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学者姓名：徐哲壮
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Abstract ：
随着海上风力发电和光伏发电的快速发展,海洋输电工程的地位越来越重要,海底电缆的应用也越米越广泛.获得精确的海底电缆位置不仅有利于日常巡检,而且提高了故障检测的效率,因此,海底电缆的路由定位和故障检测将会是未来维护和维修的重要环节.由于海底电缆的小直径和内部电流的变化性,导致定位准确度的下降以及定位难度的上升.针对上述问题,首先,基于海底环境和水下机器人,利用三芯铠装海底电缆的电缆结构推导海底电缆外磁场的近似方程;然后,水下机器人根据检测到的磁感应强度值进行姿态调整,在此基础上,提出一种基于改进灰狼优化算法(improved grey wolf optimization,IGWO)的海底电缆定位算法,利用基于磁通密度模的适应度函数,设计一种用于海底电缆探测的在线路径定位方法;最后,通过仿真实验验证了 IGWO算法实现海底电缆定位的精确性和有效性.
Keyword ：
改进灰狼优化算法 改进灰狼优化算法 水下机器人 水下机器人 海底电缆 海底电缆 电磁场传播 电磁场传播 电磁定位 电磁定位 电磁探测 电磁探测
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| GB/T 7714 | 黄文超 , 温锦嵘 , 徐哲壮 . 基于改进灰狼群优化算法的水下机器人海底电缆定位算法 [J]. | 控制与决策 , 2025 , 40 (1) : 87-94 . |
| MLA | 黄文超 等. "基于改进灰狼群优化算法的水下机器人海底电缆定位算法" . | 控制与决策 40 . 1 (2025) : 87-94 . |
| APA | 黄文超 , 温锦嵘 , 徐哲壮 . 基于改进灰狼群优化算法的水下机器人海底电缆定位算法 . | 控制与决策 , 2025 , 40 (1) , 87-94 . |
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为了提高光伏发电功率的预测精度,在数据挖掘分析基础上提出双模式组合的日前光伏预测方法.首先,利用波动量分析建立输出功率与天气类型之间更精确的匹配模型,将天气划分为简单与复杂2种天气类型.其次,对于简单天气类型,采用K-means聚类分析选取最相似日和XGBoost回归组合的预测模型;对于复杂天气类型,提出基于变分模态分解(variational modal decomposition,VMD)、采用麻雀算法(sparrow search algorithm,SSA)优化极限学习机(extreme learning machine,ELM)的日前光伏预测模型.最后,选用DKASC Alice Spring光伏电站数据集对2种模型进行验证,并进行仿真实验.实验结果显示,使用双模式组合方法构建的光伏发电功率预测模型在春季和夏季2个不同数据集下,相关系数分别达到96.44％和96.61％,比其他4种常用模型具有更高的预测精度.
Keyword ：
光伏功率日前预测 光伏功率日前预测 双模式组合模型 双模式组合模型 变分模态分解 变分模态分解 极限学习机 极限学习机 波动量分析 波动量分析 麻雀优化算法 麻雀优化算法
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| GB/T 7714 | 刘丽桑 , 郭凯琪 , 徐哲壮 et al. 基于数据挖掘的双模式组合光伏功率日前预测 [J]. | 武汉大学学报（工学版） , 2024 , 57 (10) : 1459-1468 . |
| MLA | 刘丽桑 et al. "基于数据挖掘的双模式组合光伏功率日前预测" . | 武汉大学学报（工学版） 57 . 10 (2024) : 1459-1468 . |
| APA | 刘丽桑 , 郭凯琪 , 徐哲壮 , 郭琳 . 基于数据挖掘的双模式组合光伏功率日前预测 . | 武汉大学学报（工学版） , 2024 , 57 (10) , 1459-1468 . |
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Due to the similarity in appearance and dense deployment of devices in industrial environments, relying solely on machine vision makes it challenging for inspection robots to accurately identify similar devices. Although wireless signals from industrial internet of things (IoT) can serve as identification features, signal fluctuations impact the accuracy and efficiency of recognition. To address this issue, this paper proposes a proximity estimation algorithm with position adjustment for autonomous industrial inspection. The algorithm considers the spatial relationships between nodes and the topological relationship between the signal strength and variations among the nodes. By analyzing the topological relationship between the signal strength and variations among the nodes, the robot autonomously adjusts its position and selects the proximal node based on the spatial topology relationship between them. We have built an inspection platform using quadruped robots to evaluate the effectiveness of the experiments. The experimental results demonstrate that the algorithm further improves the efficiency of identifying proximal devices while ensuring the estimation accuracy of the algorithm. © 2024 IEEE.
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| GB/T 7714 | Zhou, Z. , Huang, P. , Zheng, S. et al. Proximity Estimation with Position Adjustment for Autonomous Industrial Inspection [未知]. |
| MLA | Zhou, Z. et al. "Proximity Estimation with Position Adjustment for Autonomous Industrial Inspection" [未知]. |
| APA | Zhou, Z. , Huang, P. , Zheng, S. , Xu, Z. , Zhuang, Z. . Proximity Estimation with Position Adjustment for Autonomous Industrial Inspection [未知]. |
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With the rapid development of artificial intelligence and Unmanned Aerial Vehicle (UAV) technology, AI-based UAVs are increasingly utilized in various industrial and civilian applications. This paper presents a distributed Edge-Cloud collaborative framework for UAV object detection, aiming to achieve real-time and accurate detection of ground moving targets. The framework incorporates an Edge-Embedded Lightweight (${{\text{E}}<^>{2}}\text{L}$E2L) object algorithm with an attention mechanism, enabling real-time object detection on edge-side embedded devices while maintaining high accuracy. Additionally, a decision-making mechanism based on fuzzy neural network facilitates adaptive task allocation between the edge-side and cloud-side. Experimental results demonstrate the improved running rate of the proposed algorithm compared to YOLOv4 on the edge-side NVIDIA Jetson Xavier NX, and the superior performance of the distributed Edge-Cloud collaborative framework over traditional edge computing or cloud computing algorithms in terms of speed and accuracy
Keyword ：
Attention mechanism Attention mechanism Autonomous aerial vehicles Autonomous aerial vehicles Cloud computing Cloud computing Collaboration Collaboration edge-cloud collaborative edge-cloud collaborative fuzzy neural network fuzzy neural network Image edge detection Image edge detection object detection object detection Object detection Object detection Real-time systems Real-time systems Task analysis Task analysis UAV UAV YOLOv4 YOLOv4
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| GB/T 7714 | Yuan, Yazhou , Gao, Shicong , Zhang, Ziteng et al. Edge-Cloud Collaborative UAV Object Detection: Edge-Embedded Lightweight Algorithm Design and Task Offloading Using Fuzzy Neural Network [J]. | IEEE TRANSACTIONS ON CLOUD COMPUTING , 2024 , 12 (1) : 306-318 . |
| MLA | Yuan, Yazhou et al. "Edge-Cloud Collaborative UAV Object Detection: Edge-Embedded Lightweight Algorithm Design and Task Offloading Using Fuzzy Neural Network" . | IEEE TRANSACTIONS ON CLOUD COMPUTING 12 . 1 (2024) : 306-318 . |
| APA | Yuan, Yazhou , Gao, Shicong , Zhang, Ziteng , Wang, Wenye , Xu, Zhezhuang , Liu, Zhixin . Edge-Cloud Collaborative UAV Object Detection: Edge-Embedded Lightweight Algorithm Design and Task Offloading Using Fuzzy Neural Network . | IEEE TRANSACTIONS ON CLOUD COMPUTING , 2024 , 12 (1) , 306-318 . |
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Steel plate is one of the most valuable steel products which is highly customized in specification according to the demands of users. In this case, the outbound scheduling of steel plates is a challenging issue since its efficiency and complexity are impacted by both steel plate shuffling and truck loading sequencing. To overcome this challenge, we propose to jointly optimize steel plate shuffling and truck loading sequencing (SPS-TLS) by utilizing the data of steel plates and trucks collected by Industrial Internet of Things (IIoT). The SPS-TLS problem is firstly transformed as an orders scheduling problem which is formulated as a mixedinteger linear programming (MILP) model. Then an alternating iteration algorithm based on deep reinforcement learning (AltDRL) is proposed to solve the SPS-TLS problem. In AltDRL, the deep Q network (DQN) with prioritized experience replay (PER) and the heuristic algorithm are combined to iteratively obtain the nearoptimal shuffling position of blocking plates and truck sequence. Experiments are executed based on data collected from a real steel logistics park. The results confirm that AltDRL can significantly reduce the number of plate shuffles and improve the outbound scheduling efficiency of steel plates.
Keyword ：
Deep reinforcement learning Deep reinforcement learning Industrial Internet of Things Industrial Internet of Things Optimization Optimization Steel plate shuffling Steel plate shuffling Truck loading sequencing Truck loading sequencing
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| GB/T 7714 | Xu, Zhezhuang , Wang, Jinlong , Yuan, Meng et al. Joint optimization of steel plate shuffling and truck loading sequencing based on deep reinforcement learning [J]. | ADVANCED ENGINEERING INFORMATICS , 2024 , 60 . |
| MLA | Xu, Zhezhuang et al. "Joint optimization of steel plate shuffling and truck loading sequencing based on deep reinforcement learning" . | ADVANCED ENGINEERING INFORMATICS 60 (2024) . |
| APA | Xu, Zhezhuang , Wang, Jinlong , Yuan, Meng , Yuan, Yazhou , Chen, Boyu , Zhang, Qingdong et al. Joint optimization of steel plate shuffling and truck loading sequencing based on deep reinforcement learning . | ADVANCED ENGINEERING INFORMATICS , 2024 , 60 . |
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Pickup vehicle scheduling in steel logistics parks is an important problem for determining the outbound efficiency of steel products. In a steel logistics park, each yard contains different types of steel products, which provides flexible yard selection for each pickup operation. In this case, the yard allocation and the loading sequence for each vehicle must be considered simultaneously in pickup vehicle scheduling, which greatly increases the scheduling complexity. To overcome this challenge, in this paper, we propose a pickup vehicle scheduling problem with mixed steel storage (PVSP-MSS) to optimize the makespan of pickup vehicles and the makespan of steel logistics parks simultaneously. The optimization problem is formulated as a multi-objective mixed-integer linear programming model, and an enhanced algorithm based on SPEA2 (ESPEA) is proposed to solve the problem with a high efficiency. In the ESPEA, a cooperative initialization strategy is firstly proposed to initialize the vehicle pickup sequence for each yard. Then, an insertion decoding method is designed to improve the scheduling efficiency, utilizing the idle time of a yard. Furthermore, local search technology based on critical paths is proposed for the ESPEA to improve the solution quality. Experiments are executed based on data collected from a real steel logistics park. The results confirm that the ESPEA can significantly reduce both the makespan of each pickup vehicle and the makespan of the steel logistics park.
Keyword ：
mixed storage mixed storage multi-objective optimization multi-objective optimization pickup vehicle scheduling pickup vehicle scheduling steel logistics park steel logistics park
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| GB/T 7714 | Wang, Jinlong , Xu, Zhezhuang , He, Mingxing et al. Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage [J]. | APPLIED SCIENCES-BASEL , 2024 , 14 (9) . |
| MLA | Wang, Jinlong et al. "Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage" . | APPLIED SCIENCES-BASEL 14 . 9 (2024) . |
| APA | Wang, Jinlong , Xu, Zhezhuang , He, Mingxing , Xue, Liang , Xu, Hongjie . Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage . | APPLIED SCIENCES-BASEL , 2024 , 14 (9) . |
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在工业现场自主巡检中,由于定位误差和光线角度等因素的影响,使得四足机器人仅依靠机器视觉难以实现高精度的仪表数字识别。针对上述问题,提出一种结合移动机器人运动的工业仪表数字识别方法。该方法首先基于图像感知的四足机器人控制策略实现仪表对准,来获取大小适中的仪表图片,进而使用改进自动色彩均衡(ACE)算法提高图片清晰度,并使用改进高效准确的场景文本(EAST)检测器来优化仪表数字漏检情况,最后获得仪表数字识别结果。在基于四足机器人的工业巡检实验平台中验证了该识别方法,实验结果表明上述方法对工业仪表数字识别准确率达97.75%。
Keyword ：
四足机器人 四足机器人 巡检 巡检 工业仪表 工业仪表 感知与控制 感知与控制 数字识别 数字识别 文本检测 文本检测
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| GB/T 7714 | 简荣贵 , 陈康 , 徐哲壮 et al. 基于四足机器人的工业仪表数字识别方法研究 [J]. | 物联网技术 , 2024 , 14 (03) : 3-7,11 . |
| MLA | 简荣贵 et al. "基于四足机器人的工业仪表数字识别方法研究" . | 物联网技术 14 . 03 (2024) : 3-7,11 . |
| APA | 简荣贵 , 陈康 , 徐哲壮 , 黄平 . 基于四足机器人的工业仪表数字识别方法研究 . | 物联网技术 , 2024 , 14 (03) , 3-7,11 . |
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Surface defect detection of sawn timber is a critical task to ensure the quality of wooden products. Current methods have challenges in considering detection accuracy and speed simultaneously, due to the complexity of defects and the massive length of sawn timbers. Specifically, there are scale variation, large intraclass difference and high interclass similarity in the defects, which reduce the detection accuracy. To overcome these challenges, we propose an efficient multilevel-feature integration network (EMINet) based on YOLOv5s. To obtain discriminative features of defects, the cross fusion module (CFM) is proposed to fully integrate the multilevel features of backbone. In the CFM, the local information aggregation is designed to enrich the detailed information of high-level features, and the global information aggregation is designed to enhance the semantic information of low-level features. Experimental results demonstrate that the proposed EMINet achieves better accuracy with fast speed compared with the state-of-the-art methods.
Keyword ：
information aggregation information aggregation machine vision machine vision multilevel feature integration multilevel feature integration sawn timber sawn timber surface defect detection surface defect detection
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| GB/T 7714 | Zhu, Yuhang , Xu, Zhezhuang , Lin, Ye et al. Surface defect detection of sawn timbers based on efficient multilevel feature integration [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2024 , 35 (4) . |
| MLA | Zhu, Yuhang et al. "Surface defect detection of sawn timbers based on efficient multilevel feature integration" . | MEASUREMENT SCIENCE AND TECHNOLOGY 35 . 4 (2024) . |
| APA | Zhu, Yuhang , Xu, Zhezhuang , Lin, Ye , Chen, Dan , Zheng, Kunxin , Yuan, Yazhou . Surface defect detection of sawn timbers based on efficient multilevel feature integration . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2024 , 35 (4) . |
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针对基于随机采样的RRT机械臂路径规划算法在全局工作空间下采样效率低、随机性强等问题,提出一种基于采样点优化RRT算法的机械臂路径规划算法.相对于全局工作空间采样,优化算法首先基于非障碍物空间生成随机采样点,以降低算法碰撞检测概率与冗余节点的生成,再结合一定概率的人工势场法产生启发式采样点,使得机械臂臂体于路径规划采样过程中既能保证随机采样的概率完备,又能使采样点更具目标导向性.其次,为使得路径更加简洁平滑,使用冗余节点删除策略剔除路径中的冗余节点来优化最终路径.最后在二维、三维的仿真环境中对优化算法进行对比实验分析,以验证算法在随机采样路径规划算法中的良好性能,并在IRB 1200-7/0.7机械臂上进行避障规划算法实验.仿真和实验结果都表明,所提出的算法在机械臂路径规划中可以获得更高的规划效率和更优的路径.
Keyword ：
人工势场法 人工势场法 启发式采样 启发式采样 快速随机搜索树 快速随机搜索树 机械臂运动规划 机械臂运动规划 采样点优化 采样点优化 非障碍物空间采样 非障碍物空间采样
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| GB/T 7714 | 陈丹 , 谭钦 , 徐哲壮 . 基于采样点优化RRT算法的机械臂路径规划 [J]. | 控制与决策 , 2024 , 39 (08) : 2597-2604 . |
| MLA | 陈丹 et al. "基于采样点优化RRT算法的机械臂路径规划" . | 控制与决策 39 . 08 (2024) : 2597-2604 . |
| APA | 陈丹 , 谭钦 , 徐哲壮 . 基于采样点优化RRT算法的机械臂路径规划 . | 控制与决策 , 2024 , 39 (08) , 2597-2604 . |
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Wood surface broken defects seriously damage the structure of wooden products, these defects have to be detected and eliminated. However, current defect detection methods based on machine vision have difficulty distinguishing the interference, similar to the broken defects, such as stains and mineral lines, and can result in frequent false detections. To address this issue, a multi-source data fusion network based on U-Net is proposed for wood broken defect detection, combining image and depth data, to suppress the interference and achieve complete segmentation of the defects. To efficiently extract various semantic information of defects, an improved ResNet34 is designed to, respectively, generate multi-level features of the image and depth data, in which the depthwise separable convolution (DSC) and dilated convolution (DC) are introduced to decrease the computational expense and feature redundancy. To take full advantages of two types of data, an adaptive interacting fusion module (AIF) is designed to adaptively integrate them, thereby generating accurate feature representation of the broken defects. The experiments demonstrate that the multi-source data fusion network can effectively improve the detection accuracy of wood broken defects and reduce the false detections of interference, such as stains and mineral lines.
Keyword ：
deep learning deep learning multi-source data fusion multi-source data fusion semantic segmentation semantic segmentation U-Net U-Net wood defect detection wood defect detection
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| GB/T 7714 | Zhu, Yuhang , Xu, Zhezhuang , Lin, Ye et al. A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation [J]. | SENSORS , 2024 , 24 (5) . |
| MLA | Zhu, Yuhang et al. "A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation" . | SENSORS 24 . 5 (2024) . |
| APA | Zhu, Yuhang , Xu, Zhezhuang , Lin, Ye , Chen, Dan , Ai, Zhijie , Zhang, Hongchuan . A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation . | SENSORS , 2024 , 24 (5) . |
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