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Current sensor Measurement Lag State Evaluation Method Based on Data Time Series Clustering and DE Algorithm 4

Current Sensor Measurement Lag State Evaluation Method Based...

引用

4th international conference on Energy Engineering and Power systems, EEPS 2024

作者： Zhou, Ke Jin, Qingren Meng, En Bao, Haibo Mo, Zhiyue Electric Power Research Institute Guangxi Power Grid Co. Ltd China Southern Power Grid Nanning China Nanning Power Supply Bureau China Southern Power Grid Guangxi Power Grid co. Ltd Nanning China

ISBN: (纸本)9798350366914

Current sensor is a crucial measuring equipment in power system. Due to temperature/humidity change, electromagnetic interference, mechanical vibration and other factors, its measurement lag often occurs, threatening the normal operation of current sensor. therefore, a current sensor measurement lag state evaluation method based on data timing clustering and DE algorithm is proposed. A variety of sensors are used to collect and pre-process real-time current data, sensor output data, time stamp data and environmental condition data. Based on this, the difference parameters between real-time current value and sensor output current value are calculated, data timing clustering rules are formulated, data timing clustering parameters are optimized based on DE algorithm, and more accurate data timing clustering results are obtained. the average time of measurement lag is calculated to determine the measurement lag state of the current sensor. the experimental results show that the classification dividing line of the data time series clustering results obtained by the proposed method is clear, and there are no data points with classification errors. the evaluation results of current sensor measurement lag state are consistent with the actual results, which fully proves that the proposed method has good application performance. © 2024 ieee.

关键词： Signal interference

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indexPDT: A High Scalable distributed Classification Approach with Novel Cache Structure for Geo-location 25

indexPDT: A High Scalable Distributed Classification Approac...

引用

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： Sun, Zhijie Li, Jing Xie, Jun Zheng, Binfan Zeng, Li Zhao, Rongqian Huawei Technologies Co. Ltd. Shenzhen China

ISBN: (纸本)9798350330014

Geo-location, also known as measurement report (MR) location, is a technique to determine the geographic location of user equipment (UE) and the behaviour attribute of telephone traffic based on wireless signals measured by the mobile communication network. the geographic location information can help to support network performance monitoring and evaluation. Considering accuracy and cost, we mainly adopt a hybrid location scheme combined with feature matching location and Weighted Centroid Correction Location (WCCL). As for feature matching location, over 20 billion samples gathered from tens of thousands of cells daily updated. Due to the vast data scale, feature analysis encounters a severe performance bottleneck. To address this problem, we design the indexed parallel decision tree (indexPDT) operator and integrate it into WindTensor, a self-innovated distributed machine learning (ML) engine. indexPDT is a classifier unit of the random forest (RF) algorithm with a novel cache structure. It performs structured cache processing on the dataset's meta-information, which can accompany the splitting of nodes. the cache structure can be quickly converted into statistical information to help find the optimal splitting point, effectively reducing memory usage and improving performance. Under the public datasets testing on 5 nodes, the mean speedup ratios are 86x and 3x compared with SparkML and XGBoost, respectively. In the Geo-location scenario, for a single cell, the speedup ratios are 82x and 4x compared with SparkML and XG Boost, respectively. © 2023 ieee.

关键词： Location

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Based on Deep Regulation of Electric Heating Loads for distributed Photovoltaic Consumption 4

Based on Deep Regulation of Electric Heating Loads for Distr...

引用

4th international conference on Electrical Engineering and Control Science, IC2ECS 2024

作者： Li, Jisen Chen, Nan Gao, Luchen Guo, Qiuting Xu, Heli Qi, Ning State Grid Beijing Miyun Electric Power Supply Company Beijing China Tsinghua University Department of Electrical Engineering Beijing China

ISBN: (纸本)9798331510107

With the advancement of China's "Carbon Peak, Carbon Neutrality"strategic goals, the proportion of electric heating loads in northern regions during winter has been steadily increasing, alongside the growing prevalence of distributed photovoltaic (PV) systems in rural areas. this paper proposes an optimization operation strategy for electric heating and PV integration based on the adjustable potential of electric heating loads. By employing thermodynamic modeling to quantify the adjustability of electric heating loads and aiming to maximize PV consumption, a joint optimization model is established. the study validates this approach using a 10kV distribution line with distributed PV systems in a suburban area of Beijing. Results show that this strategy significantly enhances PV consumption while ensuring user comfort, alleviating transmission congestion in distribution networks. It provides theoretical support and a technical pathway for achieving efficient energy management and green transformation. © 2024 ieee.

关键词： Photovoltaics

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Modelling Virtual sensors for Indoor Environments with Machine Learning 18

Modelling Virtual Sensors for Indoor Environments with Machi...

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18th Annual international conference on distributed computing in sensor systems (DCOSS)

作者： Polanski, Dawid Marek Angelopoulos, Constantinos Marios Bournemouth Univ Dept Comp & Informat Poole Dorset England

ISBN: (纸本)9781665495127

Virtual sensors model the sensing operation of physical sensors deployed in an area of interest by generating sensory data with accuracy and precision close to those collected by physical sensors. their use in applications such as augmenting the infrastructure of IoT facilities and test beds, monitoring and calibrating the operation of physical sensors, and developing Digital Twins of physical systems have led virtual sensors to attract research attention. Machine learning provides methods for modelling patterns in complex and big data generated by IoT sensing devices, allowing to model the behaviour of these devices. In this work, we investigate ML methods as means of implementation for virtual sensors. In particular, we evaluate the performance of six ML methods in terms of their effectiveness, accuracy and precision in generating sensory data based on data from physical sensors. In our study, we use a multi-modal dataset comprising IoT sensory data for temperature, humidity and illumination collected over a period of two years in an office space at University of Geneva. Our results show that the best performing model at predicting an output of a missing sensor is the Random Forest method, achieving MAPE error below 3%, 5% and 18% respectively for temperature, humidity and illuminance. the worst performing models were the linear radial basis function neural network and linear regression. In future research, we plan to deploy the best performing models natively on IoT devices, making use of tinyML and extreme edge computing methods.

关键词： Internet of things sensor Virtual sensor Machine Learning

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Anomaly Detection Approach for sensor Networks in Coal Mine Solid Backfilling Working Faces Based on Transformer 4

Anomaly Detection Approach for Sensor Networks in Coal Mine ...

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4th international conference on Information Science, Parallel and distributed systems, ISPDS 2023

作者： Yang, Shangqing Bo, Lei Zhang, Zihang Liu, Yang Beijing China

ISBN: (纸本)9798350337181

In this paper, we propose a comprehensive anomaly detection model for the perception system of backfilling working faces. the model consists of three main components: the position encoding module, the time series prediction module, and the anomaly detection module. the position encoding module extracts global temporal information, including year, month, and day, based on the timestamp information of the data. the time series prediction module utilizes the Temporal Fusion Transformers model, which considers the latent correlations among different variables for accurate predictions. the anomaly detection module assesses the data's abnormality by comparing the predicted values with the actual values. We validate the performance of the model on a real-world dataset, demonstrating its high accuracy and strong generalization capabilities for various types of sensor data in backfilling working faces. this research provides a viable solution for anomaly monitoring and management in backfilling operations, with significant practical implications. © 2023 ieee.

关键词： Anomaly detection

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Basketball Sports Posture Recognition based on Neural computing and Visual sensor 4

Basketball Sports Posture Recognition based on Neural Comput...

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4th international conference on Smart systems and Inventive Technology, ICSSIT 2022

作者： Wang, Hong The Faculty of Physical Education China West Normal University Sichuan Nanchong637009 China

ISBN: (纸本)9781665401180

In basketball training, the analysis of athletes can visually show the performance of athletes for coaches, formulate corresponding training plans, and improve team performance. With the application of video analysis technology, it is possible to analyze the actions of basketball players in training, introduce methods for estimating body posture, and provide players with accurate action analysis results. Based on the vision sensor, this paper combines neural computing and neural network algorithms to study the recognition of basketball gestures. © 2022 ieee

关键词： Motion estimation

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4th international conference on Dynamic Data Driven Applications systems, DDDAS 2022

4th International Conference on Dynamic Data Driven Applicat...

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4th international conference on Dynamic Data Driven Applications systems, DDDAS 2022

ISBN: (纸本)9783031526695

the proceedings contain 42 papers. the special focus in this conference is on Dynamic Data Driven Applications systems. the topics include: Physics-Aware Machine Learning for Dynamic, Data-Driven Radar Target Recognition;DDDAS for Optimized Design and Management of 5G and Beyond 5G (6G) Networks;DDDAS-Based Learning for Edge computing at 5G and Beyond 5G;monitoring and Secure Communications for Small Modular Reactors;Data Augmentation of High-Rate Dynamic Testing via a Physics-Informed GAN Approach;unsupervised Wave Physics-Informed Representation Learning for Guided Wavefield Reconstruction;passive Radio Frequency-Based 3D Indoor Positioning System via Ensemble Learning;deep Learning Approach for Data and computing Efficient Situational Assessment and Awareness in Human Assistance and Disaster Response and Battlefield Damage Assessment Applications;SpecAL: Towards Active Learning for Semantic Segmentation of Hyperspectral Imagery;generalized Multifidelity Active Learning for Gaussian-process-based Reliability Analysis;Multimodal IR and RF Based sensor System for Real-Time Human Target Detection, Identification, and Geolocation;learning Interacting Dynamic systems with Neural Ordinary Differential Equations;Relational Active Feature Elicitation for DDDAS;explainable Human-in-the-Loop Dynamic Data-Driven Digital Twins;transmission Censoring and Information Fusion for Communication-Efficient distributed Nonlinear Filtering;distributed Estimation of the Pelagic Scattering Layer Using a Buoyancy Controlled Robotic System;towards a Data-Driven Bilinear Koopman Operator for Controlled Nonlinear systems and Sensitivity Analysis;tracking Dynamic Gaussian Density with a theoretically Optimal Sliding Window Approach;dynamic Data-Driven Digital Twins for Blockchain systems;Adversarial Forecasting through Adversarial Risk Analysis Within a DDDAS Framework;essential Properties of a Multimodal Hypersonic Object Detection and Tracking System;power Grid Resilience: Data Gaps for Da

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Maximizing Performance and Efficiency: An Algorithm Approach to Engine sensor Optimization using Machine Learning 4

Maximizing Performance and Efficiency: An Algorithm Approach...

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4th ieee international conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Varma, Viren Verma, Khyati Mehta, Hriday Gangadharan, K.V. Dept. of Mechanical Engineering National Institute of Technology Karnataka Surathkal India

ISBN: (纸本)9798331528614

this paper presents an algorithmic methodology developed to reduce the number of sensors required in automotive engines by leveraging machine learning techniques. the sensor data used in this technique was obtained from a standard engine, which exhibited redundancy in data. the high cost associated with sensors and their integration into engine systems necessitates an efficient approach to optimize sensor utilization while maintaining reliable engine performance. By utilizing advanced data analysis and predictive modeling, our algorithm aims to identify redundant or non-critical sensors, enabling a streamlined and cost-effective sensor configuration. We achieved this by developing a tailored dimensionality reduction algorithm based on functional dependency theory. this approach transforms data from a high-dimensional space into a lower-dimensional space, preserving essential features of the original data and ideally approximating its intrinsic dimension. © 2024 ieee.

关键词： Applied Mathematics Functional Dependency Machine Learning sensor Data

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Economic analysis of cross seasonal distributed buried pipe heat storage heating system 4

Economic analysis of cross seasonal distributed buried pipe ...

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4th international conference on Energy Engineering and Power systems, EEPS 2024

作者： Xiao, Yang Tian, Chuan Li, Shuai Huang, Kailiang Liu, Xin Shenyang Jianzhu University Liaoning Shenyang11016 China Economic and Technological Research Institute State Grid Liaoning Electric Power Co. LTD. Shenyang110015 China

ISBN: (纸本)9798350366914

In order to determine the economy of cross-season distributed buried pipe heat storage technology in civil building application scenario, an office building in Shenyang was studied, and the DeST software was used to establish the building model and calculate the load. At the same time, the design scheme of cross-season distributed buried pipe heat storage system is established. Based on economic theory, the economy of the system is analyzed, the initial investment, operating cost, annual cost and investment payback period of the system are calculated, and the economic comparison is made with other systems. the results show that in the initial investment, the investment cost of buried pipe is 51%. Compared with other systems, the initial investment of the cross-season distributed buried pipe heat storage and heating system is the highest, and the annual operating cost and expense are the lowest. the payback period of the investment is as short as 2.3 years, and the longest is about 5.8 years. the cost of the system is relatively high, but due to the reduction of the operating cost due to the saving of energy costs, the system has a good economic return in long-term operation. Compared with other systems, this study provides important reference and guidance for the economic analysis of this system. © 2024 ieee.

关键词： Cost reduction

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Secure and Efficient Fraud Detection Using Federated Learning and distributed Search Databases 4

Secure and Efficient Fraud Detection Using Federated Learnin...

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4th ieee international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Shah, Milavkumar Shah, Parth Patil, Swapnil University of Texas at Dallas TX United States University of Colorado Colorado Springs United States

ISBN: (纸本)9798331518882

Credit card fraud has grown increasingly common in today's age, and with the rise in cybercrimes with fraud, several examples have been recorded in the past. the use of a distributed search plays a pivotal role in enhancing the performance of fraud detection systems. By enabling the aggregation and retrieval of data from multiple decentralized sources without compromising data privacy, it facilitates the efficient training of models on large, diverse datasets. Another technique used in controlling fraud losses is through applying Federated Learning (FL) for detecting fraudulent transactions. the models may gain the benefit of the dispersed data without actually sharing the data in this way. this paper focuses on the implementation of CNN with FL to improve a security and accuracy of fraud detection in financial transactions. the suggested model employs Kaggle credit card fraud dataset and uses enhanced techniques such as;SMOTE to work on class imbalance problem and one hot encoder to work on Categorical features. the proposed CNN-FL model surpassed other classifiers and yielded better accuracy, precision, and recall rates compared to traditional ML classifiers like NB, LR, and Gaussian Naive Bayes;accuracy 99.86%, precision 99.83%, recal 199.85%, and an F1-score of 99.84%. thus, effectiveness of suggested CNN-based federated learning approach for enriching fraud detection systems is shown, with good generalisation and high accuracy on various types of transactions. © 2025 ieee.

关键词： Federated learning

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