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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 173-181页

作者： Kant, Ravi Saini, Poonam Kumari, Julee Defence Electronics Application Laboratory Defence Research and Development Organisation Dehradun248001 India Department of Computer Science and Engineering Punjab Engineering College Chandigarh148023 India Centre for Development of Advanced Computing Mohali160071 India

Unmanned aerial vehicles (UAV's) safe flight is one of the most important tasks of ground control stations (GCS). However, sometimes due to communication failure between ground data terminal (GDT) and UAV, GCS is not able to ensure a safe flight, which could be hazardous. In such conditions, the accurate UAV position finding prediction becomes essential to serve as sustenance for UAVs. Hence, this article proposes a UAV's position finding prediction model using a deep neural network application for successfully pointing the GDT toward UAV in real time. The proposed work consists of three components, namely data preprocessing, long short-term memory autoencoder (LSTM-AE)-based deep learning model, and mathematical formulation to calculate the ground radar look angle. First, we use different preprocessing techniques on a historical raw dataset for better visualization and generate patterns. To achieve this, Tensor flow along with Keras packages have been used. Next, an LSTM autoencoder deep learning model is applied to the same dataset in order to predict an accurate 4-D position of the UAV. Finally, the output from the LSTM autoencoder model is used in a mathematical model to calculate the GDT look-angles called Azimuth and Elevation. Both the parameters have been used to point the GDT toward the predicted point on the Airspace. We evaluate our proposed model against famous evaluation matrices, namely mean absolute error, mean square error, and root-mean-square error to validate the experimental results. © 2022 IEEE.

关键词： Real time systems

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Modeling of Stepper Motor Fault Diagnosis Based on GRU Time Series Analysis 21st

Modeling of Stepper Motor Fault Diagnosis Based on GRU Time ...

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21st Asia Simulation Conference, AsiaSim 2022

作者： Liu, Zilong Chen, Gang An, Baoran Liu, Yunfei Institute of Computer Application Chinese Academy of Engineering Physics Mianyang China

ISBN: (纸本)9789811991974

There are few data sources for fault diagnosis of stepper motor motion control systems, conventional voltage, current, vibration signals cannot well characterize stepper motor faults, and the fault diagnosis capabilities of stepper motor controllers on the market are limited. In view of the problem that the stepper motor data source is limited, this paper uses the stepper motor load value to provide an effective data source for fault diagnosis and condition monitoring. The time series decomposition of motor load value data is carried out, the time series data is analyzed by GRU recurrent neural network algorithm, fault classification is performed, and a GRU stepper motor fault diagnosis model is constructed to conduct online self-diagnosis of intermittent vibration, instantaneous overload and connection loosening fault, and fault prediction is carried out on motor overload stall fault. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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Reliance Stock Market Analysis with RBF Kernel using Support Vector Machine

Reliance Stock Market Analysis with RBF Kernel using Support...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Rawat, Vandana Singh, Kanishk Sahu, Yash Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India

ISBN: (纸本)9798350323917

This paper analyzes stock market prediction using Machine Learning Techniques with varied algorithms. Financial prediction experts consider stock market forecasting to be a difficult endeavor. Different models of Supervised machine learning are used for the prediction of stock prices including the RBF Kernel Model of SVM. In SVM (support vector machines) Linear, polynomial, and RBF models are used. The reliance industry dataset has been used to train the models. After the implementation of SVM highest accuracy is provided by the RBF model which is 95% accuracy. In conclusion, results provided by the RBF model can be preferred for investment, Meanwhile, linear and polynomial models are also used which gave an accuracy of 94% and 93%, and are suitable for investing purposes. © 2023 IEEE.

关键词： Support vector machines

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Mothed and System of Visibility Analysis Based on Human Eye Imaging Process 5

Mothed and System of Visibility Analysis Based on Human Eye ...

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5th World Conference on Mechanical engineering and Intelligent Manufacturing, WCMEIM 2022

作者： Chen, Gang Chen, Chuan Ren, Qiang Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9781665473699

The human eye is the most important organ to perceive the external environment, and visibility analysis is the basis of ergonomic design and evaluation. The current mainstream visual simulation tools mainly focus on the qualitative analysis within the visual range, lacking the optical modeling process in the complex human-machine-environment system, and cannot reflect the visual features of the human eyes under different working conditions. According to the principle of the rendering pipeline of the current manifold graphics simulation engine, a human vision simulation method based on physical rendering theory is proposed. A real-time rendering engine is designed and implemented. Experiments are carried out on the components of the visual system (camera, lighting and materials), and the results are evaluated using a unified glare evaluation model. The experiments show that the proposed method is helpful to determine the appropriate position and posture of virtual human. Aiming at CATIA platform, CAA/C++technology is adopted to realize the visual analysis component, which further broadens the visibility analysis of CATIA. © 2022 IEEE.

关键词： Glare

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Federated Learning CNN for Smart Agriculture: A Modeling for Soybean Disease Detection 2

Federated Learning CNN for Smart Agriculture: A Modeling for...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Kumar, Mukesh Aeri, Manisha Chandel, Richa Kukreja, Vinay Mehta, Shiva Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Cgc Landran Department of Computer Application Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350360523

With the help of federated learning and CNNs, this research paper aims to identify and categorize soybean leaf diseases. This study uses a federated learning approach to classify disease severity into four classes using four clients. The benefit of this method is that it preserves data privacy during analytics, which is highly important in our digital age. The information we obtain from each local customer is used to conduct our analysis. Using federated averaging, we produce an aggregate global model. It is possible to assess the model's performance on a local and international level by calculating macro, micro, and weighted averages. As a result of the macro standard stressing class equality, clients yu-1 through yu-6 performed well with grades of 91.42%, 91.08%, 90.92%, 93.15%, and 93.40. Based on the weighted average of those clients with class imbalance, 91.43%, 91.07%, 90.92%, 93.13%, 93.41%, and 92.86%, respectively. Finally, the micro average averaging all inputs to the model indicates its overall accuracy with the following percentages for each client: 91.43%, 91.06%, 90.91%, and 93.11%.Using federated averaging to transform local data into a global data model proved this approach feasible in a real-world scenario. Precision, recall, and F1-scores for the studied clients indicate high sensitivity and specificity of the model. A precise agriculture technique can be adapted to control soybean foliar disorders resulting from this study. As a part of our move towards more resilient and safe farm management approaches, this study helps us solve the urgent task of detecting and classifying diseases. © 2024 IEEE.

关键词： Data privacy

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Convolutional Neural Networks Aided Reinforcement Learning for Accelerated Optimization of Antenna Topology

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Applied Computational Electromagnetics Society Journal 2025年第1期40卷 35-41页

作者： Dou, Jiangling Gong, Hao Wei, Shuaibing Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming650500 China School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College Kunming650500 China

A machine learning (ML) framework is proposed to achieve the automatic and rapid optimization of antenna topologies. A convolutional neural network (CNN) is utilized as a surrogate model (SM) and is combined with reinforcement learning (RL) algorithms. Specifically, the RL agent interacts with simulation software to learn. Data accumulated from electromagnetic (EM) simulations are used to train the SM. The CNN-based SM predicts antenna performance based on the topology of the antenna. Subsequently, the SM replaces EM simulations within the RL training environment. The RL agent interacts with the CNN-based SM to search for the optimal topology. This approach significantly reduces dependence on time-consuming EM simulations. To validate the effectiveness of the optimization method, a center-fed microstrip patch antenna is optimized. Simulation results demonstrate that, compared to other optimization methods, impedance bandwidth is improved, while the number of simulation samples and optimization time are significantly reduced. © ACES.

关键词： Convolutional neural networks

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Federal database system based on foreign data wrapper

Federal database system based on foreign data wrapper

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2022 International Conference on Frontiers of Communications, Information System and Data Science, CISDS 2022

作者： Wen, Yong Zhang, Hao Liu, Xihua Zhou, Yuanlin Gao, Wenjie Zhu, Xmglin China Academy of Engineering Physics Institute of Computer Application Mianyang China

ISBN: (纸本)9781665464468

Nowadays, the heterogeneity of databases and the diversity of systems lead to the independence of different systems and the inability to share data horizontally and vertically. It makes the data forming a data silo, between each system can only be stored in the local database. To eliminate the data isolation phenomenon between systems, because of the database heterogeneity caused by the difference of software, hardware and development mode. This paper realizes different Cross-domain connection and data exchange between system database, by building a federated database system through technologies such as PostgreSQL database extension, Foreign data wrapper and triggers. Through practical application verification, the results show that the federation database built by this paper can effectively realize the cross-domain connection and data exchange of heterogeneous databases, which effectively improves the data utilization value and productivity. © 2022 IEEE.

关键词： Electronic data interchange

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Privacy Data Synchronization Method Based on Election in Intranet 3

Privacy Data Synchronization Method Based on Election in Int...

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3rd International Conference on Big Data, Artificial Intelligence and Internet of Things engineering, ICBAIE 2022

作者： Han, Jun Deng, Kai Deng, Xiaolei Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

Privacy data synchronization is an important prerequisite for efficient data utilization on the intranet. Aiming at the lack of effective node election model and information synchronization mechanism in the current enterprise intranet, a privacy data synchronization model based on election is proposed. Firstly, the service cluster is generated according to the service type, and the cluster head election model is established to fairly identify the services in the cluster. Secondly, a privacy data synchronization method based on the heartbeat mechanism is designed. By establishing a communication channel with the central master service, the organizational structure information of all services in the cluster can be synchronized. The method proposed in this paper can realize the synchronization of privacy data safely and efficiently, and can be applied to the enterprise intranet. © 2022 IEEE.

关键词： Synchronization

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Investigation into a Medical Diagnostic System for Chronic Kidney Disease: Preparation and Execution 10

Investigation into a Medical Diagnostic System for Chronic K...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Sharma, Arvind Singh, Dalwinder School of Computer Application Lovely Professional University Punjab Phagwara India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350382471

This abstract provides a concise overview of a study focused on the development and execution of a medical diagnostic system specifically designed for Chronic Kidney Disease (CKD). The primary objective of this study is to contribute to the advancement of early detection and intervention strategies for chronic kidney disease (CKD), a prevalent and potentially incapacitating condition. The inquiry process encompasses several sequential stages, including the initial planning phase and subsequent execution phase. During this stage, comprehensive literature studies are conducted to examine the various approaches used for diagnosing chronic kidney disease (CKD), as well as the risk factors associated with its development and the biomarkers used for its detection. This literature review aids in identifying the deficiencies within the diagnostic system and highlights the potential utility of advanced techniques such as machine learning and data analytics. The development and implementation of the diagnostic system occur during the execution phase. A comprehensive database is established by the consolidation of data from various sources, including patient records, laboratory findings, and diagnostic imaging. State-of-the-art algorithms and machine learning approaches are utilized to conduct comprehensive data analysis, pattern identification, and the construction of prediction models for the diagnosis of Chronic Kidney Disease (CKD). This article provides a comprehensive examination of various healthcare systems pertaining to the management of chronic kidney disease. Additionally, it presents a systematic review of recent research undertaken by multiple scientists during the past five years. © 2023 IEEE.

关键词： Biomarkers chronic kidney disease (CKD) Diagnosis Preparation Early detection Execution Literature review Machine learning Risk factors

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AI-Powered Job Market Analysis: Transforming Workforce Trends and Employment Predictions 5

AI-Powered Job Market Analysis: Transforming Workforce Trend...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Purohit, Sanjana Shuja, Mirza Walia, Sahil Kumar, Mukesh Bajaj, Karan Bhardwaj, Vivek Jecrc University School of Computer Applications Rajasthan Jaipur India Lovely Professional University School of Computer Application Punjab Phagwara India Snowflake Inc. AtlantaGA United States Chandigarh Group of Colleges Chandigarh School of Business Department of Computer Application Punjab Mohali140307 India Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350376685

This analysis delves into the "AI-Powered Job Market Insights"dataset, offering a comprehensive exploration of how artificial intelligence (AI) and automation are influencing the modern job market. The dataset provides a snapshot of job listings across various industries, highlighting factors such as AI adoption levels, automation risk, and salary ranges. By examining these elements, we aim to uncover key trends and insights into how AI is reshaping employment opportunities and job market dynamics. The primary objective of this analysis is to identify and understand key patterns related to salaries, required skills, remote work, and job market trends influenced by AI. By employing various analytical techniques, we seek to reveal how AI adoption impacts compensation, skill requirements, and job growth, as well as how it correlates with the likelihood of remote work. © 2024 IEEE.

关键词： Job analysis

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