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Optical Sensors 2023

作者： Kostelansky, Michal Nedoma, Jan Krizan, Daniel Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Czech Republic

ISBN: (数字)9781510662650

ISBN: (纸本)9781510662643

The article describes a modification of the fiber-optic sensor based on a Bragg grating, which is implemented in oxygen glasses, which are commonly used in real medical practice. This type of experimental fiber-optic sensor can be used to monitor the respiratory activity of the human body over time. Modifications primarily consist of the design improvement of the Bragg grating implementation itself in using conventional oxygen glasses for better transfer of measured parameters to the Bragg grating itself. The practical part also describes the experimental verification of the functionality and the subsequent evaluation of the measured data. The experimental measurement was carried out on a group of 5 volunteers in laboratory conditions. © 2023 SPIE.

关键词： Fiber Bragg gratings

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A Cheap and Practical IoT Solution for Agricultural Data Monitoring 13

A Cheap and Practical IoT Solution for Agricultural Data Mon...

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13th IEEE International Conference and Expositions on Electric and Power engineering, EPEi 2024

作者： Cornei, Doru Fosalau, Cristian Cornei, Laura 'Gheorghe Asachi' Technical University Faculty of Electrical Engineering Iaşi Romania 'Alexandru Ioan Cuza' University Faculty of Computer Science Iaşi Romania

ISBN: (纸本)9798350356199

In this study we propose an agricultural data monitoring solution which uses widely available and affordable IoT components, being integrated in the local edge infrastructure. Data related to several important agricultural parameters (air and soil temperature, air and soil humidity, luminosity) is collected and stored with the use of a local server, which also performs the distribution of the information to the end-user (usually the farmer). The presented application uses open-source software solutions. It is also complete, encompassing all aspects from the used sensors to the monitoring and control application. The sensors communicate using the LoRaWAN protocol and are independent, being powered with the use of solar panels. The proposed system has been tested in real conditions for over a year, proving to be reliable and requiring no additional maintenance costs. © 2024 IEEE.

关键词： Smart agriculture

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Mapping of Cores onto Network on Chip Architectures by Hierarchical Genetic Evolution Algorithm 17th

Mapping of Cores onto Network on Chip Architectures by Hiera...

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17th Annual Conference of China Electrotechnical Society, CES 2022

作者： Pan, Wenxin Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)9789819904075

The Network on Chip (NoC) is a communication architecture of system on chip, which mainly adopts data routing and packet switching techniques. The NoC has the advantages of low delay, high bandwidth and low power consumption. The design of NoC includes topology structure generation, core mapping, and communication mechanism. The core mapping decides the position of each core in a specified topology structure, it has an important impact on the communication delay, scalability, and reliability. The core mapping needs to be carried out under the specified constraints to achieve the best system performance. A new core mapping method is proposed in this paper, the method makes use of hierarchical genetic evolution strategy, which contains following two-level evolutions: high-level evolution and low-level evolution. In each level of evolutions, its own populations and evolutionary operations are used. The experimental results show that compared with the conventional genetic algorithm, the proposed method in this paper can reduce the total computing time of the core mapping. The obtained core mapping scheme can not only meet the requirement of communication bandwidth, but also reduce the total communication power consumption. © 2023, Beijing Paike Culture Commu. Co., Ltd.

关键词： Network-on-chip

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Machine Learning Techniques for Detecting Fraud in Credit Card Transactions 31

Machine Learning Techniques for Detecting Fraud in Credit Ca...

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31st Symposium of Advanced Database Systems, SEBD 2023

作者： Mahmood, Tariq Hashemi, Seyedeh Khadijeh Mirtaheri, Seyedeh Leili Greco, Sergio Department of Computer Science Modeling Electronics and Systems Engineering University of Calabria Italy Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran

Credit cards became one of the most popular payment methods as technology advanced and e-commerce services expanded, resulting in an increase in the volume of banking transactions. Furthermore, the significant increase in the number of frauds necessitates high banking transaction costs. As a result, detecting fraudulent activities has become an intriguing topic, attracting a large number of researchers. We consider the use of class weight-tuning hyperparameters to control the weight of fraudulent and legitimate transactions in this study. We use Bayesian optimization, in particular, to optimize the hyperparameters while taking into account practical issues such as unbalanced data. We propose weight-tuning as a pre-process for unbalanced data, as well as using CatBoost and XGBoost to improve the performance of the LightGBM method by taking the voting mechanism into account. Finally, we use deep learning to fine-tune the hyperparameters, particularly our proposed weight-tuning one, in order to improve performance even further. We conducted some experiments to evaluate the proposed methods on real-world data. We use recall-precision metrics in addition to the common ROC-AUC to better cover unbalanced datasets. We use a 5-fold cross-validation method to test CatBoost, LightGBM, and XGBoost separately. Furthermore, the performance of the combined algorithms is evaluated using the majority voting ensemble learning method. According to the results, LightGBM and XGBoost achieve the best level criteria of ROC-AUC = 0.95, precision 0.79, recall 0.80, F1 score 0.79, and MCC 0.79. Also, by using deep learning and the Bayesian optimization method to tune the hyperparameters, we meet the following criteria: ROC-AUC = 0.94, precision = 0.80, recall = 0.82, F1 score = 0.81, and MCC = 0.81. This is a big improvement over the state-of-the-art methods we compared it to. © 2023 CEUR-WS. All rights reserved.

关键词： Learning systems

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A Comparison of the State-of-the-Art Evolutionary Algorithms with Different Stopping Conditions 15

A Comparison of the State-of-the-Art Evolutionary Algorithms...

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15th International Joint Conference on Computational Intelligence, IJCCI 2023

作者： Herzog, Jana Brest, Janez Bošković, Borko Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 Maribor Slovenia

ISBN: (纸本)9789897586743

This paper focuses on the comparison of the state-of-the-art algorithms and the influence of a stopping condition, the maximum number of function evaluations, on the optimization process. The main aim is to compare the chosen state-of-the-art algorithms with different predetermined stopping conditions and observe if they are comparable in reaching a certain quality of solution on a given set of benchmark functions. For this analysis, the four most recent state-of-the-art evolutionary algorithms were chosen for comparison on the latest set of benchmark functions. We utilized a fixed-budget approach with different values of stopping conditions. Small differences in the algorithms’ performances are observed and the obtained results are also statistically analyzed. Different values of the stopping conditions show different rankings of evolutionary algorithms without the significant difference. The possible reason for this is that their performances are very close. © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Evolutionary algorithms

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Data-Driven Detection of Data Falsification Cyber-Attacks in Distributed Generation

Data-Driven Detection of Data Falsification Cyber-Attacks in...

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2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Anin, Johnson Abdelsalam, Omar Nabil, Mahmoud Hu, Fei Alsharif, Ahmad University of Alabama Department of Electrical and Computer Engineering AL35487 United States Tennessee Tech University Department of Computer Science TN35405 United States University of North Carolina A&T Electrical and Computer Engineering NC27411 United States University of Alabama Department of Computer Science AL35487 United States Benha University Faculty of Engineering at Shoubra Department of Electrical Engineering Egypt

ISBN: (纸本)9798350376715

The advancement of smart grid technology has enabled consumers to become active participants in electricity generation, particularly using renewable-based distributed energy resources. While this evolution offers economic benefits by allowing consumers to sell excess energy back to the grid, it has also opened the door to fraudulent activities. Generation theft, a malicious act where consumers report falsified higher energy generation readings for financial gain, has emerged as a significant challenge, leading to estimated annual losses of approximately $6 billion in the United States alone. In response to this issue, our paper introduces a novel approach to the detection of fraudulent electricity generation within the grid. Our primary contribution is the creation of a benchmark dataset, based on actual power usage and generation data from the Ausgrid dataset, which now includes simulated fraudulent activities alongside normal usage patterns. Our investigation further explores the statistical properties of power consumption patterns among fraudulent users, revealing a distinct lack of periodicity and predictability. By employing the day-to-day correlation matrix as an input to a 2D Convolutional Neural Network (CNN), our detection system significantly improves its ability to distinguish between legitimate and fraudulent electricity use. Additionally, we propose a hybrid deep learning model that merges the strengths of 2D CNNs with the sequential pattern recognition capabilities of Long Short-Term Memory (LSTM) networks. This approach effectively captures both spatial and temporal dynamics within the dataset, leading to a remarkable performance with a detection rate of 97.04%, a false alarm rate of 0.67%, and an overall accuracy of 99.70%. © 2024 IEEE.

关键词： Cyber attacks

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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Frequency Bandwidth Reduction of High-Frequency Intra-Body Propagation Characteristics Used for Attacker's Touch Detection in Fingerprint Authentication 12

Frequency Bandwidth Reduction of High-Frequency Intra-Body P...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Tomura, Shunichi Yamada, Koki Yoshida, Takahiro Tokyo University of Science Graduate School of Engineering Department of Electrical Engineering Tokyo Japan Tokyo University of Agriculture and Technology Department of Electrical Engineering and Computer Science Tokyo Japan Tokyo University of Science Faculty of Engineering Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350340181

In previous studies, we have proposed the attacker's touch detection method in fingerprint authentication using high-frequency intra-body propagation characteristics to detect attacks in that an attacker holds the user's finger/wrist and presses the user's finger to a fingerprint scanner. However, the previous method used a wide range of frequency bands from 300 kHz to 10 MHz, and this caused problems in the circuit scale, cost, and size of the device in development for practical use. Therefore, we searched effective frequency bands and applied K-nearest neighbor with data augmentation in this study. As results, we achieved a 99.1 % touch detection rate with a single frequency and a 99.9 % touch detection rate with a combination of two frequencies. We achieved a significant reduction in the frequency bandwidth of the propagation characteristics. © 2023 IEEE.

关键词： Bandwidth

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Method To Improve The Accuracy Of PV Curtailment To Maintain The System Stability in North Sulawesi and Gorontalo Power Grid 4

Method To Improve The Accuracy Of PV Curtailment To Maintain...

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4th International Conference on High Voltage engineering and Power Systems, ICHVEPS 2023

作者： Pratama, Arif Majiid Nusa Hashfi, Muhammad Irfan Dermawan, Ido Fandy Banjar-Nahor, Kevin Marojahan Hariyanto, Nanang School of Electrical Engineering and Informatics Bandung Indonesia Faculty of Computer Science Jakarta Indonesia Tomohon Indonesia

ISBN: (纸本)9798350318678

The penetration of renewable energy with high intermittencies, such as solar power, presents a challenge for the North Sulawesi and Gorontalo power grid. Currently, the largest photovoltaic plant in the region is Likupang PV with a capacity of 15 MWp, followed by Isimu PV with a capacity of 10 MWp. The daytime peak load of the electrical system is 330 MW, with the initial penetration of photovoltaics into the system at 7.58%. Due to the low stiffness of the system, which was measured at 22.32 Mw/Hz in 2022, the intermittence has a direct impact on the system's operation. The study method used for PV curtailment first determines the magnitude by analyzing the correlation between changes in multiple PV and changes in demand affecting frequency. The Pearson correlation coefficient is used to determine the magnitude of the correlation among these variables. If Likupang PV has a significant influence on changes in frequency, then it can become a curtailment target to limit the amount of power change due to intermittence. Secondly, through data mining, we can understand the function of the influence of changes in PV power on frequency changes. From this function, we can determine the operating limits of the PV to achieve the desired frequency operating range. Thirdly, in this study, this function is tested through simulations and experiments in the field to verify the accuracy of the required PV curtailment execution. By applying this approach, business owners can optimize the planning, regulation, and reliability of the electricity system, thereby mitigating power interruptions in solar power plants. © 2023 IEEE.

关键词： Data mining

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Comparison of Graph and REST APIs 10

Comparison of Graph and REST APIs

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10th Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Gluhak, Marko Heričko, Marjan University of Maribor Faculty of Electrical Engineering and Computer Science Koroška cesta 46 Maribor Slovenia

As APIs have become more important it is crucial to offer flexibility in their use. REST API has been the dominating architectural style over the past two decades. With the API Gateway and other improvements, it has been able to keep pace with the ever-changing customer demands. Novel approaches such as the Graph API offer such features as a native property of their design philosophy. They offer benefits to API consumers and providers which are still not understood in full. In this paper we explore Graph API, how it works, performs, and what are the differences between it and the standard REST API. We experiment and assess the performance of GitHub, GitLab and Star Wars APIs in REST and Graph forms. Conclusion is that neither approach is ideal in all situations, but they supplement each other where the other falls short. © 2023 Copyright for this paper by its authors.

关键词： API performance API principles Graph API REST API

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