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检索条件"任意字段=4th Conference on Learning and Intelligent Optimization"

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Promoting Collective Reflection through intelligent Multi-modal Analysis: A Case Study 4

Promoting Collective Reflection through Intelligent Multi-mo...

引用

4th International conference on Educational Technology, ICET 2024

作者： Li, Jimei Liu, Bingxue Xu, Miao Long, Taotao Wang, Mengke Central China Normal University Faculty of Artificial Intelligence in Education Wuhan China

ISBN: (纸本)9798350376944

Collective reflection refers to both the process by which group members engage in thoughtful consideration through social interaction and the resultant products of such engagement. It has been applied to improve teacher professional development. Based on active learning theory, this study explored the use of a collective reflection approach with video-based intelligent multi-modal analysis technology to enhance pre-service teachers' teaching practice. Twenty second- and third-year pre-service science teachers were involved in this case study. this study found that the video-based intelligent multi-modal analysis technology played an important role in facilitating pre-service teachers' critical reflection and evaluation of their teaching behaviors and performance. they showed positive changes in teaching behaviors and performance through conducting collective reflection with the video-based intelligent multi-modal analysis report. Design principles for future technology-integrated collective reflection activities to achieve active learning are also discussed. © 2024 IEEE.

关键词： Video analysis

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Determination of Fatigue Strength of Steel Using Deep learning Techniques 4th

Determination of Fatigue Strength of Steel Using Deep Learni...

引用

4th International conference on Advanced Engineering optimization through intelligent Techniques

作者： Kumar, Punit Anand, Ayush Kavitha, B. Natl Inst Technol Warangal Dept Civil Engn Hanamkonda Telangana India Natl Inst Technol Warangal Fac Civil Engn Hanamkonda Telangana India

ISBN: (纸本)9789819796618;9789819746545;9789819746538

Steel is one of the primary components of a structure from a strength perspective. Since the load applied to some structures is cyclic, fatigue strength becomes a crucial factor for durability. However, due to the complex nature of fatigue strength formation in steel, it is not yet completely understood. Fortunately, the evolution of new technologies like artificial intelligence and machine learning has led to the development of regression models that can accurately predict the fatigue strength of steel. Although machine learning models have a defined structure, their performance improvement is limited after reaching a certain threshold. However, this limitation can be overcome with neural networks (NN), which are highly customizable and improve with the addition of data. In this study, seven different NN models were constructed with successive enhancements to predict the fatigue strength of steel with high accuracy. Various performance metrics were utilized to evaluate the models. the dataset used in this project is the fatigue dataset for steel from Japan NIMS (National Institute for Materials Science), which is an open-source and the world's largest dataset on steel fatigue strength. It comprises 437 instances with 25 different factors influencing the property, such as chemical composition (9), heat treatment conditions (12), and upstream processing details (4). these instances include 371 carbon and low-alloy steels, 48 carburizing steels, and 18 spring steels. the data covers various heats of each steel grade and different processing conditions. After testing various neural networks and determining the best train-test split, a maximum accuracy of 99.7% was achieved on the test data. Furthermore, the model was deployed as a web application, making it accessible to everyone.

关键词： Fatigue strength of steel Material science Deep learning Artificial neural network Model deployment

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Fourth International conference on Mechanical Engineering, intelligent Manufacturing, and Automation Technology, MEMAT 2023

Fourth International Conference on Mechanical Engineering, I...

引用

4th International conference on Mechanical Engineering, intelligent Manufacturing, and Automation Technology, MEMAT 2023

ISBN: (纸本)9781510674820

the proceedings contain 140 papers. the topics discussed include: experimental study on potential energy recovery and generation current characteristics of heavy electric forklift;research on material transportation robot based on PSO-PID controller;modeling and simulation of a solar array deployment mechanism;plc control method for speed regulation of automatic frequency converter of high-voltage electrical equipment;simulation design and optimization of multi-robot production line based on RobotStudio;a deep reinforcement learning based fault diagnosis algorithm for unlabeled and imbalanced data;research on the fastening scheme of screw group of electronic components based on finite element simulation;improved RRT-based path planning algorithm for 2D mobile robots;and research on transformer load forecasting based on deep learning.

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intelligent control of extra-high arch dams 4

Intelligent control of extra-high arch dams

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2024 4th International conference on Mechatronics, Automation and intelligent Control, MAIC 2024

作者： Yang, Yunbao Lv, Runqi Han, Shenqiu Li, Yang Yue, Jiali Wu, Jianming Zhejiang Guangchuan Engineering Consulting Co. Ltd. Hangzhou China Jinyun County Water Conservancy Bureau Lishui China

At this stage, the intelligent construction system integrating sensing technology, communication transmission, data processing, and intelligent decision-making is becoming increasingly mature. It is fully applied in the construction industry. As a large-volume concrete building with concrete temperature control, the extra-high arch dam faces challenges such as high construction difficulty, management difficulty, and other thorny issues. By introducing an intelligent control system, real-time sensing during the pouring process of the extra-high arch dam allows for autonomous analysis to be conducted in a timely manner. Anticipation of various risks in the construction process and autonomous problem-solving are achieved through intelligent decision-making within the system. Artificial assistance makes up for the shortcomings of the intelligent construction system. through the system's own independent learning ability and continuous optimization, the problem of intelligent construction can be solved. It also can solve most of the problems in the dam pouring process, accelerate the project progress, significantly reduce the construction difficulty, improve the quality of dam construction, and improve the overall safety performance of the dam. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Arch dams

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Enhancing Road Safety in Diminished Vision Conditions: A Deep learning Approach Using Faster MobileNet for Real-time Object Detection 4

Enhancing Road Safety in Diminished Vision Conditions: A Dee...

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Subbaiah, D. Venkata Sricharan, Byreddi Aman, Shaik Zeenath Gopinath, Kasarla Department of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350349900

Despite significant enhancements in car safety technology, traffic accidents still happen, so finding creative ways to reduce the risk is necessary, particularly in situations with poor vision, like fog. this study presents a unique method using deep learning, namely the Faster MobileNet model, to reduce traffic accidents in situations where eyesight is impaired. the suggested solution makes use of a live video feed from the car's front-mounted camera and Faster MobileNet to identify and highlight things on the driver's display instantly. To improve image quality, the system incorporates image processing methods such as data augmentation, normalization, and scaling. Convolutional and pooling layers are used in feature extraction, together with a Region Proposal Network (RPN) to find potential object areas. Classifiers are used in object detection to recognize different kinds of objects and give the driver pertinent information. Non-Maximum Suppression (NMS) is incorporated into the implementation as a post-processing step to eliminate duplicate detections and improve the result. By acting as an intelligent helper, the created application greatly increases poor vision drivers' visibility and promotes a safer driving environment. © 2024 IEEE.

关键词： intelligent systems

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Forecasting India's Air Quality: A Machine learning Approach for Comprehensive Analysis and Prediction 4

Forecasting India's Air Quality: A Machine Learning Approach...

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4th International conference on intelligent Technologies, CONIT 2024

作者： Kothari, Osho Sah, Nabin Kumar Kumar, Konda V S Harshith Nair, Priyanka C Sampath, Nalini Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350349900

With rising industrialization, India confronts increasing difficulties in maintaining air quality regulations. this research proposes a comprehensive analysis and prediction framework based on machine learning approaches for assessing and forecasting the Air Quality Index (AQI) in different place of India. this model takes into account a wide variety of pollutant concentrations that are monitored at regular intervals. this approach, which uses historical data from numerous Indian cities, employs a variety of machine learning methods, including k-Neighbour regression, decision tree regressor, linear regression, and random forest regressor, in addition to support vector regressor. Every model had performed well but the Decision Tree Regression and Random forest has performed best achieving RMSE value as 0.0016 and 0.00095 respectivelly whereas R-Squared value as 0.99 for both the model, indicating strong performance and a substantial progress in air quality prediction. the environmental health risks associated with air pollution have been mitigated by the addition of important knowledge. © 2024 IEEE.

关键词： Support vector regression

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Element Extraction and Reconstruction of Lingnan Clothing Based on Machine learning 4

Element Extraction and Reconstruction of Lingnan Clothing Ba...

引用

4th International conference on intelligent Design, ICID 2023

作者： Mo, Lan Aris, Asliza Bt Zhang, Xiaopu Gu, Haiping University of Science and Technology School of Arts and Media. Guangzhou Vocational and Technical Guangzhou China Universiti Teknologi MARA Faculty of Art and Design Selangor Malaysia

ISBN: (纸本)9798350342574

With the rapid development of science and technology and intelligence, the development of Lingnan clothing has gradually declined, and even a crisis of extinction has occurred. In order to inherit and innovate Lingnan clothing, this paper realizes the extraction and reconstruction of Lingnan clothing elements based on machine learning. Firstly, the intelligent extraction model of Lingnan clothing element contour is constructed based on GrabCut algorithm, and the intelligent extraction model of Lingnan clothing element color is established based on density peak clustering algorithm. then, the shape grammar is used to construct the intelligent reconstruction model of Lingnan clothing elements, and the integrated clothing products of Lingnan clothing and modern fashion are designed. © 2023 IEEE.

关键词： Machine learning

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Optimizing Energy Efficiency and Network Performance in Smart Street Lighting Systems Using LoRaWAN Technology 3

Optimizing Energy Efficiency and Network Performance in Smar...

引用

3rd IEEE International conference on intelligent Techniques in Control, optimization and Signal Processing, INCOS 2024

作者： Sivakumar, S. Jayanth, R. Mughesh Kumar, N.R. Srm Institute of Science and Technology Dept. of Comp. Sci. and Engineering Specialization in Artificial Intelligence and Machine Learning Tamil Nadu Tiruchirapalli India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361186

this paper presents a groundbreaking Smart Street Lighting System utilizing LoRaWAN technology to overcome the challenges of scalability, energy inefficiency, and communication limitations inherent in older smart street technologies. the system employs seamless, long-range communication for real-time monitoring, adapting to dynamic urban environments, and achieving a balance between energy conservation and public safety. Advanced components, including a Passive Infrared (PIR) sensor and microwave motion sensor, enhance object detection and urban safety. A unique feature of the system is its ability to automatically dim the street lights to 1/4th intensity when no object is detected, while instantly adjusting to full brightness upon object detection. Furthermore, the incorporation of a response factor ensures accurate detection and swift response to communication disruptions. Notably, the system achieves an outstanding 70% energy savings through optimized lighting and dynamic remote control, significantly contributing to the evolution of intelligent and energy-efficient street lighting solutions in smart city infrastructures. © 2024 IEEE.

关键词： Smart city

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Real-Time Ragging Notification Using Machine learning Algorithms and MediaPipe 4

Real-Time Ragging Notification Using Machine Learning Algori...

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Dhanesh, R. Shanthi, H.J. Hindustan Institute of Technology & Science Chennai Department of Computer Applications India

ISBN: (纸本)9798350349900

Ragging is disorderly practice of senior students harassing their juniors that violates rules of the institutions. Addressing critical issues such as ragging, suicidal ideation, and medical emergencies within college campuses necessitates the integration of innovative technological solutions. the utilization of innovative technologies such as MediaPipe and machine learning algorithms plays a pivotal role in addressing critical issues like ragging, suicidal ideation, and medical emergencies within college environments. this research paper integrates OpenCV, machine learning algorithms, MediaPipe, including support vector machine, random forest, k-nearest neighbour, and support vector machine, to develop a model for hand tracking and key point recognition. through the deployment of a camera-enabled device, real-time footage is captured and compared against the trained model. Predictions and classification is detected from hand gestures, and when the sign matches the designated sign then it triggers an alarm and sends a screenshot of the person who needs help. this ensures prompt alerts authorities to take necessary actions in response to identified situations, thereby enhancing campus safety and welfare. © 2024 IEEE.

关键词： Decision trees

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Parameters optimization of Selected Advanced Manufacturing Processes 1

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4th International conference on Advanced Engineering optimization through intelligent Techniques

作者： Rao, Ravipudi Venkata Salve, Ajinkya K. Sardar Vallabhbhai Natl Inst Technol Dept Mech Engn Surat Gujarat State India

ISBN: (数字)9789819746545

ISBN: (纸本)9789819796618;9789819746545;9789819746538

In any manufacturing process, selecting the optimum process parameters for advanced manufacturing processes is essential to reduce machining costs and increase productivity. optimization methods like the genetic algorithm (GA), particle swarm optimization (PSO), and others have recently substantially aided the challenges of advanced manufacturing processes. However, when the population size increases, these metaheuristic algorithms take excessive time to converge. To address this, newly developed Rao algorithms will be used to provide the best results of the process parameters for several advanced manufacturing processes, namely focused ion beam micro-milling (FIBM), fused deposition modelling (FDM), and wire electric discharge machining (WEDM). Rao algorithms are used to calculate output parameters in both single-objective and multi-objective priori approaches, and they have demonstrated improved performance.

关键词： Advanced manufacturing Rao algorithms WEDM optimization FDM FIB

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