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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Dindar, Burak Saner, Can Berk Polat, Dogukan Yigit Çakmak, Huseyin Kemâl Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany National University of Singapore Department of Electrical and Computer Engineering Singapore National University of Singapore School of Computing Singapore

ISBN: (纸本)9789531842976

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time. © 2024 IEEE.

关键词： Differential privacy

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Differential Morphing Attack Detection via Triplet-Based Metric Learning and Artifact Extraction 23

Differential Morphing Attack Detection via Triplet-Based Met...

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23rd International Conference of the Biometrics Special Interest Group, BIOSIG 2024

作者： Liu, Chengcheng Ferrara, Matteo Franco, Annalisa Borghi, Guido Zhong, Dexing Xi'an Jiaotong University School of Automation Science and Engineering Xi'an710049 China University of Bologna Department of Computer Science and Engineering Cesena47521 Italy University of Modena and Reggio Emilia Department of Education and Humanities Reggio Emilia42121 Italy

ISBN: (纸本)9798350373714

Face morphing attack has been demonstrated to pose significant security risks to face recognition systems. Therefore, recently, developing reliable Morphing Attack Detection (MAD) techniques has become an important research priority. This paper considers the failure of regular face recognition systems in handling morphed faces from the perspective of identity feature space, arguing that the morphed image creates a 'pathway' between the two subjects involved in the morphing generation process. This 'pathway' causes automatic face recognition systems to misidentify the morphed face as either contributing subjects. Based on this insight and considering the characteristics of the Differential Morphing Attack Detection (D-MAD) scenario (i.e., both the potential morphing attack image and the trusted live face image are accessible), we propose an end-to-end D-MAD solution based on metric learning with triplet feature separation and artifact analysis. The proposed approach aims to break the 'pathway' created by the morphed image in the identity feature space and to incorporate latent artifact features of the potential morphed image for D-MAD. Comparative results on different public benchmarks indicate that the proposed solution demonstrates satisfactory performance against state-of-the-art algorithms. © 2024 IEEE.

关键词： Contrastive Learning

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In-Memory Fault-Free Vector Simulation

In-Memory Fault-Free Vector Simulation

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2023 IEEE East-West Design and Test Symposium, EWDTS 2023

作者： Devadze, David Chumachenko, Svetlana Hahanov, Ivan Davitadze, Zaza Litvinova, Eugenia Shevchenko, Olga Hahanov, Vladimir Hahanova, Anna Hacimahmud, Abdullayev Vugar Batumi Shota Rustaveli State University Department of Computer Sciences Batumi Georgia Kharkov National University of Radioelectronics Design Automation Department Kharkov Ukraine Kharkiv National University of Radio Electronics Design Automation Department Kharkiv Ukraine Azerbaijan State Oil and Industry University Computer Engineering Department Baku Azerbaijan

ISBN: (纸本)9798350314847

A technology for modeling and in-memory simulation of digital devices based on smart data structures is proposed. The fault-free method for modeling and simulation of digital devices based on vector logic, located in memory based on read-write transactions, is considered. In-memory fault-free simulation on smart data structure is focused on implementation in-memory of SoC, FPGA, RISC-V VLSI. The proposed method of in-memory simulation of logic circuits does not require synthesis and implementation in the standard base of elements. Moreover, the performance of the method depends on the size of the elements, the more inputs, the higher the parallelism of processing the input test sets as addresses. Based on good behavior simulation, a system for stuck-at-fault simulation of logical functionality, as well as logical circuits, is built. The software application is focused on teaching university students the methods of verification and testing of digital products. A convenient visual interface of the application can serve student projects that include dozens of logical elements of any complexity. © 2023 IEEE.

关键词： Data structures

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Failure Prediction Mechanism of Disk Devices Based on LSTM 2

Failure Prediction Mechanism of Disk Devices Based on LSTM

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2nd Asia-Pacific Conference on Communications Technology and computer Science, ACCTCS 2022

作者： Xu, Zhenpeng Ma, Jinwei Liu, Yu Jiangsu Automation Research Institute Department of Computer Lianyungang China School of Computer Engineering Weifang University Weifang China

ISBN: (纸本)9781665400343

In order to reduce the impact of the disk device failure on the information system performance fluctuation and business services, a disk device failure prediction mechanism based on LSTM is proposed based on the disk state feature extraction and neural network model. Firstly, based on the SMART information, the performance data and the function attenuation characteristics of the disk, the characteristic index of the disk device is selected and extracted. Then, a disk device failure prediction mechanism is established based on the improved LSTM by using the characteristic index as the training set. Finally, the active failure prediction of disk device was developed. Performance analysis shows the failure prediction mechanism of disk device proposed can obtain online prediction function through the data pre-processing methods such as SMART data item selecting, disk loss feature construction, the data item cleaning, and the sample standardization. The proposal is superior to the traditional neural network model in terms of the error square sum and correction decision coefficient. © 2022 IEEE.

关键词： Feature extraction

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Exploring Innovations in Autonomous Robotics and Mechatronics: Application in Manufacturing and Healthcare 5

Exploring Innovations in Autonomous Robotics and Mechatronic...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Shalini, T.Grace Ganeshabu, L. Chemudugunta, Priyanka Yamsani, Nagendar Kumar, Ch.Ashok Athiraja, A. School of Computing Department of Computational Intelligence Chengalpattu Tamil Nadu Kattankulathur India Rajalakshmi Engineering College Department of Robotics and Automation Thandalam Mevalurkuppam Rajalakshmi Nagar Tamil Nadu Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Robotics and Automation Tamil Nadu Chengalpattu India Sr University Department of School of Computer Science and Artificial Engineering Warangal India Malla Reddy Engineering College Department of Mechanical Engineering Main Campus Hyderabad Telangana Maisammaguda India Telangana Secunderabad India

ISBN: (纸本)9798331504403

Autonomous robotics and mechatronics have drastically changed the manufacturing and healthcare sectors by increasing productivity, precision, and flexibility. This work addresses the pressing need for new methods to advance these sectors by merging intelligent algorithms with autonomous systems. A hybrid strategy was employed, combining Deep Reinforcement Learning (DRL) and Adaptive Control Systems (ACS), to develop and optimize robotic operations. Extensive studies were conducted to assess the success of this strategy, which demonstrated a 25% reduction in operating expenses and a 30% increase in production efficiency in manufacturing settings. The method produced a precision rate of about 99% in the healthcare industry, which is significantly better than the 93% average of current systems. It also reduced patient recovery times by an estimated 20%. These results highlight the improved performance and promise of ACS with DRL combination in autonomous robotics, which may have revolutionary implications for the manufacturing and healthcare industries. The success, highlighted by economic and medical benefits, demonstrates the efficacy of our methodology and provides a strong platform for future developments in autonomous robotics and mechatronics. © 2024 IEEE.

关键词： Deep reinforcement learning

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Short-term Load Forecasting of an Integrated Energy System Based on STL-CPLE with Multitask Learning

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Protection and Control of Modern Power Systems 2024年第6期9卷 71-92页

作者： Suxun Zhu Hengrui Ma Laijun Chen Bo Wang Hongxia Wang Xiaozhu Li Wenzhong Gao the College of Energy and Electrical En-gineering Qinghai UniversityXining 810016China the School of Electrical and Automation Wuhan UniversityWuhan 430072China the Department of Electrical and Computer Engineering University of DenverDenver CO 80208USA Engineering Research Center for Renewa-ble Energy Generation and Grid Connection Technology Ministry of EducationXinjiang UniversityUrumchi 830046China

Multienergy loads in integrated energy sys-tems(IESs)exhibit strong volatility and randomness,and existing multitask sharing methods often encounter nega-tive migration and seesaw problems when addressing complexity and competition among *** line with these considerations,a short-term multienergy load joint prediction method based on seasonal-trend decomposition using LOESS(STL)and convolutional progressive lay-ered extraction(CPLE)is proposed,called ***,STL is applied to model regular and uncertain load information into interpretable trend,seasonal,and re-sidual ***,joint modeling is performed for the same type of components of multienergy loads.A one-dimensional convolutional neural network(1DCNN)is constructed to extract deeper feature *** approach works in concert with the progressive layered extraction sharing method,and convolutional shared and task-specific experts are developed to acquire common and distinctive representations of multienergy loads, re-spectively. Task-specific parameters are gradually sepa-rated through progressive routing. Finally, a subtask network is built to learn temporal dependencies using long short-term memory (LSTM). Simulation validation is performed on the IES dataset at the Tempe campus of Arizona State University, and the experiments show that the STL-CPLE method exhibits higher prediction accu-racy than do the other methods.

关键词： Integrated energy system multienergy load forecasting convolutional progressive layer extrac-tion network seasonal-trend decomposition.

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Automatic messaging system by detecting the road accidents for vehicle applications

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Materials Today: Proceedings 2023年 80卷 3124-3128页

作者： Karthik, M. Sreevidya, L. Vinodha, K. Thangaraj, M. Hemalatha, G. Sena, T. Viswak Department of Electrical and Electronics Engineering Sri Ramakrishna Engineering College Tamil Nadu Coimbatore641 022 India Department of Electrical and Electronics Engineering VSB College of Engineering Tamil Nadu Coimbatore642 109 India Department of Computer Science and Engineering New Horizon College of Engineering Karnataka Bengaluru560 103 India Department of Electrical and Electronics Engineering Coimbatore Institute of Engineering & Technology 641 055 Tamil Nadu India Department of Robotics and Automation Sri Ramakrishna Engineering College Tamil Nadu Coimbatore641 022 India

The emerging advancements in innovation has changed our lives drastically making it ease. Yet in this forefront world the rate of automobile accidents is still increasing resulting in increased fatality rate majorly due to untimely attention of emergency services. Our project will provide the best possible answer to this problem. When an automobile collides with another vehicle, the MEMS sensor senses this and sends the data, which is then processed by Arduino, according to this project. The Arduino sends an alarm message to a police control room or a rescue squad via the GSM Module, which includes the position. As a result, as soon as the police receive the information, they can use the GPS Module to track down the location. Then, once the location has been confirmed, the necessary steps will be taken. The project alerts the emergency services and the family members for immediate help by sending the location when an accident occurs. © 2021

关键词： Global system for mobile communications

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A Comparative Analysis of Transient Analysis Results for DC-DC Regulator in PSpice, Tina, and LTspice 5

A Comparative Analysis of Transient Analysis Results for DC-...

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Vince, Tibor Cizmarik, Dominik Dziak, Jozef Mamchur, Dmytro Anna, Iudina Vitalii, Naida Technical University in Kosice Department of Theoretical and Industrial Electrical Engineering Fei Kosice Slovakia Kremenchuk Mykhailo Ostrohradskyi National University Department of Computer Engineering and Electronics Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Department of Automation and Computer Systems Kremenchuk Ukraine

ISBN: (纸本)9798350359787

The paper presents a comparative analysis of the simulation results of a DC-DC buck regulator based on LM2595 on three different simulation platforms: PSpice, LTspice, and Tina. The comparative analysis includes visual comparison of selected waveforms, comparison of statistical parameters of the observed signals, and comparison based on time step signal analysis. The simulation was performed using three different maximal time step conditions. The research focused on examining the differences caused by different simulation platforms under the same maximal time step condition, as well as the differences caused by different maximal time step conditions within a single simulation platform. This analysis is crucial, particularly when comparing different simulation Spice models that require simulations to be conducted on various simulation platforms. By comparing the results obtained from different platforms under the same simulation conditions, the impact of the simulation platform on the accuracy and behavior of the models can be assessed. © 2023 IEEE.

关键词： Simulation platform

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Impact Analysis of Distributed Energy Resources in Maintaining Unbalanced Voltage Degree and DLMP 12

Impact Analysis of Distributed Energy Resources in Maintaini...

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12th International Conference on Renewable Power Generation, RPG 2023

作者： Gong, Jian Wang, Xiaofei Cai, Sheng Zhang, Menglin School of Automation China University of Geosciences Wuhan China Department of Electrical Engineering and Computer Sciences University of Tennessee Knoxville United States School of Automation Nanjing University of Science and Technology Nanjing China

The three-phase distribution system is evolving towards a more unbalanced one in terms of load consumption, which is harmful to both the distribution grid and user equipment. In this study, we integrate the voltage imbalance constraints into the three-phase distribution level market. However, the three-phase distribution locational marginal price (DLMP) may inevitably be affected and exhibit differences between phases. The deployment of distributed energy sources (DERs) is driving the distribution system to be more flexible and could help alleviate the imbalances of three-phase voltage and DLMP. First, the coupling relationship between phases is modelled, and three-phase unbalanced voltage constraints are considered by limiting the magnitude difference between phases. Next, the three-phase nonlinear power losses are linearized via the Taylor expansion. Then, a three-phase distribution market model with multiple types of DERs is established. Finally, the proposed model is tested on the IEEE 33-bus system. The simulation results show that the integration of voltage imbalance constraints and DERs can make the voltage profile between phases more balanced and significantly impact the three-phase DLMP. © The Institution of engineering & Technology 2023.

关键词： Energy resources

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Robust Control of Feeding Speed for Coal Mine Tunnel Drilling Machines 25

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Liu, Xiao Chen, Luefeng Wu, Min Cao, Weihua Lu, Chengda Pedrycz, Witold School of Automation China University of Geosciences Wuhan China School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed. © 2024 IEEE.

关键词： Robust control

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