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共 7159 条记录，以下是371-380 订阅

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Ballbot Simulation System: Modeling, Verification, and Gym Environment Development 11

Ballbot Simulation System: Modeling, Verification, and Gym E...

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11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Nashat, Abdelrahman Morsi, Abdelrahman Hassan, Mohamed M. M. Abdelrahman, Mustafa Assuit University Electrical Engineering Department Assiut Egypt University of Hull School of Engineering Hull United Kingdom

ISBN: (纸本)9798350322101

this paper introduces the development of a sophisticated simulation model for the ball-balancing robot (ballbot), implemented as a Gymnasium (Gym) environment in Python. the main purpose of this environment is to facilitate the application of Reinforcement learning (RL) techniques to effectively control the ballbot system. Initially, a standard ballbot model is created using Solidworks, and a Unified Robot Description Format (URDF) file is generated to precisely capture the dynamics of the ballbot. To validate the model, simulations are performed within MATLAB/Simulink, comparing the ballbot URDF model against its nonlinear mathematical model with a simple PID controller. the simulation results demonstrate that the URDF model accurately represents the ballbot dynamics, exhibiting a comparable response to the mathematical model. Subsequently, the high-fidelity URDF model is integrated into the Gym environment using the Pybullet simulator. the ongoing objective of this research is to utilize the developed ballbot environment for RL-based control design. © 2023 IEEE.

关键词： Reinforcement learning

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Design and Implementation of Successful Envelope intelligent Analysis System for Spacecraft 11th

Design and Implementation of Successful Envelope Intelligent...

引用

11th international conference on Signal and Information Processing, Network and Computers, ICSINC 2023

作者： Li, Peng Diao, Weihe Beijing Institute of Spacecraft System Engineering Beijing100094 China

ISBN: (纸本)9789819721153

the architecture design of successful envelope intelligent analysis system for spacecraft includes management of successful envelope analysis object, construction and management of successful envelope, intelligent data acquisition, successful envelope analysis, and data integration. the successful envelope intelligent analysis system compares and analyzes the envelope range of the spacecraft data to be analyzed and the data of the successful flight spacecraft, obtains the data envelope status of the spacecraft to be analyzed, and evaluates the health status of spacecraft. the system architecture has been successful applied in launch sites testing and flight control of manned spacecraft, which realizes standardized description of envelope analysis objects, digitization envelope construction and analysis processes, and intelligence of envelope data acquisition and result analysis. the successful envelope intelligent analysis method and system architecture design of spacecraft have universality and expansibility in practice, which can guide other batch production to carry out successful envelope analysis. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data acquisition

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Innovative Pulse Monitoring: Exploring Auscultation-Based Diagnostic Technique 24

Innovative Pulse Monitoring: Exploring Auscultation-Based Di...

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11th international conference on Biomedical and Bioinformatics engineering, ICBBE 2024

作者： Kumar, Saurav Tayade, Apakrita Kumar, Prabhat Bhallamudi, Ravi Department of Mechanical Engineering Indian Institute of Technology Bombay Mumbai India Department of Neurosurgery University of California San FranciscoCA United States

ISBN: (纸本)9798400718274

Pulse Rate Variability (PRV) refers to the slight variations in the time between each pulse beat. these variations reflect the effect of the autonomic nervous system on the heart rate. this makes PRV a valuable tool to assess cardiovascular health. this study presents an acoustic system using an electret condenser microphone to measure variations in the pulse cycles. the system extracts the signals from the radial artery, having minimal external electronic or interference noise. Signal processing techniques are applied to the acquired waveform to extract useful features. Signals were obtained from two groups of participants - 14 diabetic and 16 healthy age-matched control (AMC) groups, respectively. Various PRV measures were evaluated. It was observed that the PRV indexes of the diabetic group were significantly lower than those of the AMC group. Further acquisition of more data sets could be applied to train a machine learning model to classify and predict the signal waveforms in cardiovascular diseases like diabetes. Copyright © 2024 held by the owner/author(s).

关键词： Cardiology

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Predicting Unsettled Debts in Imbalanced data using Resampling Methods 11

Predicting Unsettled Debts in Imbalanced Data using Resampli...

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11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Helmy, Abdelmoneim Elnaghy, Shaimaa Ramadan, Nagy Cairo University Faculty of Graduate Studies for Statistical Research Department of Information Systems and Technology Cairo Egypt

ISBN: (纸本)9798350322101

Unsettled debt is a central global problem for banks and businesses. Being able to accurately predict which loans will default would be incredibly valuable for understanding the state of the economy. Machine learning models can be used to predict debt defaults, which can help these organizations make better decisions and reduce their risk. this study contributed a unique dataset collected from Egyptian Social Insurance organization (NOSI) with a proposed approach for defaulted credit prediction using classification learning and resampling methods for solving imbalanced data, which is a common challenge in debt default and credit data analysis studies. the approach was evaluated using three tree-based classification algorithms: Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). the Similarity Oversampling and Under-sampling Pre-processing (SOUP) resampling technique outperformed all other resampling techniques when applied with RF, achieving high micro-average precision and recall of 66.67% and 67.38%, respectively, compared to the original dataset's 50.42% and 50.39%. © 2023 IEEE.

关键词： Forecasting

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Efficient Residual Neural Network for Human Activity Recognition using WiFi CSI Signals 9

Efficient Residual Neural Network for Human Activity Recogni...

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9th international conference on Information and Education Innovations (ICIEI)

作者： Hnoohom, Narit Mekruksavanich, Sakorn theeramunkong, thanaruk Jitpattanakul, Anuchit Mahidol Univ Dept Comp Engn Fac Engn Salaya 73170 Nakhon Pathom Thailand Univ Phayao Sch Informat & Commun Technol Dept Comp Engn Phayao 56000 Thailand Thammasat Univ Sch Informat & Comp Technol Sirindhorn Int Inst Technol Pathum Thani 12000 Thailand King Mongkuts Univ Technol North Bangkok Fac Sci Appl Dept Math Bangkok 10800 Thailand King Mongkuts Univ Technol North Bangkok Sci & Technol Res Inst Intelligent & Nonlinear Dynam Innovat Res Ctr Bangkok 10800 Thailand

ISBN: (纸本)9798400716409

the use of deep learning (DL) technology for the purpose of human activity recognition (HAR) is an important research area. Vision and sensor-based methods can provide good data but at the cost of privacy and convenience. Furthermore, Wi-Fi-based sensing has become popular for collecting human activity data, as it is ubiquitous, versatile, and performs well. the utilization of channel state information (CSI) obtained from Wi-Fi networks has the potential to facilitate the recognized activities. Traditional machine learning relies on hand-crafted features, but DL is more appropriate for automated feature extraction from raw CSI data. this work presented a generic HAR framework using CSI and studied various deep networks. We proposed a deep residual network that would automatically extract informative features from raw CSI. In this study, we conducted a comparative analysis of five fundamental deep networks;namely, convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM, gated recurrent unit (GRU), and bidirectional GRU. Experiments on a publicly benchmark dataset named the CSI-HAR dataset showed that the proposed recognition model performed the best for CSI-based HAR with the highest accuracy of 98.60%, thus improving the accuracy by up to 3.60% over prior methods. therefore, deep residual networks would be considered to be a suitable option for HAR tasks that would encompass Wi-Fi CSI data.

关键词： Technology channel state information Wi-Fi human activity recognition deep residual network

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UNSUPERVISED 3D OBJECT learning thROUGH NEURON ACTIVITY AWARE PLASTICITY 11

UNSUPERVISED 3D OBJECT LEARNING THROUGH NEURON ACTIVITY AWAR...

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11th international conference on learning Representations, ICLR 2023

作者： Kang, Beomseok Chakraborty, Biswadeep Mukhopadhyay, Saibal School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States

We present an unsupervised deep learning model for 3D object classification. Conventional Hebbian learning, a well-known unsupervised model, suffers from loss of local features leading to reduced performance for tasks with complex geometric objects. We present a deep network with a novel Neuron Activity Aware (NeAW) Hebbian learning rule that dynamically switches the neurons to be governed by Hebbian learning or anti-Hebbian learning, depending on its activity. We analytically show that NeAW Hebbian learning relieves the bias in neuron activity, allowing more neurons to attend to the representation of the 3D objects. Empirical results show that the NeAW Hebbian learning outperforms other variants of Hebbian learning and shows higher accuracy over fully supervised models when training data is limited. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Neurons

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Improved UNet for Semi-Supervised Multi-Scale CMRI Right Ventricular Segmentation Method 9

Improved UNet for Semi-Supervised Multi-Scale CMRI Right Ven...

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9th international conference on intelligent Computing and Signal Processing, ICSP 2024

作者： Cui, Hongzhen Wang, Shichao Zhu, Xiaoyue Peng, Yunfeng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Electronics and Information Engineering Liaoning University of Technology Liaoning China

ISBN: (纸本)9798350376548

Segmentation of the right ventricle (RV) holds significant clinical implications for the assessment of cardiac function in conditions such as pulmonary hypertension. Precise delineation of the right ventricle from cardiac magnetic resonance images (CMRI) is crucial in the diagnosis of various cardiac conditions. It provides clinicians with essential information for patient evaluation, treatment planning, and subsequent rehabilitation strategies. Currently, the development of fully automated segmentation models for the right ventricle heavily relies on deep learning techniques, necessitating meticulous manual annotation of CMRI data to achieve optimal segmentation results. However, the labor-intensive nature of labeling CMRI data requires significant medical expertise and consumes considerable time and resources. In response to these challenges, this study proposes a semi-supervised multi-scale segmentation approach for the right ventricle in CMRI. the method leverages unlabeled data to enhance segmentation accuracy while minimizing the reliance on annotated data. Experimental findings demonstrate the effectiveness of the proposed method in accurately delineating the right ventricle from CMRI datasets. © 2024 IEEE.

关键词： Magnetic resonance imaging

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4th international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Srishwal, Dhruvi Shibu, P.S. Vardan, Harsha Sajjad Ibrahim, A. Gouthaman, P. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department Of Networking And Communications Kattankulathur603203 India Srm Institute of Science and Technology Faculty of Engineering and Technology and Technology Department Of Networking And Communications Kattankulathur603203 India

ISBN: (纸本)9798331529635

Heart arrhythmias, if not detected early, can lead to severe conditions like stroke or heart failure, making timely and accurate classification critical. Traditional methods of analyzing Electrocardiogram (ECG) data are manual, time-consuming, and prone to errors due to the complexity of ECG signals. this paper presents an automated approach for arrhythmia classification using vector databases and similarity search algorithms. By converting raw ECG time-series data into vectorized representations, the system enables efficient pattern recognition and real-time analysis. Machine learning models applied to the vectorized data enhance classification accuracy, while the use of a vector database allows for quick similarity searches, facilitating real-time comparison of patient data with known arrhythmia patterns. the proposed method significantly reduces analysis time and increases precision, offering a robust framework for real-time monitoring and early detection, thereby improving patient outcomes and enabling timely medical interventions. © 2024 IEEE.

关键词： Dimensionality reduction

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A Next-Generation Platform for Law Enforcement Vehicle Detection 11

A Next-Generation Platform for Law Enforcement Vehicle Detec...

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11th international conference on Wireless Networks and Mobile Communications (WINCOM) - Inclusive and intelligent Connectivity

作者： Moumen, Idriss Rafalia, Najat Abouchabaka, Jaafar Ibn Tofail Univ Fac Sci Dept Comp Sci Kenitra Morocco

ISBN: (纸本)9798350377873;9798350377866

this paper presents an in-depth exploration of a novel platform designed to modernize police detection of wanted vehicles, situated at the intersection of Flask, a versatile web framework, and MongoDB, a leading NoSQL database platform. Our objective is to streamline vehicle detection processes while investigating MongoDB's potential in AI-driven applications, particularly in law enforcement. through thorough analysis, rigorous experimentation, and insightful observations, we expound MongoDB's role in augmenting AI capabilities, focusing on vehicle detection and surveillance. the platform integrates two AI models: YOLOv8, renowned for accurate object detection including license plates, and EasyOCR, specialized in precise text extraction from images. By detailing the methodologies and contributions of these models, we aim to inspire advancements in law enforcement through innovative technology integration. the accuracy obtained from this system reaches 98%. Our research delves into the architecture and development methodologies of the platform, highlighting the synergies between Flask and MongoDB. Additionally, we discuss the practical implications of MongoDB's flexible document-oriented structure, enabling seamless integration with AI models for efficient data processing. through comprehensive experimentation, we demonstrate the platform's efficacy in enhancing law enforcement efforts, offering insights into its potential applications in surveillance and crime prevention.

关键词： Vehicle Detection License Plate recognition Deep learning Convolutional Neural Network NoSQL database

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An intelligent system for analyzing the dependence of a chess player's rating on inaccurate moves 11

An intelligent system for analyzing the dependence of a ches...

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11th international Research conference Information Technology and Implementation - Workshop: intelligent Systems and Security, IT and I-WS 2024: ISS

作者： Dudnik, Andriy Moroz, Yurii Leshchenko, Olga Makhovych, Oleksandr Kolisnyk, Ihor Kyiv National Taras Shevchenko University 60 Volodymyrska Str. Kyiv03022 Ukraine Interregional Academy of Personnel Management 2 Frometivska str Kyiv03039 Ukraine

this paper presents an intelligent system we devloped to analyze the relationship between chess players' ratings and the mistakes they make during the game. To achieve the goal of this study, we used various techniques and algorithms of machine learning. With the help of big data tools, we analyzed thousands of chess games and received a large number of statistical insights about them. We created an algorithm that processes the obtained data and returns the results of the computations. Using different tools of information technologies, based on this algorithm, it will be possible to build numerous frameworks, such as humanlike bots, in further development. © 2024 Copyright for this paper by its authors.

关键词： intelligent systems

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