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Reinforcement Learning Based Optimization of Autonomous Aircraft Performance 11

Reinforcement Learning Based Optimization of Autonomous Airc...

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11th International conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Sari, Metin Caliskan, Fikret Istanbul Technical University Department of Mechatronics Engineering Istanbul Turkey Turkish Aerospace Industries Ankara Turkey Istanbul Technical University Department of Control and Automation Istanbul Turkey

ISBN: (纸本)9798331509293

This research introduces an advanced flight control system for optimizing autonomous aircraft performance, leveraging deep reinforcement learning (DRL) to address the complexities of nonlinear flight dynamics. Using a six-degree-of-freedom (6-DoF) rigid aircraft flight dynamics model, we develop a Deep Deterministic Policy Gradient (DDPG) controller tailored for waypoint navigation and attitude stabilization tasks. A custom reward framework and extensive hyperparameter tuning enable effective training within a high-fidelity MATLAB/Simulink environment, achieving high rewards and precise control. Although computationally intensive, the simulations demonstrate robust performance across diverse flight conditions, with potential for real-world applications and future extensions to multi-agent scenarios. © 2025 ieee.

关键词： Deep reinforcement learning

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STAY Diffusion: Styled Layout Diffusion Model for Diverse Layout-to-Image Generation

STAY Diffusion: Styled Layout Diffusion Model for Diverse La...

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2025 ieee/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Wang, Ruyu Hou, Xuefeng Schmedding, Sabrina Huber, Marco F. Bosch Center for Artificial Intelligence Renningen Germany Institute of Industrial Manufacturing and Management Iff University of Stuttgart Stuttgart Germany Fraunhofer Institute for Manufacturing Engineering and Automation Ipa Stuttgart Germany

ISBN: (纸本)9798331510831

In layout-to-image (L2I) synthesis, controlled complex scenes are generated from coarse information like bounding boxes. Such a task is exciting to many downstream applications because the input layouts offer strong guidance to the generation process while remaining easily reconfigurable by humans. In this paper, we proposed STyled LAYout Diffusion (STAY Diffusion), a diffusion-based model that produces photo-realistic images and provides fine-grained control of stylized objects in scenes. Our approach learns a global condition for each layout, and a self-supervised semantic map for weight modulation using a novel Edge-Aware Normalization (EA Norm). A new Styled-Mask Attention (SM Attention) is also introduced to cross-condition the global condition and image feature for capturing the objects' relationships. These measures provide consistent guidance through the model, enabling more accurate and controllable image generation. Extensive benchmarking demonstrates that our STAY Diffusion presents high-quality images while surpassing previous state-of-the-art methods in generation diversity, accuracy, and controllability. © 2025 ieee.

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Non-Invasive Measurement and Multi-Channel Attention Network for Fault Diagnosis of Rotating Machinery Under Variable Operating Conditions 26

Non-Invasive Measurement and Multi-Channel Attention Network...

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26th International conference on Industrial Technology, ICIT 2025

作者： Wu, Qi Mou, Binbin Li, Zhijian Zhang, Wen-An Wang, Yao-Wei Wang, Zhenbin College of Information Engineering Zhejiang University of Technology Hangzhou China Department of Automation Zhejiang University of Technology Hangzhou310023 China Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou310023 China College of Information Science and Engineering Wuhan University of Science and Technology Wuhan China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430064 China Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan University of Science and Technology Wuhan430064 China

ISBN: (纸本)9798331521950

Rotating machinery is critical in industries such as wind power, aerospace, and manufacturing. These machines often operate under high loads and intense conditions, leading to frequent failures in critical components like bearings and gears. Such failures can result in significant economic losses and safety concerns. Fault diagnosis technology, which uses operational data to analyze and identify fault types, is essential for maintaining reliable operation. Traditional vibration signal analysis is effective but costly and invasive, while acoustic analysis is non-invasive and cost-effective but vulnerable to environmental noise. In contrast, three-phase current signals, measured using current transformers, offer a low-cost, minimally invasive alternative with excellent noise resistance. This paper proposes a method for fault diagnosis using three-phase current signals. The method employs Butterworth filtering to improve the signal-to-noise ratio, followed by advanced data analysis techniques such as multi-scale feature extraction and a multi-channel attention mechanism to realized fault diagnosis of rotating machinery. Experimental results demonstrate the effectiveness of this approach in achieving accurate fault detection, with a diagnostic accuracy of 96.11%. The proposed method shows robust diagnostic capabilities across various operating conditions. © 2025 ieee.

关键词： Bearings (machine parts)

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Classification of Millet Cultivars Using Image Processing and Machine Learning Techniques

Classification of Millet Cultivars Using Image Processing an...

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2025 ieee International conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Bharambe, Dewendra Aher, Pushpalata Sandip University Department of Computer Science and Engineering Nashik India

ISBN: (纸本)9798331512965

India is one of the leading producers of various millet cultivars. Indian millets are a class of highly nutritious, drought-resistant crops that are mostly cultivated in the country's semi-arid and drought-prone regions. The most popularly cultivated millet varieties in India are Sorghum (Jowar), Pearl millet (Bajra), Barnyard Millet (Bhagar), Foxtail millet (Kangi), Amaranth (Rajgira), Red sorghum (Chikani), Finger millet (Nachani), and White sorghum (Dadar). In addition to being a wholesome dietary source, millets offer several health advantages. Since they don't contain gluten, they're an excellent alternative for anyone with gluten sensitivity or coeliac disease. Additionally low in glycemic index;millets are a great meal choice for diabetics. Each cultivar has a different nutritional composition and uses. Also, the majority of the time the farmers adapted the millet seeds obtained from their crop hence to preserve the purity of seeds and quality of produce it is very important to classify different millet cultivars. However, the classification of millet cultivars is a complex and time-consuming process. automation in millet cultivar classification becomes greatly beneficial with improved accuracy and efficiency. This study presents the effective classification of eight different millet cultivars using different machine-learning classifiers along with image processing techniques, based on the derived color features. © 2025 ieee.

关键词： Seed

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Design of an Intelligent Picking Robot Based on Raspberry Pie and Arduino Control

Design of an Intelligent Picking Robot Based on Raspberry Pi...

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2024 International conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Ma, Weiming Zhang, Yuan'an Li, Zhijian Chen, Xinxin School of Electrical and Electronic Engineering Guang Dong Technology College China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

The development of modern agriculture is inseparable from the promotion of science and technology, and agricultural robot has become one of the important directions of agricultural technology innovation. Intelligent harvesting robots can effectively reduce the labor force of manual harvesting and improve harvesting efficiency. This article aims to design an intelligent picking machine based on raspberry pie and Arduino, explore its effectiveness in agricultural production, and provide new, efficient, and low-cost technical means for agricultural production. The significance of this study lies in improving the level of agricultural production automation, promoting the transformation and upgrading of agricultural methods, and laying the foundation for achieving agricultural modernization, technological advancement, and sustainable development. This paper will introduce an intelligent picking robot scheme based on Raspberry Pie and Arduino control, design an intelligent picking machine, design an intelligent picking machine, which can automatically detect and identify fruits, and pick fruits through mechanical devices, including hardware design, control logic and application scenarios, hoping to provide some reference value for the development and application of agricultural robot. © 2025 SPIE.

关键词： Agricultural robots

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Utilizing Transforming Portfolio Management Through automation Using Advanced Deep Reinforcement Learning Algorithms for Optimized Investment Strategies 3

Utilizing Transforming Portfolio Management Through Automati...

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3rd ieee International conference on automation and Computation, AUTOCOM 2025

作者： Praveenraj, D. David Winster Hemalatha, D. Al-Farouni, Mohammed Priya, S. Arunkumar, B. Mittal, Prakhar School of Business and Management Christ University Bangalore India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Kcg College of Technology India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Department of It Atricure MasonOH United States

ISBN: (纸本)9798331542375

This paper focuses on the future possibility of enhancing the applications of DRL in autonomously managing a portfolio for better investment plans. Having used past financial data and a highly developed case of DRL, the proposed system shows better performance compared to conventional investment strategies and indices. This process includes data gathering from the financial databases, the steps of preprocessing and feature extraction, and the use of the DQN structure. After that, the system's training and validation are done by a finite portion of real-world data and a large number of synthesized data to improve stability. The result shows that the new method achieves superior cumulative return, Sharpe ratio, maximum drawdown, and annualized volatility;therefore, it suggests that the proposed system can flexibly predict the fluctuating stock market trends and make appropriate investment decisions. Thus, the present research adds importance to the use of DRL in improving return potential and risk management in portfolio management. Thus, this study adds to the existing literature and practice by allowing for the automation of the optimization and testing for investment solutions at a larger scale, while opening up opportunities for future developments in the application of financial technology and investment tools. © 2025 ieee.

关键词： Risk management

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Automating Power System Optimization and Analysis Through ETAPPy Integration

Automating Power System Optimization and Analysis Through ET...

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2025 ieee SoutheastCon, SoutheastCon 2025

作者： Rooker, Elias Keshtkar, Hessam Mohammadi, Farideh Doost Department of Physics Computer Science and Engineering Newport NewsVA United States

ISBN: (纸本)9798331504847

This paper introduces a novel algorithm developed using EtapPy to optimize power system settings, focusing on reducing short circuit currents and improving overall system efficiency. The study utilizes a modified ieee 9-Bus microgrid system, incorporating renewable energy sources such as wind and solar energy. Unlike traditional approaches, this study pioneers a rare and innovative integration of Python with E TAP, unlocking the potential for significantly more autonomous features within ETAP's framework. As a proof of concept, the algorithm automates the adjustment of transformer settings in the modeled microgrid system, demonstrating its capability to streamline system optimization processes. The results validate that by enabling automated adjustments and analyzing their effects through short circuit studies, this method lays the groundwork for more comprehensive and complex automation in future power system optimization efforts. © 2025 ieee.

关键词： Solar fuels

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Towards Precision Cardiovascular Medicine - A Novel Framework for Risk Prediction Using MultiOmics and Imaging Data 3

Towards Precision Cardiovascular Medicine - A Novel Framewor...

引用

3rd ieee International conference on automation and Computation, AUTOCOM 2025

作者： Lalitha, S. Sujay, V. Krishna, A.P. Jaya Navaneethan, S. Abijith, G.R. Priya, S. Sugantha Department of Cse Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Tamil Nadu Chennai India Krishna University College of Engineering and Technology Department of Computer Science and Engineering Krishna Andhra Pradesh Machilipatnam India Sona College of Technology Department of Electronics and Communication Engineering TamilNadu Salem India Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Information Technology St.Joseph's Institute of Technology Tamil Nadu Chennai India Department of Computer Science Dr Sns Rajalakshmi College of Arts and Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798331542375

The framework integrated multi-omics data and imaging characteristics to boost predictive precision for cardiovascular risk. Existing studies chiefly relied on restricted clinical information using common logistic regression designs unable to capture cardiovascular diseases (CVD's) intricacy. Such dated techniques experienced diminished accuracy and lack personalized risk prediction. But these restrictions are surpassed by the suggested system since it uses deep learning algorithms such as Random Forest, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for structured data and visual information. To prepare the data, standardization and simplification of complexity firstly applied preprocessing techniques such as PCA and t-SNE first, before utilizing feature selection via RFE and LASSO. Results demonstrated substantially improved outcomes with 0.85 accuracy, 0.82 sensitivity and an AUC-ROC of 0.90, delivering a more trustworthy tool for early intervention and customized cardiovascular care. Complex sentences mixed with shorter, simpler constructions to vary structure and keep readers engaged throughout the lengthier description. © 2025 ieee.

关键词： Recurrent neural networks

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Automated X-Ray Image Classification for COVID-19 Detection Using DenseNet-121 and SVM 3

Automated X-Ray Image Classification for COVID-19 Detection ...

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3rd ieee International conference on automation and Computation, AUTOCOM 2025

作者： Ameta, Gaurav Kumar Khekare, Ganesh Phulre, Ajay Kumar Jayachitra, S. Singh, Vijendra Pratap Vijayan, Vinodh P. Parul Institute of Technology Parul University Department of Computer Science and Engineering Gujarat Vadodara India School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computing Science Engineering and Artificial Intelligence Vit Bhopal University Sehore India Psna College of Engineering and Technology Department of Ece Dindigul India Mahatma Gandhi Kashi Vidyapith Department of Computer Science & Applications Uttar Pradesh Varanasi India Mangalam College of Engineering Kerala Kottayam India

ISBN: (纸本)9798331542375

This paper provides an algorithm to detect COVID-19 from the chest X-Ray images using Two-class classification with DenseNet-121 and Support Vector Machine. For effective identification of patterns suggestive of COVID-19 in the provided X-ray images, we leverage the DenseNet-121 model due to its feature extracting prowess, complemented by the SVM classifier to accurately classify the images between the COVID-19 to non-COVID-19 positive cases. The method delivers a high accuracy, which is more than 93% for accuracy, precision, recall, and F1-score, which makes it efficient in the COVID-19 quick screening. The Improved DenseNet-121 + SVM was compared with other deep learning models like ResNet50, VGG16, and confirmed that the proposed model has better efficiency in classifying COVID-19 cases. The performance of the model is checked with a five fold cross validation to ensure consistency and reliability on different sets of data not only in terms of performance with the IMDB data but also with the test data. The working paper is relevant to the current research practices in an effort to construct machine diagnostic systems that can be used by practicing healthcare professionals for identifying COVID-19 in the early stages, thus enhancing outcomes in patients' treatment. © 2025 ieee.

关键词： COVID-19

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space 26

Safe Reinforcement Learning for Social Motor Coordination in...

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26th International conference on Industrial Technology, ICIT 2025

作者： Yang, Fan Huang, Yuening Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing HoustonTX United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (纸本)9798331521950

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints. © 2025 ieee.

关键词： Reinforcement learning

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