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arXiv 2024年

作者： Rieyan, Sakib Anwar News, Raisul Kabir Muntasir Rahman, A.B.M. Khan, Sadia Afrin Jawad Zaarif, Sultan Tasneem Alam, Golam Rabiul Hassan, Mohammad Mehedi Ianni, Michele Fortino, Giancarlo Department of Computer Science and Engineering School of Data and Sciences BRAC University 66 Mohakhali Dhaka1212 Bangladesh Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Department of Informatics Modeling Electronics and Systems University of Calabria CS Rende87036 Italy

Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple parties can collaborate in training a machine-learning model without exchanging raw data but using the learned or fused features. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method’s effectiveness through a case study on pituitary tumor classification, achieving a significant level of accuracy. However, the primary focus of the study is on the development and evaluation of federated learning and partially homomorphic encryption as tools for secure medical image analysis. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning. © 2024, CC BY.

关键词： Medical imaging

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Effect of Environment Temperature and Relative Humidity on Thermal Emissivity: Study Case of Mango Fruit 22

Effect of Environment Temperature and Relative Humidity on T...

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22nd IEEE International Symposium on Intelligent systems and Informatics, SISY 2024

作者： Sirisomboon, Panmanas Sripinyowanich Jongyingcharoen, Jiraporn Junto, Apiwat Phanomsophon, Thitima Dachoupakan Sirisomboon, Cheewanun Rerngsamran, Panan Pokhrel, Dharma Raj Sharma, Sneha Posom, Jetsada Pornchaloempong, Pimpen Terdwongwarakul, Anupun Chongstitvatana, Prabhas School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Agricultural Engineering Bangkok Thailand School of Engineering and Innovation Rajamangala University of Technology Tawan-Ok Department of Agricultural Engineering Chon Buri Thailand School of Agricultural Technology King Mongkut's Institute of Technology Ladkrabang Office of Administrative Interdisciplinary Program on Agricultural Technology Bangkok Thailand Chulalongkorn University Faculty of Science Department of Microbiology Bangkok Thailand University of Melbourne Department of Infrastructure Engineering Victoria Australia Khon Kaen University Faculty of Engineering Department of Agricultural Engineering Khon Kaen Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Food Engineering Bangkok Thailand Kasetsart University Faculty of Engineering at Kanpangsan Department of Agricultural Engineering Thailand Chulalongkorn University Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350385601

This research was to study the effect of the environment condition during image captured including temperature and relative humidity in the packaging house of the mango exporting factory and in the orchard on the emissivity of mango fruit. The result showed that in the controlled environment of the packaging house in factory, the emissivity was increased (the mango emitted more energy) when the surface temperature was lower and the emissivity of mango is 0.71-0.84 and in the uncontrolled environment of open-air packaging yard in the mango orchard, the different transpiration rates of mango effected mainly by ambient temperature and relative humidity make the morning condition emissivity of 0.44-0.66 and the afternoon condition of 0.72-0.98. There was the effect of the different measured positions on the fruits where the physiology was different and the fluctuated environment in the latter condition made the wide range of emissivity of mangoes. This indicated the shortcoming of thermal imaging of horticultural product if the correct emissivity varied and difficult to set in the thermal camera setting, hence the inaccurate thermogram to be obtained. © 2024 IEEE.

关键词： Fruits

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FFNET: An End-To-End Framework Based on Feature Pyramid Network and Filter Network for Pulmonary Nodule Detection

FFNET: An End-To-End Framework Based on Feature Pyramid Netw...

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IEEE International Symposium on Biomedical Imaging

作者： Xiaoxi Lu Na Zeng Xingyue Wang Jingqi Huang Yan Hu Jiansheng Fang Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China CVTE Research Guangzhou China

Accurate nodule detection with high sensitivity is essential for early lung cancer diagnosis. Focusing on small nodule detection, we propose an end-to-end framework, which includes a backbone, a candidate detection network, and a filter network. The backbone learns multi-layer features so that the region proposal network with feature pyramid structure detects nodules of various sizes, especially small ones. Moreover, the filter net is designed to further classify the proposals with low confidence, which utilizes the decoupled feature maps to make the features of nodules more discriminative. We validate our framework on the LUNA16 dataset. The results show that our framework detects more small nodules, and achieves a comparable performance with other CAD systems.

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Merging UAV-Derived Metrics with Crop Physiology to Estimate Sunflower Yield

Merging UAV-Derived Metrics with Crop Physiology to Estimate...

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Frontiers of Information Technology (FIT)

作者： Usama Athar Muhammad Ali Zuhair Zafar Haris Khurshid Karsten Berns Muhammad Moazam Fraz School of Electrical Engineering and Computer Science National University of Sciences and Technology (NUST) Islamabad Pakistan Oilseeds Research Program National Agricultural Research Center (NARC) Islamabad Pakistan Department of Computer Science Rheinland-Pfälzische Technische Universität Kaiserslautern Germany

ISBN: (数字)9798331510503

ISBN: (纸本)9798331510510

Accurate and timely estimation of sunflower yield is crucial for agricultural researchers, farmers, and breeders. Use of Unmanned Aerial Vehicles (UAVs) with multi-spectral sensors has been adopted to meet the need for precise sunflower seed yield predictions. This study proposes a combined approach that integrates 12 remotely sensed vegetation indices (VIs) with 3 key physiological traits - fresh weight, dry weight, and moisture factor - to capture the complex dynamics affecting sunflower crop. Predictive performance of VIs alone and in combination with physiological traits is evaluated using Root Mean Squared Error (RMSE). Our results show that incorporating these two sets of features, along with their temporal, spectral, and statistical characteristics, leads to consistent improvements in sunflower seed yield estimation that can reduce the root mean squared error to as low as 0.4075 ( $\text{kg} / \text{plot}$ ).

关键词： Vegetation mapping Moisture Autonomous aerial vehicles Physiology Seeds (agriculture) Sensors Yield estimation Reliability Vehicle dynamics Remote sensing

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Controllable Structure Planning for Energy Internet

Controllable Structure Planning for Energy Internet

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Cyber-Energy systems and Intelligent Energy (ICCSIE), International Conference on

作者： Yushuai Li Tianyi Li Yonghao Gui David Wenzhong Gao Yan Zhang Department of Informatics University of Oslo Oslo Norway Department of Computer Science Aalborg University Aalborg Denmark Department of Electronic Systems Aalborg University Aalborg Denmark Department of Electrical and Computer Engineering University of Denver Denver USA

Secure system operations rely on reliable network structures. The loss of controllability may be the main reason to cause cascaded failures for complex network, e.g., Energy Internet (EI). However, the existing studies do not consider the network controllability to guide the system reconfiguration. To address this issue, the paper proposes a new structuring planning method for EI with consideration of controllability and economy. Firstly, the structure planning problem is modeled as a dynamic optimization problem with the tradeoff objectives of maximum social welfare and minimum driven nodes for long-term period. Then, a mixed maximum matching and deep deterministic policy gradient method is presented to obtain the approximate optimal planning solution with strong adaptability. Finally, simulation results demonstrate the effectiveness of the proposed method.

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Control-oriented modeling and passivity analysis of thermal dynamics in a multi-producer district heating system

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IFAC-PapersOnLine 2023年第1期56卷 175-180页

作者： Saeed Ahmed Juan E. Machado Michele Cucuzzella Jacquelien M.A. Scherpen Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen 9747 AG Groningen The Netherlands Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

Climate change and geopolitics have led to the conception of plans for reducing greenhouse gas emissions and improving the sustainability of existing fossil-based energy systems. In this respect, district heating has been identified as an indispensable player for its potential to integrate seamlessly environmentally-friendly heat sources. To improve the efficiency of these district heating systems, optimal operation schemes can be devised and enforced through control systems. To this end, we present a control-oriented nonlinear ODE-based model of temperature dynamics in a multi-producer district heating system. The model features a modular design and comprises the thermal dynamics of heat exchangers of producers and consumers interconnected by a distribution network of meshed topology. Then, we establish passivity properties and zero-state detectability for the modeled temperature dynamics that could be exploited for controller design and solving constrained optimization problems.

关键词： District heating thermal dynamics Krasovskii passivity shifted passivity zero-state detectability

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Queue Management Priority-Based Traffic engineering Method 9

Queue Management Priority-Based Traffic Engineering Method

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9th IEEE International Conference on Problems of Infocommunications science and Technology, PIC S and T 2022

作者： Lemeshko, Oleksandr Yeremenko, Oleksandra Persikov, Anatoliy Yevdokymenko, Maryna Mersni, Amal Harkusha, Serhii Kharkiv National University of Radio Electronics V.V. Popovskyy Department of Infocommunication Engineering Kharkiv Ukraine International University of Sarajevo Department of Engineering Computer Sciences and Engineering Program Sarajevo Bosnia and Herzegovina Poltava University of Economics and Trade Department of Document Studies and Information Activities in Economic Systems Poltava Ukraine

ISBN: (纸本)9798350398915

The work proposes the improvement of queue management priority-based Traffic engineering method. It is based on the interaction prediction principle to coordinate decisions at various levels. The lower level of calculations is responsible, firstly, for the distribution and aggregation of packet flows between the macro queues and sub-queues organized on the router interface (congestion management task), and, secondly, for the balanced distribution of interface bandwidth among sub-queues, weighted relative to their priorities (resource allocation task). The upper level of the method's calculations is accountable for distributing the interface's bandwidth between macro queues by performing the iterative procedure. The numerical research results of the proposed two-level queue management method confirmed its effectiveness in ensuring high scalability, balanced priority- based distribution of packet flows and interface bandwidth between the macro queues and sub-queues organized on it. The method demonstrated high coordination procedure convergence and the final quality of centralized calculations. © 2022 IEEE.

关键词： Bandwidth

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An Attention-Based Deep Network for Plant Disease Classification

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Machine Graphics and Vision 2024年第1期33卷 47-67页

作者： Bera, Asish Bhattacharjee, Debotosh Krejcar, Ondrej Department of Computer Science and Information Systems Birla Institute of Technology and Science Rajasthan Pilani India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Králové Czech Republic Škoda Auto University Mladá Boleslav Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia

Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method. © 2024 Institute of Information Technology, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： Convolutional neural networks

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Absolute longitudinal distance measurement verification of a standard polystyrene nanoparticle near a surface in water by means of multi-wavelength evanescent field 22

Absolute longitudinal distance measurement verification of a...

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22nd International Conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2022

作者： Blattler, Aran Khajornrungruang, Panart Suzuki, Keisuke Baba, Akiyoshi Goto, Daiki Graduate School of Computer Science and System Engineering Mechanical Information Science and Technology Course Kyushu Institute of Technology Fukuoka Japan Department of Intelligent and Control Systems Advanced Mechanical Division Kyushu Institute of Technology Fukuoka Japan Center of Microelectronic System Kyushu Institute of Technology Fukuoka Japan School of Computer Science and System Engineering Advanced Mechanical Course Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9781998999118

In the wet semiconductor process, abrasive nanoparticles are widely used in the chemical mechanical polishing (CMP) process to polish wafer surfaces for high planarization. During polishing, nanoparticles move with a high-speed three-dimensional (3D) position in the solution. In the 3D particles's moving positions, Z is a perpendicular longitudinal distance between the surface to the nanoparticle that plays a significant role in polishing, which could not be measured by general microscopy without longitudinal scanning. To understand nanoparticle phenomena near-surface, we have proposed and developed the apparatus with real-time fastmotion tracking on optical multi-wavelength evanescent fields microscopy to measure the 3D motion of the nanoparticle in water without scanning. In euspen's 2020, we could verify the relative distance ?Z of nanoparticles in water, but the absolute distance Z is unknown yet. In this manuscript, we have therefore developed an invisible nano-step height in water to verify the absolute height Z of an individual nanoparticle. The invisible nano-step height in water is made from an optical resin with a refractive index of 1.35 that is close to a refractive index of water to avoid unnecessary light during experimental verification. The nano-step height was less than 100 nm on a glass surface, fabricated using nanoimprint lithography. Polystyrene f100 nm standard particles in water were used to verify the height Z. The results of height Z of nanoparticles could be measured less than 100 nm with the measurement uncertainty ±10 nm (2s), which is related to the interferometer result of the nano-step height. © European Society for Precision engineering and Nanotechnology, Conference Proceedings - 22nd International Conference and Exhibition, EUSPEN 2022. All rights reserved.

关键词： Nanoparticles

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Block-FeST: Blockchain-Enhanced Federated Sparse Transformers for Privacy-Preserving RES Forecasting in Internet of Vehicles systems

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IEEE Internet of Things Journal 2025年

作者： Hameed, Aroosa Danish, Syed Muhammad Ranjha, Ali Srivastava, Gautam Carleton University Department of Systems and Computer Engineering Ottawa Canada Algoma University School of Computer Science and Technology Brampton Canada University of the Fraser Valley School of Computing BC Canada Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India

Internet of Vehicles (IoV) frameworks integrate smart vehicles, roads, network infrastructures, and users into one system, enhancing environmental awareness, increasing efficiency, and reducing accidents. Although IoV is widely adopted, it has resulted in an increase in global energy demand, necessitating more robust and reliable energy solutions. In order to meet this growing demand, both traditional and distributed energy generation technologies have been developed, particularly renewable energy sources (RES). In order to ensure seamless operation of smart vehicles and related infrastructure, integrating renewable energy into existing grid infrastructure is essential. This enables stable and efficient power supply to IoV systems, especially during time periods of high demand. As part of this shift, it is important to accurately forecast the energy generation load of individual prosumers—entities that both produce and consume energy—because of their intermittent and dynamic nature. Therefore, we propose Block-FeST, a blockchain-based Federated Learning (FL) framework designed to predict the energy generation patterns of RES prosumers while preserving their private and sensitive data. Within this Block-FeST framework, a Sparse Transformer model is used to forecast energy generation among prosumer clients. Additionally, blockchain technology is integrated into the Block-FeST framework to enable distributed aggregation and securely validate and record the local parameters shared by clients. The results indicate that Block-FeST is superior to the second-best baseline method, with improvements of 20.4% in the mean square error (MSE), 13.7% in mean absolute error (MAE) and 19.3% in root mean square error (RMSE) for a long sequence length of 128. © 2014 IEEE.

关键词： Federated learning

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