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IEEJ Transactions on Electronics, Information and systems 2020年第4期140卷 471-475页

作者： Takeuchi, Masaya Kishihara, Mitsuyoshi Fukuoka, Takao Yamaguchi, Akinobu Utsumi, Yuichi Laboratory of Advanced Science and Technology for Industry University of Hyogo 3-1-2 Koto Kamigori-cho Ako-gun Hyogo678-1205 Japan Faculty of Computer Science and System Engineering Okayama Prefectural University 111 Kuboki Soja-shi Okayama719-1197 Japan Graduate School of Engineering Kyoto University Bldg. C3 Kyoto Daigaku Katsura Nishikyo-ku Kyoto615-8540 Japan

We demonstrate the on-chip synthesis of Au nanoparticles achieved by a microwave-induced reaction in a microfluidic channel. The chip structure consists of a waveguide and a microchannel through the inside of the waveguide. A post-wall waveguide, which configures the metallic post on the side wall continuously, is utilized to confine microwave fields. It allows to insert a solution inside the waveguide by microchannel passing between the metallic posts. In order to realize a miniaturization of the chip, the 24.125 GHz industrial, scientific, and medical (ISM) band is utilized instead of the commonly used 2.45 GHz ISM band. When pure water is heated in the microchannel under the microwave input power of 3.0 W, it has been confirmed that the temperature increases to 70℃. In this paper, this chip is adopted to synthesis Au nanoparticles via microwave-induced reaction. After the irradiation of the microwave power 3.0 W and the irradiation time 5 min, we have observed absorbance and dynamic light scattering of the reactant, and the results indicated generation of Au nanoparticles. This microwave synthesis system allows us to achieve automatic and fast selective synthesis of nanoparticles in the solution. © 2020 The Institute of Electrical Engineers of Japan.

关键词： Gold nanoparticles

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State Estimation for Integrated Energy systems: Motivations, Advances, and Future Work

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IEEE Transactions on Power systems 2024年

作者： Zhao, Junbo Srivastava, Ankur Guo, Ye Cetenovic, Dragan Lin, Yuzhang Levi, Victor Yin, Guanxiong Huang, Manyun Zhang, Tingting Li, Zhigang Chen, Yanbo Exposito, Antonio G. Zhang, Wen Zheng, Weiye Chakrabarti, Saikat Li, Yang Sharma, Ankush Kamwa, Innocent Guo, Qinglai Terzija, Vladimir Mili, Lamine University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States Tsinghua University Tsinghua-Berkeley Shenzhen Institute Shenzhen518055 China The University of Manchester Department of Eee ManchesterM13 9PL United Kingdom University of Kragujevac Faculty of Technical Sciences Čačak Čačak32000 Serbia New York University Department of Electrical and Computer Engineering NY11201 United States Tsinghua University Department of Electrical Engineering Beijing100084 China Hohai University College of Energy and Electrical Engineering Nanjing210098 China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan250061 China The Chinese University of Hong Kong-Shenzhen School of Science and Engineering Shenzhen518172 China North China Electric Power University School of Electrical and Electronic Engineering Beijing102206 China University of Sevilla Spain Sevilla41092 Spain South China University of Technology School of Electrical Power Engineering Guangzhou510641 China Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur208016 India Northeast Electric Power University School of Electrical Engineering Jilin132012 China Laval University Department of Electrical and Computer Engineering QuebecQCG1V 0A6 Canada Tsinghua University State Key Laboratory of Power Systems Department of Electrical Engineering Beijing100084 China Newcastle University School of Engineering Newcastle upon TyneNE1 7RU United Kingdom Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering Falls ChurchVA22043 United States

This paper summarizes the technical activities of a three-year-long IEEE Task Force (TF) on State Estimation (SE) for Integrated Energy systems (IES). It presents the formal definition and characteristics of IES, along with the comprehensive discussion on Electric Power systems (EPS) model, and static and dynamic models associated with heating and natural gas systems. The paper also identifies the barriers of SE for IES, such as estimation modeling, observability analysis, and measurement requirements, together with addressing multi-scale dynamics. An extensive comparative analysis between Integrated Energy systems-State Estimation (IES-SE) and more established Electric Power system-State Estimation (EPS-SE) is presented. The paper also provides future research needs and directions related to IES-SE. © 1969-2012 IEEE.

关键词： Power system analysis

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PollenNet: A novel architecture for high precision pollen grain classification through deep learning and explainable AI

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Heliyon 2024年第19期10卷 e38596页

作者： Shamrat, F M Javed Mehedi Idna Idris, Mohd Yamani Zhou, Xujuan Khalid, Majdi Sharmin, Sharmin Sharmin, Zeseya Ahmed, Kawsar Moni, Mohammad Ali Department of Computer System and Technology Universiti Malaya Kuala Lumpur 50603 Malaysia School of Business University of Southern Queensland Toowoomba Australia Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Australia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Bio-photomatiχ Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh AI & Digital Health Technology Artificial Intelligence & Cyber Future Institute Charles Stuart University Bathurst 2795 NSW Australia AI & Digital Health Technology Rural Health Research Institute Charles Stuart University Orange 2800 NSW Australia

Pollen grains play a critical role in environmental, agricultural, and allergy research despite their tiny dimensions. The accurate classification of pollen grains remains a significant challenge, mainly attributable to their intricate structures and the extensive diversity of species. Traditional methods often lack accuracy and effectiveness, prompting the need for advanced solutions. This study introduces a novel deep learning framework, PollenNet, designed to tackle the intricate challenge of pollen grain image classification. The efficiency of PollenNet is thoroughly evaluated through stratified 5-fold cross-validation, comparing it with cutting-edge methods to demonstrate its superior performance. A comprehensive data preparation phase is conducted, including removing duplicates and low-quality images, applying Non-local Means Denoising for noise reduction, and Gamma correction to adjust image brightness. Furthermore, Explainable AI (XAI) is utilized to enhance the interpretability of the model, while Receiver Operating Characteristic (ROC) curve analysis serves as a quantitative method for evaluating the model's capabilities. PollenNet demonstrates superior performance when compared to existing models, with an accuracy of 98.45 %, precision of 98.20 %, specificity of 98.40 %, recall of 98.30 %, and f1-score of 98.25 %. The model also maintains low Mean Squared Error (0.03) and Mean Absolute Error (0.02) rates. The ROC curve analysis, the low False Positive Rate (0.016), and the False Negative Rate (0.017) highlight the reliability and dependability of the model. This study significantly improves the efficacy of classifying pollen grains, indicating an important advancement in the application of deep learning for ecological research. © 2024 The Authors

关键词： Deep learning Explainable AI Image processing Pollen grain classification PollenNet model

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Machine learning in health condition check-up: An approach using Breiman's random forest algorithm

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Measurement: Sensors 2022年 23卷

作者： Abd Algani, Yousef Methkal Ritonga, Mahyudin Kiran Bala, B. Al Ansari, Mohammed Saleh Badr, Malek Taloba, Ahmed I. Department of Mathematics Sakhnin College Israel Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel Muhammadiyah University of West Sumatra Indonesia Samayapuram 621112 Trichy India College of Engineering Department of Chemical Engineering University of Bahrain Bahrain The University of Mashreq Research Center Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad10021 Iraq Department of Computer Science College of Science and Arts in Qurayyat Jouf University Saudi Arabia Information System Department Faculty of Computers and Information Assiut University Assiut Egypt

Nowadays majority of the college students' physical condition is worrying. They are not physically and also mentally healthy. If so, why? Their selection of foods is not consistent. Thus, they are more likely to suffer from chronic illnesses such as diabetes, hypertension, stress, etc. in the future. Awareness should be created to prevent such diseases before they occur. Physiological parameters measured included Systolic (SBP) and Diastolic (DBP) Blood Pressure, Body mass Index (BMI), Blood Serum Cholesterol (BSC), and percentage of Body Fat (%BF). These parameters are retrieved and classified to check the physical health or predict if any abnormalities are found in the health condition of college students. Therefore, to predict and classify their health status using Breiman's Random Forest (RF) Algorithm is proposed in this paper. Of all the classification methods available, random forests offer the greatest accuracy. Random forest method also handles large data with thousands of variables. When a class is more sparse than further classes in the data it can spontaneously balance the data sets. The outcome shows that the proposed Random Forest algorithm is accurate in predicting and checking the health condition of students. Students' physical condition should be diagnosed through this method. By knowing the healthy body parameters of the students, a physician can know whether they are healthy or not. © 2022

关键词： Machine learning

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Shooting-projection method for a small object moving under the influence of a force 10

Shooting-projection method for a small object moving under t...

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202 10th International Conference on engineering Mathematics and Physics, ICEMP 2021

作者： Filipov, S.M. Faragó, I. Avdzhieva, A. Department of Computer Science Faculty of Chemical and System Engineering University of Chemical Technology and Metallurgy 8 St. Kliment Ohridski Blvd. Sofia1756 Bulgaria Department of Differential Equations Institute of Mathematics Budapest University of Technology and Economics MTA-ELTE Research Group Egry József u. 1 Budapest1111 Hungary Department of Numerical Methods Faculty of Mathematics and Informatics Sofia University St. Kliment Ohridski 5 James Bourchier Blvd. Sofia1164 Bulgaria

We consider a small object in 3D moving under the influence of a force that may depend explicitly on time, on the position of the object, and on its velocity. The equations of motion of classical mechanics are assumed to hold. If the position of the object is specified at some initial and some final time, obtaining the trajectory of the object requires the solution of a two-point boundary value problem. To solve the problem various numerical technics can be applied. This paper extends the recently proposed shooting-projection method to 3D. We introduce a Lagrangian from which, applying the principle of least action, the projection trajectory is derived. Analysis of the action reveals the meaning of the projection trajectory. Using the shooting-projection method, the considered two-point boundary value problem is solved for the case of a projectile motion in the presence of air resistance and wind. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Trajectories

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Suitable parameters to optimize the information and communication technology resources in a public company of ecuador

Suitable parameters to optimize the information and communic...

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2020 Asia Conference on computers and Communications, ACCC 2020

作者： Toapanta, Segundo Moises T. Gallegos, Luis Enrique Mafla De Los, Angeles Ureta M. Estefania Solis, Maximo Prado Information Technology Consultant Management of Technologies for World 'Gtm' Quito Ecuador Faculty of Engineering System Quito Ecuador Department Computer Science Guayaquil Ecuador Universidad Técnica Luis Vargas Torres of Esmeraldas Career Social Sciences Esmeraldas Ecuador

ISBN: (纸本)9781728189673

The problems to optimize the resources of information and communication technologies in a public company in Ecuador are evident, it is necessary to improve the management of the use of ICT to provide services effectively and efficiently to users. The objective of this research is to analyze the appropriate information technology parameters to optimize management in a public company. The deductive method and exploratory research were used to analyze the information. It turned out that the implementation of the architecture integrated by COBIT and ITIL are options to establish the conditions to generate spaces for communicability, generate common benefits, improve public management, use of technological resources and reduce costs. It was concluded that the use of the COBIT and ITIL models are alternatives to improve the management of public companies, optimize the quality of services, generate trust in users, reduce risks in projects, improve company administration and reduce costs. © 2020 IEEE.

关键词： Information management

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LeNet enabled intrusion detection system for iot- networks

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AIP Conference Proceedings 2024年第1期3072卷

作者： Muhammad Allah Rakha Inam Ullah Khan Salma El Hajjami Abdelkarim El Hajjami Farhood Nishat Keshav Kaushik Department of Computer Science FAST National University of Computer and Emerging Sciences Peshawar 25000 Pakistan Department of Electronic Engineering School of Engineering and Applied Sciences (SEAS) Isra University Islamabad 76000 Pakistan LabSIV Department of Computer Science Faculty of Science Ibn Zohr University Agadir 80060 Morocco Department of Computer Science and Telecommunications INP-ENSEEIHT University of Toulouse Toulouse 31000 France Department of Information Security Institute of System Engineering Riphah University Islamabad 45320 Pakistan School of Computer Sciences University of Petroleum and Energy Studies Dehradun 248007 India

Now a day’s Internet of Thing’s (IoT’s) is considered growing technology. Ever increasing demand of IoT devices must have reliable channels for communication therefore, security needs to be ensured. Wireless enabled IoT use to have many real-time applications which include smart education, intelligent transportation, smart home, smart grid, health care and farming etc. Data collection can be made possible with the help of massive IoT devices. Intruder directly sends illegal data packets which lead to hijack overall network. IoT-networks have improved human experience in many real-time applications. Cyber-security threats usually limit computational resources. Therefore, detection of cyber-threats is the main problem for IoT-networks. This paper presents a novel concept of leNet model-based IDS to detect cyber- attacks. Although, two benchmark datasets such as IoTID20 and CICIDS2017 are utilized for simulation. LeNet model is compared with other traditional techniques like linear discriminant analysis, quadratic discriminant analysis, support vector machine and multi-layer perceptron. Accordingly, metrics like accuracy, precision, recall, F1-score, confusion matrix and correlation matrix are used for performance evaluation. Extensive analysis is performed which indicates that with IoTIDS20 dataset, LeNet achieves recall-99.91%, precision-99.74%, F1-score-99.83% and accuracy-99.68%. While, using CICIDS2017 dataset, LeNet presents optimal results in terms of recall-97.96%, precision-96.41%, F1-score- 97.18% and accuracy-98.87%. Proposed LeNet model is light weighted and adaptable.

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Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

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Intelligent Automation & Soft Computing 2024年第3期39卷 417-436页

作者： Muhammad S.Alam Farhan B.Mohamed Ali Selamat Faruk Ahmed AKM B.Hossain Department of Emergent Computing School of ComputingUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Malaysia-Japan International Institute of Technology(MJIIT) Universiti Teknologi MalaysiaKuala Lumpur54100Malaysia Media and Game Innovation Centre of Excellence(MaGICX) Universiti Teknologi MalaysiaJohor Bahru81310Malaysia Department of Engineering Technology University of MemphisMemphisTN38152USA Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Department of Information System and Cyber Security College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance *** scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and *** research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image *** annotated image dataset trains the proposed system and predicts the camera pose in *** system mainly improved the localization performance of indoor cameras by more accurately predicting the camera *** also recognizes the scene changes during the sequence and evaluates the effects of these *** system achieved high accuracy and real-time *** scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact ***,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

关键词： Camera pose estimation indoor camera localization real-time localization scene change detection simultaneous localization and mapping(SLAM)

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Is your web design sustainable? Empirical evaluation of university website top 50 UI greenmetrics rankings

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AIP Conference Proceedings 2023年第1期2706卷

作者： Dimas Sasongko Candra Zonyfar Aris Sudaryanto Arlis Dewi Kuraesin Faculty of Engineering Universitas Muhammadiyah Magelang Magelang Indonesia Faculty Engineering and Computer Science Universitas Buana Perjuangan Karawang Karawang Indonesia Faculty of Engineering Universitas 17 Agustus 1945 Surabaya Surabaya Indonesia Accounting information system Sekolah Tinggi Ilmu Ekonomi Muhammadiyah Jakarta Jakarta Indonesia

Internet technology has developed rapidly in recent years. User access to internet technology services such as social media, websites, and mobile applications causes an increase in power consumption. While the Internet currently produces around 3.8% of global carbon emissions, that number will continue to increase as users access more data. Sustainable web design is an approach to designing website interfaces that prioritize the health and sustainability of the planet we live on, focusing on energy consumption and reducing carbon emissions. This study aims to calculate the amount of carbon footprint generated when visiting the website and create a ranking based on the results of carbon footprint testing on the university website. The benchmarking method is used to compare website performance and the resulting carbon emissions. Carbon emissions testing using digitalbeacon and websitecarbon tools. This study identified that most university websites still have bad ratings in producing a carbon footprint above 1.5g each time a user visits. In addition, it was found that the size of the website content influences the resulting carbon emissions.

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Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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