检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Wu, Yanmin Zhang, Yunzhou Zhu, Delong Deng, Zhiqiang Sun, Wenkai Chen, Xin Zhang, Jian Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China College of Information Science and Engineering Northeastern University Shenyang110819 China The Department of Electronic Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this paper, we present a comprehensive object SLAM framework that focuses on object-based perception and object-oriented robot tasks. First, we propose an ensemble data association approach for associating objects in complicated conditions by incorporating parametric and nonparametric statistic testing. In addition, we suggest an outlier-robust centroid and scale estimation algorithm for modeling objects based on the iForest and line alignment. Then a lightweight and object-oriented map is represented by estimated general object models. Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multi-map matching. Finally, we suggest an object-driven active exploration strategy to achieve autonomous mapping in the grasping scenario. A range of public datasets and real-world results in mapping, augmented reality, scene matching, relocalization, and robotic manipulation have been used to evaluate the proposed object SLAM framework for its efficient performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

A Novel Underwater Image Enhancement and Improved Underwater Biological Detection Pipeline

arXiv

引用

arXiv 2022年

作者： Liu, Zheng Zhuang, Yaoming Jia, Pengrun Wu, Chengdong Xu, Hongli Liu, Zhanlin Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang110819 China Warner Music Group New York10019 United States

For aquaculture resource evaluation and ecological environment monitoring, automatic detection and identification of marine organisms is critical. However, due to the low quality of underwater images and the characteristics of underwater biological, a lack of abundant features may impede traditional hand-designed feature extraction approaches or CNN-based object detection algorithms, particularly in complex underwater environment. Therefore, the goal of this paper is to perform object detection in the underwater environment. This paper proposed a novel method for capturing feature information, which adds the convolutional block attention module (CBAM) to the YOLOv5 backbone. The interference of underwater creature characteristics on object characteristics is decreased, and the output of the backbone network to object information is enhanced. In addition, the self-adaptive global histogram stretching algorithm (SAGHS) is designed to eliminate the degradation problems such as low contrast and color loss caused by underwater environmental information to better restore image quality. Extensive experiments and comprehensive evaluation on the URPC2021 benchmark dataset demonstrate the effectiveness and adaptivity of our methods. Beyond that, this paper conducts an exhaustive analysis of the role of training data on performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

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...

引用

Chinese Control and Decision Conference, CCDC

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

ISBN: (纸本)9781665478977

In recent years, convolutional neural networks have effectively improved the accuracy of semantic segmentation tasks. However, semantic segmentation of indoor scenes is still a challenging problem due to the complexity of indoor environments. With the advent of depth sensors, the use of depth information to improve semantic segmentation has been considered. Most 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 features. In this paper, we propose the network of attention-aware multimodal fusion for RGB-D indoor semantic segmentation method. By designing an attention-aware multimodal fusion module, the multilevel RGB features and depth features are effectively fused. The 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 information. The method can effectively promote the synergistic interaction of multimodal features and further improve the effect of semantic segmentation. Experimental 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： Training Semantic segmentation Feature extraction Multitasking Sensors Indoor environment Data mining

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

Chinese Control and Decision Conference, CCDC

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

ISBN: (纸本)9781665478977

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

关键词： Robust control Couplings Adaptation models Torque Admittance Transient analysis robots

来源：评论

学校读者我要写书评

暂无评论

Capturing Contact Surfaces by a Frustrated Total Internal Reflection System Using a Curved Plate for Comparison of the Beginning of Touching Motions by Humanitude Experts and Novices

Capturing Contact Surfaces by a Frustrated Total Internal Re...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Akishige Yuguchi Mayuki Toyoda Sung-Gwi Cho Atsushi Nakazawa Jun Takamatsu Koichiro Yoshino Tsukasa Ogasawara Faculty of Advanced Engineering Tokyo University of Science Katsushika-ku Tokyo Japan Guardian Robot Project RIKEN Seika-cho Sorakugun Kyoto Japan Nara Institute of Science and Technology Ikoma Nara Japan School of Science and Engineering Tokyo Denki University Hatoyama-machi Hiki-gun Saitama Japan Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University Kita-ku Okayama Japan Applied Robotics Re-search Microsoft Corporation Redmond WA USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Analyzing time-series changes of the contact surface by touching is important to elucidate the touching skills of Humanitude as one of the pervasive multimodal comprehensive care methodologies. For the analysis, there is a frustrated total internal reflection (FTIR) method to capture contact surfaces on a transparent flat plate by a camera. However, this conventional flat plate is far from the actual surfaces of care receivers because the surfaces of humans consist of curved shapes. In this paper, we propose an FTIR sensing system using a transparent curved plate to capture more ideal contact states with a surface shape more similar to the human body. We collect the contact surface data of the beginning of touching motions by Humanitude experts and novices using the FTIR sensing system with the curved and flat plates. Then, we compare the data by the experts and novices in terms of time-series contact areas and the quantitative indices and subjective evaluation and discuss the analysis results. Through these experiments, we confirm that the proposed system has the potential for the novices to perform more correctly the beginning of Humanitude's touching motions.

关键词： Correlation Shape Receivers Cameras Reflection Sensors Cybernetics

来源：评论

学校读者我要写书评

暂无评论

Short-Term Load Forecasting with TDCI: Temporal Decomposition and Channel Independence 7

Short-Term Load Forecasting with TDCI: Temporal Decompositio...

引用

7th International Conference on Electrical, Mechanical and Computer engineering, ICEMCE 2023

作者： Li, Wenxue Cheng, Chi Xu, Peihua Liu, Renfeng Zhang, Yun Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Prov. Wuhan430205 China Wuhan Institute of Technology Hubei Provincial Key Laboratory of Intelligent Robot Hubei Prov. Wuhan430205 China Hubei Meteorological Service Center Wuhan430079 China School of Educational Information Technology Central China Normal University Faculty of Artificial Intelligence Education Wuhan430079 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China

ISBN: (纸本)9798350382877

Given the substantial load fluctuations, pronounced stochasticity, and non-linearity influenced by factors like weather and temperature in power load forecasting, we present a short-term load forecasting model based on TDCI. This model introduces several enhancements to boost forecasting accuracy. Firstly, TDCI employs temporal decomposition, separating time-series data into seasonal and trend components, enabling the capture of inherent seasonal patterns and long-term trends that significantly impact load forecasting accuracy. Additionally, the incorporation of locality-sensitive hashing attention efficiently processes relevant temporal patterns, enhancing model performance while reducing computational complexity. Lastly, a channel independence approach is applied to each variable, allowing tailored processing based on individual feature characteristics. This adaptive feature handling ensures effective capture of distinct properties, thereby enhancing forecasting accuracy. Experimental evaluations on a real-world power load dataset demonstrate the superior forecasting precision of our proposed model compared to existing models. © 2023 IEEE.

关键词： Electric power plant loads

来源：评论

学校读者我要写书评

暂无评论

Amos-SLAM: An Anti-Dynamics Two-stage SLAM Approach

arXiv

引用

arXiv 2023年

作者： Zhuang, Yaoming Jia, Pengrun Liu, Zheng Li, Li Wu, Chengdong Cui, Wei Liu, ZhanLin Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China College of Information Science and Engineering Northeastern University Shenyang110819 China JangHo School of Architecture Northeastern University Shenyang110819 China Institute for Infocomm Research Astar 138632 Singapore Microsoft RedmondWA98052 United States

The traditional Simultaneous Localization And Mapping (SLAM) systems rely on the assumption of a static environment and fail to accurately estimate the system's location when dynamic objects are present in the background. While learning-based dynamic SLAM systems have difficulties in handling unknown moving objects, geometry-based methods have limited success in addressing the residual effects of unidentified dynamic objects on location estimation. To address these issues, we propose an anti-dynamics two-stage SLAM approach. Firstly, the potential motion regions of both prior and non-prior dynamic objects are extracted and pose estimates for dynamic discrimination are quickly obtained using optical flow tracking and model generation methods. Secondly, dynamic points in each frame are removed through dynamic judgment. For non-prior dynamic objects, we present a approach that uses super-pixel extraction and geometric clustering to determine the potential motion regions based on color and geometric information in the image. Evaluations on multiple low and high dynamic sequences in a public RGB-D dataset show that our proposed method outperforms state-of-the-art dynamic SLAM methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamics

来源：评论

学校读者我要写书评

暂无评论

UAV-Based Optimization for Fruit Counting in Greenhouses

UAV-Based Optimization for Fruit Counting in Greenhouses

引用

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： João Vítor R. Vasconcelos Thayron M. Hudson André G. dos Santos Martin Saska Alexandre S. Brandão Department of Electrical Engineering Núcleo de Especialização em Robótica Graduate Program on Computer Science Federal Univer-sity of Vicosa Vicosa MG Brazil Graduate Program on Computer Science Federal University of Vicosa Vicosa MG Brazil Department of Cybernetics Multi-robot Systems Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper presents a solution for counting fruit in agricultural greenhouses using Unmanned Aerial Vehicles (UAV s). Initially, a heuristic based on Simulated Annealing was used to optimize the UAV's trajectory, ensuring efficient coverage of the beds. Next, digital image processing (DIP) techniques were implemented to count the fruit, including depth segmentation, application of bounding boxes, color filtering, and element counting. The DIP accuracy was evaluated in multiple scenarios and the results indicate high reliability in fruit counting, with the potential to optimize agricultural operations and provide valuable information to producers. Possible future improvements could include further refinements in image processing to increase the accuracy of counting other fruits. Ultimately, this work contributes to the advancement of automation in agriculture by offering a viable and efficient solution for counting fruit in greenhouses using UAV s.

关键词： Image segmentation Accuracy Image color analysis Greenhouses Simulated annealing Autonomous aerial vehicles Trajectory

来源：评论

学校读者我要写书评

暂无评论

Multi-person interaction action recognition based on co-occurrence graph convolutional network

Multi-person interaction action recognition based on co-occu...

引用

Chinese Control and Decision Conference, CCDC

作者： Zhongyu Bai Hongli Xu Haopeng Zhang Haitao Gu Faculty of Robot Science and Engineering Northeastern University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665478977

Skeleton-based human action recognition has attracted more and more attention with the accessibility of depth sensors and the development of pose estimation technology. The recognition of multi-person interaction behavior with skeleton data has wide applications in real life. However, the existing skeleton-based action recognition methods mainly focus on single-person actions, and lack the research on the semantic information of the skeleton co-occurrence relationship in the spatial dimension. Here, a two-stream co-occurrence graph convolutional network (2s-CGCN) is proposed for multi-person interactive action recognition. The topological connection structure of the proposed model establishes connections between the human limbs and the head to capture the co-occurrence feature. In addition, the coordinates and co-occurrence information of the joints are simultaneously input into the model. The experiments of mutual actions in the NTU-RGB+D dataset show the effectiveness of the proposed model.

关键词： Convolution Semantics Pose estimation Data integration Skeleton Sensors Behavioral sciences

来源：评论

学校读者我要写书评

暂无评论

Critical Information Selection for Affective Brain-computer Interfaces Based on Brain Function Networks

Critical Information Selection for Affective Brain-computer ...

引用

IEEE International Conference on robotics and Biomimetics

作者： Jinying Bi Fei Wang Xin Yan Jingyu Ping College of Information Science and Engineering Northeastern University Shenyang China Faculty of Robot Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665405362

With the increasing importance of emotional factors in human-computer interaction, research on affective brain-computer interface (aBCI) has received extensive attention. To study the key frequency bands (FBs) and channels in the aBCI, we employ the brain function network (BFN) to obtain key information on SEED. Firstly, we adopt Pearson correlation to establish the BFN under three threshold densities and extract four feature parameters in each FB. Support vector machine (SVM) and logistic regression (LR) respectively perform recognition on single and splicing features on traditional bands and full-band splicing data (FBSD). Secondly, achieve the best threshold density: 15% and the optimal FBs: beta and gamma. According to the degree centrality of the 62 channels, mainly considering eight schemes: 10 or 12 top-ranked channels. Then, the LR with the grid search is used to classify the new BFN feature parameters on the best threshold density and FBs in eight schemes, to get the combination of critical channels. Finally, two methods are employed to verify the rationality of the critical information: SVM performance evaluation and residual network accuracy analysis. For the latter, the average accuracy is 72.08% and the mean highest accuracy reaches 95.23% on FBSD. The acquisition of critical information can reduce the workload of EEG emotion recognition, and simplify the electrode cap system.

关键词： Support vector machines Electrodes Emotion recognition Splicing Transfer learning Feature extraction Brain modeling

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：