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Technologies for Risk Mitigation and Support of Impaired Drivers

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第5期23卷 4736-4738页

作者： Ahlstrom, Christer Diederichs, Frederik Teichmann, Daniel Swedish Natl Rd & Transport Res Inst S-58195 Linkoping Sweden Linkooping Univ Dept Biomed Engn S-58183 Linkoping Sweden Fraunhofer Inst Optron Syst Technol & Image Exploitat D-76131 Karlsruhe Germany Univ Southern Denmark SDU Hlth Informat & Technol DK-5230 Odense Denmark MIT Inst Med Engn & Sci Cambridge MA 02139 USA

This editorial serves as an extended introduction to the Special Issue on Technologies for Risk Mitigation and Support of Impaired Drivers. It gives the context to recent advances in assisted and automated driving and the new challenges that arise when modern technology meets human users. The Special Issue focuses on the development of robust sensors and detection algorithms for driver state monitoring of fatigue, stress, and inattention, and on the development of personalized multimodal, user-oriented, and adaptive information, warning, actuation, and handover strategies. A summary of more recent developments serves as a motivation for each article that follows.

关键词： Special issues and sections Risk management Biomedical monitoring Human factors Human computer interaction Automation detection algorithms Prediction algorithms

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Development of Automatic Wheeze detection Algorithm for Children With Asthma

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IEEE ACCESS 2021年 9卷 126882-126890页

作者： Kuo, Ho-Chang Lin, Bor-Shing Wang, Yan-Di Lin, Bor-Shyh Kaohsiung Chang Gung Mem Hosp Dept Pediat Kaohsiung 83301 Taiwan Kaohsiung Chang Gung Mem Hosp Kawasaki Dis Ctr Kaohsiung 83301 Taiwan Chang Gung Univ Coll Med Taoyuan 33302 Taiwan Natl Taipei Univ Dept Comp Sci & Informat Engn New Taipei 237303 Taiwan Natl Yang Ming Chiao Tung Univ Inst Imaging & Biomed Photon Tainan 71150 Taiwan Chi Mei Med Ctr Dept Med Res Tainan 71004 Taiwan

Asthma is a symptom of tracheal obstruction caused by bronchospasm, and it is among the most prevalent chronic obstructive pulmonary diseases. Auscultation is the most commonly used approach for the clinical diagnosis of asthma. However, recognizing wheezes through auscultation requires experienced physicians, and this approach is not sufficiently objective. Therefore, developing a method for recognizing wheezes objectively is crucial. Most studies have used the spectral features of lung sounds to detect wheezes;however, they have not achieved sufficiently high performance owing to the poor discrimination of spectral features. Several studies have attempted to extract wheezing features from lung sound spectrograms;however, their approaches were easily affected by variations in the wheezing frequency and background noise. The present study proposes a novel automatic wheeze detection algorithm for extracting lung sound features in the time-frequency domain and automatically detecting wheezes. The proposed algorithm applies canonical correlation analysis to successfully detect wheezing features in a lung sound spectrogram. Moreover, a neural network technique is used to effectively classify healthy and wheezing sounds. The experimental results indicated that the proposed algorithm showed excellent performance in detecting wheezing.

关键词： Lung Feature extraction Spectrogram Correlation detection algorithms Time-frequency analysis Neural networks Asthma wheezes canonical correlation analysis neural network

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Data-Driven False Data Injection Attack: A Low-Rank Approach

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IEEE TRANSACTIONS ON SMART GRID 2022年第3期13卷 2479-2482页

作者： Mukherjee, Debottam Indian Inst Technol BHU Varanasi Dept Elect Engn Varanasi 221005 Uttar Pradesh India

False data injection attack on the state estimation algorithms have already showcased its detrimental effects for modern grid operation. This letter promotes a novel attack vector formulation policy for the linear state estimation algorithm by exploring the low-rank subspace of the mapping matrix. An extensive analysis on the IEEE 14 bus test bench promotes the aforementioned propositions.

关键词： Sparse matrices Matrix decomposition State estimation Approximation algorithms Detectors Singular value decomposition detection algorithms Cyber security false data injection attack smart grid state estimation

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Exactly optimal Bayesian quickest change detection for hidden Markov models

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AUTOMATICA 2023年第1期157卷

作者： Ford, Jason J. James, Jasmin Molloy, Timothy L. Queensland Univ Technol QUT Sch Elect Engn & Robot Brisbane Qld 4000 Australia Univ Queensland UQ Sch Mech & Min Engn Brisbane Qld 4072 Australia Australian Natl Univ Sch Engn Acton ACT 2601 Australia

This paper considers the quickest detection problem for hidden Markov models (HMMs) in a Bayesian setting. We construct an augmented HMM representation of the problem that allows the application of a dynamic programming approach to prove that Shiryaev's rule is an (exact) optimal solution. This augmented representation highlights the problem's fundamental information structure and suggests possible relaxations to more exotic change event priors not appearing in the literature. Finally, this augmented representation allows us to present an efficient computational method for implementing the optimal solution.& COPY;2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Bayesian quickest change detection detection algorithms Markov models Shiryaev test

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Load Voltage Control Operation Method for Hybrid Distribution Transformers Based on Improved Voltage detection Algorithm

Load Voltage Control Operation Method for Hybrid Distributio...

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Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Zixia Sang Xu Zheng Jiaqi Huang Jiong Yan Junyao Liu Qixin Wang State Grid HBEPC Economic & Technology Research Institute Wuhan China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

The changes in the distribution network load will affect the distribution network voltage, so the hybrid distribution transformer (HDT) needs to start or stop the load voltage compensation equipment according to the specific load situation. Due to the disadvantage of commonly used voltage rise and fall detection algorithms, this paper proposes a novel dq0 transformation detection algorithm, which takes the derivative of each phase voltage to construct a virtual three-phase voltage, and then performs dq0 transformation on three sets of virtual three-phase voltages. Finally, through simple logical judgment, the detection of single-phase power supply voltage drops and other conditions can be achieved. The advantage of this algorithm is that it only requires the addition of a filter with a smaller time constant to filter out higher-order harmonics, effectively avoiding the delay caused by the larger filter time constant in traditional detection algorithms, thereby improving the real-time and accuracy of detection. The simulation verification of the improved detection algorithm is implemented with the Saber simulation platform about the start and stop of the load voltage compensation device based on the detection signal.

关键词： Low-pass filters Distribution networks Filtering algorithms Transformers Real-time systems Delays Time factors detection algorithms Voltage control Power harmonic filters

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Hierarchical Community detection and Network Analysis: A 2-Level Approach for Social Media Networks

Hierarchical Community Detection and Network Analysis: A 2-L...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Vishakha Shelke Priyan Parmar Darshit Adhiya Department of Computer Science and Engineering (ICB) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

As social networking site users and their relationships generate enormous amounts of data, these networks become complicated, requiring sophisticated research approaches to extract valuable information. This research presents an innovative 2-level hierarchical method for community discovery. Greedy Modularity and Label Propagation community detection algorithms applied in the first level to find communities. Next, node similarity is assessed using similarity metrics such Jaccard, Cosine, Katz, and Adamic-Adar. These nodes are grouped utilizing K-Nearest Neighbors (KNN) using common neighbors and vicinity to centroids once initial communities are identified, which refines the neighborhood framework to form a 2-tiered hierarchy. NetworkX visualizations for link prediction techniques used to highlight additional network topology insights. The proposed method improves on conventional community detection by identifying intricate hierarchical patterns, providing a more thorough comprehension of the dynamics of social media networks. The effectiveness of the presented hierarchical community detection methodology is assessed using quantitative indicators, such as F1 score, precision, and recall. The outcomes show the reliability of our approach in accurate community detection.

关键词： Social networking (online) Scalability Network analyzers Nearest neighbor methods Network architecture Gain measurement Real-time systems Reliability detection algorithms Informatics

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Single and Multi-Spectral Methods for Pedestrian detection: A Comprehensive Review

Single and Multi-Spectral Methods for Pedestrian Detection: ...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Jagrati Gupta Shobha Sharma Electronics and Communication Engineering IGDTUW Delhi

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Real-world object identification applications and theoretical research, for both of them, pedestrian detection is still a crucial task. In order to combine classifiers and characterize pedestrian characteristics, traditional pedestrian detection algorithms need specialists to design features. Convolutional neural networks (CNNs) and their evolved forms have become a key component of deep learning. This review presents detailed spectral-based pedestrian detection methods. The review reveals that single spectral deep feature based pedestrian identification system performs better than channel feature based approaches, but for dark environments, pedestrian detection multi-spectral methods and datasets need to be focused. In addition, key accessible data sets and evaluation parameters have been addressed with respect to the pedestrian detection problem. Future developments in pedestrian detection could focus on enhancing the precision and speed of detection under all environmental conditions.

关键词： Deep learning Pedestrians Reviews Feature extraction Object recognition Convolutional neural networks detection algorithms Collaborative intelligence

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Dim and Small Target detection Based on Spatio-Temporal Filtering and High-Order Energy Estimation

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2023年第2期15卷

作者： Xiangsuo, Fan Wenlin, Qin Juliu, Li Qingnan, Huang Fan, Zhang Guangxi Univ Sci & Technol Sch Automat Liuzhou 545006 Peoples R China Guangxi Univ Sci & Technol Guangxi Collaborat Innovat Ctr Earthmoving Machine Liuzhou 545006 Peoples R China Guangxi Univ Sci & Technol Sch Elect Engn Liuzhou Peoples R China

To improve the detection ability of infrared dim and small targets in complex cloud backgrounds, we present a novel method based on motion energy estimation. First, according to the characteristic of the gradient between the target and the background, we propose a new diffusion function and combine with the gradient difference to complete the background suppression. Then an improved high-order correlation algorithm is proposed to enhance the target signal through the motion correlation properties of the target, which build the gradient difference discrimination model between the target and four adjacent directions to establish the region of interest model, then realizes the high-order energy enhancement of the region of interest and further eliminate the clutters at the same time. Finally, the motion model of the target in the inter-frame spatial-temporal domain is constructed to estimate the motion direction of the target, then realizes the target energy accumulation along the motion direction and obtains the maximum energy of the target to detect the real target. In the experiment, we select six scenes for testing, the experimental results show that the proposed method has an excellent detection performance compared with six traditional detection algorithms in infrared dim and small target detection.

关键词： Filtering Clutter Object detection Correlation Prediction algorithms Image edge detection detection algorithms Background estimation dim and small target high-order energy correlation kernel diffusion filtering motion estimation

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Ratio-and-Scale-Aware YOLO for Pedestrian detection

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2021年 30卷 934-947页

作者： Hsu, Wei-Yen Lin, Wen-Yen Natl Chung Cheng Univ Dept Informat Management Chiayi 62102 Taiwan Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat Chiayi 62102 Taiwan Natl Chung Cheng Univ Ctr Innovat Res Aging Soc CIRAS Chiayi 62102 Taiwan

Current deep learning methods seldom consider the effects of small pedestrian ratios and considerable differences in the aspect ratio of input images, which results in low pedestrian detection performance. This study proposes the ratio-and-scale-aware YOLO (RSA-YOLO) method to solve the aforementioned problems. The following procedure is adopted in this method. First, ratio-aware mechanisms are introduced to dynamically adjust the input layer length and width hyperparameters of YOLOv3, thereby solving the problem of considerable differences in the aspect ratio. Second, intelligent splits are used to automatically and appropriately divide the original images into two local images. Ratio-aware YOLO (RA-YOLO) is iteratively performed on the two local images. Because the original and local images produce low- and high-resolution pedestrian detection information after RA-YOLO, respectively, this study proposes new scale-aware mechanisms in which multiresolution fusion is used to solve the problem of misdetection of remarkably small pedestrians in images. The experimental results indicate that the proposed method produces favorable results for images with extremely small objects and those with considerable differences in the aspect ratio. Compared with the original YOLOs (i.e., YOLOv2 and YOLOv3) and several state-of-the-art approaches, the proposed method demonstrated a superior performance for the VOC 2012 comp4, INRIA, and ETH databases in terms of the average precision, intersection over union, and lowest log-average miss rate.

关键词： Image resolution Deep learning Proposals Object detection Feature extraction detection algorithms Classification algorithms Multiresolution fusion pedestrian detection ratio-aware scale-aware

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Misclassifications of Contact Lens Iris PAD algorithms: Is it Gender Bias or Environmental Conditions? 23

Misclassifications of Contact Lens Iris PAD Algorithms: Is i...

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23rd IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Agarwal, Akshay Ratha, Nalini Noore, Afzel Singh, Richa Vatsa, Mayank IISER Bhopal Bhopal India SUNY Buffalo Buffalo NY USA Texas A&M Univ Kingsville TX USA IIT Jodhpur Jodhpur Rajasthan India

ISBN: (纸本)9781665493468

One of the critical steps in biometrics pipeline is detection of presentation attacks, a physical adversary. Several presentation (adversary) attack detection (PAD) algorithms, including iris PAD, have been proposed and have shown superlative performance. However, a recent study, on a small-scale database, has highlighted that iris PAD may have gender biases. In this research, we present a rigorous study on gender bias in iris presentation attack detection algorithms using a large-scale and gender-balanced database. The paper provides several interesting observations which can help in building future presentation attack detection algorithms with aim of fair treatment of each demography. In addition, we also present a robust iris presentation attack detection algorithm by combining gender-covariate based classifiers. The proposed robust classifier not only reduces the difference in accuracy between different genders but also improves the overall performance of the PAD system.

关键词： Computer vision Databases Demography Pipelines Buildings Classification algorithms detection algorithms

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