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检索条件"任意字段=22nd International Conference on Intelligent Data Engineering and Automated Learning"

共 2237 条记录，以下是381-390 订阅

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Adaptive Cloud Movement Prediction for Photovoltaic Systems Using Real-Time Sensor data and Deep learning

Adaptive Cloud Movement Prediction for Photovoltaic Systems ...

引用

international conference on intelligent Systems Applications to Power Systems (ISAP)

作者： Marek Bobček Róbert Štefko Zsolt Čonka Ladislav Főző Dept. of Electrical Power Engineering Technical University of Košice Košice Slovakia KVK Department of Power Systems Óbuda University Budapest Hungary Technical University of Košice Košice Slovakia

ISBN: (数字)9798331531751

ISBN: (纸本)9798331531768

Advancements in solar forecasting have been achieved through various techniques of computer vision and deep learning. This paper introduces a novel approach for cloud position prediction over photovoltaic (PV) power plants using real-time sensor data. We gather voltage drop data from sensors placed on the strings of solar panels, convert this data into matrices representing the PV system over time, and design a deep learning model to predict future cloud positions.

关键词： Deep learning Cloud computing Adaptation models Accuracy Voltage Predictive models Real-time systems data models Forecasting Optimization

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Convolutional Neural Network for Performance Monitoring and Modulation Formation Recognition in Communication System 22

Convolutional Neural Network for Performance Monitoring and ...

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22nd IEEE international conference on Communication Technology, ICCT 2022

作者： Zhang, Lifang Zhang, Ting Wang, Danshi Chen, Wenbin The Intelligent Network Innovation Center of Chinaunicom China Unicom Beijing China Beijing University of Posts and Telecommunications State Key Laboratory of Information Photonics and Optical Communications Beijing China

ISBN: (纸本)9781665470674

In this paper, a deep learning-based method is proposed for system damage identification and modulation format recognition in satellite communication system. Four typical damages are studied, including atmospheric channel impairments and hardware imperfections. In addition, total of ten modulation formats are generated, from low-order phase modulation to complex quadrature amplitude modulation. All the data are converted into image data and processed from the perspective of image recognition using convolutional neural network without feature engineering. © 2022 IEEE.

关键词： Damage detection

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Risk Factor Prediction of Heart Disease using Machine learning Approaches 22

Risk Factor Prediction of Heart Disease using Machine Learni...

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22nd international conference on Cyberworlds, CW 2023

作者： Dkhil, Mejdi Ben Rabbouch, Bochra Saadaoui, Foued Higher Institute of Applied Science and Technologies Sousse Tunisia Research Laboratory in Algebra Numbers and Technologies Theory and Intelligent Systems Faculté des Sciences Monastir Tunisia King Abdulaziz University Department of Statistics Jeddah Saudi Arabia

ISBN: (纸本)9798350315653

Heart disease is a widespread condition that impacts a significant number of individuals and continues to be the leading cause of death globally. It encompasses a range of conditions that affect the heart. There are numerous risk factors associated with heart disease, making it crucial to develop accurate, reliable, and practical methods for early diagnosis and effective management. Artificial intelligence techniques and machine learning algorithms have become widely used in healthcare and offer a means to process vast amounts of medical data. Researchers employ those techniques to analyze complex medical data, assisting healthcare professionals in predicting the likelihood of heart disease. In this research paper, regression techniques are investigated to predict risk factors of heart disease by focusing on different attributes related to this disease. For this aim we pretrained some machine learning approaches such as ElasticNet, K-Nearest Neighbor, XG Boost, Support Vector Regression and Random Forest. The study utilizes an existing dataset from the American Centers for Disease Control and Prevention that contains 319795 lignes and 18 attributes. The objective of this research paper is to estimate risk factors encountred on developing heart disease in patients in order to delay this disease as much as possible. © 2023 IEEE.

关键词： Diseases

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TA-Explore: Teacher-Assisted Exploration for Facilitating Fast Reinforcement learning 22

TA-Explore: Teacher-Assisted Exploration for Facilitating Fa...

引用

22nd international conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Beikmohammadi, Ali Magnússon, Sindri Department of Computer and Systems Sciences Stockholm University Stockholm Sweden

Reinforcement learning (RL) is crucial for data-driven decision-making but suffers from sample inefficiency. This poses a risk to system safety and can be costly in real-world environments with physical interactions. This paper proposes a human-inspired framework to improve the sample efficiency of RL algorithms, which gradually provides the learning agent with simpler but similar tasks that progress toward the main task. The proposed method does not require pre-training and can be applied to any goal, environment, and RL algorithm, including value-based and policy-based methods, as well as tabular and deep-RL methods. The framework is evaluated on a Random Walk and optimal control problem with constraint, showing good performance in improving the sample efficiency of RL-learning algorithms. © 2023 international Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Efficiency

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Preliminary Investigation into Machine-Learned 2D automated Video Surveillance Systems 22

Preliminary Investigation into Machine-Learned 2D Automated ...

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22nd international Carpathian Control conference (ICCC)

作者： Surowka, Adam Cracow Univ Technol Fac Elect & Comp Engn Ul Warszawska 24 PL-31155 Krakow Poland

ISBN: (纸本)9781728186092

The purpose of this study is to explore the possibility of using 2D human pose estimation algorithms in automated video surveillance systems. The proposed concept boils down to develop an intelligent application that can handle the incoming results and even prevent the occurrence of critical events in the monitored areas. The so-called critical events represent the acts of vandalism, crime, burglary, theft, fights, etc. The core aspect of the described solution is the use of conventional 2D imaging devices, i.e. CCTV cameras, IP webcams or even mobile phone cameras and an AI (artificial intelligence) neural network trained on a set of images. That makes the proposed solution relatively cheap, universal and easy to install across a wide range of target environments without the need for additional sensors. In the paper the authors highlights the architecture of the system, presents details of the video data acquisition stage followed by a description of the 2D human pose estimation toolkit. It is then complemented by summarizing the data extraction stage from pre-recorded clips for machine learning.

关键词： video surveillance systems human activity recognition 2D pose estimation machine learning

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learning Rewards to Optimize Global Performance Metrics in Deep Reinforcement learning 22

Learning Rewards to Optimize Global Performance Metrics in D...

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22nd international conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Qian, Junqi Weng, Paul Tan, Chenmien Shanghai Jiao Tong University Shanghai China University of Nottingham Ningbo China Ningbo China

When applying reinforcement learning (RL) to a new problem, reward engineering is a necessary, but often difficult and error-prone task a system designer has to face. To avoid this step, we propose LR4GPM, a novel (deep) RL method that can optimize a global performance metric, which is supposed to be available as part of the problem description. LR4GPM alternates between two phases: (1) learning a (possibly vector) reward function used to fit the performance metric, and (2) training a policy to optimize an approximation of this performance metric based on the learned rewards. Such RL training is not straightforward since both the reward function and the policy are trained using non-stationary data. To overcome this issue, we propose several training tricks. We demonstrate the efficiency of LR4GPM on several domains. Notably, LR4GPM outperforms the winner of a recent autonomous driving competition organized at DAI'2020. © 2023 international Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Reinforcement learning

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ML Support for Conformity Checks in CMDB-Like databases 22nd

ML Support for Conformity Checks in CMDB-Like Databases

引用

22nd international conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Niewiadomski, Szymon Mzyk, Grzegorz Wroclaw Univ Sci & Technol Wroclaw Poland

ISBN: (纸本)9783031425073;9783031425080

During data input into databases like CMDB (Configuration Management databases) we usually have a conformity check. The conventional approach is to create hundreds of rules to check data quality. This article shows several ideas on how to use ML algorithms to support the quality management of CMDB. We focus on naming conventions commonly used in CI (Configuration Items) - attributes like hostnames, serial numbers, and application names. Such attributes should be consistent with some dictionary data (operating system names, vendors) and existing relationships (location, applications). We review several strategies for feature extraction including tokenization and analyze the usability of CNB, RVAE, or NN to this particular problem. We also show the results of experiments on a public dataset (USA car database) to demonstrate the efficiency and inspire other researchers to work on similar topics. Algorithms used in the experiment are published as Jupiter Lab files.

关键词： data management Machine learning Feature extraction Error detection Kernel methods

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intelligent Onboard Routing in Stochastic Dynamic Environments using Transformers 22

Intelligent Onboard Routing in Stochastic Dynamic Environmen...

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22nd international conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Chowdhury, Rohit Murugan, Raswanth Subramani, Deepak Indian Institute of Science Bangalore Bangalore India Indian Institute of Technology Palakkad Palakkad India

Autonomous marine agents find extensive applications in environmental data collection, naval security, and exploration of harsh ocean regions. As intelligent agents, they must perform onboard routing, collect data about their surroundings and update their route to minimize mission travel time, energy, or data collection. While Markov Decision Processes (MDPs) and Reinforcement learning (RL) are often used for path planning, they are computationally expensive for onboard routing as they need in-mission re-planning. In the present paper, we develop a novel, deep learning method based on the decision transformers for optimal path planning and onboard routing of autonomous marine agents. The transformer architectures convert the RL-based optimal path planning problem into a supervised learning problem via sequence modeling. Before the mission, during the offline planning phase, the environment is first modeled as a stochastic dynamic ocean flow with dynamically orthogonal flow equations. A training dataset for the transformer model is created by solving the stochastic dynamically orthogonal Hamilton-Jacobi level set partial differential equations or a dynamic programming solution for MDPs. These paths are then processed to obtain sequences of states, actions and returns for our transformer models, where the agent's state is typically its spatio-temporal coordinate and other collectible data. We propose and analyze multiple state modeling choices against the agent's state estimation capabilities and scenarios with multiple target locations. We demonstrate that (i) a trained agent learns to infer the surrounding flow and perform optimal onboard routing when the agent's state estimation is accurate, (ii) specifying the target locations (in case of multiple targets) as a part of the state enables a trained agent to route itself to the correct destination, and (iii) a trained agent is robust to limited noise in state transitions and is capable of reaching target locations in

关键词： Reinforcement learning

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intelligent Traffic Management System: An Advanced Solution for Helmet Compliance, Traffic Signal Violation Detection, Number Plate Identification, Cell Phone Usage Tracking, and Proximity-Based Police Station Alerts 2

Intelligent Traffic Management System: An Advanced Solution ...

引用

2nd international conference on Computer, Communication and Control, IC4 2024

作者： Kayalvizhi, S. Jayanth, J. Karthikeyan, V. Nithish Kumar, R. Computer Science and Engineering Chennai89 India

ISBN: (纸本)9798350387933

intelligent Traffic Management System (ITMS) is a sophisticated solution designed to address a variety of urban traffic management challenges. This comprehensive system employs cutting-edge technologies to ensure helmet compliance, detect traffic signal violations, identify license plates, track cell phone usage, and provide police stations with proximity-based alerts. The system can accurately identify helmetless individuals by implementing computer vision algorithms. ITMS uses computer vision and machine learning techniques to detect traffic signal violations in addition to monitoring helmet compliance. The system can identify and record instances of red light running by analyzing live video feeds from traffic cameras, allowing law enforcement to issue appropriate penalties. Furthermore, ITMS includes a number plate identification feature that extracts license plate information using optical character recognition. This enables efficient and automated vehicle movement monitoring, allowing for effective traffic surveillance and the identification of vehicles involved in criminal activities. Another important feature of ITMS is its ability to monitor cell phone usage while driving. The system can detect drivers using cell phones and generate alerts to deter distracted driving by leveraging mobile network data and smart devices. Finally, ITMS sends proximity-based alerts to police stations, allowing for faster response times and more efficient deployment of law enforcement personnel in the event of an emergency or traffic incident. In summary, ITMS is a sophisticated and all-encompassing solution that combines advanced technologies to ensure helmet compliance, detect traffic signal violations, identify license plates, track cell phone usage, and provide proximity-based police station alerts. ITMS aims to improve road safety and streamline traffic management by combining these features into a single system for a better commuting experience. © 2024 IEEE.

关键词： Motor transportation

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Transfer learning-based Driver Distraction Detection 2

Transfer Learning-based Driver Distraction Detection

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2nd international conference on Sustainable Computing and data Communication Systems, ICSCDS 2023

作者： Goel, Lipika Chennamaneni, Sukruthi Golla, Anjali Puchhakayala, Sravya Rudraram, Adithi Gokaraju Rangaraju Institute of Engineering and Technology Department of CSE Telangana India

ISBN: (纸本)9781665491990

Due to driver's preoccupation, there have been more accidents on the roads recently all around the world. One of the key explanations is attributed to human behavior. Accidents have been linked mostly to drivers' distraction. This sudden road crush frequently causes injuries, property loss, or even fatalities. The automated interpretation of the driver's behavior is one of the trickiest subjects in the field of intelligent transportation systems (ITSs). This study looks on the human action recognition aspect of distracted driving posture recognition. There have been several reports of inattentive driving causing auto accidents. The goal is to use cutting-edge transfer learning techniques like MobileNetV2 and DenseNet to identify distracted driving activities more effectively. As driving is a complex task, it requires complete attention of the driver. Distractions of the driver can be due to talking phone calls, texting, talking to another passenger, drinking, operating radio. To identify distractions while driving, it is crucial to observe and evaluate the driver's behavior. Images of the driver serve as the main source of data and that comprises of face, arms, and hands of the person inside the car. To decrease vehicle accidents and enhance transportation security, it is extremely desirable and has received a significant amount of study to develop a system that can detect distracted driving. In this study, transfer learning models like DenseNet and MobileNetV2 are used to classify and detect driver distraction. The best performance was produced by MobileNetV2, which has an accuracy of 93.8. © 2023 IEEE.

关键词： Accidents

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