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IEEE International Conference on Industrial Informatics (INDIN)

作者： Lecong Li Guizhen Yu Han Li Qi Xia Xiangyu Zhang Han Cai School of Transportation Science and Engineering State Key Lab of Intelligent Transportation System The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Open-pit mining areas have special geological structures, complex road networks, multi-rotation sections and poor road conditions, which bring many challenges to the operation of mining autonomous transport vehicles. Due to the large size and high control difficulty of mining autonomous transport vehicles, poor control effect and inaccurate steering mechanism implementation are prone to occur during the driving process, which leads to the vehicle departure from the reference path. To ensure the safety of autonomous operation in intelligent mine, this paper proposes a method for predicting driving departure of mining autonomous vehicles based on Gated Recurrent Unit (GRU). Firstly, the vehicle's historical trajectory data is obtained via on-board sensors, followed by data cleaning and normalization processes. key features are extracted, and a vehicle driving scene recognition module based on a GRU-based network is designed using deep learning. Subsequently, a GA-seq2seqGRU trajectory prediction module is constructed utilizing the scene recognition results, and the Genetic Algorithms (GA) is employed to tune the network hyper-parameters. Based on the predicted trajectories, the overall departure risk is calculated using two departure judgment methods based on cross-lane time and predicted lateral deviation, which are mapped to the departure level. Simulation experiments demonstrate that the accuracy of the driving scene recognition model in the proposed method in this paper reaches 0.9721, which is better than the 0.9585 of the Long Short Memory Neural Network (LSTM) model, and the trajectory prediction model based on the results of the driving scene recognition has a smaller RMSE value than the LSTM, GRU, and GA-seq2seqLSTM models when the prediction time domains are 1s, 2s, 3s, 4s, and 5s, and the departure detection module The average time consumed is 0.142ms.

关键词： Accuracy Roads Predictive models Feature extraction Trajectory Safety Data mining Long short term memory Autonomous vehicles Genetic algorithms

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DRIP: unleashing diffusion priors for joint foreground and alpha prediction in image matting 24

DRIP: unleashing diffusion priors for joint foreground and a...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Xiaodi Li Zongxin Yang Ruijie Quan Yi Yang The State Key Lab of Brain-Machine Intelligence and CCAI College of Computer Science and Technology Zhejiang University Hangzhou China DBMI HMS Harvard University Boston

ISBN: (纸本)9798331314385

Recovering the foreground color and opacity/alpha matte from a single image (i.e., image matting) is a challenging and ill-posed problem where data priors play a critical role in achieving precise results. Due to the limited matting datasets, traditional methods usually struggle to produce high-quality estimation. To address this, we explore the potential of leveraging vision priors embedded in pre-trained latent diffusion models (LDM) for estimating foreground RGBA values in challenging scenarios and rare objects. We introduce Drip, a novel approach for image matting that harnesses the rich prior knowledge of LDM models. Our method incorporates a switcher and a cross-domain attention mechanism to extend the original LDM for joint prediction of the foreground color and opacity. This setup facilitates mutual information exchange and ensures high consistency across both modalities. To mitigate the inherent reconstruction errors of the LDM's VAE decoder, we propose a latent transparency decoder to align the RGBA prediction with the input image, thereby reducing discrepancies. Comprehensive experimental results demonstrate that our approach achieves state-of-the-art performance in foreground and alpha predictions and shows remarkable generalizability across various benchmarks.

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Cable Segmentation Based on Mask2Former in Open-Pit Mining Area

Cable Segmentation Based on Mask2Former in Open-Pit Mining A...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Liyun Wang Bin Zhou Songyue Yang Wentao Liu Huazhi Li Shengdi Sun The Key Laboratory of Autonomous Transportation Technology for Special Vehicles School of Transportation Science and Engineering State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

The development of unmanned transportation technology has improved operational efficiency and safety in open-pit mining areas. However, there remain significant challenges to be addressed. One pressing issue is the need for mining trucks to pass through the area where the cable is laid on the ground. Accidentally crushing or damaging these cables would lead to significant risk to the mining area operation. However, due to the complexity of the mining environment and the characteristics of cables being thin and curved, the existing methods such as edge segmentation are difficult to meet the requirements for cable segmentation. This paper introduces a cable segmentation method based on Mask2Former and carries out comprehensive experiments to examine the effectiveness of the method. Experimental results show that the proposed method achieves IoU of 66.04% and PA of 80.08%, which can meet the requirements of practical applications.

关键词： Image segmentation Accuracy Computational modeling Transportation Pressing Network architecture Real-time systems Safety Optimization Cables

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Real-Time 3D Object Detection Based on Dynamic Sparse Voxel Transformer in Mining Area

Real-Time 3D Object Detection Based on Dynamic Sparse Voxel ...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Shengdi Sun Zhangyu Wang Runsen Liu Songyue Yang Liyun Wang Wentao Liu State Key Lab of Intelligent Transportation System The Key Laboratory of Autonomous Transportation Technology for Special Vehicles School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

To improve the safety and efficiency of mining operation, the development of unmanned transportation technologies for mining trucks is extraordinarily rapid. The application of LiDAR in autonomous mining trucks has also become increasingly popular. However, due to the significant difference in the size of objects and the complex noise, the problem of point cloud object detection in mining scenarios remains a major challenge. Existing methods, such as those based on point cloud clustering algorithms, have proved to be difficult to meet practical application requirements. In this paper, a real-time point cloud object detection based on Dynamic Sparse Voxel Transformer (DSVT) is introduced. A series of experiments were conducted to examine the efficacy of the proposed methodology. The experimental results demonstrated that the proposed method attained a mean average precision of 75%. Additionally, the detection latency of the proposed method was observed to be 62 ms. Collectively, the detection speed and accuracy both align with the practical application requirements.

关键词： Point cloud compression Three-dimensional displays Runtime Heuristic algorithms Transportation Object detection Transformers Real-time systems Safety Data mining

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Application and Implementation of Convolutional Neural Network Accelerator Based on FPGA in Environmental Sound Classification

Application and Implementation of Convolutional Neural Netwo...

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International Conference on Computers, Communications, and systems (ICCCS)

作者： Jianwei Zhang Haonan Yan Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System School of Biomedical Engineering & School of Artificial Intelligence Dalian University of Technology Dalian China Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System School of Biomedical Engineering Dalian University of Technology Dalian China

With the development of artificial intelligence, convolutional neural network (CNN) has achieved great success in various learning tasks, and has been applied to environmental sound classification tasks, achieving a high classification accuracy. With the development of convolutional neural network in the application of environmental sound classification, the design of hardware accelerator for convolutional neural network has become a hot topic. Compared with CPU, GPU and ASIC, FPGA has the unique advantages of low power consumption and high energy efficiency in accelerating deep learning, and has become the preferred hardware platform for neural network hardware implementation. In this paper, a convolution neural network accelerator based on FPGA is proposed, which can realize convolution, tanh nonlinear activation, average pooling and other operations. The hardware accelerator can realize the inference process of environmental sound classification, and achieve high energy efficiency and low resource consumption while having a high classification accuracy. The network was tested on Xilinx Virtex VC-707 FPGA board. The experimental results show that its energy efficiency is 547.60 and 377.36 times higher than that of CPU and GPU respectively.

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Droflysim: A Collective Behavior Analysis and Evaluation Tool 10

Droflysim: A Collective Behavior Analysis and Evaluation Too...

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10th IEEE Smart World Congress, SWC 2024

作者： Zhu, Rui Zeng, Yudian Yang, Chuying Wang, Bangchuan Zheng, Nenggan Zhejiang University Qiushi Academy for Advanced Studies College of Computer Science and Technology Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China Zhejiang University Qiushi Academy for Advanced Studies School of Software Technology Hangzhou China Zhejiang University Qiushi Academy for Advanced Studies The State Key Lab of Brain-Machine Intelligence College of Computer Science and Technology Hangzhou China Bengbu University Bengbu China

ISBN: (纸本)9798331520861

The collective behaviors of various organisms exhibit complex patterns and statistical characteristics, especially evident within large populations. The complexity of these behaviors necessitates in-depth analysis of extensive trajectory data to reveal underlying dynamics and patterns. Current methods fall short in quantitative analysis and multi-angle assessment of collective behaviors, particularly in terms of the limitations of macroscopic indicators and the singularity of evaluation methods. This study introduces Droflysim, an efficient computational platform for analyzing and assessing large-scale trajectory data of collective behavior. Droflysim enhances data processing speed through parallel processing technology and integrates a variety of simulation models and multi-angle evaluation methods, overcoming the limitations of traditional tools. Notably, Droflysim emphasizes the statistical analysis of trajectories under environmental disturbances, providing a new perspective and deeper understanding of the complexity of collective behavior by establishing a trajectory fitting model. The design of Droflysim includes an intuitive interactive interface, modular architecture, and parallel processing capabilities, enhancing the efficiency and scalability of data analysis. By quantitatively analyzing trajectories, Droflysim lays the foundation for precise model calibration and authentic reproduction of behavior, offering a powerful tool for the study of collective behavior. © 2024 IEEE.

关键词： Collective Behavior Analysis Computational Modeling Environmental Disturbances Statistical Characteristics Trajectory Data Visualization

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Distributed Sliding Mode Control with Disturbance Observer Of Multi-manipulator systems

Distributed Sliding Mode Control with Disturbance Observer O...

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第43届中国控制会议

作者： Yu Lin Jiayu Zou Kuo Wang Hai-Tao Zhang School of Artificial Intelligence and Automation Engineering Research Center of Autonomous Intelligent Unmanned Systems and the State Key Lab of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper proposes a distributed control strategy for the composite disturbance problem in multimanipulator ***,the disturbance observer is used for the access to observe the composite disturbance and motion state of the *** a slide mode control law is developed to improve the accuracy of the tracking ***,sufficient conditions are derived to guarantee asymptotical convergency of the developed control ***,the numerical simulations are conducted to verify the effectiveness of the present control scheme.

关键词： Multi-manipulator systems sliding mode control composite disturbance the disturbance observer

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FEDERATED NEAREST NEIGHBOR MACHINE TRANSLATION

arXiv

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arXiv 2023年

作者： Du, Yichao Zhang, Zhirui Wu, Bingzhe Liu, Lemao Xu, Tong Chen, Enhong University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Tencent AI Lab United States

To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention. Training neural machine translation (NMT) models with traditional FL algorithms (e.g., FedAvg) typically relies on multi-round model-based interactions. However, it is impractical and inefficient for translation tasks due to the vast communication overheads and heavy synchronization. In this paper, we propose a novel Federated Nearest Neighbor (FedNN) machine translation framework that, instead of multi-round model-based interactions, leverages one-round memorization-based interaction to share knowledge across different clients and build low-overhead privacy-preserving systems. The whole approach equips the public NMT model trained on large-scale accessible data with a k-nearest-neighbor (kNN) classifier and integrates the external datastore constructed by private text data from all clients to form the final FL model. A two-phase datastore encryption strategy is introduced to achieve privacy-preserving during this process. Extensive experiments show that FedNN significantly reduces computational and communication costs compared with FedAvg, while maintaining promising translation performance in different FL settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Real-time Terrain Recognition based on Transformer and Gait Prediction based on CNN-LSTM-Attention for Exosuit

Real-time Terrain Recognition based on Transformer and Gait ...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Yang, Fangliang Chen, Chunjie Wang, Zhuo Xu, Yinsheng Liu, Yao Wu, Xinyu University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Guilin University of Technology Guilin541004 China

ISBN: (纸本)9798350327182

For wearable soft exosuits, an imprecise control strategy can easily injure the wearer, while real-time terrain recognition and accurate gait phase estimation can effectively improve the control strategy of the exosuit and make the wearer comfortable and safe. In this paper, a real-time terrain recognition algorithm based on Transformer and a gait estimation algorithm based on CNN-LSTM-Attention are implemented. The Transformer-based recognition of different terrains improves the recognition accuracy to some extent, and the CNN-LSTM-Attention based feature extraction for temporal signals such as gait phase is also extremely noticeable. Experiments show that the Transformer-based algorithm achieves 99.64 % recognition accuracy in six different terrain environments. In the gait phase estimation experiment, CNN-LSTM-CBAM achieved the best performance with an evaluation index r2 of 0.9221. The aforementioned terrain recognition algorithms and gait phase estimation algorithms may have a positive impact on soft exosuit and dynamic prosthetics research. © 2023 IEEE.

关键词： Landforms

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Advancing Logic in AI: Insights from the LNGAI 2021 Workshop

Advancing Logic in AI: Insights from the LNGAI 2021 Workshop

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作者： Liao, Beishui Liu, Fenrong van der Torre, Leendert Zhou, Yi School of Philosophy Zhejiang University Hangzhou310058 China The State Key Lab of Brain-Machine Intelligence Hangzhou310058 China Department of Philosophy Tsinghua University Beijing100084 China Department of Computer Science University of Luxembourg Esch-sur-AlzetteL-4365 Luxembourg School of Information Science and Technology University of Science and Technology of China HeFei230026 China

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