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Experimental Evaluation of Machine Learning Models for Gait Segmentation

Experimental Evaluation of Machine Learning Models for Gait ...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jacob A. Strick Jason J. Wiebrecht Ryan J. Farris Jerzy T. Sawicki Center for Rotating Machinery Dynamics and Control (RoMaDyC) Cleveland State University Cleveland USA Department of Engineering Messiah University Mechanicsburg USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Accurate estimation of the gait phase is extremely important in exoskeleton control, as various stages in the gait cycle require different control objectives. This study involved the evaluation of seven machine learning models utilizing inertial measurement unit and joint angle data from eight healthy subjects wearing the Ekso Indego exoskeleton for the purpose of gait segmentation. During the experiments, subjects walked on a level instrumented treadmill with gravity compensation assistance for three trials and underwent a fourth trial simulating impairment. A six-state model of gait was employed, where bilateral heel strike, toe off, and tibia vertical determined state transitions. True state transitions were determined using optical motion capture and ground reaction force data. Of the evaluated models, the Support Vector Machine achieved the highest performance, with an average accuracy of 94.5% and 94.1% for normal walking and impaired walking, respectively. Future research should focus on assessing the model’s real-time performance among exoskeleton users before considering its application as the basis for an exoskeleton controller.

关键词： Legged locomotion Support vector machines Accuracy measurement units Biological system modeling Exoskeletons Real-time systems Data models Motion capture Reliability

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From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation

From Handcrafted Features to LLMs: A Brief Survey for Machin...

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international joint conference on Neural Networks (IJCNN)

作者： Haofei Zhao Yilun Liu Shimin Tao Weibin Meng Yimeng Chen Xiang Geng Chang Su Min Zhang Hao Yang Shool of Computer Science and Engineering Northeastern University Shenyang China Huawei China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of machine-translated text in real time without the need for reference translations, which is of great importance for the development of MT. After two decades of evolution, QE has yielded a wealth of results. This article provides a comprehensive overview of QE datasets, annotation methods, shared tasks, methodologies, challenges, and future research directions. It begins with an introduction to the background and significance of QE, followed by an explanation of the concepts and evaluation metrics for word-level QE, sentence-level QE, document-level QE, and explainable QE. The paper categorizes the methods developed throughout the history of QE into those based on handcrafted features, deep learning, and Large Language Models (LLMs), with a further division of deep learning-based methods into classic deep learning and those incorporating pre-trained language models (LMs). Additionally, the article details the advantages and limitations of each method and offers a straightforward comparison of different approaches. Finally, the paper discusses the current challenges in QE research and provides an outlook on future research directions.

关键词： Deep learning Learning systems Surveys measurement Annotations Neural networks Estimation

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Speed Control of Six-Switch and Four-Switch BLDC Motor Using PID Controller

Speed Control of Six-Switch and Four-Switch BLDC Motor Using...

引用

international conference on Information Technology, computer and Electrical Engineering (ICITACEE)

作者： Andi Ahmad Badaruddin Faizal Arya Samman Muh Anshar Departement of Electrical Engineering Faculty of Engineering Universitas Hasanuddin Gowa Indonesia

ISBN: (数字)9798350389302

ISBN: (纸本)9798350389319

Brushless DC (BLDC) motors are becoming the first choice in industrial applications and electric vehicle development due to their high efficiency, long service life, and easy maintenance and control. The BLDC drive system of primary interest in this paper uses 6-switch and 4-switch inverters. In this paper, a comparative study is conducted between a three-phase six-switch inverter (SSTPI) and a three-phase four-switch inverter (FSTPI). The purpose of the study is to investigate the speed performance of both inverters in controlling BLDC motors using PID control techniques. Simulations were performed using Power Simulation (PSIM) software with the same motor parameters, control circuit topology and load. The PID (Proportional Integral Derivative) parameter values are given to both circuits for SSTPI with values of Kp=10, Ki=0.1 and Kd=0.1 while for FSTPI with values of Kp=4, Ki=0.1 and Kd=0.1 and simulation time of 0.1 seconds. The maximum performance results of both are obtained based on trial and error experiments as many as 31 iterations until the best velocity waveform is found, the best waveform is selected based on the Root Mean Square Error (RMSE) value. This comparative study provides options for selecting and designing two drive systems to improve system performance based on system requirements.

关键词： Torque System performance Velocity control Motors Inverters Software Torque measurement Integrated circuit modeling Load modeling Switching circuits

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Parameter Identification Method for Air Conditioning Load Models Based on Improved Harris Hawk Optimization Algorithm

Parameter Identification Method for Air Conditioning Load Mo...

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international conference on Information systems and computer Aided Education (ICISCAE)

作者： Kaihang Wu Yang Yu Henan University of Science and Technology Luoyang China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

The air conditioning load holds a significant proportion in demand-side resources, and the air conditioning load model serves as the foundation for air conditioning load regulation. However, it overly relies on the accuracy of model parameters. This paper addresses this issue by proposing a parameter identification method for the air conditioning load model based on the Improved Harris's Hawks Optimization (IHHO) algorithm to enhance parameter identification accuracy. The Harris's Hawks Optimization (HHO) algorithm simulates the cooperative predation behavior of Harris's hawks in nature and is effective in solving problems in parameter space but is prone to local optima. To tackle this, an Improved Harris's Hawks Optimization (IHHO) algorithm is introduced for parameter identification. Initially, a Latin Hypercube Sampling method is employed for population initialization to achieve a more uniform initial population distribution. Subsequently, algorithm performance is enhanced, and the issue of optimizing local optimal solutions is addressed through a nonlinear energy updating strategy and a Cauchy-Gaussian mutation perturbation strategy. Finally, the IHHO algorithm is combined with a second-order equivalent thermal parameter (ETP) model to minimize the error between simulated and measured temperatures of the second-order equivalent thermal parameter model as the optimization objective. Experimental results indicate that the fitness values of parameters obtained through IHHO optimization for the second-order ETP model are optimal in various experimental scenarios compared to those obtained through HHO and PSO optimizations. The simulated temperatures based on IHHO optimization are also closer to the actual temperatures. In conclusion, the proposed air conditioning load model parameter identification based on the IHHO algorithm exhibits improved accuracy, satisfying the requirements of load regulation.

关键词： Air conditioning Temperature measurement Parameter estimation Accuracy Atmospheric modeling Perturbation methods Sampling methods Regulation Optimization Load modeling

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HC-HackerRank: Identifying Key Hackers in Cybercrime Social Network Forums

HC-HackerRank: Identifying Key Hackers in Cybercrime Social ...

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international conference on Advanced Communication Technologies and Networking (CommNet)

作者： Abdoul Nasser Hassane Amadou Anas Motii Mohammed Jouhari College of Computing University Mohammed VI Polytechnic Ben Guerir Morocco LSIA Laboratory Moroccan School of Engineering Sciences (EMSI) Tanger Morocco

ISBN: (数字)9798350367027

ISBN: (纸本)9798350367034

Underground forums are a refuge for malicious activities, emphasizing anonymity while circumventing conventional online oversight. Within the murky depths of these forums, cybercriminals gather to exchange illicit knowledge, tools, and tactics, fueling a spectrum of cyber threats ranging from hacking techniques to the distribution of stolen data, malware, and zero-day exploits. Identifying the key instigators, particularly key hackers driving these activities, is essential but remains a significant challenge. In this paper, we propose Hybrid Centrality Hacker Rank (HC-HackerRank), an automated system designed to identify key hackers within underground social network forums. Our system analyzes user profiles, interactions, and activities to determine user influence. The process begins with a comprehensive evaluation of cybercrime subforums, extracting users’ topic preferences through topic modeling. This approach highlights each user’s expertise and interests, allowing us to remove those with no technical skills, and focus on presumed skilled cybercriminals. Additionally, we apply multiple centrality measures and an influencer score to rank each user. The framework was applied to the Hack-Forums dataset, followed by an in-depth manual analysis of the identified key hackers. The results of our approach demonstrate its effectiveness in identifying key hackers in underground forums.

关键词： Couplings computer hacking Social networking (online) Filtering Computational modeling Manuals Real-time systems Malware Distance measurement computer crime

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Improving the Performance of Job Backfilling Based on Job's Weight and Back Propagation Neural Network

Improving the Performance of Job Backfilling Based on Job's ...

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Computational Intelligence Approaches and Applications (ICCIAA), international conference on

作者： Hazem Alnajjar Nadia Al-Rousan Syed Sahal Nazli Alhady Syed Hassan Junita Mohamad-Saleh Department of Computer Science College of Information Technology University of Petra Amman Jordan Department of computer engineering College of Engineering AlBalqa Applied University Salt Jordan Department of Computer Engineering Faculty of Electrical Engineering and Information Systems German Jordanian University Amman Jordan Colleage of Engineering and Architecture School of Electrical & Electronic Engineering Engineering Branch Campus Universiti Sains Malaysia Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Nowadays, the grid computing environment faces many difficulties executing new jobs, especially jobs requiring large resource requirements and long execution times. This motivates researchers and scholars to find cheap and fast methods to improve the efficiency of grid environments. One of the cheap and fast methods is to implement job scheduling algorithms based on cheap and fast techniques. This paper proposes a new job ranking backfilling algorithm based on the job's weight and back propagation neural network. To define the weight of the job, first, the proposed model will use a clustering algorithm to cluster the job's dataset into groups, and then the groups will be ranked using an experimental ranking equation. A discrete event simulator is used to validate the proposed algorithm's capability and robustness. The average results revealed that the new algorithm outperforms previous algorithms. The improvement of the studied metrics is between 1.19 and 6.30, respectively. The results proved that the proposed model is efficient and can be used with low overhead in a real environment.

关键词： Backpropagation measurement Scheduling algorithms Computational modeling Neural networks Clustering algorithms Grid computing Mathematical models Robustness Faces

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Gait Cycle Analysis Algorithm Based on Depth Camera measurement

Gait Cycle Analysis Algorithm Based on Depth Camera Measurem...

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international conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Zhenfu Yu Shaoxiang Guo Xiaoyu Zhan Xinyuan Song Junyu Dong Department of Computer Science and Technology Ocean University of China Qingdao China

ISBN: (数字)9798331510633

ISBN: (纸本)9798331510640

Gait analysis is widely used in the evaluate in medical gait diagnosis, patient rehabilitation training, and scientific training guidance. Gait analysis system effectively reconstruct 3D human joints model to obtain related gait information through specialized calculations. In this paper, we present a Real-Time Gait Analysis Algorithm (RTGA) based on RGB-D images input, which replaces traditional methods that capturing human body motion trail from wearable inertial measurement units (IMU) equipment. Combining real-time gait data acquisition and a deep learning-based gait medical feature analysis algorithm, we propose a gait measurement and acquisition system based on reliable 3D human joints distribution. We first take joint sequence smooth algorithm to filter the noise data the original 3D motion curve. Then, we perform joints transformation and position correction for the proposed method, which eliminates the gait cycle detection error caused by random noise interference. Finally, we propose a well-designed evaluation system to identify gait cycle and calculate each medical index (such as step width, speed, frequency). We employ Azure-Kinect DK to acquire 3D human body semantic content as input and validate the effectiveness of our RTGA system in the wild.

关键词： Training Three-dimensional displays measurement units Noise Estimation Inertial navigation Cameras Real-time systems Medical diagnostic imaging Gait recognition

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SciTrust: Evaluating the Trustworthiness of Large Language Models for Science

SciTrust: Evaluating the Trustworthiness of Large Language M...

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High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the international conference for

作者： Emily Herron Junqi Yin Feiyi Wang Oak Ridge National Laboratory Oak Ridge Tennessee

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

This work presents SciTrust, a comprehensive framework for assessing the trustworthiness of large language models (LLMs) in scientific contexts, with a focus on truthfulness, accuracy, hallucination, and sycophancy. The framework introduces four novel open-ended benchmarks in computer Science, Chemistry, Biology, and Physics, and employs a multi-faceted evaluation approach combining traditional metrics with LLMbased evaluation. SciTrust was applied to five LLMs, including one general-purpose and four scientific models, revealing nuanced strengths and weaknesses across different models and benchmarks. The study also evaluated SciTrust’s performance and scalability on high-performance computing systems. Results showed varying performance across models, with Llama3-70B-Instruct performing strongly overall, while Galactica-120B and SciGLM-6B excelled among scientific models. SciTrust aims to advance the development of trustworthy AI in scientific applications and establish a foundation for future research on model robustness, safety, and ethics in scientific contexts. We have open-sourced our framework, including all associated scripts and datasets, at https://***/herronej/SciTrust.

关键词： measurement Computational modeling Large language models High performance computing Biological system modeling Scalability Benchmark testing Robustness Safety Physics

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Impact of energy leakage on the energy performance of green IoT nodes

Impact of energy leakage on the energy performance of green ...

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international Symposium on modeling, Analysis and Simulation of computer and Telecommunication systems (MASCOTS)

作者： Godlove Suila Kuaban Tadeusz Czachórski Erol Gelenbe Piotr Pecka Valery Nkemeni Piotr Czekalski Polish Academy of Sciences (IITiS PAN) Institute of Theoretical and Applied Informatics Gliwice Poland Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology University of Buea Cameroon Faculty of Automatic Control Electronics and Informatics Silesian University of Technology Gliwice Poland

ISBN: (数字)9798331531300

ISBN: (纸本)9798331531317

In the present paper, we investigate the impact of imperfections (non-idealities) of the energy storage system (e.g., batteries, capacitors, or supercapacitors) on the energy performance of green IoT nodes. In particular, we investigate the impact of energy leakage from the Energy Storage System (ESS) on important energy performance metrics, such as service outage probability, the density of the lifetime of the node, and the time-dependent mean number of energy packets (EPs) in the ESS. Also, we explore various strategies that can be employed to compensate for the impact of energy losses due to energy leakage on the energy performance metrics. Specifically, we examine two potential strategies for improving the energy performance of the IoT nodes: (i) through increasing the energy generation rate of the energy harvesters (e.g., by adding additional solar panels or replacing the existing solar panels with more efficient ones that can produce more energy), and (ii) through reducing the energy consumption rate of the IoT node (e.g., by configuring ESS energy thresholds below which the nodes are forced to operate in low energy consumption states).

关键词： measurement Energy consumption Green products Production Probability Energy efficiency Sensors Power system reliability Solar panels Internet of Things

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ICEL: Learning with Inconsistent Explanations

ICEL: Learning with Inconsistent Explanations

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Biao Liu Xiaoyu Wu Bo Yuan Department of Computer Science & Engineering Southern University of Science and Technology RAMS Lab Huawei

Generating the heatmaps is one of the explanation methods to show what regions the model use to predict in vision tasks. GradCAM is a popular approach to provide such heatmaps. However, GradCAM is post-hoc, and its heatmaps are not always meeting human annotations. Inspired by CGC (Contrastive GradCAM consistency), we propose ICEL (InConsistent Explanation Learning) method which introduces inconsistent explanation loss measured by cosine similarity on heatmaps. We show that our method can preserve classification accuracy, while the heatmaps generated by explanation methods are more consistent with human annotations, and the computational complexity is reduced from O(n 2 ) to O(n), compared with CGC.

关键词： Heating systems Learning systems Annotations Computational modeling Signal processing Predictive models Loss measurement

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