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Human Activity Recognition for People with Knee Abnormality Using Surface Electromyography and Knee Angle Sensors

Human Activity Recognition for People with Knee Abnormality ...

International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Ponnipa Jantawong Narit Hnoohom Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Computer Engineering Image Information and Intelligence Laboratory Faculty of Engineering Mahidol University Nakorn Pathom Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

With the onset of technological advancements, biosensors are being effectively used in a variety of contexts, including the diagnosis of diseases, the promotion of their prevention and rehabilitation, the monitoring of patient health, and human activity recognition (HAR). Recently, HAR research results have been obtained and applied in many commercial applications such as fall detection using smart watch sensors, activity classification using smart home sensors. However, previous HAR models utilizing wearable sensors primarily utilized data from healthy individuals, and such models are frequently inaccurate when applied to individuals with medical mobility impairments. In this work, surface electromyography and goniometer sensors were used to categorize various types of rehabilitation activities based on HAR models developed for individuals with knee abnormalities. To achieve the goal of our study, a deep residual network called ResNeXt was proposed to detect human activities with high performance. To investigate the classification performance of deep learning models, a 5-fold cross-validation technique was applied for both training and testing. Based on the results of our study, the combination of features extracted from the goniometer and electromyograph signals resulted in the highest F1-score (93.31%) and the best accuracy (93.52%).

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Vision-Based Traffic Accident Detection and Anticipation: A Survey

arXiv

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arXiv 2023年

作者： Fang, Jianwu Qiao, Jiahuan Xue, Jianru Li, Zhengguo College of Transportation Engineering Chang’an University Xi’an China Applied Mathematics and Artificial Intelligence Institute of Information Technologies and Computer Science National Research University Moscow Power Engineering Institute Russia Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Singapore

Traffic accident detection and anticipation is an obstinate road safety problem and painstaking efforts have been devoted. With the rapid growth of video data, Vision-based Traffic Accident Detection and Anticipation (named Vision-TAD and Vision-TAA) become the last one-mile problem for safe driving and surveillance safety. However, the long-tailed, unbalanced, highly dynamic, complex, and uncertain properties of traffic accidents form the Out-of-Distribution (OOD) feature for Vision-TAD and Vision-TAA. Current AI development may focus on these OOD but important problems. What has been done for Vision-TAD and Vision-TAA? What direction we should focus on in the future for this problem? A comprehensive survey is important. We present the first survey on Vision-TAD in the deep learning era and the first-ever survey for Vision-TAA. The pros and cons of each research prototype are discussed in detail during the investigation. In addition, we also provide a critical review of 31 publicly available benchmarks and related evaluation metrics. Through this survey, we want to spawn new insights and open possible trends for Vision-TAD and Vision-TAA tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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A Coupled CS-DSG3/BEM Method for Hydroelastic Analysis of Pontoon-Type VLFS with Arbitrary Plan Shape 3rd

A Coupled CS-DSG3/BEM Method for Hydroelastic Analysis of Po...

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3rd International Conference on Sustainable Civil engineering and Architecture, ICSCEA 2023

作者： Luong, Van Hai Nguyen, H.P. Nguyen-Thoi, T. 268 Ly Thuong Kiet Street District 10 Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Linh Trung Ward Thu Duc District Ho Chi Minh City Viet Nam School of Civil Engineering The University of Queensland St LuciaQLD4072 Australia Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical-Electrical and Computer Engineering Van Lang University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819974337

The cell-based smoothed discrete shear gap method (CS-DSG3) was introduced for analysing Mindlin plates. This method employs three-node triangular elements and can produce solutions with high accuracy. This research aims to combine CS-DSG3 with the Boundary Element Method (BEM) to perform hydroelastic analyses on pontoon-type Very Large Floating Structures (VLFS) with varying plan shapes. In this study, the VLFS is conceptualized as a Mindlin plate resting on an ideal fluid. The structural and fluid domains are discretized using three-node triangular elements. The structural equation is formulated using CS-DSG3, while the water equation is established using BEM. The hydroelastic analysis is conducted within the frequency domain. To validate the accuracy of the CS-DSG3/BEM approach, its results are compared with available published solutions. The proposed method is then employed in parametric investigations involving various VLFS plan shapes to explore how the shape influences hydroelastic responses. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Boundary element method

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Two Stages of Algebraic Matrix Decomposition in Optimal Image Steganographic Approach

Two Stages of Algebraic Matrix Decomposition in Optimal Imag...

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International Conference on engineering Technology and their Applications (IICETA)

作者： bJumana Waleed Ihab Mahdi Almaameri Areej M. Abduldaim Waleed Al-Azzawi Department of Computer Science College of Science University of Diyala Iraq Department of Computer Technical Engineering College of Technical Engineering The Islamic University Najaf Iraq Applied Sciences Department Mathematics and Computer Applications University of Technology Baghdad Iraq Medical Technical College Al-Farahidi University Baghdad Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

With the evolution of social networks, the necessitate of providing information security methods becomes increasingly significant. Therefore, these methods should be studied and analyzed for presenting more secure and appropriate techniques. Since the images represent the most common and widely utilized visual formats, an optimized approach of image steganography based on algebraic matrix decomposition has been proposed in this paper. In particular, matrix decomposition methods represent a significant tool for increasing the security of the whole blent of digital steganographic approaches. In this paper, two stages of the Hessenberg matrix decomposition are implemented on the original image using modulo operation to show the impact of the matrix decomposition in the steganographic process. Additionally, a Chameleon Swarm optimization (CSO) algorithm is utilized to appoint the optimal positions in the embedding stage. The obtained results have been evaluated to show the difference between the utilization of decomposition or not.

关键词： Wavelet transforms Resistance Steganography Visualization Social networking (online) Information security Robustness

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A system capable of verifiably and privately screening global DNA synthesis

arXiv

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arXiv 2024年

作者： Baum, Carsten Berlips, Jens Chen, Walther Cui, Hongrui Damgard, Ivan Dong, Jiangbin Esvelt, Kevin M. Foner, Leonard Gao, Mingyu Gretton, Dana Kysel, Martin Li, Juanru Li, Xiang Paneth, Omer Rivest, Ronald L. Sage-Ling, Francesca Shamir, Adi Shen, Yue Sun, Meicen Vaikuntanathan, Vinod Van Hauwe, Lynn Vogel, Theia Weinstein-Raun, Benjamin Wang, Yun Wichs, Daniel Wooster, Stephen Yao, Andrew C. Yu, Yu Zhang, Haoling Zhang, Kaiyi Department of Computer Science Aarhus University Denmark DTU Compute Technical University of Denmark Denmark SecureDNA Foundation Switzerland Department of Computer Science and Engineering Shanghai Jiao Tong University China Institute for Interdisciplinary Information Sciences Tsinghua University China Media Lab Massachusetts Institute of Technology United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Department of Applied Mathematics Weizmann Institute of Science Israel Department of Computer Science Northeastern University United States China National GeneBank China Department of Political Science Massachusetts Institute of Technology United States Shanghai Qi Zhi Institute China

Printing custom DNA sequences is essential to scientific and biomedical research, but the technology can be used to manufacture plagues as well as cures. Just as ink printers recognize and reject attempts to counterfeit money, DNA synthesizers and assemblers should deny unauthorized requests to make viral DNA that could be used to ignite a pandemic. There are three complications. First, we don't need to quickly update printers to deal with newly discovered currencies, whereas we regularly learn of new viruses and other biological threats. Second, anti-counterfeiting specifications on a local printer can't be extracted and misused by malicious actors, unlike information on biological threats. Finally, any screening must keep the inspected DNA sequences private, as they may constitute valuable trade secrets. Here we describe SecureDNA, a free, privacy-preserving, and fully automated system capable of verifiably screening all DNA synthesis orders of 30+ base pairs against an up-to-date database of hazards, and its operational performance and specificity when applied to 67 million base pairs of DNA synthesized by providers in the United States, Europe, and China. Copyright © 2024, The Authors. All rights reserved.

关键词： DNA sequences

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PARTITIONS AND ELEMENTARY SYMMETRIC POLYNOMIALS - AN EXPERIMENTAL APPROACH

arXiv

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arXiv 2024年

作者： Ballantine, Cristina Beck, George Merca, Mircea Department of Mathematics and Computer Science College of the Holy Cross WorcesterMA01610 United States Department of Mathematics and Statistics Dalhousie University HalifaxNSB3H 4R2 Canada Department of Mathematical Methods and Models Fundamental Sciences Applied in Engineering Research Center University Politehnica of Bucharest BucharestRO-060042 Romania

Given a partition λ, we write ej(λ) for the jth elementary symmetric polynomial ej evaluated at the parts of λ and ejpA(n) for the sum of ej(λ) as λ ranges over the set of partitions of n with parts in A. For ejpA(n), we prove analogs of the classical formula for the partition function, (formula presented), where σ1 is the sum of divisors function. We prove several congruences for e2p4(n), the sum of e2 over the set of partitions of n into four parts. Define the function prej(λ) to be the multiset of monomials in ej(λ), which is itself a partition. If A is a set of partitions, we define prej(A) to be the set of partitions prej(λ) as λ ranges over A. If P(n) is the set of all partitions of n, we conjecture that the number of odd partitions in pre2(P(n)) is at least the number of distinct partitions. We prove some results about pre2(B(n)), where B(n) is the set of binary partitions of n. We conclude with conjectures on the log-concavity of functions related to ejp(n), the sum of ej(λ) for all λ ∈ P(n).MSC Codes 05E05, 11P81 © 2024, CC BY.

关键词： Polynomials

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PCA: Semi-Supervised Segmentation with Patch Confidence Adversarial Training

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IEEE Transactions on Network science and engineering 2025年第4期12卷 2473-2486页

作者： Xu, Zhenghua Yang, Runhe Xu, Zihang Zhang, Shuo Yang, Yuchen Liu, Weipeng Xu, Weichao Chen, Junyang Lukasiewicz, Thomas Leung, Victor C. M. Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Tianjin China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The Johns Hopkins University Department of Applied Mathematics and Statistic Baltimore United States Hebei University of Technology School of Artificial Intelligence Tianjin China Hebei Provincial Hospital of Traditional Chinese Medicine Digestive Disease Center Shijiazhuang China Shenzhen University College of Computer Science and Software Engineering Shenzhen China University of Oxford Department of Computer Science University of Oxford United Kingdom Department of Computer Science United Kingdom Vienna University of Technology institute of Logic and Computation nstitute of Logic and Computation Vienna Austria

Deep-learning-based semi-supervised learning (SSL) methods have achieved a strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preferences. In this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. The PCA method's discriminator penalizes patch-level structures, guiding the generator to optimize different patch areas, by leveraging pixel context, the generator is driven to focus on high-frequency features, making it harder to deceive the discriminator and easy to converge to an ideal state, which more effectively guides the segmenter to generate high-quality pseudo-labels. Furthermore, at the discriminator's input, we supplement image information constraints, making it simpler to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation. © 2025 IEEE. All rights reserved.

关键词： Adversarial machine learning

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Fault detection in closed-loop systems using a double residual generator

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IFAC-PapersOnLine 2022年第6期55卷 298-303页

作者： Henrik Niemann Niels Kjølstad Poulsen Dept. of Electrical Engineering Technical University of Denmark DK-2800 Kgs. Lyngby Denmark Dept. of Applied Mathematics and Computer Science Technical University of Denmark DK-2800 Kgs. Lyngby Denmark

A double residual generator for fault detection in closed-loop systems is presented in this paper. The residual generator gives the standard residual vector and a dual residual vector as outputs. The standard residual vector can be applied directly for fault detection. The dual residual vector depends on known inputs to the system e.g. reference inputs and auxiliary inputs but decoupled from the disturbance in the system. It is also an input vector that can be applied for active fault detection (AFD). The dual residual vector can be considered as a generalized input vector in connection with AFD in closed-loop systems. This gives the possibility to use all known inputs for detection. The dual residual vector gives a link between passive and AFD in closed-loop systems. The double residual generator is given directly as the left Bezout matrix from the Bezout equation.

关键词： Fault detection residual generators parametric faults closed-loop systems coprime factorization

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Bayesian Parameter Inference for Partially Observed Diffusions using Multilevel Stochastic Runge-Kutta Methods

arXiv

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arXiv 2023年

作者： Moral, Pierre Del Hu, Shulan Jasra, Ajay Ruzayqat, Hamza Wang, Xinyu Institut de Mathematiques de Bordeaux FR Bordeaux33405 France School of Mathematics and Statistics Wenlan School of Business China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Zhongnan University of Economics and Law CN China

We consider the problem of Bayesian estimation of static parameters associated to a partially and discretely observed diffusion process. We assume that the exact transition dynamics of the diffusion process are unavailable, even up-to an unbiased estimator and that one must time-discretize the diffusion process. In such scenarios it has been shown how one can introduce the multilevel Monte Carlo method to reduce the cost to compute posterior expected values of the parameters for a pre-specified mean square error (MSE);see [12]. These afore-mentioned methods rely on upon the Euler-Maruyama discretization scheme which is well-known in numerical analysis to have slow convergence properties. We adapt stochastic Runge-Kutta (SRK) methods for Bayesian parameter estimation of static parameters for diffusions. This can be implemented in high-dimensions of the diffusion and seemingly under-appreciated in the uncertainty quantification and statistics fields. For a class of diffusions and SRK methods, we consider the estimation of the posterior expectation of the parameters. We prove that to achieve a MSE of O(ϵ2), for ϵ > 0 given, the associated work is O(ϵ−2). Whilst the latter is achievable for the Milstein scheme, this method is often not applicable for diffusions in dimension larger than two. We also illustrate our methodology in several numerical examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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Scalable method for Bayesian experimental design without integrating over posterior distribution

arXiv

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arXiv 2023年

作者： Hoang, Vinh Espath, Luis Krumscheid, Sebastian Tempone, Raúl Mathematics for Uncertainty Quantification RWTH Aachen University Germany Faculty of Science University of Nottingham United Kingdom Steinbuch Center for Computing Institute for Applied and Numerical Mathematics Karlsruhe Institute of Technology Karlsruhe76131 Germany Computer Electrical and Mathematical Sciences and Engineering KAUST Alexander von Humboldt professor in Mathematics of Uncertainty Quantification RWTH Aachen University Germany

We address the computational efficiency in solving the A-optimal Bayesian design of experiments problems for which the observational map is based on partial differential equations and, consequently, is computationally expensive to evaluate. A-optimality is a widely used and easy-to-interpret criterion for Bayesian experimental design. This criterion seeks the optimal experimental design by minimizing the expected conditional variance, which is also known as the expected posterior variance. This study presents a novel likelihood-free approach to the A-optimal experimental design that does not require sampling or integrating the Bayesian posterior distribution. Our proposed approach is developed based on two properties of the conditional expectation: the law of total variance and the property of orthogonal projection. The expected conditional variance is obtained via the variance of the conditional expectation using the law of total variance, and we take advantage of the orthogonal projection property to approximate the conditional expectation. We derive an asymptotic error estimation for the proposed estimator of the expected conditional variance and show that the intractability of the posterior distribution does not affect the performance of our approach. We use an artificial neural network (ANN) to approximate the nonlinear conditional expectation in the implementation of our method. We then extend our approach for dealing with the case that the domain of experimental design parameters is continuous by integrating the training process of the ANN into minimizing the expected conditional variance. Specifically, we propose a nonlocal approximation of the conditional expectation and apply transfer learning to reduce the number of evaluations of the observation model. Through numerical experiments, we demonstrate that our method greatly reduces the number of observation model evaluations compared with widely used importance sampling-based approaches. This reduction is c

关键词： Design of experiments

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