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Focuses on the development of an office systems to support clerical work. Accuracy and cost effectiveness in clerical work; Difficulty of describing clerical work; Description of business regulations in constraint.

A smart home needs to be human-centric, where it tries to fulfill human needs given the devices it has. Various works are developed to provide homes with reasoning and planning capability to fulfill goals, but most do not support complex sequence of plans or require significant manual effort in devising subplans. This is further aggravated by the need to optimize conflicting personal goals. A solution is to solve the planning problem represented as constraint satisfaction problem (CSP). But CSP uses hard constraints and, thus, cannot handle optimization and partial goal fulfillment efficiently. This paper aims to extend this approach to weighted CSP. Knowledge representation to help in generating planning rules is also proposed, as well as methods to improve performances. Case studies show that the system can provide intelligent and complex plans from activities generated from semantic annotations of the devices, as well as optimization to maximize personal constraints’ fulfillment. Note to Practitioners—Smart home should maximize the fulfillment of personal goals that are often conflicting. For example, it should try to fulfill as much as possible the requests made by both the mother and daughter who wants to watch TV but both having different channel preferences. That said, every person has a set of goals or constraints that they hope the smart home can fulfill. Therefore, human-centric system that automates the loosely coupled devices of the smart home to optimize the goals or constraints of individuals in the home is developed. Automated planning is done using converted services extracted from devices, where conversion is done using existing tools and concepts from Web technologies. Weighted constraint satisfaction that provides the declarative approach to cover large problem domain to realize the automated planner with optimization capability is proposed. Details to speed up planning through search space reduction are also given. Real-time case studies are run in a prototype smart home to demonstrate its applicability and intelligence, where every planning is performed under a maximum of 10 s. The vision of this paper is to be able to implement such system in a community, where devices everywhere can cooperate to ensure the well-being of the community. [ABSTRACT FROM AUTHOR]

This paper deals with the impulsive formation control of spacecraft in the presence of constraints on the position vector and time. Determining a set of path constraints can increase the safety and reliability in an impulsive relative motion of spacecraft. Specially, the feasibility problem of the position norm constraints is considered in this paper. Under assumptions, it is proved that if a position vector be reachable, then the reach time and the corresponding time of impulses are unique. The trajectory boundedness of the spacecraft between adjacent impulses are analyzed using the Gerschgorin and the Rayleigh–Ritz theorems as well as a finite form of the Jensen's inequality. Some boundaries are introduced regarding the Jordan–Brouwer separation theorem which are useful in checking the satisfaction of a constraint. Two numerical examples (approximate circular formation keeping and collision-free maneuver) are solved in order to show the applications and visualize the results. [ABSTRACT FROM AUTHOR]

A growing number of companies deploy their applications in green data centers (GDCs) and provide services to tasks of global users. Currently, a growing number of GDC providers aim to maximize their profit by deploying green energy facilities and decreasing brown energy consumption. However, the temporal variation in the revenue, price of grid, and green energy in tasks’ delay bounds makes it challenging for GDC providers to achieve profit maximization while strictly guaranteeing delay constraints of all admitted tasks. Unlike existing studies, a time-aware task scheduling (TATS) algorithm that investigates the temporal variation and schedules all admitted tasks to execute in GDC meeting their delay bounds is proposed. In addition, this paper provides the mathematical modeling of task refusal and service rates. In each iteration, TATS solves the formulated profit maximization problem by hybrid chaotic particle swarm optimization based on simulated annealing. Compared with several existing scheduling algorithms, TATS can increase profit and throughput without violating delay constraints of all admitted tasks. Note to Practitioners—This paper investigates the profit maximization problem for a green data center (GDC) while meeting delay constraints for all admitted tasks. Previous task scheduling algorithms do not jointly investigate temporal variation in revenue, green energy, and price of grid. Thus, they fail to meet the delay constraints of all admitted tasks. In this paper, a new approach that overcomes drawbacks of existing algorithms is proposed. It is obtained by using a hybrid metaheuristic algorithm that solves a constrained nonlinear optimization problem. Simulation results show that compared with several existing algorithms, it increases both throughput and profit. It can be readily incorporated into real-life industrial GDCs. The future work needs to investigate the repair/failure effect of GDCs on the proposed time-aware task scheduling. [ABSTRACT FROM AUTHOR]

This paper addresses the fundamental theoretical development of a linear optimal noncausal control for wave energy converters (WECs) in a closed analytic form. It is well known that the WEC control is a noncausal control problem, i.e., the future wave information contributes to the present control action. This paper provides a reliable, efficient, and simple linear optimal controller with guaranteed stability for the WEC control problem. The proposed WEC linear optimal control consists of a causal linear state feedback part and an anticausal linear feedforward part to incorporate the influence of future incoming waves. The stability of the closed-loop WEC control system with the proposed linear optimal controller is proven. The contribution of the noncausal term using wave prediction information to the optimal control and the energy output is analyzed quantitatively. The proposed linear controller can be more preferred when constraint satisfaction becomes a less important issue for mild sea states and some well-designed WECs with an ample operation range. The proposed optimal control strategy can be extended for a generic class of energy maximization problems. Numerical simulations are presented to justify the efficacy of the proposed WEC optimal control. [ABSTRACT FROM AUTHOR]

A hierarchical model predictive control (MPC) approach is developed for energy management of aircraft electro-thermal systems. High-power electrical systems on board modern and future aircraft perform a variety of mission- and flight-critical tasks, while thermal management systems actively cool these electronics to satisfy component-specific temperature constraints, ensuring safe and reliable operation. In this paper, coordination of these electrical and thermal systems is performed using a hierarchical control approach that decomposes the multi-energy domain, constrained optimization problem into smaller, more computationally efficient problems that can be solved in real-time. A hardware-in-the-loop (HIL) experimental testbed is used to evaluate the proposed hierarchical MPC in comparison to a baseline controller for a scaled, laboratory representation of an aircraft electro-thermal system. Experimental results demonstrate that the proposed approach outperforms the baseline controller across a range of electrical loading in terms of both efficient energy management and constraint satisfaction. [ABSTRACT FROM AUTHOR]

This paper studies a single beacon-based three-dimensional multirobot localization (MRL) problem. Unlike most of existing localization algorithms which use extended Kalman filter or maximum a posteriori, moving horizon estimation (MHE), and convex optimization are novelly designed to perform MRL with constraints and unknown initial poses. The main contribution of this paper is three-fold: 1) a constrained MHE-based localization algorithm, which can bound localization error, impose various constraints and compromise between computational complexity and estimator accuracy, is proposed to estimate robot poses; 2) constrained optimization is examined in the perspective of Fisher information matrix to analyze why and how multirobot information and constraints are able to reduce uncertainties; 3) a semidefinite programming-based initial pose estimation, which can efficiently converge to global optimum, is developed by using convex relaxation. Simulations and experiments are conducted to verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Geomagnetically induced currents (GICs) introduced by geomagnetic disturbances (GMDs) can damage transformers, increase reactive power losses, and cause reliability issues in power systems. Finding an optimal strategy to place blocking devices (BDs) at transformer neutrals is essential to mitigating the negative impact of GICs. In this paper, we study and solve the problem of optimal BD placement (OBP), aiming at minimizing the damages of GMDs, subject to various practical constraints. In particular, our approach accounts for the potential impact of BD placement to neighboring interconnected systems; we also consider the time-varying nature of the geoelectric field in BD placement. To the best of our knowledge, neither has been considered in the existing works. Under these constraints, the combined complexity of solving the OBP problem on a large-scale system poses a big challenge. To address this, we developed a simulated annealing-based algorithm that can achieve near-optimal solutions for the OBP problem at a reduced computational complexity, while taking the above constraints into account. More importantly, this paper provides a general mathematical framework that can be used to solve various OBP problems, with different objective functions and constraints. We demonstrated the effectiveness and the efficiency of our method by using power systems of various sizes. [ABSTRACT FROM AUTHOR]

Planning motions for two robot arms to move an object collaboratively is a difficult problem, mainly because of the closed-chain constraint, which arises whenever two robot hands simultaneously grasp a single rigid object. In this paper, we propose a manipulation planning algorithm to bring an object from an initial stable placement (position and orientation of the object on a support surface) toward a goal stable placement. The key specificity of our algorithm is that it is certified-complete: for a given object and a given environment, we provide a certificate that the algorithm will find a solution to any bimanual manipulation query in that environment whenever one exists. Moreover, the certificate is constructive: at run-time, it can be used to quickly find a solution to a given query. The algorithm is tested in software and hardware on a number of large pieces of furniture. Note to Practitioners—This paper presents an algorithm to solve a difficult class of bimanual manipulation planning problems where a movable object can be moved only when grasped by two robots. This problem arises naturally when manipulating a large and/or heavy object such as a piece of furniture and is therefore essential to industrial automation. The algorithm first precomputes a certificate, a set of robot motions to move the object between different placement classes that helps guarantee that the algorithm will find a solution to any planning query whenever one exists. This certificate is then used to quickly construct a solution trajectory to a planning query and can be reused under the same environment. The algorithm has been empirically verified through software and hardware experiments on a number of large pieces of furniture. An open-source implementation is provided. [ABSTRACT FROM AUTHOR]

The survey propagation (SP) algorithm for solving k-SAT problems has been shown recently as an instance of the belief propagation (BP) algorithm. In this paper, we show that for general constraint-satisfaction problems, SP may not be reducible from BP. We also establish the conditions under which such a reduction is possible. Along our development, we present a unification of the existing SP algorithms in terms of a probabilistically interpretable iterative procedure -- weighted probabilistic token passing. [ABSTRACT FROM AUTHOR]

When tuning the parameters of a constrained model predictive controller (MPC), one usually will use closed-loop simulations in order to predict closed-loop performance. Closed-loop simulation can be very time-consuming and inconvenient fur large-scale constrained MPC, such as paper machine cross-directional (CD) predictive control. Paper machine CD processes are two-dimensional (2-D) (temporal and spatial) systems with up to 600 inputs and 6000 outputs. It is very important to predict the steady-state values for the closed-loop CD MPC systems during the tuning process, as the variances of these values are used as the control performance indexes in paper making industry. This article proposes to use a direct one-step static optimizer for approximating the closed-loop steady-state performance of constrained CD MPC. The parameters of this static optimizer can be obtained through minimizing the difference of two closed-loop transfer functions. Experiments with industrial data demonstrate that the static optimizer is computationally much more efficient (up to two orders of magnitude) than closed-loop simulation while reliably and accurately predicting the steady-state performance. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural network (NN) constraint control method is studied for a class of uncertain nonlinear nonstrict feedback systems with state constraints. The restrictive assumption that the unknown internal dynamics must possess the monotonically increasing characteristics in previous results is removed. The property of radial basis function (RBF)NNs is used to solve the algebraic loop problem based on the approximation structure. In order to achieve full state constraint satisfactions, the barrier Lyapunov functions (BLFs) are employed in each design procedure. Based on the backstepping and less adjustable parameters techniques, the controllers and the adaptive laws are obtained. By using the Lyapunov stability theory, the boundedness of all signals in the closed-loop system is proved. Therefore, the scheme not only solves the stability problem of the nonstrict feedback system but also overcomes the influence of the state constraint on the control performance. Finally, the effectiveness of the control method is verified by two simulation examples. [ABSTRACT FROM AUTHOR]

Enforcing constraints on the maximum deliverable power is essential to protect lithium-ion batteries and to maximize resource utilization. This paper describes an algorithm to address the estimation of power capability of battery systems accounting for thermal and electrical constraints. The algorithm is based on model inversion to compute the limiting currents and, hence, power capability. The adequacy of model inversion significantly depends on the accuracy of model states and parameters. Herein, these are estimated by designing cascading estimators whose structure is determined by quantifying the relative estimability of states and parameters. The parameterized battery model and the estimation algorithms are integrated with a power management system in a model of a series hybrid electric vehicle to demonstrate their effectiveness. [ABSTRACT FROM AUTHOR]

This paper considers the problem of transmitting linear functions of two correlated Gaussian sources over a two-user additive Gaussian noise multiple access channel. The goal is to recover this linear function within an average mean squared error distortion criterion. Each transmitter has access to only one of the two Gaussian sources and is limited by an average power constraint. In this paper, a lattice coding scheme and two lower bounds on the achievable distortion are presented. The lattice scheme achieves within a constant of a distortion lower bound if the signal-to-noise ratio is greater than a threshold. Furthermore, for the difference of correlated Gaussian sources, uncoded transmission is shown to be worse in performance to lattice coding methods for correlation coefficients above a threshold. [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]

Determining contingency aware dispatch decisions by solving a security-constrained optimal power flow (SCOPF) is challenging for real-world power systems, as the high problem dimensionality often leads to impractical computational requirements. This problem becomes more severe when the SCOPF has to be solved not only for a single instance, but for multiple periods, e.g. in the context of electricity market analyses. This paper proposes an algorithm that identifies the minimal set of constraints that exactly define the space of feasible nodal injections for a given network and contingency scenarios. By internalizing the technical limits of the nodal injections and enforcing a minimal worst-case impact of contingencies to line flows, computational effort can be further improved. The case study applies and analyzes the methods on the IEEE 118 and A&M 2000 bus systems, as well as the German and European transmission systems. In all tested cases the proposed algorithm identifies at least 95% of the network and security constraints as redundant, leading to significant SCOPF solve time reductions. Scalability and practical implementation are explicitly discussed. The code and input data of the case study is published supplementary to the paper under an open-source license. [ABSTRACT FROM AUTHOR]

The quay crane scheduling problem (QCSP) is one of the most important problems for the operations at container ports. The QCSP aims to decide a QC schedule for loading and unloading containers so as to minimize the vessel turnaround time. The QCSP is subject to various kinds of constraints, e.g., task precedence constraints and QC noninterference constraints. This paper extends the QCSP by taking into consideration the stability constraints, which are crucial for the safety reason but often omitted in the existing literature. We provide a mathematical model for the QCSP with stability constraints (QCSPSCs). A bicriteria evolutionary algorithm is proposed to solve the QCSPSC. The algorithm consists of a sliding-window heuristic to fix the schedule, which violates the stability constraints. Extensive experiments are conducted to demonstrate the effectiveness of the algorithm. The computational results of the traditional QCSP and the QCSPSC are also compared and analyzed. Note to Practitioners—Stability is a vital factor for the safety of the ship and the goods inside. At a terminal, when a berthed container ship receives loading and unloading operations performed by quay cranes (QCs), it is required that the ship stability criterion does not exceed a certain threshold. In practice, ship operation managers are keen in minimizing the ship’s turnaround time via an efficient QC schedule for loading and unloading containers without leaning the ship to one side. The schedule is then implemented by automatic scheduling systems or simulation tools in the terminal routines. This paper presents some practical stability constraints and integrates them into the traditional QC scheduling model. The results show that by considering the stability constraints, the ship will become more stable, but the turnaround time will not be affected significantly. We believe that the introduction of ship stability constraints can not only fill in the gap between the research on QC scheduling and the real-world ship handling practices but also enrich the studies on logistics planning and port operations. [ABSTRACT FROM AUTHOR]

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

In this paper, Bayesian quickest change detection problems with sampling right constraints are considered. In particular, there is a sequence of random variables whose probability density function will change at an unknown time. The goal is to detect this change in a way such that a linear combination of the average detection delay and the false alarm probability is minimized. Two types of sampling right constrains are discussed. The first one is a limited sampling right constraint, in which the observer can take at most \(N\) observations from this random sequence. Under this setup, we show that the cost function can be written as a set of iterative functions, which can be solved by Markov optimal stopping theory. The optimal stopping rule is shown to be a threshold rule. An asymptotic upper bound of the average detection delay is developed as the false alarm probability goes to zero. This upper bound indicates that the performance of the limited sampling right problem is close to that of the classic Bayesian quickest detection for several scenarios of practical interest. The second constraint discussed in this paper is a stochastic sampling right constraint, in which sampling rights are consumed by taking observations and are replenished randomly. The observer cannot take observations if there are no sampling rights left. We characterize the optimal solution, which has a very complex structure. For practical applications, we propose a low complexity algorithm, in which the sampling rule is to take observations as long as the observer has sampling rights left and the detection scheme is a threshold rule. We show that this low complexity scheme is first order asymptotically optimal as the false alarm probability goes to zero. [ABSTRACT FROM AUTHOR]

This paper presents an experimental analysis of a nonlinear model predictive controller combined with an integral sliding mode control component and an explicit dead-time compensator applied to the closed-loop control of FlatFish, an autonomous underwater vehicle (AUV). The proposed strategy is composed of three control instances that can be integrated to deal with constraints, disturbances, and dead time. The integral sliding mode control instance is considered to attenuate unmeasured disturbance effects, the filtered Smith predictor component is used to compensate for the dead-time effect, and the model predictive control layer aims at systematically optimizing closed-loop performance with constraint satisfaction. The discussion highlights that any of these control layers can be either included or removed from the control chain, which depends on the challenges presented by the AUV problem. The benchmark for such evaluation is the original control chain of FlatFish, which is based on a P cascade controller. Experimental tests were performed with FlatFish in a saltwater tank located in Bremen, Germany, and showed the superior performance of the proposed system. The experimental results show that the proposed control chain may be effectively used to provide improved tracking performance as expected from theoretical analysis. [ABSTRACT FROM AUTHOR]

The recently approved regulation about self-consumption in Spain will dramatically change the level of penetration of photovoltaic distributed generation combined with storage devices at residential level in short term. In this paper, we will study the impact of the massive installation of such technologies under the constraints imposed by the new regulation, over the terminal distribution network. As it will be demonstrated, this impact will be drastic in terms of energy demand, voltage profile, and load curves. [ABSTRACT FROM AUTHOR]

This paper presents a robust gain-scheduling controller design based on the parameter-space approach. The gain-scheduling control is of interest for systems with varying dynamics at different operating conditions. Requirements of robustness of stability and performance are also necessary if the system is subject to other parametric or model uncertainties. The parameter space approach is capable of projecting these requirements to the space of design parameters and facilitate the design of robust gain-scheduling control. This method is applied to automated vehicle lateral control to ensure its tracking accuracy and stability subject to uncertainties in vehicle load, speed and tire saturation. The proportional feedback gain and look-ahead distance are scheduled with respect to speed while sustaining robust stability, mixed sensitivity bound constraint and satisfaction of performance indices. Realistic hardware-in-the-loop (HIL) simulation with a validated vehicle model and road tests are conducted to demonstrate the robust performance of the designed gain-scheduling control in the presence of model uncertainty and disturbances. Another general robust gain-scheduling approach, the linear-matrix-inequality- (LMI-) design is also conducted for benchmarking purposes and the comparisons are discussed. [ABSTRACT FROM AUTHOR]

This paper focuses on statistical optimization and, more specifically, timing yield (TY)-constrained optimization. For cell replacement in timing-constrained optimization, we need an indicator that examines whether or not a timing violation occurs and gives the available timing for a gate. In deterministic optimization, the timing slack is used for this indicator. Although there is an analogous concept of TY slack in statistical optimization, it has not been well utilized. This paper proposes an effective way to use the TY slack for successful statistical optimization. To achieve this, we present an efficient method to calculate the TY slacks of gates and a strategy that uses timing resources for effective statistical optimization. Based on this work, we propose a novel statistical leakage minimization method that uses the TY slack for a gate change metric. The use of TY-based metrics that are appropriate for statistical design ensures that our method has a better optimization performance at a higher speed. Experimental results on ISCAS-85 benchmark circuits show that the leakage minimization method reduces leakage by 25.2% compared to the statistical benchmark method. In addition, our method has a better runtime when the number of gates is high. [ABSTRACT FROM PUBLISHER]

The current state-of-the art in high-level synthesis of asynchronous circuits is syntax directed translation, which performs a one-to-one mapping of an HDL-description into a corresponding circuit. This paper presents a method for behavioral synthesis of asynchronous circuits which builds on top of syntax directed translation, and which allows the designer to perform automatic design space exploration guided by area or speed constraints. This paper presents an asynchronous implementation template consisting of a data-path and a control unit and its implementation using the asynchronous hardware description language Balsa. This "conventional" template architecture allows us to adapt traditional synchronous synthesis techniques for resource sharing, scheduling, binding, etc., to the domain of asynchronous circuits. A prototype tool has been implemented on top of the Balsa framework, and the method is illustrated through the implementation of a set of example circuits. The main contributions of this paper are the fundamental idea, the template architecture and its implementation using asynchronous handshake components, and the implementation of a prototype tool. [ABSTRACT FROM AUTHOR]

In this paper, a contribution to solve the system prognostic problem is proposed. For that, the concept is defined in this work as a problem of predictive diagnosis under temporal constraint. Generally, this problem is treated using mainly approaches that are based on dynamic systems, experts' knowledge or are data-driven. Here, in order to describe the behavior of a process, we consider dynamic models that are composed of differential equations. The goal of this work is twofold. First, we present a new strategy for system prognosis based on observer design. Second, we propose a comparative study of two methodologies, dedicated to observer design, with application to an electromechanical process. To illustrate the performances of the approaches, simulation results are proposed. [ABSTRACT FROM AUTHOR]

We present an approach for planning ensembles of channels, ribbons, within 3D printed implants for facilitating radiation therapy treatment of cancer. The ribbons are traced out by sweeping a constant width rigid body (cuboid) along spatial curves. We propose a method for planning multiple disjoint and mutually collision-free ribbons of finite thickness along curvature and torsion constrained curves in 3D space. This is equivalent to planning motions for the cross section of the ribbon along a spatial curve such that the cross section is oriented along the unit binormal to the curve defined according to the Frenet–Serret frame. We propose a two-stage planning approach. In the first stage, a customized sampling-based planner uses rapidly exploring random trees (RRTs) to generate feasible curvature and torsion constrained ribbons. In the second stage, the curvature and torsion along each ribbon is locally optimized using sequential quadratic programming (SQP). We use this approach to design curved radiation delivery channels inside custom 3D printed implants that allow temporary insertion of a high-dose radioactive source that is threaded through the channels using a wire and allowed to dwell for specified times to expose cancerous tumors for intracavitary brachytherapy treatment. Constraints on the curvature and torsion are required for 3D printing (to allow flushing of sacrificial material) and for smooth insertion of radioactive sources. In simulation experiments, this approach achieves an improvement of 46% in tumor coverage compared with a greedy approach that generates channels sequentially. [ABSTRACT FROM AUTHOR]

This paper describes the design analysis and optimization of a novel 3-degrees of freedom (DOF) wearable parallel robot developed for ankle rehabilitation treatments. To address the challenges arising from the use of a parallel mechanism, flexible actuators, and the constraints imposed by the ankle rehabilitation treatment, a complete robot design analysis is performed. Three design stages of the robot, namely, kinematic design, actuation design, and structural design are identified and investigated, and, in the process, six important performance objectives are identified which are vital to achieve design goals. Initially, the optimization is performed by considering only a single objective. Further analysis revealed that some of these objectives are conflicting, and hence these are required to be simultaneously optimized. To investigate a further improvement in the optimal values of design objectives, a preference-based approach and evolutionary-algorithm-based nondominated sorting algorithm (NSGA II) are adapted to the present design optimization problem. Results from NSGA II are compared with the results obtained from the single objective optimization and preference-based optimization approaches. It is found that NSGA II is able to provide better design solutions and is adequate to optimize all of the objective functions concurrently. Finally, a fuzzy-based ranking method has been devised and implemented in order to select the final design solution from the set of nondominated solutions obtained through NSGA II. The proposed design analysis of parallel robots together with the multiobjective optimization and subsequent fuzzy-based ranking can be generalized with modest efforts for the development of all of the classes of parallel robots. [ABSTRACT FROM AUTHOR]

Coordinated beamforming has been optimized to maximize the sum rate under the transmit power constraints or to minimize the transmit power under the data rate constraints. In this paper, we design a precoder that maximizes the energy efficiency (EE) of multicell multiantenna downlink network with coordination and, in the meantime, ensures the minimal data rate requirement of each user. To find a solution of such a nonconvex optimization problem, we construct a convex subset of the original constraint set and a quasi-concave lower bound of the EE. Then, we propose an iterative algorithm to maximize the lower bound of the EE within the convex subset, which is shown to rapidly converge to a local optimum of the original problem. We evaluate the EE of the proposed algorithm through simulations under different user locations, data rate requirements, and numbers of antennas. The results demonstrate that the proposed precoder performs closely to an upper bound derived from interference-free assumption. When the circuit power consumption is dominant, the proposed precoder is much more energy efficient than the transmit power minimization precoder, as well as a precoder, including an interference alignment (IA) beamformer and the optimized transmit power maximizing the EE. [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]

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]

The effective dismantling of discarded products regardless being used or not is critically important to their reuse, recovery, and recycling. However, the existing product disassembly planning methods pay little or no attention to resource constraints, e.g., limited numbers of disassembly operators and tools. Thus, a resulting plan when being executed may be ineffective in practice. This paper presents a dual-objective optimization model for selective disassembly sequences by considering multiresource constraints such that disassembly profit is maximized and time is minimized. A scatter search is adopted to solve the proposed dual-objective optimization model. It embodies the generation of diverse initial solutions, global assessment of objective functions, a crossover combination operator, a local search strategy for improved solutions, and a reference set update method. To analyze the effect of different weights on its performance, simulations are conducted on different products. Its effectiveness is verified by comparing its optimization results and those of genetic local search. Note to Practitioners—This work deals with a sequence modeling and planning problem of product disassembly. It establishes a novel dual-objective optimization model for product disassembly subject to multiresource constraints. Previously, such a problem is handled through a methodology based on the optimization of a single objective, i.e., disassembly time or cost. The resultant solution is insufficient without fully considering disassembly resources, e.g., labors and tools. Also, in an actual disassembly process, a decision-maker may want to maximize disassembly profit, as well as minimize disassembly time. This work considers both objectives and proposes scatter search to solve disassembly problems. The results demonstrate that the proposed approach can solve them effectively. The obtained solutions give decision makers some desired choices to select a right disassembly process when an actual product is disassembled. [ABSTRACT FROM AUTHOR]

Cloud gaming as the emerging game service has attracted significant attention. However, traditional video streaming approach suffers from high bandwidth consumption, and traditional graphics streaming approach requires a long initial period to download game models. In this paper, we propose a novel hybrid streaming framework, jointly applying video streaming and graphics streaming to provide a high-quality gaming experience. In the proposed framework, cloud servers not only transmit the encoded video frames but also progressively transmit the graphics data, which are used to render a game frame to provide an additional reference to the video encoder. Based on the proposed framework, we investigate the delay–rate–distortion optimization problem, where the source rate between the video stream and the graphics stream is optimized to minimize the overall distortion under the bandwidth and response delay constraints. The experimental results demonstrate that the proposed hybrid streaming can achieve the lowest distortion under the constraints of bandwidth and response delay, compared with the traditional video streaming and graphics streaming. [ABSTRACT FROM PUBLISHER]

The problem of finding network codes for general connections is inherently difficult in capacity constrained networks. Resource minimization for general connections with network coding is further complicated. Existing methods for identifying solutions mainly rely on highly restricted classes of network codes, and are almost all centralized. In this paper, we introduce linear network mixing coefficients for code constructions of general connections that generalize random linear network coding for multicast connections. For such code constructions, we pose the problem of cost minimization for the subgraph involved in the coding solution and relate this minimization to a path-based constraint satisfaction problem (CSP) and an edge-based CSP. While CSPs are NP-complete in general, we present a path-based probabilistic distributed algorithm and an edge-based probabilistic distributed algorithm with almost sure convergence in finite time by applying communication free learning. Our approach allows fairly general coding across flows, guarantees no greater cost than routing, and shows a possible distributed implementation. Numerical results illustrate the performance improvement of our approach over existing methods. [ABSTRACT FROM PUBLISHER]

QoS routing plays a critical role in providing QoS support in the Internet. Most existing QoS routing algorithms employ the strategy of unidirectional search in route selection. Bidirectional search has been recognized as an effective strategy for fast route acquisition in identifying the shortest path connecting a pair of nodes. However, its efficiency has not been well established in the context of route selection subject to multiple additive constraints, which is in general NP-Complete. In this paper, we study how to employ bidirectional search to support efficient QoS routing subject to multiple additive constraints. The major contributions in this paper are as follows. First, we propose a k shortest path algorithm using bidirectional search, whose complexity is deduced to be O(\sqrtk\vert V \vert\lg (\vert V \vert) + k\vert E\vert), where \vert V \vert and \vert E \vert represent the number of nodes and links in the network, respectively. Second, we show that bidirectional search can significantly accelerate the convergence of several existing QoS routing algorithms. Third, we propose a novel cost-effective bidirectional multi-constrained routing algorithm, which can greatly alleviate the forwarding state scalability issue by supporting stateless QoS routing in IP networks via IP tunneling or constraints-based alternate routing in MPLS networks via label stacks. It has the fastest known on-line running time O(\vert V\vert). Theoretical and simulation results are given to demonstrate the high performance of our proposed algorithm in identifying QoS-satisfied paths and also in efficient resource utilization as compared with existing algorithms. [ABSTRACT FROM PUBLISHER]

Speech signals recorded in real environments may be corrupted by ambient noise and reverberation. Therefore, noise reduction and dereverberation algorithms for speech enhancement are typically employed in speech communication systems. Although microphone arrays are useful in reducing the effect of noise and reverberation, existing methods have limited success in significantly removing both reverberation and noise in real environments. This paper presents a method for noise reduction and dereverberation that overcomes some of the limitations of previous methods. The method uses a spherical microphone array to achieve plane-wave decomposition (PWD) of the sound field, based on direction-of-arrival (DOA) estimation of the desired signal and its reflections. A multi-channel linearly-constrained minimum-variance (LCMV) filter is introduced to achieve further noise reduction. The PWD beamformer achieves dereverberation while the LCMV filter reduces the uncorrelated noise with a controllable dereverberation constraint. In contrast to other methods, the proposed method employs DOA estimation, rather than room impulse response identification, to achieve dereverberation, and relative transfer function (RTF) estimation between the source reflections to achieve noise reduction while avoiding signal cancellation. The paper includes a simulation investigation and an experimental study, comparing the proposed method to currently available methods. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of multicast scaling performance in multichannel multiradio (MC-MR) networks. Under the assumption of both limited bandwidth and node tunability, a total fixed bandwidth (W) is equally split into (c) channels with (0

Unmanned aerial vehicles (UAVs) play a significant role in various 5G or Beyond-5G (B5G)-enabled Internet-of-Things (IoT) applications. However, the UAV performance in an air-ground cooperative network is significantly affected by its mobility and air-to-ground (A2G) communication and computation behaviors. In this paper, a UAV-oriented computation offloading system is investigated, where the UAV desires to complete its onboard computation demands with the assistance of a ground edge-computing infrastructure, i.e., a road-side unit (RSU). The objective is to maximize the energy efficiency of the UAV. Specifically, a non-convex constrained optimal control problem is formulated to optimize the overall energy efficiency of UAV by jointly considering the coupled effects of UAV's longitudinal mobility, A2G communication, and computation dynamics. To address the coupled complexity and non-convexity of the original problem, a primal decomposition approach is developed to transform the problem into a convex subproblem and a primary problem, and then a closed-form optimal transmission power control is derived by solving the subproblem, which is dependent on mobility information. By embedding the closed-form optimal power control into the primary problem, a gradient projection-based iterative algorithm is proposed to obtain a joint optimal solution for both the longitudinal acceleration control and the power control, the feasibility and convergence of which is also theoretically proven. Extensive simulations have been conducted to validate the effectiveness of the proposed method in terms of constraint satisfaction and convergence speed, and comparative results also demonstrate that it can outperform other benchmark methods in terms of global energy efficiency. [ABSTRACT FROM AUTHOR]

The design of electric vehicle batteries is a challenge requiring significant computational, human and material resources. This paper proposes a new framework to design a battery pack that allows quick iterations between designs and computes optimized configurations to give more information about the best available solutions. The battery pack is represented as a series/parallel configuration in a Constraint Satisfaction Problem (CSP) that is solved to exploit every possible configuration for specific vehicle requirements. The result is then used in a multi-objective Constrained Optimization Problem (COP) to determine the non-dominated (Pareto optimal) solutions according to selected objectives functions. Non-dominated solutions are computed by optimizing weighted-sum scalar functions as well as minimizing different types of distances to a design reference. A case study is explored to design a battery pack for an electric motorcycle. A 24s/6p battery module designed with this methodology has been validated by experiments and the battery pack has successfully been used at MotoStudent in 2018. [ABSTRACT FROM AUTHOR]