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Physics-Informed Machine learning for UAV Control

Physics-Informed Machine Learning for UAV Control

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international conference on Electrical engineering, Computing Science and Automatic Control (CCE)

作者： Carlos Alexander Osorio Quero Jose Martinez-Carranza Computer Science Department Instituto Nacional de Astrofísica Optica y Electrónic a (INAOE) Puebla Mexico

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

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.

关键词： Accuracy Riccati equations Noise Neural networks Estimation Autonomous aerial vehicles Control systems stability analysis Trajectory Vehicle dynamics

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Enhanced ELMAN Neural Network learning Using Sine Cosine Optimization

Enhanced ELMAN Neural Network Learning Using Sine Cosine Opt...

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international conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Ali Iqtidar Li Jianping Fatima Pervez Attaullah Narejo Jiaheng Luo Arafat Yeasin Abdus Saboor Liu Yulu School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan China Institute of Computer Science and Information Technology The University of Agriculture Peshawar Pakistan

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

This paper explores an enhanced learning model of the Elman Neural Network with a view of addressing problems of local minima and slow convergence time by using the sine cosine algorithm. Elman Neural Network that is a sort of Artificial Neural Networks is also widely applied to most classification tasks due to its simplicity and effectiveness. However, it faces challenges like; it is slower in learning and when performing an optimization process it gets trapped in local Optima. The research presents a SCA-ElmanNN model which incorporates of exploration and exploitation aspect of sine cosine algorithm to improve Elman Neural Network performance. Using Thyroid dataset, the study examines the effectiveness of the proposed hybrid model with other models for the comparison purpose, including SCA-BPNN and SCA-ANN. The results clarify that upgraded SCA-ElmanNN model enhances the accuracy criteria and minimizes mean square error in both training and testing sets. The training set of Thyroid dataset had an accuracy of 99.9587% and mean square error of 0.000473411 with SCA-ElmanNN, outperforming all the assessed models. Thus, some amount of enhancement has been visible in the Thyroid dataset. This study shows that an integration of SCA with ElmanNN results to improved classification performance and highlights the applicability of meta-heuristic algorithms like Sine Cosine in handling of optimization problems within neural networks. The findings inform the future research on the integration of intelligent optimization algorithms for training artificial neural networks.

关键词： Training Accuracy Metaheuristics Artificial neural networks Mean square error methods Classification algorithms Optimization Thyroid Genetic algorithms Testing

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Enhanced Smart-Shoe System for Gait Analysis with Real-Time Monitoring and Optimized Sampling

Enhanced Smart-Shoe System for Gait Analysis with Real-Time ...

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IEEE India conference (INDICON)

作者： Madhavan Bharanidivya Samiappan Dhanalakshmi Department of Electronics and Communication Engineering College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Gait analysis plays a vital role in medical diagnostics, with applications spanning treatment, rehabilitation, therapy, and physical training. However, conventional gait analysis typically requires a specialized laboratory setting, limiting accessibility and failing to capture natural gait patterns. In this study, we introduce an innovative approach to enhance the accuracy and efficiency of gait monitoring systems, particularly for knee osteoarthritis (KOA) detection. Our innovation, the Smart Insole, is a wearable device designed to overcome the limitations of traditional inertial measurement unit (IMU) methods. It offers superior feature extraction, real-time visualization, and advanced age-group classification. The device, which resembles an insole, is engineered to monitor pressure and inertial data from both feet, providing a discreet, lightweight, and user-friendly option for continuous gait analysis in everyday environments. we have developed a smartphone-based graphical user interface that displays real-time sensor data via a Wi-Fi module. The device's performance has been rigorously validated through a series of experiments and user trials, where it was benchmarked against traditional gait analysis techniques. Our evaluation also focused on the Smart Insole's comfort and usability for regular wear, particularly for KOA detection. Using sensor data and a deep learning algorithm, our method achieved an accuracy of 94.74% in disease prediction.

关键词： Knee Accuracy Inertial sensors data visualization Real-time systems Osteoarthritis Biomedical monitoring Gait recognition Monitoring Diseases

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Cross Layer Optimization and Distributed Reinforcement learning for Wireless 360° Video streaming

Cross Layer Optimization and Distributed Reinforcement Learn...

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IEEE Internatonal conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Anis Elgabli Mohammed S. Elbamby Cristina Perfecto Mounssif Krouka Mehdi Bennis Vaneet Aggarwal *Department of Industrial and Systems Engineering Interdisciplinary Research Center for Communication Systems and Sensing (IRC-CSS) Interdisciplinary Research Center for Intelligent Secure Systems (IRC-ISS) King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Telefónica Research Spain Bilbao School of Engineering University of the Basque Country (UPV/EHU) Spain Centre for Wireless Communications University of Oulu Finland School of Industrial Engineering Purdue University USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Wirelessly streaming high quality 360 degree videos is still a challenging problem. When there are many users watching different 360 degree videos and competing for the computing and communication resources, the streaming algorithm at hand should maximize the average quality of experience (QoE) while guaranteeing a minimum rate for each user. In this paper, we propose a cross layer optimization approach that maximizes the available rate to each user and efficiently uses it to maximize users' QoE. Particularly, we consider a tile based 360 degree video streaming, and we optimize a QoE metric that balances the tradeoff between maximizing each user's QoE and ensuring fairness among users. We show that the problem can be decoupled into two interrelated subproblems: (i) a physical layer subproblem whose objective is to find the download rate for each user, and (ii) an application layer subproblem whose objective is to use that rate to find a quality decision per tile such that the user's QoE is maximized. We prove that the physical layer subproblem can be solved optimally with low complexity and an actor-critic deep reinforcement learning (DRL) is proposed to leverage the parallel training of multiple independent agents and solve the application layer subproblem. Extensive experiments reveal the robustness of our scheme and demonstrate its significant performance improvement compared to several baseline algorithms.

关键词： Wireless communication Measurement Cross layer design streaming media Physical layer Ad hoc networks Robustness Quality of experience Optimization Videos

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Clustering Protocols and Algorithms for IoT and IIoT: Survey, Classifications and Challenges

Clustering Protocols and Algorithms for IoT and IIoT: Survey...

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IEEE India conference (INDICON)

作者： Aylapogu. Pramod Kumar Kartikey Shukla Harshit Jain Dhanaveera Pavan Kumar B Nidhi Agarwal Dept. of AIML & ECE CMR Engineering College NIT Andhra Pradesh Hyderabad India Dept. of CSE Apolis United State of America USA Dept.of CSE Galgotias Universty Greater noida India Dept. of CSE Malla Reddy Engineering College Hyderabad India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Now a day's most of the electronic gadgets are revolving towards the forthcoming technology such as Internet of Things (IoT) and as well as the Industrial Internet of Things (IIoT). We required efficient process techniques to transform the IoT data. In this regards the Clustering algorithms are more helpful in gathering and formulation of data in a structured form. In this survey article, we present a classification and challenges of clustering protocols things will become more reliable and smarter with the development of technologies that will allow them to act in an autonomous way. In the suggested work, we focus on the adaptive clustering algorithms for smart city services, intelligent building management and factory automation. The Internet of things devices plays a pivotal role in the transmit sensed data to the host in real time manner. To reduce the number of internet connection, instead of the data transmission. By configuring the loT networks in this way the energy utilization of the sensor is decreased. The main intention of this article is to minimize the transmission time delay between the cluster head to nodes, as well as the optimization with minimal cost. It is more helpful in the autonomy-based systems. This case study provides unambiguous review on clustering protocols and algorithms, specifically for IoT and IIoT.

关键词： Surveys Protocols Costs Smart cities Buildings Clustering algorithms Classification algorithms Reliability Optimization Industrial Internet of Things

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Arogya -An intelligent Ayurvedic Herb Management Platform

Arogya -An Intelligent Ayurvedic Herb Management Platform

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international Scientific conference on Information Technology and Management Science of Riga Technical University (ITMS)

作者： Nirmani Pathiranage Nilfa Nilfa Mithula Nithmali Nadee Kumari Lokesha Weerasinghe Ishara Weerathunga Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9781728191058

ISBN: (纸本)9781728191065

Ayurvedic means a science of life and well-being with its unique approaches to social and spiritual life. Especially in Sri Lanka we have our own set of rare Ayurvedic herbs which have been utilized by generations as medicinal treatments for a variety of diseases. Absence of specialists in this area makes proper identification as well as classification of valuable herbal plants a tedious task, which is essential for better treatment. Hence, a fully automated system for herb detection and classification, information visualization regarding them is highly desirable. There are existing applications which can identify plants with low prediction accuracies, as well as to give information regarding them. However, these applications are based on foreign plant data sets that do not include valuable herbs and shrubs with medicinal qualities. Hence this research proposes an application unique to medicinal plants, which can perform all these functionalities in both online and offline approach. Here, a new Ayurvedic plant dataset prepared from scratch, and preliminary results for classification of 5 types of herbs, compared with several deep Convolutional Neural Network (CNN) models based on transfer learning are presented. Experimental results indicate Marker-based Watershed algorithm as the best object detection algorithm in a complex background, VGG-16 as the best deep CNN classification model which reached a promising testing accuracy of 99.53%, and Seq2Seq LstM model as the best deep learning model with optimum accuracy in abstractive information summarization.

关键词： Biomedical imaging Image segmentation Training Software engineering Classification algorithms Testing Deep learning

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Medicare Fraud Detection using CatBoost

Medicare Fraud Detection using CatBoost

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IEEE international conference on Information Reuse and Integration (IRI)

作者： John Hancock Taghi M. Khoshgoftaar College of Engineering and Computer Science Florida Atlantic University Boca Raton Florida

ISBN: (数字)9781728110547

ISBN: (纸本)9781728110554

In this study we investigate the performance of CatBoost in the task of identifying Medicare fraud. The Medicare claims data we use as input for CatBoost contain a number of categorical features. Some of these features, such as the procedure code and provider zip code, have thousands of possible values. One contribution we make in this study is to show how we use CatBoost to eliminate some data pre-processing steps that authors of related works take. A second contribution we make is to show improvements in CatBoost's performance in terms of Area Under the Receiver Operating Characteristic Curve (AUC), when we include another one of the categorical features (provider state) as input to CatBoost. We show that CatBoost attains better performance than XGBoost in the task of Medicare fraud detection with respect to the AUC metric. At a 99% confidence level (with p-value 0) our experiments show that XGBoost obtains a mean AUC value of 0.7615 while CatBoost obtains a mean AUC value of 0.7851, validating the significance of CatBoost's performance improvement over XGBoost. Moreover, when we include an additional categorical feature (healthcare provider state) in our data analysis, CatBoost yields a mean AUC value of 0.8902, which is also statistically signficant at a 99% confidence interval level (with p-value 0). Our empirical evidence clearly indicates CatBoost is a better alternative to XGBoost for Medicare fraud detection, especially when dealing with categorical features.

关键词： Decision trees Task analysis Machine learning Insurance Medical services Encoding Cats

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Braintransformer: Subject-Wise Patch Embed Transformer for Cross-Subject Brain Visual Information Decoding

Braintransformer: Subject-Wise Patch Embed Transformer for C...

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international conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Du Zongxin Li Lin Faculty of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Visual information decoding aims to infer the visual content perceived by a subject based on their brain responses, representing a cutting-edge area of neuroscience research. Functional magnetic resonance imaging (fMRI) provides millimeter-level spatial resolution and a relatively high signal-to-noise ratio, making it an ideal tool for neural information decoding. However, practical applications remain limited, primarily due to the reliance on subject-specific models. In this work, we propose BrainTransformer, a novel framework for cross-subject visual information decoding. To overcome the limitations, we develop a self-supervised patch embedding transformer trained using a masked signal modeling approach on large-scale fMRI data, enabling the learning of robust and generalizable neural representations across subjects. By aligning these learned fMRI representations with CLIP's image and text embeddings, BrainTransformer leverages rich semantic information from both visual and textual modalities. This alignment allows us to employ a multimodal latent diffusion model guided by CLIP's embeddings to generate images consistent with brain activity. Experimental results demonstrate BrainTransformer's effectiveness in decoding visual information across subjects, marking a significant step toward scalable and generalizable brain decoding.

关键词： Training Visualization Semantics Functional magnetic resonance imaging Brain modeling Transformers Diffusion models data models Decoding Spatial resolution

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AI Framework for Urban and Natural Scene Classification Using Multiscale Attention for SAR/Multispectral Big data

AI Framework for Urban and Natural Scene Classification Usin...

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international Bhurban conference on Applied Sciences & Technology, IBCAst

作者： Nawaz Amjad Jie Chen Wei Yang Scool of Electrionics and Information Engineering Beihang University Beijing China

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

Urban and natural scene classification has drawn increasing interest in recent years and has been used in various remote sensing applications, such as forest monitoring, planning for cities and controlling vegetation and land use. In order to make better use of the knowledge that already exists, a novel attention based classification is suggested as a solution to the shortage of annotated data and lack of generalization capacity of existing models. While the conditional-distribution holds similar significance in practical applications, current adaption techniques for cross scenes evaluation solely take into account the marginal distribution. Furthermore, deep learningbased methods align the feature distribution taken from a singlescale structure, which results in information loss. An Attention based deep learning module is suggested for cross-scene classification tasks in order to get around the aforementioned problems. Conditional distributions (CD) is also used with a marginal distribution (MD) in the suggested neural network. Additionally, more information is extracted using the multiscale technique and the attention mechanism. Comparing experimental findings using So2Sat LCZ42 dataset, it is clear that attention mechanism performs noticeably better than the baseline model.

关键词： Scene classification Adaptation models Attention mechanisms Urban areas Neural networks Vegetation mapping Feature extraction Planning Remote sensing Periodic structures

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AW4C: A Commit-Aware C dataset for Actionable Warning Identification

AW4C: A Commit-Aware C Dataset for Actionable Warning Identi...

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international Mining Software Repositories, MSR

作者： Zhipeng Liu Meng Yan Zhipeng Gao Dong Li Xiaohong Zhang Dan Yang School of Big Data and Software Engineering Chongqing University China Shanghai Institute for Advanced Study Zhejiang University China

ISBN: (数字)9798400705878

ISBN: (纸本)9798350363982

Excessive non-actionable warnings generated by static program analysis tools can hinder developers from utilizing these tools effectively. Leveraging learning-based approaches for actionable warning identification has demonstrated promise in boosting developer productivity, minimizing the risk of bugs, and reducing code smells. However, the small sizes of existing datasets have limited the model choices for machine learning researchers, and the lack of aligned fix commits limits the scope of the dataset for research. In this paper, we present AW4C, an actionable warning C dataset that contains 38,134 actionable warnings mined from more than 500 repositories on GitHub. These warnings are generated via Cppcheck, and most importantly, each warning is precisely mapped to the commit where the corrective action occurred. To the best of our knowledge, this is the largest publicly available actionable warning dataset for C programming language to date. The dataset is suited for use in machine/deep learning models and can support a wide range of tasks, such as actionable warning identification and vulnerability detection. Furthermore, we have released our dataset 1 and a general framework for collecting actionable warnings on GitHub 2 to facilitate other researchers to replicate our work and validate their innovative *** Concepts• Software and its engineering → Software maintenance tools; Software creation and management; Software testing and debugging;• Mathematics of computing → data mining.

关键词： Software testing Productivity Software maintenance Computer languages Codes Computer bugs Mathematics

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