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Performance improvement of multi-parameter measurement optical fiber sensors based on machine learning methods 21

Performance improvement of multi-parameter measurement optic...

引用

21st international conference on Optical Communications and Networks, ICOCN 2023

作者： Zhou, Yi-Fan Han, Bo Jin, Po Zhang, Ya-Nan Zhao, Yong Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China

A machine learning based spectral data analysis method is proposed to solve the problem of limited measurement range of optical fiber sensors. This method expands the measurement range of the sensor and improves measu... 详细信息

ISBN: (纸本)9798350343502

关键词： Spectrum analysis

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Physics-Informed Machine learning for UAV Control 21

Physics-Informed Machine Learning for UAV Control

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21st international conference on Electrical engineering, Computing Science and Automatic Control, CCE 2024

作者： Osorio Quero, Carlos Alexander Martinez-Carranza, Jose Computer Science Department Puebla Mexico

ISBN: (纸本)9798350377545

The integration of Dynamic Mode Decomposition (DMD) control with Physics-Informed Neural Networks (PINNs) offers a novel approach to enhancing UAV quadcopter control systems. This method leverages DMD techniques and PINNs to solve the Riccati equation, which is essential for precise UAV position estimation. By framing the UAV control problem within physics-based constraints, the approach ensures that the learned models adhere to the physical laws governing UAV dynamics. Utilizing a comprehensive dataset-including UAV flight parameters such as position, velocity, and control inputs-DMD extracts fundamental dynamic modes from the data, providing a reduced-order representation that captures the dominant UAV dynamics. This reduced representation is then incorporated into the PINN framework to accurately solve the Riccati equation. The combination of DMD and PINNs results in a robust control strategy that significantly improves position estimation accuracy and optimizes control performance. Real-Time validation of this method was conducted in a Unity-based physics simulation environment, incorporating factors like gravity and perturbation noise. The results demonstrate substantial improvements in estimation accuracy and control stability compared to traditional methods. © 2024 IEEE.

关键词： Dynamic mode decomposition

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Identification of Phtato Leaf Disease Based on The TS-Resnet Deep Learing Method 21

Identification of Phtato Leaf Disease Based on The TS-Resnet...

引用

21st international conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2023

作者： Duan, Xiao-Yue Gu, Shen-Ming School of information Engineering Zhejiang Ocean University Zhoushan316022 China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9798350303810

At present, the incidence of potato disease is large, the disease is complex, and it is difficult to control, which seriously affects the potato yield and quality. Therefore, in order to realize the potato disease image recognition technology under small sample data, by using attention mechanism, transfer learning and data enhancement technology, the potato leaf disease image is accurately analyzed. This paper proposes a potato leaf disease recognition method based on TS-ResNet(Transform learning and SE) deep learning method. Based on the original ResNet101 model, its full connectivity layer is modified to add transfer learning and attention mechanism. In order to increase the number of potato disease part of the data set, the original graph can be flipped, horizontal flip and other operations to avoid the phenomenon of overfitting in the original model. The results of this study can provide some support for the intelligent research of potato leaf disease identification. © 2023 IEEE.

关键词： Disease recognition Potato ResNet101 SE attention mecha-nism Transfer learning

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intelligent Analysis Method of E-Commerce data Based on Multiple Machine learning Algorithms 4

Intelligent Analysis Method of E-Commerce Data Based on Mult...

引用

4th international conference on Machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Jiang, Xiaotian Li, You Jilin Agricultural Science and Technology University Jilin Jilin China

Machine learning algorithm is one of the most popular research fields in the 21st century, and it is widely used in various industries. This paper first discusses the development status, characteristics and intelligent classification technology of e-commerce based on various methods. Then, through statistical analysis of multiple samples, this paper establishes a model to predict the quantity of goods and other time-related changes in the future period of time, and proposes an improved variance estimate to achieve the best effect under different data scenarios. Finally, the experiment proves that the response time of the linear regression algorithm fluctuates between 20ms and 25ms, and the overall fluctuation is small. For different samples, the response time of the linear regression algorithm is maintained in a relatively stable range, showing the stability of different input data. The response time of the decision tree algorithm fluctuates between 28ms and 33ms, showing a certain fluctuation. For different samples, the response time of decision tree algorithm fluctuates greatly, which may be affected by the characteristics of input data and show different processing efficiency. The response time of support vector machine algorithm fluctuates between 14ms and 18ms, and the change is relatively stable. For different samples, the response time of SVM algorithm is kept at a relatively stable level, showing the consistency of input data processing ability. © 2024 The Authors.

关键词： Adversarial machine learning

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Hybrid Computational Framework for Fault Detection in Coil Winding Manufacturing Process Using Knowledge Distillation 21

Hybrid Computational Framework for Fault Detection in Coil W...

引用

IEEE 21st international conference on Industrial Informatics (INDIN)

作者： Escudero-Ornelas, Izhar Oswaldo Tiwari, Divya Farnsworth, Michael Zhang, Ze Tiwari, Ashutosh Dept Automat Control & Syst Engn ACSE Sheffield England

ISBN: (纸本)9781665493130

This paper proposes a hybrid computational framework for fault detection during the coil winding manufacturing process by using a combination of Discrete Event Simulation (DES) model with a Supervised Machine learning (SML) algorithm. The conventional End of the Line (EoL) tests are insufficient in detecting faults during process resulting in increased manufacturing costs and lead times. The proposed methodology utilises a Knowledge Distillation (KD) approach to address the challenges associated with the technique and optimise the student model's performance by employing architecture search and data augmentation. Multiple SML algorithms were evaluated to determine their effectiveness in predicting faults during manufacturing. The random forest algorithm demonstrated superior performance due to its ability to handle complex data and identify the impact of interdependencies of process parameters on the final product quality. The method was validated by conducting physical experiments on a linear coil-winding machine, and the results indicated that the random forest algorithm has the potential to decrease simulation time from 2 minutes to less than a second. The proposed methodology has the potential to reduce manufacturing time, enhance stator quality, and ultimately improve their reliability and safety.

关键词： Knowledge Distillation Discrete Event Simulation Supervised Machine learning modelling winding faults

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Predicting the Impact of data Visualization on Decision-Making in Higher Education 21

Predicting the Impact of Data Visualization on Decision-Maki...

引用

21st IEEE international Symposium on intelligent Systems and Informatics, SISY 2023

作者： Llaha, Olta Aliu, Azir Kadena, Esmeralda South East European University Faculty of Contemporary Sciences and Technologies Tetovo Macedonia Obuda University Bánki Donát Faculty of Mechanical and Safety Engineering Budapest Hungary

ISBN: (纸本)9798350343366

data visualization plays a crucial role in comprehending and leveraging interactive and complex data by presenting information in graphical or pictorial form. As the vast and open data field continues to evolve with technological advancements, it necessitates diverse levels of interaction for a comprehensive understanding. This paper focuses on the practical application of data visualization techniques and machine learning algorithms. Through a case study that compares various algorithms., we analyze information and forecast the influence of data visualization on decision-making. Presenting substantial evidence, our findings strongly support the impact of data visualization on decision-making. Moreover, we investigate how data visualization technology influences decision-making among administrative staff, academic staff, and students. Additionally, we thoroughly assess the implications of data visualization on decision-making within higher education institutions, highlighting its remarkable capacity to empower stakeholders to make faster and more informed decisions. © 2023 IEEE.

关键词： Forecasting

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9028 | Uncertainty quantification for wheeled locomotion machine learning predictions on soft soil 21

9028 | Uncertainty quantification for wheeled locomotion mac...

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21st international and 12th Asia-Pacific Regional conference of the international Society for Terrain-Vehicle Systems, IstVS 2024

作者： Fediukov, Vladyslav Huhne, Jana Dietrich, Felix Buse, Fabian Weßling Germany School of Computation Information and Technology Technische Universität München Garching b. München Germany

ISBN: (纸本)9781942112570

Surrogate modeling with machine learning (ML) techniques is becoming increasingly popular in engineering and physical fields. Models based on statistical inference often lack uncertainty measures, which are crucial for comprehensive predictions. Uncertainty quantification (UQ) addresses these challenges, especially in tasks lacking analytical solutions or extensive experimental data, such as modeling wheel locomotion on soft soils. High-fidelity data from real experiments or precise particle-level simulations are scarce, adding inherent uncertainty to statistical models. In our paper we analyzed the UQ aspect of the terramechanical surrogate modeling. Our surrogate model leverages the probabilistic nature of Gaussian processes to facilitate uncertainty calculation and make further analysis easier. We extend UQ analysis into a new multi-fidelity model for wheel locomotion. Our work aims to improve the model's interpretability and optimization through uncertainty propagation, sensitivity analysis and uncertainty decomposition. © 2023 international Society for Terrain-Vehicle Systems. All rights reserved.

关键词： Sensitivity analysis

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AI-Driven HR Optimization strategies in Finance and Marketing: Methodological Framework and Applications

AI-Driven HR Optimization Strategies in Finance and Marketin...

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2024 international conference on Innovative Computing, intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Lekshmi, R.S. Mary, V. Sheela Arasuraja, G. Krishnamoorthy, V. Kaliappan, S. Selvameena, R. St.Joseph's College of Engineering Department of Management Studies Tamil Nadu Chennai India Aarupadai Veedu Institute of Technology Vinayaka Mission's Research Foundation Department of Management Studies Tamil Nadu Chennai India Kongu Engineering College Department of Management Studies Perundurai Tamil Nadu Erode India Lovely Professional University Division of Research and Development Punjab Phagwara India Dr.M.G.R Educational and Research Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

The proposed systems that include AI and the IoT present innovative financial and marketing opportunities. AI analytics capabilities and networked devices with the capability to provide real-time data mean the possibility to significantly improve recruitment, employee engagement, performance management, and retention. It combines natural language processing and machine learning algorithms, offering personalized recommendations, and in the vision insights, and in the case of environmental sensors, smart devices, and wearable health monitors, it facilitates accumulation of significant data. This framework will integrate IoT with AI to enhance HR procedures: more engaging, efficient, and enjoyable work environment. Apart from better decision-making and automated occupations, it also advocates for a data-driven approach to HRM. Further, it can result in the formation of intelligent and responsive HR systems that have the potential of improving organizational effectiveness and work productivity. © 2024 IEEE.

关键词： Marketing

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Investigating the Effectiveness of Artificial Intelligence in Watermarking and steganography for Digital Media Security 21

Investigating the Effectiveness of Artificial Intelligence i...

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21st international Multi-conference on Systems, Signals and Devices, SSD 2024

作者： Abdalwahid, Shadan M.J.A Hashim, Wassan Adnan Saeed, Mohammed Ganim Altaie, Sarmad A. Kareem, Shahab Wahhab Engineering College Erbil Polytechnic University Information System Engineering Dept. Erbil Iraq AIQAlam University College Medical Instruments Techniques Dept Kirkuk Iraq Dohuk Polytechnic University Department of Information Technology Dohuk Iraq University of Technology Computer Engineering Department Baghdad Iraq Erbil Technical Engineering College Erbil Polytechnic University Information System Engineering Dept. Erbil Iraq

ISBN: (纸本)9798350374131

Watermarking and steganography are methods of embedding digital information within images or other media, such as text or audio, for the purpose of Copyright Protection or covert communication. This field of study is not recent and has been ongoing for several years, culminating in its current advanced stage. The utilization of Artificial Intelligence algorithms has played a pivotal role in revolutionizing various aspects, including security concerns and the precision of outcomes, as compared to traditional methods. This paper focuses on doing an in-depth analysis of cutting-edge research, techniques, and methodologies employed in the domain of Watermarking and steganography, specifically in conjunction with Artificial Intelligence. By thoroughly examining and evaluating a collection of recent studies in this domain, we have scrutinized the outcomes of each study with respect to its research goal, the acquired results, the employed algorithm, and the research's robustness in terms of susceptibility to various forms of attacks and the technique of data embedding. Our findings indicate that the use of Artificial Intelligence algorithms has a substantial influence on enhancing result precision, system resilience, and establishing data ownership. Deep Neural Networks (DNN) are essential in Watermarking and steganography due to their robustness, effectiveness, and accuracy. Novel methodologies and systems improve security, incorporation rates, precision of detection, and speed of convergence. Deep learning is being investigated in techniques such as data concealment, information hiding, and steganography to improve security. © 2024 IEEE.

关键词： Embeddings

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Transformer-Based Biometrics Method for Smart Phone Continuous Authentication 21

Transformer-Based Biometrics Method for Smart Phone Continuo...

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21st international conference on Networking, Sensing and Control, ICNSC 2024

作者： Guo, Zhida Shen, Wuqiang Xiao, Mingqian Cui, Lei Xie, Dehua Huizhou Power Supply Bureau Guangdong Power Grid Co. Ltd. Huizhou China China Southern Power Grid Cyberspace Security Joint Laboratory Guangdong Power Grid Co. Ltd. Guangzhou China College of Electronics and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9798350365221

As a widely used personal device, smartphones handle substantial amounts of sensitive information, rendering their security a critical concern. To ensure a seamless user experience and achieve robust continuous authentication, this work introduces a deep learning-based model leveraging a transformer architecture. The model captures complex temporal dependencies by learning user behavior patterns from keystroke dynamics and sensor data (e.g., accelerometer and gyroscope), significantly improving the precision and robustness of multimodal biometric recognition. A dynamic behavior-weighted module is incorporated to optimize the multi-head attention mechanism, while the integration of a quadruple loss function further enhances biometric recognition accuracy. Experimental results validate the model's efficacy, demonstrating high usability and a low error rate in multimodal biometric datasets, positioning it as a viable solution for continuous implicit authentication on smartphones. © 2024 IEEE.

关键词： Smartphones

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