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Ultra-short-term Probability Prediction Method of Photovoltaic Power Considering Satellite image 4

Ultra-short-term Probability Prediction Method of Photovolta...

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4th international conference on Smart Power and internet Energy systems (SPIES)

作者： Zhou, Zijie Zhang, Xuemin Zhu, Cunhao Li, Zhi Li, Zheng Tang, Haiyan Tsinghua Univ Dept Elect Engn Beijing Peoples R China China Huaneng Clean Energy Res Inst Technol Co Lt Beijing Peoples R China

ISBN: (纸本)9781665489577

As cloud shielding is one of the main factors leading to the fluctuation of photovoltaic power, an ultra-short-term probability prediction method of photovoltaic power considering satellite cloud images is proposed. Firstly, a cloud displacement vector algorithm for the outer edge of the cloud cluster is designed, based on the block-area-center-positioned strategy, which improves the calculation efficiency and precision;Then, based on the Gaussian mixture model of multi-station output, a conditional probability model considering cloud occlusion is constructed. The model can give the probability prediction of power for target photovoltaic station, using the historical power of reference stations and predicted power of target station. The model corrects the correlation of adjacent power stations by introducing time delay, which is calculated using the result of cloud displacement vector and Pearson correlation coefficient of power. The cases show that the proposed method is superior to the existing probability prediction methods both in reliability and sharpness.

关键词： satellite images cloud displacement vector photovoltaic power ultra-short-term prediction probability prediction conditional probability

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Supervision system on internet advertising based on artificial intelligence technology

Supervision system on Internet advertising based on artifici...

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international conference on Information systems Engineering (ICISE)

作者： Dongyang Qin Zuyang Li Yu Yan School of Literature and Law Wuhan Donghu University Wuhan

With the rapid development of internet advertising, regulatory issues have become increasingly acute, and traditional regulatory methods have been failed to meet the changing needs of internet advertising. Therefore, this paper, based on artificial intelligence technologies such as natural language processing, image recognition, machine learning and data tracking, designs an internet advertising supervision system for strengthening supervision, improving recognition rates, establishing a unified database and data prediction system. This study is conducive to the optimization and upgrading of advertising supervision and the sound development of the internet advertising industry.

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Automated Decision Support Framework for IoT: Towards a Cyber Physical Recommendation System 25

Automated Decision Support Framework for IoT: Towards a Cybe...

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25th international conference on Enterprise Information systems, ICEIS 2023

作者： Choaib, Mohammad Garouani, Moncef Bouneffa, Mourad Waldhoff, Nicolas Ahmad, Adeel Mohanna, Yasser Univ. Littoral Côte d'Opale LISIC Laboratoire d'Informatique Signal et Image de la Côte d'Opale France Department of Electronics and Physics Lebanese University Lebanon Univ. Littoral Côte d'Opale UDSMM Unité de Dynamique et de Structure des Matériaux Moléculaires France

ISBN: (纸本)9789897586484

Among the key factors of Industry 4.0 are the intensive use of Cyber Physical systems (CPSs). These systems can be implemented at many application levels for various application domains. Each application requires a personalized CPS architecture concerning the Physical sensors as well as the management of Cyber systems. Furthermore, it requires also the maintenance of the communications among these different sub-systems. However, researchers and engineers may lack the essential knowledge to design and build an autonomous CPS. In this paper, we propose the concept of a novel Cyber Physical Recommendation System (CPRS) in order to address this open challenge. The proposed approach is subjected to assist the competencies of researchers and engineers to design and build more efficient CPS according to the given objective, domain, and input application scenarios. In this regard, CPRS recommend the components of the desired CPS, based on a novel architecture model of the components, connections, and tasks of the CPSs. The proposed system is eventually intended to aide the progress in leading factories towards the maturity of fourth industrial revolution. Copyright © 2023 by SCITEPRESS - Science and technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Cyber Physical System

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Deep Metric Learning for Transparent Classification of Covid-19 X-Ray images

Deep Metric Learning for Transparent Classification of Covid...

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international IEEE conference on signal-image Technologies and internet-based System

作者： Salvatore Calderaro Giosuè Lo Bosco Riccardo Rizzo Filippo Vella DMI Università Degli Studi di Palermo Palermo Italy

This work proposes an interpretable classifier for automatic Covid-19 classification using chest X-ray images. It is based on a deep learning model, in particular, a triplet network, devoted to finding an effective image embedding. Such embedding is a non-linear projection of the images into a space of reduced dimension, where homogeneity and separation of the classes measured by a predefined metric are improved. A K-Nearest Neighbor classifier is the interpretable model used for the final classification. Results on public datasets show that the proposed methodology can reach comparable results with state of the art in terms of accuracy, with the advantage of providing interpretability to the classification, a characteristic which can be very useful in the medical domain, e.g. in a decision support system.

关键词： COVID-19 Training Deep learning image color analysis Pulmonary diseases Particle measurements Extraterrestrial measurements

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A Review of Artificial Intelligence-Enhanced Security Solutions for the internet of Things

A Review of Artificial Intelligence-Enhanced Security Soluti...

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Artificial Intelligence, Virtual Reality and Visualization (AIVRV), international conference on

作者： Qingqing Yue Shanshan Tu Wenlong Li Hongchen Li Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (数字)9798331528744

ISBN: (纸本)9798331528751

The rapid development of the internet of Things (IoT) is fundamentally transforming various industries, with many IoT systems increasingly driven by artificial intelligence (AI). Advances in AI introduce new changes and breakthroughs in IoT, while the swift progress of IoT technology also presents significant security and privacy challenges. This paper reviews the major security issues and solutions within AI-driven IoT environments, focusing on key management protocols, intrusion detection systems, physical layer authentication, and data encryption. It examines the advantages and limitations of symmetric and asymmetric key management, the roles of network-based and host-based intrusion detection systems, and how physical layer authentication enhances device security through wireless signal characteristics. Additionally, the paper discusses the role of homomorphic encryption and privacy protection technologies, suggesting that integrating these methods represents an effective strategy for addressing IoT security challenges. Future research directions include developing scalable security protocols, advanced threat detection systems, and incorporating AI to enhance the resilience and efficiency of IoT security measures.

关键词： Privacy Protocols Reviews Intrusion detection Authentication Physical layer Threat assessment Security internet of Things Artificial intelligence

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Research on Performance Optimization of Embedded Microcontrollers based on Convolutional Neural Networks

Research on Performance Optimization of Embedded Microcontro...

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Computer Applications and Information technology (ISCAIT), international Symposium on

作者： Peng Ding School of Intelligent Manufacturing and Electronic Engineering Wenzhou University of Technology Wenzhou China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

This paper focuses on the performance optimization of embedded microcontrollers using convolutional neural networks (CNNs). With the continuous development of the internet of Things and edge - computing technologies, embedded microcontrollers are required to handle complex tasks such as image and signal processing. CNNs, renowned for their outstanding performance in feature extraction and pattern recognition, offer a promising solution for enhancing the capabilities of embedded microcontrollers. This research first reviews the basic principles of CNNs and embedded microcontrollers. Then, it delves into the challenges of integrating CNNs into embedded microcontrollers, including limited computing resources and power consumption constraints. Through in - depth experimental studies, real - world data is collected and analyzed to evaluate the impact of different optimization strategies on the performance of embedded microcontrollers. The results show that through appropriate optimization methods, the performance of embedded microcontrollers in tasks related to CNN - based processing can be significantly improved, which has important practical significance for promoting the application of CNNs in the field of embedded systems.

关键词： Performance evaluation Quantization (signal) Power demand Microcontrollers Computational modeling Memory management Optimization methods Convolutional neural networks internet of Things Hardware acceleration

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Automated Blood Group Detection System Using image Processing for Non- Invasive Antigen Feature Extraction and Classification

Automated Blood Group Detection System Using Image Processin...

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Intelligent Data Communication Technologies and internet of Things (IDCIoT), international conference on

作者： Gopu Chandrasekhar Reddy Gajavada SaiKrishna A. Annie Micheal Department of CSE with AIML Sathyabama Institute of Science and Technology Chennai India Department of CSE Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Blood group detection is essential in medical diagnostics and transfusion medicine but often relies on invasive methods requiring specialized infrastructure. This research introduces an automated, non-invasive blood group detection system using YOLOv8 object detection and advanced image processing. The system addresses challenges such as human error, lack of accessibility in resource-limited settings, and delays in traditional methods. By analyzing high-resolution blood sample images, the system extracts antigen features and classifies agglutination patterns for A, B, and D blood groups. With a mean average precision (mAP) of 97.4% and an inference time of 15 milliseconds per image, the system delivers scalable, real-time, and robust diagnostics. This research advances healthcare accessibility and precision medicine objectives, offering an efficient and practical alternative to traditional laboratory-based methods.”

关键词： Antigens Precision medicine Object detection Feature extraction Real-time systems Delays Medical diagnosis internet of Things Blood Medical diagnostic imaging

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A Bayesion Rule based Aggregation Method In Federated Learning 8

A Bayesion Rule based Aggregation Method In Federated Learni...

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8th international conference on Big Data and Information Analytics, BigDIA 2022

作者： Zhihan, Shou Jingxuan, Zhou Xiongtao, Zhang Zhengyi, Zhong National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

Federal Learning(FL) has been proposed to solve the problem of the data isolation. The traditional Federated Averaging Alogrithm(Fed-Avg) [1] requires IID dataset and treats the client indiscriminately. However, due to the data heterogeneity, the client models has different equality, so it is unreasonable to aggregate the client models equally. To address the issues, we raise a new aggregation method based on Bayesian rules. This method sets the accuracy of each client models on a global validation dataset as the prior probality, then get the likelihood probility with the 2-norm of the weights between each client and server. Furthermore, we also do research on the Non-IID splits [2] and use regularization to improve the generalization ability. Experienments on the CIFAR-10 [3] and private image collections from internet show that the Bayesion Method based model converges faster and enhances the global model compared with Federated Averaging Alogrithm in both situations, and the accuracy has 1%-2% increase. © 2022 IEEE.

关键词： image enhancement

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Smart Vision and Intelligent Object Recognition System for the Visually Impaired 15th

Smart Vision and Intelligent Object Recognition System for t...

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15th international conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Suresh, Merugu Sandhyarani Shaik, Abdul Subhani Premalatha, B. Ghinea, George Reddy, Avala Raji Raju, K. Srujan Kumar, Voruganti Naresh Department of ECE CMR College of Engineering and Technology Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India Mulsemedia Computing Brunal University London Wilfred Brown Building 215 Uxbridge United Kingdom

ISBN: (纸本)9783031810824

In an effort to increase the functional dependence for the visually impaired people, have identified the disadvantages and drawbacks in the present existing solutions and have tried to include the loopholes in the existing systems. Identified and addressed the problems like: object recognition, identification of packaged goods, identification of currency coins and notes, ability to read texts recognize people and faces. This paper provides complete support to the visually impaired friends. The idea of this paper is an augmented reality wearable spectacle along with a wearable smart vision band on the wrist. The Spectacles are equipped with camera, earphones, GPS module and internet connection (Wi-Fi &4G). The wearable smart vision system contains ultrasonic sensors, IR sensors to recognize the objects that are present before/behind the visually impaired person and can alert using a vibration. The Spectacles and the band are connected using Bluetooth. The outcome of the assistive device should be in the form of audible signal received using the earphones, that can be clearly usable for a visually impaired friend. This research paper works on the principle of Computer Vision for image processing using OpenCV and Google Cloud Vision API. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image coding

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IMPROVING IQA PERFORMANCE based ON DEEP MUTUAL LEARNING 29

IMPROVING IQA PERFORMANCE BASED ON DEEP MUTUAL LEARNING

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IEEE international conference on image Processing (ICIP)

作者： Yue, Guanghui Cheng, Di Wu, Honglv Jiang, Qiuping Wang, Tianfu Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr Shenzhen Peoples R China Ningbo Univ Sch Informat Sci & Engn Ningbo Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

In this paper, we propose a novel solution, termed DML-IQA, for the image quality assessment (IQA) tasks. DML-IQA holds a dual-branch network architecture and builds the IQA model through a deep mutual learning (DML) strategy. Specifically, the two branches extract stable feature representations by feeding different transformed images into the classical CNNs. The DML strategy first calculates the prediction loss of each branch and the consistency loss across two branches, followed by updating the network iteratively to converge. Overall, DML-IQA has the following advantages: 1) It is flexible to adapt to diverse backbones for tackling the IQA issues in both the laboratory and wild;2) It improves the baseline's performance by approximately 1%similar to 2%, especially performs well in the case of small samples. Extensive experiments on four public datasets show that the proposed DML-IQA can handle the IQA tasks with considerable effectiveness and generalization.

关键词： image quality assessment deep mutual learning convolutional neural networks

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