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SSRN 2023年

作者： Román-Messina, Arturo Murillo Aguirre, E. Rafael Villalón, Osvaldo Rodríguez Graduate Studies Program in Electrical Engineering CINVESTAV Jal. Guadalajara45015 Mexico Department of Electrical Engineering DICIS Universidad de Guanajuato Gto. Salamanca36885 Mexico

Data fusion of multitype data is a fundamental problem in power system monitoring. This paper proposes a method for graph spectral clustering and visualization based on the fusion of similarity networks. The adopted approach enables the integration or fusion of multiple types of measurements, leading to the identification of a reduced-order model (a coarse-grained representation) and the variables that are influential in system dynamics.A systematic dynamic framework for data fusion is developed based on diffusion geometry and the fusion of similarity networks. This approach utilizes the statistical characteristics of Markov processes to construct a similarity network or graph for each data type or information. This allows for capturing the global data structure by establishing a Markov chain that describes transition probabilities between different sensor time series. Integrating this approach with the concept of graph community aids in identifying important buses and transmission branches for *** considerations concerning the application of spectral analysis techniques and improvements upon previous work are deliberated. Additionally, promising methodologies for investigating the multimodal attributes of data are highlighted to address these challenges robustly. Simulation studies conducted on a 14-machine, 58-bus test system serve to illustrate the efficacy of diffusion-based similarity network fusion techniques. © 2023, The Authors. All rights reserved.

关键词： Data fusion

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A DEA-CCR Model Application in Clustered Stocks Portfolio with Technical Investment Strategies and Mean-Variance Model

A DEA-CCR Model Application in Clustered Stocks Portfolio wi...

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2023 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2023

作者： Navarro, M.M. Young, M.N. Prasetyo, Y.T. Camino, J.L. Navarro, B.B. Ramos, V.T. School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines School of Graduate Studies Mapúa University Manila Philippines Technological Institute of the Philippines Department of Industrial Engineering Quezon City Philippines Yuan Ze University International Bachelor Program in Engineering Taiwan Technological Institute of the Philippines Department of Civil Engineering Quezon City Philippines Technological Institute of the Philippines Department of Electrical Engineering Quezon City Philippines

ISBN: (纸本)9798350323153

The stock market is one of the investment options that offer the best potential returns. However, investors are aware of the risk associated with their investments, which is why stock portfolios are established to diversify investments or distribute investments. Managing a portfolio may be challenging because there are so many different stocks. One of the challenges is deciding how much money to invest and how to allocate it for the best results. This paper used the K-means Algorithm for cluster analysis in identifying Decision Making Unit (DMU), the stocks will be grouped according to their characteristics considering their risk and return. Asset allocation per clustered portfolio was performed and the Mean-Variance Model was used to optimize clustered portfolios. This paper aims to use Data Envelopment Analysis (DEA) to evaluate the efficiency of these clustered portfolios/DMUs. In addition, this paper introduces subjective choice in decision-making using the criterion's level of importance to help investors decide which among these efficient DMUs is a better option. The results show 4 Efficient DMUs for 2019 Data, and 3 Efficient DMUs in 2020 Data. All simulations were carried out using the MATLAB environment platform. © 2023 IEEE.

关键词： Data Envelopment Analysis K-means Mean-Variance Model Stock Market Technical Investment Strategy

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Preprocessing Algorithm for Seismic Signals Monitored by Fiber Optic Distributed Sensors

Preprocessing Algorithm for Seismic Signals Monitored by Fib...

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SBMO/IEEE MTT-S International Conference on Microwave and Optoelectronics (IMOC)

作者： Erik Tarlles Silveira Beatriz Brusamarello Uilian J. Dreyer Guilherme H. Weber Danilo F. Gomes Sidnei H. C. Teixeira Gilson A. Brunetto Luis F. P. Melegari Cicero Martelli Jean Carlos Cardozo da Silva Graduate Program in Electrical and Computer Engineering (CPGEI-UTFPR) Curitiba Parana Brazil Graduate Program in Energy Systems (PPGSE-UTFPR) Curitiba Parana Brazil Civil Engineering Studies Center (CESEC-UFPR) Curitiba Parana Brazil CPFL Energia Brasil Universidade Tecnológica Federal do Paraná Curitiba Paraná Brazil

Fiber optic-based sensors have experimented with significant technological advances in the last two decades. Distributed acoustic sensors represent their most recent iteration. This sensing technology become all the cable length into a sensor, a favorable condition for seismic studies. Data preprocessing is a crucial step in this study, in which the seismic signals captured by optical interrogators must be converted to the apparent velocity of particles that sensitized the fiber. Amongst the main conversion algorithms, the ones based on Fourier Transforms are more adopted at large-scale surveys. This paper presents an algorithm implementation for this conversion, based on the 2D Fourier Transform, in a small-scale laboratory investigation. The results are favorable and show that it is a possible use. It brings more definition to individualizing first break time arrivals, equalizing signal amplitude, and aligning oscillation peaks, essential parameters for applying reflections and refraction methods in seismic studies.

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Mapping the Optimal Sites for Offshore Wind Power Plants and Green Hydrogen Production: South and Southeast Brazilian Case Study

Mapping the Optimal Sites for Offshore Wind Power Plants and...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Karen de Paula Hayro Pumalloclla Mahdi Pourakbari-Kasmaei Joel D. Melo Djalma Falcão Graduate Program in Energy Federal University of ABC Santo André Brazil Dept. Electrical Engineering and Automation Aalto University Espoo Finland Alberto Luiz Coimbra Institute for Graduate Studies and Engineering Research Federal University of Rio de Janeiro Rio de Janeiro Brazil

The demand for renewable energy solutions has become a global trend due to the current climate change scenario and the increased demand for electricity. Hence, in several countries, the share of renewable sources in the electric matrix has grown with the insertion of new energy sources, such as offshore wind. Brazil presents optimal conditions for the construction of offshore wind farms. The Brazilian 10-year Energy Expansion Plan 2029, in addition to placing this source as an expansion for the coming years, intensely focuses on hydrogen. For this reason, this study aims to assist planners and agents interested in running new construction projects of offshore wind farms with green hydrogen production. Such a study case seeks better use of the available wind resources in the high energy consumption regions of Brazil’s South and Southeast, assessing the wind resource considering technical, environmental, social, and economic restrictions to identify the best locations for installing wind farms. As such, it processes the data within a geographic information system, and then a fuzzy logic-based model is used for the joint evaluation of constraints. Results show that Rio de Janeiro and Espírito Santo are the most favorable places.

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Analyzing the Performance of Radial-Flux PMSGs Through Different Types of Windings

Analyzing the Performance of Radial-Flux PMSGs Through Diffe...

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IEEE Central America and Panama Convention (CONCAPAN)

作者： E. Ortiz-García R. Iracheta-Cortez E. Vázquez Dept. of Electrical Engineer CINVESTAV-Guadalajara Zapopan Jalisco México Posgraduate Studies Division CONAHCYT-Universidad del Istmo Tehuantepec Oaxaca México Electrical Engineering Graduate Program FIME-UANL Monterrey Nuevo León México

In this paper is presented an improved version of a general methodology for calculating the optimal solution of non-conventional winding distribution for applications in Wind Turbines based on a direct-drive permanent magnet synchronous generator. The proposed methodology is also well suited for different conventional windings in electrical rotating machines. Moreover, the aim of this paper is to compare the performance of wind turbines based on PMSGs with different types of windings such as concentric, integer and distributed. To achieve this, an exhaustive parametric study through 2D and 3D finite element analysis is carried out in order to measure variables such as the copper mass, losses, the power quality and, cogging and ripple torques. Finally, in a comparative table are described the main characteristics of each type of winding and the best performance for the purpose of using in wind turbines.

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An Efficient Three-Factor Authenticated Key Agreement Technique Using FCM Under HC-IoT Architectures

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Computers, Materials & Continua 2022年第7期72卷 1373-1389页

作者： Chandrashekhar Meshram Agbotiname Lucky Imoize Sajjad Shaukat Jamal Parkash Tambare Adel R.Alharbi Iqtadar Hussain Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post-Graduation CollegeCollege of Chhindwara UniversityBetul460001M.P.India Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr University44801BochumGermany Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia Water Resources&Applied Mathematics Research Lab Nagpur440027India College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia Mathematics Program Department of MathematicsStatistics and PhysicsCollege of Arts and SciencesQatar University2713DohaQatar

The Human-Centered Internet of Things(HC-IoT)is fast becoming a hotbed of security and privacy *** users can establish a common session key through a trusted server over an open communication channel using a three-party authenticated key *** of the early authenticated key agreement systems relied on pairing,hashing,or modular exponentiation processes that are computationally intensive and *** order to address this problem,this paper offers a new three-party authenticated key agreement technique based on fractional chaotic *** new scheme uses fractional chaotic maps and supports the dynamic sensing of HC-IoT devices in the network architecture without a password *** projected security scheme utilized a hash function,which works well for the resource-limited HC-IoT *** results show that our new technique is resistant to password guessing attacks since it does not use a ***,our approach provides users with comprehensive privacy protection,ensuring that a user forgery attack causes no ***,our new technique offers better security features than the techniques currently available in the literature.

关键词： Three-party authenticated key agreement anonymity fractional chaotic maps Chebyshev polynomial password table human-centered internet of things(HC-IoT)

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A DEA-CCR Model Application in Clustered Stocks Portfolio with Technical Investment Strategies and Mean-Variance Model

A DEA-CCR Model Application in Clustered Stocks Portfolio wi...

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IEEE International Conference on Industrial engineering and engineering Management

作者： M.M. Navarro M. N. Young Y.T. Prasetyo J.L. Camino B.B. Navarro V.T. Ramos School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines School of Graduate Studies Mapúa University Manila Philippines Department of Industrial Engineering Technological Institute of the Philippines Quezon City Philippines International Bachelor Program in Engineering Yuan Ze University Taiwan Department of Civil Engineering Technological Institute of the Philippines Quezon City Philippines Department of Electrical Engineering Technological Institute of the Philippines Quezon City Philippines

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The 2020 power blackout in the brazilian state of amapá: Lessons learned and opportunities

The 2020 power blackout in the brazilian state of amapá: Le...

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2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2021

作者： Do Prado, Josue Campos Santos, Marcos Vinicius Ramos, Lucas Feksa Canha, Luciane Neves Konzen, Gabriel School of Engineering Computer Science Washington State University VancouverWA98686 United States School of Engineering Newcastle University Newcastle Upon TyneNE1 7RU United Kingdom Federal University of Rondônia Department of Electrical Engineering Porto Velho Brazil Graduate Program in Electrical Engineering Federal University of Santa Maria Santa Maria Brazil Division of Energy Economics and Environmental Studies Rio de Janeiro Brazil

ISBN: (纸本)9781665444217

The Brazilian state of Amapa=-notable for having 70% of its area consisting of unexplored Amazonian forests-experienced a massive power blackout in November 2020. An internal short-circuit in a transformer led to an explosion followed by a fire at the Macapa substation. This event left about 90% of the Amapa's population without power for several days in the middle of the COVID-19 pandemic, disrupting the lives of hundreds of thousands of people and raising concerns from several electricity sector stakeholders. This paper discusses select lessons learned from the power blackout and presents future opportunities to improve power system reliability and resilience for the grids in Amapá, and other similar Brazilian states, with a special focus on the increasingly important role of of distributed energy resources and smart grid technologies. © 2021 IEEE.

关键词： Outages

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EEG dataset classification using CNN method 5

EEG dataset classification using CNN method

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5th International Conference on Technology and Vocational Teachers, ICTVT 2019

作者： Mao, W.-L. Fathurrahman, H.I.K. Lee, Y. Chang, T.W. Department of Electrical Engineering Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliu64002 Taiwan Bachelor Program in Interdisciplinary Studies College of Future National Yunlin University of Science and Technology Taiwan Department of Digital Media Design National Yunlin University of Science and Technology Taiwan

This paper proposes a simple Convolutional Neural Network (CNN) program to classify epileptic seizure. The diagnosis of epileptic seizure involves the identification and different characteristic of the Electroencephalography (EEG) signal. As such, it needs a method for identifying and classifying epileptic seizure. Deep learning is part of a neural network that has the ability and pattern to identify and classify epileptic seizure. CNN has been demonstrated high performance on image classification and pattern detection. In this paper, we combine the continuous wavelet transform (CWT) and CNN to classify epileptic seizure. This experiment uses the wavelet transform to convert signal data of EEG to time-frequency domain images. The output of the wavelet transform is an image that will classify into five attributes. In this experiment, we develop a simple program that will compare with other CNN approach (AlexNet and GoogleNet). The results of this experiment are two kinds of data, accuracy, and loss. The resulting accuracy is 72.49%, and the loss is 0.576. This result has a better learning time than GoogleNet and smaller loss result than AlexNet. © Published under licence by IOP Publishing Ltd.

关键词： Electroencephalography

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Structural and performance comparison between SU-8 microfabricated and 3D-printed microneedle electrodes

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Journal of 3D Printing in Medicine 2020年第1期4卷 29-44页

作者： Soltanzadeh, Ramin Afsharipour, Elnaz Anssari, Neda Mansouri, Behzad Shafai, Cyrus Biomedical Graduate Studies Program University of Manitoba Winnipeg R3T 5V6 MB Canada Electrical and Computer Engineering Department University of Manitoba Winnipeg R3T 5V6 MB Canada Department of Internal Medicine (Neurology) University of Manitoba Winnipeg R3A 1R9 MB Canada

Aim: This study is aimed to compare the structure and performance of 3D-printed and microfabricated microneedle arrays (MNAs). Materials & methods: MNAs were produced using either stereolithography printing or were micro-fabricated using SU-8 polymer. The structure of both MNA types were tested and compared through scanning electron microscopy. MNA performance was also evaluated through electrical impedence spectroscopy and nerve conduction velocity testing. Results & conclusion: The 3D-printed MNAs were shown to have higher gain than microfabricated MNAs at high frequencies. However, the selectivity of the microfabricated MNAs is more than 3D-printed MNAs. Scanning electron microscopy image comparison also showed significant differences between the output of the 3D-printed MNAs and the microfabricated MNAs. It was understood that the microfabricated MNAs' geometries are closer to the primary design than the 3D-printed MNAs. © 2020 Future Medicine Ltd.

关键词： 3D printing microfabrication microneedle array performance difference strucutral difference

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