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学者姓名：袁蒙
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Abstract ：
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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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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Electric-powered wheelchairs play a vital role in ensuring accessibility for individuals with mobility impairments. The design of controllers for tracking tasks must prioritize the safety of wheelchair operation across various scenarios and for a diverse range of users. In this study, we propose a safety-oriented speed tracking control algorithm for wheelchair systems that accounts for external disturbances and uncertain parameters at the dynamic level. We employ a set-membership approach to estimate uncertain parameters online in deterministic sets. Additionally, we present a model predictive control scheme with real-time adaptation of the system model and controller parameters to ensure safety-related constraint satisfaction during the tracking process. This proposed controller effectively guides the wheelchair speed toward the desired reference while maintaining safety constraints. In cases where the reference is inadmissible and violates constraints, the controller can navigate the system to the vicinity of the nearest admissible reference. The efficiency of the proposed control scheme is demonstrated through high-fidelity speed tracking results from two tasks involving both admissible and inadmissible references.
Keyword ：
Model predictive control (MPC) Model predictive control (MPC) robotic wheelchair robotic wheelchair safety constraints safety constraints speed tracking speed tracking
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| GB/T 7714 | Yuan, Meng , Wang, Ye , Li, Lei et al. Safety-Based Speed Control of a Wheelchair Using Robust Adaptive Model Predictive Control [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2023 , 54 (8) : 4464-4474 . |
| MLA | Yuan, Meng et al. "Safety-Based Speed Control of a Wheelchair Using Robust Adaptive Model Predictive Control" . | IEEE TRANSACTIONS ON CYBERNETICS 54 . 8 (2023) : 4464-4474 . |
| APA | Yuan, Meng , Wang, Ye , Li, Lei , Chai, Tianyou , Ang, Wei Tech . Safety-Based Speed Control of a Wheelchair Using Robust Adaptive Model Predictive Control . | IEEE TRANSACTIONS ON CYBERNETICS , 2023 , 54 (8) , 4464-4474 . |
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Detecting wood broken defects through machine vision is challenging due to the similar appearance of defect and defect-free regions on images. Laser profilometer is a reasonable solution, nevertheless, imperfect point cloud representation, such as slope profile, incontinuity of tiny defects and similarity between broken defects and sound area, poses obstacles. To overcome these challenges, this study proposes a multi-line detection method based on bidirectional long-and short-term memory network (Bi-LSTM) for real-time wood broken defect detection. The feature that represents the extent of surface damage in line-level is designed by residual extraction and sorting operation. The Bi-LSTM combines adjacent information to exaggerate semantic information of detection line. Context information extracted by Bi-LSTM are concatenated for multi -line detection to reduce computation complexity. Finally, detection results are modified by considering the information of adjacent lines of point cloud. Experimental results show that the proposed method achieves real-time detection with high accuracy.
Keyword ：
Bi-LSTM Bi-LSTM Feature extraction Feature extraction Laser profilometer Laser profilometer Multi-line detection Multi-line detection Wood broken defect Wood broken defect
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| GB/T 7714 | Xu, Zhezhuang , Lin, Ye , Chen, Dan et al. Wood broken defect detection with laser profilometer based on Bi-LSTM network [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 242 . |
| MLA | Xu, Zhezhuang et al. "Wood broken defect detection with laser profilometer based on Bi-LSTM network" . | EXPERT SYSTEMS WITH APPLICATIONS 242 (2023) . |
| APA | Xu, Zhezhuang , Lin, Ye , Chen, Dan , Yuan, Meng , Zhu, Yuhang , Ai, Zhijie et al. Wood broken defect detection with laser profilometer based on Bi-LSTM network . | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 242 . |
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