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IEEE International Conference on Communications in China (ICCC)

作者： Shicong Liu Zhen Gao Gaojie Chen Yu Su Lu Peng MIIT Key Laboratory of Complex-Field Intelligent Sensing Beiing Institute of Technology Beijing China Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Jinan China Yangtze Delta Region Academy of Beijing Institute of Technology Beijing Institute of Technology Jiaxing China Institute for Communication Systems University of Surrey Guildford United Kingdom China Mobile (Chengdu) Institute of Research and Development Chengdu Sichuan

The Space-Air-Ground-Sea integrated network calls for more robust and secure transmission techniques against jamming. In this paper, we propose a textual semantic transmission framework for robust transmission, which utilizes the advanced natural language processing techniques to model and encode sentences. Specifically, the textual sentences are firstly split into tokens using wordpiece algorithm, and are embedded to token vectors for semantic extraction by Transformer-based encoder. The encoded data are quantized to a fixed length binary sequence for transmission, where binary erasure, symmetric, and deletion channels are considered for transmission. The received binary sequences are further decoded by the transformer decoders into tokens used for sentence reconstruction. Our proposed approach leverages the power of neural networks and attention mechanism to provide reliable and efficient communication of textual data in challenging wireless environments, and simulation results on semantic similarity and bilingual evaluation understudy prove the superiority of the proposed model in semantic transmission.

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EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms

arXiv

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arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization laboratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

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Decision Tree Pruning Method Using Delayed Sampling

Decision Tree Pruning Method Using Delayed Sampling

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International Conference on Information Technologies (InfoTech)

作者： Sergei Alexandrovich Mitrofanov Eugene Stanislavovich Semenkin Department of System Analysis and Operations Research Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russian Federation Scientific and Educational Center "Artificial Intelligence Technologies" Bauman Moscow State Technical University Moscow Russian Federation Laboratory "Hybrid Methods of Modeling and Optimization in Complex Systems" Siberian Federal University Krasnoyarsk Russian Federation

ISBN: (数字)9798350365689

ISBN: (纸本)9798350365696

Algorithms based on decision trees have been around for quite some time, however, they are still one of the most popular because of their efficiency and the interpretability of their results. One of the main problems of decision trees is their high tendency to overfit. There are various ways to solve this problem, but this paper proposes a new method based on tree pruning using delayed sampling. The tree is trained using the Separation Measure and Differential Evolution algorithms. The proposed approach improves the standard method and is compared with it. The proposed approach demonstrates higher efficiency than the standard algorithm, and also improves the interpretability of the resulting trees.

关键词： Visualization Machine learning algorithms Accuracy Measurement uncertainty Machine learning Sampling methods Decision trees Stakeholders Information technology Standards

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Tunable All-in-One Bimodal Porous Membrane of Ultrahigh Molecular Weight Polyethylene for Highly Efficient Solar-to-Vapor Generation

SSRN

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SSRN 2022年

作者： Guo, Yuhang Wu, Hong Guo, Shaoyun Qiu, Jianhui The State Key Laboratory of Polymer Materials Engineering Sichuan Provincial Engineering Laboratory of Plastic/Rubber Complex Processing Technology Polymer Research Institute of Sichuan University Chengdu610065 China Department of Mechanical Engineering Faculty of Systems Science and Technology Akita Prefectural University Akita015‐0055 Japan

Solar-to-vapor generation is a promising technology to produce freshwater by clean energy source. Rational design of the porous structure of photothermal membranes for efficient energy conversion and vapor generation in long-term use is a key challenge. Herein, tunable all-in-one bimodal porous membrane of ultrahigh molecular weight polyethylene with interconnected porous network is first designed, which can effectively regulate porous structure by thermal-induced phase separation and NaCl-templet leaching. Carboxylated carbon nanotubes are evenly dispersed and embedded in the pore walls, efficiently absorbing solar energy and converting it into heat, exhibiting a satisfactory solar absorptance nearly 96% and photothermal conversion efficiency about 96.3% . Particularly, polydopamine is allowed to enter the porous membrane and deposit on the surface of the pore walls for water transport and water activation by flowing deposition, achieving water evaporation rate of 1.53 kg m-2 h-1 at ~97.6% energy efficiency under 1 sun illumination . The membrane with light weight, high porosity and abundant porous structure is able to stably desalinate in both 3.5 wt% and 10 wt% saline due to quick water exchange that suppresses salt crystallization during evaporation. This bimodal porous structure design is generic and can be expanded to other photothermal systems for advanced solar-thermal applications. © 2022, The Authors. All rights reserved.

关键词： Porosity

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Kinetic Modeling of Secondary Organic Aerosol in a Weather-Chemistry Model: Parameterizations, Processes, and Predictions for GOAmazon

ACS ES&T Air

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ACS ES&T Air 2025年第2期2卷 249-263页

作者： Yicong He Kelsey R. Bilsback Manish Shrivastava Rahul A. Zaveri John E. Shilling John H. Seinfeld Bin Zhao Shuxiao Wang Christopher D. Cappa Jeffrey R. Pierce Shantanu H. Jathar Department of Mechanical Engineering Colorado State University Fort Collins Colorado 80523 United States；State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Tsinghua University Beijing 100084 China；State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084 China Department of Mechanical Engineering Colorado State University Fort Collins Colorado 80523 United States；PSE Healthy Energy Oakland California 94612 United States Pacific Northwest National Laboratory Richland Washington 99354 United States Divison of Chemistry and Chemical Engineering California Institute of Technology Pasadena California 91125 United States State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Tsinghua University Beijing 100084 China；State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084 China Department of Atmospheric Science Colorado State University Fort Collins Colorado 80521 United States Department of Civil and Environmental Engineering University of California Davis Davis California 95616 United States Department of Mechanical Engineering Colorado State University Fort Collins Colorado 80523 United States

Secondary organic aerosol (SOA) forms and evolves in the atmosphere through many pathways and processes, over diverse spatial and time scales. Hence, there is a need to represent these widely varying kinetic processes in large-scale atmospheric models to allow for accurate predictions of the abundance, properties, and impacts of SOA. In this work, we integrated a kinetic, process-level model (simpleSOM-MOSAIC) into a weather-chemistry model (WRF-Chem) to simulate the oxidation chemistry and microphysics of atmospheric SOA. simpleSOM-MOSAIC simulates multigenerational gas-phase chemistry, autoxidation reactions, aqueous chemistry, heterogeneous oxidation, oligomerization, and phase-state-influenced gas/particle partitioning of SOA. As a case study, the integrated WRF-Chem-simpleSOM-MOSAIC (WC-SSM) model was used to simulate the photochemical evolution downwind of a large city (Manaus, Brazil) in the Amazon and, in turn, study the anthropogenic and biogenic interactions in an otherwise pristine environment. Consistent with previous work, we found that OA was enhanced by up to a factor of 4 in the urban plume due to elevated hydroxyl radical (OH) concentrations, relative to the background, and that this OA was dominated by SOA from biogenic precursors (80%). In addition to accurately simulating the OA enhancement in the urban plume, the model reproduced the magnitude of the OA oxygen-to-carbon (O:C) ratio and broadly tracked the evolution of the aerosol number size distribution. Our work highlights the importance of including an integrated, kinetic representation of SOA processes in an atmospheric model.

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A Solution Analysis of U.S. Weapon Equipment systems-of-systems Development and Application

A Solution Analysis of U.S. Weapon Equipment Systems-of-Syst...

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International Conference on Information science and Control Engineering (ICISCE)

作者： Menglong Lin Shuanghui Yi Mengmeng Zhang Zhen Shu Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China National Key Laboratory for Complex System Simulation Beijing China

In recent years, the development and deployment of the U.S. weapon equipment systems-of-systems (SoS) have yielded rich research results. However, there are still large gaps in the SoS development, joint testing, and design and acquisition of our own equipment SoS. The barriers of these steps have not yet been opened up. This article mainly analyzes the solutions of the U.S. weapon equipment SoS from three aspects, such as capability-based requirement generation and architecture design technology, interconnected-oriented integration test technology, and weapon equipment design and acquisition technology based on complex adaptive systems. We explore key technologies involved in order to form an overall understanding and explore related research approaches.

关键词： Technological innovation Adaptive systems Weapons Agent-based modeling Planning Sustainable development System analysis and design

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An Event-Triggered Fault Detection Method for State-Saturated systems with Time-Delay and Nonlinearities

An Event-Triggered Fault Detection Method for State-Saturate...

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第40届中国控制会议

作者： Weilu Chen Jun Hu Dongyan Chen Xiaoyang Yu Zhihui Wu School of Measurement and Communication Harbin University of Science and Technology Department of Mathematics Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology

The fault detection problem is addressed in this paper for a class of state-saturated systems with time-delay and nonlinearities under communication *** particular,the phenomenon of state saturation is introduced to characterize the physical limitations of the ***,the event-triggered communication strategy is adopted to achieve the purpose of saving network resources and preventing data ***,an event-based fault detection filter is *** selecting the appropriate Lyapunov functional,several sufficient conditions are obtained such that the addressed augmented system is globally asymptotically stable with a satisfactory H performance ***,the filter parameters are gained through solving certain matrix ***,the effectiveness of the proposed fault detection approach is demonstrated by a numerical example.

关键词： State-saturated systems Fault detection Event-triggered mechanism Time-delay

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EiHi Net: Out-of-Distribution Generalization Paradigm

arXiv

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arXiv 2022年

作者： Wei, Qinglai Yuan, Beiming Chen, Diancheng State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Systems Engineering Macau University of Science and Technology 999078 China

This paper develops EiHi to solve the out of distribution (O.o.D.) problem in recognition, especially the distribution shift of background. EiHi net is a model learning paradigm that can be blessed on any visual backbone. Different from the traditional deep model, namely find out correlated relationship between sample features and corresponding categories, EiHi net suffers less from spurious correlations. Fusing SimCLR and VIC-Reg via explicit and dynamical establishment of original - positive - negative sample pair as a minimal learning element, EiHi net iteratively establishes a relationship close to the causal one between features and labels, while suppresses the spurious correlations. To further reinforce the ability of the proposed model on causal reasoning, we develop a human-in-the-loop strategy, with few guidance samples, to prune the representation space directly. Finally, it is shown that the developed EiHi net makes significant improvements in the most difficult and typical O.o.D. dataset NICO, compared with the current SOTA results, without any domain (e.g. background) information. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Design of a novel flexible assistive hand exoskeleton

Design of a novel flexible assistive hand exoskeleton

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Guotao Li Long Cheng Zhengrun Gao Jilong Lu Yanjiang Zhao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The National Clinical Research Center for Aging and Medicine Fudan University Shanghai China Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education Harbin University of Science and Technology Harbin China Intelligent Machine Institute Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781665465373

Hand paralysis caused by stroke, spinal cord injury, or neurological trauma has a significant impact on the independence and quality of life of the patients. The hand exoskeleton can provide hand assistance and improve the patient’s grasp. In this article, a novel wearable flexible hand exoskeleton is proposed to assist users in activities of daily life. To create a simple and lightweight assistive hand exoskeleton, 3D printing is used to manufacture most of the components, and a remote drive system is designed to reduce the weight of the hand exoskeleton. A ball pair device and a slider device are used to ensure the freedom of the thumb and the adaptability of the hand exoskeleton to different hand sizes. Finally, the experiments are conducted to verify that the hand exoskeleton can achieve motion assistance.

关键词： Exoskeletons Thumb Metals Stroke (medical condition) Three-dimensional printing Motion capture Paralysis

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Robust Imagined Speech Production from Electrocorticography with Adaptive Frequency Enhancement

Robust Imagined Speech Production from Electrocorticography ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xu Xu Chong Fu Junxin Chen Gwanggil Jeon David Camacho School of Computer Science and Engineering Northeastern University Shenyang China School of Software Dalian University of Technology Dalian China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China College of Information Technology Incheon National University Incheon Korea Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Imagined speech production with electrocorticography (ECoG) plays a crucial role in brain-computer interface system. A challenging issue is the great variation underlying the frequency bands of the ECoG signals’ encode information, which makes current methods difficult to generate imagined speech with stable quality among different persons. To this end, we propose a robust model to generate high-quality imagined speech from ECoG. A frequency enhancement branch is first designed to adaptively modulate the frequency information, whose product is fed into the following multi-scale channel attention module for robust feature extraction and fusion. By incorporating both the mel-spectrum and audio as training constraints, a multi-constraint decoder branch is finally constructed for imagined speech production. The performance of our model is evaluated on a high-quality dateset, i.e, Single Word Production Dutch-iBIDS. It yields Pearson correlation scores that are all above 0.8, and the standard deviationsare are all below 0.2 in different volunteers. Experimental results demonstrate that our model is effective and robust for ECoG based imagined speech production, and has advantages over peer methods.

关键词： Training Adaptation models Frequency modulation Correlation Neuroscience Production Speech enhancement

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