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17th International Conference on Telecommunication systems, Services, and Applications, TSSA 2023

作者： Raheel, Muhammad Salman Tubbal, Faisel Coyte, James Iranmanesh, Saeid Ogunbon, Philip Odeh, Nidhal Raad, Raad Theoharis, Panagiotis Ioannis University of Wollongong School of Electrical Computer and Telecommunications Engineering Wollongong Australia The University of Sydney Electronics & Development Engineer at Jericho Smart Sensing Laboratory Sydney Australia

ISBN: (纸本)9798350309164

Monitoring vital signs regularly helps the early recognition of abnormal physiological parameters in deteriorating patients. Impulse radio ultra-wideband (IR-UWB) technology is a suitable non-invasive approach to monitor the vital signs such as heart and breathing rates. In this technique, the IR-UWB radar antenna propagates signal towards the patient and the reflected spectrum can determine the status of the vital signs. This function is ideal where patient touching should be avoided such as a potentially infected COVID-19 patients. This paper designs an algorithm to identify the chest displacement and to separate the heart and breathing signals. In addition, we propose an adaptive filter and a smoothing filter used in the function to reduce the measurement noise from the surrounding vibrations. We have implemented the device and validated the performance over different sets of real experiments. The results show that the proposed technology can effectively estimate the breathing and heart rates with up to 98% accuracy as compared to the values obtained using an electrocardiograph (ECG). © 2023 IEEE.

关键词： COVID-19

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Preliminary Results on the Surge Current Withstand Capability of Natural Ester Oil

Preliminary Results on the Surge Current Withstand Capabilit...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Tsovilis, Thomas E. Hadjicostas, Alexandros Y. Staikos, Evangelos T. Tsekouras, Konstantinos Datsios, Zacharias G. Peppas, George D. School of Electrical & Computer Engineering Aristotle University of Thessaloniki High Voltage Laboratory Thessaloniki54124 Greece Raycap Sa Research & Development Department Drama Greece

ISBN: (纸本)9798350320169

Natural ester oils are an environmentally friendly substitute for mineral oils that are commonly used as dielectric insulating liquid and coolant in power transformers. In this study, the real relative permittivity and DC breakdown voltage of a natural ester oil are experimentally examined. An accelerated degradation test employing 15 current impulses is employed to investigate the change of the basic electrical characteristics of the oil under study with the aid of a 1 MV/ 7 kJ Marx generator. Experimental results on the surge current withstand capability of the natural ester oil are presented and discussed. © 2023 IEEE.

关键词： Esters

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Distributed Power Allocation for 6-GHz Unlicensed Spectrum Sharing via Multi-agent Deep Reinforcement Learning

Distributed Power Allocation for 6-GHz Unlicensed Spectrum S...

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2023 IEEE International Conference on Industrial Technology, ICIT 2023

作者： Zhang, Xiang Bhuyan, Arupjyoti Kasera, Sneha Kumar Ji, Mingyue University of Utah Department of Electrical and Computer Engineering United States Idaho National Laboratory United States Kahlert School of Computing University of Utah United States

ISBN: (纸本)9798350336504

We consider the problem of spectrum sharing by multiple cellular operators. We propose a novel deep Reinforcement Learning (DRL)-based distributed power allocation scheme which utilizes the multi-agent Deep Deterministic Policy Gradient (MA-DDPG) algorithm. In particular, we model the base stations (BSs) that belong to the multiple operators sharing the same band, as DRL agents that simultaneously determine the transmit powers to their scheduled user equipment (UE) in a synchronized manner. The power decision of each BS is based on its own observation of the radio environment (RF) environment, which consists of interference measurements reported from the UEs it serves, and a limited amount of information obtained from other BSs. One advantage of the proposed scheme is that it addresses the single-agent non-stationarity problem of RL in the multi-agent scenario by incorporating the actions and observations of other BSs into each BS's own critic which helps it to gain a more accurate perception of the overall RF environment. A centralized-training-distributed-execution framework is used to train the policies where the critics are trained over the joint actions and observations of all BSs while the actor of each BS only takes the local observation as input in order to produce the transmit power. Simulation with the 6 GHz Unlicensed National Information Infrastructure (U-NII)-5 band shows that the proposed power allocation scheme can achieve better throughput performance than several state-of-the-art approaches. © 2023 IEEE.

关键词： Reinforcement learning

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Study on Landslide Susceptibility Evaluation Based on Improved Multivariable Weighted Model: A Case Study of Lushan County, China 3

Study on Landslide Susceptibility Evaluation Based on Improv...

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3rd IEEE International Conference on Software engineering and Artificial Intelligence, SEAI 2023

作者： Zhao, Li Duan, Gonghao Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China Automation for Complex Systems Hubei Key Laboratory of Advanced Control and Intelligent Wuhan China

ISBN: (纸本)9798350337952

The evaluation of regional geological hazard susceptibility is of great significance to the prevention and control of geological hazard. In this paper, the "4-20"Lushan earthquake disaster area as the research area, combined with GIS and characteristics of the research area, through correlation analysis, selected 9 influencing factors as the evaluation factors. The study was conducted using a weighted information volume-logistic regression model (WI-LR). The results were classified into five sensitivity levels: very low, low, medium, high and very high. The results show that the landslide prone areas in Lushan County are concentrated in the area below the middle of the county seat, mostly near rivers, faults and areas with peak acceleration greater than 0.4. Among them, WI-LR model (AUC=0.918) > deterministic factor model (CF =0.908) > weighted information quantity model (WI) (AUC=0.896) > information quantity model I (AUC=0.853) > information quantity logistic regression model I-LR (AUC=0.799). It indicates that WI-LR model has high evaluation accuracy. © 2023 IEEE.

关键词： Hazards

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On the Optimal Delay Growth Rate of Multi-Hop Line Networks: Asymptotically Delay-Optimal Designs and the Corresponding Error Exponents

On the Optimal Delay Growth Rate of Multi-Hop Line Networks:...

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作者： Ogbe, Dennis O. Wang, Chih-Chun Love, David J. NASA Jet Propulsion Laboratory PasadenaCA91109 United States Purdue University School of Electrical and Computer Engineering West LafayetteIN47907 United States

Multi-hop line networks have emerged as an important abstract model for modern and increasingly dense communication networks. In addition, the growth of real-time and mission-critical services has created high demand for and increased research interest in low-latency communications. The combination of these facts motivates a new investigation of data transmission schemes for L-hop line networks from a delay-vs-throughput perspective. To this end, this work defines a metric called the delay amplification factor for a target throughput R, denoted by AF R, which characterizes the growth rate of the (asymptotic) delay with respect to the number of hops. We show that all existing relay schemes, e.g., Decode-&-Forward (DF), have limRC DAF(R)=Ω (L), which is consistent with the decades-old perception that delay grows linearly with respect to L. We then design a scheme satisfying limRC DAF R=1, if the bottleneck hop is the last hop, i.e., its asymptotic delay does not grow with respect to L. The results imply that this linearly growing delay is an artifact of the existing DF designs, and it is possible to surpass it and attain the true fundamental limit with a new delay-centric solution. In the second half of this work, we further show that if variable-length coding and one-bit stop-feedback are allowed, we can relax the condition bottleneck being the last hop and attain limRC DAF (R)=1 for any arbitrary line networks. © 1963-2012 IEEE.

关键词： Reliability analysis

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MRIAD: A Pre-clinical Prevalence Study on Alzheimer’s Disease Prediction Through Machine Learning Classifiers 4th

MRIAD: A Pre-clinical Prevalence Study on Alzheimer’s Dise...

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4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023

作者： Loba, Jannatul Mia, Md. Rajib Mahmud, Imran Mahi, Md. Julkar Nayeen Whaiduzzaman, Md. Ahmed, Kawsar Department of Software Engineering Daffodil International University Dhaka Bangladesh School of Information Systems Queensland University of Technology Brisbane Australia Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada

ISBN: (纸本)9783031423161

Alzheimer’s disease (AD) is a neurological illness that worsens with time. The aged population has expanded in recent years, as has the prevalence of geriatric illnesses. There is no cure, but early detection and proper treatment allow sufferers to live normal lives. Furthermore, people with this disease’s immune systems steadily degenerate, resulting in a wide range of severe disorders. Neuroimaging Data from magnetic resonance imaging (MRI) is utilized to identify and detect the disease as early as possible. The data is derived from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) collection of 266 people with 177 structural brain MRI imaging, DTI, and PET data for intermediate disease diagnosis. When neuropsychological and cognitive data are integrated, the study found that ML can aid in the identification of preclinical Alzheimer’s disease. Our primary objective is to develop a model that is reliable, simple, and rapid for diagnosing preclinical Alzheimer’s disease. According to our findings (MRIAD), the Logistic Regression (LR) model has the best accuracy and classification prediction of about 98%. The ML model is also developed in the paper. This article profoundly, describes the possibility to getting into Alzheimer’s disease (AD) information from the pre-clinical or non-preclinical trial datasets using Machine Learning Classifier (ML) approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Subjective and Objective Quality Assessments of AI-Generated Images for Language E-Learning

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IEEE Access 2025年 13卷 100667-100682页

作者： Chibuike Onuoha Vu Hai Dang Nguyen Duc Nam Truong Thu Huong Pham Ngoc Nam Truong Cong Thang Department of Computer Science and Information Systems The University of Aizu Aizuwakamatsu Japan School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam College of Engineering and Computer Science VinUniversity Hanoi Vietnam

AI-generated images (AIGIs) are becoming popular and can be employed in many applications, owing to Generative AI (GAI). Researchers have developed models that can be used to generate images for different scenarios. In addition, researchers have proposed datasets of natural scene images for language learning and different AIGI-quality datasets for general applications. For e-learning, particularly in the context of language learning, no AIGI dataset is currently available. To fill this gap, we first proposed an AIGI quality dataset for language learning. Both subjective and objective assessments have been conducted on the proposed dataset. The findings from subjective assessment show that higher perceptual quality also corresponds to a more substantial alignment. It also shows that the average MOS scores of images generated from Stability AI models are similar and lower than images generated by the Dall.E3 model. The results of the objective assessment indicate that the performance of off-the-shelf quality models is generally low. In addition, results from finetuning learning-based quality models show that significant gains and improvements can be achieved using the dataset. The results of the alignment evaluation show that the HPS model is the best, and realistic images in the dataset produced the best alignment correlation compared to the other styles in the dataset. The findings also show that multimodal large language models, such as vision-enabled GPT-4 (GPT-4V), still struggle to produce alignment scores that correlate with humans.

关键词： Electronic learning Visualization Text to image Quality of experience Quality assessment Diffusion models Training data Training Generators Generative AI

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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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AggFollower: Aggressiveness Informed Car-Following Modeling

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Chen, Xianda Yuan, Xinyi Zhu, Meixin Zheng, Xinhu Shen, Shaojie Wang, Xuesong Wang, Yinhai Wang, Fei-Yue Guangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR China School of Transportation Engineering Tongji University Shanghai China Department of Civil and Environmental Engineering University of Washington Seattle WA USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Car-following is the most common driving scenario where a following vehicle follows a lead vehicle in the same lane. One crucial factor of car-following behavior is driving style which affects speed and gap selection, acceleration pattern, and fuel consumption. However, existing car-following research used limited categories of driving style through pre-defined patterns and failed to encode driving style into data-driven car-following models. To address these limitations, we propose the Aggressiveness Informed Car-Following (AICF) modeling approach, which embeds driving style as a dynamic input feature in data-driven car-following models. In detail, We design driving aggressiveness tokens using four physical quantities (jerk, acceleration, relative speed, and relative spacing) to capture the heterogeneity of driving aggressiveness. These tokens were then embedded into a physics-informed Long Short-Term Memory (LSTM) based car-following model for trajectory prediction. To evaluate the effectiveness of our approach, we conducted extensive experiments based on 12,540 car-following events extracted from the HighD dataset and 24,093 events from the Lyft dataset. Compared to models devoid of considerations for driving aggressiveness levels, AICF exhibits superior efficacy in mitigating the Mean Square Error (MSE) of spacing and collision rate. To the best of our knowledge, this is the first work to directly incorporate real-time driving aggressiveness tokens as input features into data-driven car-following models, enabling a more comprehensive understanding of aggressiveness in car-following behavior. IEEE

关键词： Long short-term memory

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Optimization of Quality Process Control and Preventive Maintenance Strategy: A Case Study

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Operations Research Forum 2024年第4期5卷 84页

作者： Sadraoui, Youssef Er-ratby, Mohamed Kadiri, Moulay Saddik Kobi, Abdessamad Laboratory of Process Engineering Computer Science and Mathematics LIPIM National School of Applied Sciences of Khouribga Sultan Moulay Slimane University Beni Mellal Morocco Laboratory of Science and Engineering Techniques LaSTI National School of Applied Sciences of Khouribga Sultan Moulay Slimane University Beni Mellal Morocco LARIS Angevin Laboratory for Systems Engineering Research Polytech Angers University of Angers Angers France

The studied process concerns a wet grinding and cycloning unit, set up to ensure that the particle size it processes complies with the requirements in terms of pipe transport mode. Frequent shutdowns, low reliability, the risk of delivering a product with inappropriate particle size, and the lack of operational parameters’ control are all obstacles for the unit, having enormous repercussions on its relevance and performance. To improve this situation, the implementation of an innovative 5-month quality and process control study has proven crucial to establish a culture of quality among operators, aiming to achieve the targeted grain size. However, the innovative aspects of our approach lie in its thoroughness and inclusiveness since it addresses the process both qualitatively and quantitatively, focusing not only on theoretically modeling and simulating the grinding circuit but also providing an integrated method to address every component of the unit. By going through the seven stages of this study, we were able to enhance the overall performance of the unit controlling the particle size at the output. After establishing a set of quality control tools, we observed a remarkable development in mastering various parameters influencing the targeted particle size. The benefits generated by the study are extremely satisfying for the unit at different levels, particularly in terms of quality, process control, and resource optimization. Furthermore, the integration of maintenance management into the process has proven essential to ensure the reliability and sustainability of the installation, thereby contributing significantly to the overall efficiency of the unit. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Optimization Preventive maintenance Process capability Quality Statistical process control SPC Wet grinding

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