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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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Development of Inter-Vehicle Communication for Electric Bus

Development of Inter-Vehicle Communication for Electric Bus

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Dimas Fajar Uman Putra Imam Wahyudi Farid Vicky Andria Kusuma Aji Akbar Firdaus Novian Patria Uman Putra Shafira Zahra Atar Fuady Babgei Nasyith Hananur Rohiem Eunike Widya Adinda Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Electrical Automation Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Electrical Engineering Institut Teknologi Kalimantan Balikpapan Indonesia Department of Engineering Universitas Airlangga Surabaya Indonesia Department of Electrical Engineering Institut Teknologi Adhi Tama Surabaya Surabaya Indonesia Department of Electrical and Electronic Information Engineering Toyohashi University of Technology Toyohashi Japan Department of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

This paper discusses the importance of electric vehicles in modern society and the challenges associated with communication systems in such vehicles. To overcome these challenges, the paper proposes a model for supporting communication on electric buses by connecting each sensor device to a control area network (CAN). However, the severe nonlinearity and high complexity of the message frame frequency pose significant obstacles to successfully implementing this research in the CAN protocol. The paper highlights the need for continued research and development in this area to enable the effective use of electric buses in our daily lives.

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An Online Self-learning Graph-based Lateral Controller for Self-Driving Cars

arXiv

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arXiv 2024年

作者： Samiuddin, Jilan Boulet, Benoit Wu, Di The Intelligent Automation Lab the Centre for Intelligent Machines the Department of Electrical and Computer Engineering McGill University MontrealQCH3A 0E9 Canada

The hype around self-driving cars has been growing over the past years and has sparked much research. Several modules in self-driving cars are thoroughly investigated to ensure safety, comfort, and efficiency, among which the controller is crucial. The controller module can be categorized into longitudinal and lateral controllers in which the task of the former is to follow the reference velocity, and the latter is to reduce the lateral displacement error from the reference path. Generally, a tuned controller is not sufficient to perform in all environments. Thus, a controller that can adapt to changing conditions is necessary for autonomous driving. Furthermore, these controllers often depend on vehicle models that also need to adapt over time due to varying environments. This paper uses graphs to present novel techniques to learn the vehicle model and the lateral controller online. First, a heterogeneous graph is presented depicting the current states of and inputs to the vehicle. The vehicle model is then learned online using known physical constraints in conjunction with the processing of the graph through a Graph Neural Network structure. Next, another heterogeneous graph – depicting the transition from current to desired states – is processed through another Graph Neural Network structure to generate the steering command on the fly. Finally, the performance of this self-learning model-based lateral controller is evaluated and shown to be satisfactory on an open-source autonomous driving platform called CARLA. © 2024, CC BY-NC-ND.

关键词： Graph neural networks

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Evolving Restricted Boltzmann Machine-Kohonen Network for Online Clustering

arXiv

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arXiv 2024年

作者： Senthilnath, J. Bhattiprolu, Adithya Singh, Ankur Zhou, Bangjian Wu, Min Benediktsson, Jón Atli Li, Xiaoli Singapore138632 Singapore The Department of Electrical & Electronics Engineering Birla Institute of Technology and Science Pilani333031 India The Faculty of Electrical and Computer Engineering University of Iceland Reykjavik107 Iceland School of Computer Science and Engineering Nanyang Technological University Singapore and A*STAR Centre for Frontier AI Research Singapore138632 Singapore

A novel online clustering algorithm is presented where an Evolving Restricted Boltzmann Machine (ERBM) is embedded with a Kohonen Network called ERBM-KNet. The proposed ERBM-KNet efficiently handles streaming data in a single-pass mode using the ERBM, employing a bias-variance strategy for neuron growing and pruning, as well as online clustering based on a cluster update strategy for cluster prediction and cluster center update using KNet. Initially, ERBM evolves its architecture while processing unlabeled image data, effectively disentangling the data distribution in the latent space. Subsequently, the KNet utilizes the feature extracted from ERBM to predict the number of clusters and updates the cluster centers. By overcoming the common challenges associated with clustering algorithms, such as prior initialization of the number of clusters and subpar clustering accuracy, the proposed ERBM-KNet offers significant improvements. Extensive experimental evaluations on four benchmarks and one industry dataset demonstrate the superiority of ERBM-KNet compared to state-of-the-art approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Scientometric Analysis in the Field of Big Data and Artificial Intelligence in Industry 1

Scientometric Analysis in the Field of Big Data and Artifici...

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1st International Conference on Information System and Information Technology, ICISIT 2022

作者： Maulana, Fairuz Iqbal Febriantono, M. Aldiki Hamim, Miftahul Fajri, Bayu Ramadhani Arifuddin, Rahman Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Entrepreneurship Business Creation BINUS Business School Jakarta11480 Indonesia Faculty of Engineering Universitas Negeri Padang Department of Animation Padang Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (数字)9781665402002

ISBN: (纸本)9781665402002

Big Data and Artificial Intelligence (BDAI) in Industry have grown so prevalent, and the potential they provide is so revolutionary that they are seen as critical for competitive growth. Because the number of organizations BDAI on Industry technology is increasing exponentially, so is the need for BDAI on Industry practitioners. Until we conducted this research, only 1399 academic documents on BDAI in Industry found from 2002 to 2020 were obtained by searching the Scopus database. BDAI in the industrial sector is examined in-depth in this paper. This study uses bibliometric analysis and indexed digital methods to map scientific publications worldwide. This study uses the Scopus database to collect information and online analysis via the Scopus website and VOSViewer to demonstrate bibliometric network mapping. We use the article selection process, starting with the keywords to be searched for, the year limitation, then the database is exported into RIS and CSV format files. From the database, we also perform network mapping using VOSViewer. Researchers in China have the most articles published and indexed by Scopus among the most prolific authors (373), followed by the United States (239) and India with 125 academic publications. Data analysis reveals an upward trend in the number of worldwide publications in BDAI in Industry, as measured by the Scopus index. © 2022 IEEE.

关键词： Big data

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HEAVY TRAFFIC LIMIT WITH DISCONTINUOUS COEFFICIENTS VIA A NON-STANDARD SEMIMARTINGALE DECOMPOSITION

arXiv

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arXiv 2025年

作者： Atar, Rami Miyazawa, Masakiyo Viterbi Faculty of Electrical and Computer Engineering Technion – Israel Institute of Technology Haifa Israel Department of Information Sciences Tokyo University of Science Chiba Noda Japan

This paper studies a single server queue in heavy traffic, with general inter-arrival and service time distributions, where arrival and service rates vary discontinuously as a function of the (diffusively scaled) queue length. It is proved that the weak limit is given by the unique-in-law solution to a stochastic differential equation in [0, ∞) with discontinuous drift and diffusion coefficients. The main tool is a semimartingale decomposition for point processes introduced in [6], which is distinct from the Doob-Meyer decomposition of a counting process. Whereas the use of this tool is demonstrated here for a particular model, we believe it may be useful for investigating the scaling limits of queueing models very *** Codes 60K25, 60H10, 60H30 © 2025, CC BY.

关键词： Stochastic systems

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Optimizing Art Studio Connectivity: A Haversine and Greedy Algorithm Approach for Navigation in Cirebon Indonesia

Optimizing Art Studio Connectivity: A Haversine and Greedy A...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Indra Surya Permana Teguh Arlovin Taufik Hidayat Ivany Sarief Hanhan Hanafiah Solihin Cecep Deni Mulyadi Department of Informatics Engineering Institut Teknologi dan Kesehatan Mahardika Cirebon Indonesia Department of Computer Engineering Universitas AMIKOM Yogyakarta Indonesia Department of Electrical Engineering Universitas Indonesia Jakarta Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Sangga Buana Bandung Indonesia Department of Information System Universitas Sangga Buana Bandung Indonesia Faculty of Engineering Universitas Sangga Buana Bandung Indonesia

The proposed geographic information system (GIS) provides a systematic approach to locating and connecting art studios, enabling cultural institutions, artists, and students to collaborate and share resources more efficiently. The implementation of a structured GIS and Haversine Formula and Greedy Algorithm can improve the management and connectivity of art studios in Cirebon, fostering connections and collaboration between art studios, artists, and cultural institutions. This research used a greedy algorithm to find the shortest route from user’s current location to the destination point, i.e., Art Gallery. The Greedy Algorithm is a problem-solving approach that aims to find an optimal solution by iteratively selecting the best local option. The Haversine formula is used to calculate the distance from each connected node using the haversine formula, which converts latitudes and longitudes from degrees to radians and calculates the differences in latitude and longitude. In implementing the nearest art studio search system, the Haversine Formula combined with Greedy Algorithm can be employed as an alternative for search logic since it can directly calculate the distance from the initial point or user’s selected point by utilizing easily identifiable reference points.

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Multi-Band Radio Frequency Energy Predictor for Advanced Energy Harvesting Cellular Bands Systems

Multi-Band Radio Frequency Energy Predictor for Advanced Ene...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Shaimaa H. Mohammed Ashraf S. Mohra Ashraf Y. Hassan Ahmed F. Elnokrashy Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt Benha Faculty of Engineering Benha University Benha Egypt School of Information Technology and Computer Science Nile University Giza Egypt

Radio Frequency (RF) energy harvesting has been employed to power wireless devices. Nevertheless, RF energy harvesting encounters restrictions regarding the quantity of power it can harvest depending on signal accessibility. As a result, accurately predicting energy levels becomes crucial for enhancing the performance of energy harvesting circuits. Most research efforts have concentrated on enhancing power harvesting policies or theoretically estimating the energy obtained through RF energy harvesting. Moreover, the existing literature has primarily focused on single-band prediction approaches. This paper presents a multi-band RF energy prediction approach for RF energy harvesting systems. We collect real-time RF energy using software-defined radio technology. The proposed approach leverages Long Short-Term Memory (LSTM) neural networks to accurately predict the mean RF energy in different frequency bands for the next 100 samples, which corresponds to approximately one hour and a half. The research explores the research gap in modeling the radio frequency signal and the need for multi-band prediction techniques. The results demonstrate the effectiveness of the proposed approach in predicting RF energy across different frequency bands, with average accuracies above 98%.

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Improved Runtime for the Synchronous Multi-door Filling

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica electrical engineering and computer science 2022年第1期66卷 12-19页

作者： Hideg, Attila Lukovszki, Tamás Forstner, Bertalan Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Magyar tudósok krt. 2 BudapestH-1117 Hungary Faculty of Informatics Eötvös Loránd University Pázmány Péter sétány 1/C. BudapestH-1117 Hungary

In this paper, a particular type of dispersion is further investigated, which is called Filling. In this problem, robots are injected one by one into an a priori not known area and have to travel across until the whole area is covered. The coverage is achieved by a robotic team whose hardware capabilities are restricted in order to maintain low production costs. This includes limited viewing and communication ranges. In this work, we present an algorithm solving the synchronous Filling problem in O((k + ∆)·n) time steps by n robots with a viewing range of 1 hop, where k is the number of doors, n is the number of vertices of the graph, and ∆ is the maximum degree of the graph. This improves the best previously known running time bound of O(k · ∆ · n). Furthermore, we remove the constraint from the previous algorithm that the door vertices need to have a degree of 1. © 2022 Budapest University of Technology and Economics. All rights reserved.

关键词： Multipurpose robots

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Statistical Time Series Forecasting Models for Pandemic Prediction

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computer Systems Science & engineering 2023年第10期47卷 349-374页

作者： Ahmed ElShafee Walid El-Shafai Abeer D.Algarni Naglaa F.Soliman Moustafa H.Aly Department of Electrical Engineering Faculty of EngineeringAhram Canadian University6th October CityGizaEgypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease *** this case,COVID-19 data is a time-series dataset that can be projected using different ***,this work aims to gauge the spread of the outbreak severity over ***,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus *** have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML *** of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive ***,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best ***,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.

关键词： Forecasting COVID-19 predictive models medical viruses mathematical model market research diseases

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