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IEEE Symposium on intelligent Vehicle

作者： Zhipeng Shen Chao Huang Hailong Huang Yutong Wang Fei-Yue Wang Abbas Jamalipour Duc Truong Pham Ljubo Vlacic Andrey V. Savkin Department of Aeronautical and Aviation Engineering the Hong Kong Polytechnic University Hong Kong Department of Industrial and Systems Engineering the Hong Kong Polytechnic University Hong Kong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Engineering Macau University of Science and Technology Macau China School of Electrical and Information Engineering University of Sydney Sydney Australia Department of Mechanical Engineering University of Birmingham Birmingham UK Institute of Intelligent & Integrated Systems and the School of Engineering and Built Environment Griffith University Brisbane Australia School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In this paper, we propose the innovative use of intelligent Vehicle Carriers (IVCs) as a key solution to address the energy constraints of small-scale unmanned intelligent Vehicles (IVs). IVCs function as both transporters and charging stations, significantly boosting the operational range and efficiency of IVs. Our research delves into the IV-IVC collaborative framework, highlighting the existing challenges, exploring potential solutions, and examining a range of applications. This study offers a visionary approach to revolutionizing intelligent transportation systems by leveraging the synergistic relationship between IVs and IVCs.

关键词： intelligent vehicles Collaboration Charging stations Boosting

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Riddling: Chimera's dilemma

arXiv

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arXiv 2018年

作者： Santos, V. Szezech, J.D. Batista, A.M. Iarosz, K.C. Baptista, M.S. Ren, H.P. Grebogi, C. Viana, R.L. Caldas, I.L. Maistrenko, Y.L. Kurths, J. Graduate in Science Program State University of Ponta Grossa Ponta Grossa Paraná84030-900 Brazil Department of Mathematics and Statistics State University of Ponta Grossa Ponta Grossa Paraná84030-900 Brazil Potsdam Institute for Climate Impact Research Potsdam Brandenburg14473 Germany Institute of Physics University of São Paulo São Paulo05508-900 Brazil Department of Physics Humboldt University Berlin Brandenburg12489 Germany Institute for Complex Systems and Mathematical Biology SUPA University of Aberdeen Aberdeen ScotlandAB24 3UE United Kingdom Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian University of Technology Xi'an710048 China Xian Technological University Xi'an710021 China Department of Physics Federal University of Paraná Curitiba Paraná80060-000 Brazil Institute of Mathematics Centre for Medical and Biotechnical Research National Academy of Sciences of Ukraine Tereshchenkivska St. 3 Kyiv01030 Ukraine

We investigate the basin of attraction properties and its boundaries for chimera states in a circulant network of Hénon maps. It is known that coexisting basins of attraction lead to a hysteretic behaviour in the diagrams of the density of states as a function of a varying parameter. Chimera states, for which coherent and incoherent domains occur simultaneously, emerge as a consequence of the coexistence of basin of attractions for each state. Consequently, the distribution of chimera states can remain invariant by a parameter change, as well as it can suffer subtle changes when one of the basins ceases to exist. A similar phenomenon is observed when perturbations are applied in the initial conditions. By means of the uncertainty exponent, we characterise the basin boundaries between the coherent and chimera states, and between the incoherent and chimera states, respectively. This way, we show that the density of chimera states can be not only moderately sensitive but also highly sensitive to initial conditions. This chimera's dilemma is a consequence of the fractal and riddled nature of the basins boundaries. Coupled dynamical systems have been used to describe the behaviour of real complex systems, such as power grids, neuronal networks, economics, and chemical reactions. Furthermore, these systems can exhibit various kinds of interesting nonlinear dynamics, e.g. synchronisation, chaotic oscillations, and chimera states. The chimera state is a spatio-temporal pattern characterised by the coexistence of coherent and incoherent dynamics. It has been observed in a great variety of systems, ranging from theoretical and experimental arrays of oscillators, to in phenomena such as the unihemispheric sleep of cetaceans. We study the chimera state in a circulant network of Hénon maps, seeking to determine how the density of states in the network depends on the system parameters and the initial conditions. We have found that, as expected, the density of states might be inva

关键词： Neurons

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Design and Implement the Continuous Flickering SSVEP-BCI in Augmented Reality

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Journal of Physics: Conference Series 2020年第1期1631卷

作者： Ruiyan Wang Yufeng Ke Shuang Liu Jiale Du Cheng Xu Bohao Xing Dong Ming Tianjin International Engineering Institute Tianjin University Tianjin University 300072 China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin 300072 China School of Precision Instruments & Opto-Electronics Engineering Tianjin University Tianjin 300072 China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory No. 40 Yun'gangbeili Fengtai District Beijing 100074 China

Brain-computer interface (BCI) provides a new way to express our minds without peripheral nerves and muscles. In this work, a process control recognition method based on continuous flickering is proposed to output continuous commands. Phase matching method was applied in this work, matching the test data with the template with the same phase to improve the accuracy. We also put forward a high tolerance criterion and give a new definition to the recognition result of attention shift period. We first conducted a screen-based continuous flickering feasibility verification experiment using correlation component analysis algorithm (TRCA) method, and the recognition accuracy reached 85% and 90% under high tolerance criterion. The average offline simulation information translates rate (ITR) was 455.8 bit/min, and the highest ITR reached 524.7bit/min, which certificated the feasibility. Furthermore, we carried out a drone control experiment based on augmented reality (AR)-BCI using extended filter bank canonical correlation analysis (extended-FBCCA), and achieved an average accuracy of 90% and ITR of 49.1 bit/min, having better performance than repetitive visual stimulus (RVS) with intervals.

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一种改进的纯组分物性预测机器学习模型（英文）

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Engineering 2024年第8期 61-73页

作者：曹欣羽贡铭 Anjan Tula 陈曦 Rafiqul Gani Venkat Venkatasubramanian State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Department of Physics Bard College at Simon's Rock PSE for SPEED Company Sustainable Energy and Environment Thrust The Hong Kong University of Science and Technology (Guangzhou) Department of Applied Sustainability Széchenyi István University Complex Resilient Intelligent Systems Laboratory Department of Chemical Engineering Columbia University

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product design. However, the lack of extensive data and high experimental costs hinder the development of prediction techniques for these properties. Moreover,accuracy and predictive capabilities still limit the scope and applicability of most property estimation methods. This paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach. A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different compounds. Prior selection techniques, including prior elicitation and prior predictive checking, are also applied during the building procedure to provide the model with more information from previous research findings. The framework is assessed using datasets of varying sizes for 20 pure component properties. For 18 out of the 20 pure component properties,the new models are found to give improved accuracy and predictive power in comparison with other published models, with and without machine learning.

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An optimized energy management strategy for fuel cell hybrid vehicles

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IOP Conference Series: Materials science and Engineering 2019年第4期612卷

作者： Wenxin Xiao Lei Wang Dongyin Liu Wenwen Zhang Chinese-German College Tongji University Shanghai China Department of Electrical and Computer Engineering Technische Universität München Munich Germany Research Center of Control Science and Intelligent System University of Electronic Science and Technology of China Chengdu China College of Electronics and Information Engineering Tongji University Shanghai China

Fuel cell hybrid vehicles are environmentally friendly and have good development prospects. Their energy management is very important, but there are currently few studies on this topic. In this paper, an optimized energy management strategy specifically applied to fuel cell hybrid vehicles is proposed. Fuel cell hybrid vehicles have some different characteristics from ordinary hybrid vehicles, which need to be considered in energy management. For the fuel cell to remain activated during driving, its output power must remain above a minimum power. In addition, it takes some time for the fuel cell to start up. These characteristics are considered in the vehicle model. The energy management strategy proposed in this paper maximizes energy use efficiency by controlling the SOC value of the battery. The SOC value of the battery is in the proper range during driving. At high speeds, fuel is consumed as a source of energy and the battery is charged to keep its SOC at a high level. When approaching the destination or when the vehicle is traveling at a low speed, the battery is discharged to reduce fuel consumption. Applying this strategy to the ECE-15 and EUDCL environments, the simulation results show that this optimized energy management strategy improves the energy efficiency of the fuel cell and enables the battery to operate at the appropriate power.

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Antitumor Effects of Cds-Ppix Adducts for Nuclear-Targeted Sonodynamic Therapy

SSRN

引用

SSRN 2023年

作者： Xue, Juanjuan Jia, Muyue Sang, Shengbo Li, Huaqian Wang, Xiaojuan Guo, Li Duan, Qianqian Xi, Yanfeng Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030024 China Department of Pathology Shanxi Cancer Hospital Shanxi Hospital Affiliated to CancerHospital Chinese Academy of Medical Sciences Cancer Hospital Affiliated to Shanxi Medical University Shanxi Taiyuan030013 China Shanxi Key Laboratory of Bone and Soft Tissue Injury Repair Department of Orthopaedics Second Hospital of Shanxi Medical University Taiyuan030001 China Department of Pathology Shanxi Tumor Hospital Taiyuan030013 China

To achieve high-efficiency treatment to diseases, accurate delivery of drugs with a carrier into the specific organelles is of great importance. The nucleus is the ultimate target for a large number of drugs and direct delivery of drugs into nucleus can greatly improve the therapeutic treatment. Whereas, in sonodynamic therapy (SDT), the fact that most drug delivery systems can only deliver drugs to the cytoplasm of the cells and cannot act directly on the nucleus. In this paper, nucleolus-targeted carbondots (CDs) are prepared. Also, the conjugate of CDs and PpIX is synthesized by amide cross-linking and then release singlet oxygen to kill cancer cells under ultrasound radiation. The resulting CDs-PpIX nanoparticles showed significant nuclear targeting properties, and greatly improved the efficiency of acoustic dynamics treatment, as the nucleus exhibited poor tolerance to reactive oxygen species (ROS) generated during acoustic dynamics treatment. In addition, ROS as an extracellular signal can activate the p38MAPK protein pathway, which is the main mechanism of ultrasound killing of cancer cells. Our results demonstrate the great potential of CDs-PpIX in targeting cell nuclei for acoustic dynamics treatment. © 2023, The Authors. All rights reserved.

关键词： Cancer cells

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2-D GCANet Applied to Denoise 1-D EEG Signals in Online Remote Teaching Scenarios

Journal of Network Intelligence

引用

Journal of Network Intelligence 2023年第4期8卷 1289-1302页

作者： Huang, Ting-Qing Wang, Chuan-Sheng Chen, Zhao-Qi Zhang, Fu-Quan Meng, Xiang-Long Grau, Antoni Chen, Yang Huang, Jing-Wei College of Computer and Control Engineering Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulong Jiangbei Avenue Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music No. 2 Wannianchang Street Chenghua District Sichuan Province Chengdu City China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town NO. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 599 Quanxiu Road Juyuanzhou Ecological Civilization District Fujian Province Fuzhou City China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fujian Province Fuzhou City China

When EEG signals are used to assess the level of student engagement in online teaching tasks, they are often interfered by noise. It is a challenge to effectively remove these noises. Currently, deep learning methods have been applied to the field of EEG denoising. However, existing methods still have some problems. The denoised signals still have obvious noise residue, or the original EEG signals are damaged, and the model fitting speed is too slow. Image dehazing, as a typical denoising task in the field of image enhancement, has achieved great success in recent years. Therefore, inspired by advanced models in this field, we introduce CNN into EEG denoising tasks. In this paper, we take GCANet, an excellent image enhancement model, as an example. The dilated convolutions and gate fusion subnetworks included in GCANet enable more efficient EEG signal denoising. The results demonstrate that the proposed model effectively reduces noise while preserving essential features. Furthermore, in comparison to other state-of-the-art models, our proposed model exhibits enhanced robustness and faster convergence, as evidenced by achieving lower loss values after five epochs. Its good performance provides a new development idea for the field of EEG denoising. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Image enhancement

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Liquid Metal Manifold Microchannel Heat Sink for Ultra-High Heat Flux Cooling

SSRN

引用

SSRN 2024年

作者： Zhang, Mingkuan Gao, Qi Zhao, Zhiyuan Guo, Luna Li, Xuan Zhang, Chao Zhang, Xudong Rao, Wei Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China School of mechanical engineering Tianjin University of Commerce Tianjin300134 China Institute of Refrigeration and Cryogenics Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Engineering Mechanics Tsinghua University Beijing Beijing100084 China CAS Key Laboratory of Cryogenics Technical Institute of Physics and Chemistry Chinese Academy of Sciences China Beijing Key Lab of CryoBiomedical Engineering and Key Lab of Cryogenics Beijing100190 China

The reduction in transistor dimensions and advancements in chip integration have led to a dramatic increase in the heat flux of chip. Advanced heat dissipation technology has become the pivotal in ensuring the normal operation of chips and fully utilizing chip performance. Room temperature liquid metal convection cooling technology has demonstrated its efficacy as a viable approach for addressing high heat flux heat dissipation. However, the smaller specific heat capacity of liquid metal leads to a large temperature rise, imposing a constraint on the progression of liquid metal cooling technology. To overcome this drawback, this paper proposed a liquid metal manifold channel structure designed to convert the continuous long-range flow into a segmented short-range flow. The good matching between liquid metal and manifold structures is adequately demonstrated through a comparison with alternative coolant and channel structure. The simulation results demonstrate the effectiveness of liquid metal manifold channel cooling in dealing with a heat flux of 1000 W/cm², ensuring that the maximum temperature of the chip remains below 351.7 K. Moreover, the convective heat transfer coefficient even reaches 106 W/(m2·K), which is ten times larger than that of water conventional microchannel heat sink. The orthogonal experiments were conducted to analyze how the structural parameters affect the dissipation performance, including the height of the microchannel, ratio of manifold length to fin length, length of fin in manifold, width of channel, and ratio of fin with to channel width. It concluded that the height of the microchannel has been identified as the most critical factor. This manifold structure mitigates the inherent limitation of the lower specific heat capacity of liquid metal, paving the way for effective thermal management of electronic devices subjected to ultra-high heat flux. © 2024, The Authors. All rights reserved.

关键词： Heat flux

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Transient Voltage Stability Assessment Method based on gcForest

引用

Journal of Physics: Conference Series 2021年第1期1914卷

作者： Sun Zhiyuan LI Mingpo Zhang Jie Hu Binjiang Qi Guo Zhu Yihua Guangxi Power Grid Electrical Power Research Institute Supported by the Science and Technology Project of Guangxi Power Grid (Contract No(***DL00006)). State Key Laboratory of HVDC Electric Power Research Institute CSG China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System CSG Key Laboratory of Power System Simulation

Aiming at the fact that the traditional method cannot quickly and accurately judge the power system transient voltage stability, and the deep neural network method needs many sample data sets and has a large number of hyperparameters, a power system transient voltage stability assessment method based on deep forest (gcForest) is proposed. gcForest effectively extracts features through ensemble learning, multi-layer feature transformation, and dynamically adjusting the number of model layers. Finally, this method is applied to transient voltage stability assessment of an electrolytic aluminium load-intensive area in Guangxi Power Grid. The results show that the method has high accuracy, the feasibility and effectiveness of this method are verified. This method can assist dispatchers to judge the transient voltage stability of the power grid.

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AFDM-SCMA: A Promising Waveform for Massive Connectivity over High Mobility Channels

arXiv

引用

arXiv 2023年

作者： Luo, Qu Xiao, Pei Liu, Zilong Wan, Ziwei Nikolaos, Thomos Gao, Zhen He, Ziming The 5G & 6G Innovation Centre University of Surrey United Kingdom The School of Computer Science and Electronics Engineering University of Essex United Kingdom The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The 5G & 6G Innovation Centre University of Surrey GuildfordGU2 7XH United Kingdom BIT Zhuhai519088 China The MIIT Key Laboratory of Complex-Field Intelligent Sensing BIT Beijing100081 China Jinan250307 China Jiaxing314019 China The Samsung Cambridge Solution Centre System LSI Samsung Electronics CambridgeCB4 0DS United Kingdom

This paper studies the affine frequency division multiplexing (AFDM)-empowered sparse code multiple access (SCMA) system, referred to as AFDM-SCMA, for supporting massive connectivity in high-mobility environments. First, by placing the sparse codewords on the AFDM chirp subcarriers, the input-output (I/O) relation of AFDM-SCMA systems is presented. Next, we delve into the generalized receiver design, chirp rate selection, and error rate performance of the proposed AFDM-SCMA. The proposed AFDM-SCMA is shown to provide a general framework and subsume the existing OFDM-SCMA as a special case. Third, for efficient transceiver design, we further propose a class of sparse codebooks for simplifying the I/O relation, referred to as I/O relation-inspired codebook design in this paper. Building upon these codebooks, we propose a novel iterative detection and decoding scheme with linear minimum mean square error (LMMSE) estimator for both downlink and uplink channels based on orthogonal approximate message passing principles. Our numerical results demonstrate the superiority of the proposed AFDM-SCMA systems over OFDM-SCMA systems in terms of the error rate performance. We show that the proposed receiver can significantly enhance the error rate performance while reducing the detection complexity. © 2023, CC BY.

关键词： Mean square error

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