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SSRN 2023年

作者： Bi, Jinying Wang, Fei Ping, Jingyu Qu, Gangguo Hu, Fangzhou Li, Hao Han, Shuai Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China College of Instrument Science and Engineering Southeast University Nanjing210096 China College of Instrument Science and Engineering Northeastern University Shenyang110169 China Department of Neurosurgery Shengjing Hospital of China Medical University Shenyang110004 China

Background and Objective: Brain networks in electroencephalogram (EEG) emotion recognition are gaining popularity for their ability to visualize and quantify. It shows potential in medical cognition and human-computer interaction. However, single brain space is limited, and multi-channel recognition poses challenges with calculation volume and information redundancy. Therefore, this paper is committed to studying the affective brain-computer interface (aBCI) and critical lead acquisition of the fusion brain network. Method: This paper employs two correlation algorithms to build functional and effective networks. Then a new fusion brain network (FBN) is proposed to obtain overall emotional information in the space domain. Secondly, a deep network recognition model (FBN-TCN) is proposed to capture the dynamic changes of spatial domain emotional information in time series. According to the brain causal network, a critical channel combination with a high emotional correlation between multiple channels is obtained. All proposed models were performed on the SEED dataset. Results: All experimental results show that FBN can cover more emotional information to improve emotional recognition. The critical channel aBCI can also achieve the recognition effect of full-channel aBCI. The recognition accuracy of the two in the full frequency band is 95.51%/3.15% and 96.34%/1.62% respectively. The ablation experiment has also proved the effectiveness of the model we proposed. Conclusion and Significance: This article can simplify the electrode cap device and achieve the purpose of making full use of EEG spatial domain emotional information. It is of great significance to improve the performance of human-computer interaction aBCI systems. © 2023, The Authors. All rights reserved.

关键词： Emotion Recognition

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AI-Based Maize and Weeds Detection on the Edge with CornWeed Dataset

AI-Based Maize and Weeds Detection on the Edge with CornWeed...

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Federated Conference on Computer science and Information Systems (FedCSIS)

作者： Naeem Iqbal Christoph Manss Christian Scholz Daniel König Matthias Igelbrink Arno Ruckelshausen Plan-based Robot Control DFKI Osnabrueck Germany Marine Perception DFKI Oldenburg Germany Faculty of Engineering and Computer Science University of Applied Sciences Osnabrueck Osnabrueck Germany

Artificial intelligence (AI) is used more heavily in agricultural applications. Yet, the lack of wireless-fidelity (Wi-Fi) connections on agricultural fields makes AI cloud services unavailable. Consequently, AI models have to be processed directly on the edge. In this paper, we evaluate state-of-the-art detection algorithms for their use in agriculture, in particular plant detection. Thus, this paper presents the CornWeed data set, which has been recorded on farm machines, showing labelled maize crops and weeds for plant detection. The paper provides accuracies for the state-of-the-art detection algorithms on the CornWeed data set, as well as frames per second (FPS) metrics for the considered networks on multiple edge devices. Moreover, for the FPS analysis, the detection algorithms are converted to open neural network exchange (ONNX) and TensoRT engine files as they could be used as future standards for model exchange.

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Linear Active Disturbance Rejection Anti-Backlash Control for Dual-Motor Coaxial Driving robotic Joint

Linear Active Disturbance Rejection Anti-Backlash Control fo...

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第34届中国控制与决策会议

作者： Jiqian Xu Lijin Fang Qiankun Zhao Huaizhen Wang Faculty of Robot Science and Engineering Northeastern University Institute of Shandong New Generation Information Industry Technology Inspur group

This paper describes a linear active disturbance rejection anti-backlash controller for a dual-motor coaxial driving robotic *** type of robotic joint is an over-actuated system that dual motors collaboratively drive load each ***,a dual-motor driving model is built considering backlash and then a simplified control-orient model is proposed based on the principle of dual-motor anti-backlash ***,a linear active disturbance rejection control-based dual-motor position tracking framework is developed in accordance with the control-orient ***,in order to deal with backlash coupling with unknown external torque,an admittance control-based adaptive position compensator is *** other control strategies,it practically achieves high tracking performance without requiring any backlash and external torque *** effectiveness and simplicity of proposed control strategy is verified through comparative experiments.

关键词： Anti-backlash Dual-motor Linear active disturbance rejection control Admittance control robotic joint

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The Network of Attention-Aware Multimodal fusion for RGB-D Indoor Semantic Segmentation Method

The Network of Attention-Aware Multimodal fusion for RGB-D I...

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第34届中国控制与决策会议

作者： Qiankun Zhao Yingcai Wan Lijin Fang Huaizhen Wang Faculty of Robot Science and Engineering Northeastern University Institute of Shandong New Generation Information Industry Technology Inspur group

In recent years,convolutional neural networks have effectively improved the accuracy of semantic segmentation ***,semantic segmentation of indoor scenes is still a challenging problem due to the complexity of indoor *** the advent of depth sensors,the use of depth information to improve semantic segmentation has been *** of the previous studies simply use equal-weight stitching or summation operations to fuse RGB features and depth features,failing to make full use of the complementary information between RGB features and depth *** this paper,we propose the network of attention-aware multimodal fusion for RGB-D indoor semantic segmentation *** designing an attention-aware multimodal fusion module,the multilevel RGB features and depth features are effectively *** cross-modal attention mechanism is also introduced in the multimodal fusion module,and the features are fused at different scales so that RGB features and depth features can guide and optimize each other using complementary information,thus extracting feature representations rich in spatial location *** method can effectively promote the synergistic interaction of multimodal features and further improve the effect of semantic *** results on publicly available datasets SUN RGB-D,NYU Depth v2 and ScanNet show that our designed algorithm outperforms other RGB-D image semantic segmentation algorithms.

关键词： Convolutional neural network Multimodal fusion Attention-aware mechanism Deep learning

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EverySync: An Open Hardware Time Synchronization Sensor Suite for Common Sensors in SLAM

EverySync: An Open Hardware Time Synchronization Sensor Suit...

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Xuankang Wu Haoxiang Sun Rongguang Wu Zheng Fang Faculty of Robot Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Multi-sensor fusion systems have been widely applied in various fields, including mobile robot, simultaneous localization and mapping (SLAM), and autonomous driving. For a tightly coupled multi-sensor fusion system, strict time synchronization between sensors will improve the accuracy of the system. However, there is currently a lack of open-source and general-purpose hardware synchronization systems for Cameras, IMUs, LiDARs, GNSS/RTK in the academic community. Therefore, we propose EverySync, an open hardware time synchronization system to address this gap. The synchronization accuracy of the system was evaluated through multiple experiments, achieving an accuracy of less than 1 ms. And, real-world experiments proved that hardware time synchronization improves the accuracy of the SLAM system. This open-source system is available on GitHub.

关键词： Simultaneous localization and mapping Accuracy Laser radar robot vision systems Sensor fusion Cameras Hardware Sensor systems Synchronization Software development management

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ASML-VDIO: Visual-Depth-Inertial Odometry using Selected Accurate and Stable Multi-Modal Landmarks in Structural Environments

ASML-VDIO: Visual-Depth-Inertial Odometry using Selected Acc...

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Xingjian Luo Chenglin Pang Xuankang Wu Zheng Fang Faculty of Robot Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In complex indoor structural scenes such as shopping centers and malls, camera pose estimation using pure point features is easy to fail due to the difficulty in extracting sufficient and stable point features from weak textures or dynamic environments. Recent works have attempted to address these challenges by introducing line features. However, the addition of line features increases the number of parameters and landmarks for BA (Bundle Adjustment), leading to efficiency reduction. This is a common issue in multi-modal SLAM (Simultaneous Localization And Mapping). To address this issue, this paper proposes a novel visual-depth-inertial odometry (ASML-VDIO) framework by combining RGB-D and IMU sensors. To improve the efficiency of BA, the proposed landmark classification method classifies 3D landmarks into accurate landmarks and other landmarks based on spatial consistency verification and depth range limitation. Then, accurate landmarks are fixed, and only other landmarks are optimized in the optimization of BA. Furthermore, to remove line features extracted from dynamic objects (pedestrian, shopping-car, etc), we propose a dynamic line removal method that combines geometric constraints and motion constraints of line features. Finally, the method is evaluated on public and author-collected datasets, showing competitive accuracy and robustness in complex indoor structural scenes while 71% speedup on optimization thread with same constraints.

关键词： Accuracy Simultaneous localization and mapping Dynamics Pose estimation Feature extraction Cameras Robustness Sensors Odometry Optimization

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Geometry-aided Underwater 3D Mapping Using Side-scan Sonar

Geometry-aided Underwater 3D Mapping Using Side-scan Sonar

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Yiqiao Yang Chenglin Pang Chengdong Wu Zheng Fang Faculty of Robot Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In recent years, the interest in underwater exploration with Autonomous Underwater Vehicles (AUVs) equipped with side-scan sonars (SSS) has grown considerably. However, state-of-the-art SSS Simultaneous Localization and Mapping (SLAM) systems encounter challenges in data association across large viewpoint changes. Additionally, these systems assume that the seabed is a flat surface, leading to significant mapping error in uneven underwater terrains. To address these challenges, we propose a framework that leverages the side-scan sonar geometry to facilitate data association and improve mapping accuracy. The framework begins with a preprocessing module that extracts feature points and provides initial estimates of the elevation angles of the landmarks. Then, a non-consecutive data association module applies epipolar line search to establish correspondences between the current and historical frames. Finally, the mapping module uses side-scan sonar bundle adjustment to recover the positions of the landmarks. The proposed method is evaluated using an underwater terraced fields dataset. Our method achieves over 90% matching rate and reduces the average mapping error from 3.799 to 0.134.

关键词： Geometry Bundle adjustment Autonomous underwater vehicles Simultaneous localization and mapping Accuracy Geometric modeling Feature extraction Side-scan sonar Optimization Intelligent robots

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Sub-Pixel Depth Perception Based on Flexible and Separated Structured Light 11

Sub-Pixel Depth Perception Based on Flexible and Separated S...

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11th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2021

作者： Wang, Hongyu Li, Dongxue Xu, Fang Wang, Yixiao Tsinghua University Department of Mechanical Engineering Beijing100084 China Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China Central Research Institute SIASUN Robot Automation Co Ltd Shenyang110168 China College of Sciences Northeastern University Shenyang110819 China

ISBN: (纸本)9781665425278

We develop the method of machine learning for sub-pixel depth perception based on flexible and separated structured light, and describe novel framework for getting the extrinsic parameters of the system without complicated calibration in advance. We combined the extrinsic parameter calculation process into the structured light decoding process. PSO-C4.5 Decesion Tree algorithm is used to classify and match sub-pixel based on the Label. According to the matching result, the disparity map at the sub-pixel level is obtained, and the extrinsic parameters of the measurement system are obtained according to the fundamental matrix and initial pose information. In contrast to previous work, we formulate the problem of correspondence matching as a classification problem, which allows us to correctly take into account effects caused by scene movement. All optimization problems are solved with machine learning techniques. We test our method on a number of real-world examples as well as our new benchmark dataset for depth map estimation, and compare results to the state-of-The-Art methods. The proposed method is both faster as well as more accurate. © 2021 IEEE.

关键词： Decision trees

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Machine Learning-based Energy Consumption Model for Data Center

Machine Learning-based Energy Consumption Model for Data Cen...

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Chinese Control and Decision Conference, CCDC

作者： Lin Qiao Yuanqi Yu Qun Wang Yu Zhang Yueming Song Xiaosheng Yu Department Information Communication Branch of State Grid Liaoning Electric Power Co.Ltd. Shenyang China Faculty of Robot Science and Engineering Northeastern University Shenyang China

The accurate prediction of time series data in the industrial production process can provide important guidance for the scheduling and decision-making of industrial systems, and is also an important part of predictive control technology. In this paper, a time series prediction model which introduces the graph neural network (GCN) is proposed. This model mainly consists of a time series feature extraction module and a relational modeling module. In the time series feature extraction module, the Att-LSTM model is proposed to extract the feature information of time series data. In the relational modeling module, a novel M-GCN network is proposed to model the relevance among different time series nodes. In addition, based on the time series prediction model, a time series multi-classification model is also proposed. The proposed model can predict the energy consumption conditions of the data center accurately. The experimental results demonstrate that the propose model can provide a desirable performance superior to some traditional models in accuracy and robustness.

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FlowTrack: Point-level Flow Network for 3D Single Object Tracking

FlowTrack: Point-level Flow Network for 3D Single Object Tra...

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Shuo Li Yubo Cui Zhiheng Li Zheng Fang Faculty of Robot Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

3D single object tracking (SOT) is a crucial task in fields of mobile robotics and autonomous driving. Traditional motion-based approaches achieve target tracking by estimating the relative movement of target between two consecutive frames. However, they usually overlook local motion information of the target and fail to exploit historical frame information effectively. To overcome the above limitations, we propose a point-level flow method with multi-frame information for 3D SOT task, called FlowTrack. Specifically, by estimating the flow for each point in the target, our method could capture the local motion details of target, thereby improving the tracking performance. Meanwhile, to handle scenes with sparse points, we present a learnable target feature as the bridge to efficiently integrate target information from past frames. Moreover, we design a Instance Flow Head to transform dense point-level flow into instance-level motion, effectively aggregating local motion information to obtain global target motion. Finally, our method achieves competitive performance with improvements of 5.9% on the KITTI and 2.9% on the NuScenes, compared to the next best method.

关键词： Bridges Target tracking Three-dimensional displays Aggregates Transforms Object tracking Intelligent robots Autonomous vehicles

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