Yazar "Song, Yanjie" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    Assessing growth potential of careers with occupational mobility network and ensemble framework
    (Pergamon-Elsevier Science Ltd, 2024) Liu, Jiamin; Wang, Tao; Yao, Feng; Pedrycz, Witold; Song, Yanjie; He, Renjie
    The growth potential of a career reflects its future prospects and is an important consideration for individuals and organizations when career planning. There is still a lack of quantitative assessment tools for growth potential of careers. In this study, considering the key role of human capital in human resource management, as well as the excellent performance of complex network and machine learning in big data analysis and prediction, a career growth potential assessment model with human capital ensemble is proposed through human capital-based occupational mobility network and ensemble learning. First, an occupational mobility network is constructed based on online professional dataset to associate occupations with each other. Then, five dimensions of human capital measurements are designed to quantify human capital in terms of education, experience, social capital, occupational size, and concentration. These are then combined with the occupational mobility network to create a new network that depicts human capital flows among occupations. Finally, an ensemble framework for assessing career growth potential is constructed to integrate multidimensional human capital information in the network and obtain quantitative scores of growth potential. This study is the original attempt to adopt a data-driven idea and an intelligent approach to understand career growth potential. The experimental results show that it also makes a useful exploration for modeling human capital flows and intelligent assessment of career prospects.
  • Küçük Resim Yok
    Öğe
    Generalized Model and Deep Reinforcement Learning-Based Evolutionary Method for Multitype Satellite Observation Scheduling
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Song, Yanjie; Ou, Junwei; Pedrycz, Witold; Suganthan, Ponnuthurai Nagaratnam; Wang, Xinwei; Xing, Lining; Zhang, Yue
    Multitype satellite observation, including optical observation satellites, synthetic aperture radar (SAR) satellites, and electromagnetic satellites, has become an important direction in integrated satellite applications due to its ability to cope with various complex situations. In the multitype satellite observation scheduling problem (MTSOSP), the constraints involved in different types of satellites make the problem challenging. This article proposes a mixed-integer programming model and a generalized profit representation method in the model to effectively cope with the situation of multiple types of satellite observations. To obtain a suitable observation plan, a deep reinforcement learning-based genetic algorithm (DRL-GA) is proposed by combining the learning method and genetic algorithm. The DRL-GA adopts a solution generation method to obtain the initial population and assist with local search. In this method, a set of statistical indicators that consider resource utilization and task arrangement performance are regarded as states. By using deep neural networks to estimate the $Q$ value of each action, this method can determine the preferred order of task scheduling. An individual update strategy and an elite strategy are used to enhance the search performance of DRL-GA. Simulation results verify that DRL-GA can effectively solve the MTSOSP and outperforms the state-of-the-art algorithms in several aspects. This work reveals the advantages of the proposed generalized model and scheduling method, which exhibit good scalability for various types of observation satellite scheduling problems.
  • Küçük Resim Yok
    Öğe
    Learning Adaptive Genetic Algorithm for Earth Electromagnetic Satellite Scheduling
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Song, Yanjie; Ou, Junwei; Suganthan, Ponnuthurai Nagaratnam; Pedrycz, Witold; Yang, Qinwen; Xing, Lining
    Earth electromagnetic exploration satellites are widely used in many fields due to their wide detection range and high detection sensitivity. The complex environment and the proliferating number of satellites make management a primary issue. In this article, a learning adaptive genetic algorithm (LAGA) is proposed for the Earth electromagnetic satellite scheduling problem (EESSP). Control parameters are essential to the successful performance of evolutionary algorithms, and their sensitivity to the problem makes tuning parameters very time-consuming. In the LAGA, a gated recurrent unit (GRU) neural network model is used to control the parameters of variation operators. The neural network model is capable of leveraging real-time information to achieve dynamic parameter adjustment during population search. Moreover, a policy gradient-based reinforcement learning method is utilized to update the parameters of GRU. An adaptive evolution mechanism is employed in LAGA for the autonomous selection of crossover operators. Additionally, the heuristic initialization method, elite strategy, and local search method are incorporated into LAGA to enhance overall performance. Simulation experiments demonstrate the effectiveness of LAGA in solving the EESSP. This study highlights the advantages of utilizing reinforcement learning to optimize neural network models for controlling genetic algorithm searches. Learning adaptive planning methods can effectively address complex problem scenarios and enhance satellite scheduling system performance.