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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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With the rapid development of offshore wind power and photovoltaic power generation, the importance of marine power transmission projects is increasing and the application of submarine cables is also becoming increasingly widespread. Accurate identification of the location of submarine cables is not only beneficial for routine inspections but also enhances the efficiency of fault detection. Therefore, the routing, positioning, and fault detection of submarine cables will play a crucial role in future repair and maintenance operations. The small diameter of submarine cables and the variability of internal currents have led to a decrease in positioning accuracy and an increase in positioning difficulty. To address the aforementioned issues, this paper first derives the approximate equation for the external magnetic field of submarine cables based on the underwater environment and underwater robotics, utilizing the cable structure of three-core armored submarine cables. The underwater robotic system performs attitude adjustments based on the detected magnetic induction intensity values. Building upon this, a submarine cable positioning algorithm is proposed using an improved grey wolf optimization (IGWO) algorithm. The algorithm utilizes a fitness function based on the magnetic flux density model and incorporates an online path localization method specifically designed for submarine cable detection. Finally, the accuracy and effectiveness of the IGWO algorithm are validated through simulation experiments. © 2025 Northeast University. All rights reserved.
Keyword ：
Structural optimization Structural optimization Underwater equipment Underwater equipment
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| GB/T 7714 | Huang, Wen-Chao , Wen, Jin-Rong , Xu, Zhe-Zhuang . Submarine cable localization algorithm for underwater robots based on improved grey wolf optimization algorithm [J]. | Control and Decision , 2025 , 40 (1) : 87-94 . |
| MLA | Huang, Wen-Chao 等. "Submarine cable localization algorithm for underwater robots based on improved grey wolf optimization algorithm" . | Control and Decision 40 . 1 (2025) : 87-94 . |
| APA | Huang, Wen-Chao , Wen, Jin-Rong , Xu, Zhe-Zhuang . Submarine cable localization algorithm for underwater robots based on improved grey wolf optimization algorithm . | Control and Decision , 2025 , 40 (1) , 87-94 . |
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The efficient pointer meter reading methods have been proposed based on machine vision to replace timeconsuming manual inspections for the industrial monitoring. However, the interference factors, such as rain or dirt, can occlude meter, which poses obstacles in the recognition and labeling of pointer and scales. To solve these problems, we propose a multi-task network with pointer and main scale detection (PMSD-Net) for the occluded meter reading with synthetic data generation technology. Specifically, dense parallel dilated convolution block is proposed for correlating the pointer and main scale features with large receptive field. Multi-scale feature fusion is designed to purify noisy features for the detailed information extraction. The relation reconstruction mechanism is designed to reconstruct the feature relation under severe occlusion. Moreover, the keypoint detection branch is designed to detect meter center and pointer tip according to the segmented pointer, which can identify changeable position of the segmented pointer tip to determine the pointer orientation. Finally, the synthetic data generation technology is developed to generate massive labeled data with simulated interference factors in the meter for the training, which enhances the generalization ability of PMSD-Net in various occlusion scenes. Experimental results indicate that PMSD-Net can segment more accurate regions of pointer and main scale and detect the changeable position of pointer tip for occluded meters, thereby improving the accuracy in reading occluded meters.
Keyword ：
Keypoint detection Keypoint detection Multi-task network Multi-task network Occluded meter reading Occluded meter reading Pointer and main scale segmentation Pointer and main scale segmentation Synthetic data generation Synthetic data generation
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| GB/T 7714 | Lin, Ye , Xu, Zhezhuang , Wu, Yiying et al. A multi-task network for occluded meter reading with synthetic data generation technology [J]. | ADVANCED ENGINEERING INFORMATICS , 2025 , 64 . |
| MLA | Lin, Ye et al. "A multi-task network for occluded meter reading with synthetic data generation technology" . | ADVANCED ENGINEERING INFORMATICS 64 (2025) . |
| APA | Lin, Ye , Xu, Zhezhuang , Wu, Yiying , Yuan, Meng , Chen, Dan , Zhu, Jinyang et al. A multi-task network for occluded meter reading with synthetic data generation technology . | ADVANCED ENGINEERING INFORMATICS , 2025 , 64 . |
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Abstract ：
The meter reading with machine vision greatly improves the efficiency of industrial monitoring. However, the pointer and scales of the meter can be occluded by rain or dirt, which greatly reduces the accuracy of the meter reading recognition. To solve this problem, we propose a generative adversarial network (PMS-GAN) with pointer generation and main scale detection for occluded meter reading. Specifically, dilated convolution block is designed to correlate separated pointer features. Then multi-scale feature fusion mechanism is proposed to guarantee the precision of pointer generation and main scale detection with guidance of semantic information. Moreover, feature enhancement mechanism is proposed to construct the long -range relationship for generating pointer under high occlusion. Finally, the reading is accomplished by calculating local angle with generated pointer and detected main scales. Experiments show that PMS-GAN can generate more intact pointer and detect main scales to guarantee the success and accuracy of occluded meter reading.
Keyword ：
Generative adversarial network Generative adversarial network Local angle calculation Local angle calculation Main scale detection Main scale detection Occluded meter reading Occluded meter reading Pointer generation Pointer generation
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| GB/T 7714 | Lin, Ye , Xu, Zhezhuang , Yuan, Meng et al. Pointer generation and main scale detection for occluded meter reading based on generative adversarial network [J]. | MEASUREMENT , 2024 , 234 . |
| MLA | Lin, Ye et al. "Pointer generation and main scale detection for occluded meter reading based on generative adversarial network" . | MEASUREMENT 234 (2024) . |
| APA | Lin, Ye , Xu, Zhezhuang , Yuan, Meng , Chen, Dan , Zhu, Jinyang , Yuan, Yazhou . Pointer generation and main scale detection for occluded meter reading based on generative adversarial network . | MEASUREMENT , 2024 , 234 . |
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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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In order to solve the problem of difficult mathematical modeling of complex industrial processes, this thesis proposes a data-driven modeling method that performs dynamic linearization near the working point. Since all real industrial processes have a certain working range, they can be regarded as linear objects containing unmodeled dynamics within this range. Therefore, a dynamic modeling algorithm based on incremental least squares is designed in this thesis. This algorithm is able to compute a linearized model of the dynamics in the vicinity of the operating point based on actual production data. Considering the controller implementation problems in the industrial field, the model is computed up to the 2nd order, i.e., the higher-order characteristics are also regarded as part of the unmodeled dynamics. Finally, on the basis of the dynamic model, Smith predictor is used to simulate the control. The simulation results show that control based on the model derived from this algorithm results in better dynamic and steady state characteristics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Keyword ：
Linearization Linearization
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| GB/T 7714 | Wei, Yongyao , Chen, Jian , Xu, Zhezhuang . A Data-Driven Modeling and Control Scheme Design Methodology for a Class of SISO Industrial Processes [C] . 2024 : 379-386 . |
| MLA | Wei, Yongyao et al. "A Data-Driven Modeling and Control Scheme Design Methodology for a Class of SISO Industrial Processes" . (2024) : 379-386 . |
| APA | Wei, Yongyao , Chen, Jian , Xu, Zhezhuang . A Data-Driven Modeling and Control Scheme Design Methodology for a Class of SISO Industrial Processes . (2024) : 379-386 . |
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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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