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Decision Aid Sciences and Application (DASA), International Conference on

作者： Tariq Samarah Muder Almiani Ala Mughaid Shadi AlZu'bi Amer Al-Rahayfeh Electronic Marketing and Social Media Zarqa University Zarqa Jordan Department of MIS GUST Engineering and Applied Innovation Research Center (GEAR) Gulf University of Science & Technology Kuwait Department of Computer Science Alzaytoonah University of Jordan Amman Jordan Faculty of information technology The World Islamic Sciences and Education University Amman Jordan

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This research explores the application of AI-driven strategies in engineering project management, focusing on optimizing decision-making in areas such as risk assessment, resource distribution, and performance tracking. The study reveals that AI-based predictive analytics enhance risk identification accuracy, allowing for proactive measures that reduce project delays. Resource optimization models effectively streamline allocations, minimizing waste and ensuring consistent resource availability. Furthermore, real-time performance monitoring provides project managers with actionable insights, improving responsiveness to emerging challenges and contributing to reliable projects. While these methods demonstrate significant advantages, the study identifies challenges related to technical expertise and data requirements, which may limit AI implementation. The findings underscore AI's role in supporting complex project decisions, suggesting that further research could expand its applicability and establish ethical guidelines for its use in decision-making frameworks.

关键词： Optimization models Decision making Project management Real-time systems Resource management Risk management Reliability Predictive analytics Monitoring Guidelines

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Development of H-Slotted DGS Based Dual Band Antenna Using ANN for 5G Applications

Development of H-Slotted DGS Based Dual Band Antenna Using A...

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15th European Conference on Antennas and Propagation, EuCAP 2021

作者： Nakmouche, Mohammed Farouk Allam, A.M.M.A. Fawzy, Diaa E. Bing Lin, Ding Abo Sree, Mohamed Fathy Izmir University of Economics Faculty of Engineering Izmir Turkey German University in Cairo Department of Communication Engineering Cairo Egypt National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan Arab Academy of Science Technology and Maritime Transportation Cairo Egypt

ISBN: (纸本)9788831299022

The proposed work conducts a new approach for modeling a dual band monopole antenna design using DGS assisted by ANN. In the aim of efficient dual band antenna design with gain and optimal impedance matching, the Artificial Neural Networks technique (ANN) is used for the development process. This work presents a modeling for H-slotted Defected Ground Structure (DGS) based dual band antenna using ANN for 5G Sub-6 GHz applications. The designed antenna operates at 3.76 GHz and 6.1 GHz. The antenna gain is 2.18 dB and 2.75 dB at both frequencies, respectively. Firstly, a simulation is performed using CST EM simulator, then the predicted results in term of return losses and frequencies are fed into the ANN model. Secondly using a hybrid algorithm based on both feed-forward back-propagation and Levenberg-Marquart (LM) learning algorithm, the optimal position of the H-Slotted DGS in terms of 5G Sub-6 GHz band is extracted. Finally, the experimental validation is conducted and compared with the simulation results, a good agreement is obtained. © 2021 EurAAP.

关键词： Finite element method

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Citizen science "Thundercloud Project" – multi-point radiation measurements of gamma-ray glows from accelerated electrons in thunderstorms 38

Citizen science "Thundercloud Project" – multi-point radiat...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Enoto, Teruaki Tsurumi, Miwa Ikkatai, Yuko Wu, Ting Wang, Daohong Shinoda, Taro Nakazawa, Kazuhiro Tsuji, Naoki Diniz, Gabriel Sousa Kataoka, Jun Kamogawa, Masashi Takagaki, Toru Miyake, Shoko Morimoto, Takeshi Nakamura, Yoshitaka Tsuchiya, Harufumi Department of Physics Graduate School of Science Kyoto University Kyoto Japan Extreme Natural Phenomena RIKEN Hakubi Research Team Cluster of Pioneering Research RIKEN Saitama Japan Graduate School of Science and Engineering Aoyama Gakuin University Kanagawa Japan Institute of Human and Social Sciences Kanazawa University Ishikawa Japan Department of Electrical Electronic and Computer Engineering Gifu University Gifu Japan Institute for Space-Earth Environmental Research Nagoya University Aichi Japan Kobayashi-Maskawa Institute for the Origin of Particles and the Universe Nagoya University Aichi Japan Faculty of Science and Engineering Waseda University Tokyo Japan Global Center for Asian and Regional Research University of Shizuoka Shizuoka Japan TAC Inc. Kyoto Japan Ibaraki College Ibaraki Japan Faculty of Science and Engineering Kindai University Osaka Japan Department of Electrical Engineering Kobe City College of Technology Hyogo Japan Nuclear Science and Engineering Center Japan Atomic Energy Agency Ibaraki Japan

It has been a long-standing question whether cosmic rays promote the triggering of lightning and how cosmic-ray air showers interact with the electric field of thunderclouds. The strong electric field in the thunderclouds accelerates electrons to the relativistic regime, of which seed electrons are thought to be supplied from cosmic-ray air shower. Such relativistic electrons emit bremsstrahlung photons in gamma rays, which have been detected by on-ground measurements called gamma-ray glows. Low-altitude winter thunderstorm in Japan provides an ideal environment for observations of gamma-ray glows. We newly launched the citizen science "Thundercloud Project" to construct a multi-point radiation mapping campaign for glows from winter thunderstorms around Kanazawa, Japan. We developed a new handy radiation monitor and shipped about 60 detectors to citizen supporters. The radiation data are stored in the microSD cards in the detectors, and a part of them is remotely sent to the web server so that researchers and supporters can watch the real-time data. In addition, an automatic alert is sent to public Twitter from the server when a glow is detected. The purpose of this project is (1) to characterize the methodological condition of electron acceleration, (2) to investigate whether accelerated relativistic electrons can enhance the chance of the initiation of lightning discharges, and (3) to find a new way of the citizen science to join in the cutting edge science in the physics field. Here we report this growing citizen science project and examples of successful gamma-ray glow observations. Our first scientific result from this citizen science project was published in Tsurumi et al., GRL 2023 [1], where we reported lightning discharges started in or near the electron acceleration site of a gamma-ray glow. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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Lightning flash started near the electron acceleration region in the thundercloud 38

Lightning flash started near the electron acceleration regio...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Tsurumi, Miwa Enoto, Teruaki Ikkatai, Yuko Wu, Ting Wang, Daohong Shinoda, Taro Nakazawa, Kazuhiro Tsuji, Noki Gabriel, Diniz Kaoataoka, Jun Iwashita, Ryoji Koshikawa, Nanase Kamogawa, Masashi Takagaki, Toru Miyake, Shoko Tomioka, Dai Morimoto, Takeshi Nakamura, Yoshitaka Tsuchiya, Harufumi Department of Physics Graduate School of Science Kyoto University Kyoto Japan Graduate School of Science and Engineering Aoyama Gakuin University Kanagawa Japan Extreme Natural Phenomena RIKEN Hakubi Research Team Cluster of Pioneering Research RIKEN Saitama Japan Institute of Human and Social Sciences Kanazawa University Ishikawa Japan Department of Electrical Electronic and Computer Engineering Gifu University Gifu Japan Institute for Space-Earth Environmental Research Nagoya University Aichi Japan Kobayashi-Maskawa Institute for the Origin of Particles and the Universe Nagoya University Aichi Japan Faculty of Science and Engineering Waseda University Tokyo Japan Global Center for Asian and Regional Research University of Shizuoka Shizuoka Japan TAC Inc. Kyoto Japan Ibaraki College Ibaraki Japan Faculty of Science and Engineering Kindai University Osaka Japan Department of Electrical Engineering Kobe City College of Technology Hyogo Japan Nuclear Science and Engineering Center Japan Atomic Energy Agency Ibaraki Japan

The interaction between cosmic rays and thunderclouds has attracted much attention in recent years. Cosmic rays have been considered as one possible cause of the trigger of lightning flashes: e.g., a possible correlation between the number of observed lightning and cosmic ray flux (e.g.,Themis G. Chronis, journal of Climate,2009). In thunderclouds, electric fields are thought to cause relativistic acceleration of electrons as an interaction with cosmic ray air shower and emit bremsstrahlung photons, known as gamma-ray glows. One unresolved is that such electron acceleration, and thus gamma-ray glows, may be related to initiating lightning flashes. To verify this hypothesis, we conducted winter thundercloud observations using mapping radiation measurements in Japan. At 04:06–04:08 JST on December 30, 2021, five radiation detectors recorded a gamma-ray glow, terminated with the lightning flash recorded by two lightning mapping systems (FALMA and DALMA). Our multi-location observation revealed that the gamma-ray glow region had a horizontal size of about 2 km×2.5 km. The XRAIN radar observation showed that the high reflectivity area, presumably corresponding to the electron acceleration region, reaches more than 2 km above the gamma-ray detected radiation detectors. The initiation of this lightning discharge started from 1.6 km altitude from the ground around the gamma-ray glow region [1]. This result indicates that the lightning flash began in the vicinity of the electric field region and a possible connection between the cosmic ray interaction with the electric fields in thundercloud and the triggering of the lightning flash. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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A Framework of FAS-RIS Systems: Performance Analysis and Throughput Optimization

arXiv

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arXiv 2024年

作者： Yao, Junteng Lai, Xiazhi Zhi, Kangda Wu, Tuo Jin, Ming Pan, Cunhua Elkashlan, Maged Yuen, Chau Wong, Kai-Kit The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The School of Computer Science Guangdong University of Education Guangdong Guangzhou China The School of Electrical Engineering and Computer Science Technical University of Berlin Berlin10623 Germany The School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Yonsei Frontier Laboratory the School of Integrated Technology Yonsei University Seoul03722 Korea Republic of

In this paper, we investigate reconfigurable intelligent surface (RIS)-assisted communication systems which involve a fixed-antenna base station (BS) and a mobile user (MU) that is equipped with fluid antenna system (FAS). Specifically, the RIS is utilized to enable communication for the user whose direct link from the base station is blocked by obstacles. We propose a comprehensive framework that provides transmission design for both static scenarios with the knowledge of channel state information (CSI) and harsh environments where CSI is hard to acquire. It leads to two approaches: a CSI-based scheme where CSI is available, and a CSI-free scheme when CSI is inaccessible. Given the complex spatial correlations in FAS, we employ block-diagonal matrix approximation and independent antenna equivalent models to simplify the derivation of outage probabilities in both cases. Based on the derived outage probabilities, we then optimize the throughput of the FAS-RIS system. For the CSI-based scheme, we first propose a gradient ascent-based algorithm to obtain a near-optimal solution. Then, to address the possible high computational complexity in the gradient algorithm, we approximate the objective function and confirm a unique optimal solution accessible through a bisection search method. For the CSI-free scheme, we apply the partial gradient ascent algorithm, reducing complexity further than full gradient algorithms. We also approximate the objective function and derive a locally optimal closed-form solution to maximize throughput. Simulation results validate the effectiveness of the proposed framework for the transmission design in FAS-RIS systems. © 2024, CC BY.

关键词： Antennas

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Research on Fine-Grained Modeling and Optimal Control Used for Occupant-Centric Smart Lighting Systems

SSRN

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SSRN 2024年

作者： Ning, Chenguang Shen, Dongdong Dong, Yanzhi Wang, Yingjie Duan, Peiyong School of Information Science and Engineering Shandong Normal University Jinan250014 China School of Physics and Electronic Information Yantai University Yantai264005 China School of Computer and Control Engineering Yantai University Yantai264005 China Faculty of Electronics Electrical and Control Qilu University of Technology Shandong Academy of Sciences Jinan250353 China

In order to reduce energy consumption of the lighting system and improve the visual comfort of occupants in the illuminated area, it is essential to effectively and precisely control the illumination of the luminaires. The smart lighting systems can utilize the occupant's positional data to adjust lamps switching, leading to a significant decrease in energy consumption and an enhancement in illumination comfort. However, there are still several challenges associated with achieving precise and non-invasive multi-person positioning for smart lighting systems, and the implementation of the Occupant-Centric Smart Lighting System (OCSLS) is still pending. In this paper, using 3D reconstruction of human keypoints extracted from multi-view video, the robust positioning algorithm for multi-person is proposed. The algorithm achieves a positioning error of less than 0.3 meters by combining the constraints of epipolar geometry and the appearance characteristics of occupants for multi-view matching. Subsequently, a novel fine-grained model of the OCSLS is proposed through the integration of the Illumination Demand Matrix (IDM), which is derived from the distribution of occupant position, and the Illumination Supply Matrix Library (ISML), which is based on the characteristics of lighting area luminaires. Using the OCSLS model proposed in this study, the Occupant-Centric Control (OCC) strategy is constructed through the optimization algorithm based on the autoencoder and the K-Nearest Neighbor (KNN) algorithm. A verification platform has been created, and the experimental results indicate a decrease in the energy usage of the lighting system by over 40%. © 2024, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Low-power emerging memristive designs towards secure hardware systems for applications in internet of things

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Nano Materials Science 2021年第2期3卷 186-204页

作者： Nan Du Heidemarie Schmidt Ilia Polian Department Nano Device Technology Fraunhofer Institute for Electronic Nano SystemsTechnologie-Campus 309126ChemnitzGermany Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology(TU Chemnitz)Straße der Nationen 6209126ChemnitzGermany Department of Quantum Detection Leibniz Institute of Photonic Technology(IPHT)Albert-Einstein-Str.907745JenaGermany Institute for Solid State Physics Friedrich Schiller University JenaFürstengraben 107743JenaGermany Institute of Computer Engineering and Computer Architecture(ITI) University of StuttgartPfaffenwaldring 4770569StuttgartGermany

Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and inmemory computing(IMC),but there is a rising interest in using memristive technologies for security applications in the era of internet of things(IoT).In this review article,for achieving secure hardware systems in IoT,lowpower design techniques based on emerging memristive technology for hardware security primitives/systems are *** reviewing the state-of-the-art in three highlighted memristive application areas,*** non-volatile memory,memristive reconfigurable logic computing and memristive artificial intelligent computing,their application-level impacts on the novel implementations of secret key generation,crypto functions and machine learning attacks are explored,*** the low-power security applications in IoT,it is essential to understand how to best realize cryptographic circuitry using memristive circuitries,and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their *** review article aims to help researchers to explore security solutions,to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs.

关键词： Memristive technology Nanoelectronic device Low-power consumption Miniaturization Nonvolatility Reconfigurability In memory computing Artificial intelligence Hardware security primitives Machine learning-related attacks and defenses

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Palm Bunch Grading Technique Using a Multi-Input and Multi-Label Deep Neural Network

SSRN

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SSRN 2022年

作者： Pipitsunthonsan, Pronthep Pan, Liangrui Peng, Shaoliang Khaorapapong, Thanate Nakasathien, Sutkhet Channumsin, Sittiporn Chongcheawchamnan, Mitchai Faculty of Engineering Prince of Songkla University 90110 Thailand College of Computer Science and Electronic Engineering Hunan University 410000 China Faculty of Agriculture Kasetsart University 10900 Thailand Geo-Informatics and Space Technology Development Agency Thailand

Oil palm bunches, which are the raw materials for several oil palm products, are traded to a crude palm oil (CPO) factory or a middleman. Since high oil-content palm bunches are desired to produce CPO, the factories and middlemen conventionally hire experts to choose high-grade palm bunches based on bunch colour, shape, and size. The reliability of this conventional method depends on their skill, experience, and working hours. In this paper, we propose a deep learning-based model working with three sensors (RGB camera, infrared sensor, and a load cell) for classifying a palm bunch. The classifying framework of the algorithm is a multi-input and multi-label deep convolutional neural network. Based on 1,585 datasets for 14 varieties of palm bunches collected from four trading sites, the sensors were installed, the algorithm was trained, validated, and tested with 70%, 20% and 10% of the overall datasets, respectively. The cross-entropy loss function was applied to investigate what? during the training process. Based on the results, the proposed model achieves an accuracy of 90.26%, precision of 89.86%, recall of 89.54%, and an F1-score of 89.68%. © 2022, The Authors. All rights reserved.

关键词： Grading

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Efficient Human Face Recognition in Real-Life Applications using the Discrete Wavelet Transformation (HFRDWT)

TechRxiv

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TechRxiv 2022年

作者： Eragi, Saddam M. BenSaid, Fatma Alimi, Adel M. College of Computer Science and Information Technology Sudan University of Science and Technology Sudan BP 1173 Sfax3038 Tunisia Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

Human Face receives major attention and acquires most of the efforts of the research and studies of Machine Learning in detection and recognition. In real-life applications, the problem of quick and rapid recognition of the Human Face is always challenging researchers to come out with powerful and reliable techniques. In this paper, we proposed a new human face recognition system using the Discrete Wavelet Transformation named HFRDWT. The proposed system showed that the use of Wavelet Transformation along with the Convolutional Neural Network to represent the features of an image had significantly reduced the face recognition time, which makes it useful in real-life areas, especially in public and crowded places. The Approximation coefficient of the Discrete Wavelet Transformation played the dominant role in our system by reducing the raw image resolution to a quarter while maintaining the high level of accuracy rate that the raw image had. Results on ORL, Japanese Female Facial Expression, extended Cohn-Kanade, Labeled Faces in the Wild datasets, and our new Sudanese Labeled Faces in the Wild dataset showed that our system obtained the least recognition timing (average of 24 milliseconds for training and 8 milliseconds for testing) and acceptable high recognition rate (average of 98%) compared to the other systems. © 2022, CC BY.

关键词： Face recognition

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Importance of Features Selection,Attributes Selection,Challenges and Future Directions for Medical Imaging Data:A Review

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computer Modeling in engineering & Sciences 2020年第10期125卷 315-344页

作者： Nazish Naheed Muhammad Shaheen Sajid Ali Khan Mohammed Alawairdhi Muhammad Attique Khan Faculty of Engineering&Information Technology Foundation University IslamabadIslamabadPakistan College of Computing and Informatics Saudi Electronic UniversityRayadhSaudi Arabia Department of Computer Science HITEC UniversityTaxilaPakistan

In the area of pattern recognition and machine learning,features play a key role in *** famous applications of features are medical imaging,image classification,and name a few *** the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of *** data repositories contain details information essential to support medical diagnostic decisions and also improve patient care *** the other hand,this growth also made it difficult to comprehend and utilize data for various *** results of imaging data can become biased because of extraneous features present in larger *** selection gives a chance to decrease the number of components in such large *** selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization *** correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast *** paper presents a review of feature selection techniques and attributes selection measures for medical *** review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining *** review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced ***,this review provides the importance of feature selection for correct classification of medical *** the end,critical analysis and future directions are provided.

关键词： Medical imaging imaging data feature selection data mining attribute selection medical challenges future directions

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