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28th International Conference on Computational Linguistics, COLING 2020

作者： Wang, Zikang Li, Linjing Zeng, Daniel State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Shenzhen China

ISBN: (纸本)9781952148279

Natural Language Inference (NLI) is a vital task in natural language processing. It aims to identify the logical relationship between two sentences. Most of the existing approaches make such inference based on semantic knowledge obtained through training corpus. The adoption of background knowledge is rarely seen or limited to a few specific types. In this paper, we propose a novel Knowledge Graph-enhanced NLI (KGNLI) model to leverage the usage of background knowledge stored in knowledge graphs in the field of NLI. KGNLI model consists of three components: a semantic-relation representation module, a knowledge-relation representation module, and a label prediction module. Different from previous methods, various kinds of background knowledge can be flexibly combined in the proposed KGNLI model. Experiments on four benchmarks, SNLI, MultiNLI, SciTail, and BNLI, validate the effectiveness of our model. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Knowledge graph

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MixedFusion: 6D Object Pose Estimation from Decoupled RGB-Depth Features

MixedFusion: 6D Object Pose Estimation from Decoupled RGB-De...

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International Conference on Pattern Recognition

作者： Hangtao Feng Lu Zhang Xu Yang Zhiyong Liu The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences

Estimating the 6D pose of objects is an important process for intelligent systems to achieve interaction with the real-world. As the RGB-D sensors become more accessible, the fusion-based methods have prevailed, since the point clouds provide complementary geometric information with RGB values. However, due to the difference in feature space between color image and depth image, the network structures that directly perform point-to-point matching fusion do not effectively fuse the features of the two. In this paper, we propose a simple but effective approach, named MixedFusion. Different from the prior works, we argue that the spatial correspondence of color and point clouds could be decoupled and reconnected, thus enabling a more flexible fusion scheme. By performing the proposed method, more informative points can be mixed and fused with rich color features. Extensive experiments are conducted on the challenging LineMod and YCB-Video datasets, which shows that our method significantly boosts the performance without introducing extra overheads. Furthermore, when the minimum tolerance of metric narrows, the proposed approach performs better for the high-precision demands.

关键词： Measurement Three-dimensional displays Image color analysis Fuses Pose estimation Color Pattern recognition

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Anomaly Detection in Digital Twin Model

Anomaly Detection in Digital Twin Model

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Yutong Wang Yansong Cao Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Macau Institute of Systems Engineering Macau University of Science and Technology Macau China

As the simulation model of a physical system, digital twin has been widely used in many complicated control systems. Providing an effective way to perform simulation, digital twin makes the evaluation, prediction and optimization process cheaper and easier than on physical systems. For smart manufacturing, digital twin achieves high productivity with less operation and maintenance cost. However, the advantages of digital twin in anomaly detection during manufacturing are always neglected. Taking a two-speed transmission system as an example, we generate three kinds of faults on this digital twin model. After extracting features from the system output, we train an anomaly detection model on these features to classify each type of fault. By this digital twin model, we can find the fault in the whole system at the very beginning, and reduce the time and cost of debugging and diagnosing.

关键词： Productivity Costs Digital twin Time series analysis Predictive models Maintenance engineering Feature extraction

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Adaptive Fusional Localization for Robot Fish Based on Dynamic-weight Fuzzy Inference

Adaptive Fusional Localization for Robot Fish Based on Dynam...

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Chinese Automation Congress (CAC)

作者： Yuzhuo Fu Xiaocun Liao Ben Lu Qianqian Zou Zhuoliang Zhang Yaming Ou Chao Zhou State Key Laboratory of Management and Control for Complex Systems Institute of Automation CAS Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426480

Accurate localization of robots in a specific environment often requires the cooperation of multiple sensors, and how to establish a more general data fusion model is always a difficult problem. For the localization of robot fish in an indoor pool environment, this paper proposes an adaptive fusional algorithm based on fuzzy inference of dynamic weights. This paper firstly constructs a confidence probability table of sensors’ signals based on the calibration data sets of BLE and UWB nodes at different distances, as the basis for updating the weights of the BLE or UWB nodes; secondly, the data obtained by each node in a single sampling period in the robot fish movement is vectorized to form a judgment matrix, and then the estimated distance is obtained by fuzzy inference together with the sensor weight; finally, the coordinates are calculated by the four-point positioning method. In this paper, more than 40 sets of experiments have been carried out with a simplified carangidae-like robot fish. The results show that the average positioning error is about 0.189m, which is 88.3% and 31.8% lower than that of using BLE only and using UWB only. In this paper, the fusional positioning method based on statistics combines the advantages of different sensors to reduce the data scale and achieve data denoising while fusing sensor data, which provides a reference for indoor positioning of robot fish and multi-sensor data fusion.

关键词： Location awareness Fuzzy logic Robot kinematics Heuristic algorithms Data integration Sensor fusion Probability

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Stress Detection Using Wearable Devices based on Transfer Learning

Stress Detection Using Wearable Devices based on Transfer Le...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinting Wu Yujia Zhang Xiaoguang Zhao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665429825

Excessive stress will have a negative impact on people’s physical and mental health, especially for some special occupations. Because stressful stimuli can trigger a variety of physiological responses, analyzing physiological signals collected by wearable devices has become an important way to evaluate the stress state in recent years. However, the number of available subjects of a target group may be small, and collecting a large amount of data when the target group changes is costly and time-consuming. To solve this problem, we propose a stress detection framework for a small target group which uses adversarial transfer learning method to learn shared knowledge about stress between different groups. In order to verify the performance of the framework, we establish a dataset consisting of 264 ordinary college students and 32 police school students, aiming to evaluate the acute stress state of police school students under video stimuli for psychological training in the future. Comprehensive experiments show that our algorithm has achieved a significant improvement in the target group compared with the baseline methods.

关键词： Training Protocols Law enforcement Wearable computers Transfer learning Signal processing algorithms Feature extraction

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Interfacial Thermal Transport and Energy Dissipation in Multilayer PdSe2 Field Effect Transistors

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Advanced Materials 2025年第0期 e2503264页

作者： Xie, Jiaqiu Yu, Zehao Sun, Yuanchen Gan, Qikang Liu, Chenhan Wang, Lei Zhang, Lifa Zhao, Yunshan Phonon Engineering Research Center of Jiangsu Province Ministry of Education Key Laboratory of NSLSCS Center for Quantum Transport and Thermal Energy Science Institute of Physics Frontiers and Interdisciplinary Sciences School of Physics and Technology Nanjing Normal University Nanjing210023 China National Laboratory of Solid-State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China Micro- and Nano-scale Thermal Measurement and Thermal Management Laboratory Jiangsu Key Laboratory for Numerical Simulation of Large-Scale Complex Systems School of Energy and Mechanical Engineering Nanjing Normal University Nanjing210046 China

The continuous miniaturization of 2D electronic circuits results in increased power density during device operation, leading to heat localization and placing higher demands on their performance thresholds. The risk to thermal breakdown and subsequent damage, due to the energy dissipation in the 2D semiconductor field-effect transistors (FETs) supported on the bulk substrates, represents a significant challenge in maintaining their optimal performance. Herein, this study investigates energy dissipation behavior in multilayer PdSe2 FETs for the first time. The high-field breakdown behavior is firstly studied in multilayer PdSe2 FETs on SiO2/Si substrates, where a maximum current density of ≈2.74 MA cm−2 is observed, which is comparable to that of multilayer black phosphorus FET and significantly higher—by about five times—than that of multilayer MoS2 FET. Additionally, the thermal boundary conductance (TBC) of PdSe2/SiO2 interface is measured at room temperature using Raman thermometry. The TBC is found to be ≈12–13 MW m−2 K−1, which is relatively low compared to the other known solid–solid interfaces, indicating that enhancing the performance of PdSe2 FETs can be possible by optimizing the TBC at the PdSe2/SiO2 interface. These findings provide valuable insights for design of high-quality and high-performance PdSe2 electronic and optoelectronic devices. © 2025 Wiley-VCH GmbH.

关键词： Multilayers

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Consensus control of Leader-following Multi-agent System in Partial Directed Topology 2

Consensus Control of Leader-following Multi-agent System in ...

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2nd International Conference on Industrial Artificial Intelligence, IAI 2020

作者： Yan, Chunsheng Zheng, Yongjian Huang, Chuan Xue, Simeng Sun, Hongwei Wang, Xin Wei, Qinglai LiaoNing Electric Power Development Stock CO. LTD Shenyang China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728182162

ISBN: (纸本)9781728182162

In this paper, the consensus control of linear multi-agent system in partial directed topology is discussed. The key innovation of this paper is that we propose a novel consensus control by using partial directed topology with the external stochastic disturbance. Since the noise inference is unavoidable in real life, the external disturbance produced by the Gauss white noise through Brown motion is taken into account when the states of the leader and followers are described. To settle this kind of problem, a distributed controller based on Riccati inequalities with an adaptive law is designed for leader-following multi-agent systems. We give proof through Lyapunov function and get the result which is the same as the theory. Finally, a simulation example is given to support the Theorems. © 2020 IEEE.

关键词： Multi agent systems

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Automated Assembly by Two-Fingered Microhand for Fabrication of Soft Magnetic Microrobots

Automated Assembly by Two-Fingered Microhand for Fabrication...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yue Zhao Xiaoming Liu Ruixi Wang Dan Liu Masaru Kojima Qiang Huang Tatsuo Arai Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Department of Intelligent Manufacturing Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Materials Engineering Science Osaka University Osaka Japan Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Micro-assembly is an emerging method to fabricate microrobots with multiple modules or particles. However, there is always a lack of a flexible and efficient method to freely create the desired magnetic soft microrobots. In this paper, an automated assembly system based on a two-fingered microhand is presented for fabricating magnetic soft microrobots. Our proposed system can automatically pick and place components to assemble microrobots with a two-fingered micromanipulator, and orient these components through an external magnetic field. The automated assembly has the advantages of high accuracy, high speed, and high success rate. It can endow magnetic microrobots with flexible material selection, arbitrary geometry design, and programable magnetization profile. We can make full use of this system to fabricate multiple magnetic soft microrobots. The experiment results demonstrate that this system can efficiently fabricate microrobots with excellent mechanical properties, which have application potential in robotics, biomedical engineering, and environmental governance.

关键词： Three-dimensional displays Shape Micromanipulators Magnetization Manuals Three-dimensional printing Soft magnetic materials

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Multi-View Spatio-Temporal Deep Graph Neural Network for Travel Demand Prediction

SSRN

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SSRN 2022年

作者： Zhao, Tianhong Huang, Zhengdong Tu, Wei Biljecki, Filip Chen, Long School of Architecture and Urban Planning Shenzhen University China Department of Architecture National University of Singapore Singapore Department of Real Estate National University of Singapore Singapore State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

Predicting travel demand is an essential task in urban mobility management. Previous methods use the spatio-temporal dependencies of travel demand to improve the prediction accuracy, but pay less attention to the cross-modal dependency among different transportation modes (bus, metro, taxi, etc.). We propose a multi-view spatio-temporal graph neural networks (MSTGNN) model to predict the travel demand. The development, bridging the gaps of the state of the art, emphasizes the ability to capture cross-view dependencies, derived from the interaction of multiple transportation modes. Specifically, a multi-view graph with the bus, metro, and taxi views is constructed according to spatial adjacency and transportation interaction adjacency. A multi-view attention temporal graph convolution module is designed to capture spatio-temporal and cross-view dependencies. Additionally, the cross-view features spatial consistency loss is presented to overcome the problem of uneven distribution of multi-view features. Taking the bus travel demand as an example, intensive experiments are conducted using real-world bus, metro, and taxi data in Shenzhen. The results indicate that the MAE of the MSTGNN model improves baseline methods by 9.13%. The contributions of different transportation modes and local/global cross-view dependencies are demonstrated in the ablation experiments. The attention mechanism in the MSTGNN model demonstrates certain interpretability of the interaction between different transportation modes. The dataset generated in this research has been released as open data. © 2022, The Authors. All rights reserved.

关键词： Smart cards

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The Quality Evaluation of Academic Papers in Carbon Capture, Utilization, and Storage

The Quality Evaluation of Academic Papers in Carbon Capture,...

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Machine Learning, control, and Robotics (MLCR), International Conference on

作者： Hangyue Zhang Jie Bai School of Computer Science and Technology Sun Yat-sen University Guangzhou P. R. China City State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing P. R. China

ISBN: (纸本)9781665454605

Carbon capture, utilization, and storage (CCUS) technology is a research hotspot worldwide owing to global climate change and warm gas control. CCUS papers from the last twenty years, derived from the Web of Science database, were analyzed to investigate the impact and citation of academic papers using machine learning methods. Firstly, nine author characteristics, keywords, and three other categories were extracted from the literature. Then, the natural logarithm distribution of the annual average number of citations was obtained, and five evaluation grades were decided based on the citations. Next, the keyword evolution of papers in the CCUS field is analyzed, and a model on the keyword evolution trend index is proposed. Data integration and imbalance processing were conducted after random sampling. A citation prediction model was proposed to evaluate the quality of CCUS academic papers and was verified on the recent five years' CCUS papers by comparing it with nine other mainstream machine learning models. The results indicate that the random forest model exhibits the best performance, with an accuracy rate reaching 88.7%; an ablation experiment was also conducted using the same algorithm. The results prove the effectiveness of our proposed method, shedding light on the quality of academic papers and technology development in CCUS.

关键词： Machine learning Climate change Random forests Modeling Energy storage

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