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International Conference on Communication Technology (ICCT)

作者： Yiwen Ding Wenlong Fu Lihong Cao Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781665432078

Computational neuroscience is interdisciplinary and plays an essential role in facilitating the development of cognitive neuroscience and Artificial General Intelligence (AGI). To explore the computational efficiency of Spiking Neural Network (SNN) on a computational hardware platform, we deploy SNN on the Xilinx Zynq7 Zedboard Field Programmable Gate Array (FPGA). Specifically, we use multi-compartmental Hodgkin-Huxley (HH) neuron model to construct neural circuits via C++ code and adopt a biologically plausible calcium concentration-based plasticity model as the learning rule of SNN. Our experimental results show that the FPGA platform can support parallel computation of the multi-compartment HH model with synaptic plasticity. Compared to other methods, FPGA-based computing has a significant speed advantage, suggesting that FPGA can be a good choice for implementing brain-inspired computation.

关键词： Computational modeling Biological system modeling Neurons Neural circuits Logic gates Brain modeling Hardware

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Joint Partial Offloading and Resource Allocation for Distributed Multi-access Edge computing Enabled Vehicular Network

TechRxiv

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TechRxiv 2022年

作者： Ma, Guifu Hu, Manjiang Wang, Xiaowei Wu, Di Li, Haoran Bian, Yougang Zhu, Konglin The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body The College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China Hunan University Wuxi214072 China The College of Design Hunan University Changsha410082 China The Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha410082 China The College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

In the foreseeable intelligent Transportation System (ITS), intelligent Connected Vehicles (ICVs) will play an important role in improving travel efficiency and safety. However, it is challenging for ICVs to support the resource-hungry autonomous driving applications due to the limitation of hardware computing power. Fortunately, the emergence of Multi-access Edge computing (MEC) helps overcome this limitation effectively. In this paper, we investigate the vehicle and edge server collaborative computation offloading problem by jointly optimizing the partial offloading ratio and resource allocation ratio. Different from existing two-level centralized solution methods, we give a new perspective to deal with multi-variable non-convex optimization problem in a distributed manner. That is, by analyzing the feasibility conditions of the problem, we separate the coupling between variables and derive the bounds of partial offloading ratio and resource allocation ratio based on the constraints. We further separate the coupling variables among ICVs and transform the original problem into an equivalent form and decompose it into a two-level convex problem, where each sub-problem satisfies the task offloading requirements. We then develop two low-complexity distributed algorithms to obtain the optimal solution. Finally, extensive simulations demonstrate that our proposed algorithms can converge faster and significantly outperform the baseline schemes in terms of system cost. © 2022, CC BY.

关键词： Resource allocation

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A Survey on Neural Machine Translation Applied to Sign Language Generation

A Survey on Neural Machine Translation Applied to Sign Langu...

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International Conference on Applied Machine Learning (ICAML)

作者： Yue Zhang Lihong Cao State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781665421263

Sign languages are the primary medium of communication for the deaf and hearing-impaired community, and the number of human beings with hearing impairment is increasing at a rapid rate. There is a significant communication barrier between the hearing-impaired and normal-hearing communities. One solution to this issue is to offload the effort of text understanding for the deaf and hearing-impaired community in the form of Sign Language Generation (SLG). In this paper, first of all, we systematically review the mainstream Neural Machine Translation (NMT)-based SLG approaches. Specifically, according to their model architectures, we divide them into two categories: the Recurrent Neural Networks (RNNs)-based and Transformer-based models, and discuss their advantages and limitations. Next, we introduce the existing publicly available datasets and the data preprossessing methods in the field of SLG. Then, we summarize the performance evaluation methods for NMT-based SLG models. Finally, we offer two possible future research directions for better development in the field of SLG.

关键词： Performance evaluation Recurrent neural networks Pipelines Data preprocessing Gesture recognition Machine learning Auditory system

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Self-Supervised Rotation Learning for 3D Segmentation on Nasopharyngeal Carcinoma MRI Images

Self-Supervised Rotation Learning for 3D Segmentation on Nas...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Changtai Li Ruohui Jiang Shihua Yin Jinzhu Yang Xiaojuan Ban Beijing Advanced Innovation Center for Materials Genome Engineering School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Second Affiliated Hospital of Guangxi Medical University Nanning China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China

Segmenting the tumors of nasopharyngeal carcinoma (NPC) in Magnetic Resonance Imaging (MRI) images is critical for its diagnosis and treatment. In medical image segmentation, it is vital to exploit the rich information contained in 3D sliced images. However, the precise identification and location of lesion regions often require plenty of labels. To alleviate this situation, in this article, we propose 3DRotNPC, a framework based on a tailored Self-Supervised Learning (SSL) strategy, to accurately segment tumor regions for NPC under the circumstance of label limitation. To learn the rich 3D spatial and geometric nature of MRI images in a Self-Supervised way, the proxy task of randomly selecting and rotating images in consecutively sliced data is designed. After the SSL pre-training stage, the learned parameters of the Convolutional Neural Networks (CNNs) based model are transferred to adapt the downstream segmentation task. We verify the capability of 3DRotNPC on the NPC tumor dataset which is collected and curated from the clinical treatment in the representative hospital. Extensive experiments demonstrate that our approach delivers considerable gains in downstream 3D voxel segmentation, especially with a 10% to 50% number of labels.

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An Ontology-based Knowledge Modeling towards Eco-design for Additive Manufacturing

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Procedia CIRP 2024年 122卷 760-765页

作者： Yanan Wang Tao Peng Samyeon Kim Yi Xiong Yunlong Tang Renzhong Tang State Laboratory of Fluid Power Components and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou 310058 China Department of Mechanical Engineering Jeonju University Jeonju-si Jeollabuk-do 55069 South Korea School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen 518055 China Mechanical and Aerospace Engineering Department Monash University Melbourne 3800 Australia Materials Science and Engineering Department Monash University Melbourne 3800 Australia Zhejiang Province Key Laboratory of Advanced Manufacturing Technology School of Mechanical Engineering Zhejiang University Hangzhou 310058 China

Owing to the unique technological merits of additive manufacturing (AM), design for AM, particularly eco-design for AM, has been gaining attention in improving the sustainable performance of AM-fabricated (AMed) parts. The main challenge in harvesting potential sustainability benefits is how to capture the impact of design on sustainable performance. Though several existing studies adopted quantitative evaluation on AM sustainability and investigated the relationships between design information and sustainability information of AMed part, the sustainability information is still unavailable in the design phase. This hardly guides eco-design for AM in practice. To support the increasing industrial applications of AM, this paper illustrates related knowledge, that is, material-process-structure-ecoProperty (MPSeP) relationships to connect design and sustainability information in eco-design for AM. An ontology-based method is proposed to model the MPSeP relationships of the AMed part in a structured way. An example of an AMed hydraulic valve body was used to demonstrate the MPSeP modeling process. It is discussed that how the constructed model effectively assists AM designers through providing eco-design recommendation, and enables design software tools to perform sustainability analysis towards eco-design for AM.

关键词： additive manufacturing eco-design for additive manufacturing ontology model sustainability

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Unauthorized UAV Countermeasure for Low-Altitude Economy: Joint Communications and Jamming based on MIMO Cellular Systems

arXiv

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arXiv 2024年

作者： Li, Zhuoran Gao, Zhen Wang, Kuiyu Mei, Yikun Zhu, Chunli Chen, Lei Wu, Xiaomei Niyato, Dusit Beijing100081 China BIT Zhuhai519088 China The MIT Key Laboratory of Complex-Field Intelligent Sensing BIT Beijing100081 China Jinan250307 China Jiaxing314019 China The State Key Laboratory of CNS ATM & State Key Laboratory of Explosion Science and Safety Protection Beijing Institute of Technology Beijing100081 China China Tower Corporation Limited No.9 Don-gran North Street Haidian District Beijing100089 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Research Institue of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China

To ensure the thriving development of low-altitude economy, countering unauthorized unmanned aerial vehicles (UAVs) is an essential task. The existing widely deployed base stations hold great potential for joint communication and jamming. In light of this, this paper investigates the joint design of beamforming to simultaneously support communication with legitimate users and countermeasure against unauthorized UAVs based on dual-functional multiple-input multiple-output (MIMO) cellular systems. We first formulate a joint communication and jamming (JCJ) problem, relaxing it through semi-definite relaxation (SDR) to obtain a tractable semi-definite programming (SDP) problem, with SDR providing an essential step toward simplifying the complex JCJ design. Although the solution to the relaxed SDP problem cannot directly solve the original problem, it offers valuable insights for further refinement. Therefore, we design a novel constraint specifically tailored to the structure of the SDP problem, ensuring that the solution adheres to the rank-1 constraint of the original problem. Finally, we validate effectiveness of the proposed JCJ scheme through extensive simulations. Simulation codes are provided to reproduce the results in this paper: https://***/LiZhuoRan0. The results confirm that the proposed JCJ scheme can operate effectively when the total number of legitimate users and unauthorized UAVs exceeds the number of antennas. Copyright © 2024, The Authors. All rights reserved.

关键词： Jamming

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A Low SAR, Small size, High gain, Circularly polarized Antenna for Wearable Application 2

A Low SAR, Small size, High gain, Circularly polarized Anten...

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2020 2nd International Conference on Artificial Intelligence Technologies and Application, ICAITA 2020

作者： Wang, Chao Zhang, Liang Cheng, Yi Li, XueMei Wu, Xianliang Key Lab of Ministry of Education of Intelligent Computing and Signal Processing Anhui University Hefei Anhui230601 China AnHui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Hefei Anhui230601 China

A planar, small size, high gain, low specific absorption rate (SAR) and circularly-polarized (CP) wearable antenna is proposed in this paper. The antenna is fabricated on a substrate with a dielectric constant of 3.5 with a thickness of 0.5 mm and sized at 42×42 mm only. Both the simulation results show that the overlap bandwidth of impedance and axial ratio of the proposed antenna is 13.7% (5.15-5.91 GHz). To isolate the antenna against the influence of the human body, a artificial magnetic conductor (AMC) plane is added on the reverse side of the radiator. The AMC structure effectively isolates the human body from the radiation of the antenna and reduces the specific absorption rate (SAR) of it by more than 96.1% at 5.5 GHz, which improves the antenna gain and the peak gain reaches 5.09 dBi at 5.5 GHz. The wearable performance of the antenna was analyzed, and it is observed that it can sustain good performance even under human body loading. Beside, the antenna has a small size, which makes it ideal for wearable applications. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Wearable antennas

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DEEPACC:Automate Chromosome Classification Based On Metaphase Images Using Deep Learning Framework Fused With Priori Knowledge

DEEPACC:Automate Chromosome Classification Based On Metaphas...

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IEEE International Symposium on Biomedical Imaging

作者： Li Xiao Chunlong Luo Ningbo HuaMei Hospital University of the Chinese Academy of Sciences (UCAS) Advanced Computer Research Center Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science

Chromosome classification is an important but difficult and tedious task in karyotyping. Previous methods only classify manually segmented single chromosome, which is far from clinical practice. In this work, we propose a detection based method, DeepACC, to locate and fine classify chromosomes simultaneously based on the whole metaphase image. We firstly introduce the Additive Angular Margin Loss to enhance the discriminative power of the model. To alleviate batch effects, we transform decision boundary of each class case-by-case through a siamese network which make full use of priori knowledges that chromosomes usually appear in pairs. Furthermore, we take the clinically seven group criteria as a prior-knowledge and design an additional Group Inner-Adjacency Loss to further reduce inter-class similarities. A private metaphase image dataset from clinical laboratory are collected and labelled to evaluate the performance. Results show that the new design brings encouraging performance gains comparing to the state-of-the-art baseline models.

关键词： Deep learning Image segmentation Additives Biological system modeling Knowledge based systems Transforms Performance gain

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Eis-Based State-of-Health Estimation of Lithium-Ion Batteries Using Multiple Correlation Analysis-Based Feature Screening and Info-Esn Estimation Model

SSRN

引用

SSRN 2024年

作者： Dai, Houde Lai, Yuan Yu, Hui Yang, Yuxiang Zhu, Liqi School of Advanced Manufacturing Fuzhou University Fujian Jinjiang362251 China Quanzhou Institute of Equipment Manufacturing Fujian Institute of Research The Structure of Matter Chinese Academy of Sciences Fujian Jinjiang362215 China School of Engineering and Design Hunan Normal University Hunan Changsha410081 China Fujian Key Laboratory of Special Intelligent Equipment Safety Measurement and Control Fujian Special Equipment Inspection and Research Institute Fuzhou350008 China

Lithium-ion batteries (LIBs) are widely employed in electric vehicles (EVs) owing to their high energy density, and superior longevity. As a vital evaluation index for the degradation state of LIBs, the state of health (SOH) must be accurately estimated. This study adopts an electrochemical impedance spectroscopy (EIS)-based equivalent circuit model (ECM) to estimate battery SOH. The internal dynamic electrical behaviors of LIBs are decoupled by the distribution of relaxation times (DRT) method, where the critical degradation features are extracted from the DRT curves and the fitted ECM parameters. Subsequently, referring to the ensemble learning approach (ELA), the multiple correlation analysis (MCA) is utilized to identify the most pertinent degradation features. The battery data are analyzed for various types of correlations to ascertain the optimal number and combination of health indicators (HIs). Furthermore, the weighted mean of vectors (INFO) algorithm is applied to optimize the hyperparameters in the echo state network (ESN) model for SOH estimation. The INFO-ESN model is verified with six types of LIBs under different operating conditions. Experimental results of the proposed method manifest that the RMSE and MAE of the battery SOH are within 1%, thereby verifying the accuracy and robustness of the proposed method. © 2024, The Authors. All rights reserved.

关键词： Relaxation time

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design of wearable squat booster for automobile assembler based on ergonomics 6

Design of wearable squat booster for automobile assembler ba...

引用

2020 6th International Forum on Engineering Materials and Manufacturing Technology, IFEMMT 2020

作者： Chen, Zhewen Qiao, Zhifeng Bao, Tianxu Dou, Fuchao Chen, Xinpu Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin China

Aiming at the problem that automobile assembler need to squat for a long time, a wearable squatting assistance system with low cost, simple control and easy to squat and walk is designed. Based on ergonomics and finite element theory, UG was used to draw a three-dimensional solid model of the wearable squatting power system for automobile assemblers. With the help of ANSYS software, the statics analysis of key components is carried out to check their strength and stiffness. It is concluded that the wearable squatting power assist device for automotive fitters based on ergonomics has reached the design requirements in terms of function use and strength and stiffness. © Published under licence by IOP Publishing Ltd.

关键词： Ergonomics

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