The Experts below are selected from a list of 2526 Experts worldwide ranked by ideXlab platform

Yang Shi - One of the best experts on this subject based on the ideXlab platform.

  • an effective genetic algorithm for the flexible job shop scheduling problem
    Expert Systems With Applications, 2011
    Co-Authors: Guohui Zhang, Liang Gao, Yang Shi
    Abstract:

    In this paper, we proposed an effective genetic algorithm for solving the flexible job-shop scheduling problem (FJSP) to minimize makespan time. In the proposed algorithm, Global Selection (GS) and Local Selection (LS) are designed to generate high-quality initial population in the Initialization Stage. An improved chromosome representation is used to conveniently represent a solution of the FJSP, and different strategies for crossover and mutation operator are adopted. Various benchmark data taken from literature are tested. Computational results prove the proposed genetic algorithm effective and efficient for solving flexible job-shop scheduling problem.

Guohui Zhang - One of the best experts on this subject based on the ideXlab platform.

  • an effective genetic algorithm for the flexible job shop scheduling problem
    Expert Systems With Applications, 2011
    Co-Authors: Guohui Zhang, Liang Gao, Yang Shi
    Abstract:

    In this paper, we proposed an effective genetic algorithm for solving the flexible job-shop scheduling problem (FJSP) to minimize makespan time. In the proposed algorithm, Global Selection (GS) and Local Selection (LS) are designed to generate high-quality initial population in the Initialization Stage. An improved chromosome representation is used to conveniently represent a solution of the FJSP, and different strategies for crossover and mutation operator are adopted. Various benchmark data taken from literature are tested. Computational results prove the proposed genetic algorithm effective and efficient for solving flexible job-shop scheduling problem.

Liang Gao - One of the best experts on this subject based on the ideXlab platform.

  • an effective genetic algorithm for the flexible job shop scheduling problem
    Expert Systems With Applications, 2011
    Co-Authors: Guohui Zhang, Liang Gao, Yang Shi
    Abstract:

    In this paper, we proposed an effective genetic algorithm for solving the flexible job-shop scheduling problem (FJSP) to minimize makespan time. In the proposed algorithm, Global Selection (GS) and Local Selection (LS) are designed to generate high-quality initial population in the Initialization Stage. An improved chromosome representation is used to conveniently represent a solution of the FJSP, and different strategies for crossover and mutation operator are adopted. Various benchmark data taken from literature are tested. Computational results prove the proposed genetic algorithm effective and efficient for solving flexible job-shop scheduling problem.

Alex Pappachen James - One of the best experts on this subject based on the ideXlab platform.

  • probabilistic neural network with memristive crossbar circuits
    International Symposium on Circuits and Systems, 2019
    Co-Authors: Yerbol Akhmetov, Alex Pappachen James
    Abstract:

    The scalability and non-ideality issues of the memristor circuits poses several challenges to the implementation of analog memristive probabilistic neural networks in hardware. To meet the emerging challenges of faster edge AI computing devices, the integration of neural networks within or near to the sensor can improve the data processing times, reduce bandwidth requirements, and reduce data transfer errors. The fast learning in probabilistic neural network (PNN) make it an attractive solution for energy efficient computing in edge devices. The PNN estimates the density function of the categories and classifies the input based on the Bayes decision rule. It avoids backpropagation, since weights are derived from training samples directly and set in the first Initialization Stage. The proposed hardware realization of the PNN is based on a memristor crosssbar architecture. The simulations demonstrate that the accuracy of the hardware realization of the PNN can be as high as 93.3% for the MNIST dataset if a proper smoothing parameter is selected.

  • feature extraction without learning in an analog spatial pooler memristive cmos circuit design of hierarchical temporal memory
    arXiv: Emerging Technologies, 2018
    Co-Authors: Olga Krestinskaya, Alex Pappachen James
    Abstract:

    Hierarchical Temporal Memory (HTM) is a neuromorphic algorithm that emulates sparsity, hierarchy and modularity resembling the working principles of neocortex. Feature encoding is an important step to create sparse binary patterns. This sparsity is introduced by the binary weights and random weight assignment in the Initialization Stage of the HTM. We propose the alternative deterministic method for the HTM Initialization Stage, which connects the HTM weights to the input data and preserves natural sparsity of the input information. Further, we introduce the hardware implementation of the deterministic approach and compare it to the traditional HTM and existing hardware implementation. We test the proposed approach on the face recognition problem and show that it outperforms the conventional HTM approach.

Mario I Chaconmurguia - One of the best experts on this subject based on the ideXlab platform.

  • temporal weighted learning model for background estimation with an automatic re Initialization Stage and adaptive parameters update
    Pattern Recognition Letters, 2017
    Co-Authors: Graciela Ramirezalonso, Juan A Ramirezquintana, Mario I Chaconmurguia
    Abstract:

    Abstarct Background Initialization and background update are two important Stages considered in the design of most background modeling algorithms. Commonly, these algorithms implement strategies in which their parameters have a very high adaptability in the background Initialization Stage in order to learn all the variations of the background. Contrary, in the background update phase, these parameters adapt slowly, in most cases with an exponential decay. This paper presents the BE-AAPSA method which automatically determines if the background Initialization and the background update need to be re–initialized. Re -Initialization is triggered if the video scene presents high variations, allowing the background to be defined more accurately. BE-AAPSA is based on a previously developed system, where two adaptive background models based on weight arrays with temporal learning mechanism identify dynamic objects within a video scene. The system implements four independent modules to treat the different factors that affect a correct definition of the dynamic object. In BE-AAPSA, the objective is to create a robust background estimation model where the learning rates for each pixel are calculated according to the results of the two adaptive weight arrays and the module where the video is classified. This approach allows handling different strategies to update learning rates at a pixel resolution. BE-AAPSA is validated with the SBI and SBMnet video databases and with a video created by concatenating scenes of the video categories presented in the CDnet 2014 database. According to the findings, BE-AAPSA produced highly accurate results with SBI and SBMnet and surpassed state-of-the-art methods with the CDnet video. These results demonstrate the importance of using an automatic re-Initialization scheme in the background Initialization and background update Stages when the video scene presents a major change or involves jittering. Furthermore, it shows the benefits of handling in separate modules the analysis of the background estimation results.