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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Joshi, Apoorva Preethi, d. Gill, Amandeep Noida Institute of Engineering & Technology Master of Computer Application Uttar Pradesh Greater Noida India Department of Computer Science Bangalore India Vivekananda Global University Department of R&d Cell Jaipur India

ISBN: (纸本)9798350357769

Conventional methods like classification or clustering have limits when it comes to handling the complex task for dispersed data mining across heterogeneous sources and divergent data formats. This is especially true in scenarios where stochastic models or mathematical formulas may not be feasible. data centralization is a prevalent trend that exacerbates problems including communication channel congestion and privacy difficulties related to sensitive data flows. In order to overcome these obstacles, this paper presents the distributed Academic Engine (dAE), an adaptive edge-based scattered analytic engine designed for distributed networks. The dAE routinely breaks down and divides computations that need data from multiple sources among the nodes that are already in place, enabling the sharing of partial results without disclosing the original data. To get the full expression output, the master node combines these partial results. The expression is decomposed and processing jobs are distributed among nodes using three computational intelligence techniques: genetic algorithm, genetic algorithm using evolution control, the particle swarm optimization. This method's effectiveness is illustrated with a case study on smart grid KPI identification, which shows a noteworthy (over 91%) decrease in communication messages over conventional approaches. This demonstrates how well the suggested method works to reduce the difficulties related to distributed data mining. © 2023 IEEE.

关键词： Computational Intelligence data Integration and Heterogeneous data Source Integration distributed Analytics Edge Computing Genetic Algorithm Particle Swarm Optimization Smart Grid

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Analysis of distributed Photovoltaic Hosting Capacity Based on Power Flow Calculation

Analysis of Distributed Photovoltaic Hosting Capacity Based ...

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New Power System and Power Electronics (NPSPE), International Conference on

作者： Mingli Wang Biaolong Su Siyang Shao Xiaoxiao Lu dong Xia Suyan Lu System Application R&D Department NARI Technology Co. Ltd. Nanjing China

ISBN: (数字)9798350389869

ISBN: (纸本)9798350389876

the analysis of distributed photovoltaic carrying capacity based on load flow calculation is studied. First, the concept of distributed photovoltaic carrying capacity is defined, that is, the maximum value of photovoltaic power generation capacity that can be borne when the distributed power source is connected to the grid and the photovoltaic energy is eliminated, under the premise of ensuring grid security and stability. Next, the constraints affecting the carrying capacity of distributed PV in the power grid are analyzed, including node voltage constraints, grid capacity, thermal stability and power quality. By comparing experimental data between static and dynamic hosting capacity analysis methods, it is found that the dynamic algorithm allows for higher PV generation capacity and exhibits stronger adaptability. The purpose of this paper is to provide important theoretical support and technical methods for grid planning and the rational integration of PV systems by analyzing the constraints on the maximum absorption capacity of PV generation in the distribution network.

关键词： Photovoltaic systems Heuristic algorithms Power system dynamics Power quality Voltage Power system stability Stability analysis security Thermal stability Load flow

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A Transmit Array Antenna for 5G application

A Transmit Array Antenna for 5G Application

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International Symposium on Antennas and Propagation (ISAP)

作者： Shailendra Kaushal Koichiro Masuko Ning Guan Millimeter-Wave Device Research Department Electronic Application Technology R&D Center Fujikura Ltd. Sakura Chiba Japan Electronic Application Technology R&D Center Fujikura Ltd. Sakura Chiba Japan

ISBN: (数字)9798350364774

ISBN: (纸本)9798350364781

This paper describes a meta-surface based transmit array (TA) antenna design that operates at n257 band of 5G Nr. A transmit array composed of four substrates with single side printed layers is proposed that provides full 360 deg phase sweep for transmission loss less than 2 dB. These substrate layers have air between them that helps to get wider range of the phase sweep. Two types of transmit array antennas have been fabricated and checked with wide beam horn antenna. The fabricated and simulated models produce similar results of more than 6 dBi broadside gain enhancement beyond 26 GHz. Total bandwidth obtained is more than 10 GHz.

关键词： Phased arrays 5G mobile communication Horn antennas Transmitting antennas Propagation losses Aperture antennas Substrates Gain Antenna arrays Lenses

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Key Intrinsic security Technologies in 6G Networks

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ZTE Communications 2022年第4期20卷 22-31页

作者： LU Haitao YAN Xincheng ZHOU Qiang dAI Jiulong LI rui ZTE Corporation Shenzhen 518057China Shenzhen Key Enterprise R&D Institute of Wireless Mobile Technology(ZTE) Shenzhen 518055China Shenzhen Key Laboratory of 5G RAN Security Technology Research and Application Shenzhen 518055China

Intrinsic security is a hot topic in the research of 6G network security.A revolution from the traditional“pluginbased”and“patchbased”network security protection mechanism to a self-sensing,self-adaptive and self-growing network immunity system is a general view of 6G intrinsic security in the *** connection security,physical-layer security,blockchain,and other 6G candidate intrinsic security technologies are analyzed based on 6G applications,especially hot scenarios and key technologies in the ToB(oriented to business)field.

关键词： 6G intrinsic security ToB application massive connection physical-layer security

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Optimizing diagnostic Accuracy in Ultrasound Imaging Through Cloud-Enabled deep Learning Techniques 4

Optimizing Diagnostic Accuracy in Ultrasound Imaging Through...

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4th International Conference on Soft Computing for security applications, ICSCSA 2024

作者： Kalpana, V. Girija, M. Tharageswari, K. Yobu, d. revathi, J. Aruna, r. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Jeppiaar University School of Engineering and Technology Chennai India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Computer Science and Engineering Cyber Security J.J College of Engineering and Technology Tricy India Department of Mathematics Kongu Engineering College Erode India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331515720

This research aims to study predicting and optimizing the diagnostic accuracy by cloud-enabled deep learning techniques in ultrasound imaging for kidney disease diagnosis. The main findings are that CNN gives 92% accurate outcomes in terms of sensitivity, precision, specificity, AUC-rOC, and other terms used for predictive outcomes in the current research. The other outcomes are 88% accuracy and 91% specificity in predicting kidney diseases by using the rNN model. The other two models used for learning are transfer learning and federated learning which also provide good outcomes which are 94% accurate and 91% accurately in diagnosing with the above-said models. These learning approaches optimize or increase the accuracy rates such as 93% by the transfer learning approach and 90% accuracy outcomes by the federated learning mechanism. These above-given models have been taken based on advanced technologies that are used to predict or forecast the diseases through learning processes. The success of these learning mechanisms is explained by the reliable processes used in the learning of the models that are possible by the neural network used in the learning mechanisms. The high accuracy rates of the models show the successfulness of such clinics, who are in rural areas and are unable to meet their necessary outcome-oriented goals, then measures can be taken for running tele-clinics using the above models of learning techniques to diagnose the diseases at the correct time by 'n' number of people in rural areas. Mine is one of them working as a health worker for which measures need to be taken for finding an accurate solution that operates on cloud-enabled deep learning. This model help run family life and be able to diagnose certain conditions at desired rural clinics out of the above learning models. These tools of learning are studied on cloud-based deep learning techniques and performed well in diagnosing kidney diseases on rural tele-medicine and these learning techni

关键词： Ultrasonic imaging

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Traceable ring signature schemes based on SM2 digital signature algorithm and its applications in the data sharing scheme

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Frontiers of Computer Science 2024年第2期18卷 251-253页

作者： Yongxin ZHANG Hong LEI Bin WANG Qinghao WANG Ning LU Wenbo SHI Bangdao CHEN Qiuling YUE R&D Department SSC Holding CompanyLtd.Chengmai 571924China School of Cyberspace Security(School of Cryptography) Hainan UniversityHaikou 570228China College of Computer Science and Engineering Northeastern UniversityShenyang 110819China School of Computer Science and Technology Xidian UniversityXi'an 710071China Blockchain Research Group Oxford-Hainan Blockchain Research InstituteChengmai 571924China

1 Introduction Identity privacy concerns hinder data sharing by casting doubt on the safeguarding of personal information,eroding trust,and impeding the willingness of individuals and organizations to exchange their data[1,2].The traceable ring signatures(TrSs)addresses the contradiction between identity privacy and regulation[3],no scheme has been developed thus far that is based on SM2,the Chinese cryptographic public key algorithm standard,without relying on centralized trust.

关键词： SM2 signature scheme

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Unveiling the Future: Exploring the Impacts and Challenges of Artificial Intelligence in Workplace applications 3

Unveiling the Future: Exploring the Impacts and Challenges o...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Pragith, S. Supriya, S. Sasikala, S. Sateesh, rayala Swamy, Hemanth Karpagam, P. Vellore Institute of Technology Department of Computer Science Engineering Vellore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering India Tagore Engineering College Department of ECE Chennai India MLR Institute of Technology Department of ECE Hyderabad India Motorola Solutions United States Dhanalakshmi College of Engineering Department of Cyber Security Chennai India

ISBN: (纸本)9798350354171

Unveiling the Future: Exploring the Impacts and Challenges of Artificial Intelligence in Society' embarks on a comprehensive journey through the transformative landscape shaped by Artificial Intelligence (AI). This abstract offers a nuanced examination of AI's profound implications, ranging from its disruptive potential to its ethical complexities, and the societal imperatives it underscores. delving into the evolution of AI technologies, from foundational concepts to cutting-edge innovations, the abstract elucidates their growing significance across diverse sectors including healthcare, finance, transportation, and beyond. However, amidst the promises of efficiency and innovation, ethical concerns loom large. The abstract navigates through the ethical intricacies of AI, addressing issues of bias, accountability, and the erosion of privacy. Furthermore, it ventures into the societal ramifications of AI adoption, exploring its impact on employment dynamics, socioeconomic disparities, and the fabric of human interaction. By interrogating the dual narratives of promise and peril, this abstract offers a holistic understanding of AI's role in shaping the future of society. It underscores the imperative for thoughtful governance, ethical stewardship, and inclusive decision-making to ensure that AI serves as a force for collective progress rather than exacerbating existing divides. Through its interdisciplinary lens, this abstract serves as a roadmap for stakeholders across academia, industry, and policymaking to navigate the complex terrain of AI's integration into society, ultimately paving the way for a future where AI augments human capabilities while upholding fundamental values of fairness, equity, and justice. © 2024 IEEE.

关键词： decision making

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Unscented Kalman Filter-based Multi-IMU Combination for UAV GPS Anomaly Navigation System 8

Unscented Kalman Filter-based Multi-IMU Combination for UAV ...

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8th CAA International Conference on Vehicular Control and Intelligence, CVCI 2024

作者： Huang, Annan Li, Jingyan Zhu, Longlong Chen, Yuanzhe Tao, Fazhan Li, Mengyang College of Information Engineering Henan University of Science and Technology Luoyang China Wuhan Polytechnic University Faculty of Humanities and Media Wuhan China Henan High Score Application Information Technology Research Institute Co. R&d & Innovation Department Zhengzhou China Luoyang Normal University Faculty of Physics and Electronic Information Luoyang China

ISBN: (纸本)9798331504892

In the current unmanned aerial vehicle (UAV) technology, accurate positioning and navigation systems are very important to ensure flight safety and perform precise tasks. Especially when the GPS signal is blocked or unavailable, the traditional navigation system combined with a single micro-electro-mechanical system-inertial measurement unit (MEMS-IMU) often shows significant performance degradation. This paper proposes a multi-IMU combination technology based on unscented Kalman filtering (UKF) to optimize the navigation system of UA V s in the environment of GPS signal failure. By comparing the performance of a single IMU system with that of a system integrated with three IMU s in the simulation environment, and using the NaveGo framework to simulate the system, the research methods include simulating the two system configurations in the scenarios where GPS signals are shielded, to evaluate their position tracking ability and error distribution. The results show that the errors in the upward velocity estimation of each system are relatively stable, mainly within 0.5 m/s. This remarkable performance improvement proves the effectiveness of the multi-IMU system in improving error distribution and enhancing the robustness of the navigation system. The value of this research lies in that it not only improves the navigation accuracy of UAVs when the GPS signal fails but also provides strong technical support for the design of advanced navigation systems for UAV s in the future. © 2024 IEEE.

关键词： Kalman filters

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Improved Piano Music recognition Algorithm Based on Artificial Intelligence 2

Improved Piano Music Recognition Algorithm Based on Artifici...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Sahaya Sheela, M. Muthu Krishnammal, P. Shetty, Surendra Surendran, S. Vinothkumar, K. Venkataramanaiah, B. Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Avadi India School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Master of Computer Application NMAM Institute of Technology Nitte Deemed University Karnataka India Department of Computer Science and Engineering Tagore Engineering College Tamil Nadu Chennai India Department Of ECE PSNA College Of Engineering And Technology Tamilnadu Dindigul India Department Of ECE Veltech rangarajan Dr sagunthala R&D institute of science and Technology Chennai Avadi India

ISBN: (纸本)9798350305418

Not only do people enjoy listening to piano music, but it may also be differentiated from vocal music in the realm of digital music. With its ongoing development, the piano music recommendation algorithm has found solutions to a great number of practical issues, and its technical path has also progressed from its initial recommendation based on the behavioral preferences of users to the correlation recommendation among users to the mining of users' potential preferences in recent years. The birth of The ONE smart piano means that a new teaching mode will be produced, that is, piano learning and user experience will be integrated, and the latest achievements of modern science and technology will be applied and rooted in the field of music education and teaching, so that the piano, a large musical instrument, can be easily combined with a variety of intelligent devices. This is significant in terms of the combination of offline artificial intelligence and music education because it means that a new teaching mode will be produced. Additionally, this new teaching mode will The improved piano music recognition system is investigated in this study, which uses artificial intelligence as its foundation. In the beginning, both the overall architecture of the system and the functional module design plan are shown. It is well known that the feature matrix of a piano is distinct from that of other musical instruments. This distinction was discovered when a digital recognition approach was used to extract the music timbre feature matrix of a piano. The findings of the experiments indicate that the suggested system has a high recognition accuracy for the purpose of timbre recognition of piano arrangements, which demonstrates that it is deserving of further promotion and application. © 2023 IEEE.

关键词： Artificial intelligence improved recognition algorithm piano music

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Novel Gender and Age Prediction from real Time Facial Images using Linear regression Algorithm 8

Novel Gender and Age Prediction from Real Time Facial Images...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Nalini, N. Shahila, d. Ferlin deva rosi, A. Tamilselvi, M. Giri, Jayant Sherideh, Ala'a Al Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Saveetha University Saveetha School of Engineering Department of Ece Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University College of Information Technology Department of Cyber Security zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (纸本)9798350367904

The proposed novel algorithm utilizes linear regression in real-time facial image analysis for predicting both gender and age. By leveraging features extracted from facial data, the model aims to provide accurate and instantaneous predictions. The emergence of social media platforms has made the automatic classification of gender and age increasingly relevant. real-time frame processing is possible with OpenCV. The expected gender and age are provided as the output, and this frame is provided as the input. The capacity to autonomously ascertain age and gender from facial images because of its versatility in facial analysis applications. However, the current models are still behind the required accuracy level, which is required for these models to be used in real-world applications, because of the significant intraclass variance of face images (such as difference in lighting, position, scale, and occlusion). In this work, a deep learning framework uses an ensemble of residual and attentional convolutional networks to reliably determine the gender and age group of facial photographs. By employing an attention mechanism, this model can concentrate on the important and instructive aspects of the face, increasing its prediction accuracy to 90.15%. © 2024 IEEE.

关键词： Prediction models

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