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arXiv 2020年

作者： Goldstein, Yoav Schätz, Martin Avigal, Mireille Department of Mathematics and Computer Science Open University of Israel Raanana Israel University of Chemistry and Technology in Prague Department of Computing and Control Engineering Czech Republic

Although the field of sleep study has greatly developed over the recent years, the most common and efficient way to detect sleep issues remains a sleep examination performed in a sleep laboratory, in a procedure called Polysomnography (PSG). This examination measures several vital signals during a full night’s sleep using multiple sensors connected to the patient’s body. Yet, despite being the golden standard, the connection of the sensors and the unfamiliar environment inevitably impact the quality of the patient’s sleep and the examination itself. Therefore, with the novel development of more accurate and affordable 3D sensing devices, new approaches for non-contact sleep study emerged. These methods utilize different techniques with the purpose to extract the same sleep parameters, but remotely, eliminating the need of any physical connections to the patient’s body. However, in order to enable reliable remote extraction, these methods require accurate identification of the basic Region of Interest (ROI) i.e. the chest area of the patient, a task that is currently holding back the development process, as it is performed manually for each patient. In this study, we propose an automatic chest area segmentation algorithm, that given an input set of 3D frames of a sleeping patient, outputs a segmentation image with the pixels that correspond to the chest area, and can then be used as an input to subsequent sleep analysis algorithms. Except for significantly speeding up the development process of the non-contact methods, accurate automatic segmentation can also enable a more precise feature extraction and it is shown it is already improving sensitivity of prior solutions on average 46.9% better compared to manual ROI selection. All mentioned will place the extraction algorithms of the non-contact methods as a leading candidate to replace the existing traditional methods used today. Copyright © 2020, The Authors. All rights reserved.

关键词： Sleep research

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A New Method for Quantifying Workload of Drivers and Application in Analyzing Stabilization Time after Critical Events in Varying Automation Levels

SSRN

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SSRN 2022年

作者： Zhai, Junda Lu, Guangquan Chen, Facheng Liu, Miaomiao School of Transportation Science and Engineering Beihang University Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Traffic Management School People’s Public Security University of China Beijing100038 China Beijing100088 China

The workload of drivers increases rapidly after the critical events and the period of time for workload to stabilize is worthy to investigate due to the close relationship between the workload and driving performance of drivers. It is yet unclear if the stabilization time of drivers varies across the different automation levels. In previous studies, a number of measurements have been utilized in evaluating the workload of drivers while most of the existing methods have limitations in studying the stabilization time. Therefore, this study introduces a new method for quantifying the cognitive workload of drivers with a measurement frequency of 2 seconds based on the performance of drivers to a newly designed calculating NDRT. A driving simulator experiment containing three automated driving levels and two critical events was conducted for the verification and application of the quantification method. Data were collected from 52 participants. The workload before and after critical events was analyzed and the results of quantified workload, the measurement acquired through the quantification method, showed a high similarity with heart rate and pupil diameter, which supported that the measurement could well reflect the workload of drivers with and without critical events. Moreover, the quantification method was applied to analyzing stabilization time after the critical situations and it was observed that quantified workload was a more appropriate measurement in determining the stabilization time of workload compared to heart rate and pupil diameter. In addition, the analysis results based on the workload quantification method showed that the effect of automation levels on the stabilization time of drivers after the critical events occurred was not significant. © 2022, The Authors. All rights reserved.

关键词： Automobile drivers

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Reinforcement Learning Algorithms for Online Single-Machine Scheduling

Reinforcement Learning Algorithms for Online Single-Machine ...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Yuanyuan Li Edoardo Fadda Daniele Manerba Roberto Tadei Olivier Terzo LINKS Foundation-Advanced Computing and Applications Torino Italy Department of Control and Computer Engineering Politecnico di Torino Torino Italy Department of Information Engineering University of Brescia Brescia Italy

ISBN: (数字)9788395541674

ISBN: (纸本)9781728141473

Online scheduling has been an attractive field of research for over three decades. Some recent developments suggest that Reinforcement Learning (RL) techniques have the potential to deal with online scheduling issues effectively. Driven by an industrial application, in this paper we apply four of the most important RL techniques, namely Q-learning, Sarsa, Watkins's Q(λ), and Sarsa(λ), to the online single-machine scheduling problem. Our main goal is to provide insights on how such techniques perform. The numerical results show that Watkins's Q(λ) performs best in minimizing the total tardiness of the scheduling process.

关键词： field surveys Learning learning (artificial intelligence) machine scheduling scheduling process

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Mobile robot navigation for complete coverage of an environment

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IFAC-PapersOnLine 2018年第22期51卷 512-517页

作者： Šelek, Ana Seder, Marija Petrovi, Ivan Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Croatia

Inspired by the Spanning Tree Covering (STC) algorithm of Gabriely and Rimon, a novel algorithm of complete coverage for known environments is developed. Unlike the original STC algorithm, we detect dynamic elements of the environment and efficiently update the solution when changes are observed. The contribution is a path replanning algorithm that reduces overlapping when the part of the environment is changed. Experiments show that the proposed algorithm is capable of planning complete coverage robot paths with 70.27% coverage and without overlapping. © 2018

关键词： Mobile robots Collision avoidance Motion planning Robot programming Trees (mathematics) Complete coverages Dynamic elements Known environments Mobile Robot Navigation Novel algorithm Path re planning Robot path Spanning tree

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Heterogeneous influence of individuals’ behavior on mask efficacy in gathering environments

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Frontiers of engineering Management 2022年第4期9卷 550-562页

作者： Haochen SUN Xiaofan LIU Zhanwei DU Ye WU Haifeng ZHANG Xiaoke XU College of Information and Communication Engineering Dalian Minzu UniversityDalian 116600China Web Mining Laboratory Department of Media and CommunicationCity University of Hong KongHong KongChina WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong KongChina Laboratory of Data Discovery for Health Hong Kong Science and Technology ParkHong KongChina Computational Communication Research Center Beijing Normal University(Zhuhai)Zhuhai 519087China School of Journalism and Communication Beijing Normal UniversityBeijing 100875China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical ScienceAnhui UniversityHefei 230601China

Wearing masks is an easy way to operate and popular measure for preventing *** masks can slow down the spread of viruses,their efficacy in gathering environments involving heterogeneous person-to-person contacts remains ***,we aim to investigate the epidemic prevention effect of masks in different real-life gathering *** study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering *** transmission of COVID-19 is simulated using the Monte Carlo simulation *** heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general ***,the heterogeneity of gathering environments causes the epidemic prevention effect of masks to *** masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools,high schools,and ***,the use of masks alone in primary schools and hospitals cannot control *** high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse,masks can meet the need for *** the heterogeneity of individual behavior,if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing,the epidemic prevention effect of masks can be ***,asymptomatic infection has varying effects on the prevention effect of masks in different *** effect can be weakened or eliminated by increasing the usage rate of masks in high schools and ***,the effect on primary schools and hospitals cannot be *** study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention.

关键词： COVID-19 masks behavioral heterogeneity asymptomatic infection

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Hybrid quantum-classical convolutional neural networks

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Science China(Physics,Mechanics & Astronomy) 2021年第9期64卷 1-8页

作者： Junhua Liu Kwan Hui Lim Kristin LWood Wei Huang Chu Guo He-Liang Huang Information Systems Technology and Design Singapore University of Technology and DesignSingapore 487372Singapore Forth AI Singapore 487372Singapore College of Engineering Design and ComputingUniversity of Colorado DenverColorado 80208USA Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic TechnologyGuilin 541004China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and ApplicationsHunan Normal UniversityChangsha 410081China Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Shanghai Branch CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum PhysicsUniversity of Science and Technology of ChinaHefei 201315China Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou 450000China

Deep learning has been shown to be able to recognize data patterns better than humans in specific circumstances or *** parallel,quantum computing has demonstrated to be able to output complex wave functions with a few number of gate operations,which could generate distributions that are hard for a classical computer to *** we propose a hybrid quantum-classical convolutional neural network(QCCNN),inspired by convolutional neural networks(CNNs)but adapted to quantum computing to enhance the feature mapping *** is friendly to currently noisy intermediate-scale quantum computers,in terms of both number of qubits as well as circuit’s depths,while retaining important features of classical CNN,such as nonlinearity and *** also present a framework to automatically compute the gradients of hybrid quantum-classical loss functions which could be directly applied to other hybrid quantum-classical *** demonstrate the potential of this architecture by applying it to a Tetris dataset,and show that QCCNN can accomplish classification tasks with learning accuracy surpassing that of classical CNN with the same structure.

关键词： quantum computing quantum machine learning hybrid quantum-classical algorithm convolutional neural network

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COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images

arXiv

引用

arXiv 2020年

作者： Hemdan, Ezz El-Din Shouman, Marwa A. Karar, Mohamed Esmail Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Egypt Department of Computer Engineering College of Computing and Information Technology Shaqra University Saudi Arabia

Background and Purpose: Coronaviruses (CoV) are perilous viruses that may cause Severe Acute Respiratory Syndrome (SARS-CoV), Middle East Respiratory Syndrome (MERS-CoV). The novel 2019 Coronavirus disease (COVID-19) was discovered as a novel disease pneumonia in the city of Wuhan, China at the end of 2019. Now, it becomes a Coronavirus outbreak around the world, the number of infected people and deaths are increasing rapidly every day according to the updated reports of the World Health Organization (WHO). Therefore, the aim of this article is to introduce a new deep learning framework;namely COVIDX-Net to assist radiologists to automatically diagnose COVID-19 in X-ray images. Materials and Methods: Due to the lack of public COVID-19 datasets, the study is validated on 50 Chest X-ray images with 25 confirmed positive COVID-19 cases. The COVIDX-Net includes seven different architectures of deep convolutional neural network models, such as modified Visual Geometry Group Network (VGG19) and the second version of Google MobileNet. Each deep neural network model is able to analyze the normalized intensities of the X-ray image to classify the patient status either negative or positive COVID-19 case. Results: Experiments and evaluation of the COVIDX-Net have been successfully done based on 80-20% of X-ray images for the model training and testing phases, respectively. The VGG19 and Dense Convolutional Network (DenseNet) models showed a good and similar performance of automated COVID-19 classification with f1-scores of 0.89 and 0.91 for normal and COVID-19, respectively. The worst classification performance is obtained for the InceptionV3 model with f1-scores of 0.67 for normal cases and 0.00 for COVID-19 cases. Conclusions: This study demonstrated the useful application of deep learning models to classify COVID-19 in X-ray images based on the proposed COVIDX-Net framework. Clinical studies are the next milestone of this research work. Copyright © 2020, The Authors. All ri

关键词： Coronavirus

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Influence of lateral stress on stick-slip characteristics of granite fault: A true three-dimensional experimental study

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Physics of Fluids 2025年第6期37卷

作者： Liang, Qinming Song, Zhenlong Huang, Gun Huang, Xinyu Lu, Yu Yao, Jingming Cheng, Guojian State Key Laboratory of Coal Mine Disaster Dynamics and Control School of Resources and Safety Engineering Chongqing University Chongqing400044 China High Performance Computing Department National Supercomputing Center in Shenzhen Guangdong Shenzhen518055 China China Coal Technology and Engineering Group Chongqing Research Institute Chongqing40037 China National Key Laboratory of Coal Mine Disaster Prevention and Control Chongqing400037 China

Understanding the stick-slip behavior of fault is crucial for studying earthquake nucleation and disaster prevention. However, experimental research on fault slip under true three-dimensional stress condition is relatively rare, particularly those focusing on the effect of lateral stress (σp) parallel to the fault plane on stick-slip characteristics. In this study, direct shear experiments under true three-dimensional stress condition were conducted to investigate the influence of σp on the mechanical and acoustic emission (AE) properties of stick-slip in granite fault. The results show that increasing σp enhances the stick-slip initiation strength (τslip) and the corresponding friction coefficient of fault, leading to an elevation in peak slip velocity and fault slip seismic parameters. The strengthening effect of σp also increases the proportion of high-energy stick-slip events, accompanied by a rise in the proportion of shear cracks during fault slip, further influencing τslip. Additionally, higher σp results in an elevated average AE b-value, indicating a gradual decrease in AE magnitudes, corresponding to the enhancement in τslip. Furthermore, b-value decline prior to stress drop can serve as a key indicator for predicting stick-slip events. These findings provide valuable theoretical insights for fault activation-induced hazards and the assessment of such hazards. © 2025 Author(s).

关键词： Stick-slip

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Construction of Cu7s4@Cuco2o4 Yolk-Shell Microspheres Composite and Elucidation of its Enhanced Photocatalytic Activity, Mechanism, and Pathway for Carbamazepine Degradation

SSRN

引用

SSRN 2022年

作者： He, Zuming Yang, Hanpei Wong, Ngie Hing Ernawati, Lusi Sunarso, Jaka Huang, Zhengyi Xia, Yongmei Wang, Yong Su, Jiangbin Fu, Xiaofei Wu, Mi Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes Ministry of Education College of Environment Hohai University Nanjing210098 China Huaide School Changzhou University Jingjiang214500 China School of Microelectronics and Control Engineering Changzhou University Changzhou213164 China Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia Department of Chemical Engineering Institut Teknologi Kalimantan Balikpapan76127 Indonesia School of Materials and Engineering Jiangsu University of Technology Changzhou213001 China School of Pharmaceutical and Materials Engineering Taizhou University Jiaojiang318000 China

Water pollution caused by the massive use of medicines has caused major environmental problems. This work first reported the synthesis and characterization of the Cu7S4/CuCo2O4 (CS/CCO) yolk-shell microspheres via hydrothermal and annealing methods, then investigated their photocatalytic performance in removing organic water pollutants. The 10-CS/CCO composite with yolk-shell microspheres exhibited the highest photodegradation rate of carbamazepine (CBZ), reaching 96.3% within 2 h. The 10-CS/CCO also demonstrated more than 2 times higher photodegradation rates than the pure (Cu7S4) CS and (CuCo2O4) CCO. This outstanding photocatalytic performance can be attributed to the unique yolk-shell structure and the Z-scheme charge transfer pathway, reducing multiple reflections of the acting light. These factors enhanced the light absorption efficiency and efficiently transferred photoexcited charge carriers. In-depth photocatalytic degradation pathways of CBZ were systematically evaluated via the identification of degradation intermediates with Fukui index calculation. © 2022, The Authors. All rights reserved.

关键词： Microspheres

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Rate-Splitting for Cell-Free Massive MIMO: Performance Analysis and Generative AI Approach

arXiv

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arXiv 2024年

作者： Zheng, Jiakang Zhang, Jiayi Du, Hongyang Zhang, Ruichen Niyato, Dusit Dobre, Octavia A. Ai, Bo The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The College of Computing and Data Science Nanyang Technological University Singapore Faculty of Engineering and Applied Science Memorial University Canada The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

Cell-free (CF) massive multiple-input multiple-output (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Rate-splitting (RS) effectively extracts data by decoding interference, yet its effectiveness is limited by the weakest UE. In this paper, we investigate an RS-based CF massive MIMO system, which combines strengths and mitigates weaknesses of both approaches. Considering imperfect channel state information (CSI) resulting from both pilot contamination and noise, we derive a closed-form expression for the sum spectral efficiency (SE) of the RS-based CF massive MIMO system under a spatially correlated Rician channel. Moreover, we propose low-complexity heuristic algorithms based on statistical CSI for power-splitting of common messages and power-control of private messages, and genetic algorithm is adopted as a solution for upper bound performance. Furthermore, we formulate a joint optimization problem, aiming to maximize the sum SE of the RS-based CF massive MIMO system by optimizing the power-splitting factor and power-control coefficient. Importantly, we improve a generative AI (GAI) algorithm to address this complex and non-convexity problem by using a diffusion model to obtain solutions. Simulation results demonstrate its effectiveness and practicality in mitigating interference, especially in dynamic environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

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