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检索条件"任意字段=Scientific Conference on Mechatronics Engineering and Computer Science"

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A New Bearing Fault Diagnosis Method Based on Multi-Scale CNN and LSTM

A New Bearing Fault Diagnosis Method Based on Multi-Scale CN...

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2023 International conference on mechatronics and Intelligent Control, ICMIC 2023

作者： Sun, Haibin Fan, Yueguang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9781510668393

Detection accuracy of bearing faults is crucial in saving economic loss for industrial applications. Various diagnostic methods proposed by researchers can produce highly accurate bearing fault diagnosis, but it is difficult to obtain accurate and effective diagnosis results under variable working conditions and noise conditions. Thus, A hybrid model M-SWCAL based on multi-scale convolutional neural network (CNN), artificial neural network (ANN) and long short-term memory (LSTM) neural network is proposed for fault diagnosis of rolling bearings. The model uses multiscale CNN to extract the frequency domain information of the vibration signal at different resolutions, and the multiscale structure can effectively enrich the number of feature information. After that, the features of different channels are fused into one long sequence by using splicing fusion. Then ANN is used to filter and sort out the noise and redundant information in the features, and finally LSTM is used to further learn the sequence characteristics in the new sequence. Experiments show that the model can generally achieve more than 99% diagnostic accuracy for different data sets, and the generalization performance and noise immunity of the model are completely better than other existing methods. © 2023 SPIE.

关键词： Long short-term memory

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Research on the simulation of emergency evacuation of rail transit stations based on Anylogic 7

Research on the simulation of emergency evacuation of rail t...

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7th IEEE Information Technology and mechatronics engineering conference, ITOEC 2023

作者： Deng, Jiatai Meng, Yadong Fu, Yanrong Du, Yingjie Guo, He Tianjin University of Technology and Education School of Automobile and Transportation Tianjin300222 China

ISBN: (纸本)9798350334197

As the epidemic passed and rail operations continued to expand, rail passenger traffic flow also recovered. Due to the complex layout of rail transit station facilities and conflicting passenger lines, passenger evacuation has become a major challenge. The current mainstream simulation analysis mainly focuses on the layout of station facilities and pays less attention to passenger lines. This paper takes a subway station as an example, uses Anylogic simulation software to simulate the evacuation process under emergency situation in the station, finds the evacuation bottleneck by analyzing the simulation process, and finally reduces the intersection and convergence of passenger flow lines by interfering with the flow lines, making full use of the station resources, and reasonably guiding the diversion of pedestrians, and controls the safe evacuation time within 6 minutes. The research results show that: evacuation under the original model conditions, evacuation time cannot meet the requirements;in the optimized model, passenger evacuation time is shortened from 444.6 seconds to 354.8 seconds, with an optimization rate of 25.34%. The research results have some reference value for the design of emergency evacuation of stations with the same structure. © 2023 IEEE.

关键词： computer software

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Advanced Technological Improvements in Making of Smart Production Using AI and ML 4

Advanced Technological Improvements in Making of Smart Produ...

引用

4th IEEE International conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Praveenraj, D. David Winster Vijayaraj, T. Kumar, K. Sambath Sakthivel, N.K. Multani, Fahim Nagaveni, P. Christ University School of Business and Management TN Bangalore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering TN India Department of Electronics and Communication Engineering Chennai Tamil Nadu Avadi India Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu T.M. Palayam India Ies College of Technology Department of Computer Science& Engineering M.P Bhopal India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering TN India

ISBN: (纸本)9798350360165

The necessity for adaptation and creativity in the manufacturing sector demonstrates the importance of sustainable manufacturing by the merging of advanced technologies. To encourage sustainability, a global view on the integration of smart manufacturing procedures is important. Artificial intelligence (or AI) has appeared as a crucial factor in achieving environmentally conscious manufacturing, with methods like the use of machine learning (ML) getting popularity. This study carefully studies the scientific papers related to the usage of AI and ML in business. The emergence of Industry 4.0 as a whole has positioned machine learning (ML) and artificial intelligence (AI) as drivers for the smart industry change. The study categorizes material based on release year, writers, scientific field, country, institution, and terms, applying the Web of Biology and SCOPUS databases. Utilize UCINET alongside NVivo 12 software, thereby the analysis covers empirical studies on machine learning (ML) and artificial intelligence (AI) via 1999 until the present, showing their growth before and after the start of Industry 4.0. Notably, the USA displays a substantial addition to this area, with a noticeable surge in desire following the rise of Industry 4.0. © 2024 IEEE.

关键词： Smart manufacturing

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Real Time Face Recognition System using Raspberry Pi4 3

Real Time Face Recognition System using Raspberry Pi4

引用

3rd International conference on Advanced Electrical engineering, ICAEE 2024

作者： Boukerche, Alaa Eddine Chelali, Fatma Zohra University of Science and Technology Houari Boumediene Usthb Faculty of Electrical Engineering Telecommunications Department Algeria University of Science and Technology Houari Boumediene Usthb Speech Communication and Signal Processing Laboratory Faculty of Electrical Engineering Algeria

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349351

Modern technologies like computer vision (CV), Artificial Intelligence (AI), the internet of things (IoT), and (AIoT) Artificial Intelligence of Things have advanced in many fields, particularly surveillance systems, which need to process faces in real time to guarantee security. This paper proposed a comparative analysis for a real time face recognition system from video sequences with two architectures including traditional application using a personal computer and a Raspberry Pi, respectively. Our study offers an implementation for a real-time face recognition system employing Viola-Jones algorithm and Deep neural networks techniques such as Convolutional Neural Networks (CNN) for face detection and face recognition. Our face recognition system seeks to replace the conventional approach of keeping track of attendance by implementing a Raspberry Pi-based attendance monitoring system that can be utilized by all institutions and organizations. Significant progress has been made thanks to the use of CNN models which has allowed an accuracy rate of 94%. © 2024 IEEE.

关键词： Convolutional neural networks

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Progress in Automated Cancer Detection with Recursive Neural Networks

Progress in Automated Cancer Detection with Recursive Neural...

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2024 International conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Richa Patro, B.D.K. Kumar, Mukesh Pal, Atul Kumar, Anuj Aeri, Manisha Rajkiya Engineering College Kannauj Affiliated To Dr. A.P.J. Aktu Department Of Computer Science And Engineering Lucknow India Graphic Era Hill University Department Of Computer Science And Engineering Uttarakhand Dehradun248002 India Affiliated To Ggsip University Department Of Information Technology New Delhi India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350366846

Detecting cancer is a crucial step in the prognosis and treatment of most cancers and consequently, there has been significant progress in the development of automatic strategies for detecting cancer. One approach that has shown promising results in this area is the usage of Recursive Neural Networks (RNNs). RNNs are a sort of artificial neural network that has the capacity to method sequential statistics, making them appropriate for reading clinical pictures, which include those used in cancer detection. Using RNNs in cancer detection has advanced extensively in recent years, with several researches demonstrating their potential to appropriately detect most cancers with various sorts of clinical images. One observer applied RNNs to research breast tissue images and completed an accuracy of 98.6%, outperforming conventional gadget learning algorithms. Some other examiner used RNNs to research colonoscopy pictures and detected most cancers with an accuracy of 96.6%, surpassing the performance of each human and traditional gadget mastering tactics. One of the key benefits of the use of RNNs in cancer detection is their capability to analyze from statistics and make selections based on patterns and relationships in the records, without the need for characteristic engineering or manual annotation. This makes them nicely-applicable for analyzing complicated scientific photographs wherein traditional methods may lag. In most cancers detection, short and accurate diagnosis is critical for timely treatment and enhancing effects. Therefore, the performance of RNN-based models is a vital aspect in their overall performance analysis. © 2024 IEEE.

关键词： Medical imaging

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Sensitive information identification method combining MTL and fuzzy classification 8

Sensitive information identification method combining MTL an...

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8th IEEE Information Technology and mechatronics engineering conference, ITOEC 2025

作者： Yang, Siyao Liu, Wei Li, Bo Zhang, Ao Liu, Yang Liu, Fangfei Guan, Yong Cai, Chunlei Shenyang Ligong University College of Information Science and Engineering Liaoning China

ISBN: (纸本)9798331517915

To address the limitations of traditional sensitive information classification methods when handling complex data structures and fuzzy boundaries, a method combining multi-task learning and fuzzy logic is proposed. The method constructs a multi-task(MTL) model based on LSTM and attention mechanisms, with classification adjustments made through fuzzy logic. Comparative experimental results show that the proposed method significantly outperforms traditional methods in metrics such as accuracy, precision, recall, F1 score, and AUC, achieving an accuracy of 99.97%, an F1 score of 0.9993, and an AUC of 0.9994. Compared to models like Logistic Regression, Random Forest, and Neural Networks, this method demonstrates a clear performance advantage. Ablation experiments further validate the contribution of multi-task learning and fuzzy logic in improving model performance. Overall, the model performs excellently across various evaluation metrics, proving its effectiveness and practicality in sensitive information recognition tasks. © 2025 IEEE.

关键词： Network security

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Hybrid K-Mean PSO Clustering Algorithm for Energy-Efficient of Object Tracking in WSNs

Hybrid K-Mean PSO Clustering Algorithm for Energy-Efficient ...

引用

2023 International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2023

作者： Ahmed, Sahar Hamad Noori Rashid, Ahmed University Headquarter University of Anbar Anbar Iraq University of Anbar College of Computer Science and Information Technology Anbar Iraq

ISBN: (纸本)9798350322972

Target tracking can be a time-consuming and resource-hungry process due to the limitations in resource, power, sensing capability, communication, memory, and computational capability, it is a challenge to implement traditional target track solutions in wireless sensor networks (WSNs) applications. As any of the operations of data collection, processing, and dissemination leads to an increase in resource consumption, the target tracking algorithms in WSN should effectively utilize the resource and also reduce the computational complexity. in this paper, a new and effective multiple objects tracking approach was proposed by combining dynamic and static clustering techniques with the predictive tracking technique using the Long Short-Term Memory (LSTM) which is the extension of Recurrent Neural Networks (RNNs). In this work two models from the LSTM were used, where the first model will classify the objects' motion path and then discover the object's new location, and the second model was used to predict the three nodes that will track the objects, and computing the energy consumption for this approach and proposed anew algorithm called Hyper K-Mean PSO Clustering algorithm (HKM-PSO) to improve the energy consumption and increasing the lifetime of the network. The approaches and the algorithm that proposed in this work were very effective. The most important contribution was optimizing the energy consumed in the process of tracking multiple objects and increasing the life of the network by improve the energy through the process of selecting the head cluster and increasing the life of the network. © 2023 IEEE.

关键词： Energy utilization

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Big Data Framework to Detect and Mitigate Distributed Denial of Service(DDoS) Attacks in Software-Defined Networks (SDN)

Big Data Framework to Detect and Mitigate Distributed Denial...

引用

2023 International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2023

作者： Hamedani, Amirreza Fazely Aziz, Muzzamil Wieder, Philipp Yahyapour, Ramin Göettingen University Department of Computer Science Göttingen Germany EScience Group Göttingen Germany

ISBN: (纸本)9798350322972

The software-defined network is in recent years come into the sight of so many network designers as a successor to the traditional network. This type of network is vulnerable to DDoS attacks, targeting a different part of the SDN network architecture by continuously injecting fake flows. It imposes substantial processing on the controller, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to legitimate *** paper proposes a scalable and reliable real-time DDoS attack detection and mitigation framework using machine learning incorporated with a big data pipeline infrastructure for the SDN environment. The framework supports the detection of DDoS attacks with the ability of processing and analyzing the huge amount of network traffic in near real-time. The framework employs the power of big data solutions, such as Apache Kafka, and Apache Spark in order to realize a scalable pipeline for big data *** main objective of this research is to offer a vital supporter which can assist network security systems in addressing the DDoS threats in the SDN environment. For this purpose, machine learning algorithms are also utilized to provide a viable opportunity for classifying network data and detecting DDoS attacks in this regard. © 2023 IEEE.

关键词： Machine learning

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An Integration of a Neural-Network-Based computer Vision Model and a 2-DOF Object Tracker Robot 11

An Integration of a Neural-Network-Based Computer Vision Mod...

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11th RSI International conference on Robotics and mechatronics, ICRoM 2023

作者： Azimi, Mohammad Gharekhani, Ali Ebadollahi, Saeed Iran University of Science and Technology Faculty of Electrical Engineering Department of Control Tehran Iran

ISBN: (纸本)9798350308105

This paper presents a system for object tracking that integrates a neural-network-based computer vision model and a 2-DOF robot. The system consists of two main parts: the first part detects objects in the environment and enables the user to select a target for tracking, while the second part establishes a control loop and tracks the selected target using a PID controller. The system uses Yolo neural network for object detection and StrongSort neural network for sorting and assigning IDs to objects. Two methods are proposed for distinguishing the target right before beginning the control loop phase: Landmark-Based Identification (LBI) and Feature-Based Identification. LBI detects a specific and unique sign in the environment to locate the target, while Feature-Based Identification utilizes the StrongSort neural network to extract features of detected objects and compares them to match the desired target. Finally, the tracking procedure begins with the help of StrongSort model and the system calculates the position of the target, generates a control signal to track it even if it's moving. This system demonstrates the potential of integrating advanced technologies for object tracking applications. © 2023 IEEE.

关键词： Feature extraction

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Leveraging Machine Learning and Deep Learning Percepts for Air Pollution Analysis 15

Leveraging Machine Learning and Deep Learning Percepts for A...

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15th International conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Gahlot, Mansi Katiyar, Maitreyi Katiyar, Maitri Agarwal, Ayushi Computer Science and Engineering Abes Engineering College India Abes Engineering College Computer Science and Engineering India

ISBN: (纸本)9798331300579

The main aim of this project is to teach the computer to recognize patterns in air quality data, such as how pollution levels change over time. We use the LSTM network to make sense of different things like pollution amounts, weather conditions, and past AQI numbers. What makes this model smart is that it improves at understanding the air by looking at a lot of data. It can handle the tricky and ever-changing nature of the atmosphere. The computer learns from what happened before and adjusts to what's going on right now, which is super useful for predicting air quality in the future. Utilizing deep learning's predictive powers to improve AQI forecast accuracy is the main goal of this research. The LSTM model exhibits higher adaptability to the dynamic character of meteorological circumstances by assimilation of complex patterns included in the air quality data. The model learns the intricate correlations between different contaminants, climatic variables, and AQI through extensive training. But this study isn't just about computers and data;it's about making our cities and lives better. The computer predictions can help our leaders make wise decisions to protect our health, especially in busy cities. It's like having a friend that always watches out for us, letting us know when things might not be great so we can make changes and live in a cleaner and safer environment. © Grenze scientific Society, 2024.

关键词： Air quality

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