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4th International Conference on Computer Vision and Information Technology, CVIT 2023

作者： Lin, Zhiyuan Zhu, Feng Wang, Jianyu Wang, Qun Zhao, Pengfei Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Faculty of Robot Science and Engineering Northeastern University Shenyang110169 China

ISBN: (纸本)9781510672864

The amount of semantic contribution of each object in an image to understand the scene is different. Understanding the relative importance of objects in a scene for scene semantics is critical for various computer vision applications, such as scene recognition and image captioning. In this paper, we refer to the contribution of an object to scene semantics as the degree of its gist and propose a method for Estimating the degree of the Gist of an Instance (EGoI). In the EGoI method, an object gist degree is estimated by the semantic features comparison strategy and the semantic distance comparison strategy. In the first strategy, the image is represented as a scene graph first, then the aggregation features and the node features of different graph node combinations are calculated to estimate the instance gist that is not in the combination of the nodes. In the second strategy, the captions of the complete and incomplete images are generated, then the semantic distance of these captions is used to estimate the instance gist deleted in the scene. Different strategies for estimating the gist degree of instances are tested in the experiments. The results show that the proposed method can effectively quantify the contribution of an instance to scene semantics. Among these strategies, the method that compares semantic features has better discriminative power for the gist degree of various instances in the scene. © 2024 SPIE.

关键词： Semantics

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An Interactive Annotation Method Based on Incremental Learning 2

An Interactive Annotation Method Based on Incremental Learni...

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2nd International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Duan, Xiusheng Sun, Guohua Cao, Jingya Zhang, Yuyang Yang, Jingchao Han, Duyu Hebei Polytechnic Institute Department of Artificial Intelligence and Big Data Shijiazhuang China Hebei Provincial University Road Traffic Perception Intelligent Application Technology Research and Development Center Shijiazhuang China Shijiazhuang Tiedao University School of Mechanical Engineering Shijiazhuang China Northeastern University Faculty of Robot Science and Engineering Shenyang China Hebei Jiaotong Vocational and Technical College Shijiazhuang China

ISBN: (纸本)9798350331417

In recent years, military science and technology has been developed rapidly, and new equipments has been equipped in the army. Augmented Reality (AR) technology provides the possibility to solve the problem of equipments operation training. But in the training process, One of the essential technologies is how to location and identify the operation keys. Obviously, the sample set of equipments operation keys is the basis of key recognition, and a certain scale of sample set is an indispensable element to ensure the performance of the recognition model. On the basis of manually establishing a small sample set, this paper puts forward a mechanism based on interactive automatic sample labeling and sample set expanding, the target recognition model was updated by incremental learning method at the same time. © 2023 IEEE.

关键词： data annotation incremental learning interaction sample augmentation

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3D Lidar Point Cloud Dynamic Object Segmentation Based on Multi-Hierarchical Feature Extraction

3D Lidar Point Cloud Dynamic Object Segmentation Based on Mu...

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第34届中国控制与决策会议

作者： Fang Li Fengshan Zou Zhenjun Du Faculty of Robots Science and Engineering Northeastern University SIASUN Robot & Automation CO. Ltd

Normally,SLAM algorithms are based on the assumption of static *** quality of lidar input directly affects the final performance of SLAM algorithm during this *** important factor that affects the input quality is the dynamic objects in the ***,how to segment dynamic objects from the 3 D point cloud to improve the quality of input is a question worth paying attention *** contrast to traditional methods,we use convolutional neural network to segment points on moving ***'s achallenge to improve the segmentation accuracy with a low amount of computation effort by carefully designing the network structure to control the number of parameters of the network.A method was proposed to convert the 3 D point cloud into a range image and combine the residual information as the input of CNN and the multi-hierarchical feature extraction module was used to improve the accuracy of the existing *** focus on segmenting moving objects rather than dividing the points into their semantic *** have shown that the proposed method can effectively improve the accuracy of dynamic object segmentation while keeping the neural network model lightweight.

关键词： 3D Lidar Point Cloud Dynamic Object Segmentation Multi-Hierarchical Feature Extraction

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Lexicographic Multi-objective Order Picking Optimization for robotic Mobile Fulfillment Systems

Lexicographic Multi-objective Order Picking Optimization for...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Hanying Wang Ziyan Zhao Jiaqi Liang Xingyang Li Shixin Liu College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University Foshan China Département de mathématiques et génie industriel Polytechnique Montréal Montréal Qc Canada Faculty of Robot Science and Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao Hebei China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In light of advancements in artificial intelligence, the Internet of Things, and mechatronics, robots are increasingly integrated into e-commerce warehouses to enable smart order picking solutions and foster intelligent automation. A robot-assisted order picking process revolutionizes the traditional labor-intensive person-to-goods order picking technology, leading to a goods-to-person (G2P) smart warehouse. Within it, robots transport pods to predefined picking stations, where human pickers retrieve the requested goods from these pods to fulfill customer orders. The allocation of pods to robots and the scheduling of picking operations are key optimization issues in G2P order picking systems. Although they play a key role in improving operational efficiency, existing research has paid limited attention to their joint optimization. This study considers a lexicographic multi-objective optimization problem to shorten the order picking cycles under the premise of optimizing the picking efficiency evaluated by makespan. We build a mixed integer program for the newly proposed problem and develop a matheuristic algorithm by integrating a commodity-order model into a metaheuristic algorithm to solve it. Experimental results show that the proposed method can significantly shorten the total order picking cycles while keeping the minimum makespan. It outperforms a recent state-of-the-art algorithm. This work emphasizes the importance of joint optimization within G2P smart warehouses and reveals the high potential of the proposed method to be used in practice.

关键词： Intelligent automation Adaptation models Mechatronics Metaheuristics Search problems Scheduling Resource management Internet of Things Electronic commerce robots

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Constraint Programming for a Novel Integrated Optimization of Blocking Job Shop Scheduling and Variable-Speed Transfer robot Assignment

Constraint Programming for a Novel Integrated Optimization o...

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IEEE International Conference on Networking, Sensing and Control

作者： Xingyang Li Jun Fu Zixi Jia Ziyan Zhao Siyi Li Shixin Liu Faculty of Robot Science and Engineering Northeastern University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University Foshan China

ISBN: (纸本)9781665472449

Blocking job shop scheduling problems are common in industrial environments. Various existing studies tackle them to enhance the production efficiency of job shops with machine blocking properties. In the environment of intelligent manufacturing, robots are commonly used to transfer the jobs to be processed among different processes. However, no previous work considers the integrated optimization of blocking job shop scheduling and transfer robot assignment. Facing the new and key demand of production scheduling, this work considers a novel blocking job shop scheduling problem with transfer robots whose speed varies with or without cargo load. It is first formulated by using constraint programming as a baseline model. By analyzing the characteristics of both the considered problem and baseline model this work proposes an improved constraint programming model. Numerous experiments on an adapted benchmark dataset show that the improved constraint programming model can well solve the concerned problem. Comparing with a baseline model, it can greatly enhance the solution efficiency and accuracy. Its great performance shows its high potential to be used in practical industrial scenarios.

关键词： Constraint handling Adaptation models Analytical models Job shop scheduling Service robots Production robot sensing systems

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FBN-TCN: Temporal Convolutional Neural Network Based on Spatial Domain Fusion Brain Networks for Affective Brain-Computer Interfaces

SSRN

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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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Bi-objective Optimization of Real-time AGC Dispatch in Performance-based Frequency Regulation Market

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CSEE Journal of Power and Energy Systems 2024年第6期10卷 2360-2370页

作者： Xiaoshun Zhang Tian Tan Tao Yu Bo Yang Xiaoming Huang Foshan Graduate School of Innovation Northeastern UniversityFoshan 528311China Meizhou Power Supply Bureau Guangdong Power Grid Co.Ltd.Meizhou 514000China College of Electric Power South China University of TechnologyGuangzhou 510640China Faculty of Electric Power Engineering Kunming University of Science and TechnologyKunming 650500China Southern Power Grid Digital Enterprise Technology(Guangdong)Co. Ltd.Guangzhou 510000China

This paper illustrates a new bi-objective optimization model of real-time automatic generation control dispatch in a performance-based frequency regulation *** attempts to simultaneously minimize the total power deviation and the regulation mileage payment via optimally distributing the realtime total generation command to different regulation *** handle this problem,an efficient non-dominated sorting genetic algorithm II is adopted to rapidly obtain a high-quality Pareto front for real-time AGC dispatch.A dynamic ideal point based decision making technique is designed to select the best compromise solution from the obtained Pareto front according to the minimization of the total regulation variation,which can effectively avoid an excessive regulation variation for each ***,two testing systems are used to verify the performance of the proposed technique.

关键词： AGC dispatch bi-objective optimization dynamic ideal point based decision making performance-based frequency regulation regulation mileage

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Constrained Behavior Cloning for robotic Learning

arXiv

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arXiv 2024年

作者： Liang, Wensheng Xie, Jun Wang, Zhicheng Tan, Jianwei Ma, Xiaoguang School of Mechanical Engineering and Automation Northeastern University Wenhua Road Heping District Liaoning Shenyang110819 China Faculty of Robot Science and Engineering Northeastern University Chuangxin Road Hunnan District Liaoning Shenyang110169 China Foshan Graduate School of Innovation Northeastern University Shunde District Guangdong Foshan528311 China

Behavior cloning (BC) is a popular supervised imitation learning method in the societies of robotics, autonomous driving, etc., wherein complex skills can be learned by direct imitation from expert demonstrations. Despite its rapid development, it is still affected by limited field of view where accumulation of sensors and joint noise bring compounding errors. In this paper, we introduced geometrically and historically constrained behavior cloning (GHCBC) to dominantly consider high-level state information inspired by neuroscientists, wherein the geometrically constrained behavior cloning were used to geometrically constrain predicting poses, and the historically constrained behavior cloning were utilized to temporally constrain action sequences. The synergy between these two types of constrains enhanced the BC performance in terms of robustness and stability. Comprehensive experimental results showed that success rates were improved by 29.73% in simulation and 39.4% in real robot experiments in average, respectively, compared to state-of-the-art BC method, especially in long-term operational scenes, indicating great potential of using the GHCBC for robotic learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Dynamic Stiffness Estimation of Human Body Based on sEMG

Dynamic Stiffness Estimation of Human Body Based on sEMG

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： ZiDa An ShiGuang Wen Jing Liu Qi Wu Lu Han Fei Wang Faculty of Robot Science and Engineering Northeastern University Shen Yang LiaoNing CHINA LiaoNing Disabled Persons'Service Center Shen Yang LiaoNing CHINA

Rehabilitation robots can effectively assist stroke patients in motor function rehabilitation. robots need to have movement flexibility to ensure comfortable and active interaction among stroke patients. As a mechanical arm with good flexibility, the stiffness adjustment characteristics of the human body have received widespread attention. However, static estimation methods are mostly used to estimate human body impedance, which can lead to inaccurate human body impedance information. To achieve accurate man-machine impedance transfer, this paper studies muscle contraction and proposes a framework for human dynamic stiffness identification based on sEMG and static stiffness. In this paper, experiments were conducted to estimate the dynamic stiffness of human upper limbs. The conclusion proves that the dynamic stiffness calculated by this framework is consistent with the changes in arm stiffness during actual human motion. The framework proposed in this paper can be widely used in control systems that require human-machine impedance transfer.

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Experimental and quantum chemical studies of carboxylates as corrosion inhibitors for AM50 alloy in pH neutral NaCl solution

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Journal of Magnesium and Alloys 2022年第2期10卷 596-611页

作者： Junjie Yang Pingli Jiang Youmin Qiu Chih-Yu Jao Carsten Blawert Sviatlana Lamaka Anissa Bouali Xiaopeng Lu Mikhail L.Zheludkevich Wei Li Institute of Advanced Wear&Corrosion Resistant and Functional Materials Jinan UniversityGuangzhou 510632PR China Helmholtz-Zentrum Hereon Institute of Surface ScienceGeesthacht 21502Germany School of Materials Science and Engineering Zhengzhou UniversityZhengzhou 450001PR China Shenyang National Laboratory for Materials Science Northeastern University3-11 Wenhua RoadShenyang 110819PR China Faculty of Engineering University of KielKaiserstraße 2Kiel 24143Germany

Sodium salts of mono-and di-carboxylic acids(glycolic,fumaric and benzoic acid)were studied as corrosion inhibitors for AM50 alloy in pH neutral aqueous NaCl *** evolution,electrochemical and surface characterization techniques were employed to reveal their corrosion inhibition mechanism,whilst the molecular features of inhibitors were investigated by quantum chemical *** inhibitors reduced anodic dissolution of AM50 and their efficiency generally increased with time and concentration from 5 mM to 100 *** inhibition mechanism can be described as physisorption of inhibitive molecules on the surface of the intrinsic oxide layer followed by chemisorption with Mg^(2+)and Al^(3+),and the difference in inhibition action among these inhibitors was explained on the molecular scale.

关键词： Mg-Al magnesium alloy Neutral NaCl solution Corrosion inhibitors Electrochemistry Density functional theory

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