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UAV navigation in high dynamic environments:A deep reinforcement learning approach

Chinese Journal of Aeronautics 2021年第2期34卷 479-489页

作者： Tong GUO Nan JIANG Biyue LI Xi ZHU Ya WANG Wenbo DU School of Electronic and Information Engineering Beihang UniversityBejing 100083China Key Laboratory of Advanced Technology of Near Space Information System(Beihang University) Ministry of Industry and Information Technology of ChinaBeijing 100083China Research Institute of Frontier Science Beihang UniversityBeijing 100083China College of Software Beihang UniversityBeijing 100083China State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100083China

Unmanned Aerial Vehicle(UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions in harsh environments. The recently emerging Deep Reinforcement Learning(DRL) methods have shown promise for addressing the UAV navigation problem,but most of these methods cannot converge due to the massive amounts of interactive data when a UAV is navigating in high dynamic environments, where there are numerous obstacles moving *** this work, we propose an improved DRL-based method to tackle these fundamental *** be specific, we develop a distributed DRL framework to decompose the UAV navigation task into two simpler sub-tasks, each of which is solved through the designed Long Short-Term Memory(LSTM) based DRL network by using only part of the interactive data. Furthermore, a clipped DRL loss function is proposed to closely stack the two sub-solutions into one integral for the UAV navigation problem. Extensive simulation results are provided to corroborate the superiority of the proposed method in terms of the convergence and effectiveness compared with those of the state-of-the-art DRL methods.

关键词： Autonomous vehicles Deep learning Motion planning Navigation Reinforcement learning Unmanned Aerial Vehicle(UAV)

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Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification

arXiv

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arXiv 2024年

作者： Peng, Ruimin Du, Zhenbang Zhao, Changming Luo, Jingwei Liu, Wenzhong Chen, Xinxing Wu, Dongrui Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China AI Platform Software Engineering Research Center Dongfeng Corporation Research & Development Institute Wuhan430072 China China Electronic System Technology Co. Ltd. Beijing100089 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China

Cross-subject electroencephalogram (EEG) based seizure subtype classification is very important in precise epilepsy diagnostics. Deep learning is a promising solution, due to its ability to automatically extract latent patterns. However, it usually requires a large amount of training data, which may not always be available in clinical practice. This paper proposes Multi-Branch Mutual-Distillation (MBMD) Transformer for cross-subject EEG-based seizure subtype classification, which can be effectively trained from small labeled data. MBMD Transformer replaces all even-numbered encoder blocks of the vanilla Vision Transformer by our designed multi-branch encoder blocks. A mutual-distillation strategy is proposed to transfer knowledge between the raw EEG data and its wavelets of different frequency bands. Experiments on two public EEG datasets demonstrated that our proposed MBMD Transformer outperformed several traditional machine learning and state-of-the-art deep learning approaches. To our knowledge, this is the first work on knowledge distillation for EEG-based seizure subtype classification. © 2024, CC BY.

关键词： Electroencephalography

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Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach

arXiv

引用

arXiv 2024年

作者： Su, Cheng Wen, Jinbo Kang, Jiawen Wang, Yonghua Su, Yuanjia Pan, Hudan Zhong, Zishao Hossain, M. Shamim School of Automation The GuangdongHongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Automation The Guangdong Basic Research Center of Excellence for Ecological Security and Green Development Key Laboratory for City Cluster Environmental Safety Green Development The Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation The Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong University of Technology Guangzhou510006 China Guangzhou University of Traditional Chinese Medicine State Key Laboratory of Traditional Chinese Medicine Syndrome The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing integration of the Internet of Medical Things (IoMT). The rise of generative artificial intelligence has further elevated Multi-modal Large Language Models (MLLMs) as essential tools for managing and optimizing healthcare data in IoMT. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including security and freshness issues of healthcare data, affecting the output quality of MLLMs. To this end, in this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLM framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share their fresh data, mitigating information asymmetry during data sharing. Finally, we utilize a generative diffusion model-based deep reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management. Copyright © 2024, The Authors. All rights reserved.

关键词： Information management

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Geospatial data ontology:the semantic foundation of geospatial data integration and sharing

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Big Earth Data 2019年第3期3卷 269-296页

作者： Kai Sun Yunqiang Zhu Peng Pan Zhiwei Hou Dongxu Wang Weirong Li Jia Song State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application NanjingPeople’s Republic of China Center for Collaborative Innovation in Baiyangdian River Basin Ecological Protection and Beijing-Tianjin-Hebei Sustainable Development BaodingPeople’s Republic of China Appraisal Center for Environment and Engineering Ministry of Ecology and EnvironmentBeijingPeople’s Republic of China f State Environmental Protection Key Laboratory of Numerical Modeling for Environment Impact Assessment BeijingPeople’s Republic of China Beijing GEOWAY Software Co. LtdBeijingPeople’s Republic of China

Effective integration and wide sharing of geospatial data is an important and basic premise to facilitate the research and applications of geographic information ***,the semantic heterogeneity of geospatial data is a major problem that significantly hinders geospatial data integration and *** are regarded as a promising way to solve semantic problems by providing a formalized representation of geographic entities and relationships between them in a manner understandable to ***,many efforts have been made to explore ontology-based geospatial data integration and ***,there is a lack of a specialized ontology that would provide a unified description for geospatial *** this paper,with a focus on the characteristics of geospatial data,we propose a unified framework for geospatial data ontology,denoted GeoDataOnt,to establish a semantic foundation for geospatial data integration and ***,we provide a characteristics hierarchy of geospatial ***,we analyze the semantic problems for each characteristic of geospatial ***,we propose the general framework of GeoDataOnt,targeting these problems according to the characteristics of geospatial *** is then divided into multiple modules,and we show a detailed design and implementation for each *** limitations and challenges of GeoDataOnt are identified,and broad applications of GeoDataOnt are discussed.

关键词： Geospatial data ontology geospatial semantics semantic heterogeneity data integration data sharing

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B-spline surface fitting to mesh vertices

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Science China(Information Sciences) 2017年第7期60卷 259-261页

作者： Yang LU Junhai YONG Kanle SHI Hejin GU Ping ZHENG Jean-Claude PAUL School of Software Tsinghua University Department of Computer Science and Technology Tsinghua University Key Laboratory for Information System Security Ministry of Education of China Tsinghua National Laboratory for Information Science and Technology Tsinghua University Department of Research & Development Jiangxi Academy of Science Department of Mathematics and Computer Science Nanchang Normal University

Here we propose a novel method of fitting B-spline surfaces to input mesh *** a mesh model that is difficult to fit with one B-spline surface,we divide it into several patches that form a tree structure(which is called hierarchical splines[1]),and for each patch,we use one B-spline surface for ***,only a few patches are required,and G2continuity can be achieved between the adjacent surfaces.

关键词： B-spline surface fitting to mesh vertices

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A Test Language for Avionics system

A Test Language for Avionics System

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2017 IEEE 8th International Conference on software Engineering and Service Science

作者： Yanfang Liu Jianghua Lv Wei Wang Tao Li Shilong Ma Department of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment System Engineering Research Institute of China State Shipbuilding Corporation

In this paper, we proposed an automated test framework, logical test device which makes complex avionics systems transparent and jump machines which ensure data security of SUT(system Under Test). Based on this test framework, we mainly proposed a universal test language for avionics systems. The key feature of our test language is to introduce device type data and device collaboration operations which support automatic collaboration between logical test device and jump machines, meanwhile make test language more universal. This test language has been applied to the actual avionics system testing in China Academy of Electronics and Information Technology. Relative to avionics system testing before automation, our test language changes work pattern of avionics system testing and improves the test efficiency.

关键词： Avionics system test framework test language automated testing device collaboration

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Towards Cost-Effective Cloud Downloading with Tencent Big Data

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Journal of Computer Science & Technology 2015年第6期30卷 1163-1174页

作者：李振华刘刚嵇智源 Roger Zimmermann School of Software Tsinghua University Beijing 100084 China Tsinghua National Laboratory for Information Science and Technology Beijing 100084 China QQXuanfeng System Group Tencent Co. Ltd Shanghai 200233 China High Technology Research and Development Center Ministry of Science and Technology Beijing 10004 China Department of Computer Science National University of Singapore Singapore 117417 Singapore

The cloud downloading scheme, first proposed by us in 2011, has effectively optimized hundreds of millions of users＇ downloading experiences. Also, people start to build a variety of useful Internet services on top of cloud downloading. In brief, by using cloud facilities to download （and cache） the requested file from the ＂best-effort＂ Internet on behalf of the user, cloud downloading ensures the data availability and remarkably enhances the data delivery speed. Although this scheme seems simple and straightforward, designing a real-world cloud downloading system involves complicated and subtle trade-offs （between deployment cost and user experience） when serving a large number of users： 1） how to plan the cloud cache capacity to achieve a high and affordable cache hit ratio, 2） how to accelerate the data delivery from the cloud to numerous users, 3） how to handle the dense user requests for highly popular files, and 4） how to judge a potential downloading failure of the cloud. This paper addresses these design trade-offs from a practical perspective, based on big data from a nationwide commercial cloud downloading system, i.e., Tencent QQXuanfeng. Its running traces help us find reasonable design strategies and parameters, and its real performances confirm the efficacy of our design. Our study provides solid experiences and valuable heuristics for the developers of similar and relevant systems.

关键词： cloud downloading cost-effective design trade-off big data

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

引用

Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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Tunnel ventilation control system using real-time traffic and ventilation simulator

Tunnel ventilation control system using real-time traffic an...

引用

19th Intelligent Transport systems World Congress, ITS 2012

作者： Azuma, Shin'ichiro Fushihara, Motohiro Vardy, Alan E. Yuhara, Minoru Mitani, Atsushi Engineering Development Team Central Nippon Expressway Company Limited Japan Facilities Research Department Nippon Expressway Research Institute Company Limited Japan Civil Engineering Division University of Dundee United Kingdom System Engineering Department Dosys Co. Ltd Japan Software Engineering Department Sohatsu System Laboratory Inc Japan

This paper describes a new tunnel ventilation control system called MPVC (Model-based Predictive Ventilation Control). The new system uses real-time traffic and ventilation simulators and it is possible to reduce at least 20 % or more of electricity consumption compared to previous tunnel ventilation control systems. This new control software has been already installed in 7 tunnels including the 1st and 2nd longest tunnels in Japan.

关键词： Control systems

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On the Comparison of User Space and Kernel Space Traces in Identification of software Anomalies

On the Comparison of User Space and Kernel Space Traces in I...

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(CSMR) European Conference on software Maintenance and Reengineering

作者： Syed Shariyar Murtaza Afroza Sultana Abdelwahab Hamou-Lhadj Mario Couture Software Behaviour Analysis (SBA) Research Laboratory Concordia University Montreal QUE Canada System of Systems Section Software Analysis and Robustness Group Defence Research and Development Canada Valcartier QUE Canada

Corrective software maintenance consumes 30-60% time of software maintenance activities. Automated failure reporting has been introduced to facilitate developers in debugging failures during corrective maintenance. However, reports of software with large user bases overwhelm developers in identification of the origins of faults, and in many cases it is not known whether reports of failures contain information about faults. Prior techniques employ different classification or anomaly detection algorithms on user space traces (e.g., function calls) or kernel space traces (e.g., system calls) to detect anomalies in software behaviour. Each algorithm and type of tracing (user space or kernel space) has its advantages and disadvantages. For example, user space tracing is useful in detailed analysis of anomalous (faulty) behaviour of a program whereas kernel space tracing is useful in identifying system intrusions, program intrusions, or malicious programs even if source program code is different. If one type of tracing or algorithm is infeasible to implement then it is important to know whether we can substitute another type of tracing and algorithm. In this paper, we compare user space and kernel space tracing by employing different types of classification algorithms on the traces of various programs. Our results show that kernel space tracing can be used to identify software anomalies with better accuracy than user space tracing. In fact, the majority of software anomalies (approximately 90%) in a software application can be best identified by using a classification algorithm on kernel space traces.

关键词： Europe software maintenance

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