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Traffic Flow Optimizer: Traffic Signal control With Vehicle Density and Algorithms

Traffic Flow Optimizer: Traffic Signal Control With Vehicle ...

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2023 international conference on Recent advances in Science and engineering Technology, ICRASET 2023

作者： Vayadande, Kuldeep Wakodikar, Nidhish Oswal, Kriya Chauhan, Tilaksingh Shivade, Vaishnavi Ubale, Shreenath Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350306927

The goal of this work is to create an intelligent traffic signal control system that will enhance intersection traffic flow. The major goals are to reduce traffic congestion, expedite travel, and improve all-around road safety. To estimate traffic density in real time, the system uses image processing methods, specifically vehicle detection algorithms. The computer vision library is used to evaluate surveillance camera footage at traffic lights in order to precisely count vehicles and gauge traffic density. To detect vehicles and precisely count them, a variety of image processing techniques are applied, such as object recognition and background subtraction. The number of vehicles per area or lane is then taken into account when calculating traffic density. A central server receives this data for further processing. © 2023 IEEE.

关键词： Traffic congestion

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An intelligent Forecasting Classification Model for Traffic Analysis using Machine Learning Techniques 4

An Intelligent Forecasting Classification Model for Traffic ...

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4th international conference on advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kishor, Bodla Tiwari, Rajesh Patra, Raj Kumar Pinjakar, Latika Singh, Mahesh Cmr Engineering College Dept. of Computer Science and Engineering Hyderabad India Cmr Technical Campus Dept. of Computer Science and Engineering Hyderabad India Symbiosis Institute of Technology Dept. of Computer Science and Engineering Nagpur India Shri Shankaracharya Technical Campus Dept. of Eee Durg Bhilai India

ISBN: (纸本)9798350343670

In addition to taking care of the normal citizens, Road Traffic protection is also one of the main issue for corporations governing the transportation. We use data mining techniques to get the hidden matter from the database. The applications of Data mining is there in various fields such as fraud detection, marketing and many more. Many countries were affected due to many decisions that were taken by the governments one such issue is globalization. This has brought some drastically changes in the intake level and the financial related activities and this has given a boost to transportation department. Understanding the importance of the street protection the authorities are putting effort to know the reasons behind the street accidents. The increase in the street injuries made it difficult to determine the limitations of the street injuries. This paper explains the street injuries frequency from analysing the database. This work is about 95% efficient. © 2024 IEEE.

关键词： classification data mining machine learning traffic analysis Transportation models

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Design of Software engineering Adaptive Learning System Based on Artificial Intelligence 5

Design of Software Engineering Adaptive Learning System Base...

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5th international conference on advances in Electrical engineering and computer Applications, AEECA 2024

作者： Chen, Jing Wang, Yufan Yunnan Land and Resources Vocational College Kunming China Wucheng Primary School Kunming China

ISBN: (纸本)9798350355253

The complexity of modern software systems necessitates adaptability to better handle complex environments. This paper, from an object-oriented perspective, investigates adaptive learning methods using software agent technology. The study thoroughly examines the software engineering issues of adaptive systems from four perspectives: model, mechanism, language, and platform. The project integrates reinforcement learning and software agent technology, delving into the adaptive mechanisms of reinforcement learning, constructing adaptive intelligent agent software models, and exploring frameworks for adaptive system construction and adaptive learning algorithms. The case analysis of a robotic dog demonstrates that through continuous interaction with the environment, the robotic dog updates its understanding of the environment in real time, allowing it to adjust its strategy to adapt to changes in prey movement patterns and promptly adjust its role to replenish energy as needed to complete its tasks. This research validates the effectiveness of the proposed models, mechanisms, languages, and platforms, highlighting the potential impact of adaptive systems in enhancing system stability. © 2024 IEEE.

关键词： Adversarial machine learning

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Aerodynamic Design of Ultra-High Load Axial Flow Compressor

Aerodynamic Design of Ultra-High Load Axial Flow Compressor

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2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Wu, Minyi Zhou, Zhenggui College of Energy and Power Engineering Nanjing University of Aeronautics and Astronautics China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

In this paper, the use of a negative air outlet Angle superbending diffuser blade, the compressor rotor blade design, to achieve the given pressure ratio, flow, compressor ultra-high load, ultra-low speed design. In this paper, the design load coefficient of 1.7771, much higher than the conventional load coefficient 0.4;In the process of two-dimensional blade design, the automatic optimization design method is adopted. computer numerical simulation results show that the designed compressor stage can reach the given design point flow rate and pressure ratio, and has a high stage efficiency;And because the rear section of the rotor blade channel convergence is not easy to produce flow separation, it has a large surge margin. © 2025 SPIE.

关键词： Axial-flow compressors

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A Novel Approach to Optimize Energy Consumption in Industries Using IIoT and Machine Learning

A Novel Approach to Optimize Energy Consumption in Industrie...

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2024 international conference on advances in Data engineering and intelligent Computing Systems, ADICS 2024

作者： Yadav, Ritesh Kumar Malavika, V. Rajendran, P.Selvi Hindustan Institute Of Technology And Science Department Of Computer Science Engineering Chennai India

ISBN: (纸本)9798350364828

Managing Electrical Energy has become crucial nowadays where everything we use works on electrical energy. Utilizing energy in an efficient and effective way can save a lot of energy. Reducing unnecessary usage of electrical current can lead to lowering carbon emission and also can directly benefit the production cost in industries. This reduction is possible when we continuously monitor the usage of the current when it's being utilized. In order to overcome this problem our novel idea proposes a state of art algorithm such as the Autoregressive Integrated Moving Average (ARIMA) model which is the link between one observation and several lagged observations of the same series modeled by the autoregressive component. In essence, it depicts how the current value is linearly dependent on its own historical values. Linear regression is a prevalent statistical technique utilized to model the association between a dependent variable and one or more independent variables. When it comes to energy consumption, linear regression can be used to examine and forecast patterns of energy use depending on a variety of influencing factors. To improve the accuracy of the proposed work, the use of Time Series Analysis aids in making the system more reliable and efficient. We have developed a system in which an all-in-one energy management solution provides a platform where we can control this wastage and manage all equipment in one place. Our proposed system monitors electrical energy using various IOT sensors to continuously monitor energy utilization and predicting the usage earlier to help the industries to make better financial plans. © 2024 IEEE.

关键词： Energy utilization

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Research on Voice Communication Enhancement of Network Learning Platform Based on Human-computer intelligent Interaction 6

Research on Voice Communication Enhancement of Network Learn...

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6th international conference on Communications, Information System and computer engineering, CISCE 2024

作者： Wang, Lihong Gou, Weijie School of Basic Education Beijing Polytechnic Beijing China School of Automotive Engineering Beijing Polytechnic Beijing China

ISBN: (纸本)9798350353174

In order to improve the resource dispatching performance of network learning platform and enhance the ability of voice communication enhancement of network learning platform, a voice communication enhancement model of network learning platform based on human-computer intelligent interaction is proposed. The resource allocation method of high concurrent access requests is adopted to conduct channel vertical switching processing of voice communication. An optimal control model of voice communication of network learning platform is established with network congestion degree and user data transmission rate as constraint objective parameters. Through artificial intelligence interaction and adaptive control, combined with multi-objective optimization switching and analysis, the comprehensive benefit value is obtained to realize voice communication enhancement. Experiments show that the method used for processing voice communication enhancement of network learning platform can improve communication stability, reduce output interference and improve the ability of resource adaptive allocation of English network learning platform. © 2024 IEEE.

关键词： Federated learning

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Research on the Automatic Statistical Technology of Construction Progress of Substation engineering Based on AI and AR 5

Research on the Automatic Statistical Technology of Construc...

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5th international conference on advances in Electrical engineering and computer Applications, AEECA 2024

作者： Qin, Kuangyu Zhong, Bin Lan, Qiaoqian State Grid Shanghai Municipal Electric Power Company State Grid Shanghai Electric Power Company Development Planning Department Shanghai China

ISBN: (纸本)9798350355253

Currently, mobile augmented reality (AR)-based construction visualization technologies offer diverse interaction modes but struggle with in-depth applications such as schedule and investment statistics due to issues of low accuracy and inadequate system integration. To mitigate these challenges, this study leverages simultaneous localization and mapping (SLAM) technology to capture real-time, comprehensive images from construction sites. This approach is grounded in the investment management "cascade neural network model."By integrating building information modeling (BIM) and image recognition technologies, we achieve real-time registration of the construction scene and BIM models using mobile AR technology. This enables the comparison of engineering quantities, analysis of project completion, and precise monitoring of project progress, thereby laying a critical foundation for subsequent digital and intelligent statistical analyses. © 2024 IEEE.

关键词： Investments

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Flower Classification using Pre-trained ResNet Models in computer Vision 15

Flower Classification using Pre-trained ResNet Models in Com...

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15th international conference on advances in Computing, control, and Telecommunication Technologies, ACT 2024

作者： Chandra, Jyotish K. Jishnu Ashirwaad Sarbesh Pavithra, G. Ninawe, Swapnil S. Dayananda Sagar College of Engineering Bangalore India Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9798331300579

This report explores a methodology for flower classification using a pre-trained ResNet *** approach involves fine-tuning the ResNet model on a flower dataset to achieve high classification *** processes include data preprocessing, augmentation, model training, and evaluation, resulting in a validation accuracy of 95%.The project "Flower Classification Using Pre-trained ResNet Models in computer Vision" aims to explore the capabilities of advanced deep learning techniques in accurately classifying flower *** pre-trained Residual Networks (ResNet), this study demonstrates the efficacy of transfer learning in the domain of image classification, particularly for flora *** abstract outlines the methodology, key findings, and potential applications of the project, emphasizing the significant improvements in classification accuracy and computational efficiency achieved through the use of pre-trained models. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Research on computer Cloud intelligent System Based on intelligent Virtualization Technology 3

Research on Computer Cloud Intelligent System Based on Intel...

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3rd IEEE international conference on Electrical engineering, Big Data and Algorithms, EEBDA 2024

作者： Shi, Qingfeng Zhao, Fei College of Physical Education Taishan University Tai'an271000 China Zibo High-sports Industry Development Zone Education and Sports Center Zibo City China

ISBN: (纸本)9798350380989

This paper is mainly the smart home system and some other intelligent devices and robot technology organic integration together, so that it can be more comprehensive for the robot service. At the same time, computer cloud computing technology is also integrated with the API of other third-party cloud computing platforms, so that it has more business capabilities. The goal of computer cloud computing architecture is to improve the computing and storage performance of robots with the help of massive data. The integration of third-party cloud computing services with intelligent hardware technology expands the resources and business functions of robots. By using cloud computing simulation technology, the inconsistency between observer and predictor is corrected, and a new cloud computing predictive control algorithm is designed to effectively suppress complex network attacks. Secondly, the D cloud computing system with predictive control mechanism is modeled, and the state convergence criterion in D cloud computing is established by using Lyapunov stability theory and LMI theory. Through remote operation of mobile terminals, the collaboration between mobile terminals based on cloud computing and third-party clouds is realized. © 2024 IEEE.

关键词： in the clouds service framework service model Virtualization

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Design of Reinforcement Learning Environment with Time Sensitive Characteristics 4

Design of Reinforcement Learning Environment with Time Sensi...

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4th international conference on computer Science, Electronic Information engineering and intelligent control Technology, CEI 2024

作者： Hu, Haomeng Wang, Zhenya Zhang, Weili Chen, Xiuheng Li, Boyao Xie, Haidong China Academy of Aerospace Science and Innovatio Advanced Intelligent Algorithm Center Laboratory Beijing China School of Automation Hangzhou Dianzi University Beijing China

ISBN: (纸本)9798350378542

Self-play theory is an important branch of reinforcement learning. Different levels of complexity in scenarios are also necessary to make reinforcement learning more sophisticated. In this paper, a new reinforcement learning scenario environment is designed. This scene is a game where the red and blue teams throw snowballs at each other to attack each other. In this scenario, it is more sensitive to time. The reward for the action depends on the influence of the snowball after a period of time, rather than immediate feedback. The IPPO and MAPPO algorithms are tested in this scenario. At the same time, we designed six different difficulty level rule strategies. This paper tested the trained model against six rules, counted the number of wins and losses between them, and provided the test results. © 2024 IEEE.

关键词： reinforcement learning reward delay self-play time-sensitive

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