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5th International Conference on Electrical, Control and Instrumentation engineering, ICECIE 2023

作者： Yoshikawa, Yuta Okai, Takayuki Oya, Hidetoshi Hoshi, Yoshikatsu Nakano, Kazushi Tokyo City University Graduate School of Integrative Science and Engineering Tokyo Japan Tokyo City University Department of Computer Science Tokyo Japan The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan

ISBN: (纸本)9798350325041

This paper proposes a prediction system of the effect of electrical defibrillation based on the wavelet transform with pseudo-differential operators. To construct this system first, we analyze and extract features from pre-shock (before defibrillation) ECGs (ElectroCardioGrams) by using Gabor wavelet transform with pseudo-differential operators. Next, the effective feature parameters are selected based on the ?2 test. Finally, this system predicts two states such as "Effective"and "Ineffective"defibrillation by using the SVM (Support Vector Machine). In this way, we show the effectiveness of the proposed feature extraction method and prediction system. © 2023 IEEE.

关键词： Wavelet transforms

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3D Virtual Reality Application with Design Approach for Introduction of Istano Basa Pagaruyung Tourist Attractions 23

3D Virtual Reality Application with Design Approach for Intr...

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11th International Conference on computer and Communications Management, ICCCM 2023

作者： Adrian, Qadhli Jafar Hidayat, Robby Rahmat Pasha, Donaya Maharini, Anggita Septia Lubis, Muharman Lubis, Arif Ridho Department of Information System Universitas Teknokrat Indonesia Indonesia Department of Information Technology Universitas Teknokrat Indonesia Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Medan Indonesia

ISBN: (纸本)9798400707735

This research is about the development of proper media for the tourist attraction, which in this case related to the Istano Basa Pagaruyung in order to increase the engagement during Covid-19 pandemic, which is the Android-based. It aims to produce interactivity and innovation product service by using Virtual Reality (VR) technology through design approach method as cognitive and practical process. For the purpose of testing, there are three aspects that were evaluated namely the functional conformity, the usability and the portability. In the first aspect, the results indicated almost 100% successful presentation, while the usability aspect provide the presentation value of 86.7% and the last aspects show zero errors result. Thus, it can conclude that the application can be one alternative solution to promote tourist attraction in the midst of the Covid-19 pandemic in term of marketing or learning mechanism. It also presents the useful practice on the utilization of design approach that can be aligned with multimedia development life cycle (MDLC) to have non-linear and iterative process in order to redefine the problem for designing the alternative solution © 2023 ACM.

关键词： Virtual reality

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Predicting Fish-Mortality: Artificial Neural Networks vs Symbolic Regression 14

Predicting Fish-Mortality: Artificial Neural Networks vs Sym...

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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Aravanis, Theofanis Ilias, Aristeidis Hatzilygeroudis, Ioannis Spiliopoulos, Georgios University of the Peloponnese Dept of Mechanical Engineering Patras Greece University of Patras Dept of Computer Engin. and Informatics Patras Greece Kefalonia Fisheries S.A. Kefalonia Greece

ISBN: (纸本)9798350318067

In this paper, we present a comparison between a collection of Artificial Neural Networks (ANNs) - specifically, Multilayer Perceptrons - and a collection of Symbolic Regression (SR) models, all developed for predicting fish-deaths due to infectious diseases in aquaculture. The implemented Machine-Learning models are able to identify patterns that indicate an increased risk of fish-deaths, and provide early warning-alerts to fish-farmers and other stakeholders, enabling them to take necessary measures to prevent or minimize losses of fishes. The models were trained using real-world data acquired from a large Greek fish-farming unit, and evaluated in a validation dataset (distinct from the training dataset) based on their Mean Absolute Error (MAE) performance. The study found that, for each disease considered, the corresponding ANN model outperformed the respective SR model in terms of MAE. However, the ANNs sacrificed interpretability for higher performance, while the developed SR models, despite lower performance, managed to produce transparent and understandable mathematical expressions for estimating fish-deaths. Overall, this study not only provides a valuable method for generating early warning-alerts of pathogenetic circumstances to the interested stakeholders, improving the efficiency and sustainability of fish-farming operations, but it also sheds light on the strengths and limitations of the developed ANN and SR models. © 2023 IEEE.

关键词： Forecasting

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A Hydroelectric Power Plant Brief: Classification and Application of Artificial Intelligence 17

A Hydroelectric Power Plant Brief: Classification and Applic...

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17th IEEE International Symposium on Applied Computational Intelligence and informatics, SACI 2023

作者： Shahgholian, Ghazanfar Moazzami, Majid Zanjani, Sayed Mohammadali Mosavi, Amir Fathollahi, Arman Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark

ISBN: (纸本)9798350321104

Hydropower is a reliable, clean, and efficient alternative to conventional fossil fuels and other renewable energy sources. The hydro turbine is the core of a hydropower plant, and the proper maintenance and operation of all other components are essential for maximizing energy production. Besides electricity generation, hydropower plants play a critical role in storing irrigation and drinking water and controlling floods. This study presents a concise overview of hydroelectric power plant classification based on the output power generated by peak water drop and storage. Pumped storage water plants are the most applicable classification based on water conditions. The study reviews the application of Artificial Intelligence (AI) in various aspects of hydropower plants, highlighting the potential benefits and challenges of integrating AI technologies in the energy production process. This paper emphasizes the importance of proper maintenance and operation of hydropower plants and provides insights into AI's potential role in optimizing energy production and improving plant efficiency. © 2023 IEEE.

关键词： Hydroelectric power plants

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Controllable Structure Planning for Energy Internet 1

Controllable Structure Planning for Energy Internet

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1st International Conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2022

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

ISBN: (纸本)9781665452151

Secure system operations rely on reliable network structures. The loss of controllability may be the main reason to cause cascaded failures for complex network, e.g., Energy Internet (EI). However, the existing studies do not consider the network controllability to guide the system reconfiguration. To address this issue, the paper proposes a new structuring planning method for EI with consideration of controllability and economy. Firstly, the structure planning problem is modeled as a dynamic optimization problem with the tradeoff objectives of maximum social welfare and minimum driven nodes for long-term period. Then, a mixed maximum matching and deep deterministic policy gradient method is presented to obtain the approximate optimal planning solution with strong adaptability. Finally, simulation results demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Controllability

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Career-Based Explainable Course Recommendation 18th

Career-Based Explainable Course Recommendation

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18th International Conference on Information for a Better World: Normality, Virtuality, Physicality, Inclusivity, iConference 2023

作者： Striebel, Jacob Myers, Rebecca Liu, Xiaozhong Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States

ISBN: (纸本)9783031280313

The type and quality of education that a student receives can have a profound impact on their career. In contrast to education that is not intentionally organized to help students achieve specific career objectives, career-based education seeks to provide students with the skills that are required to achieve specific career goals. In this work, we propose a course recommendation framework that is designed to recommend courses based directly on their ability to teach skills relevant to a user-specified career goal. Within our framework, course recommendations are generated in a transparent manner, using skills to bridge between jobs and courses in a knowledge-based inference procedure. Due to the procedure’s transparency, our system is able to provide faithful template-based explanations detailing why each recommended course was chosen for recommendation. Our framework contrasts with other course recommendation systems in the literature that lack the ability to explain their choices and therefore may lack trustworthiness from the perspective of a user. The proposed framework has several applications, including assisting students with course planning, as well as aiding with curriculum evaluation and development by providing insight into the usefulness of specific courses to specific careers. We conduct a preliminary evaluation of our system, and its performance is competitive against two baselines. We provide all the resources needed to reproduce our results. (http://***/striebel/cbecr ) © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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Design and Implementation of HDB3 Clock Recovery Module with Large Frequency Range 2

Design and Implementation of HDB3 Clock Recovery Module with...

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2nd IEEE International Conference on Electrical engineering, Big Data and Algorithms, EEBDA 2023

作者： Li, Haoxiang Lu, Shengbo Zhao, Zixiang Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hefei University of Technology School of Electrical Engineering and Automation Hefei230009 China College of Oceanography and Space Informatics Qingdao266580 China

ISBN: (纸本)9781665462532

With the development of the communication and information industry, the demand for high-speed data transmission is growing. One of the essential tasks of the receiver is to extract synchronization information from the received data polluted by noise and recover the data and clock accurately. Therefore, clock data recovery (CDR) is crucial in optical transmission systems and other digital transmission fields. However, the traditional bit synchronous clock recovery can only recover signals of a specific frequency. If a front-end frequency meter is added at the beginning of signal input to measure the input signal frequency, and the real-time control bit synchronization extraction circuit uses the input signal frequency as its working frequency, the operating range of the bit synchronization clock recovery circuit can be significantly improved. This will be the perfect combination of clock recovery performance and time cost. This paper studies the basic principles of bit synchronous clock recovery circuit and dynamic frequency meter. The HDB3 dynamic clock in a wide frequency range is extracted in FPGA using an all-digital design method. Through simulation and verification, the bit synchronous CDR with a pre-dynamic frequency meter has a more comprehensive recovery range, reaching 64Kb/s to 32.768Mb/s. © 2023 IEEE.

关键词： Real time control

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An Uncertainty Based Predictive Analysis of Smart Water Distribution System Using Bayesian LSTM Approach 6

An Uncertainty Based Predictive Analysis of Smart Water Dist...

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6th IEEE International Conference on Universal Village, UV 2022

作者： Zaman, Mostafa Islam, Maher Al Tantawy, Ashraf Abdelwahed, Sherif Virginia Commonwealth University Department of Electrical and Computer Engineering Richmond United States De Montfort University School of Computer Science and Informatics Leicester United Kingdom

ISBN: (纸本)9781665474771

A well-designed water distribution system is crucial for maintaining high service standards in any modern smart city. Moreover, as the population is sky-rocketing, the demand for energy and water is increasing more rapidly than a decade before. Therefore, ensuring a steady clean water supply with optimized energy and water consumption has become necessary. To accurately monitor water distribution systems, the accuracy of input data plays a vital role in determining how accurate the system's status estimations are. There must be a way for system operators to know what is going on at any given time to make practical decisions about how reliable the data they are receiving is. The input data uncertainty can induce flow and pressure calculation inaccuracies, which can be fatal while planning for future demands and needs to be *** the degree of uncertainty in predicting the water distribution system's capacity or load can help people better prepare for future capacity or load predictions. Accurate uncertainty calculations are critical to time series forecasting. Probabilistic formulae are widely employed with classical time series models to estimate uncertainty. But incorporating new data and fine-tuning these models is a challenging task. This research paper presents a Bayesian LSTM network that computes both time series prediction and uncertainty assessment at the same time. In this paper, a real-time data set from VCU's OpenCity test bed is employed to evaluate the efficacy of the suggested strategy. © 2022 IEEE.

关键词： Water distribution systems

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What Drives Success in Data Science Projects: A Taxonomy of Antecedents

What Drives Success in Data Science Projects: A Taxonomy of ...

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International Conference on Computing, Intelligence and Data Analytics, ICCIDA 2022

作者： Gökay, Gonca Tokdemir Nazlıel, Kerem Şener, Umut Gökalp, Ebru Gökalp, Mert Onuralp Gençal, Nergiz Dağdaş, Gizemnur Eren, P. Erhan Graduate School of Informatics Middle East Technical University Ankara Turkey Department of Computer Engineering Hacettepe University Ankara Turkey Data Analytics Center Tupras İstanbul Turkey

ISBN: (纸本)9783031270987

Organizations have been trying to reshape their business processes and transform them into a smart environment to attain sustainable competitive advantage in their markets. Data science enables organizations to define interconnected and self-controlled business processes by analyzing the massive amount of unstandardized and unstructured high-speed data produced by heterogeneous Internet of Things devices. However, according to the latest research, the success rate of data science projects is lower than other software projects, and the literature review conducted reveals a fundamental need for determining success drivers for data science projects. To address these research gaps, this study investigates the determinants of success and the taxonomy of antecedents of success in data science projects. We reviewed the literature systematically and conducted an expert panel by following a Delphi method to explore the main success drivers of data science projects. The main contributions of the study are twofold: (1) establishing a common base for determinants of success in data science projects (2) guiding organizations to increase the success of their data science projects. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Project management

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Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits

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Information Processing in Agriculture 2023年第3期10卷 289-300页

作者： Inna Novianty Ringga Gilang Baskoro Muhammad Iqbal Nurulhaq Muhammad Achirul Nanda Computer Engineering Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Informatics Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Technology and Plantation Production Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Department of Agricultural and Biosystem Engineering Faculty of Agro-Industrial TechnologyUniversitas PadjadjaranJatinangor 45363Indonesia

Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production,starting from the upstream and *** content can be used to monitor the progress of the oil palm fresh fruit bunch(FFB)and be applied to identify product *** on the near-infrared(NIR)signals,this study proposes an empirical mode decomposition(EMD)technique to decompose signals and predict the oil content of palm ***,350 palm fruits with Tenera varieties(Elaeis guineensis ***),at various ages of maturity,were harvested from the Cikabayan Oil Palm Plantation(IPB University,Indonesia).Second,each sample was sent directly to the laboratory for NIR signal measurements and oil content ***,the EMD analysis and arti-ficial neural network(ANN)were employed to correlate the NIR signals and oil ***,a robust EMD-ANN model is generated by optimizing the lowest possible *** on performance evaluation,the proposed technique can predict oil content with a coefficient of determination(R2)of 0.933±0.015 and a root mean squared error(RMSE)of 1.446±*** results demonstrate that the model has a good predictive capacity and has the potential to predict the oil content of palm fruits directly,without neither solvents nor reagents,which makes it environmentally ***,the proposed technique has a promising potential to be applied in the oil palm *** like this will lead to the effective and efficient management of oil palm production.

关键词： Artificial neural network Empirical mode decomposition Oil palm Oil content prediction

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