When dealing with engineering design problems, designers often encounter nonlinear and nonconvex features, multiple objectives, coupled decision making, and various levels of fidelity of sub-systems. To realize the de...
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When dealing with engineering design problems, designers often encounter nonlinear and nonconvex features, multiple objectives, coupled decision making, and various levels of fidelity of sub-systems. To realize the design with limited computational resources, problems with the features above need to be linearized and then solved using solution algorithms for linearprogramming. The adaptive linear programming (ALP) algorithm is an extension of the Sequential linearprogramming algorithm where a nonlinear compromise decision support problem (cDSP) is iteratively linearized, and the resulting linearprogramming problem is solved with satisficing solutions returned. The reduced move coefficient (RMC) is used to define how far away from the boundary the next linearization is to be performed, and currently, it is determined based on a heuristic. The choice of RMC significantly affects the efficacy of the linearization process and, hence, the rapidity of finding the solution. In this paper, we propose a rule-based parameter-learning procedure to vary the RMC at each iteration, thereby significantly increasing the speed of determining the ultimate solution. To demonstrate the efficacy of the ALP algorithm with parameter learning (ALPPL), we use an industry-inspired problem, namely, the integrated design of a hot-rolling process chain for the production of a steel rod. Using the proposed ALPPL, we can incorporate domain expertise to identify the most relevant criteria to evaluate the performance of the linearization algorithm, quantify the criteria as evaluation indices, and tune the RMC to return the solutions that fall into the most desired range of each evaluation index. Compared with the old ALP algorithm using the golden section search to update the RMC, the ALPPL improves the algorithm by identifying the RMC values with better linearization performance without adding computational complexity. The insensitive region of the RMC is better explored using the ALPPL-the ALP
A new practical syndrome coding method is proposed in this paper to minimize a suitably defined additive distortion function using adaptive linear programming of Polar Codes (ALP-PC). We propose modifications of the A...
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ISBN:
(纸本)9781467384605
A new practical syndrome coding method is proposed in this paper to minimize a suitably defined additive distortion function using adaptive linear programming of Polar Codes (ALP-PC). We propose modifications of the ALP-PC based on a new reduced factor graph for steganographic purposes which we denote by ALP-PCS. The implementation of wet paper codes in practice is possible using ALP-PCS. Simulation results show that this method minimizes additive distortion in steganography and gives good embedding efficiency performance.
A new practical syndrome coding method is proposed in this paper to minimize a suitably defined additive distortion function using adaptive linear programming of Polar Codes (ALP-PC). We propose modifications of the A...
详细信息
ISBN:
(纸本)9781467384612
A new practical syndrome coding method is proposed in this paper to minimize a suitably defined additive distortion function using adaptive linear programming of Polar Codes (ALP-PC). We propose modifications of the ALP-PC based on a new reduced factor graph for steganographic purposes which we denote by ALP-PCS. The implementation of wet paper codes in practice is possible using ALP-PCS. Simulation results show that this method minimizes additive distortion in steganography and gives good embedding efficiency performance.
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