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IEEE Conference on Decision and control

作者： Yuhao Yi Liren Shan Philip E. Paré Karl H. Johansson Institute of Clinical Pathology West China Hospital and College of Computer Science Sichuan University Chengdu China Toyota Technological Institute at Chicago School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and they are also affiliated with Digital Futures Stockholm Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper studies efficient algorithms for dynamic curing policies and the corresponding network design problems to guarantee fast extinction of epidemic spread in a Markov process-based susceptible-infected-susceptible (SIS) model. We provide a computationally efficient curing algorithm based on the curing policy proposed by Drakopoulos, Ozdaglar, and Tsitsiklis (2014). Since the corresponding optimization problem is NP-hard, finding optimal policies is intractable for large graphs. We provide approximation guarantees on the curing budget of the proposed dynamic curing algorithm. To avoid the waiting period included in the original curing policy, we study network design problems to reduce the total infection rate by deleting edges or reducing the weight of edges. To this end, we provide algorithms with provable guarantees. In summary, the proposed curing and network design algorithms together provide an effective and computationally efficient approach that mitigates SIS epidemic spread in networks.

关键词： Epidemics Heuristic algorithms Computational modeling Curing Markov processes Approximation algorithms Linear programming Computational efficiency Resource management Optimization

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Exploring Gender Differences Based on Functional Magnetic Resonance Images by Deep Learning and Gradient-weighted Class Activation Mapping

Exploring Gender Differences Based on Functional Magnetic Re...

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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Jie Bin Luo Ji Chao Ma Yan Jiang Wang Xue Chen College of Control Science and Engineering China University of Petroleum (East China) Qingdao China The Institute for Digital Medicine and Computer-Assisted Surgery Qingdao University Qingdao China

Previous studies have shown gender differences in cognitive function and the prevalence of some neurological diseases. Understanding gender differences in functional connectivity (FC) will provide a new perspective for understanding these differences. We wanted to develop an effective model for the gender prediction and identify the major source of gender differences in functional connectivity. In this study we used FC generated from functional Magnetic Resonance Imaging (fMRI) from the Human Connectome Project in 1038 healthy subjects. We apply deep learning to test whether functional connectivity networks can effectively predict gender and identify the main sources of gender differences in FC. The accuracy of predicting gender by using the functional connectivity network has reached more than 90%, and we found that there are significant gender differences in the FC of interhemispheric brain areas and the FC in the right hemisphere, and in addition to the previously discovered default mode network (DMN), frontoparietal network (FPN), somatomotor network (SMN), there were gender differences in visual network (VIN), auditory network (AUN), and dorsal attention network (DAN) as well, and the gender differences within each resting-state network (RSN) were also large. Our results suggest that gender differences exist in functional connectivity networks and certain subnetworks can be focused on when studying gender-related cognitive functions and neurological disorders.

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All-optical logic gates based on hierarchical photonic crystal modulated photoluminescence of perovskite nanocrystals

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Science China(Technological Sciences) 2023年第9期66卷 2735-2742页

作者： WANG Yun ZHA YanFang YANG YunYi LIU CiHui DI YunSong CAO GuiYuan WEI ShiBiao CHEN ZhiHui GAN ZhiXing Center for Future Optoelectronic Functional Materials School of Computer and Electronic InformationSchool of Artificial IntelligenceNanjing Normal UniversityNanjing 210023China Centre for Translational Atomaterials School of ScienceSwinburne University of TechnologyHawthorn VIC 3122Australia Nanophotonics Research Center Shenzhen Key Laboratory of Micro-Scale Optical Information TechnologyShenzhen UniversityShenzhen 518060China Key Laboratory of Advanced Transducers and Intelligent Control Systems Ministry of Education and Shanxi ProvinceCollege of Electronic Information and Optical EngineeringTaiyuan University of TechnologyTaiyuan 030024China

Halide perovskites exhibit high performance in all sorts of optoelectronic and photonic areas, suggesting their huge potential in integrated photonic devices. However, until now, all optical logic gates based on perovskites are still rarely explored, hindering the development of all-optical networks and computing. Herein, a new concept of all-optical logic gates is proposed based on the modulation of photoluminescence(PL) from perovskite nanocrystals(PNCs). A hierarchical photonic crystal(Hie PhC) is developed by self-assembling polystyrene(PS) and SiO2nanoparticles, which exhibit a higher PL enhancement than that of a monolayer PhC. Moreover, the light-controlled PL is realized by taking advantage of the high thermal response of the PL from PNCs/Hie PhC on polyethylene(PE) substrate, assisted by a graphene layer for light-heat conversion. Consequently, optical diode and triode are achieved based on the modulated PL, which exhibit contrast ratios(CR) of 24.7 and 74.0 dB, ***-optical logic gates, including “AND”, “OR” and “NOT”, are realized based on the optical diode and triode.

关键词： perovskite nanocrystals photonic crystals all-optical logic gates modulation of photoluminescence

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Optimized Vectorizing of Building Structures with Switch: High-Efficiency Convolutional Channel-Switch Hybridization Strategy

arXiv

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arXiv 2023年

作者： Lin, Moule Jing, Weipeng Li, Chao Jung, András College of Computer and Control Engineering Northeast Forestry University Harbin150040 China Faculty of Informatics Eötvös Loránd University Budapest1117 Hungary

The building planar graph reconstruction, a.k.a. footprint reconstruction, which lies in the domain of computer vision and geoinformatics, has been long afflicted with the challenge of redundant parameters in conventional convolutional models. Therefore, in this letter, we proposed an advanced and adaptive shift architecture, namely the"Switch" operator, which incorporates non-exponential growth parameters while retaining analogous functionalities to integrate local feature spatial information, resembling a high-dimensional convolution operation. The"Switch" operator, cross-channel operation, architecture implements the XOR operation to alternately exchange adjacent or diagonal features, and then blends alternating channels through a 1x1 convolution operation to consolidate information from different channels. The SwitchNN architecture, on the other hand, incorporates a group-based parameter-sharing mechanism inspired by the convolutional neural network process and thereby significantly reducing the number of parameters. We validated our proposed approach through experiments on the SpaceNet corpus, a publicly available dataset annotated with 2,001 buildings across the cities of Los Angeles, Las Vegas, and Paris. Our results demonstrate the effectiveness of this innovative architecture in building planar graph reconstruction from 2D building images. © 2023, CC BY-NC-ND.

关键词： Convolution

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Deep Learning for Personalized Electrocardiogram Diagnosis: A Review

arXiv

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arXiv 2024年

作者： Ding, Cheng Yao, Tianliang Wu, Chenwei Ni, Jianyuan Department of Biomedical Engineering Georgia Institution of Technlogy Atlanta United States Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University No. 1239 Siping Road Shanghai200092 China Department of Biomedical Engineering University of Rochester Rochester United States Department of Information Technology and Computer Science Juniata College Huntingdon United States

The electrocardiogram (ECG) remains a fundamental tool in cardiac diagnostics, yet its interpretation traditionally reliant on the expertise of cardiologists. The emergence of deep learning has heralded a revolutionary era in medical data analysis, particularly in the domain of ECG diagnostics. However, inter-patient variability prohibit the generalibility of ECG-AI model trained on a population dataset, hence degrade the performance of ECG-AI on specific patient or patient group. Many studies have address this challenge using different deep learning technologies. This comprehensive review systematically synthesizes research from a wide range of studies to provide an in-depth examination of cutting-edge deep-learning techniques in personalized ECG diagnosis. The review outlines a rigorous methodology for the selection of pertinent scholarly articles and offers a comprehensive overview of deep learning approaches applied to personalized ECG diagnostics. Moreover, the challenges these methods encounter are investigated, along with future research directions, culminating in insights into how the integration of deep learning can transform personalized ECG diagnosis and enhance cardiac care. By emphasizing both the strengths and limitations of current methodologies, this review underscores the immense potential of deep learning to refine and redefine ECG analysis in clinical practice, paving the way for more accurate, efficient, and personalized cardiac diagnostics. © 2024, CC BY.

关键词： Generative adversarial networks

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Data Alignment Design for Robotic Programming by Demonstration Based on IMU and Optical Tracker

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IEEE Transactions on Instrumentation and Measurement 2023年 72卷

作者： Song, Kai-Tai Li, Bing-Yi Ou, Szu-Chen National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering College of Electrical and Computer Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Graduate Degree Program of Robotics Hsinchu300093 Taiwan

This study proposes a data alignment design for sensor fusion of an optical target (OT) and an inertial measurement unit (IMU) to track the trajectory of a handheld tool for robot programming by demonstration (PbD). A novel method is proposed for coordinate and temporal alignment between the OT and the IMU sensor to increase tracking accuracy. The IMU measurements are aligned with the corresponding angular velocities and accelerations derived from the OT to calibrate the IMU-OT relationship. Global and local optimizations are combined to reduce the computation time for the temporal alignment and coordinate calibration. Experimental results show that the proposed method provides improved calibration results with shorter calculation time compared with the two existing methods. The sensor fusion experiments verify the accuracy of trajectory tracking of 1.3 mm using the calibration result, an improvement from 1.4 mm of the data alignment using ruler measurement. Integrated PbD experiments using a 6-DOF manipulator were carried out to evaluate the proposed method. The manipulator imitated the hand motion of a bottle transfer task with a trajectory error of 0.66 mm. Practical robotic experiments demonstrate that the method is suitable for robot programming applications. © 1963-2012 IEEE.

关键词： Calibration

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Empirical Analysis of Vulnerabilities Life Cycle in Golang Ecosystem

arXiv

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arXiv 2023年

作者： Hu, Jinchang Yang, Sen Zhang, Lyuye Liu, Chengwei Huang, Song Liu, Yang College of Command and Control Engineering Army Engineering University of PLA Nanjing China Continental-NTU Corporate Lab Nanyang Technological University Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

Open-source software (OSS) greatly facilitates program development for developers. However, the high number of vulnerabilities in open-source software is a major concern, including in Golang, a relatively new programming language. In contrast to other commonly used OSS package managers, Golang presents a distinctive feature whereby commits are prevalently used as dependency versions prior to their integration into official releases. This attribute can prove advantageous to users, as patch commits can be implemented in a timely manner before the releases. However, Golang employs a decentralized mechanism for managing dependencies, whereby dependencies are upheld and distributed in separate repositories. This approach can result in delays in the dissemination of patches and unresolved vulnerabilities. To tackle the aforementioned concern, a comprehensive investigation was undertaken to examine the life cycle of vulnerability in Golang, commencing from its introduction and culminating with its rectification. To this end, a framework was established by gathering data from diverse sources and systematically amalgamating them with an algorithm to compute the lags in vulnerability patching. It turned out that 66.10% of modules in the Golang ecosystem were affected by vulnerabilities. Within the vulnerability life cycle, we found two kinds of lag impeding the propagation of vulnerability fixing. By analyzing reasons behind non-lagged and lagged vulnerabilities, timely releasing and indexing patch versions could significantly enhance ecosystem security. © 2023, CC BY.

关键词： Open source software

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Decentralized Trading of High-Proportion Renewable Energy Microgrid Based on Master-Slave Game and Slime Mould Algorithm (SMA) 1

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16th Annual Conference of China Electrotechnical Society, ACCES 2021

作者： Liu, Zhengguang Tan, Qinyue Rui, Jiajing Ding, Yueming Xiong, Di Shang, Wenqiang Pang, Bowen Kang, Dingyi College of Water Resources and Architectural Engineering Northwest A&F University Yangling712100 China School of Control and Computer Engineering North China Electric Power University Beijing102208 China Rizhao Power Supply Bureau of State Grid Shandong Electric Power Company Rizhao276800 China

ISBN: (数字)9789811915321

ISBN: (纸本)9789811915314

Microgrids has always been the hotspot of academic research, and the decentralized trading of electrical energy through artificial intelligence algorithms has been widely applied in recent years. However, the randomness and volatility of wind power and photovoltaic have brought huge uncertainty to the transaction. For this reason, this paper proposes a decentralized trading model based on the master-slave game. Aiming at maximizing the interests of agents and users in the microgrid, a master-slave game model is constructed. Through Karush-Kuhn-Tucker optimality condition and duality theorem, the multi-objective nonlinear optimization model is transformed into a mixed linear programming problem for solution. The simulation results show that the real-time electricity price is 0.32 yuan/kWh higher than the negotiated electricity price based on the optimal electricity price strategy, power grid can reduce energy storage pressure. The results proved that the transaction model is feasible and effective in terms of operational efficiency and economic welfare. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Game theory

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Predicting Off-Design Performance of Axial Compressors Using Multilayer Neural Network Model for Surge control

SSRN

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SSRN 2023年

作者： Elmelegy, Mohamed Ali Elhosseini, Mostafa A. Ali, Hesham Arafat Haikal, Amira Y. Instrument Maintenance support Engineer DLNG Damietta LNG Egypt College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Department of Computers and Control Systems Engineering Faculty of Engineering Mansoura University Mansoura Egypt

The incidence of surge within axial compressors profoundly influences the efficacy and reliability of aero-engines. Conventional methodologies in engine design have predominantly concentrated on the precise and efficient forecasting of critical characteristics during such occurrences. This paper presents a novel approach for predicting the surge phenomenon in axial compressors. Surge is a major issue in the operation of axial compressors, causing reduced performance, increased wear and tear, and in some cases, complete machine failure. The proposed approach utilizes a neural network model based on the compressor's design data to forecast the behavior of the compressor's working map under off-design conditions. The model is verified using real data collected over five years during compressor operation, including the opening angle of the Inlet Guided Van (IGV), an important parameter often ignored or assumed to be fixed in previous studies. The results show that the neural network-based approach effectively predicts the compressor's behavior under different operating conditions, providing valuable insights into the onset of surge and offering a real-time surge prediction and control tool. The ability to predict the compressor's working map enables optimizing its performance and controlling instability. This model was validated by comparing its predictions with real operating conditions observed over five years across various compressor operating modes. As a result, this predictive model enables the safe operation of axial compressors by mitigating instability risks during operation. This study's findings highlight the model's effectiveness, as evidenced by the minimal error observed between the predicted and actual data collected across various compressor operating modes. The model exhibits a strong performance, characterized by a high regression factor of 0.92667 and a low mean squared error of 3.04465 x 10^-3. These numerical values underscore the reliability and pr

关键词： Forecasting

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Self-feedback LSTM regression model for real-time particle source apportionment

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Journal of Environmental Sciences 2022年第4期34卷 10-20页

作者： Wei Wang Weiman Xu Shuai Deng Yimeng Chai Ruoyu Ma Guoliang Shi Bo Xu Mei Li Yue Li Trusted AI System Laboratory College of Computer ScienceNankai UniversityTianjin 300350China KLMDASR Tianjin Key Laboratory of Network and Data Security TechnologyTianjin 300350China State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and EngineeringNankai UniversityTianjin 300071China Institute of Mass Spectrometry and Atmospheric Environment Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution Jinan UniversityGuangzhou 510632China Guangdong-Hongkong-Macao Joint Laboratory of Collaborative Innovation for Environmental Quality Guangzhou 510632China

Atmospheric particulate matter pollution has attracted much wider attention *** recent years,the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments *** demands are summarized,in this paper,as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances,such as the existence of secondary source and similar *** this study,we firstly analyze the possible and potential restraints in single particle source apportionment,then propose a novel three-step self-feedback long short-term memory(SF-LSTM)network for approximating the source *** proposed deep learning neural network includes three modules,as generation,scoring and refining,and regeneration *** from the scoring modules,SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment,meanwhile,the regeneration module calculates the source contribution in a non-linear *** results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators(residual sum of squares,stability,sparsity,negativity)for the ***,in short time-resolution analyzing,SF-LSTM provides better results under the restraint of stability.

关键词： Time series Regression Self-feedback LSTM network Particle source apportionment

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