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4th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022

作者： Zhang, Dongtian Tian, Weiwei Yin, Yifan Liu, Xiufeng Cheng, Xu Shi, Fan School of Computer Science and Engineering Tianjin University of Technology Tianjin China Norwegian University of Science and Technology Department of Ocean Operations and Civil Engineering Aalesund Norway Management and Economics Technical University of Denmark Department of Technology Produktionstorvet Denmark Section of Energy Markets Smart Innovation Norway Halden Norway

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

With the development of sensor technology, wind turbines are equipped with an increasing number of sensors, which are collecting more and more information. However, redundant features and the high cost of maintaining the internet connection between the server and the clients far from the urban area make it difficult when applying federated learning (FL) to wind turbine icing detection. To address these issues, first, this paper conducts a human knowledge-based feature selection technique to choose the informative features. Then, a neural network combines long short-Term memory (LSTM) and a three-layer convolutional neural network (CNN) is built for wind turbine blade icing detection. In federated training, the server considers the data size effect and time effect when aggregating the updates of participating clients. Furthermore, a compression method is used on both the client-side and the server-side to reduce the size of packages exchanged between clients and the server. Experiments show that the human knowledge-based feature selection method helps neural networks become sensitive to icing class and when the compression method is used with a sparsity ratio set to 0.01, it saves 99.72% of bits of updates per client with an acceptable performance drop. © 2022 IEEE.

关键词： Network layers

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Adaptive Compression-Encryption Scheme for Medical Image Based on Improved Compressive Sensing and Deoxyribonucleic Acid Coding-Compression

Adaptive Compression-Encryption Scheme for Medical Image Bas...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xianglian Xue Haiyan Jin Changjun Zhou School of Computer Science and Engineering Xi’an University of Technology Xi’an China Section of Computer Teaching and Research Shaanxi University of Chinese Medicine Xianyang China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China School of Computer Science and Technology Zhejiang Normal University Jinhua China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Medical images often occupy large storage space and contain patient privacy or sensitive information, which makes them difficult and unsafe to be transmitted through the network. This paper proposed an adaptive compression-encryption scheme based on improved compressive sensing (CS) and deoxyribonucleic acid (DNA) coding-compression, which helps to solve the above problem. In the proposed scheme, first the original medical image was compressed to a floating point CS matrix using the discrete wavelet transform and the partial Hadamard matrix. The CS matrix was then normalized and rounded for matrix quantification. The result of quantification was fed to DNA fixed encoding and DNA run length coding for encryption and secondary compression. Finally the compressed-encrypted image was obtained after DNA dynamic encoding-decoding and regroups operation. The proposed scheme was tested against 9 images and proved to be effective in reducing the quantization errors and enhancing the compression performance. For instance, when the benchmark compression ratio (CR) is 0.5, the CR can be reduced by 5%(CR = 0.4453) ∼ 23%(CR = 0.2636), with the corresponding peak signal-to-noise ratio values consistently surpassing the benchmark. Furthermore, the execution of DNA compression and DNA dynamic encoding-decoding provided double guarantee for the algorithm’s security.

关键词： Image coding Heuristic algorithms DNA Transforms Benchmark testing Encoding Sensors Compressed sensing Secure storage Biomedical imaging

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Effective integration of vision foundational models for semantic segmentation to quantify grape foliage powdery mildew infection

Effective integration of vision foundational models for sema...

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2024 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2024

作者： Lin, Yiyuan Underhill, Anna Cadle-Davison, Lance Jimenez, Ana Riaz, Summaira Jiang, Yu School of Electrical and Computer Engineering College of Engineering Cornell University IthacaNY14850 United States Grape Genetics Research Unit United States Department of Agriculture-Agricultural Research Service GenevaNY14456 United States Plant Pathology and Plant-Microbe Biology Section School of Integrative Plant Science Cornell University GenevaNY14456 United States San Joaquin Valley Agricultural Center United States Department of Agriculture-Agricultural Research Service ParlierCA93648 United States Horticulture Section School of Integrative Plant Science Cornell University GenevaNY14456 United States

ISBN: (纸本)9798331302214

Deepening the understanding of powdery mildew (PM) and developing PM-resistant crop cultivars are crucial for developing sustainable, resilient agrifood systems, particularly for valuable crops like wine grapes. Advances in sensing and AI have enhanced high-throughput phenotyping, aiding genetic research and breeding. However, most analysis pipelines rely on extensive data annotation and struggles with generalizability across geographic variations and genetic background. Vision foundational models (VFMs) show great potential in image-based tasks but adapting them to specific agricultural challenges requires significant customization to incorporate specialized knowledge and appearances. In this study, we proposed a simple yet effective method to integrate two VFMs, CLIP and SAM, and applied this approach to grape PM segmentation. Image datasets were collected using a custom active illumination imaging system from vineyards in CA and NY. The merged SAM-CLIP model was fine-tuned with the NY dataset into a model yielding PM-SAM-CLIP that possesses a strong semantic and spatial understanding of powdery mildew. The two datasets were segmented by SAM-CLIP and PM-SAM-CLIP to generate masks of grapevine canopy and PM infection, which were then used to quantify the infection severity. Our results showed that PM-SAM-CLIP outperformed the fine-tuned SAM model and the DeepLab v3+ model. This suggested the effectiveness of integrating and fine-tuning multimodal VFMs and the efficacy of using VFMs for domain-specific applications. Therefore, the developed training strategy would significantly facilitate the development of data analysis pipelines for high throughput phenotyping and increase the total throughput and outcome of genetics studies and breeding programs. © 2024 ASABE Annual International Meeting. All rights reserved.

关键词： Semantic Segmentation

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Fractional order swarming intelligence for multi-objective load dispatch with photovoltaic integration

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engineering Applications of Artificial Intelligence 2024年第PartA期137卷

作者： Muhammad, Yasir Chaudhary, Naveed Ishtiaq Sattar, Babar Siar, Babar Awan, Saeed Ehsan Raja, Muhammad Asif Zahoor Shu, Chi-Min Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Attock Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan

In this research work, the concept of fractional calculus is integrated inside the mathematical model of the canonical particle swarm optimization (i.e., FPSO) to enhance its optimization characteristics, namely its convergence rate, for solving the combined economic emission and load dispatch problems in the power systems. The proposed FPSO is exploited by tuning the output of the thermal and photovoltaic generation to reduce the overall operational cost, emissions, and line losses while meeting the consumer power demand in the standard Institute of Electrical and Electronic engineering (IEEE) 30 bus network. The outcomes of the FPSO are compared with their recent state-of-the-art counterparts to endorse its efficacy for solving economic load dispatch problem, while the reliability and consistency of the FPSO are additionally confirmed through statistical illustrations that include the minimum fitness gauges in each independent execution, boxplot illustrations, histograms, plots for normal distribution, boxplots for time complexity, plots for Weibull distribution, plots for the cumulative distribution function, and quantile–quantile plots as a gauge of the diversity and central tendency. © 2024 Elsevier Ltd

关键词： Particle swarm optimization (PSO)

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Simulation Framework for 6LoWPAN Networks Using Mininet-WiFi

Simulation Framework for 6LoWPAN Networks Using Mininet-WiFi

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IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR

作者： Sorin Buzura Vasile Dadarlat Adrian Peculea Hugo Bertrand Raphaë l Chevalier Computer Science Department Technical University of Cluj-Napoca Cluj-Napoca Romania Computer Science Section CESI Engineering School Rouen France

The Internet of Things (IoT) continuously grows as applications require connectivity and sensor networks are being deployed in multiple application domains. With the increased applicability demand, the need for testing and development frameworks also increases. This paper presents a novel simulation framework for testing IPv6 over Low Power Wireless Personal Networks (6LoWPAN) networks using the Mininet-WiFi simulator. The goal of the simulation framework is to allow easier automation testing of large-scale networks and to also allow easy configuration. This framework is a starting point for many development scenarios targeting traffic management, Quality of Service (QoS) or security network features. A basic smart city simulation is presented which demonstrates the working principles of the framework.

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Novel Federated Decision Making for Distribution of Anti-SARS-CoV-2 Monoclonal Antibody to Eligible High-Risk Patients

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International Journal of Information Technology and Decision Making 2024年第1期23卷 197-268页

作者： AlSereidi, Abeer Salih, Sarah Qahtan M. Mohammed, R.T. Zaidan, A.A. Albayati, Hassan Pamucar, Dragan Albahri, A.S. Zaidan, B.B. Shaalan, Khaled Al-Obaidi, Jameel Albahri, O.S. Alamoodi, Abdulah Majid, Nazia Abdul Garfan, Salem Al-Samarraay, M.S. Jasim, A.N. Baqer, M.J. Faculty of Engineering & IT The British university in Dubia United Arab Emirates United Arab Emirates Department of Computer Center Middle Technical University's Baghdad Iraq Sulaymaniyah Iraq Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq University of Defence in Belgrade Department of Logistic Pavla Jurisica Sturma 33 Belgrade11000 Serbia Baghdad Iraq Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliu64002 Taiwan Department of Biology Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Perak 35900 Malaysia Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia Institute of Biological Sciences Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia Foundation of Alshuhda Baghdad Iraq

Context: When the epidemic ¯rst broke out, no speci¯c treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment e®orts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The ¯rst is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes ¯ve sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates

关键词： Coronavirus

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A Novel Approach to Smart Grid Energy Management Using the Osprey Optimization Algorithm 4

A Novel Approach to Smart Grid Energy Management Using the O...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rudrapogu, Nissisolomon Hemalatha, N. Warkad, Sanjay B Jayanthi, A. Vinoth, K. Jayaram, B. Lecturer in Eee Nissisolomon Rudrapogu Mbts Government Polytechnic College Department of Electrical and Electronics Engineering Section Andhra Pradesh Guntur522005 India Bharath Institute of Higher Education and Research Department of Electrical and Electronics Engineering Tamil Nadu Chennai600073 India P. R. Pote Patil College of Engineering & Management Department of Electrical Engineering Maharashtra Amravati444602 India Jeppiaar Institute of Technology Department of Physics Tamil Nadu Sriperumbudur631502 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600062 India Rmk Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601206 India

ISBN: (纸本)9798331523923

Smart Grid Energy Management (SGEM) denotes the function of optimizing energy flow in the grid while enhancing grid efficiency and stability. One major issue is that increased integration of Renewable Energy Sources (RESs) may add a layer of complexity to EM, which may, in turn, reduce the reliability and stability of the grid. Keeping such challenges in mind, this paper proposes the Osprey Optimization Algorithm (OOA) on its part to enhance SGEM through various facets of load balancing and energy distribution. The objective of this method is to minimize the Mean Absolute Percentage Error (MAPE), enhancing the precision of energy predictions and optimizing grid performance. The OOA optimizes energy distribution in micro grids, improving efficiency and reducing power losses. The performance of the suggested OOA method is assessed by contrasting it with other optimization approaches that are currently in use, including Particle Swarm Optimization (PSO), Multi Objective Genetic Algorithm (MOGA), Sparrow Search Algorithm (SSA), and Multi Objective Wind Driven Optimization (MOWDO). The OOA method is implemented on the MATLAB platform. The outcomes show that the proposed OOA method provides superior performance in minimizing MAPE, achieving the lowest MAPE value of 0.28%, indicating its enhanced accuracy in energy demand forecasting and optimization. © 2025 IEEE.

关键词： Clean energy

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A Mathematical Model for the Selection of Software Requirements Elicitation Techniques

A Mathematical Model for the Selection of Software Requireme...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Virat Raj Saxena Rajeev Yadav Azra Parveen Mohd. Sadiq Department of Computer Science and Engineering Mangalayatan University Aligarh Uttar Pradesh India Indraprastha Research Laboratory Indraprastha Institute of Information Sciences Private Limited New Delhi India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi India

Various techniques have been developed for the identification of different types of requirements like interview, questionnaire, group elicitation techniques, attributed goal-oriented requirements analysis, fuzzy based goal-oriented requirements analysis method, non-functional requirements framework, etc. Based on the critical analysis of requirements elicitation methods, we found that single elicitation technique is not suitable to understand the need of the stakeholders. In real life applications, multiple elicitation techniques are required to elicit and select the requirements of an information system because each technique has its strength and limitations. Therefore, to address this issue, this paper presents a mathematical model for the selection of requirements elicitation techniques so that complete set of requirements along with its priority can be identified before the development of an information system. The institute examination system is considered to show the applicability of the proposed mathematical model.

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Development of Low Cost Plasmonic Tracker Via a Microstepping with PC Based System

Development of Low Cost Plasmonic Tracker Via a Microsteppin...

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2023 International Conference on engineering Technology and Technopreneurship, ICE2T 2023

作者： Abdullah, Muhammad Rosli Harun, Noor Hasmiza Wahab, Azimah Abdul Ibrahim, S Noorjannah Jamilan, Mohd Azerulazree Rahim, Nur Aina Syuhada Universiti Kuala Lumpur British Malaysian Institute Medical Engineering Technology Section Selangor Malaysia Universiti Kuala Lumpur Institute of Medical Science Technology Selangor Malaysia Engineering International Islamic University Department Of Electrical & Computer Gombak Selangor 53100 Malaysia Ministry of Health Malaysia NMCRC Institute for Medical Research Selangor Shah Alam40170 Malaysia

ISBN: (纸本)9798350304107

A plasmonic tracker had been developed for detecting a plasmonic energy at Total Internal Reflectance angle. It is crucial to determine thin film thicknesses, resonance angle and refractive index of a dielectric material. However, a low cost and flexible plasmonic tracker could offers a precise control for accurate measurement system. The main objective is to develop manual, automated and smart control of the plasmonic tracker at high resolution via a PC based system. The plasmonic tracker uses two 1.8° bipolar stepper motor as a prism and silicon photodiode stages. Both of the stepper motors were operated in microstepping that generate 3200 steps at 0.1125° pulse per step. A stable microstepping operation was observed at distance, period, phase angle, torque, RPM and power respectively at 1250 mm/ sec, 90 μS, 3.4°, 0.22 Nm, 200 RPM and 4.64 W. A plasmonic curve and resonance angle were plotted and displayed from a customized software interface which provides high sensitivity, throughput, performance and efficiency. © 2023 IEEE.

关键词： Plasmonics

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Write-Once-Read-Many-Times Nonvolatile Memory Characteristics of Sol-Gel Hafnium Zirconium Oxide

SSRN

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SSRN 2023年

作者： Jhang, Wun-Ciang Hsieh, Tsung-Chun Zhang, Xuan-Zhi Hsu, Chih-Chieh Joodaki, Mojtaba Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Section 3 Douliu Yunlin64002 Taiwan Graduate School of Electronic Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Douliu Yunlin64002 Taiwan School of Computer Science and Engineering Constructor University Bremen28759 Germany

This paper demonstrates write-once-read-many-times (WORM) memory behavior of HfZrO (HZO) ferroelectric material. A stoichiometric Hf0.5Zr0.5O2 thin film prepared using a sol-gel process is used as a resistive switching (RS) layer. The top and bottom electrodes are Al and n+-Si, respectively. The Al/HZO/n+-Si memory shows a low current of 10-11 A at a read voltage of 0.6 V in OFF state. A writing process occurs when the voltage on Al electrode increases to ~2.8 V. The ratio of OFF-state resistance (ROFF) to ON-state resistance (RON) is 106. Data retention and stress tests both indicate that the ROFF and RON can remain the same for over 104 s. Moreover, data stability is further confirmed at an elevated temperature of 85 °C. The switching speed of the HZO memory is ~40 ns, corresponding to a power consumption of 0.98 nJ. The RS mechanism is verified by examining device size dependencies of ROFF and RON. The carrier conduction mechanisms are studied by curve fitting and analysis of current-voltage characteristics at different temperatures. © 2023, The Authors. All rights reserved.

关键词： Zirconium compounds

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