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This paper is concerned with the control of linear systems with switched actuators subject to lower bounds on dwell times. The problem addressed herein consists of driving the system state to a desired periodic trajectory through suitable perturbations in the switching times. For this purpose, a linearization procedure is derived to describe the relationship between the switching-time perturbations and the resulting state trajectory. This procedure allows the constrained predictive control problem to be cast in the form of a convex quadratic program, which is a key contribution of the present work. Simulation examples with single-input and two-input models are presented for illustration. In all cases, the system state is driven to the desired trajectory with satisfaction of the dwell-time constraints on the switched input signal. [ABSTRACT FROM AUTHOR]

The relative state constraints is inevitable in multi-agent systems(MASs) because of limited sensing capabilities of the sensors. In this paper, the consensus problem of nonlinear MASs with relative state constraints is investigated in an edge perspective. First, the consensus problem is proved to be equivalent to a stabilization problem characterized by the edge dynamics. Then the edge system is confined to a hypercube for the satisfaction of relative constraints. Finally, an output event-triggered control protocol that can exponentially stabilize the edge dynamics with constraints is proposed. Sufficient conditions on system stability are provided, the Singular triggering and the Zeno-behaviour are also excluded. A simple learning-based iterative algorithm is proposed to calculate the diagonally dominant matrix, which is crucial to the controller gain design. The effectiveness of the results is illustrated through numerical examples. • Edge Laplacian framework is used to transform the consensus problem with relative state constraints into stabilization problem with edge dynamics, and being exponentially stabilized by the ETC protocol. • Singular-triggering and Zeno-behaviour will not occur under the edge-based framework. • An iterative-learning algorithm is designed to compute the diagonally dominant matrix. • The proposed method has been extended to solve the consensus problem with connectivity preservation. [ABSTRACT FROM AUTHOR]

The goal of this paper is to develop the parallel algorithms that, on input of a learning sample, identify a regular language by means of a nondeterministic finite automaton (NFA). A sample is a pair of finite sets containing positive and negative examples. Given a sample, a minimal NFA that represents the target regular language is sought. We define the task of finding an NFA, which accepts all positive examples and rejects all negative ones, as a constraint satisfaction problem, and then propose the parallel algorithms to solve the problem. The results of comprehensive computational experiments on the variety of inference tasks are reported. The question of minimizing an NFA consistent with a learning sample is computationally hard. [ABSTRACT FROM AUTHOR]

This paper proposes a baseline constrained PPP method to enhance the performance. The approach is based on constrained Kalman filter, and applied to BeiDou PPP in static, pseudo-kinematic, real kinematic modes. Both zero baseline and short baseline constrained PPP are verified in static mode, while a marine geodesy environment is designed to evaluate the performance in kinematic mode. The experiment results show that, compared to the PPP without constraint, the baseline constrained PPP can significantly improve the positional accuracy. Furthermore, the accuracy of baseline constrained static PPP can reach centimetre level, while the kinematic PPP can reach decimetre level. [ABSTRACT FROM AUTHOR]

Suhaid language is spoken in Seberuang and Suhaid river basins, Western Kalimantan (Indonesia). This language exhibited its uniqueness with the C+h (consonant + glottal fricative) sound system, which is similar to the aspirated occlusive voice. This aspirated occlusive is derived from the deletion of /É™/ in the penultimate syllable position. Since in Suhaid, r is correspondence to /h/, the C+h form will formed after the deletion of schwa. This paper discusses the existence of the aspirated occlusive voice from the perspective of Optimality phonology. This study has consolidated that this theory is a practicable approach to explain the generation of two different output after the deletion of schwa. This study found that in a disyllabic input, this deletion of schwa has lead to a floating mora which later being attached to immediate vowel and formed a vowel lengthening. On the other hand, for the trisyllabic input, the floating mora will attach to the onset nearby and produce a geminate consonant. This consonant later will undergo the process of degemination, segmental coalescence and eventually appeares as a breathy voiced in the output. Based on analysis, the *GEM (NO GEMINATE) and *COMPLEX ONSET (no more than one segment may associate to any one syllabic constituent) constraints played crucial role in terms of constraint satisfaction and violation. All the candidates with geminate consonant will be ruled out as fatal violation since *GEM dominates the other contraints in the constraint hierarchy. *COMPLEX ONSET on the other hand is an additional constraint that play specific roles to generate dwisyllabic output (from trisyllabic input). This constraint (as well as other constraints) will eliminates all the /C+h/ candidates and the candidate with breathy voice which fully satisfies the given constraint is the optimal candidate. [ABSTRACT FROM AUTHOR]

Presented paper concerns the competency-driven staff assignment and scheduling approach to the management of project portfolios subject to perturbations caused by employee absences and/or unexpected arrival of high priority jobs. Proactive strategy is considered, which exploits the concept of employee substitutability to improve the robustness of personnel allocation in the case of occurrence of specific types of disruptions. Solutions obtained using the model of a constraint satisfaction problem developed in this study are validated in series quantitative and qualitative experiments. With a view to future implementation in a Decision Support Systems dedicated to prototyping of proactive personnel allocation, a methodology employing the concept of a competency framework-based robustness measure is proposed. Implemented in a declarative framework, the proposed approach allows one to find a redundant competency framework robust to a given set of disruptions. [ABSTRACT FROM AUTHOR]

Under the background of excess capacity and energy saving in iron and steel enterprises, the hot rolling batch scheduling problem based on energy saving is a multi-objective and multi constraint optimization problem. In this paper, a hybrid multi-objective prize-collecting vehicle routing problem (Hybrid Price Collect Vehicle Routing Problem, HPCVRP) model is established to ensure minimum energy consumption, meet process rules, and maximize resource utilization. A two-phase Pareto search algorithm (2PPLS) is designed to solve this model. The improved MOEA/D with a penalty based boundary intersection distance (PBI) algorithm (MOEA/D-PBI) is introduced to decompose the HPCVRP in the first phase. In the second phase, the multi-objective ant colony system (MOACS) and Pareto local search (PLS) algorithm is used to generate approximate Pareto-optimal solutions. The final solution is then selected according to the actual demand and preference. In the simulation experiment, the 2PPLS is compared with five other algorithms, which shows the superiority of 2PPLS. Finally, the experiment was carried out on actual slab data from a steel plant in Shanghai. The results show that the model and algorithm can effectively reduce the energy consumption in the process of hot rolling batch scheduling. [ABSTRACT FROM AUTHOR]

• A robust approximation-based model predictive control scheme for constrained networked nonlinear systems is proposed. • A novel constraint tightening approach for the formulation of the FHOCP is proposed. • Both model error and inter-sampling behavior are considered. • The state and control input constraints are satisfied in continuous-time sense. • A guideline to determine the allowable sampling period is provided. In this paper, a robust approximation-based model predictive control (RAMPC) scheme for the constrained networked control systems (NCSs) subject to external disturbances is proposed. At each sampling instant, the approximate discrete-time model (DTM) is utilized for solving the optimal control problem online, and the control input applied to continuous-time systems can then be determined. Such RAMPC scheme enables to implement MPC for the continuous-time systems in the digital environment, and meanwhile, achieves the state and control input constraints satisfaction in continuous-time sense. Furthermore, we also provide a guideline to determine the allowable sampling period. Sufficient conditions for the feasibility of the RAMPC scheme as well as the associated stability are developed. Finally, the effectiveness of the RAMPC scheme is shown through a numerical simulation. [ABSTRACT FROM AUTHOR]

This paper considers the dynamic vehicle routing problem where a fleet of vehicles deals with periodic deliveries of goods or services to spatially dispersed customers over a given time horizon. Individual customers may only be served by predefined (dedicated) suppliers. Each vehicle follows a pre-planned separate route linking points defined by the customer location and service periods when ordered deliveries are carried out. Customer order specifications and their services time windows as well as vehicle travel times are dynamically recognized over time. The objective is to maximize a number of newly introduced or modified requests, being submitted dynamically throughout the assumed time horizon, but not compromising already considered orders. Therefore, the main question is whether a newly reported delivery request or currently modified/corrected one can be accepted or not. The considered problem arises, for example, in systems in which garbage collection or DHL parcel deliveries as well as preventive maintenance requests are scheduled and implemented according to a cyclically repeating sequence. It is formulated as a constraint satisfaction problem implementing the ordered fuzzy number formalism enabling to handle the fuzzy nature of variables through an algebraic approach. Computational results show that the proposed solution outperforms commonly used computer simulation methods. [ABSTRACT FROM AUTHOR]

This paper presents the solution to cost-based unit commitment (CBUC) problem with and without ramp rate limits of thermal power plants using general algebraic modelling system (GAMS) with BARON solver. The BARON solver in GAMS environment takes care of different units and system constraints to find an optimal solution. To validate the effectiveness of the proposed GAMS solution, simulations have been performed on six different systems consisting of 10-units, 20-units, 40-units, 60-units, 80-units and 100-units, respectively. The analysis also includes the valve-point loading along with the ramp rate limits of thermal units. Results obtained with BARON solver in GAMS have been compared with other approaches available in literature. Comparative analysis shows that the performance of GAMS is better as compared to other existing techniques in terms of operating cost obtained and satisfaction level of constraints. [ABSTRACT FROM AUTHOR]

Calibration approach is becoming more important in survey sampling. Calibration estimation helps in improving the estimates of population parameters by making use of auxiliary information. This paper proposes a new calibration estimator for estimating the population mean in the stratified random sampling with a set of new calibration constraints using known coefficient of variation of the auxiliary variable. The result has also been extended to the case of double sampling in stratified sampling. The proposed estimator has been compared with the estimator developed by Tracy et al. (2003) with the help of simulation study on real datasets. [ABSTRACT FROM AUTHOR]

Cloud data centers consume substantial amount of energy to meet up the growing demand of cloud resources. Recently, increase in enormous data processing and computing in data centers has resulted rigorous energy consumption that leads to large amount of carbon footprint and creates the harmful impact on the environment. The two main components of a data center that are contributing to energy consumption: computing energy and cooling energy. In this paper, we have considered the problem of scheduling parallel applications consisting of dependent tasks having precedence constraints in heterogeneous cloud computing environment. Recently many heuristics have been developed to optimize the execution time, i.e., makespan only without giving much deliberation to the computing energy and cooling energy needed for cooling the data center while performing the tasks. Reducing energy consumption leads to reduction in operating costs. Therefore, we proposed an algorithm namely power and temperature-aware workflow-scheduling (PATA-WSD) algorithm with user-specified SLA constraint (deadline) to optimize computing as well as cooling energy. The proposed algorithm minimizes the energy consumption (computing + cooling) of executing tasks along with satisfying deadline constraints with the use of dynamic voltage and frequency scaling technique. The simulation results are obtained using random task graphs and real-time scientific workflows such as Cybershake and Montage. Results demonstrated that the proposed algorithm optimizes the computing and cooling energy consumption. [ABSTRACT FROM AUTHOR]

In this paper the proposed constrained gravitational search algorithm (CGSA) is extended and used to solve multi-reservoir operation optimization problem. Tow constrained versions of GSA named partially constrained GSA (PCGSA) and fully constrained GSA (FCGSA) are outlined to solve this optimization problem. In the PCGSA, the problem constraints are partially satisfied, h owever, in the FCGSA, all the problem constraints are implicitly satisfied by providing the search space for each agent which contains only feasible solution and hence leading to smaller search space for each agent. These proposed constrained versions of GSA are very useful when they are applied to solve large scale multi-reservoir operation optimization problem. The constrained versions of GSA are formulated here for both possible variables of the problem means considering water release or storage volumes as the decision variables of the problem and therefore first and second formulations of these algorithms are proposed. The proposed algorithms are used to solve the well-known four and ten reservoir operation optimization problems and the results are presented and compared with those of original form of the GSA and any available results in the literature. The results indicate the superiority of the proposed algorithms and especially FCGSA over existing methods to optimally solve large scale multi-reservoir operation optimization problem. [ABSTRACT FROM AUTHOR]

In the paper, an adaptive neural controller for the tracking problem of a direct-current (DC) motor is investigated. Because the unknown functions are included in the systems, the neural networks are used to estimate the unknown functions. In this study, the state variables of DC motor are required to be constrained in the compact set. The main contribution of this paper is that the proposed scheme is successfully to integrate barrier Lyapunov function to avoid the violation of the constraints. Based on Lyapunov analysis, it is proved that the output of the DC motor follows a desired trajectory and all the signals of the systems are guaranteed to be bounded. A simulation result is shown to confirm the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Purpose – In multidisciplinary design optimization (MDO), if the relationships between design variables and some output parameters, which are important performance constraints, are complex implicit problems, plenty of time should be spent on computationally expensive simulations to identify whether the implicit constraints are satisfied with the given design variables during the optimization iteration process. The purpose of this paper is to propose an ensemble of surrogates-based analytical target cascading (ESATC) method to tackle such MDO engineering design problems with reduced computational cost and high optimization accuracy. Design/methodology/approach – Different surrogate models are constructed based on the sample point sets obtained by Latin hypercube sampling (LHS) method. Then, according to the error metric of each surrogate model, the repeated ensemble of surrogates is constructed to approximate the implicit objective functions and constraints. Under the framework of analytical target cascading (ATC), the MDO problem is decomposed into several optimization subproblems and the function of analysis module of each subproblem is simulated by repeated ensemble of surrogates, working together to find the optimum solution. Findings – The proposed method shows better modeling accuracy and robustness than other individual surrogate model-based ATC method. A numerical benchmark problem and an industrial case study of the structural design of a super heavy vertical lathe machine tool are utilized to demonstrate the accuracy and efficiency of the proposed method. Originality/value – This paper integrates a repeated ensemble method with ATC strategy to construct the ESATC framework which is an effective method to solve MDO problems with implicit constraints and black-box objectives. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of image restoration with box-constraints. Image restoration problem is ill-conditioned and the regularization approach has widely been used to stabilize the solution. The restored image highly depends on the choice of the regularization parameter. The regularization parameter is generally determined by trial-and-error method when no true original image is available. Obviously, it is time consuming. The main aim in this paper is to develop an algorithm to choose the regularization parameter automatically when the box-constraints are imposed. In the proposed algorithm, the regularization parameter is adaptively determined by the previous iterative solution. Numerical simulations are used to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The multi-objective scheduling of maximizing the profit and minimising the emission of a Hydro -Thermal System (HTS) using Lagrangian Relaxation-Evolutionary Programming (LR-EP) technique has been presented in this paper. The various constraints of the hydro and thermal power system network considered are power balance, reservoir storage, turbine flow rate and loading limits of both thermal and hydro plants. Numerical results for a sample test system consist of four hydro and three thermal plants have been presented. The increase in demand for electricity has lead to the introduction of hydro, nuclear and tidal power stations. Under such cases, it is essential to interconnect hydro and thermal plants so that effective utilization of all the available energy resources can be carried out and demand can be met easily. Since the operational cost of hydropower plant is low, it can be integrated with thermal power plant so that most of the demand can be met by hydro power plant resulting in less usage of fossil fuel consumption for thermal power plants. Unit Commitment (UC) for thermal plants in the deregulated environment has been dealt with in the earlier works showing considerable profit. Here, in this paper a new attempt has been carried out for multiobjective scheduling of hydrothermal power plants for a Generating company (GENCO). It is observed that there is a increase in profit on the inclusion of non-renewable energy source such as hydro power plants, since it reduces the fuel consumption of thermal power plants. This proves the improved effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A new sub-category of MANET is vehicular ad hoc network where vehicles communicate with each other. Because of constraint roads and very high speed of vehicles, routing is an issue in VANET. Most of the papers analyzed the performance of topology based routing protocols. This paper analyzed the performance of distance-effect routing algorithm for mobility (DREAM) and location aided routing (LAR) location-based protocols for city and highway vehicular environment. Metrics for analysis of performance like PDR, routing overhead, delay, throughput and lost packet ratio are evaluated using ns2 with IEEE802.11p. The simulation results are analyzed using AWK programming script. [ABSTRACT FROM AUTHOR]

This paper presents new approaches for maximal constraint satisfaction problems (Max-CSPs). Inspired by the honeybee marriage process, our approaches try to reach a solution that satisfies as many constraints as possible in a reasonable period of time. Our approaches are a centralized and distributed honey-bee optimization enhanced by a new parameter called local optimum detector. The latter allows detection of the local optimum. In this paper, newer algorithms and their experimental results are presented. [ABSTRACT FROM AUTHOR]

This paper presents a new model and solution for the multi-objective Vehicle Routing Problem with Soft Time Windows ( VRPSTW ) using a hybrid metaheuristic technique. The proposed methodology is developed on the basics of a new swarm based Artificial Bee Colony ( ABC ) algorithm combined with two-step constrained local search for neighborhood selection. VRPSTW involves computing the routes of a set of vehicles with fixed capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. Here, the time window constraints are relaxed into “soft”, that is penalty terms are added to the solution cost whenever a vehicle serves a customer outside of his time window. The solution of routing problems with soft time windows has valuable practical applications. This paper uses a direct interpretation of the VRPSTW as a multi-objective optimization problem where the total traveling distance, number of window violations and number of required vehicles are minimized while capacity and time window constraints are met. Our work aims at using ABC inspired foraging behavior of honey bees which balances exploration and exploitation to avoid local optima and reach the global optima. The algorithm is applied to solve the well known benchmark Solomon׳s problem instances. Experimental results show that our suggested approach is quite effective, as it provides solutions that are competitive with the best known results in the literature. Finally, we present an analysis of our proposed algorithm in terms of computational time. [ABSTRACT FROM AUTHOR]

This paper investigates group consensus for the first-order multi-agent systems with nonlinear input constraints. And group consensus protocols for discrete-time and continuous-time multi-agent systems are proposed. By using the tools of the Lyapunov function, LaSalle׳s invariance principle and graph theory, group consensus/finite-time group consensus conditions are obtained. Finally, two simulation examples are provided to illustrate the effectiveness of our proposed methods in the paper. [ABSTRACT FROM AUTHOR]

Constraint programming is a paradigm wherein relations between variables are stated in the form of constraints. Many real life problems come from uncertain and dynamic environments, where the initial constraints and domains may change during its execution. Thus, the solution found for the problem may become invalid. The search for robust solutions for constraint satisfaction problems (CSPs) has become an important issue in the field of constraint programming. In some cases, there exists knowledge about the uncertain and dynamic environment. In other cases, this information is unknown or hard to obtain. In this paper, we consider CSPs with discrete and ordered domains where changes only involve restrictions or expansions of domains or constraints. To this end, we model CSPs as weighted CSPs (WCSPs) by assigning weights to each valid tuple of the problem constraints and domains. The weight of each valid tuple is based on its distance from the borders of the space of valid tuples in the corresponding constraint/domain. This distance is estimated by a new concept introduced in this paper: coverings. Thus, the best solution for the modeled WCSP can be considered as a most robust solution for the original CSP according to these assumptions. [ABSTRACT FROM AUTHOR]

E-learning is a knowledge management concept where content creators have to arrange a set of learning resources, to present them in a clear and comprehensive way to the learners. In this paper, we formulate a new approach for obtaining better learning paths for different learners groups as a constraint satisfaction problem (CSP) in which meta-data and competencies are used to define the relationships between the learning objects (LOs), where the course materials are used to formulate LOs sequence. The main aim of this paper is to obtain a dynamic learning path for the considered CSP problem by using the swarm intelligence technique, which is a sub-set of the artificial intelligence technique. Further, the proposed model is tested in a simulated environment, which gives an optimized LO sequencing. The simulation results reveal that the artificial ants gives solution to the proposed problem in an optimized way. More precisely, suitable learning path can be obtained by applying ant colony optimization (ACO) technique. From the obtained results it is concluded that the proposed model supports the e-learning portal administrator in getting benefits in terms of less processing time and minimal sequencing cost. [ABSTRACT FROM AUTHOR]

The acceptance of autonomous control technologies in planetary science has met significant resistance. Many within this scientific community question the efficacy of autonomous technologies for making decisions regarding what data to collect, how to process it and its processing. These technologies, however, can be used to significantly increase the scientific return on mission investment by removing limitations imposed by communications bandwidth constraints and communications and human decision making delays. A fully autonomous mission, in an ideal case, could be deployed, perform most of the substantive work itself (possibly relying on human assistance for dealing with any unexpected or unexplained occurrences) and return an answer to a scientific question along with data selected to allow scientists to validate software performance. This paper presents the results of a survey of planetary scientists which attempts to identify the root causes of the impediments to the use of this type of technology and identify pathways to its acceptance. Previous work considered planetary science as a single large community. This paper contrasts the differences in acceptance between component fields of planetary science. [ABSTRACT FROM AUTHOR]

Purpose: Volumetric modulated arc therapy (VMAT) optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. High-performance graphics processing units (GPUs) have been used to speed up the computations. However, GPU's relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs, and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation-based VMAT algorithm, previously developed in the authors' group, on a multi- GPU platform to solve the memory limitation problem. While the column-generation-based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. The authors also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. Methods: The column-generation approach generates VMAT apertures sequentially by solving a pricing problem (PP) and a master problem (MP) iteratively. In the authors' method, the sparse DDC matrix is first stored on a CPU in coordinate list format (COO). On the GPU side, this matrix is split into four submatrices according to beam angles, which are stored on four GPUs in compressed sparse row format. Computation of beamlet price, the first step in PP, is accomplished using multi-GPUs. A fast inter-GPU data transfer scheme is accomplished using peer-to-peer access. The remaining steps of PP and MP problems are implemented on CPU or a single GPU due to their modest problem scale and computational loads. Barzilai and Borwein algorithm with a subspace step scheme is adopted here to solve the MP problem. A head and neck (H&N) cancer case is then used to validate the authors' method. The authors also compare their multi-GPU implementation with three different single GPU implementation strategies, i.e., truncating DDC matrix (S1), repeatedly transferring DDC matrix between CPU and GPU (S2), and porting computations involving DDC matrix to CPU (S3), in terms of both plan quality and computational efficiency. Two more H&N patient cases and three prostate cases are used to demonstrate the advantages of the authors' method. Results: The authors' multi-GPU implementation can finish the optimization process within ~1 min for the H&N patient case. S1 leads to an inferior plan quality although its total time was 10 s shorter than the multi-GPU implementation due to the reduced matrix size. S2 and S3 yield the same plan quality as the multi-GPU implementation but take ~4 and ~6 min, respectively. High computational efficiency was consistently achieved for the other five patient cases tested, with VMAT plans of clinically acceptable quality obtained within 23-46 s. Conversely, to obtain clinically comparable or acceptable plans for all six of these VMAT cases that the authors have tested in this paper, the optimization time needed in a commercial TPS system on CPU was found to be in an order of several minutes. Conclusions: The results demonstrate that the multi-GPU implementation of the authors' columngeneration- based VMAT optimization can handle the large-scale VMAT optimization problem efficiently without sacrificing plan quality. The authors' study may serve as an example to shed some light on other large-scale medical physics problems that require multi-GPU techniques. [ABSTRACT FROM AUTHOR]

Surface quality and accuracy are the most important factors that affect the performance and life cycle of products. Surface deviations are confirmed to be nonstationary random signals. They show characteristics of randomness, irregularity and multiscale. For precise components, those deviations, i.e., the location/orientation deviations, form deviations, waviness and roughness are constrained by different scales of tolerances. In addition, tolerancing principles are specified to deal with the relationship between geometrical and dimensional deviations of the workpiece. Conventional modeling methods are unable to express surfaces with those features. This paper proposed a new method for geometrical modeling of multiscale toleranced workpiece with consideration of tolerancing principles. The workpiece deviations are decomposed into different scale of deviations. Each scale of deviations is expressed as the products of the normalized deviations and deviation factors. To balance the conflict between computing time and accuracy, a multilevel displaying method is developed. Those deviations are presented at different levels. In the simulation, each scale of tolerances and the tolerancing principles are integrated into the geometrical model of the workpiece. At the end of this paper, a case study was carried out to validate the proposed method. [ABSTRACT FROM AUTHOR]

This paper examines the effects of different types of complexity facing novice designers in constraint satisfaction tasks. The nature of the complexity in a design task was varied by manipulating different aspects of the extrinsic constraints, which refer to restrictions concerning how design components can be assembled. We investigated the effect of the number of constraints (Study 1) and the number of different types of constraint (Study 2) in a simulated office design task. Results indicated that tackling a design task with a greater number of constraints, or more types of constraint, resulted in decrements in performance. Study 3 examined the effect of reasoning about constraints that involved a fixed location in the office layout and those that did not. It was found that having a higher proportion of constraints that referenced a fixed location led to better design performance. The theoretical and practical aspects of these results are discussed. Practitioner summary:This paper identifies sources of constraint complexity facing the novice designer in an office design task. Three features of constraints proved problematic: the number of constraints, the number of types of constraint and whether the constraint involved a specific location. Training and decision support solutions are discussed. [ABSTRACT FROM AUTHOR]

Cloud computing is the key and frontier field of the current domestic and international computer technology, workflow task scheduling plays an important part of cloud computing, which is a policy that maps tasks to appropriate resources to execute. Effective task scheduling is essential for obtaining high performance in cloud environment. In this paper, we present a workflow task scheduling algorithm based on the resources' fuzzy clustering named FCBWTS. The major objective of scheduling is to minimize makespan of the precedence constrained applications, which can be modeled as a directed acyclic graph. In FCBWTS, the resource characteristics of cloud computing are considered, a group of characteristics, which describe the synthetic performance of processing units in the resource system, are defined in this paper. With these characteristics and the execution time influence of the ready task in the critical path, processing unit network is pretreated by fuzzy clustering method in order to realize the reasonable partition of processor network. Therefore, it largely reduces the cost in deciding which processor to execute the current task. Comparison on performance evaluation using both the case data in the recent literature and randomly generated directed acyclic graphs shows that this algorithm has outperformed the HEFT, DLS algorithms both in makespan and scheduling time consumed. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper discusses the short-term combined economic emission scheduling of hydrothermal power systems with cascaded reservoirs [Energy Convers Manage. 50 (2009) 97–104], while differential evolution algorithm has been employed to solve the optimization problem. However, this problem is subjected to several constraints like the generation limitations of generating units. The solutions reported in the original paper do not satisfy the constraint on the initial and final reservoir storage volumes of hydro units as well as the constraint on the generated power by such units, which should be positive at any time of the scheduling period. Thus, this paper intends to prove this issue and solve the problem using Normal Boundary Intersection (NBI) method, in order to propose the correct solutions satisfying all the constraints of the short-term hydrothermal scheduling problem. [ABSTRACT FROM AUTHOR]

Environmental economic dispatch of fixed head of hydrothermal power systems is viewed as a mulitobjective optimization problem in this paper. The practical hydrothermal system possesses various constraints which make the problem of finding global optimum difficult. This paper develops an improved multiobjective estimation of distribution algorithm to solving the above problem. A local learning operation is added into the original regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) in the improved approach so as to improve the local search ability and enhance the convergence efficiency. Furthermore, a repair mechanism is employed to repair the searched infeasible solutions in order to be able to search in the feasible region. In the experiment, the results obtained by the proposed approach have been compared with those from other three MOEAs: NSGA-II, NNIA, and RM-MEDA. Results from some pervious reported methods have also been employed to compare with our method. In addition, the results demonstrate the superiority of this proposed method as a promising MOEA to solve this power system multiobjective optimization problem. [ABSTRACT FROM AUTHOR]

This paper presents a novel parameter automation strategy for particle swarm optimization algorithm for solving non-convex emission constrained economic dispatch (NECED) problems. Many evolutionary techniques such as particle swarm optimization, differential evolution have been applied to solve these problems and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. This paper presents a new improved particle swarm optimization technique called self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC) for non-convex emission constrained economic dispatch (NECED) problems to avoid premature convergence. Generator ramp rate limits and prohibited operating zones are taken into account in problem formulation. Non-convex emission constrained economic dispatch (NECED) problem is obtained by considering both the economy and emission objectives. The performance of the proposed method is demonstrated on two sample test systems. The results of the proposed method are compared with other methods. It is found that the results obtained by the proposed method are superior in terms of fuel cost, emission output and losses. [ABSTRACT FROM AUTHOR]

Distribution systems are most commonly operated in a radial configuration for a number of reasons. In order to impose radiality constraint in the optimal network reconfiguration problem, an efficient algorithm is introduced in this paper based on graph theory. The paper shows that the normally followed methods of imposing radiality constraint within a mixed-integer programming formulation of the reconfiguration problem may not be sufficient. The minimum-loss network reconfiguration problem is formulated using different ways to impose radiality constraint. It is shown, through simulations, that the formulated problem using the proposed method for representing radiality constraint can be solved more efficiently, as opposed to the previously proposed formulations. This results in up to 30% reduction in CPU time for the test systems used in this study. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of data gathering with compressive sensing (CS) in wireless sensor networks (WSNs). Unlike the conventional approaches, which require uniform sampling in the traditional CS theory, we propose a random walk algorithm for data gathering in WSNs. However, such an approach will conform to path constraints in networks and result in the non-uniform selection of measurements. It is still unknown whether such a non-uniform method can be used for CS to recover sparse signals in WSNs. In this paper, from the perspectives of CS theory and graph theory, we provide mathematical foundations to allow random measurements to be collected in a random walk based manner. We find that the random matrix constructed from our random walk algorithm can satisfy the expansion property of expander graphs. The theoretical analysis shows that a k-sparse signal can be recovered using \ell_1 minimization decoding algorithm when it takes m=O(k\;\log (n/k)) independent random walks with the length of each walk t=O(n/k) in a random geometric network with n nodes. We also carry out simulations to demonstrate the effectiveness of the proposed scheme. Simulation results show that our proposed scheme can significantly reduce communication cost compared to the conventional schemes using dense random projections and sparse random projections, indicating that our scheme can be a more practical alternative for data gathering applications in WSNs. [ABSTRACT FROM PUBLISHER]

Role mining is a critical step for organizations that migrate from traditional access control mechanisms to role based access control (RBAC). Additional constraints may be imposed while generating roles from a given user-permission assignment relation. In this paper we consider two such constraints which are the dual of each other. A role-usage cardinality constraint limits the maximum number of roles any user can have. Its dual, the permission-distribution cardinality constraint, limits the maximum number of roles to which a permission can belong. These two constraints impose mutually contradictory requirements on user to role and role to permission assignments. An attempt to satisfy one of the constraints may result in a violation of the other. We show that the constrained role mining problem is NP-Complete and present heuristic solutions. Two distinct frameworks are presented in this paper. In the first approach, roles are initially mined without taking the constraints into account. The user-role and role-permission assignments are then checked for constraint violation in a post-processing step, and appropriately re-assigned, if necessary. In the second approach, constraints are enforced during the process of role mining. The methods are first applied on problems that consider the two constraints individually, and then with both considered together. Both methods are evaluated over a number of real-world data sets. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed model predictive control (MPC) strategy for a large-scale system that consists of several dynamically coupled nonlinear systems with decoupled control constraints and disturbances. In the proposed strategy, all subsystems compute their control signals by solving local optimizations constrained by their nominal decoupled dynamics. The dynamic couplings and the disturbances are accommodated through new robustness constraints in the local optimizations. The paper derives relationships among, and designs procedures for, the parameters involved in the proposed distributed MPC strategy based on the analysis of the recursive feasibility and the robust stability of the overall system. The paper shows that, for a given bound on the disturbances, the recursive feasibility is guaranteed if the sampling interval is properly chosen. Moreover, it establishes sufficient conditions for the overall system state to converge to a robust positively invariant set. The paper illustrates the effectiveness of the proposed distributed MPC strategy by applying it to three coupled cart-(nonlinear) spring–damper subsystems. [ABSTRACT FROM AUTHOR]

Many practical applications in control require that constraints on the inputs and states of the system are respected, while some performance criterion is optimized. In the presence of model uncertainties or disturbances, it is often sufficient to satisfy the state constraints for at least a prescribed share of the time, such as in building climate control or load mitigation for wind turbines. For such systems, this paper presents a new method of Scenario-Based Model Predictive Control (SCMPC). The basic idea is to optimize the control inputs over a finite horizon, subject to robust constraint satisfaction under a finite number of random scenarios of the uncertainty and/or disturbances. Previous SCMPC approaches have suffered from a substantial gap between the rate of constraint violations specified in the optimal control problem and that actually observed in closed-loop operation of the controlled system. This paper identifies the two theoretical explanations for this gap. First, accounting for the special structure of the optimal control problem leads to a substantial reduction of the problem dimension. Second, the probabilistic constraints have to be interpreted as average-in-time, rather than pointwise-in-time. Based on these insights, a novel SCMPC method can be devised for general linear systems with additive and multiplicative disturbances, for which the number of scenarios is significantly reduced. The presented method retains the essential advantages of the general SCMPC approach, namely a low computational complexity and the ability to handle arbitrary probability distributions. Moreover, the computational complexity can be adjusted by a sample-and-remove strategy. [ABSTRACT FROM AUTHOR]

In this paper, a resource allocation problem is formulated to maximize the throughput of vehicular user equipments (VUEs) in both licensed, and unlicensed frequency bands under constraints of reliability, and latency for vehicular communications as well as the Quality of Service (QoS) for WiFi network based on a network system with coexisting VUEs, and WiFi user equipments (WUEs). In the system, the VUEs are able to access to the unlicensed frequency bands, and the interference among the VUEs, and WUEs are mitigated by the listen-before-talk (LBT) scheme or the duty cycle scheme. Due to the mixed integer nonlinear programming (MINLP) objective in the optimization problem, the problem is hard to be solved directly. Instead, we solve the problem by two steps. The number of VUEs offloaded to unlicensed frequency bands as well as the time factor for duty cycle scheme is firstly determined, and then, the optimization problem is converted into a convex optimization problem, which is solved by the proposed Lagrange Duality Method (LDM). Numerical results show the efficiency of our proposed application scenario compared to case with only LTE system or only WiFi system. Moreover, compared to traditional Greedy algorithm, the proposed algorithm performs better in terms of throughput with a guaranteed QoS of WUEs. [ABSTRACT FROM AUTHOR]

Traffic management strategies grounded on reliable traffic demand data can reduce the costs associated with traffic congestions. While traffic video imaging can deliver real-time turning movement (TM) information, their cost can be prohibitive, especially to smaller agencies. The Network Flow (NETFLO) model uses the Minimum Cost Flow optimization by generating network flows along weighted arcs from a sparse set of detected link flows. The network is constructed so that weighted arcs represent both turning movements and links. When constrained, the NETFLO algorithm can infer reliable TM estimates from link flows detected in quasi-real-time. The research presented here establishes a consistently robust constraint regime that enables reliable estimation of urban intersection TMs. Turning movements are identified as through, left, and right. The optimal NETFLO constraint regime assigns a weight of 35 for lefts, 2 for throughs, 28 for right intersection turns. A weight of one applies to all other arcs. The optimal weighting scheme consistently outperforms a non-weighted regime, when tested estimating 5-minute TM flows on a real network in Orem, Utah, U.S.A. The optimal weights yield a mean coefficient of determination (R²) of 93% between observed and modeled TM flows, while the non-weighted regime produces an R² of 78%. With the optimal weight set, almost all through, 50% of right and 40% of left movements at intersections are reliably predicted. The lower reliability of left and right estimates can be attributed to their low observed flows, when compared to the dominant through flows. This paper shows the details of how NETFLO is constrained to give consistent urban intersection TM estimations. [ABSTRACT FROM AUTHOR]

In this paper, a model-based prognosis method where the degradation cannot be directly measured but only detectable through the drift of a model parameter is considered. This parameter drift is viewed as an unknown input, whose reconstruction allows the estimation of the degradation state. Model-based prognosis is divided into a filtering step where the current degradation state is estimated, and an uncertainty propagation step where the future degradation state is predicted. During these two steps, model uncertainty and measurement uncertainty are taken into account within the set-membership framework using interval techniques. The filtering step is performed with an interval unknown input observer for linear time-invariant discrete systems. Then, based on a set-membership constraint satisfaction methodology, interval propagation is performed to estimate the bounds that include the future degradation state until some failure threshold is reached, allowing the deduction of the interval including the remaining useful life. In order to demonstrate the efficiency of the proposed model-based prognosis methodology, the degradation of a suspension system is studied. [ABSTRACT FROM AUTHOR]

Most of modern technological networks that can perform their tasks with various distinctive levels of efficiency are multistate networks, and reliability is a fundamental attribute for their safe operation and optimal improvement. For a multistate network, the two-terminal reliability at demand level d, defined as the probability that the network capacity is greater than or equal to a demand of d units, can be calculated in terms of multistate minimal paths, called d-minimal paths (d-MPs) for short. This paper presents an efficient algorithm to find all d-MPs for the multistate two-terminal reliability problem. To advance the solution efficiency of d-MPs, an improved model is developed by redefining capacity constraints of network components and minimal paths (MPs). Furthermore, an effective technique is proposed to remove duplicate d-MPs that are generated multiple times during solution. A simple example is provided to demonstrate the proposed algorithm step by step. In addition, through computational experiments conducted on benchmark networks, it is found that the proposed algorithm is more efficient. [ABSTRACT FROM AUTHOR]

Since drop-impact is the most frequent cause of failure of mobile devices and the display is the major channel for users to interact with mobile devices, the reliability of the display module by drop-impact is one of the most important concerns for both manufacturers and customers. In this paper, we propose- a drop-impact reliability assessment method for the mobile display module using a statistical modeling approach. First, the general likelihood functions that consider various censored data are proposed to obtain accurate estimates. Second, under the constraints of the test budget and the sample size, the optimization problem for minimizing the mean squared error of the target of interest with the decision variables was proposed. The proposed method was applied to the real application example to determine the most reliable mobile display module design. [ABSTRACT FROM AUTHOR]

An efficient strategy for weakly-supervised segmentation is to impose constraints or regularization priors on target regions. Recent efforts have focused on incorporating such constraints in the training of convolutional neural networks (CNN), however this has so far been done within a continuous optimization framework. Yet, various segmentation constraints and regularization priors can be modeled and optimized more efficiently in a discrete formulation. This paper proposes a method, based on the alternating direction method of multipliers (ADMM) algorithm, to train a CNN with discrete constraints and regularization priors. This method is applied to the segmentation of medical images with weak annotations, where both size constraints and boundary length regularization are enforced. Experiments on two benchmark datasets for medical image segmentation show our method to provide significant improvements compared to existing approaches in terms of segmentation accuracy, constraint satisfaction and convergence speed. [ABSTRACT FROM AUTHOR]

The existing W asserstein distance has achieved great success in the transfer learning. However, the implementation of Lipschitz constraint was not good by previous methods. To overcome this problem, this paper proposed a new learning domain adaptation ability approach, namely spectral normalization W asserstein distance transfer network ( SNWDTN) . This method first calculated the spectral norm of the weight matrix, and then used the spectral norm to normalize the weight matrix to design the spectral normalization layer that could satisfy the Lipschitz constraint condition. Thus it provided better constraint condition satisfaction for the use of W asserstein distance. The experimental results of public dataset show that SNWDTN achieves better results than previous methods. [ABSTRACT FROM AUTHOR]