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The Past, Present, and Future of Robotic Systems in Automotive Applications 23

The Past, Present, and Future of Robotic Systems in Automoti...

引用

23rd international Arab conference on Information Technology, ACIT 2022

作者： Shaout, Adnan Overbeck, Sarah College of Engineering and Computer Science The University of Michigan-Dearborn Department of Electrical and Computer Engineering DearbornMI48128 United States

This paper focuses on the automotive applications of robotic systems and their evolution on two fronts: manufacturing and autonomous vehicles. The first will be divided into chronological generations and while the sec... 详细信息

ISBN: (纸本)9798350320244

关键词： Robotics

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Active Learning for High-Quality Sawtooth Wave Dataset Generation in Sleep Analysis 6

Active Learning for High-Quality Sawtooth Wave Dataset Gener...

引用

6th international conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Chen, Jinghuang Bao, Xiaoyu Ye, Yuntao Huang, Qiyun School of Automation Science and Engineering South China University of Technology Guangzhou China Research Center for Brain-Computer Interface Pazhou Laboratory Guangzhou China

ISBN: (纸本)9798331541750

Sawtooth Waves (STW) that occur during Rapid Eye Movement (REM) sleep are closely associated with memory consolidation and the regulation of brain functions. However, due to challenges in data acquisition and the high cost of manual annotation, there is currently an absence of a publicly massive STW annotated dataset. This study focuses on constructing a high-quality STW dataset, comprising 4912 samples from 200 participants, and developing classification algorithms for STW detection. We utilized three prevalent algorithms-Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN)-to assess the dataset's quality. The dataset achieved a maximum classification accuracy of 95.62% with the CNN, confirming the gold standard of STW dataset. Finally, we studied age and gender difference in physiological characteristics of STW, including average frequency of STW, peak-to-peak amplitude(PTP), root mean square(RMS) and spectrum power. This study created a public STW dataset and a STW detection model, laying the groundwork for analyzing the mechanisms of STW events. © 2024 IEEE.

关键词： Convolutional neural networks

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VPN Traffic Classification Based on CNN 14

VPN Traffic Classification Based on CNN

引用

14th IEEE international conference on computer Research and Development, ICCRD 2022

作者： Feng, Ruixiao Hu, Taiwei Jia, Xiajin College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9781728177212

Classifying network traffic has long been an active field of research, and it is also a key part of network traffic analysis. However, as the use of network traffic encryption and masking grows in the recent years, traditional network classification methods have become less effective. To overcome the traditional methods' disadvantages of not being accurate enough to classify real world scenario network traffic that is encrypted and randomly mixed with flows from different services and applications, we proposed a method that utilizes payload-based technique and a convolutional neural network model that was modified from existing network structures. Our data preprocessing converts the byte flow of the packet payload into two dimensional arrays for training and classification. Comparing to existing methods that are not purposed for encrypted traffic classification, our method achieves better accuracy, yet remains less resource-intensive relative to using other deep neural networks, making it more suitable to deploy in network infrastructures for practical use. © 2022 IEEE.

关键词： Cryptography

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Innovative Motion Sensing System with Labview 14th

Innovative Motion Sensing System with Labview

引用

14th EAI international conference on Broadband Communications, Networks, and Systems, BROADNETS 2024

作者： Vidyadhar, R. Phani Ravindra, J.V.R. Kumar, G. A. E. Satish Sandeep, Yanigandla Ashwitha, Kanugula Mantri, Devansh Vishvakumari, Faldu Department of ECE Vardhaman College of Engineering Telangana Hyderabad India RK University Gujarat Rajkot India

ISBN: (纸本)9783031811678

Nowadays children’s used to play and going near restricted areas like transformers and borewells due to that they are getting affected by such incidents ensure that we are performing a system called Innovative Motion Sensing with LabVIEW. This system will be built to instantly detect motion and send alerts or initiate actions according to the motion. LabVIEW software will be used for terminating this project. This Motion Detection System can be used for automotive safety in addition to restricted locations. The system’s primary objective will be to detect motion in real time and send out notifications or start events according to the motion. Several motion situations will be used to test and evaluate the system, including performance measures like accuracy and detection, different motion situations will be used to test and validate the system, and performance metrics including accuracy and detection time will be assessed. In this project, a GSM module is utilized to communicate with a nearby CELL Tower and transmit the message to a mobile device. Intelligent Motion Detection System for effectively identifying movement or motion inside a predetermined area and prompt timely replies or actions. With the help of this system, security, monitoring, and automation are all intended to be improved in a number of applications, and motion-related events will be handled proactively. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Motion sensors

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A Comprehensive Study on Industry 4.0 Technologies 1

引用

international conference on Production and Industrial engineering, CPIE 2023

作者： Kadne, Atharva Kamath, Pratham Karvat, Manav Bodkhe, Mohan Sharma, Sanjeev Department of Mechanical Engineering SVKM’s NMIMS Mukesh Patel School of Technology Management and Engineering Mumbai400056 India Research Scholar Department of Mechanical Engineering Amity University Haryana Gurugram India

ISBN: (数字)9789819983438

ISBN: (纸本)9789819983421

This paper discusses the impact of Industry 4.0 on manufacturing industries and distribution networks, as well as the design principles that helped create it, including service orientation, virtualization, real-time capability, decentralization, modularity, and interoperability. Also, Industry 4.0 integrates information technology and operational technology systems, creating a network between autonomous manufacturing equipment and wider computer systems. The paper presents several case studies of successful implementations of Industry 4.0 technologies, including Renault's use of automation, data analytics, and artificial intelligence to improve its manufacturing processes, a case study of an industrial company's transformation into a smart factory through the adoption of technologies in Industry 4.0, and the benefits of digital technologies in transforming traditional supply chain operations into next-generation digital supply chains. The paper concludes with case study of Covid-19 and a discussion of the intense competition faced by aerospace and defense companies and how Industry 4.0 technologies could help them stay ahead. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Industry 4.0

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Application of X-ray fluorescence technology in lithology discrimination

Application of X-ray fluorescence technology in lithology di...

引用

2023 international conference on computer Application and Information Security, ICCAIS 2023

作者： Liu, Guili Zhai, Juan Zhang, Wei Lai, Wanchang Zhao, Ziqi Li, Wen Wang, Guangxi Yang, Qiang Chen, Ran College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu610059 China Chengdu Institute of Product Quality Inspection Co. Ltd. Sichuan Chengdu610100 China Chengdu Zhongfu Technology Co. Ltd. Sichuan Chengdu610100 China

ISBN: (纸本)9781510675032

X-ray fluorescence data are not only an important indicator in petroleum exploration, but they are also an advantage in lithology discrimination. From the perspective of the application of nuclear technology in drilling and exploration, in this paper, the X-ray fluorescence count rate of the core is used as the basic data for core logging, and the geology and multivariate statistical data are analyzed to analyze the lithology of the core. X-ray fluorescence analysis can provide the fluorescence count rates of 35 elements in a core sample. If there are n core samples, an n*35 matrix can be created. If 35 types of fluorescence count rates are used as the independent variables to directly identify the lithology, the dataset is very large, and some of the independent variables are not strongly correlated. Therefore, the discrimination effect is insufficient for achieving the purpose. Therefore, based on the diagenetic characteristics of sedimentary rocks and the main rock-forming elements and using multivariate statistical methods, in this paper, the count rates of five elements (i.e., Si, Fe, Al, K, and Ca) are selected as independent variables for the lithology discrimination. It is reasonably transformed to obtain the matrix coefficients before dimensionality reduction. The original five-dimensional dataset is reduced to two dimensions using matrix coefficients. Then, Fisher's discriminant method is applied to the dimensionally reduced data to discriminate and classify the lithology. Based on the discrimination results, the X-ray fluorescence count rate and mathematical statistics can be used to obtain a good effect in lithology discrimination. © 2024 SPIE.

关键词： Lithology

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Long Short-Term Memory (LSTM) Networks for Stock Market Prediction 4

Long Short-Term Memory (LSTM) Networks for Stock Market Pred...

引用

4th international conference on Computation, automation and Knowledge Management, ICCAKM 2023

作者： Dhabliya, Dharmesh Al-Jawahry, Hassan M. Sharma, Viklap Jayadurga, R. Jasmin, M. Vishwakarma Institute of Information Technology Department of Information Technology Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Department of Computer Science & Engineering Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Master of Business Administration Chennai India New Prince Shri Bhavani College of Engineering and Technology India

ISBN: (纸本)9798350393248

The amazing capacity of Long Short-Term Memory (LSTM) networks to record complex temporal connections in sequential data has drawn a lot of attention in recent years. In the context of stock market prediction, a field where precise price movement forecasting is crucial, this study investigates the use of LSTM networks. We explore the basic ideas behind LSTM networks and explain how they may be used for time series forecasting because of their ability to analyze and understand sequential data. The first section of the study gives a brief introduction of LSTM architecture and its benefits over more conventional models. We talk about how important feature engineering and data preparation are to improving LSTMs' predictive power for stock market forecasting. We also explore how model assessment methods and hyperparameter adjustment may be used to maximize LSTM performance. In the study, we investigate several approaches, such as single-step and multi-step forecasting, for using LSTM networks in stock market prediction. We look at how external influences affect the performance of the model, including sentiment analysis of news and economic indicators. We also discuss potential remedies to the problems of overfitting and data scarcity that are inherent in LSTM-based stock market prediction. © 2023 IEEE.

关键词： Long short-term memory

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A Q-Learning based Method for Energy-Efficient Monitoring of Activities of Daily Living using a Wearable Device and a Companion Robot 13

A Q-Learning based Method for Energy-Efficient Monitoring of...

引用

13th IEEE international conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2023

作者： Liang, Fei Sheng, Weihua School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States

ISBN: (纸本)9798350315196

In this paper, we proposed a Q-learning based algorithm to trade off the activity recognition accuracy, energy cost, privacy concerns and robot resource consumption in monitoring activities of daily living (ADLs) for elderly care using a wearable device and a companion robot. The robot uses a Q-learning algorithm to decide when to turn on what sensors for data collection by considering the above four criteria. The wearable device follows the robot's decision to turn on the relevant sensors to collect data which are sent back to the robot for activity recognition. First, we described the overall design of the system in which a wearable device and a companion robot collaborate for activity monitoring. Second we developed the Q-learning framework based on the knowledge of transition motions. Third, we trained the model and evaluated the proposed method using offline data and real time data collected from our smart home testbed. The results showed that the proposed method could recognize ADLs with high accuracy while saving about half energy compared with periodic methods. The results from real time evaluation showed that the activity detection rate is about 85%. © 2023 IEEE.

关键词： Learning algorithms

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Hybrid Machine Learning and Deep Learning CNN Models for Apple Trees Leaves Pathologies Classification 12

Hybrid Machine Learning and Deep Learning CNN Models for App...

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12th international Scientific conference on computer Science, COMSCI 2024

作者： Matov, Petar Iavorov Lazarova, Milena Kirilova Filipova-Petrakieva, Simona Kirilova Taralova, Ina Technical University of Sofia Faculty of Computer Systems and Technologies Department of Computer Systems 8 Kliment Ohridski blvd. Sofia1000 Bulgaria Technical University of Sofia Faculty of Automation Department of Theoretical Electrical Engineering 8 Kliment Ohridski blvd. Sofia1000 Bulgaria Ecole Centrale de Nantes LS2N UMR CNRS 6004 Nantes France

ISBN: (纸本)9798350392166

Agricultural development in recent years has been based entirely on smart management. Leaves diseases on apple trees is a very common problem. The present work is a continuation of previous research of the author's team to solve it. The recently proposed apple leaves pathology prediction model based on deep learning convolutional neural network (CNN) is extended to hybrid models. The latter are a combination of a deep learning CNN and classical machine learning techniques. To increase the classification accuracy two hybrid models are proposed: CNN+SVM (Support Vector Machines) and CNN+RF (Random Forest). The CNN is based on modified DenseNet architecture and SVM and RF models are integrated as CNN output is fed to SVM and RF models respectively to ensure higher classification accuracy. The suggested hybrid models are evaluated on a data set that comprises 1800 images of pathologies in apple trees' leaves. The experimental results prove the higher accuracies of the hybrid deep + machine learning models compared to the deep learning CNN model. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine Learning Strategies for Business Process Optimization 10

Machine Learning Strategies for Business Process Optimizatio...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Kumar Chaudhary, Jitendra Aarif, Mohd Rao, N. Raghava Sobti, Rajeev Suresh Kumar, M.V. Muralidhar, L.B. School of Computing Graphic Era Hill University Bhimtal Campus Uttrakhand India Global Research Network UP India Institute of Aeronautical Engineering Telangana Hyderabad India Lovely Professional University Phagwara India College of Law Koneru Lakshmaiah Education Foundation Green Fields Andhra Pradesh Guntur India School of Commerce Jain Deemed to Be University Department of Management Studies Bangalore India

ISBN: (纸本)9798350382471

The incorporation of machine learning tactics into commercial procedures heralds a paradigm-shifting period in which enterprises harness cutting-edge technology to maximise productivity, cultivate flexibility, as well as elevate client experiences. This paper examines the many applications of machine learning, demonstrating its contribution to automation, increased productivity, and customized client experiences. In terms of methodology, to comprehend the complex relationships between business processes and machine learning, an interpretivist approach is used. Using a framework for deductive reasoning, the study uses secondary data sources, such as academic journals and case studies, to confirm as well as enhance preexisting beliefs. The investigation reveals machine learning's analytical strength in identifying recurring patterns and patterns, its critical function in automation to free up human resources for strategic projects, and its forecasting power for resource optimization. Machine learning integrated into business processes provides a flexible toolkit that can be tailored to satisfy the demands of individual organizations. The case studies of Amazon, Google, and Netflix provide empirical evidence that supports the practical application of machine learning in optimizing operations, improving user experiences, and promoting and cultivating consumer loyalty. This report sheds light on how machine learning is influencing a paradigm change regarding the way businesses operate. Organizations that embrace this technology transformation are positioned to be trailblazers, resilient, and innovative, ready to navigate the future. © 2023 IEEE.

关键词： automation Business Process Optimization Machine Learning Personalization Predictive Analytics

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