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A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehicles 18

A reinforcement learning path planning approach for range-on...

引用

ieee 18th international conference on automation science and engineering (ieee CASE)

作者： Masmitja, Ivan Martin, Mario Katija, Kakani Gomariz, Spartacus Navarro, Joan Mbari Bioinspirat Lab Moss Landing CA 95062 USA CSIC Inst Ciencies Mar Barcelona 08003 Spain Univ Politecn Cataluna Knowledge Engn & Machine Learning Grp Barcelona Tech Barcelona 08034 Spain Univ Politecn Cataluna SARTI Res Grp Barcelona Tech Elect Dept Barcelona 080934 Spain

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

Underwater target localization using range-only and single-beacon (ROSB) techniques with autonomous vehicles has been used recently to improve the limitations of more complex methods, such as long baseline and ultra-short baseline systems. Nonetheless, in ROSB target localization methods, the trajectory of the tracking vehicle near the localized target plays an important role in obtaining the best accuracy of the predicted target position. Here, we investigate a Reinforcement Learning (RL) approach to find the optimal path that an autonomous vehicle should follow in order to increase and optimize the overall accuracy of the predicted target localization, while reducing time and power consumption. To accomplish this objective, different experimental tests have been designed using state-of-the-art deep RL algorithms. Our study also compares the results obtained with the analytical Fisher information matrix approach used in previous studies. The results revealed that the policy learned by the RL agent outperforms trajectories based on these analytical solutions, e.g. the median predicted error at the beginning of the target's localisation is 17% less. These findings suggest that using deep RL for localizing acoustic targets could be successfully applied to in-water applications that include tracking of acoustically tagged marine animals by autonomous underwater vehicles. This is envisioned as a first necessary step to validate the use of RL to tackle such problems, which could be used later on in a more complex scenarios.

关键词： Location awareness Autonomous underwater vehicles Target tracking Power demand computer aided software engineering Navigation Reinforcement learning

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Analysis of Axial Flux Permanent Magnet Motors Used for Electric Vehicle 13

Analysis of Axial Flux Permanent Magnet Motors Used for Elec...

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13th ieee international conference and Expositions on Electric and Power engineering, EPEi 2024

作者： Elena-Daniela, Lupu Stefan, Tabacu Ştefan cel Mare University of Suceava Faculty of Electrical Engineering and Computer Science Suceava Romania National University of Science and Technology Politehnica Bucharest Faculty of Mechanics and Technology Piteşti Suceava Romania

ISBN: (纸本)9798350356199

Currently, the electrical vehicle industry needs motors that can deliver high efficiency throughout the entire vehicle running cycle. Axial flux motors are compatible with these major developments taking place in this industry. In the first part of this paper is presented the differences between the geometry of axial flux motors and the radial-flux motors to highlight the complexity of the analysis of AFPM and their particularities. Then, in the paper are presented and analyzed different classical AFPM motor variants and emerging new variants of AFPM including transversal flux motors, bearingless AFMs and magnetically geared AFPMs pointing out their strengths and weaknesses. Also, in this paper, an electromagnetic analysis of an axial flux motor type is presented. © 2024 ieee.

关键词： Traction motors

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Unmanned aerial vehicle path planning method based on football team training algorithm 7

Unmanned aerial vehicle path planning method based on footba...

引用

7th international conference on computer Information science and Application Technology, CISAT 2024

作者： Li, Wenguang Wu, Hao Huang, Xinxin Shi, Fengming Li, Wei Zhao, Song Army Engineering University of Pla Shijiazhuang campus Shijiazhuang China Engineering University of Pla Department of Teaching and Research Army Shijiazhuang China

ISBN: (纸本)9798350375107

Aiming at the optimal solution problem of unmanned aerial vehicle path planning, the current novel metaheuristic algorithm (football team training algorithm) was applied to solve the problem of unmanned aerial vehicle path planning. First of all, it is to transform algorithm fitness function to unmanned aerial vehicle path cost. Then, the three stages of collective training, group training and individual extra training of the algorithm were adjusted. Finally, the flight path obtained by the solution is smoothed and the final flight path of the unmanned aerial vehicle is obtained. Simulation results show the algorithm can be applied to solve the problem of unmanned aerial vehicle path planning, and the algorithm has good convergence. © 2024 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Anomaly Detection in Cybersecurity: Leveraging Machine Learning for Intrusion Detection

Anomaly Detection in Cybersecurity: Leveraging Machine Learn...

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

作者： Mittal, Aditi Gupta, Anushka Bhoomi Agarwal, Kadambri Abes Engineering College Department Of Computer Science And Engineering Ghaziabad India

ISBN: (纸本)9798350366846

An intrusion Detection system is a software that monitors the network traffic and alerts the administrators if any malicious request is found. The system is able to segregate between a malicious and non-malicious request using two methods one is signature-based and the other is by training a machine learning model which is also called the Anomaly-based method. In this paper, we have achieved the highest accuracy from all existing models i.e., 99.99%. We have been able to increase the accuracy of ML models as a result of our proposed model, which is an ensemble of Random Forest, Decision tree, and KNN. Along with this, we have also applied a deep learning model ANN which has led to the effect. Since we have used four different datasets, our model is trained with a large number of datasets which has also contributed to high accuracy. © 2024 ieee.

关键词： Intrusion detection

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The Quantum Threat: Implications for Data Security and the Rise of Post-Quantum Cryptography 9

The Quantum Threat: Implications for Data Security and the R...

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9th ieee international conference for Convergence in Technology, I2CT 2024

作者： Tiwari, Amritansh Chauhan, Rahul Joshi, Nischay Devliyal, Swati Aluvala, Srinivas Kumar, Amit Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India Chitkara University Institute of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9798350394474

Securing our data is critical in today's digital environment. This study investigates the possible influence of quantum computers on data security. Research has been initiated by learning about quantum computers and their distinct qualities. These computers, which are driven by quantum bits (qubits), can do numerous jobs at the same time, but they also confront unique problems. The RSA Algorithm has been proposed here which is a key component of modern encryption. To protect our data from normal computers, one can use huge prime *** quest then takes to Shor's Algorithm, a quantum innovation designed to decipher complicated codes effectively. We explain how it works and what it means for traditional computing. Author delves into the area of post-quantum cryptography, introducing approaches like lattice cryptography, code-based cryptography, multivariate cryptography and hash-based signatures. These solutions exploit quantum features to guard against quantum computer attacks, albeit they may come with certain trade-offs. This research study emphasizes the constantly changing environment of digital security, emphasizing the importance of adaptive cryptographic systems in an increasingly linked and vulnerable world. © 2024 ieee.

关键词： Economic and social effects

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Beyond Textual Analysis: Framework for CSAT Score Prediction with Speech and Text Emotion Features

Beyond Textual Analysis: Framework for CSAT Score Prediction...

引用

2024 ieee international conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Singh, Divyanshu Pandey, Niteesh Kumar Gupta, Vedika Prajapati, Megh Senapati, Rajiv Srm University Ap Computer Science & Engineering Andhra Pradesh India

ISBN: (纸本)9798350376876

In today's competitive landscape, accurately assessing customer satisfaction is crucial for business success. This paper presents an innovative methodology for call centers, leveraging advanced speaker diarization, sentiment analysis, and speech emotion recognition (SER) to gain a nuanced understanding of customer needs and preferences. By integrating these techniques, our approach predicts customer satisfaction scores (CSAT), empowering businesses to refine products, optimize services, and make data-driven decisions. Experimental validation demonstrates the effectiveness of our methodology in uncovering actionable intelligence from call center interactions, enabling businesses to enhance customer experience and drive continuous improvement. © 2024 ieee.

关键词： Customer satisfaction

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Multi-Modal LLM Driven computer Interface 8

Multi-Modal LLM Driven Computer Interface

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8th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sowmiya, R. Revathi, P. Ragunath, D. Sindhuja, S. Gokila, P. Kalaivani, T. Sns College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India Tamilnadu Coimbatore India Info Institute of Engineering Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

This study presents a framework for multimodal models to control and interact with computers: The Multimodal-driven computer Interface. A decision-making module is used by the framework to determine what actions are acceptable after it processes user input through a variety of modalities, such as speech, text, and gestures, and runs it through a multimodal fusion algorithm. Platform-specific APIs are then used to carry out these operations on the computer. It runs on Linux, Mac, and Windows platforms and is currently integrated with the multimodal LLM. Although the framework shows promising results, there are still issues with adjusting to different user demands and preferences and increasing the accuracy of mouse click position prediction. Potentially revolutionizing human-computer interaction, the Multimodal-driven computer Interface offers new avenues for accessibility, productivity, and pleasure. © 2024 ieee.

关键词： Windows operating system

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A WPSD-Based Feature Extraction Method of EEG Signal for Motor Imagination 3

A WPSD-Based Feature Extraction Method of EEG Signal for Mot...

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3rd international conference on computer science, Electronic Information engineering and Intelligent Control Technology, CEI 2023

作者： Shi, Jing Wang, Qisong Liu, Dan Kim, Chungsong Zhang, Yan Liu, Xin School of Economics and Management Heilongjiang Institute of Technology Harbin China School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China School of Transportation Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350372052

In order to improve the performance of the Brain-computer Interface (BCI) system, it is important to extract the feature vector which reflects nonlinearity and non-stationary of Electroencephalogram (EEG) signals. This paper proposed an adaptive feature extraction method for BCI system based on multiclass motor imagery EEG signals. The proposed method extracts a feature vector related with time-frequency-spatial information of multiclass EEG signals from the continuous wavelet transform power spectral density (CWT-PSD) difference weighted by weight matrices which reflect the time-varying of EEG signals, in some channels which reflect well multiclass motor imagery. This method was evaluated on the EEG signals of BCI Competition IV Datasets 2a using SVM as classifier, and the performance results were compared with the results obtained by the Common Spatial Pattern (CSP) and Filter Band CSP (FBCSP) feature extraction method used Naïve Bayesian Parzen Window (NBPW) classifier. The average classification accuracy of the proposed method is 90.22% for the 4-class motor imagery based BCI, and the kappa coefficient of the proposed method is higher 1.38 times and 1.21 times than one of the CSP and FBCSP method, respectively. © 2023 ieee.

关键词： Feature extraction

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Observer-Structured PID Controller: Flight Controller Design Examples 4

Observer-Structured PID Controller: Flight Controller Design...

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4th international conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Sato, Masayuki Sebe, Noboru Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

ISBN: (纸本)9798350391183

This note shows two design examples of PID con-Trollers, whose state variables estimate the selected state vari-Ables of the controlled plant systems, for LTI Parametrically Dependent (LTIPD) systems. The authors have already proposed observer-structured controllers, whose structure is not the same as but is very similar to the well-established Luenberger observer-based controllers, and have also proposed a conversion method from a priori designed unstructured LTI or Linear Parameter-Varying (LPV) controllers to observer-structured LTI/LPV con-Trollers with the plant state estimation ability implemented. How-ever, the successful conversion has been reported only for full-order controllers. In this note, we show two examples, in which a priori designed LTI PID controllers are successfully converted to observer-structured PID controllers, and the designed observer-structured PID controllers well estimate the selected plant state variables in numerical simulations. © 2024 ieee.

关键词： Three term control systems

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An Intelligent Framework for Smart Automated House Implementation via Integration of IOT and DL 4

An Intelligent Framework for Smart Automated House Implement...

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4th ieee international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Venkata Lakshmi, D. Shyama, R. Anila, S. Abbineni, Srichandana Al, Sajid Abd Al-Hilali, Aqeel Vit Ap University School of Computer Science and Engineering Andhra Pradesh India Adi Shankara Institute of Engineering and Technology Department of Computer Science and Engineering Ernakulam Kerala Kalady India Hyderabad India National University of Science and Technology Khidhir Abdullah College of Technical Engineering Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

The danger environment for the Internet of Things (IoT) has grown as a result of the widespread use of internet-connected devices, and malware detection is becoming increasingly difficult. Feature extraction, selection, and classification phases of machine learning turn it into a potent weapon against many dangers in Internet of Things environments. Particularly, in order to decrease feature size and speed up detection, feature selection is critical. Three approaches are usually used by researchers: feature reduction, feature selection, or hybrid approaches. This study examines feature selection and feature reduction for IoT malware identification using deep learning (DL) and machine learning (ML) techniques. According to the findings, feature selection by itself can lead to an accuracy rate of 94%. This demonstrates that when it comes to recognizing IoT malware, feature selection works better than feature reduction. © 2024 ieee.

关键词： Adversarial machine learning

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