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Neural Network Simulation of Time-variant Waves on Arbitrary grids with applications in Active Sonar Prediction

Neural Network Simulation of Time-variant Waves on Arbitrary...

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IEEE international conference on Electro Information Technology (EIT)

作者： Ranjith, Yash Westmont High Sch Campbell CA 95008 USA

ISBN: (纸本)9798350330656;9798350330649

Wave behavior underlies useful technologies such as active sonar, passive sonar, and echolocation. For instance, wave behavior is used to determine the sonar signature of underwater vehicles for stealth designs. To obtain a sonar signature, the fastest method is to simulate a sonar testbed using physics software. However, these software rely on the finite element method or finite difference method, which are slow due to their dependence on explicit computations. To overcome this issue, researchers have begun developing physics-informed neural networks (PINNs) to learn wave behavior. As PINNs rely on soft computing and act as operators, they can learn from noisy data and make quick predictions. Our PINN differs from other PINNs that are trained on static simulation grids with open boundaries. The goal of our PINN is to predict the reflection of a pressure wave off of a dynamic obstruction in an arbitrary grid, mimicking in part, the behavior of active sonar. Given a random source location, static obstructions, and a dynamic obstruction, the PINN predicted the wave evolution for 500 timesteps in 1.54 seconds and was over 1200 times faster than the finite difference method. The PINN was trained for 180 epochs using supervised and unsupervised learning and reached a mean squared error of 3E-5. Results show that the PINN demonstrates visual and numerical accuracy and avoids learning unwanted artifacts like spurious waves, due to its ability to generalize. For future work, we will add more degrees of freedom to define complex dynamic obstruction shapes like submarines.

关键词： neural network pressure wave sonar deep learning partial differential equation

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Development of a LabVIEW-Based Data Acquisition and Monitoring System for Demand Response Laboratory 4

Development of a LabVIEW-Based Data Acquisition and Monitori...

引用

4th international conference on Smart grid Metrology, SMAGRIMET 2023

作者： Culjak, Marija Havelka, Juraj Pandzic, Hrvoje University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia KONČAR-Electrical Engineering Institute Ltd. Zagreb Croatia

ISBN: (纸本)9798350323597

Active involvement of residential consumers in smart home initiatives is a key for realization of the smart grid. In this framework, the active customers are subject to demand response programs used to efficiently reduce the amount and timing of electricity consumption. The main requirements for residential demand response programs are acquiring real-Time electricity consumption data and load profiles of residential appliances. Development of communication technologies and smart load monitoring applications has simplified the access to this information. This paper investigates and describes the design of a developed data acquisition and monitoring system for demand response implementation in the residential sector. Our smart monitoring system integrates a number of residential electric appliances to study their daily energy consumption profiles and, in the future, forecasts their potential demand response capacity. We describe the design of a LabView application for acquiring, logging, processing and visualizing data derived from the Demand Response Laboratory (DR Lab) at the University of Zagreb Faculty of Electrical Engineering and computing (UNIZG-FER). The monitoring system is implemented in a way that enables an-cillary service provisioning and participation in demand response programs. © 2023 IEEE.

关键词： Electric power utilization

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An Improved Trajectory Planning Method for Unmanned Aerial Vehicles in Complex Environments 18th

An Improved Trajectory Planning Method for Unmanned Aerial V...

引用

18th international conference on Bio-Inspired computing: Theories and applications (BIC-TA)

作者： Zhang, Chen Yu, Moduo Huang, Wentao Hu, Yi Chen, Yang Fan, Qinqin Shanghai Maritime Univ Logist Res Ctr Shanghai 201306 Peoples R China Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Minist Educ Shanghai 200240 Peoples R China State Grid Shanghai Songjiang Elect Power Supply Shanghai 200240 Peoples R China

ISBN: (纸本)9789819722747;9789819722754

Trajectory planning plays a crucial role in the execution of Unmanned aerial vehicle (UAV) missions. However, planning an optimal collision-free trajectory is a challenging task, especially in complex environments. To address the above issue, an enhanced Elliptical tangent graph algorithm (ETG-CPI) based on comprehensive performance indicator is proposed in the present study. In the proposed algorithm, the comprehensive performance indicator, which contains the obstacle avoidance frequency, the yaw angle and the distance from the start point to the candidate waypoint, is used to select promising waypoints. Moreover, the entropy weight method is used to integrate these performance indicators. The experimental results demonstrate that the proposed algorithm outperforms four competitive path planning methods in 26 different environments. Additionally, the results indicate that the proposed comprehensive path evaluation method can help the proposed algorithm find a high-quality path in complex environments.

关键词： Unmanned aerial vehicle Path planning Autonomous flight Elliptical tangent graph algorithm

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JumpCrypt: Based on Precomputing Federated Learning with Homomorphic Encryption 25

JumpCrypt: Based on Precomputing Federated Learning with Hom...

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25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wang, Yun Wang, Chong Xu, Chao Wang, Jing School of Computer Science and Engineering Southeast University Nanjing China State Grid Jiangsu Electric Power Co. Ltd Nanjing China

ISBN: (纸本)9798350330014

Federated Learning (FL) enables organizations or individuals to train a machine learning model collaboratively by aggregating local gradient updates from each client without sharing privacy-sensitive data. To ensure the security of gradient updates, the FL framework employs additively homomorphic encryption (AHE) to prevent revealing the participants' identities during aggregation. However, strategy imposes heavy communication and computation costs. Thus, this paper proposes a system solution for cross-silo FL named JumpCrypt. Our scheme uses pre-computation to build a plaintext and noise pool for faster encryption. Meanwhile, gradient sparsity is employed to reduce communication overhead. Experimental results show that our scheme achieves a 3x speedup in training compared with BatchCrypt while reducing communication overhead by 30%. © 2023 IEEE.

关键词： Sensitive data

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Combining Ensemble Learning based Hybrid Classification Algorithm with Effective Feature Selection to identify Electricity Fraud in Advanced Metering Infrastructure of Smart grid

Combining Ensemble Learning based Hybrid Classification Algo...

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2024 IEEE international conference on computing, applications and Systems, COMPAS 2024

作者： Sikder, Md. Masum Ahmed, Boshir Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331529765

Consuming electricity through Advanced Metering Infrastructure (AMI) has become an irrefutable part of our daily micro-moments. In AMI, electricity fraud is regarded as one of the most significant Nontechnical losses (NTLs) for power companies which requires substantial investigation to effectively detect theft instances with minimal false positives. In this paper, we investigated how the meticulous selection of top-ranked attributes enhances the accuracy of fraud identification. We proposed a modified-ReliefF feature selection algorithm to find the rank of attributes of our developed dataset and observed significant improvement in electricity fraud identification. Moreover, we designed a hybrid classifier called ElectroGuard SVM Bagging, which combines SVM with Bootstrap Aggregation. This approach, paired with our modified-ReliefF feature selection, outperformed three standard supervised classifiers. Finally, using 10-fold cross-validation, we compared our hybrid model's performance against the best result obtaining conventional classifier, i.e. SVM and achieved 5.94% (before applying feature selection) and 6.19% (after applying modified-ReliefF feature selection) accuracy improvement for ElectroGuard SVM Bagging. © 2024 IEEE.

关键词： Advanced metering infrastructures

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Research on The Intelligent Acquisition and Mining Technology of Metadata Based on Electric Power Database 8

Research on The Intelligent Acquisition and Mining Technolog...

引用

8th IEEE international conference on Smart Cloud, SmartCloud 2023

作者： Lou, Yiting Huang, Zhi Ke, Fangyuan Li, Jiaqin Zhang, Zhizhen Chen, Yingyang State Grid Ningbo Electric Power Supply Company Ningbo China

ISBN: (纸本)9798350313505

The intelligent metadata acquisition algorithm and the power industry database-based K-Means clustering algorithm can enhance the efficiency of data acquisition and processing, providing effective support and guidance for data analysis and applications in the power industry. The experimental results demonstrate that the K-Means clustering algorithm achieves the best clustering performance and high accuracy when the number of clusters is set to 3. The research findings of this paper offer valuable support and references for data management and applications in the electric power industry. Furthermore, they are relevant and significant for data analysis and applications in other fields. ©2023 IEEE.

关键词： Metadata

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Analysis and Evaluation of Phase-Sequence Separators for Sag Detection in Power Electronics Converter applications 20

Analysis and Evaluation of Phase-Sequence Separators for Sag...

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20th international conference on Electrical Engineering, computing Science and Automatic Control, CCE 2023

作者： Cuevas, Israel Cardenas, Victor Miranda-Vidales, Homero Alcalá, Janeth Autonomous University of San Luis Potosí Engineering Department San Luis Potosí Mexico University of Colima Engineering Department Colima Manzanillo Mexico

ISBN: (纸本)9798350306767

The incorporation of renewable energies into the electrical system has led to an increase in the use of power electronic converters and a subsequent expansion and complexity of the electrical grid. As a result, the probability of disturbances increases, with the risk of affecting the operation and continuity of the electrical service, as well as the operation of the converters connected to the grid. To ensure proper operation of these converters, it is necessary for their system control to have the appropriate reference generation schemes to avoid introducing harmonic distortion in the network. One solution is to use control systems based on DQ theory, which can avoid this issue. Alternatively, generation methods based on symmetrical component theory can also be employed. This paper analyzes two reference generation structures - the Three-Phase Sequence Tuned Filter and the Delayed Signal Cancellation - and their respective advantages and disadvantages in obtaining sequence components in real time. The operational scheme is validated using a grid-connected power inverter. © 2023 IEEE.

关键词： Power quality

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An Ensembled grid based Machine Learning Approach For PD Classification From MRI Images 4

An Ensembled Grid based Machine Learning Approach For PD Cla...

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4th international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Redhya, M. Jayalakshmi, M. Kalasalingam Academy of Research and Education Department of Computer Applications Tamil Nadu India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350343670

Parkinson's disease (PD) is a neuro-degenerative disease caused due to breakdown of brain cells in the central-part of the nervous system. As symptoms of PD appear only after 60% or more of these cells are destroyed, early detection is a tedious task. Several studies have proven that PD can be effectively diagnosed from MR-Images (MRI). This paper provides an ensembled machine learning model, Stacked-EG model, for MRI-image based PD classification. The dataset was obtained from PPMI (Parkinson's Progression Markers Initiative) online repository which included 134 PD subjects (80 males and 54 females) and 126 healthy patients (60 males and 66 females) around 60 years old on average. Feature extraction is performed through the application of filters, including wavelet transformations and intensity-based enhancements. Feature selection is conducted using optimised XGBoost model's feature significance scores. For classification, a Stacking Classifier which combines the strengths of XGBoost, SVM, and a Random Forest classifier was used. The model obtained an accuracy of 98.41% in classifying PD from healthy controls. © 2024 IEEE.

关键词： gridSearch Optimization Magnetic Resonance Imaging (MRI) Random Forest Stacking SVM XGBoost

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RESEARCH AND APPLICATION OF KEY TECHNOLOGIES FOR LARGE-SCALE TIME-SERIES DATA COLLECTION AND SHARING IN POWER grid WAMS 2

RESEARCH AND APPLICATION OF KEY TECHNOLOGIES FOR LARGE-SCALE...

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2nd international conference on Power, Communication, computing and Networking Technologies, PCCNT 2024

作者： Shi, Yingjie Wang, Xiang Zhou, Chun Shi, Jinwei Chen, Shijia Nanjing NARI Digital Service Co. Ltd Nanjing China Nanjing China

ISBN: (纸本)9781837242672

With the acceleration of the construction of new power systems and the continuous increase in the proportion of new energy generation, the demand for data sharing and application collaboration among various levels of dispatch master stations in wide area measurement system(WAMS) is becoming increasingly urgent. An analysis of the current status of WAMS systems was conducted, and based on this, a scheme and architecture for collecting and sharing WAMS temporal data across the entire network were proposed. Key technologies such as wide area WAMS data modeling, large-scale temporal data interaction, distributed parallel storage and processing, and data security were studied, and large-scale WAMS temporal data collection and global sharing based on the entire network device model were achieved. At present, the research results have been demonstrated and applied in the National Power Dispatch Center, supporting business applications such as comprehensive analysis of power grid fault data, online monitoring and analysis of grid related performance of new energy and conventional units, and identification of typical fault disturbances. © The Institution of Engineering & Technology 2024.

关键词： Spatio-temporal data

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Hybrid Solar-Powered Street Lighting System with Battery Storage and grid Integration

Hybrid Solar-Powered Street Lighting System with Battery Sto...

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2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Abhiram, S. Harishankar, M. Rahul, R. Jayasree, P.R. Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Amritapuri India

ISBN: (纸本)9798350333244

The conventional lighting systems that are present today result in the wastage of an ample amount of energy and money, as the lights will remain turned on most of the time even when it is not in use. Artificial lighting is a constant companion in street lighting systems, influencing visibility in parking spaces as well as roads and highways. In recent years, new technical solutions have extended traditional highway lighting systems to become 'smart'. This project focuses on smart lit highway systems that can drastically decrease unwanted energy usage and associated expenses. The motion sensors and Infrared sensors used in the proposed system are mainly what turn on the streetlights in front of them when they locate people or cars approaching. The sensors are supplied power from a battery, powered by a solar Photovoltaic array and are also given backup power supply from the power grid. Only in rare circumstances where the battery is insufficiently charged to power the sensors is the backup power supply used. When the system detects the presence of a vehicle, it raises the light intensity from zero level to high level. The system would automatically turn off the lights during the absence of at least one vehicle in a particular area, eliminating power wastage. A prototype which demonstrates the working of the streetlights and associated sensors has been developed. The suggested concept can have multiple applications on both a high level and a low level. © 2023 IEEE.

关键词： Light emitting diodes

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