realistic decisions. • The current paper presents the T2NN WASPAS'B approach strengthen with the help of T2NN based on the Bonferroni function. • The proposed model is applied to solve second-hand Ro-Ro vessel selection problems, which are highly complex and include uncertainties. • It has identified the selection criteria for second-hand Ro-Ro ships for the first time. • It presents a flexible decision-making approach, which is proper for real decision-making problems. • It demonstrates interrelations among factors to make it more understandable in complex situations. [ABSTRACT FROM AUTHOR]

This paper presents a new paradigm for modeling the multi-traffic signal-control synchronization problem using game theory based on the extraproximal method. The objective in a crossing is to minimize the queuing delay and the problem for a signal controller is to find an optimal signal timing strategy, i.e. establishing green timing for each signal phase. Signal controllers are considered the players of the game. Each intersection aims at finding the green time that minimizes its signal and queuing delay. The problem presents natural restrictions: (a) the number of entering cars and exiting cars is different for each street of the intersection and (b) the interval of time given for vehicles to the green light is equal to the red light in the respective opposite direction . The first restriction determines a leader–follower Stackelberg game model: streets having more traffic require more green time. The last restriction establishes a simultaneous-solution of the game as a better consideration of the real situation. Then, to take advantage of the structure of the game, the paper shows that the solution is given by a Nash equilibrium. We introduce the c -variable method for finding the optimal signal timing distribution and making the problem computationally tractable. The extraproximal method is a two-step iterated procedure: (a) the first step calculates a preliminary approximation to the equilibrium point, and (b) the second step is designed to find an adjustment of the previous step. The formulation of the game is given in terms of coupled nonlinear programming problems implementing the Lagrange principle. Tikhonov׳s regularization method is employed to ensure the convergence of the costfunctions to a Nash equilibrium point. In addition, the extraproximal method is developed in terms of Markov chains. The usefulness of the method is demonstrated by a three-way intersection example. The contributions have major implications for real-world applications. [ABSTRACT FROM AUTHOR]

In this paper, the performance of different ordinal and nominal multi-class classifiers is evaluated, in a problem of wave energy range prediction using meteorological variables from numerical models. This prediction could be used in problems of wave energy conversion in renewable and sustainable systems for energy supply. Specifically, the work is focused on ordinal classifiers , that have provided excellent performance in previous applications. The proposed techniques are novel with respect to alternative classification and regression techniques used up to date, the former not considering the order relation between classes in a multi-class problem and the latter needing, in general, more complex models. Another important novelty of the paper is to consider meteorological variables from numerical models as inputs of the classifiers, which has not been done before, to our knowledge, in this context. For this, a data matching is carried out between meteorological data, obtained from NCEP/NCAR Reanalysis Project in four points around the two buoys subjected to study (a buoy in the Gulf of Alaska and another one in the Southeast of United States), and the wave height or wave period collected by sensors in each buoy. Using this matching, the problem is tackled as an ordinal multi-class classification problem and the objective is to predict the range of height of the wave produced in each buoy and the range of energy flux generated. The classifiers to be compared and the model proposed are fully evaluated in both buoys. The results obtained are promising, showing an acceptable reconstruction by ordinal methods with respect to nominal ones in terms of wave height and energy flux. [ABSTRACT FROM AUTHOR]

The paper deals with the problem of robust fault detection using recurrent neural networks and chaos engineering. The main part of the proposed approach is a locally recurrent neural network that is composed of complex dynamic neural units for which chaotic behaviour can be obtained. Selected global and local optimization methods are connected to have diphase strategies for training this kind of neural networks. And beyond this, chaos engineering is incorporated into both the evolutionary and simulated annealing algorithm in order to improve the efficiency of the tuning procedure. The problem of selecting the most relevant input variables is solved by means of extended Hellwig׳s method of integral capacity of information. Criteria isolines and a sensitive-based method are used to identify the suitable architecture of a neural network. Moreover, the issue of stability analysis of neural models is also considered in this paper. Recurrence quantification analysis is proposed for residual evaluation in order to have the comprehensive methodology of neural model-based fault detection. The preliminary verification of the elaborated methodology in modelling tasks was carried out for both simulation and industrial data. The fundamental verification was conducted for the data made available within DAMADICS benchmark problem. The achieved results confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Influence maximization is the problem of finding a small set of nodes that maximizes the aggregated influence in social networks. The problem of influence maximization in social networks has been explored in many previous researches. They have mainly relied on similar temporal chances for every node to influence another; whereas in reality, time plays a major role in pairwise propagation rates in social networks. However, there is little research done on influence maximization considering temporal dynamics of the networks and existing approaches merely offers a mediocre performance due to ignoring trust aspects of the diffusion process. In this paper, we propose a Trust based Latency aware Influence Maximization model, abbreviated as TLIM, which selects the most influential nodes in social networks with considering time and trust simultaneously. To the best of our knowledge, we are the first to study trust in classic IC model and also the first to consider both important time and trust factors jointly in influence maximization problem. The main contributions of this paper are listed as follows: first, we extend the classic IC model to include time and trust simultaneously, which is more applicable in existing social networks. Second, we find the most influential nodes in social networks with considering time and trust together; and the last but not the least, it is applicable to well-known real social networks such as Epinions, Slashdot and Wikipedia. To explore the advantages of our approach, quite a lot of experiments with different specifications are conducted. The obtained results are very promising. [ABSTRACT FROM AUTHOR]

Model-based diagnosis in discrete event systems (DESs) is a major research topic in failure diagnosis, where diagnosability plays an important role in the construction of the diagnosis engine. To improve the solution efficiency for diagnosability, this paper proposes novel techniques to solve the problems of testing and optimizing for diagnosability. We propose a new concept, reverse twin plant, which is generated backwards from the final states of the DESs so there is no need to generate a complete copy of the DES model to determine the diagnosability. Such a design makes our testing algorithm much faster than existing methods. An efficient optimizing algorithm, which makes a non-diagnosable system diagnosable, is also proposed in the paper by expanding the minimal observable space with operation on just a part of the DES model. Examples and theoretical studies demonstrate the performance of the proposed designs. [ABSTRACT FROM AUTHOR]

In recent decades, research on supply chain management (SCM) has enabled companies to improve their environmental, social, and economic performance. This paper presents an industrial application of logistics that can be classified as an inventory-route problem. The problem consists of assigning orders to the available warehouses. The orders are composed of items that must be loaded within a week. The warehouses provide an inventory of the number of items available for each day of the week, so the objective is to minimize the total transportation costs and the costs of producing extra stock to satisfy the weekly demand. To solve this problem a formal mathematical model is proposed. Then a hybrid approach that involves two metaheuristics: a greedy randomized adaptive search procedure (GRASP) and a genetic algorithm (GA) is proposed. Additionally, a meta-learning tuning method is incorporated into our hybridized approach, which yields better results but with a longer computation time. Thus, the trade-off of using it is analyzed. An extensive evaluation was carried out over realistic instances provided by an industrial partner. The proposed technique was evaluated and compared with several complete and incomplete solvers from the state of the art (CP Optimizer, Yuck, OR-Tools, etc.). The results showed that our hybrid metaheuristic outperformed the behavior of these well-known solvers, mainly in large-scale instances (2000 orders per week). This hybrid algorithm provides the company with a powerful tool to solve its supply chain management problem, delivering significant economic benefits every week. [ABSTRACT FROM AUTHOR]

Since the start of COVID-19, a fair amount of work has been undertaken by scholars around the world to model its progression. It became clear from the start of pandemic that its progression is affected by various factors within different communities. Subsequently, the necessary means and the range of measures used to effectively control the virus would vary from place to place. And we have been witness to different approaches adopted around the world to maintain the virus under check both in the short term and the long term. So, in this unexpected situation, it is a great challenge for the world health organization (WHO) to save the lives of COVID-19 patients. For this, several mathematical models have been made for better understanding the coronavirus contagion. Mostly, these models are based on classical integer-order derivative using real numbers which cannot capture the fading memory. Thus, in this unexpected situation, fuzzy sets (FSs) are considered due to their inherent capability to deal with uncertainty. Fuzzy sets (FSs) theory has the ability to manage uncertain situations. Thus, the goal of this research is to present newly mathematical methods based on complex Pythagorean fuzzy sets (CPyFSs) and their operators, namely complex Pythagorean fuzzy Einstein weighted geometric operator, and induced complex Pythagorean fuzzy Einstein hybrid geometric operator to reduce the spreading rate of COVID-19. At the end of the paper an illustrative example is constructed to show the effectiveness, reliability of the new techniques. [ABSTRACT FROM AUTHOR]

Abstract: Techniques extracting topics from dynamic Internet are relatively matured. However, people cannot accurately predict topic trend so far. Unfortunately, for prediction of topic trend, the availability of data is always very limited owing to the short life circle of topics, especially in such a highly efficient and fast-paced era. Based on Grey Verhulst Model, the paper presents an algorithm to predict topics trend. The principle of Grey Model for prediction application is analyzed and Grey Verhulst Model is established. In the meanwhile, real-world data from Youku (the largest video site in China and something like YouTube) is applied to test our presented algorithm. The average relative error of Grey Verhulst Model is less than 3%. The results show that Grey Verhulst Model has a higher prediction precision. The main contributions of this paper are as follows. First, we introduce Grey System Theory (GST) originated from system theory to the prediction of topics trend and to some extent, solve the problem with a high accuracy; second, to the best of our knowledge, it is the first attempt to employ GST in the field of topic trend prediction. [Copyright &y& Elsevier]

Abstract: This paper presents the development of a decision support system for monitoring and diagnosis of the amine-based post-combustion carbon dioxide (CO2) capture process system at the International Test Centre for CO2 Capture (ITC) at the University of Regina. The amine-based CO2 absorption capture process system consists of dozens of components and generates more than a hundred different types of data. The vast amount of raw data produced by the system are measurements of the many reaction components, valves and pumps. The system operators often find it difficult to quickly detect, diagnose and correct any abnormal conditions that may arise during operation. Therefore, developing a decision support system for monitoring and diagnosis of the CO2 capture process system which aids the operator in monitoring and diagnosis of the system is a desirable objective. This paper describes development of the system based on the dual foundation of a domain ontology and an intelligent system framework in the domain of carbon dioxide capture process. The developed ontology provides the semantic knowledge foundation; it was implemented in the Knowledge Modeling System (KMS), and the knowledge was stored in the XML format. The intelligent system framework consists of system functions which can use the XML schema provided by the ontology and support the development process. A decision support expert system for process monitoring is a sample system developed on the dual foundation. [Copyright &y& Elsevier]

Abstract: This paper addresses the topic of model based design of experiments for the identification of nonlinear dynamic systems. Data driven modeling decisively depends on informative input and output data obtained from experiments. Design of experiments is targeted to generate informative data and to reduce the experimentation effort as much as possible. Furthermore, design of experiments has to comply with constraints on the system inputs and the system output, in order to prevent damage to the real system and to provide stable operational conditions during the experiment. For that purpose a model based approach is chosen for the optimization of excitation signals in this paper. Two different modeling architectures, namely multilayer perceptron networks and local model networks are chosen and the experiment design is based on the optimization of the Fisher information matrix of the associated model architecture. The paper presents and discusses feasible problem formulations and solution approaches for the constrained dynamic design of experiments. In this context the effects of the Fisher information matrix in the static and the dynamic configurations are discussed. The effectiveness of the proposed method is demonstrated on a complex nonlinear dynamic engine simulation model and an analysis as well as a comparison of the presented model architectures for model based experiment design is given. [Copyright &y& Elsevier]

Abstract: Agent Platforms are the software that supports the development and execution of Multi-agent Systems. There are many Agent Platforms developed by the agent community, but they hardly consider privacy. This leads to agent-based applications that invade users’ privacy. Privacy can be threatened by two main information activities: information collection and information processing. Information collection can be prevented using traditional security mechanisms. Information processing can be prevented by minimizing data identifiability, i.e., the degree by which personal information can be directly attributed to a particular individual. However, minimizing data identifiability may directly affect other crucial issues in Multi-agent Systems, such as accountability, trust, and reputation. In this paper, we present the support that the Magentix2 Agent Platform provides for preserving privacy. Specifically, it provides mechanisms to avoid information collection and information processing when they are not desired. Moreover, Magentix2 provides these mechanisms without compromising accountability, trust, and reputation. We also provide in this paper an application built on top of Magentix2 that exploits its support for preserving privacy. Finally, we provide an extensive evaluation of the support that Magentix2 provides for preserving privacy based on that application. We specifically test whether or not privacy loss can be minimized by using the support that Magentix2 provides, whether or not this support introduces a bearable performance overhead, and whether or not existing trust and reputation models can be implemented on top of Magentix2. [Copyright &y& Elsevier]

Abstract: This paper considers the Dynamic Berth Allocation Problem, in which vessels are assigned to discrete positions in berths. This problem, whose goal is to minimize the total time the vessels stay at the port, constitutes one of the most important processes at any containers terminal. We propose a hybrid metaheuristic that combines Tabu Search with Path Relinking, . The results reached by this hybrid algorithm are compared with the optimal values given by the best mathematical model that appears in the literature for this problem, GSPP, and with a tabu search algorithm from the literature, . For small instances, the algorithm is able to obtain most of the optimal solutions in an amount of computational time that is lower than the time required to solve the GSPP model. For medium and large size instances, GSPP cannot be solved to optimality, whereas the proposed hybrid algorithm outperforms . Moreover, the computational experiments carried out in this paper confirm the robustness of the proposed algorithm with respect to both the parameters governing the procedure and the problem size. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to improving the overall aeroacoustic comfort of a vehicle, an important feature of vehicle design. The traditional improvement process is extended to benefit extensively from machine learning, information retrieval and information extraction technologies to assist the wind tunnel engineers with difficult tasks. The paper first describes the general approach and then focuses on providing a detailed description of the most important task of assessing the degree of discomfort for a human caused by wind noise in a vehicle, when the noise spectrum is known. For this purpose a novel approach of learning linear regression models that are consistent with expert''s domain knowledge is presented. The results of the end user evaluation of the entire system are also presented to reflect the strengths of this approach. [Copyright &y& Elsevier]

Abstract: In this paper, a multidimensional 0–1 knapsack model with fuzzy parameters is defuzzified using triangular norm (t-norm) and t-conorm fuzzy relations. In the first part of the paper, the surrogate relaxation models of the defuzzified models are developed, and the use of surrogate constraint normalization rules is proposed as the surrogate multipliers. A methodology is proposed to evaluate some surrogate constraint normalization rules proposed in the literature as well as one rule proposed in this paper. Three distance metrics are used to find the distance of fuzzy objective function from the surrogate models to the distance of fuzzy objective function from the original models. A numerical experiment shows that the rule proposed in this paper dominates the other rules considered in this paper for three distance metrics given the whole assumptions. In the second part of the paper, a methodology is proposed for multi-attribute project portfolio selection, and optimal solutions from the original defuzzified models as well as near-optimal solutions from their surrogate relaxation models are considered as alternatives. The aggregation of evaluation results is managed using a simple yet effective method so-called fuzzy Simple Additive Weighting (SAW) method. Then, the methodology is applied to a hypothetical construction project portfolio selection problem with multiple attributes. [Copyright &y& Elsevier]

Variable Selection in large datasets is a commonly procedure in multivariate calibration, which is a field of study from chemometrics. Selecting the most informative variables becomes an important step to build mathematical models through statistical techniques in order to predict some property of interest from an analyzed sample. Recombination-based search methods such as Genetic Algorithms (GAs) have been widely used as variable selection techniques to solve several optimization problems. However, previous works from literature have emphasized the schemata disruption problem caused by genetic operators. Therefore, this paper proposes two versions of an epistasis-based implementation (EbFSA) as a novel approach for variable selection in multivariate calibration problems, where each version is deterministic and performs a different strategy. The use of epistasis concepts becomes important to assess the genes (variables) interdependence. Based on our experimental results, we are able to claim EbFSA can select the most informative variables and overcome some state-of-the-art algorithms. • A novel approach for variable selection in multivariate calibration problems is presented. • We present two versions of an Epistasis-based implementation (EbFSA). • A comparison with traditional algorithms is performed. • We obtained advantages by reducing the number of variable as well as the prediction error. • We experimentally demonstrated that EbFSA can be a good choice for the variable selection problem. [ABSTRACT FROM AUTHOR]

Abstract: There is a chronic lack of shared application domains to test advanced research models and agent negotiation architectures in Multiagent Systems. In this paper we introduce a friendly testbed for that purpose. The testbed is based on The Diplomacy Game where negotiation and the relationships between players play an essential role. The testbed profits from the existence of a large community of human players that know the game and can easily provide data for experiments. We explain the infrastructure in the paper and make it freely available to the AI community. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to present a model-based methodology to estimate the optimal amount of overlapping and communication policy with a view to minimizing product development lead time and cost. In the first step of methodology, the underlying two factors are considered in order to formulate mathematically a multi-objective function for a complete product development project. To add these objectives, incommensurate in nature, a fuzzy goal programming-based approach is adopted as the second step. In order to attain the optimal solution of formulated objective function, this paper introduces a novel approach, “Gaussian Adaptive Particle Swarm Optimization” (GA-PSO), which is embedded with two beneficial attributes: (1) Gaussian probability distribution, and (2) Time-Varying Acceleration Coefficients strategy. An illustrative hypothetical example of mobile phones is detailed to demonstrate the proposed model-based methodology. Experiments are performed on an underlying example, and computational results are reported to support the efficacy of the proposed model. [Copyright &y& Elsevier]

Abstract: The precision, performance, and robustness of model-based controllers depend, to a large extent, on the accuracy of the inverse dynamic model which is incorporated in the design of the controller. Due to complex nature of the laser cladding process and presence of time-varying uncertainties, derivation of an accurate mathematical inverse dynamic model of the process is very difficult, and involves many unknown parameters. The inverse dynamic model of the complex nonlinear laser cladding process, which is difficult to be described mathematically, can be described by a fuzzy logic-based inverse dynamic model constructed form input–output data. In this paper, the development of an adaptive fuzzy inverse dynamic model of the laser cladding process, using a systematic fuzzy modelling approach is presented. In a closed-loop laser cladding process, the scanning speed of the substrate is required to produce a clad with desired geometry and quality. In this paper, a fuzzy inverse dynamic model that describes the scanning speed as a function of the cladding parameters in particular the clad height is developed. The developed fuzzy model is validated by comparing the model output with experimental data. The results are very promising and show that fuzzy models can accurately describe the process dynamics. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a driving danger-level prediction system that uses multiple sensor inputs and statistical modeling to predict the driving risk. Three types of features were collected for the research, specifically the vehicle dynamic parameter, the driver''s physiological data and the driver''s behavior feature. To model the temporal patterns that lead to safe/dangerous driving state, several sequential supervised learning algorithms were evaluated in the paper, including hidden Markov model, conditional random field and reinforcement learning. Experimental results showed that using reinforcement learning based method with the vehicle dynamic parameters feature outperforms the rest algorithms, and adding the other two features could further improve the prediction accuracy. Based on the result, a live driving danger-level prediction prototype system was developed. Compared to many previous researches that focused on monitoring the driver''s vigilance level to infer the possibility of potential driving risk, our live system is non-intrusive to the driver, and hence it is very desirable for driving danger prevention applications. Subjective on-line user study of our prototype system gave promising results. [Copyright &y& Elsevier]

Abstract: In the past there has been no structured approach to learning from construction projects once they are completed. Now, however, the construction industry is adapting concepts of tacit and explicit knowledge management to improve the situation. Top managers generally assume that professionals in enterprises already possess tacit knowledge and experience for specific types of projects. Such knowledge is extremely important to organisations because, once a project is completed, professionals tend to forget it and start something new. Therefore, knowledge multifold utilisation is a key factor in productively executing a construction project. This paper discusses the benefits of knowledge management to construction industry organisations and projects and emphasises the significance of tacit knowledge. The main purpose of this paper is to present the integrated knowledge management model for the construction industry as well as system architecture and system of the Knowledge Based Decision Support System for Construction Projects Management (KDSS-CPM) which the authors of this paper have developed. Different knowledge management models that are presented in scientific literature are discussed and compared, and the proposed new, KDSS-CPM model, as developed by this paper’s authors, is introduced. [ABSTRACT FROM AUTHOR]

The recycling of the used products collected by the manufacturing companies for recycling from their customers and their inclusion in the production system for remanufacturing has become an essential topic regarding sustainable production systems. Using parts and equipment in the remanufacturing system, especially in electronic products and products in the automotive sector, is preferred because of the opportunities to reduce costs and increase energy and workforce efficiency. In this article, we analyzed the management of a hybrid production system that processes new or returned materials in the manufacturing system. An intuitionistic fuzzy linear mathematical model is developed to demonstrate financial benefit and make decisions about using used parts in remanufacturing systems. Our study contributes to the literature by first adapting the magnitude function to the hybrid manufacturing and remanufacturing process optimization problem. Also, the innovative scope of the paper is expanded by applying IVITFN to the remanufacturing process optimization problem for the first time using comparative analysis. We consider relevant costs, the uncertainty about the number of returned products, demand, unit costs and unit production times, and capacity limitation in determining the optimal operation condition. The proposed model results show smaller cost values against other models in the literature. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a global manifold margin learning approach for data feature extraction or dimensionality reduction, which is named locally linear representation manifold margin (LLRMM). Provided that points locating on one manifold are of the same class and those residing on the corresponding manifolds are varied labeled, LLRMM is desired to identify different manifolds, respectively. In the proposed LLRMM, it firstly constructs both a between-manifold graph and a within-manifold graph. In the between-manifold graph, for any point, its k nearest neighbors and itself must belong to different manifolds. However, any node and its neighborhood points should be on the same manifold in the within-manifold graph. Then we use the minimum locally linear representation trick to reconstruct any node with their corresponding k nearest neighbors in both graphs, from which a between-manifold graph scatter and a within-manifold graph scatter can be reasoned, followed by a novel global model of manifold margin. At last, a projection will be explored to map the original data into a low dimensional subspace with the maximum manifold margin. Experiments on some widely used face data sets including AR, CMU PIE, Yale, YaleB and LFW have been carried out, where the performance of the proposed LLRMM outperforms those of some other methods such as kernel principal component analysis (KPCA), non-parametric discriminant analysis (NDA), reconstructive discriminant analysis (RDA), discriminant multiple manifold learning (DMML) and large margin nearest neighbor (LMNN). Highlights • A novel manifold margin is globally defined with the minimum locally linear representation trick. • Based on the manifold margin, a LLRMM method is presented for multi-manifold identification. • LLRMM attempts to explore a discriminant subspace to maximize the manifold margin. • The proposed method outperforms some related methods on benchmark face data. [ABSTRACT FROM AUTHOR]

This paper investigates the two-stage assembly flow shop scheduling problem with a batched delivery system where there are m independent machines at the first stage doing the components of a job and multiple identical assembly machines at the second stage, each of which can assemble the components and complete the job. The objective is to schedule the jobs, to form them into batches so as to minimize the sum of tardiness plus delivery costs. To the best of our knowledge, the assembly flow shop scheduling problem with this objective function has not been addressed so far. A mathematical model for this problem is presented. However, due to the fact that this model happens to be a mixed integer nonlinear programming model and cannot guarantee to reach the solution at reasonable time we developed the imperialist competitive algorithm (ICA) and a hybrid algorithm (HICA) by incorporating the dominance relations. Computational results show that HICA performs better than ICA with respect to the value of the objective function, However the runtime of the ICA is less than HICA. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multi-criteria approach in order to reduce the waiting time on the assignment of users to medico-social institutions. The main goal is to ascertain whether alternative assignment policies can improve global response to users’ demands, and to assess the performances of each alternative (scenario) compared to the current practice. We propose a mathematical model of user assignment to medico-social structures and professionals, under constraints of resource capacity, professionals’ skills and user requirements (formulated in an individual support project). With alternative assignment policies, users’ needs could be covered partially. They also authorize the involvement of several structures and several professionals in the response to the request. A mixed integer linear programming solver is used to solve this assignment problem, following a lexicographic approach in case of partial coverage policy. For some assignment policies that have long calculation times, we propose a simulated annealing approach in order to speed up the resolution. In order to validate simulated annealing approach, we compared it with a greedy randomized adaptive search procedure. The problem is solved for instances where we have real size data for a set of institutions for disabled children and teenagers. We present and compare computational results over several scenarios, highlighting the improvement provided by each alternative assignment policy. [ABSTRACT FROM AUTHOR]

Vasopressors administration in intensive care units is a risky surgical procedure that can be associated with infections, especially if done urgently such as in the case of unexpected systemic shock. The early prediction of a patient׳s transition to vasopressor dependence could improve overall outcomes associated with the procedure. Personalized medicine in the ICU encompasses the customization of healthcare on the level of individual patients, with diagnostic tests, monitoring interventions and treatments being fitted to the individual rather than the “average” patient. In this scope, this paper proposes an ensemble fuzzy modeling approach to a classification problem based on subgroups of patients identified by individual characteristics. A fuzzy c-means clustering algorithm was implemented to find subgroups of patients and each subgroup was used to develop a fuzzy model. The final classification of the ensemble fuzzy approach is obtained using two output selection criteria: an a priori decision criterion based on the distance from the cluster centers to the patients׳ characteristics, and an a posteriori decision criterion based on the uncertainty of the model output. The performance of the proposed approach is investigated using a real world clinical database and nine benchmark datasets. The ensemble fuzzy model approach performs better than the single model for the prediction of vasopressors administration in the ICU, being the a posteriori approach the best performer, with an average AUC of 0.85, showing this way the advantage of a personalized approach for patient care in the ICU. [ABSTRACT FROM AUTHOR]

Because of the high competition among IT sectors, companies are planning to migrate to the cloud for effective growth and development. Driven by the importance of the cloud, new cloud vendors emerge each day to satisfy the demand of IT sectors. As a result, selection of an apt cloud vendor is critical. To this end, researchers have proposed different decision models, but these models do not effectively capture uncertainty during the decision-making process. To handle this issue, probabilistic linguistic information (PLI) is adopted in this paper, which associates occurrence probability to each term. Furthermore, weights of criteria are systematically determined using a deviation method, and cloud vendors are prioritized using a mathematical model under the PLI context. These methods are integrated to form the decision model, validated for its applicability using real case data from Cloud Armor. Finally, the advantages and weaknesses of the model are analyzed by using sensitivity analysis and comparison with extant models. [ABSTRACT FROM AUTHOR]

Physical topology design of optical networks is frequently accomplished by using evolutionary approaches. However, fitness evaluation for this type of problems is time consuming and the overall optimization process presents a huge execution time. In this paper we propose a new method that uses a multi-objective evolutionary approach to handle the design of all-optical networks. We focused on the simultaneous optimization of the network topology and the device specifications in order to minimize both the capital expenditure of the network and the network performance. Our method uses surrogate models to accelerate the fitness evaluation and a novel network generative model based on preferential attachment to generate the seeds for the evolutionary process. Our approach can provide high quality solutions with a very small execution time when compared to the previous approaches. In order to assess our proposal we performed a set of simulations aiming to analyze the convergence ability and the diversity of the generated solutions for scenarios considering uniform and non-uniform traffic matrices. From our results, we obtained an evolutionary approach that presents better solutions than previous proposals for all analyzed scenarios. Our proposal presents an execution time that is up to 84% and 88% lower than the execution time needed by the previous approaches for uniform and non-uniform traffic, respectively. [ABSTRACT FROM AUTHOR]

Fog phenomena result in airlight generation and degrade the visibility of the color image captured from the camera. To improve visibility, airlight estimation is necessary for image fog removal. As airlight is very bright, the traditional methods directly select bright pixels for airlight estimation. However, some bright pixels generated by light sources, such as train headlights, may interfere with the accuracy of the above-mentioned methods. In this paper, we propose a new airlight estimation method. Based on Gaussian distribution, the proposed method selects the airlight candidates in the brightest region of the input image. Moreover, the color similarity estimation is also applied to hierarchically refine the candidates. We then compute the average color from the refined candidate pixels for airlight estimation. Experimental results demonstrate that the proposed method is more accurate than other airlight estimation methods and has low time complexity. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to validate an artificial wavelet neural network, or wavenet model, combined with artificial bee colony optimization, a swarm intelligence paradigm, to model powertrain components of a truck engine. The arrangement of artificial neural networks with wavelet based functions, called artificial wavelet neural network or wavenet (AWNN), creates a valuable tool to represent the nonlinear multivariable systems. AWNN can be considered a particular case of the feed-forward basis function neural network model. To illustrate the use of the proposed AWNN based on ABC optimization for the black-box modeling, we apply it to model a truck engine with a cubic displacement greater than seven liters. Identification results were carried out using AWNN implemented in Matlab computational environment and the model accuracy is evaluated based on performance indices. Final results were compared with Elman network, Jordan network and kernel adaptive filtering in order to check the AWNN performance. The comparison methods were tuned with the same optimization algorithm in order to find their best tuning parameters. The simulation results show that the artificial wavelet neural network approach can be useful and a promising technique in powertrain components modeling. This proposed AWNN combined with artificial bee colony approach allows modeling the dynamical behavior of powertrain components of a truck engine. [ABSTRACT FROM AUTHOR]

Data based modeling applications require the complete automation of model creation of, in general, nonlinear processes. Numerous model architectures are available to approximate complex data structures, however, creating and selecting the best model provides a present challenge for application. Model ensembles provide excellent strategies if the prediction performance of the models can be assessed in some way. Information criteria approaches, trading off goodness of fit criteria against the average variance error of the model structures, are theoretically applicable, but demonstratively perform poorly in settings with sparse data sets and complex model structures, settings which are present for many industrial purposes. This paper proposes and discusses a methodology how to regularise the weight calculation building model ensembles. Thereby, the weights trade off goodness of fit criteria against empirical measures of risk, increasing the model ensemble prediction performance. The methodology is independent of the model class, applicable to all black box models, and improves prediction performance for a variety of data in several industrial application areas. [ABSTRACT FROM AUTHOR]

Agent activities and environment change are what make things happen in a multi-agent system (MAS). Complexity in a MAS comes from non-trivial dependencies between activities ( social interaction ), and between activities and environment change ( situated interaction ). As they are used to manage social (agent–agent) dependencies, coordination artefacts could also be used to govern situated (agent–environment) dependencies. Along this line, in this paper we propose an event-driven architecture for complex MAS that exploits coordination to handle all sorts of dependencies in a uniform way. We first motivate the underlying meta-model and put some well-known agent-based frameworks in perspective, then we discuss its general articulation along with its reification within the TuCSoN coordination middleware. [ABSTRACT FROM AUTHOR]

The cloud computing allows to use virtually infinite resources, and seems to be a new promising opportunity to solve scientific computing problems. The MapReduce parallel programming model is a new framework favoring the design of algorithms for cloud computing. Such framework favors processing of problems across huge datasets using a large number of heterogeneous computers over the web. In this paper, we are interested in evaluating how the MapReduce framework can create an innovative way for solving operational research problems. We proposed a MapReduce-based approach for the shortest path problem in large-scale real-road networks. Such a problem is the cornerstone of any real-world routing problem including the dial-a-ride problem (DARP), the pickup and delivery problem (PDP) and its dynamic variants. Most of efficient methods dedicated to these routing problems have to use the shortest path algorithms to construct the distance matrix between each pair of nodes and it could be a time-consuming task on a large-scale network due to its size. We focus on the design of an efficient MapReduce-based approach since a classical shortest path algorithm is not suitable to accomplish efficiently such task. Our objective is not to guarantee the optimality but to provide high quality solutions in acceptable computational time. The proposed approach consists in partitioning the original graph into a set of subgraphs, then solving the shortest path on each subgraph in a parallel way to obtain a solution for the original graph. An iterative improvement procedure is introduced to improve the solution. It is benchmarked on a graph modeling French road networks extracted from OpenStreetMap. The results of the experiment show that such approach achieves significant gain of computational time. [ABSTRACT FROM AUTHOR]

Word sense induction (WSI) aims to automatically identify different senses of an ambiguous word from its contexts. It is a nontrivial task to perform WSI in natural language processing because word sense ambiguity is pervasive in linguistic expressions. In this paper, we construct multi-granularity semantic spaces to learn the representations of ambiguous instances, in order to capture richer semantic knowledge during context modeling. In particular, we not only consider the semantic space of words, but the semantic space of word clusters and topics as well. Moreover, to circumvent the difficulty of selecting the number of word senses, we adapt a rival penalized competitive learning method to determine the number of word senses automatically via gradually repelling the redundant sense clusters. We validate the effectiveness of our method on several public WSI datasets and the results show that our method is able to improve the quality of WSI over several competitive baselines. [ABSTRACT FROM AUTHOR]

Sudden changes in illumination often occur in real world scenarios and may cause considerable difficulties in modeling backgrounds for the state-of-the-art background subtraction methods. In this paper, we propose a simple and effective background re-initialization method that detects sudden luminance change effectively. The purpose of the proposed method is not on the presentation of a specific solution for object detection, but is instead the improvement of the background subtraction approach so that it is capable of sudden luminance change adaptation. Two embodiments related to background subtraction, and which are based on the proposed method, are also presented. These embodiments can detect the moving objects accurately as the luminance of the background model is adjusted quickly after the proposed method is employed for generating the background model. Experimental results demonstrate that the proposed method effectively improves the background subtraction methods as measured by qualitative as well as quantitative assessments. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a capable neural network for solving strictly convex quadratic programming (SCQP) problems with general linear constraints. The proposed neural network model is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem. A block diagram of the proposed model is also given. Several applicable examples further show the correctness of the results and the good performance of the model. [Copyright &y& Elsevier]

Abstract: The automatic landing of a quadrotor is often associated with model uncertainties, measurement noises, and ground effect phenomenon. To mitigate these challenges, the accurate estimation of states especially the height above the ground, and its rate of change is vital. Moreover, the error of ground effect model can also be estimated and compensated during landing. In this paper, the recently developed continuous ant colony filter is implemented for integrated estimation of states and parameters. The estimated states are used in height control loop. To investigate the closed loop performance of the filter, two control strategies, a classical proportional-integral-derivative controller and a sliding mode controller are utilized and their performance are compared in the presence of ground effect compensation and without it. Also, the error of ground effect model is estimated and compensated in landing algorithm. Finally, a landing procedure is proposed including a ground effect estimation phase before the touchdown. The simulation results show that the proposed algorithm effectively improves the landing performance when the ground effect is properly compensated. Moreover, small estimation errors, obtained from the simulations, prove the good performance of the new filter in states and parameters estimation. [Copyright &y& Elsevier]

Abstract: Composite filters based on mathematical morphological operators (MMO) are getting considerable attraction in image denoising. Most of such approaches depend on pre-fixed combination of MMO. In this paper, we proposed a genetic programming (GP) based approach for denoising magnetic resonance images (MRI) that evolves an optimal composite morphological supervised filter (F OCMSF ) by combining the gray-scale MMO. The proposed method is divided into three modules: preprocessing module, GP module, and evaluation module. In preprocessing module, the required components for the development of the proposed GP based filter are prepared. In GP module, F OCMSF is evolved through evaluating the fitness of several individuals over certain number of generations. Finally, the evaluation module provides the mechanism for testing and evaluating the performance of the evolved filter. The proposed method does not need any prior information about the noise variance. The improved performance of the developed filter is investigated using the standard MRI datasets and its performance is compared with previously proposed methods. Comparative analysis demonstrates the superiority of the proposed GP based scheme over the existing approaches. [Copyright &y& Elsevier]

Abstract: One of the most commonly known weaknesses of Evolutionary Algorithms (eas) is the large dependency between the values selected for their parameters and the results. Parameter control approaches that adapt the parameter values during the course of an evolutionary run are becoming more common in recent years. The aim of these schemes is not only to improve the robustness of the controlled approaches, but also to boost their efficiency. In this paper we investigate the application of parameter control schemes to address a well-known variant of the Frequency Assignment Problem (fap). The controlled ea is a highly efficient diversity-based multi-objective memetic scheme. In this work, a novel general parameter control method based on Fuzzy Logic is devised. In addition, a hyper-heuristic is also considered as an established parameter control scheme. An extensive experimental evaluation of both methods is carried out that includes a comparison to a wide-range of fixed-parameter schemes. The results show that the fuzzy logic method is able to find similar or even better solutions than the hyper-heuristic and the fixed-parameter methods for several instances of the fap. In fact, this method yielded frequency plans that outperform the best previously published solutions. Finally, the generality of the fuzzy logic-based scheme is demonstrated by controlling different kinds of parameters. [Copyright &y& Elsevier]

Abstract: This paper deals with the design, implementation and analysis of an integer order (IO) and fractional order (FO) based Proportional Integral Derivative (PID) controller, for speed regulation in a chopper fed Direct Current (DC) motor drive. The interdependent parameters of PID and FOPID controllers are designed in both time and frequency domain. In both domains, designs of controllers are formulated as a single objective optimization problem based on time indices integrals and frequency domain rules. Time domain based design of controllers focus mainly on minimization of indices like rise time, settling time etc. On the other hand frequency domain based design of controllers focuses on achieving iso-damping characteristics, which tries to meet user specified gain crossover frequency or phase margin while also maintaining constant overshoot for wide range of motor gain apart from maintaining optimum time indices. A newly evolved artificial bee colony (ABC) algorithm enhanced with opposition search has been used to perform the optimization task. This type of multiple designs enables users to choose the controller based on their requirement. A comparative study has been made to highlight the advantage of using fractional order controller over conventional integer order PID control scheme for speed regulation. To illustrate the efficacy of the opposition based ABC, we also compared the performance with the conventional ABC, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. Computer simulations and extensive analysis over results obtained shows the effectiveness of the proposed approach. [Copyright &y& Elsevier]

Abstract: In this paper, the problem of clustering based procedure for the identification of PieceWise Auto-Regressive eXogenous (PWARX) models is addressed. This problem involves both the estimation of the parameters of the affine sub-models and the hyperplanes defining the partitions of the state-input regression. In fact, we propose the use of the Chiu's clustering algorithm in order to overcome the main drawbacks of the existing methods which are the poor initialization and the presence of outliers. In addition, our approach is able to generate automatically the number of sub-models. Simulation results are presented to illustrate the performance of the proposed method. An application of the developed approach to an olive oil esterification reactor is also suggested in order to validate simulation results. [Copyright &y& Elsevier]

Abstract: The study of Multiagent Systems (MASs) focuses on those systems in which many intelligent agents interact with each other. The agents are considered to be autonomous entities which contain intelligence that serves for solving their selfish or common problems, and to achieve certain goals. However, the autonomous, responsive, and proactive natures of agents make the development of agent-based software systems more complex than other software systems. Furthermore, the design and implementation of a MAS may become even more complex and difficult to implement when considering new requirements and interactions for new agent environments like the Semantic Web. We believe that both domain-specific modeling and the use of a domain-specific modeling language (DSML) may provide the required abstraction, and hence support a more fruitful methodology for the development of MASs. In this paper, we first introduce a DSML for MASs called SEA_ML with both its syntax and semantics definitions and then show how the language and its graphical tools can be used during model-driven development of real MASs. In addition to the classical viewpoints of a MAS, the proposed DSML includes new viewpoints which specifically support the development of software agents working within the Semantic Web environment. The methodology proposed for the MAS development based on SEA_ML is also discussed including its example application on the development of an agent-based stock exchange system. [Copyright &y& Elsevier]

Abstract: Email spam is an increasing problem that not only affects normal users of internet but also causes a major problem for companies and organizations. Earlier techniques have been impaired by the adaptive nature of unsolicited email spam. Inspired by adaptive algorithm, this paper introduces a modified machine learning technique of the human immune system called negative selection algorithm (NSA). A local selection differential evolution (DE) generates detectors at the random detector generation phase of NSA; code named NSA–DE. Local outlier factor (LOF) is implemented as fitness function to maximize the distance of generated spam detectors from the non-spam space. The problem of overlapping detectors is also solved by calculating the minimum and maximum distance of two overlapped detectors in the spam space. From the experiments, the results show that the detection accuracy of NSA–DE is 83.06% while the standard negative selection algorithm is 68.86% at 7000 generated detectors. [Copyright &y& Elsevier]

Abstract: Neural networks have been successfully used to model nonlinear dynamic systems. However, when a static neural network model is used in system fault detection and the model prediction error is used as the residual, the residual is insensitive to the fault if the neural network used is in dependent mode. This paper proposed the use of a radial basis function network in independent mode as the system model for fault detection, and it was found that the residual is sensitive to the fault. To enhance the signal to noise ratio of the detection the recursive orthogonal least squares algorithm is employed to train the network weights. Another radial basis function network is used to isolate fault using the information in the residual signal. The developed method is applied to a benchmark simulation model of the proton exchange membrane fuel cell stacks developed at the Michigan University. One component fault, one actuator fault and three sensor faults were simulated on the benchmark model. The simulation results show that the developed approach is able to detect and isolate the faults to a fault size of ±10% of nominal values. These results are promising and indicate the potential of the method to be applied to the real world of fuel cell stacks for dynamic monitoring and reliable operations. [Copyright &y& Elsevier]