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学者姓名：徐哲壮
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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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Machine vision-based meter reading technologies have been developed to monitor the status of devices in industrial sites. However, the integrity of the pointer can be destroyed by the occlusion on the meter, such as dirt or rain. In this case, the pointer detection becomes a challenging task in the meter reading. To overcome this challenge, in this article, the pointer generative adversarial network (Pointer-GAN) is proposed for pointer mask generation. Specifically, an occlusion simulation method is developed in the data preprocessing to provide sufficient images of occluded meters (OMs) in the training phase, and the dilated convolution is adopted in the residual block to strengthen the correlation among pointer features in the long-range. Next, the multiscale attention mechanism (MAM) is designed for preventing the pointer feature in the low-level from being affected by the noise. Finally, the dense dilated convolution block (DDC block) is utilized to integrate the pointer feature in the low and high level for the pointer mask generation. The experiments demonstrate that the Pointer-GAN can generate the pointer mask with higher accuracy for the meters under occlusion compared to the other methods, thereby improving success rates of reading meters in different occlusion scenarios.
Keyword ：
Accuracy Accuracy Convolution Convolution Deep learning Deep learning Feature extraction Feature extraction Generative adversarial network (GAN) Generative adversarial network (GAN) Generative adversarial networks Generative adversarial networks Generators Generators Meter reading Meter reading Meters Meters occlusion occlusion pointer mask generation pointer mask generation pointer meter reading pointer meter reading Training Training YOLO YOLO
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| GB/T 7714 | Lin, Ye , Xu, Zhezhuang , Chen, Dan et al. Occluded Meter Reading With Pointer Mask Generation Based on Generative Adversarial Network [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
| MLA | Lin, Ye et al. "Occluded Meter Reading With Pointer Mask Generation Based on Generative Adversarial Network" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 74 (2025) . |
| APA | Lin, Ye , Xu, Zhezhuang , Chen, Dan , Yuan, Meng , Zhu, Jinyang , Yuan, Yazhou . Occluded Meter Reading With Pointer Mask Generation Based on Generative Adversarial Network . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
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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 et al. "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 et al. "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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With the increasing frequency and severity of extreme disaster events, enhancing the resilience of urban power grids has become critical. Most studies focus on distribution networks (DNs) without considering the important coupling relationship between transportation networks (TNs) and DNs. This paper addresses this gap by proposing a tri-stage resilience improvement method for power–transportation coupled networks (PTNs), incorporating flexible resources. The approach includes three models: (1) a pre-allocation model with a line hardening strategy based on vulnerability index for defense, (2) a failure response model that analyzes the impact of typhoons on PTN and minimizes load reduction in the DN, and (3) a restoration model that integrates emergency repair crews and mobile energy storage, optimized via linear programming. The performance of the proposed method is evaluated using a PTN with the IEEE 33-bus distribution test system and its corresponding TN. Results demonstrate that the resilience of PTN can be enhanced effectively by applying the proposed tri-stage method. © 2025 Elsevier Ltd
Keyword ：
Urban transportation Urban transportation
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| GB/T 7714 | Lian, Xianglong , Qiu, Lei , Song, Chenkai et al. Enhancing resilience of power-transportation coupled networks to typhoons: A tri-stage integration strategy of multi-resources [J]. | Sustainable Energy, Grids and Networks , 2025 , 43 . |
| MLA | Lian, Xianglong et al. "Enhancing resilience of power-transportation coupled networks to typhoons: A tri-stage integration strategy of multi-resources" . | Sustainable Energy, Grids and Networks 43 (2025) . |
| APA | Lian, Xianglong , Qiu, Lei , Song, Chenkai , Liu, Lijun , Xu, Zhezhuang . Enhancing resilience of power-transportation coupled networks to typhoons: A tri-stage integration strategy of multi-resources . | Sustainable Energy, Grids and Networks , 2025 , 43 . |
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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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As the foremost protocol for low-power communication, Bluetooth Low Energy (BLE) significantly impacts various aspects of our lives, including industry and healthcare. Given BLE's inherent security limitations and firmware vulnerabilities, spoofing attacks can readily compromise BLE devices and jeopardize privacy data. In this paper, we introduce BLEGuard, a hybrid mechanism for detecting spoofing attacks in BLE networks. We established a physical Bluetooth system to conduct attack simulations and construct a substantial dataset (BLE-SAD). BLEGuard integrates pre-detection, reconstruction, and classification models to effectively identify spoofing activities, achieving an impressive preliminary accuracy of 99.01%, with a false alarm rate of 2.05% and an undetection rate of 0.36%.
Keyword ：
Deep Learning Deep Learning Mobile Systems Mobile Systems Security and Privacy Security and Privacy
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| GB/T 7714 | Cai, Hanlin , Fang, Yuchen , Huang, Jiacheng et al. Poster: Hybrid Detection Mechanism for Spoofing Attacks in Bluetooth Low Energy Networks [J]. | PROCEEDINGS OF THE 2024 THE 22ND ANNUAL INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS, APPLICATIONS AND SERVICES, MOBISYS 2024 , 2024 : 710-711 . |
| MLA | Cai, Hanlin et al. "Poster: Hybrid Detection Mechanism for Spoofing Attacks in Bluetooth Low Energy Networks" . | PROCEEDINGS OF THE 2024 THE 22ND ANNUAL INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS, APPLICATIONS AND SERVICES, MOBISYS 2024 (2024) : 710-711 . |
| APA | Cai, Hanlin , Fang, Yuchen , Huang, Jiacheng , Yuan, Meng , Xu, Zhezhuang . Poster: Hybrid Detection Mechanism for Spoofing Attacks in Bluetooth Low Energy Networks . | PROCEEDINGS OF THE 2024 THE 22ND ANNUAL INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS, APPLICATIONS AND SERVICES, MOBISYS 2024 , 2024 , 710-711 . |
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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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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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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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