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学者姓名：黄捷
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在电气工程专业实践教学过程中凝练出由Turn up、Turn away、Turn back等环节组成的3-Turns教学法。经过多轮的实践应用，3-Turns教学法在实践共同体中提供了一个兼具解释性与可操作性的教学样例。研究发现，3-Turns教学法对实践共同体促进工科生深度学习起到了提质增效的作用；3-Turns教学法为工程实践学习情境的强化设计提供了理论依据与经验支持。该研究验证了基于3-Turns教学法的实践共同体促进工科生深度学习的有效性，为助力工科人才培养质量的持续提升做出了积极的探索与实践。
Keyword ：
3-Turns教学法 3-Turns教学法 实践共同体 实践共同体 实践教学 实践教学 深度学习 深度学习
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| GB/T 7714 | 陈东毅 , 王武 , 林建新 et al. 3-Turns教学法在实践共同体促进深度学习中的应用 [J]. | 实验室研究与探索 , 2024 , 43 (04) : 151-154 . |
| MLA | 陈东毅 et al. "3-Turns教学法在实践共同体促进深度学习中的应用" . | 实验室研究与探索 43 . 04 (2024) : 151-154 . |
| APA | 陈东毅 , 王武 , 林建新 , 黄捷 , 陈建国 . 3-Turns教学法在实践共同体促进深度学习中的应用 . | 实验室研究与探索 , 2024 , 43 (04) , 151-154 . |
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针对四旋翼无人机编队变换能耗优化仿真教学，该文以多无人机编队切换应用中存在无人机能耗不平衡的案例作为典型教学内容，提出了基于无人机编队能耗优化的仿真教学模式，设计了无人机集群编队队形变化能耗最优仿真教学总体方案，阐明各个无人机无碰撞地驶向目标点的实现思路。搭建的仿真教学实例有效地解决了无人机编队飞行时间短的问题。此设计已经用于研究生“无人机控制技术”的课程教学，可激发学生在传统算法上进行优化的创新思维。
Keyword ：
仿真教学 仿真教学 无人机编队 无人机编队 编队队形变换 编队队形变换 能耗优化 能耗优化
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| GB/T 7714 | 黄捷 , 李泽毅 . 四旋翼无人机编队变换能耗优化仿真教学 [J]. | 实验技术与管理 , 2024 , 41 (04) : 102-108 . |
| MLA | 黄捷 et al. "四旋翼无人机编队变换能耗优化仿真教学" . | 实验技术与管理 41 . 04 (2024) : 102-108 . |
| APA | 黄捷 , 李泽毅 . 四旋翼无人机编队变换能耗优化仿真教学 . | 实验技术与管理 , 2024 , 41 (04) , 102-108 . |
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针对群体机器人在执行任务过程中可能出现的数据泄露等安全问题，提出了一种基于群体机器人多目标的区块链安全控制架构方案，并设计了用于数据安全防护的安全流程控制算法。首先，构建的架构基于区块链，从任务层和执行层角度避免了任务数据的丢失和泄露，确保群机器人的内部数据可以安全稳定地传输。其次，设计安全控制算法，实现群体机器人在多目标情况下的安全控制。最后仿真结果表明，所设计的架构及算法提高了机器人数据传输的隐私性和安全性，引入所提出的安全控制架构及算法后，群机器人在协同跟踪目标点运动的过程中探测误差减小了40.7%。同时，通过实验验证了所提架构在实际系统中的有效性。
Keyword ：
共识算法 共识算法 区块链 区块链 安全性 安全性 安全流程 安全流程 控制架构 控制架构 控制算法 控制算法 群机器人 群机器人
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| GB/T 7714 | 黄捷 , 李帮银 , 李东方 . 基于群体机器人多目标的区块链安全控制架构及算法 [J]. | 无人系统技术 , 2024 , 7 (01) : 59-68 . |
| MLA | 黄捷 et al. "基于群体机器人多目标的区块链安全控制架构及算法" . | 无人系统技术 7 . 01 (2024) : 59-68 . |
| APA | 黄捷 , 李帮银 , 李东方 . 基于群体机器人多目标的区块链安全控制架构及算法 . | 无人系统技术 , 2024 , 7 (01) , 59-68 . |
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针对无人机航拍图像目标检测中视野变化大、时空信息复杂等问题，文中基于YOLOv5(You Only Look Once Version5)架构，提出基于图像低维特征融合的航拍小目标检测模型.引入CA(Coordinate Attention),改进MobileNetV3的反转残差块，增加图像空间维度信息的同时降低模型参数量.改进YOLOv5特征金字塔网络结构，融合浅层网络中的特征图，增加模型对图像低维有效信息的表达能力，进而提升小目标检测精度.同时为了降低航拍图像中复杂背景带来的干扰，引入无参平均注意力模块，同时关注图像的空间注意力与通道注意力；引入VariFocal Loss,降低负样本在训练过程中的权重占比.在VisDrone数据集上的实验验证文中模型的有效性，该模型在有效提升检测精度的同时明显降低复杂度.
Keyword ：
You Only Look Once Version5(YOLOv5) You Only Look Once Version5(YOLOv5) 小目标检测 小目标检测 损失函数 损失函数 注意力机制 注意力机制
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| GB/T 7714 | 蔡逢煌 , 张家翔 , 黄捷 . 基于图像低维特征融合的航拍小目标检测模型 [J]. | 模式识别与人工智能 , 2024 , 37 (02) : 162-171 . |
| MLA | 蔡逢煌 et al. "基于图像低维特征融合的航拍小目标检测模型" . | 模式识别与人工智能 37 . 02 (2024) : 162-171 . |
| APA | 蔡逢煌 , 张家翔 , 黄捷 . 基于图像低维特征融合的航拍小目标检测模型 . | 模式识别与人工智能 , 2024 , 37 (02) , 162-171 . |
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This paper studies control strategies of intelligent vehicles at unsignalized intersections. By considering the interaction among multiple vehicles and communication disturbances, the problem is formulated as a robust differential game in which the controlled vehicle computes actions using disturbed information of locally neighboring vehicles. Both cooperative and non-cooperative differential game models are considered. The controlled vehicle optimizes its own cost in the non-cooperative game while coordinates its strategy with other vehicles to optimize a joint cost in the cooperative game. We show that the local optimal strategy of each intelligent vehicle defined by a single optimization problem converges to global robust Nash equilibrium in the non-cooperative game, and converges to the global robust Pareto-Nash equilibrium in the cooperative game. The effectiveness of the robust differential game method is verified by simulations under two scenarios. Results show that under the proposed differential game approach, vehicles pass through unsignalized intersections more quickly than methods using traditional discrete game approaches. In addition, vehicles can avoid colliding with each other and obstacles at the presence of communication disturbances. IEEE
Keyword ：
Costs Costs differential game differential game Differential games Differential games Games Games game theory game theory Nash equilibrium Nash equilibrium Optimal control Optimal control robust Nash equilibrium robust Nash equilibrium Unsignalized intersection Unsignalized intersection Vehicle dynamics Vehicle dynamics Vehicular ad hoc networks Vehicular ad hoc networks
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| GB/T 7714 | Huang, J. , Wu, Z. , Xue, W. et al. Non-Cooperative and Cooperative Driving Strategies at Unsignalized Intersections: A Robust Differential Game Approach [J]. | IEEE Transactions on Intelligent Transportation Systems , 2024 , 25 (8) : 1-15 . |
| MLA | Huang, J. et al. "Non-Cooperative and Cooperative Driving Strategies at Unsignalized Intersections: A Robust Differential Game Approach" . | IEEE Transactions on Intelligent Transportation Systems 25 . 8 (2024) : 1-15 . |
| APA | Huang, J. , Wu, Z. , Xue, W. , Lin, D. , Chen, Y. . Non-Cooperative and Cooperative Driving Strategies at Unsignalized Intersections: A Robust Differential Game Approach . | IEEE Transactions on Intelligent Transportation Systems , 2024 , 25 (8) , 1-15 . |
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Cooperative trajectory planning of aerial-ground systems is a fundamental and challenging problem, which aims to leverage the aerial information to assist the ground tasks. Existing methods often suffer from suboptimal trajectories or computation burden. In this paper, we address cooperative trajectory planning of aerial-ground systems in which an unmanned ground vehicle (UGV) plans its local trajectory in real-time with the assistance of an unmanned aerial vehicle (UAV). Firstly, the UAV generates guidance trajectory using nonlinear model predictive control (NMPC), which considers the obstacle distribution density as a factor reflecting the coupling effect of multiple obstacles on the UGV, thereby avoiding local minima problem and improving the feasibility of the planned trajectory. Secondly, a null-space-based behavioral control (NSBC) framework is employed to merge the guidance trajectory into the UGV's own planned one as a task. Finally, an event triggering task supervisor is developed for the UGV to decide the priorities of all tasks, which reduces the switching frequency of task priorities brought by traditional rule-based task supervisors. Both simulation and experiment results show that the proposed approach has superior trajectory planning performance in terms of trajectory error, on-line computation time and the success rate of task execution.
Keyword ：
Aerial-ground systems Aerial-ground systems Behavioral control Behavioral control Model predictive control Model predictive control Trajectory planning Trajectory planning
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| GB/T 7714 | Huang, Jie , Chen, Jianfei , Zhang, Zhenyi et al. On Real-time Cooperative Trajectory Planning of Aerial-ground Systems [J]. | JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS , 2024 , 110 (1) . |
| MLA | Huang, Jie et al. "On Real-time Cooperative Trajectory Planning of Aerial-ground Systems" . | JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 110 . 1 (2024) . |
| APA | Huang, Jie , Chen, Jianfei , Zhang, Zhenyi , Chen, Yutao , Lin, Dingci . On Real-time Cooperative Trajectory Planning of Aerial-ground Systems . | JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS , 2024 , 110 (1) . |
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Accurate ocean environment perception is crucial for weather and climate prediction. Environmental limitations and deployment costs constrain satellite and buoy real-time observation, leading to sparse data availability. This paper proposes a novel approach, multimodal fusion -based spatiotemporal incremental learning, enhancing the ocean environment perception under sparse observations. This method uses sparse real-time observations to comprehend, reconstruct, and predict the full environment. First, spatiotemporal disentanglement decouples intrinsic features by integrating physical principles and data learning. Subsequently, incremental extension captures the dynamic environment through stable representation updating and dynamic behavior learning. Then, multimodal information fusion synergizes multisource intrinsic features, enabling the full perception of the ocean environment. Finally, the methodology is supported by convergence analysis and error boundary evaluation. Validation with global sea surface temperature and western Pacific Ocean highdimensional temperature datasets demonstrates its potential for advancing ocean research and applications using sparse real-time observation.
Keyword ：
Incremental learning Incremental learning Information fusion Information fusion Ocean environment Ocean environment Sparse observation Sparse observation Spatiotemporal modeling Spatiotemporal modeling
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| GB/T 7714 | Lei, Lei , Huang, Jie , Zhou, Yu . Multimodal fusion-based spatiotemporal incremental learning for ocean environment perception under sparse observation [J]. | INFORMATION FUSION , 2024 , 108 . |
| MLA | Lei, Lei et al. "Multimodal fusion-based spatiotemporal incremental learning for ocean environment perception under sparse observation" . | INFORMATION FUSION 108 (2024) . |
| APA | Lei, Lei , Huang, Jie , Zhou, Yu . Multimodal fusion-based spatiotemporal incremental learning for ocean environment perception under sparse observation . | INFORMATION FUSION , 2024 , 108 . |
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To address the challenges of significant changes in the field of view and complex spatiotemporal information in unmanned aerial vehicle aerial image target detection, a model for small object detection in aerial photography based on low dimensional image feature fusion is presented grounded on the YOLOv5(you only look once version 5) architecture. Coordinate attention is introduced to improve the inverted residuals of MobileNetV3, thereby increasing the spatial dimension information of images while reducing parameters of the model. The YOLOv5 feature pyramid network structure is improved to incorporate feature images from shallow networks. The ability of the model to represent low-dimensional effective information of images is enhanced, and consequently the detection accuracy of the proposed model for small objects is improved. To reduce the impact of complex background in the image, the parameter-free average attention module is introduced to focus on both spatial attention and channel attention. VariFocal Loss is adopted to reduce the weight proportion of negative samples in the training process. Experiments on VisDrone dataset demonstrate the effectiveness of the proposed model. The detection accuracy is effectively improved while the model complexity is significantly reduced. © 2024 Science Press. All rights reserved.
Keyword ：
Aerial photography Aerial photography Antennas Antennas Complex networks Complex networks Image enhancement Image enhancement Object detection Object detection Object recognition Object recognition
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| GB/T 7714 | Cai, Fenghuang , Zhang, Jiaxiang , Huang, Jie . Model for Small Object Detection in Aerial Photography Based on Low Dimensional Image Feature Fusion [J]. | Pattern Recognition and Artificial Intelligence , 2024 , 37 (2) : 162-171 . |
| MLA | Cai, Fenghuang et al. "Model for Small Object Detection in Aerial Photography Based on Low Dimensional Image Feature Fusion" . | Pattern Recognition and Artificial Intelligence 37 . 2 (2024) : 162-171 . |
| APA | Cai, Fenghuang , Zhang, Jiaxiang , Huang, Jie . Model for Small Object Detection in Aerial Photography Based on Low Dimensional Image Feature Fusion . | Pattern Recognition and Artificial Intelligence , 2024 , 37 (2) , 162-171 . |
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A reputation management system with identity authentication and task supervisor (RMS-IATS) for swarm robotics via blockchain technology is proposed to identify Byzantine robots within the swarm robotics and avoid the security threat caused by Byzantine robots to swarm robotics. Firstly, a classical blockchain-based swarm robotics reputation management system (RMS) is improved by introducing penalty factors, and a severer reputation value penalty is imposed on the robotics with long-term Byzantine behavior. Secondly, to speed up the identification of Byzantine robots within swarm robotics, an identity authentication protocol is devised, and thus lower initial reputation scores are assigned to the robots with unauthorized identities. Next, a dual-layer communication network for communication between robots is designed to solve the communication latency issue caused by blockchain in the swarm robotics system. Finally, the feasibility of the proposed blockchain-based RMS-IATS and dual-layer communication network is proved through simulations. The identification time for different types of Byzantine robots is shortened by RMS-IATS compared with the classical RMS for swarm robotics, and the maximum communication latency of the system is reduced by the proposed dual-layer communication network compared with the blockchain. © 2024 Science Press. All rights reserved.
Keyword ：
Authentication Authentication Blockchain Blockchain Network layers Network layers Robots Robots Supervisory personnel Supervisory personnel Swarm intelligence Swarm intelligence
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| GB/T 7714 | Huang, Jie , Zeng, Jiazhou . Byzantine Robot Recognition Scheme via Blockchain-Based Reputation Management [J]. | Pattern Recognition and Artificial Intelligence , 2024 , 37 (3) : 267-281 . |
| MLA | Huang, Jie et al. "Byzantine Robot Recognition Scheme via Blockchain-Based Reputation Management" . | Pattern Recognition and Artificial Intelligence 37 . 3 (2024) : 267-281 . |
| APA | Huang, Jie , Zeng, Jiazhou . Byzantine Robot Recognition Scheme via Blockchain-Based Reputation Management . | Pattern Recognition and Artificial Intelligence , 2024 , 37 (3) , 267-281 . |
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强化学习行为控制局限于没有群体任务的单个智能体，因为其将行为优先级学习建模为马尔可夫决策过程。本文提出一种新颖的多智能体强化学习行为控制方法，该方法通过执行联合学习克服上述缺陷。具体而言，针对一组非线性二阶系统，设计一个多智能体强化学习任务监管器以在任务层分配行为优先级。通过将行为优先级切换建模为协作式马尔可夫博弈，多智能体强化学习任务监管器学习最优联合行为优先级，以减少对人类智能和高性能计算硬件的依赖。在控制层，设计了一组二阶强化学习控制器用以学习最优控制策略，实现位置和速度信号的同步跟踪。特别地，设计了一组自适应补偿器以保证输入饱和约束。数值仿真结果验证了所提出的多智能体强化学习行为控制对比有限时间、固有时间和强化学习行为控制具有更低的切换频率和控制代价。
Keyword ：
二阶系统 二阶系统 任务监管 任务监管 强化学习 强化学习 行为控制 行为控制
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| GB/T 7714 | 张祯毅 , 黄捷 , 潘聪捷 . 非线性二阶系统的多智能体强化学习行为控制 [J]. | Frontiers of Information Technology & Electronic Engineering , 2024 , 25 (06) : 869-887 . |
| MLA | 张祯毅 et al. "非线性二阶系统的多智能体强化学习行为控制" . | Frontiers of Information Technology & Electronic Engineering 25 . 06 (2024) : 869-887 . |
| APA | 张祯毅 , 黄捷 , 潘聪捷 . 非线性二阶系统的多智能体强化学习行为控制 . | Frontiers of Information Technology & Electronic Engineering , 2024 , 25 (06) , 869-887 . |
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