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学者姓名：傅明建
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Abstract ：
There are a lot of multi-objective optimization problems (MOPs) in the real world, and many multi-objective evolutionary algorithms (MOEAs) have been presented to solve MOPs. However, obtaining non-dominated solutions that trade off convergence and diversity remains a major challenge for a MOEA. To solve this problem, this paper designs an efficient multi-objective sine cosine algorithm based on a competitive mechanism (CMOSCA). In the CMOSCA, the ranking relies on non-dominated sorting, and the crowding distance rank is utilized to choose the outstanding agents, which are employed to guide the evolution of the SCA. Furthermore, a competitive mechanism stemming from the shift-based density estimation approach is adopted to devise a new position updating operator for creating offspring agents. In each competition, two agents are randomly selected from the outstanding agents, and the winner of the competition is integrated into the position update scheme of the SCA. The performance of our proposed CMOSCA was first verified on three benchmark suites (i.e., DTLZ, WFG, and ZDT) with diversity characteristics and compared with several MOEAs. The experimental results indicated that the CMOSCA can obtain a Pareto-optimal front with better convergence and diversity. Finally, the CMOSCA was applied to deal with several engineering design problems taken from the literature, and the statistical results demonstrated that the CMOSCA is an efficient and effective approach for engineering design problems.
Keyword ：
competitive mechanism competitive mechanism engineering design problem engineering design problem multi-objective algorithm multi-objective algorithm sine cosine algorithm (SCA) sine cosine algorithm (SCA)
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| GB/T 7714 | Liu, Nengxian , Pan, Jeng-Shyang , Liu, Genggeng et al. A Multi-Objective Sine Cosine Algorithm Based on a Competitive Mechanism and Its Application in Engineering Design Problems [J]. | BIOMIMETICS , 2024 , 9 (2) . |
| MLA | Liu, Nengxian et al. "A Multi-Objective Sine Cosine Algorithm Based on a Competitive Mechanism and Its Application in Engineering Design Problems" . | BIOMIMETICS 9 . 2 (2024) . |
| APA | Liu, Nengxian , Pan, Jeng-Shyang , Liu, Genggeng , Fu, Mingjian , Kong, Yanyan , Hu, Pei . A Multi-Objective Sine Cosine Algorithm Based on a Competitive Mechanism and Its Application in Engineering Design Problems . | BIOMIMETICS , 2024 , 9 (2) . |
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无信号灯左转路口是自动驾驶场景中最为危险的场景之一,如何实现高效安全的左转决策是自动驾驶领域的重大难题。深度强化学习(DRL)算法在自动驾驶决策领域具有广阔应用前景。但是,深度强化学习在自动驾驶场景中存在样本效率低、奖励函数设计困难等问题。提出一种基于专家先验的深度强化学习算法(CBAMBC SAC)来解决上述问题。首先,利用SMARTS仿真平台获得专家先验知识;然后,使用通道-空间注意力机制(CBAM)改进行为克隆(BC)方法,在专家先验知识的基础上预训练模仿专家策略;最后,使用模仿专家策略指导深度强化学习算法的学习过程,并在无信号灯路口左转决策中进行验证。实验结果表明,基于专家先验的DRL算法比传统的DRL算法更具优势,不仅可以免去人为设置奖励函数的工作量,而且可以显著提高样本效率从而获得更优性能。在无信号灯路口左转场景下,CBAM-BC SAC算法与传统DRL算法(SAC)、基于传统行为克隆的DRL算法(BC SAC)相比,平均通行成功率分别提高了14.2和2.2个百分点。
Keyword ：
模仿学习 模仿学习 深度强化学习 深度强化学习 自动驾驶 自动驾驶 行为克隆 行为克隆 驾驶决策 驾驶决策
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| GB/T 7714 | 傅明建 , 郭福强 . 基于深度强化学习的无信号灯路口决策研究 [J]. | 计算机工程 , 2024 , 50 (05) : 91-99 . |
| MLA | 傅明建 et al. "基于深度强化学习的无信号灯路口决策研究" . | 计算机工程 50 . 05 (2024) : 91-99 . |
| APA | 傅明建 , 郭福强 . 基于深度强化学习的无信号灯路口决策研究 . | 计算机工程 , 2024 , 50 (05) , 91-99 . |
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Deep learning has been widely used in single image rain removal and demonstrated favorable universality. However, it is still challenging to remove rain streaks, especially in the nightscape rain map which exists heavy rain and rain streak accumulation. To solve this problem, a single image nightscape rain removal algorithm based on Multi-scale Fusion Residual Network is proposed in this paper. Firstly, based on the motion blur model, evenly distributed rain streaks are generated and the dataset is recon-structed to solve the lack of nightscape rain map datasets. Secondly, according to the characteristics of the night rain map, multi-scale residual blocks are drawn on to reuse and propagate the feature, so as to ex-ploit the rain streaks details representation. Meanwhile, the linear sequential connection structure of multi-scale residual blocks is changed to a u-shaped codec structure, which tackles the problem that features cannot be extracted effectively due to insufficient scale. Finally, the features of different scales are com-bined with the global self-attention mechanism to get different rain streak components, then a cleaner re-stored image is obtained. The quantitative and qualitative results show that, compared to the existing algo-rithms, the proposed algorithm can effectively remove rain streaks while retaining detailed information and ensuring the integrity of image information. © 2023 Computer Society of the Republic of China. All rights reserved.
Keyword ：
Deep learning Deep learning Image fusion Image fusion Rain Rain
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| GB/T 7714 | He, Jia-Chen , Fu, Ming-Jian , Lin, Li-Qun . Multi-scale Fusion Residual Network For Single Image Rain Removal [J]. | Journal of Computers (Taiwan) , 2023 , 34 (2) : 129-140 . |
| MLA | He, Jia-Chen et al. "Multi-scale Fusion Residual Network For Single Image Rain Removal" . | Journal of Computers (Taiwan) 34 . 2 (2023) : 129-140 . |
| APA | He, Jia-Chen , Fu, Ming-Jian , Lin, Li-Qun . Multi-scale Fusion Residual Network For Single Image Rain Removal . | Journal of Computers (Taiwan) , 2023 , 34 (2) , 129-140 . |
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Radial basis function network-based autoregressive with exogenous input (RBF-ARX) models are useful in nonlinear system modelling and prediction. The identification of RBF-ARX models includes optimization of the (model lags, number of hidden nodes and state vector) and the parameters of the model. Previous works have usually ignored optimizations of the model's architecture. In this paper, the RBF-ARX architecture, which includes the selection of lags, number of nodes of the RBF network, lag orders and state vector, is encoded into a chromosome and is evolved simultaneously by a genetic algorithm (GA). This combines the advantages of the GA and the variable projection (VP) method to automatically generate a parsimonious RBF-ARX model with a high generalization performance. The highly efficient VP algorithm is used as a local search strategy to accelerate the convergence of the optimization. The experimental results demonstrate the effectiveness of the proposed method. (C) 2022 Elsevier B.V. All rights reserved.
Keyword ：
Genetic algorithms Genetic algorithms Model selection Model selection Parameter estimation Parameter estimation RBF-ARX models RBF-ARX models Time series prediction Time series prediction Variable projection Variable projection
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| GB/T 7714 | Chen, Qiong-Ying , Chen, Long , Su, Jian-Nan et al. Model selection for RBF-ARX models [J]. | APPLIED SOFT COMPUTING , 2022 , 121 . |
| MLA | Chen, Qiong-Ying et al. "Model selection for RBF-ARX models" . | APPLIED SOFT COMPUTING 121 (2022) . |
| APA | Chen, Qiong-Ying , Chen, Long , Su, Jian-Nan , Fu, Ming-Jian , Chen, Guang-Yong . Model selection for RBF-ARX models . | APPLIED SOFT COMPUTING , 2022 , 121 . |
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为了更好地提升软件工程专业人才培养质量,提高学生解决实际工程问题的能力,在探讨基于项目驱动的实验教学模式的基础上,提出基于项目驱动的软件工程实践教学模式,阐述该体系下的软件工程实践教学实施过程与具体方法,结合工程教育认证的持续改进思想,以工程能力指标作为评价标准,说明构建的实践教学体系的意义和效果。
Keyword ：
工程能力指标 工程能力指标 持续改进 持续改进 软件工程实践 软件工程实践 项目驱动 项目驱动
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| GB/T 7714 | 傅明建 , 周静平 , 汪璟玢 . 新工科背景下软件工程实践教学体系构建 [J]. | 计算机教育 , 2021 , (07) : 87-91 . |
| MLA | 傅明建 et al. "新工科背景下软件工程实践教学体系构建" . | 计算机教育 07 (2021) : 87-91 . |
| APA | 傅明建 , 周静平 , 汪璟玢 . 新工科背景下软件工程实践教学体系构建 . | 计算机教育 , 2021 , (07) , 87-91 . |
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Memory and reward have been proved as effective mechanisms for maintaining cooperation among selfish individuals. In this article, this study proposes a reward mechanism based on historical loyalty, that is, an individual who adhere to the cooperation strategy for a period of time will get additional reward. Accordingly, the reward for a loyal cooperator is undertaken by neighboring defectors equally. The results on prisoner's dilemma game show that, with appropriate loyalty threshold and reward factors, the cooperation level can be greatly enhanced. In addition, the time evolution of cooperator density and the spatial distribution of cooperators and defectors are also studied. (C) 2020 Published by Elsevier B.V.
Keyword ：
Cooperation Cooperation Memory Memory Reward Reward Spatial prisoner's dilemma game Spatial prisoner's dilemma game
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| GB/T 7714 | Fu, Mingjian , Wang, Jingbin , Cheng, Linlin et al. Promotion of cooperation with loyalty-based reward in the spatial prisoner's dilemma game [J]. | PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS , 2021 , 580 . |
| MLA | Fu, Mingjian et al. "Promotion of cooperation with loyalty-based reward in the spatial prisoner's dilemma game" . | PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 580 (2021) . |
| APA | Fu, Mingjian , Wang, Jingbin , Cheng, Linlin , Chen, Lijuan . Promotion of cooperation with loyalty-based reward in the spatial prisoner's dilemma game . | PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS , 2021 , 580 . |
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We propose a punishment mechanism in the spatial public goods game with continuous strategies. The value of strategy denotes the amount that an individual contributes to each group. In a group, the ones who contribute the least will be punished by others and punishers equally share the associated costs. It is found that the punishment fine and the number of individuals being punished in a group play important roles in the evolution of cooperation. Compared with the case of no punishment, the cooperation level increases (decreases) when the number of individuals being punished is less (more) than half of the total number of individuals in a group. For a fixed value of the enhancement factor, the cooperation level increases (decreases) as the punishment fine increases when individuals being punished are the minority (majority) in a group. Copyright (C) 2020 EPLA
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| GB/T 7714 | Yang, Han-Xin , Fu, Ming-Jian . A punishment mechanism in the spatial public goods game with continuous strategies [J]. | EPL , 2020 , 132 (1) . |
| MLA | Yang, Han-Xin et al. "A punishment mechanism in the spatial public goods game with continuous strategies" . | EPL 132 . 1 (2020) . |
| APA | Yang, Han-Xin , Fu, Ming-Jian . A punishment mechanism in the spatial public goods game with continuous strategies . | EPL , 2020 , 132 (1) . |
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It is well known that the feedback mechanism or the individual's intuitive response to the epidemic can have a vital effect on the disease's spreading. In this paper, we investigate the bifurcation behavior and the optimal feedback mechanism for an SIS epidemic model on heterogeneous networks. Firstly, we present the bifurcation analysis when the basic reproduction number is equal to unity. The direction of bifurcation is also determined. Secondly, different from the constant coefficient in the existing literature, we incorporate a time-varying feedback mechanism coefficient. This is more reasonable since the initiative response of people is constantly changing during different process of disease prevalence. We analyze the optimal feedback mechanism for the SIS epidemic network model by applying the optimal control theory. The existence and uniqueness of the optimal control strategy are obtained. Finally, a numerical example is presented to verify the efficiency of the obtained results. How the topology of the network affects the optimal feedback mechanism is also discussed.
Keyword ：
Bifurcation Bifurcation Complex networks Complex networks Epidemic dynamics Epidemic dynamics Feedback mechanism Feedback mechanism Optimal control Optimal control
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| GB/T 7714 | Chen, Lijuan , Huang, Shouying , Chen, Fengde et al. The bifurcation analysis and optimal feedback mechanism for an SIS epidemic model on networks [J]. | ADVANCES IN DIFFERENCE EQUATIONS , 2019 , 2019 (1) . |
| MLA | Chen, Lijuan et al. "The bifurcation analysis and optimal feedback mechanism for an SIS epidemic model on networks" . | ADVANCES IN DIFFERENCE EQUATIONS 2019 . 1 (2019) . |
| APA | Chen, Lijuan , Huang, Shouying , Chen, Fengde , Fu, Mingjian . The bifurcation analysis and optimal feedback mechanism for an SIS epidemic model on networks . | ADVANCES IN DIFFERENCE EQUATIONS , 2019 , 2019 (1) . |
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Reward has been proved to be an effective mechanism to sustain cooperation among selfish individuals. In this paper, we propose a history loyalty-based reward in which a cooperator can gain additional reward if the time he sticks to the cooperation strategy is over a loyalty threshold. Accordingly, defectors have to bear the cost of reward subsequently. The results on the spatial public goods game show that the cooperation could be immensely enhanced when the loyalty threshold and the reward factor are suitable. Besides, the time evolution of cooperator density and the spatial distribution of cooperators and defectors are investigated. Our work extends the form of reward in the evolution of spatial public goods game. (C) 2019 Published by Elsevier B.V.
Keyword ：
Cooperation Cooperation Loyalty Loyalty Reward Reward Spatial public goods game Spatial public goods game
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| GB/T 7714 | Fu, Mingjian , Guo, Wenzhong , Cheng, Linlin et al. History loyalty-based reward promotes cooperation in the spatial public goods game [J]. | PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS , 2019 , 525 : 1323-1329 . |
| MLA | Fu, Mingjian et al. "History loyalty-based reward promotes cooperation in the spatial public goods game" . | PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 525 (2019) : 1323-1329 . |
| APA | Fu, Mingjian , Guo, Wenzhong , Cheng, Linlin , Huang, Shouying , Chen, Dewang . History loyalty-based reward promotes cooperation in the spatial public goods game . | PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS , 2019 , 525 , 1323-1329 . |
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针对传统色彩传递算法在实际应用中灵活度不高的问题,提出一种基于感兴趣区域ROI(Regions of Interest)的色彩传递算法.通过提取目标图像的感兴趣区域,将其与用户所选图像进行拼贴,得到新目标图像,并以此进行色彩传递,解决了色彩传递用户对图像色彩处理多样化、个性化需求的问题.同时,针对传统感兴趣区域提取方法容易受纹理噪声干扰、提取尺寸不易控制等问题,提出一种基于变异度的感兴趣区域提取新算法.实验结果表明,该算法效果良好.基于变异度的感兴趣区域提取算法可更准确获取图像的感兴趣区域.基于感兴趣区域的色彩传递算法可在不影响图像主体内容表达的情况下,使得色彩传递的效果更加生动多变,更具个性化,提高了色彩传递应用的灵活度.
Keyword ：
lαβ颜色空间 lαβ颜色空间 变异度 变异度 感兴趣区域 感兴趣区域 熵 熵 色彩传递 色彩传递
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| GB/T 7714 | 程琳琳 , 陈昭炯 , 傅明建 . 基于感兴趣区域的色彩传递算法 [J]. | 计算机应用与软件 , 2019 , 36 (2) : 39-43 . |
| MLA | 程琳琳 et al. "基于感兴趣区域的色彩传递算法" . | 计算机应用与软件 36 . 2 (2019) : 39-43 . |
| APA | 程琳琳 , 陈昭炯 , 傅明建 . 基于感兴趣区域的色彩传递算法 . | 计算机应用与软件 , 2019 , 36 (2) , 39-43 . |
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