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The envisaged usage of multiple Unmanned Aerial Vehicles (UAVs) to perform cooperative tasks is a promising concept for future autonomous military systems. An important aspect to make this usage a reality is the solution of the task allocation problem in these cooperative systems. This paper addresses the problem of tasks allocation among agents representing UAVs, considering that the tasks are created by a central entity, in which the decision of which task will be performed by each agent is not decided by this central entity, but by the agents themselves. The assumption that tasks are created by a central entity is a reasonable one, given the way strategic planning is carried up in military operations. To enable the UAVs to have the ability to decide which tasks to perform, concepts from swarm intelligence and multi-agent system approach are used. Heuristic methods are commonly used to solve this problem, but they present drawbacks. For example, many tasks end up not begin performed even if the UAVs have enough resources to execute them. To cope with this problem, this paper proposes three algorithm variants that complement each other to form a new method aiming to increase the amount of performed tasks, so that a better task allocation is achieved. Through experiments in a simulated environment, the proposed method was evaluated, yielding enhanced results for the addressed problem compared to existing methods reported in the literature. [ABSTRACT FROM AUTHOR]

There is a growing expectation to realize the autonomous dispatch on flight deck, where dispatch trajectory planning is seen as the key technique. Optimal-control based method has shown great advantages in high degree of constraint satisfaction over its counterparts in the last decade. However, it suffers from low computational efficiency even numerical divergence under scenarios with complicated obstacles. To deal with such an issue, a search-resampling-optimization (SRO) framework is proposed in this paper. A hybrid A* algorithm is employed to generate a coarse path according to the boundary conditions in the search stage. Then a resampling process is implemented to pave a series of safe dispatch corridors (SDCs) along the coarse path. Finally, by replacing the common one-to-one collision-avoidance with the constructed within-SDC constraints, an optimal control problem whose scale is totally independent of the number of obstacles can be formulated. The resampled result is further fed into the optimization stage to facilitate the numerical solution. Dispatch trajectory planning for taxiing aircraft and tractor can be treated uniformly under this framework. And numerical simulations demonstrate that the SRO framework is efficient and robust even with narrow accessible tunnels. The SRO is inherently flexible and can be easily extended to the trajectory planning problem in other fields. A video of the main idea and numerical simulations in this paper is available at www.bilibili.com/video/BV1tP4y1d7xy/. [ABSTRACT FROM AUTHOR]

Maintaining and enhancing engagement of patients during stroke rehabilitation exercises are in the focus of current research. There have been various methods and computer supported tools developed for this purpose, which try to avoid mundane exercising that is prone to become a routine or even boring for the patients and leads to ineffective training. This paper proposes a strategy bundle-based smart learning mechanism (SLM) to increase the efficiency of rehabilitation exercises. The underpinning strategy considers motor, perceptive, cognitive and emotional aspects of engagement. Part of a cyber-physical stroke rehabilitation system (CP-SRS), the proposed SLM is able to learn the relationship between the actual engagement levels and applied stimulations. From a computational point of view, the SLM is based on multiplexed signal processing and a machine learning agent. The paper presents the mathematical concepts of signal processing, the reasoning algorithms, and the overall embedding of the SLM in the CP-SRS. Regression and classification are two possible solutions for this learning mechanism. Computer simulation is conducted to investigate the limitations of the proposed learning mechanism and compare the results of different machine learning methods. We simulate regression with artificial neural network (ANN), and classification with ANN and Naive Bayes (NB). Results show that classification with NB is more promising in practice since it is less sensitive to the deviations in the inputs than the applied version of ANN. [ABSTRACT FROM AUTHOR]

Multiuser museum interactives are computer systems installed in museums or galleries which allow several visitors to interact together with digital representations of artefacts and information from the museum׳s collection. In this paper, we describe WeCurate, a socio-technical system that supports co-browsing across multiple devices and enables groups of users to collaboratively curate a collection of images, through negotiation, collective decision making and voting. The engineering of such a system is challenging since it requires to address several problems such as: distributed workflow control, collective decision making and multiuser synchronous interactions. The system uses a peer-to-peer Electronic Institution (EI) to manage and execute a distributed curation workflow and models community interactions into scenes, where users engage in different social activities. Social interactions are enacted by intelligent agents that interface the users participating in the curation workflow with the EI infrastructure. The multiagent system supports collective decision making, representing the actions of the users within the EI, where the agents advocate and support the desires of their users e.g. aggregating opinions for deciding which images are interesting enough to be discussed, and proposing interactions and resolutions between disagreeing group members. Throughout the paper, we describe the enabling technologies of WeCurate, the peer-to-peer EI infrastructure, the agent collective decision making capabilities and the multi-modal interface. We present a system evaluation based on data collected from cultural exhibitions in which WeCurate was used as supporting multiuser interactive. [ABSTRACT FROM AUTHOR]

This paper proposes new techniques to calculate the dynamic gains of nonlinear systems represented by fuzzy basis function network (FBFN) models. The dynamic gain of an FBFN can be approximated by finding the maximum of norm values of the locally linearized systems or by solving a non-smooth optimal control problem. From the proposed gain calculation techniques, a novel adaptive multilevel fuzzy controller (AMLFC) with a maximum output scaling factor is presented. To guarantee the system stability, a stability condition is derived, which only requires that the output scaling factor of the AMLFC be bounded. Therefore, this paper provides a systematic and simple design practice for controlling nonlinear systems by using an AMLFC. The AMLFC is simulated in a tower crane control system. Simulation results show that AMLFC is not only robust but also provides improved transient performances compared with the robust adaptive fuzzy controller. [ABSTRACT FROM AUTHOR]

Existing workflow management systems execute complex sequences of tasks by invoking corresponding services identified at execution time and ordered according to a graph that captures the workflow execution rules and data dependencies. The technology is mature and is used across the Internet to support a wide range of business transactions by taking advantage of a relatively stable communication and service infrastructure. This paper extends the applicability of workflow technology by considering settings in which service availability is highly variable due to the mobility of people and devices and due to frequent and extended disconnections from the wired infrastructure. This paper extends workflow technology to support goal-directed coordination among physically mobile people and devices that form a transient community over a mobile ad hoc network (MANET). Our approach is based on the notion of open workflows, i.e., workflows that are constructed dynamically using available knowledge and services. Because the workflow construction and execution are situation aware, different workflows are built under different circumstances even when the need being addressed is unchanged. We also introduce a framework that co-designs workflow allocation and host scheduling in a unified way, which can handle communication range limitations in a mobile environment while previous methods cannot. Specifically, we transform the collaboration problem into a temporal planning model and then solve it using automated planners. We integrate this framework into the CiAN workflow management system, which was developed in our previous work. Results of experiments we have conducted show that our approach significantly broadens the scope of previous research by solving the challenges of availability, mobility, and communication constraints in MANET domains. [ABSTRACT FROM AUTHOR]

The Ambient Intelligence (AmI) paradigm envisions systems whose central entity is the user. AmI integrates technologies such as Artificial Intelligence, implicit Human Computer Interaction, and Ubiquitous Services. Each capability of AmI systems is oriented towards assistance of humans at work, in the classroom, or even at home. In consequence, the AmI development process usually incorporates the final user since the first stages. However, having users available during all this long process is not always possible. Agent-based social simulations where the users׳ role is played by simulated entities can be used to make the AmI development process faster and more effective. In this scenario, the modelling of CMHBs (Computational Models of Human Behaviour) is a major challenge. To address this issue, this paper proposes a methodology whose main contributions are: (1) the use of domain experts׳ knowledge to create CMHBs; (2) a common methodological framework to develop CMHBs by combining information obtained from sensors׳ perceptions and experts׳ experiences; and, (3) open source tools to support this development paradigm. The paper also presents a full case of study in a hospital which illustrates: the number of recommendations made by the methodology; the techniques proposed (mainly the use of ontologies and temporal reasoning); and, the potential of the methodology to model the personnel in a hospital. [ABSTRACT FROM AUTHOR]

In this paper, an improved artificial potential field (APF) method with predictive state is proposed to reduce the influence of communication delay and physical delay on swarm control. The input threshold is introduced to optimize the problem of excessive APF method control input. The control of large-scale swarm systems is challenging due to the complexity of the communication topology. The second-order communication topology is introduced to simplify the coupling relationship of the system. The collision avoidance and formation maintenance control of the swarm system is realized by setting the collision avoidance and formation maintenance potential functions. Based on the proposed improved artificial potential field method, a new distributed controller for swarm system is designed and its stability is analyzed. Numerical simulations are conducted to prove the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Abstract: The switched Ethernet networks are more and more deployed in industry. The Spanning Tree Protocol implemented in the switches enables management of the link connectivity. But the reconfiguration time of the Spanning Tree Protocol (STP) when link failure occurs is not adapted to satisfy industrial constraints. The objective of this paper is to propose a method based only on standard, mitigating the probability of disconnection between nodes having hard real-time properties. The approach developed in this paper consists of duplicating frames and of forwarding them on different paths. These paths are optimized and specified by using genetic algorithms. OPNET simulations show the interest of this proposal on a particular Networked Control System. [Copyright &y& Elsevier]

Abstract: This paper investigates adaptive control design for nonlinear square MIMO systems. The control scheme is based on recurrent neural networks emulator and controller with decoupled adaptive rates. Networks'' parameters are updated according to an autonomous algorithm inspired from the Real Time Recurrent Learning (RTRL). The contributions of this paper are the determination of Lyapunov sufficient stability conditions for decoupled adaptive rates of the emulator and controller and the development of new adaptation strategies based on the tracking error dynamics and Lyapunov stability analysis to improve the closed loop performances. Efficiency of the proposed controller is illustrated with nonlinear system simulations. An application of the developed approaches to a hot-air blower is presented in order to validate simulations results. [Copyright &y& Elsevier]

Abstract In this paper, we consider the scenario of exploring a planetary surface with a system formed by an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV). The goal is to reach a set of target points minimizing the travelling distance. Some expected key problems in planetary explorations are the UGVs functionality constraints to reach some target points as a single robot system and the UAVs energy constraints to reach all the target points on its own. We present an approach based on the coordination of a hybrid UGV–UAV system, in which both robots work together for reaching all the target points. Our strategy proposes the UGV as a moving charging station to solve the UAV energy constraint problem, and the UAV as the robotic system in charge of reaching the target points to solve the UGV functionality constraints. To overcome this problem, we formulate a strategy merging combinatorial classic techniques and modern evolutionary approaches aiming to optimize the travelling distance. Our solution has been tested in several simulation runs with different target points distributions. The results demonstrate that our approach is able to generate a coordinated plan for optimizing the hybrid UGV–UAV system in the exploration scenario. [ABSTRACT FROM AUTHOR]

Abstract Association-rule mining (ARM) or frequent itemset mining (FIM) is the most fundamental task in knowledge discovery, which is used to find the occurrence frequency of the item/sets in transactional database. The other factors such as weight, interestingness or unit profit of the items are not considered whether in ARM or FIM. To reveal more information, high-utility itemset mining (HUIM) was designed to consider both quantity and unit profit of items to discover the high-utility itemsets (HUIs). Several algorithms for FIM or HUIM were extensively studied but fewer works concern both frequency and utility together to provide better solutions in decision-making. In the past, the SKYMINE algorithm was designed to find the skyline frequent-utility patterns (SFUPs). A SFUP is a non-dominated pattern, in which each solution dominates the others by considering the aspects of frequency and utility. The SKYMINE algorithm needs, however, amounts of computation to level-wisely discover the SFUPs. In this paper, an efficient utility-list structure is used instead of the UP-tree structure used in SKYMINE to mine the SFUPs. Two algorithms are respectively designed by using the depth-first search (called SKYFUP-D) and breath-first search (SKYFUP-B) to mine the SFUPs. An efficient structure is also designed to record the maximal utility of the potential itemsets, thus reducing the computations for finding the SFUPs in the search space. Extensive experiments are conducted on several real-world and simulated datasets and the results indicate that the designed two algorithms have better performance than that of the state-of-the-art SKYMINE algorithm in terms of runtime, memory usage, search space size and the scalability. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the tracking control design for the interval type-2 (IT2) polynomial-fuzzy-model-based (PFMB) control system subject to time-varying delay situation is investigated. The tracking control system is formed of the IT2 polynomial fuzzy model representing a nonlinear system with time-varying delay, the stable reference model and the IT2 polynomial fuzzy controller. The control objective is to design a proper IT2 polynomial fuzzy controller which is capable of driving the states of the polynomial fuzzy model to track those in the reference model and the tracking performance is evaluated and improved by the H ∞ performance index. Also, to handle the uncertainty in the membership functions, the property of IT2 fuzzy sets is utilized to enhance the fuzzy controller’s robustness against uncertainty. In addition, considering the effect of time-varying delay, the Lyapunov–Krasovskii functional based approach is adopted to facilitate the delay-dependent stability analysis. Stability conditions depending on the time-varying delay characteristic with the consideration of H ∞ performance are obtained in terms of sum-of-squares (SOS). Furthermore, the information of the IT2 membership functions is employed in the stability analysis to relax the stability conditions, both membership-function-independent (MFI) and membership-function-dependent (MFD) approaches are presented to develop the stability conditions. Simulation examples are presented to verify the effectiveness of the proposed tracking control approach. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces an adaptive reference tracking controller based on the online genetic estimation of the parameters of the system. The main novelty of the paper relies on the fact that the stability of the genetic adaptive scheme is analytically proved and not simply validated by means of simulation as it is customary in the literature. The resulting set-up is flexible enough to be integrated within a great variety of genetic estimation algorithms, which can in many cases outperform traditional estimation procedures. The goal is achieved by using a certain two-degree-of-freedom (2-DOF) based implementation of the control law in which the reference tracking property is separated from the closed-loop stability. Within this framework, the here-presented procedure for the genetic controller synthesis just affects two time-varying pre-filter blocks that do not compromise the closed-loop stability under weak conditions. In this manner, the power and versatility of genetic algorithms can be safely used to achieve tracking performance disregarding stability, which is delegated to a static feedback controller designed on the basis of robust control theory. [Copyright &y& Elsevier]

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]

Chattering is a low amplitude and high-frequency undesired signal which typically appears in relay or switched systems as in variable structure controllers. The chattering phenomenon can be treated as a limit cycle and the describing function (DF) method results in an useful tool for predicting its existence allowing the computation of the amplitude and frequency of such oscillations. In this paper, a sliding mode control based on fuzzy logic is proposed as an alternative to reduce chattering where DF method is used to demonstrate its attenuation level at the output of the plant. Analytical results are complemented with numerical simulations made for a pneumatic linear actuator. [ABSTRACT FROM AUTHOR]

This paper studies the potential of a novel approach to ensure more efficient and intelligent assignment of capacity through medium access control (MAC) in practical wireless sensor networks. Q-Learning is employed as an intelligent transmission strategy. We review the existing MAC protocols in the context of Q-learning. A recently-proposed, ALOHA and Q-Learning based MAC scheme, ALOHA-Q, is considered which improves the channel performance significantly with a key benefit of simplicity. Practical implementation issues of ALOHA-Q are studied. We demonstrate the performance of the ALOHA-Q through extensive simulations and evaluations in various testbeds. A new exploration/exploitation method is proposed to strengthen the merits of the ALOHA-Q against dynamic the channel and environment conditions. [ABSTRACT FROM AUTHOR]

The multi-variable grey model represented by GM(1, N ) is an important causal relationship forecasting model. However, since the structure of GM(1,N) is more complicated than that of the single-variable grey forecasting model GM(1,1), it is difficult to build a useful GM(1, N ) model. The simulation and prediction errors of GM(1, N ) models are usually greater than those of GM(1,1) models. The modeling process of the traditional GM(1, N ) model is studied and three serious defects are observed in terms of “modeling mechanism”, “parameter estimation” and “model structure”, which are the major causes of low model precision. In this paper, a novel GM(1, N ) model is proposed to improve the performance of the traditional GM(1, N ) model by introducing a linear correction term and a grey action quantity term to the traditional GM(1, N) model. The new model has more reasonable modeling process and more stable structure, which solves the three defects of the traditional GM (1, N ) model. In addition, the new model is entirely compatible with the single variable discrete grey forecasting model DGM(1,1)and the multi-variable grey forecasting model GM(0, N ). To verify the effectiveness of the development, the new model is used to simulate the tensile strength of a material. The mean relative simulation and prediction percentage errors of the new model are 0.0707% and 5.7369%, in comparison with those of the traditional precision GM(1, N ) model and the classical GM(1,1) model, which are 6.0011%, 18.4280% and 1.1020%, 12.5190% respectively. The findings show that the new model has the best performance, which on one hand testifies the correctness of the defect analysis, and on the other hand validates the effectiveness of the structure reform of the traditional GM(1,N) model. [ABSTRACT FROM AUTHOR]

In this paper, a real-time camera tracking system for package transportation on omnidirectional wheeled conveyor is presented. The camera tracking system is integrated with a closed-loop controller for packages path planning. No additional sensors are used for the controller implementation, only a 2-D Camera. The package's position and orientation are detected by the camera tracking system in real-time. Two proposed systems are presented, System I is implemented using the conventional image processing technique threshold method while System II is implemented using computer vision. In System II, three computer vision models were evaluated: Detectron2 , YOLOv5 and Faster R-CNN. Experimental results in real-time show that System I have lower accuracy rate 85.7% compared to System II which reported 98% and 88.1% for YOLOv5 and Detectron2 , respectively. YOLOv5 reported the best results among the computer vision models with 1% missing rate, 45.5 FPS and average precision of 99.8%. [ABSTRACT FROM AUTHOR]

This paper proposes new methods for modeling unstructured uncertainties and robust controlling of unknown nonlinear dynamic systems by using a novel robust Takagi Sugeno fuzzy controller (RTSFC). First, a new training algorithm for an interval type-2 fuzzy basis function network (FBFN) is proposed. Next, a novel technique is presented to convert the interval type-2 FBFN to an interval type-2 Takagi Sugeno (TS) fuzzy model. Based on the interval type-2 TS and type-2 FBFN models, a robust controller is presented with an adjustable convergence rate. Since the type-2 fuzzy model with its new training technique can effectively capture the unstructured uncertainties and accurately estimate the upper and lower bounds of unknown nonlinear dynamic systems, the stability condition of the proposed control system is much less conservative than other robust control methods that are based on norm bounded uncertainties. Simulation results on an electrohydraulic actuator show that the RTSFC can reduce steady state error under different conditions while maintaining better responses than the other robust sliding mode controllers. [ABSTRACT FROM AUTHOR]

Multi-agent systems have been commonly used for implementing simulators in which several individuals or components perform actions. Although these systems usually manage large amounts of data, the process for constructing efficient multi-agent simulators is still an open issue. This paper presents a Process for developing Efficient Agent-Based Simulators, called PEABS, which combines the use of an agent-oriented modeling language with a framework adapted for obtaining efficient agent-based simulators. The proposed process covers all the development phases from the requirements elicitation until the deployment of a complete functional multi-agent system for simulation. As a case study, a simulator, which consists of an agent-based system that imitates tourist behaviors when selecting routes in the historic center of Madrid city, is constructed following the new process. The process is evaluated with this simulator and another one that simulates the evolution of student sociograms. These simulators with the same specifications were developed using an alternative process. The experimental comparison shows that the time devoted to development are at least 22% shorter with the current process. The systems constructed with this new process are more efficient in terms of execution time and memory usage than the systems developed with the other process. The final systems developed with PEABS are also more usable for the domain experts than the ones developed with the alternative process. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to medium access control for single-hop wireless sensor networks. The ALOHA-Q protocol applies Q-Learning to frame based ALOHA as an intelligent slot selection strategy capable of migrating from random access to perfect scheduling. Results show that ALOHA-Q significantly outperforms Slotted ALOHA in terms of energy-efficiency, delay and throughput. It achieves comparable performance to S-MAC and Z-MAC with much lower complexity and overheads. A Markov model is developed to estimate the convergence time of its simple learning process and to validate the simulation results. [ABSTRACT FROM AUTHOR]

The present paper deals with the defect detection and diagnosis of induction motor, based on motor current signature analysis in a quality control scenario. In order to develop a monitoring system and improve the reliability of induction motors, Clarke–Concordia transformation and kernel density estimation are employed to estimate the probability density function of data related to healthy and faulty motors. Kullback–Leibler divergence identifies the dissimilarity between two probability distributions and it is used as an index for the automatic defects identification. Kernel density estimation is improved by fast Gaussian transform. Since these techniques achieve a remarkable computational cost reduction respect the standard kernel density estimation, the developed monitoring procedure became applicable on line, as a Quality Control method for the end of production line test. Several simulations and experimentations are carried out in order to verify the proposed methodology effectiveness: broken rotor bars and connectors are simulated, while experimentations are carried out on real motors at the end of production line. Results show that the proposed data-driven diagnosis procedure is able to detect and diagnose different induction motor faults and defects, improving the reliability of induction machines in quality control scenario. [ABSTRACT FROM AUTHOR]

Nowadays, many researches focus on the selection of the team members, and aim to form a team that can solve a common task collaboratively or achieve a certain goal cooperatively. The leader or manager needs to maximize the benefit of the team by selecting the appropriate team members from the available candidates. However, the personal opinions of team members have not been considered in the team formation process. In this paper, in order to consider the mutual decision between the leader and the team members, a negotiation based team formation problem is defined firstly, in which the balance of the benefit requirements of the leader and the team members is addressed. Then, a negotiation model between the leader and the team members is proposed to implement the team formation process. According to the conflicts in the process, an incentive mechanism based on the payoff is adopted. Finally, the agent-based negotiation approach is introduced to construct the simulation environment of the negotiation. The negotiation process is iterated until the benefit requirements of the leader agent and the candidate agents are all satisfied or the deadline of the negotiation is reached. The experiment results indicate that the team can be formed successfully in different team contexts, such as different team sizes, different minimum criteria of team positions and different incentive levels. Comparing with the leader׳s unilateral decision process, the negotiation-based team formation can make the team members acquire more benefits. In addition, the different preferences of the candidates have different effects on the final benefit acquirements, and the different minimum criteria of team positions and incentive levels have different effects on the conflicts resolution. [ABSTRACT FROM AUTHOR]

This paper describes an original approach to generate scenarios for the purpose of testing the algorithms used to detect special nuclear materials (SNM) that incorporates the use of ontologies. Separating the signal of SNM from the background requires sophisticated algorithms. To assist in developing such algorithms, there is a need for scenarios that capture a very wide range of variables affecting the detection process, depending on the type of detector being used. To provide such a capability, we developed an ontology-driven information system (ODIS) for generating scenarios that can be used for testing of algorithms for SNM detection. The Ontology-Driven Scenario Generator (ODSG) is an ODIS based on information supplied by subject matter experts and other documentation. The details of the creation of the ontology, the development of the ontology-driven information system, and the design of the web user interface (UI) are presented along with specific examples of scenarios generated using the ODSG. We demonstrate that the paradigm behind the ODSG is capable of addressing the problem of semantic complexity at both the user and developer levels. Compared to traditional approaches, an ODIS provides benefits such as faithful representation of the users׳ domain conceptualization, simplified management of very large and semantically diverse datasets, and the ability to handle frequent changes to the application and the UI. The approach makes possible the generation of a much larger number of specific scenarios based on limited user-supplied information. [ABSTRACT FROM AUTHOR]

Recommender system (RS) is a special type of information systems that assists decision makers to choose appropriate items according to their preferences and interests. It is utilized in different domains to personalize its applications by recommending items, such as books, movies, songs, restaurants, news articles, jokes, among others. An important issue in RS namely the new user cold-start problem occurring when a new user migrates to the system has grasped a great attraction of researchers in recent years. Existing researches are faced with the limitations of the relied dataset, the determination of the optimal number of clusters, the similarity metric, irrelevant users and the selection of membership values. In this paper, we present a novel hybrid method so-called HU-FCF++ to deal with these drawbacks by considering the integration of existing state-of-the-arts of several groups of methods in order to combine the advantages of different groups and eliminate their disadvantages by some special procedures. A numerical example on a simulated dataset is given to illustrate the activities of the proposed approach. Experimental validation on the benchmark RS datasets show that HU-FCF++ achieves better accuracy than the relevant methods. [ABSTRACT FROM AUTHOR]

Multi-agent technology is applied in this paper to solve the distribution system service restoration problems. Specifically, switches in the distribution systems are modeled as intelligent agents and organized as local power committees in a distribution system. The advantage of this approach is to utilize the solving ability of a local committee of agents who could coordinate with neighboring committees to achieve an agreement that satisfies the global objective of the power distribution system service restoration problems without relying on a pre-determined centralized optimization algorithm as traditional approaches. The proposed coordination method helps switches in the distributed local power committees to reach a consensus among many proposed service restoration solutions after faults are detected and isolated. We also take the voltage violations into consideration in the search of solutions. Two examples of different power distribution systems are presented to demonstrate how the algorithms of the coordination method can identify the best restoration solution among many proposed local candidate solutions. The simulation results show that the proposed committee-based multi-agent approach yields better performance than the previous methods. [ABSTRACT FROM AUTHOR]

The Latin Hypercube (LH) design problem arises in computer simulations and is employed for examination and simulation of many physical events. In order to find a (near) optimal LH design, this paper proposes a new version of Particle Swarm Optimization (PSO) algorithm, which uses a population-based optimizer as the evolutionary part and a multiple-local-search procedure as the refinement part of the algorithm. To manage the problem constraints, the proposed algorithm utilizes a Ranked Ordered Value (ROV) rule, which converts the continuous space of solutions to the point-permutation space. Furthermore, to maintain the population diversity, the meta-Lamarckian learning strategy is applied to the local search procedure of the algorithm. In order to test the proposed algorithm, we compare it with a set of existing algorithms on several problem instances. To perform a fair comparison, the best parameters for all the algorithms are found and the experiments are performed based on these parameters. The experimental results show that the proposed algorithm outperforms the existing algorithms in solving large-scale LH design problem. [ABSTRACT FROM AUTHOR]

Several machine learning and knowledge discovery approaches have been proposed for count data modeling and classification. In particular, latent Dirichlet allocation (LDA) (Blei et al., 2003a) has received a lot of attention and has been shown to be extremely useful in several applications. Although the LDA is generally accepted to be one of the most powerful generative models, it is based on the Dirichlet assumption which has some drawbacks as we shall see in this paper. Thus, our goal is to enhance the LDA by considering the generalized Dirichlet distribution as a prior. The resulting generative model is named latent generalized Dirichlet allocation (LGDA) to maintain consistency with the original model. The LGDA is learned using variational Bayes which provides computationally tractable posterior distributions over the model׳s hidden variables and its parameters. To evaluate the practicality and merits of our approach, we consider two challenging applications namely text classification and visual scene categorization. [ABSTRACT FROM AUTHOR]

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: Adaptation of mobile robot controllers commonly requires the computation of optimal points of operation. Specifically, for miniature mobile robots with serious computational limitations, that are typical of embedded systems, one of the main challenges is the adaptation of efficient computational methods in order to find solutions of complex optimization problems, which demand large execution times. This drawback compels the design of high-performance parallel optimization algorithms which must run over embedded system platforms. This paper describes how adequate hardware implementations of the Particle Swarm Optimization (PSO) algorithm can be useful for real time adaptation of mobile robot controllers. For achieving this, a new architecture is proposed, which is based on an FPGA implementation of the opposition-based learning (OBL) approach applied to the PSO (for short HPOPSO), and which explores the intrinsic parallelism of this algorithm in order to adjust the weights of a neural robot controller in real time according to desired behaviors. The proposed HPOPSO was applied to the learning-from-demonstration problem in which a teacher performs executions of the desired behavior. Effectiveness of the proposed architecture was demonstrated by numerical simulations and the feasibility of the adaptive behavior of the neural robot controller was confirmed for two obstacle avoidance case studies that were preserved when one or more failures on the distance sensors occur. The HPOPSO, which uses the OBL technique, improves the quality of the solutions in comparison with the standard PSO. Comparisons of the adaptation time between hardware and software approaches have demonstrated the suitability of the FPGA implementation of the proposed HPOPSO for attending specific requirements of embedded system applications. [Copyright &y& Elsevier]

Abstract: Based on Ant Colony Optimization for Continuous Domains ( ) and Particle Filter (PF), an intelligent particle filter, namely Ant Colony Estimator (ACE), is proposed in this paper. Modeling and search abilities of are incorporated into the standard particle filtering framework to improve the estimation performance and overcome the well-known problems of Degeneracy and Sample Impoverishment. operators implicitly use measurement and previous particle information, to generate probably better particles. Simulation results are given for two examples and ACE is compared to other types of particle filters. The obtained results confirm the efficiency and applicability of ACE. [Copyright &y& Elsevier]

Abstract: Chaos control can be applied in the vast areas of physics and engineering systems, but the parameters of chaotic system are inevitably perturbed by external inartificial factors and cannot be exactly known. This paper proposes an adaptive neural complementary sliding-mode control (ANCSC) system, which is composed of a neural controller and a robust compensator, for a chaotic system. The neural controller uses a functional-linked wavelet neural network (FWNN) to approximate an ideal complementary sliding-mode controller. Since the output weights of FWNN are equipped with a functional-linked type form, the FWNN offers good learning accuracy. The robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Without requiring preliminary offline learning, the parameter learning algorithm can online tune the controller parameters of the proposed ANCSC system to ensure system stable. Finally, it shows by the simulation results that favorable control performance can be achieved for a chaotic system by the proposed ANCSC scheme. [Copyright &y& Elsevier]

Abstract: The near-future penetration of plug-in electric vehicles is expected to be large enough to have a significant impact on the power grid. If PEVs were allowed to charge simultaneously at the maximum power rate, the distribution grid would face serious problems of stability. Therefore, mechanisms are needed to coordinate various PEVs that charge simultaneously. In this paper we propose an allocation mechanism that aims at balancing allocative efficiency and fairness, providing preferential treatment to the PEVs that have a high valuation of the available power, while guaranteeing a fair share of this power to all thePEVs. [Copyright &y& Elsevier]

Abstract: In this paper, several soft computing approaches were employed for rainfall prediction. Two aspects were considered to improve the accuracy of rainfall prediction: (1)carrying out a data-preprocessing procedure and (2)adopting a modular modeling method. The proposed preprocessing techniques included moving average (MA) and singular spectrum analysis (SSA). The modular models were composed of local support vectors regression (SVR) models or/and local artificial neural networks (ANN) models. In the process of rainfall forecasting, the ANN was first used to choose data-preprocessing method from MA and SSA. Modular models involved preprocessing the training data into three crisp subsets (low, medium and high levels) according to the magnitudes of the training data, and finally two SVRs were performed in the medium and high-level subsets whereas ANN or SVR was involved in training and predicting the low-level subset. For daily rainfall record, the low-level subset tended to be modeled by the ANN because it was overwhelming in the training data, which is based on the fact that the ANN is very efficient in training large-size samples due to its parallel information processing configuration. Four rainfall time series consisting of two monthly rainfalls and two daily rainfalls from different regions were utilized to evaluate modular models at 1-day, 2-day, and 3-day lead-time with the persistence method and the global ANN as benchmarks. Results showed that the MA was superior to the SSA when they were coupled with the ANN. Comparison results indicated that modular models (referred to as ANN-SVR for daily rainfall simulations and MSVR for monthly rainfall simulations) outperformed other models. The ANN-MA also displayed considerable accuracy in rainfall forecasts compared with the benchmark. [Copyright &y& Elsevier]

Abstract: Heterarchical control architectures are essentially founded on cooperation and full local autonomy, resulting in high reactivity, no master/slave relationships and local information retention. Consequently, these architectures experience myopic decision-making, bringing entities towards local optimality rather than the system’s overall performance. Although this issue has been identified as an important problem within heterarchical control architectures, it has not been formally studied. The aim of this paper is to identify the dimensions of myopic behavior and propose mechanisms to control this behavior. This study focuses on myopic behavior found in manufacturing control. For this particular study, we propose a holonic framework and a holonic organization that integrates specific mechanisms to control the temporal and social myopia. Our proposal was validated using simulations designed for solving the allocation problem in flexible manufacturing systems. These simulations were conducted to show the improvement by integrating the new mechanisms. These simulation results indicate that the myopic control mechanisms achieve better performance than the reactive strategies, because not only they introduce a planning horizon, but also because they balance local and global objectives, seeking a consensus. [Copyright &y& Elsevier]

Abstract: Artificial Neural Networks (ANNs) have been successfully employed for predicting and forecasting groundwater levels up to some time steps ahead. In this paper, we present an application of feed forward neural networks (FFNs) for long period simulations of hourly groundwater levels in a coastal unconfined aquifer sited in the Lagoon of Venice, Italy. After initialising the model with groundwater elevations observed at a given time, the developed FNN should able to reproduce water level variations using only the external input variables, which have been identified as rainfall and evapotranspiration. To achieve this purpose, the models are first calibrated on a training dataset to perform 1-h ahead predictions of future groundwater levels using past observed groundwater levels and external inputs. Simulations are then produced on another data set by iteratively feeding back the predicted groundwater levels, along with real external data. The results show that the developed FNN can accurately reproduce groundwater depths of the shallow aquifer for several months. The study suggests that such network can be used as a viable alternative to physical-based models to simulate the responses of the aquifer under plausible future scenarios or to reconstruct long periods of missing observations provided past data for the influencing variables is available. [Copyright &y& Elsevier]

Abstract: This paper presents the first step of a research programme implemented by IFSTTAR in order to develop an integrative simulation platform able to support a Human Centred Design (HCD) method for virtual design of driving assistances. This virtual platform, named COSMO-SiVIC, implements a COgnitive Simulation MOdel of the DRIVEr (i.e. COSMODRIVE) into a Vehicle–Environment–Sensors platform (named SiVIC, for Simulateur Véhicule-Infrastructure-Capteur). From this simulation tool based on a computational driver model, the design costs of driving assistances is expected to reduce in the future, and the end-users needs during the design process are also better taken into account. This article is mainly focussed on the description of the driver model developed and implemented on the SiVIC virtual platform, which is only the first step towards a future Virtual HCD integrated tool. The first section will discuss the research context and objective, and the second one will present the theoretical background in cognitive sciences supporting our driver modelling approach. Then, the SiVIC tool is used in this research as a methodological and technical support for both empirical data collection among human drivers and as a virtual road environment to be interfaced with the COSMODRIVE model. In the result section, the functional architecture of COSMO-SIVIC (based on three complementary modules of Perception, Decision and Action) will be described, and an example of virtual simulation of human driver''s errors due to visual distraction while driving will be presented. The perspectives concerning future use of COSMO-SIVIC for virtual HCD will be then discussed in the conclusion section. [Copyright &y& Elsevier]

Abstract: The modern engineering design optimization process often replaces laboratory experiments with computer simulations, which leads to expensive black-box optimization problems. Such problems often contain candidate solutions which cause the simulation to fail, and therefore they will have no objective value assigned to them, a scenario which degrades the search effectiveness. To address this, this paper proposes a new computational intelligence optimization algorithm which incorporates a classifier into the optimization search. The classifier predicts which solutions are expected to cause a simulation failure, and its prediction is used to bias the search towards solutions for which the simulation is expected to succeed. To further enhance the search effectiveness, the proposed algorithm continuously adapts during the search the type of model and classifier being used. A rigorous performance analysis using a representative application of airfoil shape optimization shows that the proposed algorithm outperformed existing approaches in terms of the final result obtained, and performed a search with a competitively low number of failed evaluations. Analysis also highlights the contribution of incorporating the classifier into the search, and of the model and classifier selection steps. [Copyright &y& Elsevier]

Abstract: In this paper, we present a max-plus algebraic modeling and control approach for cyclically operated high-throughput screening plants. In previous work an algorithm has been developed to determine the globally optimal solution of the cyclic scheduling problem. The obtained optimal schedule is modeled in a max-plus algebraic framework. The max-plus algebraic model can then be used to generate appropriate control actions to handle unexpected deviations from the predetermined cyclic operation during runtime. [Copyright &y& Elsevier]

Abstract: Microarray technology has been widely applied in study of measuring gene expression levels for thousands of genes simultaneously. In this technology, gene cluster analysis is useful for discovering the function of gene because co-expressed genes are likely to share the same biological function. Many clustering algorithms have been used in the field of gene clustering. This paper proposes a new scheme for clustering gene expression datasets based on a modified version of Quantum-behaved Particle Swarm Optimization (QPSO) algorithm, known as the Multi-Elitist QPSO (MEQPSO) model. The proposed clustering method also employs a one-step K-means operator to effectively accelerate the convergence speed of the algorithm. The MEQPSO algorithm is tested and compared with some other recently proposed PSO and QPSO variants on a suite of benchmark functions. Based on the computer simulations, some empirical guidelines have been provided for selecting the suitable parameters of MEQPSO clustering. The performance of MEQPSO clustering algorithm has been extensively compared with several optimization-based algorithms and classical clustering algorithms over several artificial and real gene expression datasets. Our results indicate that MEQPSO clustering algorithm is a promising technique and can be widely used for gene clustering. [Copyright &y& Elsevier]

Abstract: This paper proposes and explores the use of flocking techniques to naturally support navigation in large and open virtual environments. The proposed approach comprises both a flocking algorithm (which simulates the behavior of a flock of virtual beings) and a control algorithm (which manages the navigation information and parameterizes the flocking behavior). The model was developed and evaluated through a user study which investigated whether the addition of the proposed model has an effect on the user experience when exploring a virtual environment. In the research the experiment group explored a virtual environment where the proposed flocking model was added to suggest places to visit. The participants were not informed about the guiding abilities of the virtual animals. The control group explored the same virtual environment without the animal flock. The impact of both cases was compared. The results indicate that the proposed model is adequate to suggest places to visit in large and open virtual worlds. [Copyright &y& Elsevier]

Abstract: In multi-objective particle swarm optimization (MOPSO), a proper selection of local guides significantly influences detection of non-dominated solutions in the objective/solution space and, hence, the convergence characteristics towards the Pareto-optimal set. This paper presents an algorithm based on simple heuristics for selection of local guides in MOPSO, named as HSG-MOPSO (Heuristics-based-Selection-of-Guides in MOPSO). In the HSG-MOPSO, the set of potential guides (in a PSO iteration) consists of the non-dominated solutions (which are normally stored in an elite archive) and some specifically chosen dominated solutions. Thus, there are two types of local guides in the HSG-MOPSO, i.e., non-dominated and dominated guides; they are named so as to signify whether the chosen guide is a non-dominated or a dominated solution. In any iteration, a guide, from the set of available guides, is suitably selected for each population member. Some specified proportion of the current population members follow their respective nearest non-dominated guides and the rest follow their respective nearest dominated guides. The proposed HSG-MOPSO is firstly evaluated on a number of multi-objective benchmark problems along with investigations on the controlling parameters of the guide selection algorithm. The performance of the proposed method is compared with those of two well-known guide selection methods for evolutionary multi-objective optimization, namely the Sigma method and the Strength Pareto Evolutionary Algorithm-2 (SPEA2) implemented in PSO framework. Finally, the HSG-MOPSO is evaluated on a more involved real world problem, i.e., multi-objective planning of electrical distribution system. Simulation results are reported and analyzed to illustrate the viability of the proposed guide selection method for MOPSO. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to present a simple neuro-control law in order to control a geared DC motor. The main advantage of this controller is that it does not require an exact knowledge of the values of the motor parameters. The proposed artificial neural network is characterized by two input synaptic weights, two output synaptic weights and one threshold; these parameters are used to define the performance of the closed loop system. The DC motor parameters, the synaptic weights and the ANN threshold are combined in order to construct an off-line learning condition. Such condition guarantees that the seminorm of the regulation error remains bounded (closed loop performance index) and it is constructed through a Lyapunov-like analysis. The neuro-controller is evaluated through numerical simulations and through small-scale laboratory experiments by implementing the neuro-controller with electronic hardware. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses a biped balancing task in which an unknown external force is exerted, using the so-called ‘ankle strategy’ model. When an external force is periodic, a human adaptively maintains the balance, next learns how much force should be produced at the ankle joint from its repeatability, and finally memorized it as a motion pattern. To acquire motion patterns with balancing, we propose a control and learning method: as the control method, we adopt ground reaction force feedback to cope with an uncertain external force, while, as the learning method, we introduce a motion pattern generator that memorizes the torque pattern of the ankle joint by use of Fourier series expansion. In this learning process, the period estimation of the external force is crucial; this estimation is achieved based on local autocorrelation of joint trajectories. Computer simulations and robot experiments show effective control and learning results with respect to unknown periodic external forces. [ABSTRACT FROM AUTHOR]

Abstract: Since the emergence of PID controllers, control system engineers are in pursuit of more and more sophisticated versions of these controllers to achieve better performance, particularly in situations where providing a control action to even a minimal degree of satisfaction is a problem. This work is an attempt to contribute in this field. Variations in the values of weight, the friction coefficient of the road, road inclination and other nonlinear dynamics may highly affect the performance of antilock braking systems (ABS). A self-tuning scheme seems necessary to overcome these effects. Addition of automatic tuning-tool can track changes in system operation and compensate for drift, due to aging and parameter uncertainties. The paper develops a self-tuning PID control scheme with an application to ABS via combinations of fuzzy and genetic algorithms (GAs). The control objective is to minimize the stopping distance, while keeping the slip ratio of the tires within desired range. Computer simulations are performed to verify the proposed control scheme. Results are reported and discussed. [ABSTRACT FROM AUTHOR]

Abstract: Current production engines use look-up table and proportional and integral (PI) feedback control to regulate air/fuel ratio (AFR), which is time-consuming for calibration and is not robust to engine parameter uncertainty and time varying dynamics. This paper investigates engine modelling with the diagonal recurrent neural network (DRNN) and such a model-based predictive control for AFR. The DRNN model is made adaptive on-line to deal with engine time varying dynamics, so that the robustness in control performance is greatly enhanced. The developed strategy is evaluated on a well-known engine benchmark, a simulated mean value engine model (MVEM). The simulation results are also compared with the PI control. [Copyright &y& Elsevier]

Abstract: A performance comparison of genetic algorithm (GA) and the univariate marginal distribution algorithm (UMDA) as decoders in multiple input multiple output (MIMO) communication system is presented in this paper. While the optimal maximum likelihood (ML) decoder using an exhaustive search method is prohibitively complex, simulation results show that the GA and UMDA optimized MIMO detection algorithms result in near optimal bit error rate (BER) performance with significantly reduced computational complexity. The results also suggest that the heuristic based MIMO detection outperforms the vertical bell labs layered space time (VBLAST) detector without severely increasing the detection complexity. The performance of UMDA is found to be superior to that of GA in terms of computational complexity and the BER performance. [Copyright &y& Elsevier]

Abstract: The search for an intelligent group controller that can satisfy multi-criteria requirements of an elevator group control system has become a great challenge for researchers. This paper presents the development of an elevator group controller based on fuzzy logic framework with a self-tuning scheme. Instead of basing on predicted traffic patterns to initiate modifications in the control outputs produced, the proposed group controller utilizes average waiting time (AWT) as the measured performance criterion used to adjust the membership functions and to select appropriate fuzzy rule sets, for the generation of suitable control actions. By comparing the measured performance results with the ones desired, better adjustment of the controller can be achieved to further improve the controller''s performance. Computer simulation was carried out for three different cases in three traffic peaks. The results showed considerable overall improvements in the performance criteria evaluated as compared to the performance of conventional group controllers. [Copyright &y& Elsevier]

Abstract: Petroleum industry production systems are highly automatized. Maintenance of such systems is vital, not only to maintain production efficiency but also to insure minimal safety levels. Maintenance task scheduling is difficult since some tasks are already identified because they must be done repeatedly, and other tasks need to be identified dynamically. In this paper, we present a multi-agent approach for the dynamic maintenance task scheduling for a petroleum industry production system. Agents simultaneously insure effective maintenance scheduling and the continuous improvement of the solution quality by means of reinforcement learning, using the SARSA algorithm. Reinforcement learning allows the agents to adapt, learning the best behaviors for their various roles without reducing the performance or reactivity. To demonstrate the innovation of our approach, we include a computer simulation of our model and the results of experimentation applying our model to an Algerian petroleum refinery. [Copyright &y& Elsevier]