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学者姓名：袁蒙
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Abstract ：
Atomically precise alloy nanoclusters (NCs) represent an emerging sector of metal nanomaterials as a new generation of photosensitizers for light harvesting and conversion, owing to their distinctive atom-stacking pattern, quantum confinement effect, and enriched active sites. Despite the sporadic progress made in the past few years in constructing alloy NCs photosystems, photoinduced charge transfer characteristics and photocatalytic mechanisms of alloy NCs still remain elusive. In this work, we conceptually demonstrate the rational design of alloy NC (Au1-xAgx, Au1-xPtx, and Au1-xCux)/transition metal chalcogenide (TMCs) heterostructure photosystems via a ligand-triggered self-assembly strategy. The results signify that electrons photoexcited in alloy NCs can smoothly transport to the TMC substrate with the aid of an intermediate ultrathin organic molecule layer, while holes migrate in the opposite direction, promoting the charge separation and prolonging the charge lifetime. Benefitting from the advantageous charge migration, the self-assembled alloy NC/TMC heterostructures exhibit significantly enhanced photoactivity towards selective photoredox organic transformation including selective reduction of aromatic nitro compounds to amino derivatives and selective oxidation of aromatic alcohols to aldehydes under visible light. The predominant active species during the photoredox catalysis are determined, through which alloy NC-dominated photoredox mechanisms are elucidated. Our work provides new insights into the smart construction of atomically precise alloy NC hybrid photosystems, and more importantly, paves the way for regulating the spatially vectorial charge transfer over alloy NCs to achieve solar-to-chemical energy conversion.
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| GB/T 7714 | Zheng, Bing-Xiong , Yuan, Jiao-Nan , Su, Peng et al. Alloy nanocluster artificial photosystems steering photoredox organic transformation [J]. | JOURNAL OF MATERIALS CHEMISTRY A , 2025 , 13 (7) : 4908-4920 . |
| MLA | Zheng, Bing-Xiong et al. "Alloy nanocluster artificial photosystems steering photoredox organic transformation" . | JOURNAL OF MATERIALS CHEMISTRY A 13 . 7 (2025) : 4908-4920 . |
| APA | Zheng, Bing-Xiong , Yuan, Jiao-Nan , Su, Peng , Yan, Xian , Chen, Qing , Yuan, Meng et al. Alloy nanocluster artificial photosystems steering photoredox organic transformation . | JOURNAL OF MATERIALS CHEMISTRY A , 2025 , 13 (7) , 4908-4920 . |
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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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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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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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Building electrical load forecasting, as a necessary foundation for building energy management, is of great significance for building energy efficiency and sustainable urban development. However, the accuracy of forecasting can hardly be guaranteed due to the stochastic nature of occupant behavior. To overcome this challenge, this paper proposes a data fusion-based building electrical load forecasting method with occupancy data obtained by wireless sensing technology. Firstly, a wireless sensing scheme is developed, which utilizes pre-existing wireless devices within the building energy management system (BEMS), offering a cost-effective means of obtaining occupancy information without violating occupant privacy. Moreover, to estimate the pattern of occupant behavior in the entire building, an improved stacked sparse auto-encoder (ISSAE) model is developed, which involves unsupervised feature fusion from information sources of varying significance. Finally, to cope with the time-varying and strongly fluctuating building load, a multi-source data fusion forecasting model based on the ensemble deep random vector functional link (edRVFL) is proposed. This model integrates the contributions of the latest accuracy and diversity through the ranking-based dynamic integration strategy. The effectiveness of the proposed method is validated in a commercial building. The experimental results demonstrate that, compared with the load forecasting scheme without occupancy information, the proposed method can improve the forecasting accuracy on RMSE and MAPE by 13.21% and 14.97%, respectively, while cost-effectiveness and privacy are ensured.
Keyword ：
Building electrical load forecasting Building electrical load forecasting Data fusion Data fusion Occupancy information Occupancy information Random vector functional link network Random vector functional link network Wireless signal sensing Wireless signal sensing
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| GB/T 7714 | Liu, Chi , Xu, Zhezhuang , Yuan, Meng et al. Building electrical load forecasting with occupancy data based on wireless sensing [J]. | APPLIED ENERGY , 2024 , 380 . |
| MLA | Liu, Chi et al. "Building electrical load forecasting with occupancy data based on wireless sensing" . | APPLIED ENERGY 380 (2024) . |
| APA | Liu, Chi , Xu, Zhezhuang , Yuan, Meng , Xie, Junwei , Yuan, Yazhou , Ma, Kai . Building electrical load forecasting with occupancy data based on wireless sensing . | APPLIED ENERGY , 2024 , 380 . |
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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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