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Longitudinal-vertical integrated sliding mode controller for distributed electric vehicles

science China(Information sciences) 2020年第11期63卷 312-317页

作者： Yan MA Jinyang ZHAO Haiyan ZHAO Hong CHEN Tielong SHEN State Key Laboratory of Automotive Simulation and Control Jilin University Department of Control Science and Engineering Jilin University Department of Engineering and Applied Sciences Sophia University

Dear editor,In the recent years, with the advancements in vehicular technology, the vehicle dynamics control has attracted considerable research attention [1,2].Many active stability control systems have played a significant role in the chassis control of electric vehicles (EV), promising upgraded, more secure,and intelligent versions

关键词： integrated vehicle dynamic control sliding mode control tire force distributor distributed electric vehicle CarSim-Matlab/Simulink

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Semi-supervised Open-set Recognition of Radar Active Jamming

Semi-supervised Open-set Recognition of Radar Active Jamming

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2021 CIE International Conference on Radar, Radar 2021

作者： Li, Hao Fang, Xueli Zhang, Lei Kang, Hui Zhang, Wei School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Key Laboratory of Complex Aviation Simulation System Beijing China

ISBN: (纸本)9781665498142

With the emergence of various kinds of complex radar jamming, radar jamming intelligent recognition technology has increasingly become the key to electronic counter-counter measures. To achieve radar jamming open-set recognition with a small number of tagged samples, Generative adversarial network-based pseudolabeled convolutional neural network (GAN-PL-CNN) model is proposed in this paper. Firstly, radar jamming is modelled, and the radar jamming datasets with known and unknown types are established after time-frequency transformation and image processing. Then, the adaptive pseudolabeling method is used to achieve semi-supervised recognition of radar jamming by combining the learning capability of convolutional neural networks and the characteristics of pseudolabeling to increase labeled samples intelligently. Finally, generative adversarial networks are introduced to enhance the recognition ability of the model and the discrimination ability of the unknown type through the game between the generator and the discriminator in the iterative process. Experimental results show that the GAN-PL-CNN model achieves 89.7%-95.1% for known types and 30.7%-86.8% for unknown types at 3%-6% labeling rate. As the number of labeled samples increases, the recognition effect becomes better and has a greater ability to exclude unknown type. © 2021 IEEE.

关键词： convolutional neural network open-set recognition pseudo-label Radar active jamming

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Quantum Circuits for the heat equation with physical boundary conditions via Schrödingerisation

arXiv

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arXiv 2024年

作者： Jin, Shi Liu, Nana Yu, Yue School of Mathematical Sciences Institute of Natural Sciences MOE-LSC Shanghai Jiao Tong University Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education National Center for Applied Mathematics in Hunan Xiangtan University Hunan Xiangtan411105 China

This paper explores the explicit design of quantum circuits for quantum simulation of partial differential equations (PDEs) with physical boundary conditions. These equations and/or their discretized forms usually do not evolve via unitary dynamics, thus are not suitable for quantum simulation. Boundary conditions (either time-dependent or independent) make the problem more difficult. To tackle this challenge, the Schrödingerisation method can be employed, which converts linear partial and ordinary differential equations with non-unitary dynamics into systems of Schrödinger-type equations, via the so-called warped phase transformation that maps the equation into one higher dimension. Despite advancements in Schrödingerisation techniques, the explicit implementation of quantum circuits for solving general PDEs, especially with physical boundary conditions, remains underdeveloped. We present two methods for handling the inhomogeneous terms arising from time-dependent physical boundary conditions. One approach utilizes Duhamel’s principle to express the solution in integral form and employs linear combination of unitaries (LCU) for coherent state preparation. Another method applies an augmentation to transform the inhomogeneous problem into a homogeneous one. We then apply the quantum simulation technique from [CJL23] to transform the resulting non-autonomous system to an autonomous system in one higher dimension. We provide detailed implementations of these two methods and conduct a comprehensive complexity analysis in terms of queries to the time evolution input oracle. © 2024, CC BY-NC-ND.

关键词： Ordinary differential equations

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Radar Signal Modulation Recognition Based on Bispectrum Features and Deep learning 2

Radar Signal Modulation Recognition Based on Bispectrum Feat...

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2nd International Conference on Computer engineering and Application, ICCEA 2021

作者： Dong, Zeyu Lv, Fengrong Wan, Tao Jiang, Kaili Fang, Xueli Zhang, Lei University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China Key Laboratory of Complex Aviation Simulation System Beijing China

ISBN: (纸本)9781665426169

Signal bispectral transformation can not only suppress the influence of Gaussian white noise on signal modulation recognition, but also retain the signal amplitude and phase information. It is also used to extract the non-linear characteristics. Compared with other high-order spectra, bispectrum has a simple processing flow. However, the direct use of all bispectrum as signal features will lead to two-dimensional template matching, causing lots of calculations. Converting two-dimensional bispectrum into one-dimensional sequence, for example, extracting slice information of bispectrum, or using integral bispectrum apparently reduce the amount of data to be processed while retaining part of the bispectrum information. We input the extracted bispectral transformation of radar signals into the neural network to realize modulation recognition. The simulations validate our conclusions that our proposed methods still have a high recognition probability while SNR is low. © 2021 IEEE.

关键词： Template matching

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Synergetic Planning of Multi-regional Energy System Under Climate Change and Uncertainty 7th

Synergetic Planning of Multi-regional Energy System Under Cl...

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Proceedings of 2022 7th International Conference on Environmental engineering and Sustainable Development (CEESD 2022)

作者： Li, Y.F. Li, Y.P. Huang, G.H. State Key Joint Laboratory of Environmental Simulation and Pollution Control China-Canada Center for Energy Environment and Ecology Research UR-BNU School of Environment Beijing Normal University Beijing100875 China Faculty of Engineering and Applied Science University of Regina ReginaSKS4S 0A2 Canada

ISBN: (纸本)9783031281921

Climate change and uncertainty have significant impacts on the multi-regional energy system in terms of both supply and demand. In this study, a multi-regional energy model is developed based on the interval linear programming method. The model could not only describe the complex relationship between inter-regional interaction and energy transmission in detail, but also provide optimal schemes for regulating electricity generation mix, mitigating carbon emissions, and minimizing system cost. The results show that: (i) from 2021 to 2050, the proportion of fossil fuels in energy and electricity supply will show a decreasing trend, and the regional energy and electricity structure will be optimized;(ii) under the RCP4.5 scenarios, regional carbon emissions will peak around 2040 and show a decreasing trend after the peak, with a reduction of about 1.45%;(iii) the BTH region should bear additional costs of carbon emission reduction under the RCP4.5 scenarios, the total system cost is about 1.2 times of that under the RCP8.5 scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Climate change

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Robust Federated Semi-Supervised Learning for Medical Image Classification via Pseudo-Label Filtering

Robust Federated Semi-Supervised Learning for Medical Image ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ziwei Wang Shuyu Guo Mingzhu Zhu Tian Bai College of Software Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Federated learning (FL) enables collaborative model training across multiple medical institutions to ensure data security. However, due to the variations in medical imaging equipment and regions at different medical institutions, FL methods usually suffer from insufficient data annotations and irrelevant noise within private datasets. To address these issues, a robust federated semi-supervised learning method via pseudo-label filtering (PFRFed) is introduced to utilize unlabeled data while mitigating the impact of noise data. Compared with existing federated semi-supervised learning methods, we propose a pseudo-label filtering mechanism with double dynamic thresholds, which allows the model to adopt more unlabeled data by adjusting the confidence and entropy thresholds at each stage of model training. Moreover, to reduce the degradation caused by noise data in private datasets from different clients, a noise-tolerant loss function and a grouping aggregation method based on the local model similarity are employed. The comparative experiments demonstrate the effectiveness of PFRFed, which has achieved the best classification accuracy of 95.20% and 88.72% on two public medical datasets. Also, PFRFed exhibits heightened resilience to variations in noisy data ratio and labeled data ratio, reaffirming its versatility and robustness.

关键词： Training Filtering Biological system modeling Noise Semisupervised learning Data models Robustness Noise measurement Biomedical imaging Image classification

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Noise-Consistent Hypergraph Autoencoder Based on Contrastive Learning for Cancer ceRNA Association Prediction in Complex Biological Regulatory Networks

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Journal of chemical information and modeling 2025年第12期65卷 2025 Jun 12页

作者： Xin-Fei Wang Lan Huang Yan Wang Ren-Chu Guan Zhu-Hong You Feng-Feng Zhou Yu-Qing Li Zi-Qi Zhao Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 China. School of Computer Science Northwestern Polytechnical University Xi'an 710072 China. Faculty of Arts and Science University of Toronto Toronto Ontario M4Y 0C9 Canada.

Competitive endogenous RNA (ceRNA) regulatory networks (CENA) have advanced our understanding of noncoding RNAs' roles in complex diseases, providing a theoretical basis for disease mechanisms. Existing ceRNA-disease association prediction methods are limited by traditional graph structures' inability to model long-range dependencies in biological networks. While hypergraph models partially address this, they often fail to effectively handle graph-level and node-level noise, hindering improvements in predictive performance. To address these challenges, we propose a Noise-Consistent hypeRgraph AutoEncoder framework with denoising strategies, termed NCRAE, aimed at achieving robust node embeddings in ceRNA regulatory networks and enabling the precise prediction of cancer-related ceRNA biomarkers. NCRAE employs a multiview contrastive learning strategy, integrating graph-level and node-level corruption with clean feature references to significantly enhance the robustness of hypergraph feature learning. Furthermore, to mitigate potential biases introduced by contrastive learning, NCRAE incorporates a noise consistency loss constraint, dynamically adjusting the weights of each component to further optimize the model's noise resistance and generalization ability. Combined with hypergraph convolution and Fourier KAN techniques, NCRAE achieves effective node embedding learning. Experiments on cancer-related ceRNA data sets show that NCRAE outperforms existing methods, especially in noisy conditions, demonstrating its robustness and predictive capability. Case studies further illustrate its practical value in cancer biomarker prediction, providing a powerful tool for cancer biomarker discovery.

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Community discovery algorithm and its technical improvement based on link structure–taking web community algorithm as an example

Community discovery algorithm and its technical improvement ...

引用

International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2020

作者： Gao, Rui Yang, Wenzhe Shi, Xiaohu Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University 2699 Qianjin Street Changchun130012 China

ISBN: (纸本)9783030539795

Link analysis is an important way to discover potential web communities. This paper analyzes the characteristics of the Web link structure, and studies the traditional maximum flow algorithm and the maximum flow algorithm based on HITS algorithm for edge capacity allocation. The traditional maximum flow problem, but there are still shortcomings. Based on the analysis of existing link similarity definitions, this paper proposes a new definition of link similarity and topic dissimilarity to better describe the relationship between linked pages, and to measure based on link similarity and topic dissimilarity. The similarity of the pages gives a more reasonable and efficient maximum flow-side capacity allocation scheme. The community discovery of 6 topics shows that the maximum flow algorithm proposed in this paper using the link similarity and topic dissimilarity model to allocate edge capacity can better solve the problems of existing algorithms and significantly improve the quality of the Web community. This paper proposes new methods and ideas for page similarity measurement, and provides new strategies for improving the Web community discovery algorithm based on link analysis. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Computer programming

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Numerical simulation of materials-oriented ultra-precision diamond cutting:review and outlook

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International Journal of Extreme Manufacturing 2023年第2期5卷 1-21页

作者： Liang Zhao Junjie Zhang Jianguo Zhang Houfu Dai Alexander Hartmaier Tao Sun Center for Precision Engineering Harbin Institute of TechnologyHarbin 150001People’s Republic of China State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074People’s Republic of China School of Mechanical and Engineering Guizhou UniversityGuizhou 550025People’s Republic of China Interdisciplinary Centre for Advanced Materials Simulation Ruhr-University BochumBochum 44801Germany

Ultra-precision diamond cutting is a promising machining technique for realizing ultra-smooth surface of different kinds of *** fundamental understanding of the impact of workpiece material properties on cutting mechanisms is crucial for promoting the capability of the machining technique,numerical simulation methods at different length and time scales act as important supplements to experimental *** this work,we present a compact review on recent advancements in the numerical simulations of material-oriented diamond cutting,in which representative machining phenomena are systematically summarized and discussed by multiscale simulations such as molecular dynamics simulation and finite element simulation:the anisotropy cutting behavior of polycrystalline material,the thermo-mechanical coupling tool-chip friction states,the synergetic cutting responses of individual phase in composite materials,and the impact of various external energetic fields on cutting *** particular,the novel physics-based numerical models,which involve the high precision constitutive law associated with heterogeneous deformation behavior,the thermo-mechanical coupling algorithm associated with tool-chip friction,the configurations of individual phases in line with real microstructural characteristics of composite materials,and the integration of external energetic fields into cutting models,are ***,insights into the future development of advanced numerical simulation techniques for diamond cutting of advanced structured materials are also *** aspects reported in this review present guidelines for the numerical simulations of ultra-precision mechanical machining responses for a variety of materials.

关键词： diamond cutting ultra-smooth surface material-oriented physics-based simulation model molecular dynamics finite element

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Radar Signal Detection Based on Ambiguity Function and Deep Learning 2

Radar Signal Detection Based on Ambiguity Function and Deep ...

引用

2nd International Conference on Computer engineering and Application, ICCEA 2021

作者： Lv, Fengrong Dong, Zeyu Wan, Tao Jiang, Kaili Fang, Xueli Zhang, Lei University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China Key Laboratory of Complex Aviation Simulation System Beijing China

ISBN: (纸本)9781665426169

The ambiguity function describes the joint characteristics of the signal in the time domain and the frequency domain, and is usually used for signal recognition. The time domain and frequency domain distribution of noise is more spurious than the signal. Its ambiguity function can also reflect its characteristics. However, the direct use of the ambiguity function as the signal feature will increase the amount of calculation of the detection system, so slice it, which reduces the burden on the system for detecting signal information while preserving the information. At the same time, the radar signal detection based on neural network can avoid the influence of artificially setting the threshold on the detection performance of the system. So, we propose an algorithm for inputting the extracted fuzzy function slice sequence into a neural network to realize signal detection. simulation proves that when the signal-To-noise ratio of the received signal is very low, our proposed method still has a high detection probability. © 2021 IEEE.

关键词： Signal detection

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