This paper considers the downlink of reconfigurable intelligent surface (RIS) assisted cooperative non-orthogonal multiple access (CNOMA) systems. Our objective is to minimize the total transmit ...power by jointly optimizing the active beamforming vectors, transmit-relaying power, and RIS phase shifts. The formulated problem is a mixed-integer nonlinear programming (MINLP) problem. To tackle this problem, the alternating optimization approach is utilized to decouple the variables. In each alternative procedure, the optimal solutions for the active beamforming vectors, transmit-relaying power and phase shifts are obtained. However, the proposed algorithm has high complexity since the optimal phase shifts are solved by integer linear programming (ILP) whose computational complexity is exponential in the number of variables. To strike a good computational complexity-optimality trade-off, a low-complexity suboptimal algorithm is proposed by adopting the iterative penalty function based semidefinite programming (SDP) and the successive refinement approaches. Numerical results illustrate that: i) the proposed RIS-CNOMA system, aided by our proposed algorithms, outperforms the conventional CNOMA system. ii) the proposed low-complexity suboptimal algorithm can achieve near-optimal performance. iii) whether the RIS-CNOMA system outperforms the RIS assisted non-orthogonal multiple access (RIS-NOMA) system depends not only on the users' locations but also on the RIS's location.
Failure mode and effect analysis (FMEA) is a systematic, multidisciplinary team-based risk management tool used in diverse industries to help improve the safety and reliability of systems, designs, ...processes and/or services. However, the traditional FMEA method, when applied in real situations, shows some important drawbacks regarding failure mode evaluations, risk factor weights and risk priority ranking, etc. In this paper, we aim to develop an integrated risk prioritization approach to improve the performance of FMEA by using interval-valued intuitionistic fuzzy sets (IVIFSs) and the multi-attributive border approximation area comparison (MABAC) method. Moreover, a linear programming model is developed to obtain the optimal weights of risk factors when the weight information is incompletely known a priori. Finally, a practical example is presented to illustrate the applicability and effectiveness of the proposed FMEA, and results show that the new integrated approach offers a useful and reliable tool for rational criticality analysis.
•This study proposes a new risk priority model to improve the traditional FMEA.•Diversified assessments of experts are handled by interval-valued intuitionistic fuzzy sets.•An extended MABAC method is presented for ranking failure modes.•A linear programming model is used to determine risk factor weights with partial information.•The advantages of the new model are illustrated with a healthcare risk analysis case.
This sixth edition of the popular C# guide helps you learn the building blocks of the C# language, right from variables to classes and exception handling. After getting to grips with the basics of C# ...programming, it takes you through the world of Unity game development and how you can apply C# knowledge using game development examples.
The community-integrated energy system (CIES) is an essential energy internet carrier that has recently been the focus of much attention. A scheduling model based on chance-constrained programming is ...proposed for integrated demand response (IDR) enabled CIES in uncertain environments to minimize the system operating costs, where an IDR program is used to explore the potential interaction ability of electricity-gas-heat flexible loads and electric vehicles. Moreover, power to gas (P2G) and microgas turbine (MT), as the links of multienergy carriers, are adopted to strengthen the coupling of different energy subsystems. Sequence operation theory and linearization methods are employed to transform the original model into a solvable mixed-integer linear programming model. The simulation results on a practical CIES in North China demonstrate an improvement in the CIES operational economy via the coordination of IDR and renewable uncertainties, with P2G and MT enhancing the system operational flexibility and user comprehensive satisfaction. The CIES operation is able to achieve a tradeoff between the economy and system reliability by setting a suitable confidence level for the spinning reserve constraints. Besides, the proposed solution method outperforms the hybrid intelligent algorithm in terms of both optimization results and calculation efficiency.
This paper presents second-order cone programming (SOCP) and semidefinite programming (SDP) models to solve the multiperiod optimal control problem of unbalanced three-phase distribution grids with ...battery energy storage systems. The decision variables are the active and reactive power of the battery energy storage system. The objective is to minimize the power loss and energy purchase cost from the distribution substation. The optimal dispatch problem requires solution by volt/VAR optimization. The SOCP and SDP models ensure global optimum of original nonconvex nonlinear programming problem. Mupowerltiobjective volt/VAR optimization is performed and the benefits of reactive power support from battery energy storage systems are explored. A simulation-based heuristic to extract rank-one solution from the higher rank solution of SDP model is also proposed. Empirical results show the numerical stability and exactness of the proposed three-phase SOCP and SDP models.
We revisit the sequential rate-distortion (SRD) tradeoff problem for vector-valued Gauss-Markov sources with mean-squared error distortion constraints. Our study is partly motivated by the question ...recently raised in the paper "Rate-cost tradeoffs in control" (in Proc. 54th Annu. Allerton Conf. Commun., Control, Comput. , 2016, pp. 1157-1164) regarding the correctness of its solution algorithm known in the literature. We show via a counterexample that the dynamic reverse water-filling algorithm suggested by (15) of the paper "Stochastic linear control over a communication channel" ( IEEE Trans. Autom. Control , vol. 49, pp. 1549-1561, 2004) is not applicable to this problem, and consequently, the closed-form expression of the asymptotic SRD function derived in (17) of the paper "Stochastic linear control over a communication channel" ( IEEE Trans. Autom. Control , vol. 49, pp. 1549-1561, 2004) is not correct in general. Nevertheless, we show that the multidimensional Gaussian SRD function is semidefinite representable, and thus, it is readily computable.
•We focus on the periodic rural postman problem with irregular services.•We propose an algorithm that combines heuristics and mathematical programming.•Multi-start heuristics are used to construct ...initial solutions.•Some parts of these solutions are recombined through a model-based approach.•The results of an extensive computational study are presented.
In this article we address the periodic rural postman problem with irregular services (PRPP–IS), where some arcs and/or edges of a mixed graph must be traversed (to be serviced) a certain number of times in some subsets of days of a given time horizon. The goal is to define a set of minimum cost tours, one for each day or period of the time horizon, that satisfy the service requirements. For this problem we propose a two-phase algorithm that combines heuristics and mathematical programming. In the first phase, two different procedures are used to construct feasible solutions: a multi-start heuristic based on feasibility pump and a multi-start constructive heuristic. From these solutions, some fragments (parts of tours associated with the different days) are extracted. The second phase determines a solution for the PRPP–IS by combining the fragments by means of a mathematical model. We show the effectiveness of this solution approach through an extensive experimental phase on different sets of instances.
In the paper we consider advantages, sensitivity and application efficiency of the new method for solving multi-objective linear fractional programming problems. The proposed method is an iterative ...and numerically simple method that provides a unique solution in each iteration. The obtained solution is iteratively evaluated with the possibility of coarse and fine adjustment based on aspirations and cooperation among decision makers. Some relevant examples from the literature were used to present the stated properties and advantages of the proposed method in relation to the existing ones.
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