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IEEE International Conference on Unmanned systems (ICUS)

作者： Li Jing Shen Chao Tong JiaHui Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

In this paper, the composition and operation mechanism of a multi-aircraft cooperative simulation system is analyzed, with its formal architecture identified as a discrete event system & differential equation system specifications (DEV&DESS). Then, the operation and control problem of the cooperative simulation system is converted into the simulation control problem of a specific DEV&DESS system. A coordinator-based test method for cooperative simulation is proposed and validated by tests of real-time mathematical simulation.

关键词： aircraft control control system synthesis differential equations discrete event simulation discrete event systems Aircraft control discrete event systems control system synthesis discrete event simulation Differential equations Simulation systems Differentiation Test Method

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Task-irrelevant Auditory Event-related Potentials as Mental Workload Indicators: A Between-task Comparison Study*

Task-irrelevant Auditory Event-related Potentials as Mental ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jinzhao Xu Yufeng Ke Shuang Liu Xizi Song Cheng Xu Guchuan Zhou Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Real-time monitoring of mental workload (MWL) is a crucial step to build closed-loop adaptive aiding systems for human-machine systems. MWL estimators based on spontaneous electroencephalography (EEG) and event-related potentials (ERPs) have shown great potentials to achieve this goal. However, the previous studies show that the between-task robustness of these EEG/ERP-based MWL estimators is still an unsolved intractable question. This study attempts to examine the task-irrelevant auditory event-related potentials (tir-aERPs) as MWL indicators. A working memory task (verbal n-back) and a visuo-motor task (multi-attribute task battery, MATB), both with two difficulty levels (easy and hard), were used in the experiment, along with task-irrelevant auditory probes that did not need any response from the participants. EEG was recorded from ten participants when they were performing the tasks. The tir-aERPs elicited by the auditory probes were extracted and analyzed. The results show that the amplitudes of N1, early P3a (eP3a) and the late reorienting negativity (RON) significantly decreased with the increasing MWL in both n-back and MATB. Task type has no obvious influence on the amplitudes and topological layout of the MWL-sensitive tir-aERPs features. These results suggest that the tir-aERPs are potentially more constant MWL indicators across very different task types. Therefore, the tir-aERPs should be taken into consideration in future task-independent MWL monitoring studies.

关键词： Task analysis Electroencephalography Monitoring Electric potential Probes Resource management Feature extraction

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

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S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

arXiv

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arXiv 2024年

作者： Wang, Xiao Tang, Ke Dai, Xingyuan Xu, Jintao Du, Quancheng Ai, Rui Wang, Yuxiao Gu, Weihao Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei230031 China Haomo Technology Co. Ltd. Beijing100192 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266114 China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S4TP) framework. Specifically, S4TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV’s planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S4TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs’ trajectories as input. With the integration of SADRF, S4TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S4TP in terms of safety and rationality. S4TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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An Iterative Closest Points Optimization Algorithm for Localization of Mecanum-wheeled Robots

An Iterative Closest Points Optimization Algorithm for Local...

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第三十八届中国控制会议

作者： Ning Hao Rui Xing Qi Zhao Fenghua He Hang Zhang Harbin Institute of Technology Complex System Control and Intelligent Agent Cooperation Laboratory

Over the last years, optimization based estimation methods have been applied with great success to a variety of state estimation problems. In this paper, we propose an optimization algorithm to solve the localization problem of Mecanumwheeled robots in a structured environment based on combination of odometer and laser lidar. First, provide the configuration of the mobile robot and the expression of localization problem based on probability form. Secondly, the optimization cost function is derived from the perspective of maximum posterior estimation. And then an iterative closest points optimization algorithm is proposed to address the combination optimization problem. Finally, simulation experiments are designed and implemented to validate the proposed algorithm.

关键词： localization state estimation iterative closest points optimization Mecanum-wheeled robot

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Generating infrared image from visible image using Generative Adversarial Networks

Generating infrared image from visible image using Generativ...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： An, Lei Zhao, Jiajia Di, Huijun Beijing Institute of Technology School of Computer Science and Technology Beijing China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Coorperation Laboratory Beijing China

ISBN: (纸本)9781728137926

Infrared images have properties that are unaffected by illumination compared to visible images, object can be clearly recognized at day or night. Therefore, it is a better choice to use infrared images when training deep learning algorithms in unmanned systems. However, how to obtain a large number of infrared images makes it possible to train deep learning algorithm, which is the focus of our attention. In contrast to difficulties in direct shooting, computer simulations can generate large infrared images from visible images. But models that are trained on simulated data usually do not translate well to real scenarios. To bridge the domain gap between simulated and real infrared images, we exploit recent advances in paired image-to-image translation. We extend Pixel2Pixel to generate infrared image from visible image and propose a novel way of multilayer semantic information fusion, which significantly improves the quality of generated images. Finally, the image generated by our proposed PixelPro network is almost consistent with the real image distribution, achieved the purpose of expanding the dataset. © 2019 IEEE.

关键词： Generative adversarial networks

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Regularized densely-connected pyramid network for salient instance segmentation

arXiv

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arXiv 2020年

作者： Wu, Yu-Huan Liu, Yun Zhang, Le Gao, Wang Cheng, Ming-Ming TKLNDST College of Computer Science Nankai University Tianjin300350 China ETH Zurich Switzerland School of Information and Communication Engineering University of Electronic Science and Technology of China China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels. To this end, we propose a new pipeline for end-to-end salient instance segmentation (SIS) that predicts a class-agnostic mask for each detected salient instance. To better use the rich feature hierarchies in deep networks and enhance the side predictions, we propose the regularized dense connections, which attentively promote informative features and suppress non-informative ones from all feature pyramids. A novel multi-level RoIAlign based decoder is introduced to adaptively aggregate multi-level features for better mask predictions. Such strategies can be well-encapsulated into the Mask R-CNN pipeline. Extensive experiments on popular benchmarks demonstrate that our design significantly outperforms existing state-of-the-art competitors by 6.3% (58.6% vs. 52.3%) in terms of the AP metric. The code is available at https://***/yuhuan-wu/RDPNet. © 2020, CC BY-NC-ND.

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Applications of MXenes in human-like sensors and actuators

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Nano Research 2023年第4期16卷 5767-5795页

作者： Jinbo Pang Songang Peng Chongyang Hou Xiao Wang Ting Wang Yu Cao Weijia Zhou Ding Sun Kai Wang Mark H.Rümmeli Gianaurelio Cuniberti Hong Liu Institute for Advanced Interdisciplinary Research(iAIR) Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of ShandongUniversity of JinanJinan 250022China Institute for Materials Science and Max Bergmann Center of Biomaterials Technische Universität DresdenDresden 01069Germany Center for Advancing Electronics Dresden Technische Universität DresdenDresden 01069Germany Dresden Center for Computational Materials Science Technische Universität DresdenDresden 01062Germany Dresden Center for Intelligent Materials(GCL DCIM) Technische Universität DresdenDresden 01062Germany High-Frequency High-Voltage Device and Integrated Circuits R&D Center Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Key Laboratory of Microelectronic Devices&Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Shenzhen Key Laboratory of Nanobiomechanics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China State Key Laboratory of Biobased Material and Green Papermaking Qilu University of TechnologyShandong Academy of SciencesJinan 250353China School of Bioengineering Qilu University of TechnologyShandong Academy of ScienceJinan 250353China Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Ministry of Education) Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Weihai Innovation Research InstituteQingdao UniversityQingdao 266000China School of Electrical and Computer Engineering Jilin Jianzhu UniversityChangchun 130118China Institute for Complex Materials Leibniz Institute for Solid State and Materials Research Dresden(IFW Dresden)20 Helmholtz StrasseDresden 01069Germany College of Energy Soochow Institute for Energy and Materials Innovations Soochow UniversitySuzhou 215006China Key L

Human beings perceive the world through the senses of sight,hearing,smell,taste,touch,space,and *** first five senses are prerequisites for people to *** sensing organs upload information to the nervous systems,including the brain,for interpreting the surrounding ***,the brain sends commands to muscles reflexively to react to stimuli,including light,gas,chemicals,sound,and ***,as an emerging two-dimensional material,has been intensively adopted in the applications of various sensors and *** this review,we update the sensors to mimic five primary senses and actuators for stimulating muscles,which employ MXene-based film,membrane,and composite with other functional ***,a brief introduction is delivered for the structure,properties,and synthesis methods of ***,we feed the readers the recent reports on the MXene-derived image sensors as artificial retinas,gas sensors,chemical biosensors,acoustic devices,and tactile sensors for electronic ***,the actuators of MXene-based composite are ***,future opportunities are given to MXene research based on the requirements of artificial intelligence and humanoid robot,which may induce prospects in accompanying healthcare and biomedical engineering applications.

关键词： MXenes sensors actuators gas sensors biosensors tactile sensors sound sensors artificial retina

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Regional Geomagnetic Map Construction based on Support Vector Machine Residual Kriging

Regional Geomagnetic Map Construction based on Support Vecto...

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第三十九届中国控制会议

作者： Tong Liu Xingyu Li Mengyin Fu Zhaoxiang Liang Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision Nanjing University of Science and Technology

Regional geomagnetic maps are widely used in geomagnetic navigation and magnetic anomaly detection. However,the complexity of geomagnetic spatial trend changes and the spatial sparseness of the geomagnetic data affect the accuracy of regional geomagnetic map construction. In order to improve the accuracy of regional geomagnetic maps, this paper proposes the Support Vector Machine Residual Kriging method（SVMRKriging）. First, Support Vector Machine（SVM） is used to fit the geomagnetic trend changes, then the residual component is interpolated by ordinary Kriging, and finally these two parts are added to construct a regional geomagnetic map. Experiments were performed using geomagnetic grid data and aeromagnetic data. The experiment results show that SVMRKriging method can improve the accuracy of regional geomagnetic maps with geomagnetic trend changes.

关键词： Regional Geomagnetic Map Kriging Support Vector Machine

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Network Parameter Generation for One-Shot Object Detection

Network Parameter Generation for One-Shot Object Detection

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Wang, Kang Zhao, Jiajia Wang, Yang Zhou, Shuigeng Fudan University Shanghai Key Lab of Intelligent Information Processing School of Computer Science Shanghai200433 China Beijing Electro-Mechanical Engineer Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Nanchang University School of Information Engineering 330031 China

ISBN: (纸本)9781728137926

Recent object detection models have achieved satisfactory performance by deep learning with large-scale annotated datasets. However, these models often perform poorly when the training examples are not sufficient enough, especially for one-shot object detection where there is only one training example of each target class. In this paper, we propose a network parameter generation framework for effective one-shot object detection. By leveraging the semantic relationship between the source-domain and the target-domain (new classes), our framework can generate network parameters for the new classes directly. Furthermore, we develop two implementation schemes for this framework: weighting-based parameter generation and regression-based parameter generation. These two schemes use different information to construct the relationship and transfer knowledge from the source-domain to the target-domain. Experiments on the PASCAL-OSCD benchmark show that our methods can significantly boost the performance of one-shot object detection. © 2019 IEEE.

关键词： Object detection

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