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18th International Conference on Information systems for Crisis Response and Management, ISCRAM 2021

作者： Watanabe, Masahiro Takahashi, Kenichi Kimura, Tetsuya Tadakuma, Kenjiro Mugaas, Terje Ozawa, Yu Takane, Eri Suzuki, Soichiro Marafioti, Giancarlo Koutsokeras, Miltiadis Tadokoro, Satoshi Tough Cyberphysical AI Research Center Tohoku University Japan Department of System Safety Nagaoka University of Technology Japan Department of Mathematics and Cybernetics SINTEF Digital Norway Graduate School of Information Sciences Tohoku University Japan Graduate School of Engineering Tohoku University Japan International Rescue System Institute United States Institute of Communication and Computer Systems Greece

ISBN: (纸本)9781949373615

When a large-scale disaster such as earthquake occurs, a huge number of victims will be trapped under debris in a wide area. Rescue activities in debris are technically not easy and endanger the first responders. There are several methods for improving safety and efficiency of rescue operation, but their availability is limited to a certain area or short operating time. Our project called CURSOR is developing tools to comprehensively search victims using a large number of ground-based robots entering debris transported by aerial drones. Here we show the development of the exploration robot collecting information with several sensors. The robot system was designed based on the requirements and performance was evaluated by ruggedization tests and mobility tests. No critical problem was found in the durability, and the mobility showed as the same as the ordinary wheel. To improve the mobility, we are planning to apply a proposed unique track mechanism. © 2021 Information systems for Crisis Response and Management, ISCRAM. All rights reserved.

关键词： Machine design

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Multi-modal Data based Semi-Supervised Learning for Vehicle Positioning

arXiv

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arXiv 2024年

作者： Huan, Ouwen Yang, Yang Luo, Tao Chen, Mingzhe Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

In this paper, a multi-modal data based semisupervised learning (SSL) framework that jointly use channel state information (CSI) data and RGB images for vehicle positioning is designed. In particular, an outdoor positioning system where the vehicle locations are determined by a base station (BS) is considered. The BS equipped with several cameras can collect a large amount of unlabeled CSI data and a small number of labeled CSI data of vehicles, and the images taken by cameras. Although the collected images contain partial information of vehicles (i.e. azimuth angles of vehicles), the relationship between the unlabeled CSI data and its azimuth angle, and the distances between the BS and the vehicles captured by images are both unknown. Therefore, the images cannot be directly used as the labels of unlabeled CSI data to train a positioning model. To exploit unlabeled CSI data and images, a SSL framework that consists of a pretraining stage and a downstream training stage is proposed. In the pretraining stage, the azimuth angles obtained from the images are considered as the labels of unlabeled CSI data to pretrain the positioning model. In the downstream training stage, a small sized labeled dataset in which the accurate vehicle positions are considered as labels is used to retrain the model. Simulation results show that the proposed method can reduce the positioning error by up to 30% compared to a baseline where the model is not pretrained. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Research on Restructuring Path of Children's Digital Publishing Supply and Demand Chain 2

Research on Restructuring Path of Children's Digital Publish...

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2nd International Conference on Information Science and Education, ICISE-IE 2021

作者： Chen, Huan Liu, Ying Shan, Chan Zhang, Hequn School of Journalism and Communication of Minjiang University Fujian Fuzhou350108 China Department of Software Technology Xiamen Institute of Software Technology Fujian Xiamen China Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia College of Information Engineering Jimei University Xiamen361021 China

ISBN: (纸本)9781665438292

In the era of rapid development of intelligent technology, the children's digital publishing industry is facing a disruptive change, and this change may be realized through the reconstruction of the supply and demand chain. The value power of source content creation and the ability to gather users have been deconstructed. The increase in the amount of content requires platform institutions to have a more professional content operation system. The convenience of payment technology has greatly increased the digital content payment rate of users of children's digital products. A development model of the supply and demand chain of children's digital publishing with more innovative flexibility and enabling effects. © 2021 IEEE

关键词： Economics

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Hybrid MPIE Method with Anisotropic Impedance Boundary Conditions for Layered Media

Hybrid MPIE Method with Anisotropic Impedance Boundary Condi...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Ying Zhao Wenjie Neng Zhen Zhang Dazhi Ding College of computer and information engineering (College of artificial intelligence) Nanjing Tech University Nanjing China State Key Laboratory of Millimeter Waves Nanjing China China Key System & Integrated Circuit Co. Ltd. Wuxi China Department of communication engineering Nanjing University of Science and Technology Nanjing China

A mixed potential integral equation (MPIE) method combined with anisotropic impedance boundary conditions is proposed for the analysis of scattering from layered media. In this method, anisotropic surface conductivity is modeled by the impedance boundary conditions. As a result, only these surfaces need to be discretized. Numerical example is demonstrated to show the accuracy of this method.

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Correction to: A new approach to snow avalanche rescue using UAV pictures based on convolutional neural networks

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Journal of Real-Time Image Processing 2023年第5期20卷 1-2页

作者： Zhang, Suyu Gavrilovskaya, Nadezhda Al Said, Nidal Afandi, Waleed Saeed College of Artificial Intelligence Wenzhou Polytechnic Wenzhou China Department of Computer-Aided Design Systems and Engineering Calculations Moscow Timiryazev Agricultural Academy Russian State Agrarian University Moscow Russian Federation College of Mass Communication Ajman University Ajman United Arab Emirates Department of Information System King Abdulaziz University Jeddah Kingdom of Saudi Arabia

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A 180-Degree Phase Difference Output of Wilkinson Power Divider Using Microstrip Parallel Coupled Lines

A 180-Degree Phase Difference Output of Wilkinson Power Divi...

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Conference on Power, Energy and Innovations (ICPEI)

作者： Pratya Phanomupatum Ravee Phromloungsri Rachata Tama Tiwat Pongthavornkamol Decha Wongpatsa Somchat Sonasang Department of Electronics Faculty Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand Computer and Communication Engineering Faculty of Technology Udon Thani Rajabhat University Udon Thani Thailand Location and Automatic Identification System Research Team Communication and Network Research Group National Electronic and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Electrical Elements and Wireless Devices Unit (EEWSU) Faculty Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand

ISBN: (数字)9798350356779

ISBN: (纸本)9798350356786

The design of a 180° output phase difference Wilkinson power divider for L-band applications is detailed in this article. The Wilkinson power is generated through the utilization of the parallel coupler's phase shifter. To showcase the potential of the suggested power divider, an FR4 microwave substrate is fabricated with a narrow band of 1.6 GHz to accommodate radio astronomy systems. The insertion loss, return loss, and isolation loss for the power divider's difference phase are accurately simulated at the operational frequency.

关键词： Power dividers Technological innovation Microwave integrated circuits Radio astronomy Phase shifters Insertion loss Microwave circuits Microstrip Proposals Substrates

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Enhancing Chewing Detection with a CNN-BiLSTM Network Utilizing IMU and Photoplethysmography Data from an Earable Device

Enhancing Chewing Detection with a CNN-BiLSTM Network Utiliz...

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IEEE International Conference on Software engineering and Service Sciences (ICSESS)

作者： Sakorn Mekruksavanich Wikanda Phaphan Phaisarn Jeefoo Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Geographic Information System School of Information and Communication Technology University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350389166

ISBN: (纸本)9798350389173

Detecting chewing is vital to tracking eating habits, which is necessary to preserve good well-being and health. Advancements in wearable sensors have recently enabled cap-turing a wide range of sensor data, including signals from inertial measurement units (IMUs) and photoplethysmography (PPG). This research presents a new deep-learning design that efficiently detects chewing occurrences employing IMU and PPG sensor data acquired from an early instrument. The design includes convolutional neural networks (CNN) and bidirectional long short-term memory (LSTM). The CNN-BiLSTM network presented in this study combines the advantages of both CNN and BiLSTM to capture spatial and temporal characteristics from the sensor data effectively. The CNN layers extract localized patterns and characteristics from the IMU and PPG data, whereas the BiLSTM layers represent the extended relationships and time-based changes in chewing behavior. By integrating these two formidable designs, our network attains a high level of resilience and precision in detecting chewing. We assess the efficacy of the CNN-BiLSTM network using a standardized dataset obtained from several individuals wearing wearable devices while engaging in eating events. The dataset comprises IMU and PPG sensor data that have been annotated with associated chewing sessions. The experimental findings clearly show that the suggested network outperforms the current leading approaches, obtaining an accu-racy of 98.18 % and an F1-score of 98.29 %, the highest recorded. The CNN-BiLSTM network performs better than baseline models such as CNN, LSTM, BiLSTM, GRU, and BiGRU, emphasizing the efficacy of integrating spatial and temporal modeling for chewing identification.

关键词： Performance evaluation Recurrent neural networks Measurement units Photoplethysmography Convolutional neural networks Wearable devices Long short term memory Wearable sensors Software engineering Resilience

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An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection

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Healthcare Analytics 2024年 5卷 100303-100303页

作者： Mehedi Shamrat, F M Javed Shakil, Rashiduzzaman Sharmin Hoque ovy, Nazmul Akter, Bonna Ahmed, Md Zunayed Ahmed, Kawsar Bui, Francis M. Moni, Mohammad Ali Department of Computer System and Technology University of Malaya Kuala Lumpur 50603 Malaysia Department of Computer Science and Engineering Daffodil International University Dhaka 1216 Bangladesh Department of Software Engineering Daffodil International University Dhaka 1216 Bangladesh Department of Computer Science and Engineering European University of Bangladesh Dhaka 1216 Bangladesh Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Biophotomatiχ Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia 4072 QLD Australia

Diabetic retinopathy (DR) involves retina damage due to diabetes, often leading to blindness. It is diagnosed via color fundus injections, but the manual analysis is cumbersome and error-prone. While computer vision techniques can predict DR stages, they are computationally intensive and struggle with complex data extraction. In this research, our prime objective was to automate the process of DR classification into its various stages using convolutional neural network (CNN) models. We employed the performance of fifteen pre-trained models with our novel proposed diabetic retinopathy network (DRNet13) model. We aimed to discern the most efficient model for accurate diabetic retinopathy (DR) staging based on fundus images from five DR classes. We preprocessed the image using a median filter for noise reduction and Gamma correction for image enhancement. We expanded our dataset from 3662 to 7500 images to create a more generalized training model through various augmentation techniques. We also evaluated multiple evaluation metrics, including accuracy, precision, F1-score, Sensitivity, Specificity, Area under the curve (AUC), Mean Squared Error (MSE), False Positive Rate (FPR), False Negative Rate (FNR), in addition to confusion matrices for an in-depth comparison of the performance of these models. Feature maps were employed to illuminate decision making areas in the DRNet13 model, which achieved a 97 % accuracy rate for DR detection, surpassing other CNN architectures in speed and efficiency. Despite a few misclassifications, the model's capability to identify critical features demonstrates its potential as an impactful diagnostic tool for timely and accurate identification of diabetic retinopathy. © 2024 The Authors

关键词： Classification Convolutional neural network Diabetic retinopathy network Diagnostic analytics Feature map

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Analytical Survey on the Security Framework of Cyber-Physical systems for Smart Power system Networks

Analytical Survey on the Security Framework of Cyber-Physica...

引用

Cyber Resilience (ICCR), International Conference on

作者： Ahmed Hadi Ali AL-Jumaili Ravie Chandren Muniyandi Mohammad Kamrul Hasan Mandeep Jit Singh Johnny Koh Siaw Paw Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Computer Centre Department University of Fallujah Anbar Iraq Department of Electrical Electronic and System Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Department of Electronic & Communication Engineering Universiti Tenaga Nasional Kajang Selango Malaysia

ISBN: (数字)9781665461221

ISBN: (纸本)9781665461238

Cyber-Physical Power system (CPPS) is one of the most critical infrastructure systems due to deep integration between power grids and communication networks. In the power system, cascading failure is spreading more readily in CPPS, even leading to blackouts as well as there are new difficulties with the power system security simulation and faults brought by physical harm or network intrusions. The current study summarized the cross- integration of several fields such as computer and cyberspace security in terms of the robustness of Cyber-Physical systems, viewed as Interconnected and secure network systems. Therefore, the security events that significantly influenced the power system were evaluated in this study, besides the challenges and future directions of power system security simulation technologies were investigated for posing both challenges and opportunities for simulation techniques of power system security like building a new power system to accelerate the transformation of the existing energy system to a clean, low-carbon, safe, and efficient energy system which is used to assure power system stability through fusion systems that combine the cyber-physical to integrate the battery power station, power generation and renewable energy resources through the internet with the cyber system that contains Smart energy system control and attacks.

关键词： Computational modeling Buildings Power system stability Network security Stability analysis Safety Security

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MetaFollower: Adaptable Personalized Autonomous Car Following

arXiv

引用

arXiv 2024年

作者： Chen, Xianda Chen, Kehua Zhu, Meixin Yang, Hao Shen, Shaojie Wang, Xuesong Wang, Yinhai Guangzhou511400 China Interdisciplinary Programs Office The Hong Kong University of Science and Technology Hong Kong Guangdong Provincial Key Lab of Integrated Communication Sensing and Computation for Ubiquitous Internet of Things Guangzhou511400 China Department of Civil and System Engineering Whiting School of Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong School of Transportation Engineering Tongji University Shanghai201804 China Department of Civil and Environmental Engineering University of Washington SeattleWA98195 United States

Car-following (CF) modeling, a fundamental component in microscopic traffic simulation, has attracted increasing interest of researchers in the past decades. In this study, we propose an adaptable personalized car-following framework - MetaFollower, by leveraging the power of meta-learning. Specifically, we first utilize Model-Agnostic Meta-Learning (MAML) to extract common driving knowledge from various CF events. Afterward, the pre-trained model can be fine-tuned on new drivers with only a few CF trajectories to achieve personalized CF adaptation. We additionally combine Long Short-Term Memory (LSTM) and Intelligent Driver Model (IDM) to reflect temporal heterogeneity with high interpretability. Unlike conventional adaptive cruise control (ACC) systems that rely on predefined settings and constant parameters without considering heterogeneous driving characteristics, MetaFollower can accurately capture and simulate the intricate dynamics of car-following behavior while considering the unique driving styles of individual drivers. We demonstrate the versatility and adaptability of MetaFollower by showcasing its ability to adapt to new drivers with limited training data quickly. To evaluate the performance of MetaFollower, we conduct rigorous experiments comparing it with both data-driven and physics-based models. The results reveal that our proposed framework outperforms baseline models in predicting car-following behavior with higher accuracy and safety. To the best of our knowledge, this is the first car-following model aiming to achieve fast adaptation by considering both driver and temporal heterogeneity based on meta-learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Adaptive cruise control

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