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学者姓名：冯心欣
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Abstract ：
Antimony selenosulfide (Sb-2(S,Se)(3)) solar cells have achieved an efficiency of over 10.0%. However, the uncontrollable hydrothermal process makes preparing high-quality Sb-2(S,Se)(3) thin films a bottleneck for efficient Sb-2(S,Se)(3) solar cell. To address this problem, triethanolamine (TEA) additive is innovatively utilized to regulate the reaction kinetic process of Sb-2(S,Se)(3) thin films in this work. The results show that TEA chelator can realize the time-domain control of the reaction process, optimizing the Se/(S+Se) elemental distribution of Sb-2(S,Se)(3) thin film and shrinking the bandgap offset of Sb-2(S,Se)(3) thin film. Meanwhile, the (021) and (061) crystal orientation of Sb-2(S,Se)(3) thin film are enhanced and the harmful V-Se1 defects in Sb-2(S,Se)(3) solar cells are passivated. Interestingly, a uniform back surface gradient for Sb-2(S,Se)(3) thin film is formed to reduce the minority carrier recombination at the back contact, increase the photocurrent and decrease the diode current of Sb-2(S,Se)(3) solar cells. Finally, the J(sc) and FF of Sb-2(S,Se)(3) solar cells are significantly improved by 8.6% and 5.5% respectively, and the open-circuit voltage deficit of the device is reduced by 44 mV, which leads to an efficiency of 9.94% which is the highest values of Sb-2(S,Se)(3) solar cells by sodium selenosulfate system.
Keyword ：
additive additive hydrothermal deposition hydrothermal deposition reaction kinetic reaction kinetic Sb-2(S, Se)(3) solar cells Sb-2(S, Se)(3) solar cells sodium selenosulfate sodium selenosulfate
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| GB/T 7714 | Zhu, Qiqiang , Wang, Weihuang , Chen, Zhirong et al. Fabricating High-Efficiency Sb2(S,Se)3 Solar Cells by Novel Additive-Assisted Longitudinal Component Engineering [J]. | SMALL , 2024 . |
| MLA | Zhu, Qiqiang et al. "Fabricating High-Efficiency Sb2(S,Se)3 Solar Cells by Novel Additive-Assisted Longitudinal Component Engineering" . | SMALL (2024) . |
| APA | Zhu, Qiqiang , Wang, Weihuang , Chen, Zhirong , Cao, Zixiu , Wang, Weiyu , Feng, Xinxin et al. Fabricating High-Efficiency Sb2(S,Se)3 Solar Cells by Novel Additive-Assisted Longitudinal Component Engineering . | SMALL , 2024 . |
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Predicting traffic flow effectively alleviates congestion. However, traditional methods tend to rely solely on historical traffic flow data, overlooking the correlation between multimodal traffic data, such as speed and occupancy collected by sensors placed on the road. This limitation results in low tolerance for abnormal situations. Moreover, the decentralization of multimodal data on edge devices may pose data anomalies or partial modal missing due to equipment damage or absence. To address these challenges, we propose a Block-Term tensor decomposition-based multimodal data feature fusion algorithm for traffic prediction. This approach enhances the accuracy and robustness of traffic flow prediction by considering correlations between various modal data, such as speed and occupancy rate. In response to the issues of scattered multimodal data anomalies and missing data on edge devices, and to ensure address privacy and security issues, we employ federated learning methods to achieve adaptive extraction and fusion of multi-modal data at the edges. Our method is tested on a real highway dataset, demonstrating superior prediction performance and robustness compared to traditional methods, particularly in the context of data anomalies or missing modalities. © 2024 IEEE.
Keyword ：
Federated learning Federated learning Multimodal data Multimodal data Robustness Robustness Tensor decomposition Tensor decomposition Traffic flow Traffic flow
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| GB/T 7714 | Feng, S. , Feng, X. , Xu, L. et al. BTD-GTAN: Federated Traffic Flow Prediction with Multimodal Feature Fusion Considering Anomalies [未知]. |
| MLA | Feng, S. et al. "BTD-GTAN: Federated Traffic Flow Prediction with Multimodal Feature Fusion Considering Anomalies" [未知]. |
| APA | Feng, S. , Feng, X. , Xu, L. , Zheng, H. . BTD-GTAN: Federated Traffic Flow Prediction with Multimodal Feature Fusion Considering Anomalies [未知]. |
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Cooperative perception is an advanced strategy within traffic systems designed to enhance the environmental perception capabilities of vehicles, where participants exchange cooperative perception messages (CPMs) through Vehicle-to-Everything (V2X) technology. However, most existing cooperative perception methods may ignore the communication bandwidth constraints of the system, potentially resulting in connected autonomous vehicles (CAVs) receiving outdated CPMs. In this paper, we propose a novel cooperative perception framework that enhances the accuracy of CAVs perception while reducing the transmission data size to meet the transmission delay requirements of CPMs under limited bandwidth. Furthermore, we propose a strategy for selecting cooperative partners and CPMs based on the Double Deep Q-Network (DDQN) algorithm. Additionally, an invalid action masking approach is presented to address the dynamic changes in the action space over time and reduce the size of the DDQN action space. Simulation results demonstrate that the proposed cooperative perception method consumes less data compared to some existing methods. Moreover, under limited communication bandwidth constraints, it achieves higher perception accuracy due to its ability to avoid transmission delay. © 2024 IEEE.
Keyword ：
Connected automated vehicles Connected automated vehicles cooperative perception cooperative perception deep reinforcement learning deep reinforcement learning invalid action masking invalid action masking
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| GB/T 7714 | Xu, F. , Chen, C. , Zheng, H. et al. Delay-Aware Cooperative Perception with Deep Reinforcement Learning in Vehicular Networks [未知]. |
| MLA | Xu, F. et al. "Delay-Aware Cooperative Perception with Deep Reinforcement Learning in Vehicular Networks" [未知]. |
| APA | Xu, F. , Chen, C. , Zheng, H. , Feng, X. . Delay-Aware Cooperative Perception with Deep Reinforcement Learning in Vehicular Networks [未知]. |
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Lidars and cameras are critical sensors that provide complementary information for 3D detection in autonomous driving. In recent years, the emergence of pseudo point clouds has led to an increasing number of 3D object detection tasks introducing this modality, but not every point in the pseudo point cloud generated by depth completion is reliable. In order to better utilize pseudo point clouds in 3D object detection tasks based on point cloud image fusion, we propose the EppNet framework in this paper, which enables the network to learn the anti noise features of pseudo point clouds. In this framework, we use VoxelNet [1] and VirConv Net [2] to extract features from point clouds and pseudo point clouds, respectively. Besides, we utilize a attentive RoI fusion strategy to make fuller use of information from different types of point clouds. Extensive experiments on KITTI, a benchmark for real-world traffic object identification, revealed that EppNet is able to perform favorably in comparison to earlier, well-respected detectors. © 2024 IEEE.
Keyword ：
3D modeling 3D modeling Cloud platforms Cloud platforms Image fusion Image fusion Object detection Object detection Object recognition Object recognition
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| GB/T 7714 | Chen, Yuren , Feng, Xinxin , Zheng, Haifeng . EppNet: Enhanced Pseudo and Point Cloud Fusion for 3D Object Detection [C] . 2024 : 29-32 . |
| MLA | Chen, Yuren et al. "EppNet: Enhanced Pseudo and Point Cloud Fusion for 3D Object Detection" . (2024) : 29-32 . |
| APA | Chen, Yuren , Feng, Xinxin , Zheng, Haifeng . EppNet: Enhanced Pseudo and Point Cloud Fusion for 3D Object Detection . (2024) : 29-32 . |
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Federated learning (FL) has been extensively studied as a means of ensuring data privacy while cooperatively training a global model across decentralized devices. Among various FL approaches, asynchronous federated learning (AFL) has distinct advantages in overcoming the straggler problem via server-side aggregation as soon as it receives a local model. However, AFL still faces several challenges in large-scale real-world applications, such as stale model problems and modality heterogeneity across geographically distributed and industrial devices with different functions. In this article, we propose a multimodal fusion framework for AFL to address the aforementioned problems. Specifically, a novel multilinear block fusion model is designed to fuse various multimodal information, which serves as an enhancement for perceiving and transmitting the important modality and block during local training. An adaptive aggregation strategy is further developed to fully utilize heterogeneous data by allowing the global model to favor the received local model based on both freshness and the importance of the local data. Extensive simulations with different data distributions demonstrate the superiority of the proposed framework in heterogeneity scenarios, which exhibits significant merits in the improvement of modality-based generalization without sacrificing convergence speed and communication consumption.
Keyword ：
Asynchronous federated learning (AFL) Asynchronous federated learning (AFL) block term (BT) decomposition block term (BT) decomposition multimodal fusion multimodal fusion
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| GB/T 7714 | Gao, Min , Zheng, Haifeng , Du, Mengxuan et al. Multimodal Fusion With Block Term Decomposition for Asynchronous Federated Learning [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (12) : 14083-14093 . |
| MLA | Gao, Min et al. "Multimodal Fusion With Block Term Decomposition for Asynchronous Federated Learning" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20 . 12 (2024) : 14083-14093 . |
| APA | Gao, Min , Zheng, Haifeng , Du, Mengxuan , Feng, Xinxin . Multimodal Fusion With Block Term Decomposition for Asynchronous Federated Learning . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (12) , 14083-14093 . |
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Depth information is crucial for an autonomous driving system as it helps the system understand the environment and make decisions. Most deep learning-based depth completion methods are primarily designed for high-resolution lidars (e.g. 64 scanlines). However, when the number of lidar scanlines decreases, such as with 32 scanlines or 16 scanlines lidars, existing solutions may face challenges in reliably predicting dense depth maps. To address this issue, this paper proposes an effective framework based on knowledge distillation, which incorporates mixed-scanline resolution training and feature-level fusion to train a powerful teacher network that dynamically fuses features from high-scanline resolution and low-scanline resolution inputs. By supervising the student network based on the guidance of the teacher network, the knowledge from the multi-scale fusion teacher network is effectively transferred to the low-scanline resolution student network. For the inference process, only the student network is utilized. The proposed framework has been applied to various existing depth completion networks. The experimental results show the effectiveness of the proposed method by using the KITTI dataset, which shows that it can serve as a universal framework for depth completion tasks. © 2024 IEEE.
Keyword ：
deep learning deep learning depth completion depth completion knowledge distillation knowledge distillation LIDAR LIDAR multiple sensors multiple sensors
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| GB/T 7714 | Huang, J. , Zheng, H. , Feng, X. . Multi-Scale Distillation for Low Scanline Resolution Depth Completion [未知]. |
| MLA | Huang, J. et al. "Multi-Scale Distillation for Low Scanline Resolution Depth Completion" [未知]. |
| APA | Huang, J. , Zheng, H. , Feng, X. . Multi-Scale Distillation for Low Scanline Resolution Depth Completion [未知]. |
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Federated Learning (FL), as a privacy-enhancing distributed learning paradigm, has recently attracted much attention in wireless systems. By providing communication and computation services, the base station (BS) helps participants collaboratively train a shared model without transmitting raw data. Concurrently, with the advent of integrated sensing and communication (ISAC) and the growing demand for sensing services, it is envisioned that BS will simultaneously serve sensing services, as well as communication and computation services, e.g., FL, in future 6G wireless networks. To this end, we provide a novel integrated sensing, communication and computation (ISCC) system, called Fed-ISCC, where BS conducts sensing and FL in the same time-frequency resource, and the over-the-air computation (AirComp) is adopted to enable fast model aggregation. To mitigate the interference between sensing and FL during uplink transmission, we propose a receive beamforming approach. Subsequently, we analyze the convergence of FL in the Fed-ISCC system, which reveals that the convergence of FL is hindered by device selection error and transmission error caused by sensing interference, channel fading and receiver noise. Based on this analysis, we formulate an optimization problem that considers the optimization of transceiver beamforming vectors and device selection strategy, with the goal of minimizing transmission and device selection errors while ensuring the sensing requirement. To address this problem, we propose a joint optimization algorithm that decouples it into two main problems and then solves them iteratively. Simulation results demonstrate that our proposed algorithm is superior to other comparison schemes and nearly attains the performance of ideal FL. IEEE
Keyword ：
6G 6G Atmospheric modeling Atmospheric modeling Computational modeling Computational modeling Downlink Downlink Federated learning Federated learning integrated sensing and communication integrated sensing and communication Optimization Optimization over-the-air computation over-the-air computation Radar Radar Task analysis Task analysis Uplink Uplink
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| GB/T 7714 | Du, M. , Zheng, H. , Gao, M. et al. Integrated Sensing, Communication and Computation for Over-the-Air Federated Learning in 6G Wireless Networks [J]. | IEEE Internet of Things Journal , 2024 , 11 (21) : 1-1 . |
| MLA | Du, M. et al. "Integrated Sensing, Communication and Computation for Over-the-Air Federated Learning in 6G Wireless Networks" . | IEEE Internet of Things Journal 11 . 21 (2024) : 1-1 . |
| APA | Du, M. , Zheng, H. , Gao, M. , Feng, X. , Hu, J. , Chen, Y. . Integrated Sensing, Communication and Computation for Over-the-Air Federated Learning in 6G Wireless Networks . | IEEE Internet of Things Journal , 2024 , 11 (21) , 1-1 . |
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The comprehension of 3D semantic scenes holds paramount significance in autonomous driving and robotics technology. Nevertheless, the simultaneous achievement of real-time processing and high precision in complex, expansive outdoor environments poses a formidable challenge. In response to this challenge, we propose a novel occupancy network named RTONet, which is built on a teacher-student model. To enhance the ability of the network to recognize various objects, the decoder incorporates dilated convolution layers with different receptive fields and utilizes a multi-path structure. Furthermore, we develop an automatic frame selection algorithm to augment the guidance capability of the teacher network. The proposed method outperforms the existing grid-based approaches in semantic completion (mIoU), and achieves the state-of-the-art performance in terms of real-time inference speed while exhibiting competitive performance in scene completion (IoU) on the SemanticKITTI benchmark. IEEE
Keyword ：
Decoding Decoding Deep Learning for Visual Perception Deep Learning for Visual Perception Feature extraction Feature extraction Laser radar Laser radar LiDAR LiDAR Mapping Mapping Occupancy Grid Occupancy Grid Point cloud compression Point cloud compression Real-time systems Real-time systems Semantics Semantics Semantic Scene Understanding Semantic Scene Understanding Three-dimensional displays Three-dimensional displays
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| GB/T 7714 | Lai, Q. , Zheng, H. , Feng, X. et al. RTONet: Real-Time Occupancy Network for Semantic Scene Completion [J]. | IEEE Robotics and Automation Letters , 2024 , 9 (10) : 1-8 . |
| MLA | Lai, Q. et al. "RTONet: Real-Time Occupancy Network for Semantic Scene Completion" . | IEEE Robotics and Automation Letters 9 . 10 (2024) : 1-8 . |
| APA | Lai, Q. , Zheng, H. , Feng, X. , Zheng, M. , Chen, H. , Chen, W. . RTONet: Real-Time Occupancy Network for Semantic Scene Completion . | IEEE Robotics and Automation Letters , 2024 , 9 (10) , 1-8 . |
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Target parameter estimation in high-speed scenarios is one of the main challenges in the integrated sensing and communication (ISAC) systems. In an ISAC system, the orthogonal time frequency space (OTFS) signal is able to successfully combat time-frequency-selective channels since the channel exhibits significant delay-Doppler (DD) sparsity characteristic. In this paper, we investigate the problem of parameter estimation of moving targets using OTFS modulation. We firstly derive signal model in the DD domain equivalent channel and recast the problem of parameter estimation into a compressed sensing (CS) problem. In order to improve the estimation performance, we then propose ADMM-Net by deep unfolding the iterations of the Alternating Direction Method of Multipliers (ADMM) algorithm into a deep learning network. Experimental results demonstrate that the proposed ADMM-Net algorithm outperforms the other methods in terms of estimation accuracy and running time for OTFS-based parameter estimation.
Keyword ：
ADMM ADMM deep unfolding network deep unfolding network integrated sensing and communication integrated sensing and communication Orthogonal Time Frequency Space (OTFS) Orthogonal Time Frequency Space (OTFS) target parameter estimation target parameter estimation
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| GB/T 7714 | Lin, Weizhi , Zheng, Haifeng , Feng, Xinxin et al. Deep Unfolding Network for Target Parameter Estimation in OTFS-based ISAC Systems [J]. | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 , 2024 . |
| MLA | Lin, Weizhi et al. "Deep Unfolding Network for Target Parameter Estimation in OTFS-based ISAC Systems" . | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 (2024) . |
| APA | Lin, Weizhi , Zheng, Haifeng , Feng, Xinxin , Chen, Youjia . Deep Unfolding Network for Target Parameter Estimation in OTFS-based ISAC Systems . | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 , 2024 . |
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In the 6G environment, addressing the challenges of data loss and off-grid issues during target parameter estimation poses a significant challenge for the Integrated Sensing and Communication (ISAC) system. In the ISAC framework, a commonly used method for parameter estimation is compressive sensing. However, compressive sensing may encounter off-grid issues in continuous parameter estimation. In contrast, the atomic norm proves effective in addressing off-grid problems, making it more suitable for continuous parameter estimation. We explore the application of the atomic norm in ISAC and further derive an ISAC model based on OFDM (Orthogonal Frequency Division Multiplexing) utilizing the atomic norm under conditions of incomplete data. To ensure improved convergence speed and accuracy of our algorithm, we employ the Alternating Direction Method of Multipliers (ADMM) for iterative implementation. Experimental results demonstrate that our proposed AN algorithm accurately estimates target parameters in the presence of data loss, exhibiting higher precision and robustness compared to traditional methods.
Keyword ：
ADMM ADMM Atomic norm Atomic norm ISAC ISAC Off-grid target parameter estimation Off-grid target parameter estimation
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| GB/T 7714 | Ling, Muyao , Feng, Xinxin , Zheng, Haifeng . Off-Grid Parameter Estimation for OFDM-based ISAC Systems with Incomplete Data [J]. | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 , 2024 . |
| MLA | Ling, Muyao et al. "Off-Grid Parameter Estimation for OFDM-based ISAC Systems with Incomplete Data" . | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 (2024) . |
| APA | Ling, Muyao , Feng, Xinxin , Zheng, Haifeng . Off-Grid Parameter Estimation for OFDM-based ISAC Systems with Incomplete Data . | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 , 2024 . |
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