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学者姓名：傅明建
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Abstract ：
To enhance the performance of machine learning algorithms, overcome the curse of dimensionality, and maintain model interpretability, there are significant challenges that continue to confront fuzzy systems (FS). Mini-batch Gradient Descent (MBGD) is characterized by its fast convergence and strong generalization performance. However, its applications have been generally restricted to the low-dimensional problems with small datasets. In this paper, we propose a novel deep-learning-based prediction method. This method optimizes deep neural-fuzzy systems (ODNFS) by considering the essential correlations of external and internal factors. Specifically, the Maximal Information Coefficient (MIC) is used to sort features based on their significance and eliminate the least relevant ones, and then the uniform regularization is introduced, which enforces consistency in the average normalized activation levels across rules. An improved novel MBGD technique with DropRule and AdaBound (MBGD-RDA) is put forward to train deep fuzzy systems for the training of each sub-FS in a fashion of layer by layer. Experiments on several datasets show that the ODNFS can effectively balance the efficiency, accuracy, and stability within the system, which can be used for training datasets of any size. The proposed ODNFS outperforms MBGD-RDA and the state-of-the-art methods in terms of accuracy and generalization, with fewer parameters and rules.
Keyword ：
ANFIS ANFIS Deep neural-fuzzy system Deep neural-fuzzy system Maximum information coefficient Maximum information coefficient Mini-batch gradient descent Mini-batch gradient descent
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| GB/T 7714 | Huang, Yunhu , Lin, Geng , Chen, Dewang et al. Optimize Deep Neural-Fuzzy System Algorithms for Regression Problems [J]. | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS , 2025 . |
| MLA | Huang, Yunhu et al. "Optimize Deep Neural-Fuzzy System Algorithms for Regression Problems" . | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS (2025) . |
| APA | Huang, Yunhu , Lin, Geng , Chen, Dewang , Zhao, Wendi , Fu, Mingjian . Optimize Deep Neural-Fuzzy System Algorithms for Regression Problems . | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS , 2025 . |
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A fast deblurring network, based on a high-performance convolutional network and pixel volume, is proposed to address the limitations of existing video deblurring algorithms, which often overly emphasize inter-frame information, leading to high algorithmic complexity. First, high-performance convolutional networks are utilized to prune the deblurring network, thereby reducing both the number of model parameters and computational complexity. To address the increased network computational complexity resulting from the extensive use of traditional two-dimensional convolutional layers, depthwise over-parameterized convolutions are employed to replace traditional convolutions. This substitution significantly reduces computational complexity without compromising the network's structure and performance. In addition, the Charbonnier loss function is used to approximate the mean absolute error (MAE) loss function to alleviate the over-smoothing problem. At the same time, the problem of non-differentiability of the MAE loss function at zero is solved by adding a constant, to enhance the visual quality of video images. Experimental results demonstrate that the proposed method delivers superior deblurring performance. Compared with the baseline pixel volume deblurring network framework, our method achieves a significant reduction in model complexity, demonstrating 28.73% fewer parameters and 59.96% lower floating-point operations, underscoring its theoretical significance. (c) 2025 SPIE and IS&T
Keyword ：
algorithmic complexity algorithmic complexity depthwise over-parameterized convolutions depthwise over-parameterized convolutions loss function loss function video deblurring video deblurring
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| GB/T 7714 | Xie, Shangxi , Xia, Yiming , Zhong, Wenqi et al. Lightweight pixel volume deblurring network: enhanced video deblurring via efficient architecture optimization [J]. | JOURNAL OF ELECTRONIC IMAGING , 2025 , 34 (2) . |
| MLA | Xie, Shangxi et al. "Lightweight pixel volume deblurring network: enhanced video deblurring via efficient architecture optimization" . | JOURNAL OF ELECTRONIC IMAGING 34 . 2 (2025) . |
| APA | Xie, Shangxi , Xia, Yiming , Zhong, Wenqi , Lin, Liqun , Fu, Mingjian . Lightweight pixel volume deblurring network: enhanced video deblurring via efficient architecture optimization . | JOURNAL OF ELECTRONIC IMAGING , 2025 , 34 (2) . |
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Multi-view semi-supervised learning enables to efficiently leverage multi-view information as well as labeled and unlabeled data to solve practical problems. With graph neural networks, multi-view semi-supervised learning can be smooth and robust to the label propagation process. Hypergraph learning is an approach to hypergraph topology that aims to identify and exploit high-order relations on hypergraphs to uncover data beyond one-to-one in real-world applications. However, traditional hypergraph construction methods usually consider only local correlations between samples and may ignore dependencies that exist in the wider context of the dataset. In this paper, we propose a novel multi-view high-order correlation modeling method, where the connectivity of hyperedges is determined through clustering, and complementary information from each view is integrated via a hypergraph neural network. Inspired by the divisibility of graphs revealed by spectral graph theory, the proposed method works well to capture global high-order correlations within data and uncover potential manifolds. To assess the effectiveness of hypergraph modeling, we conduct a comprehensive evaluation of a multi-view semi-supervised node classification task. The experiments illustrate that the proposed approach achieves superior performance compared to current state-of-the-art algorithms and general hypergraph learning across eight datasets.
Keyword ：
Global graph structure Global graph structure Hypergraph construction Hypergraph construction Hypergraph neural network Hypergraph neural network Multi-view learning Multi-view learning
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| GB/T 7714 | Wu, Yuze , Lan, Shiyang , Cai, Zhiling et al. SCHG: Spectral Clustering-guided Hypergraph Neural Networks for Multi-view Semi-supervised Learning [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 277 . |
| MLA | Wu, Yuze et al. "SCHG: Spectral Clustering-guided Hypergraph Neural Networks for Multi-view Semi-supervised Learning" . | EXPERT SYSTEMS WITH APPLICATIONS 277 (2025) . |
| APA | Wu, Yuze , Lan, Shiyang , Cai, Zhiling , Fu, Mingjian , Li, Jinbo , Wang, Shiping . SCHG: Spectral Clustering-guided Hypergraph Neural Networks for Multi-view Semi-supervised Learning . | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 277 . |
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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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Left-turn intersections without signal lights are among the most dangerous scenes in autonomous driving, and achieving efficient and safe left-turn decision-making is highly challenging in autonomous driving. The Deep Reinforcement Learning(DRL) algorithm has broad prospects in autonomous driving decision-making. However, its sample efficiency is low and it cannot be used to easily design reward functions in autonomous driving. Therefore, a DRL algorithm based on expert priors, abbreviated as CBAM-BC SAC, is proposed to solve the aforementioned problems. First, a Scalable Multiagent RL Training School(SMARTS) simulation platform is used to obtain expert prior knowledge. Subsequently, a Convolutional Block Attention Module(CBAM) is used to improve Behavior Cloning(BC), which pretrains and imitates expert strategies based on the prior knowledge of experts. Finally, the learning process of the DRL algorithm is guided by an imitation expert strategy and verified in a left-turn decision-making at intersection without traffic lights. Experimental results indicate that the DRL algorithm based on expert prior is more advantageous than conventional DRL algorithms. It not only eliminates the workload of manually setting reward functions, but also significantly improves sample efficiency and achieves better performance. In left-turn scene at intersection without traffic lights, the CBAM-BC SAC algorithm improves the average traffic success rate by 14.2 and 2.2 percentage points, respectively, compared with the conventional DRL algorithm SAC and the DRL algorithm BC SAC based on classic BC. © 2024, Jisuanji Gongcheng. All rights reserved.
Keyword ：
autonomous driving autonomous driving Behavioral Cloning(BC) Behavioral Cloning(BC) Deep Reinforcement Learning (DRL) Deep Reinforcement Learning (DRL) drivingdecision-making drivingdecision-making imitation learning imitation learning
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| GB/T 7714 | Fu, M. , Guo, F. . Research on Decision-Making atInter section Without Traffic Lights Based on Deep Reinforcement Learning [J]. | Computer Engineering , 2024 , 50 (5) : 91-99 . |
| MLA | Fu, M. et al. "Research on Decision-Making atInter section Without Traffic Lights Based on Deep Reinforcement Learning" . | Computer Engineering 50 . 5 (2024) : 91-99 . |
| APA | Fu, M. , Guo, F. . Research on Decision-Making atInter section Without Traffic Lights Based on Deep Reinforcement Learning . | Computer Engineering , 2024 , 50 (5) , 91-99 . |
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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(CBAM-BC 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 (5) : 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 . 5 (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 (5) , 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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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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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.
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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 , (7) : 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 7 (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 , (7) , 87-91 . |
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