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Detection and Classification of Transmission Line Transient Faults Based on Graph Convolutional Neural Network

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 456-471页

作者： Houjie Tong Robert C.Qiu Dongxia Zhang Haosen Yang Qi Ding Xin Shi Department of Electrical Engineering Center for Big Data and Artificial IntelligenceShanghai Jiao Tong UniversityShanghai 200240China School of Electronic Information and Communication Huazhong University of Science and TechnologyWuhan 430000China Electric Power Research Institute Haidian DistrictBeijing 100192China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural *** with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction *** this basis,a framework for transient fault detection and classification is *** structure is generated to provide topology information to the *** approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results ***,the proposed approach is tested in various situations and its generalization ability is verified by experimental *** results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.

关键词： Graph convolutional network(GCN) power transmission line fault detection and classification spatio-temporal data topology information

Enhanced Fault Detection in Industrial Process via a Hellinger Distance-Based Data-Driven Scheme

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Enhanced Fault Detection in Industrial Process via a Helling...

Modeling, Simulation & Intelligent Computing (MoSICom), International Conference on

作者： Mohammed Affan K. Ramakrishna Kini Muddu Madakyaru Fouzi Harrou Mukund Kumar Menon Ying Sun Department of Chemical Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

Accurate fault detection in modern chemical processes ensures safety and efficiency. However, detecting faults in noisy environments remains a significant challenge. This paper introduces an effective fault detection (FD) strategy that combines a supervised learning approach using partial least squares (PLS) with fault detection charts based on Hellinger distance (HD) and exponentially weighted moving average (EWMA). The PLS-HD-EWMA strategy utilizes PLS to generate residuals, while HD is employed to compute the divergence between actual residuals and fault-free residuals. EWMA is then applied to evaluate the HD, providing sensitivity to small changes for effective fault detection. The proposed strategy’s efficacy is demonstrated through simulations on a benchmark continuous stirred tank reactor process, evaluating three common fault scenarios. The monitoring performance indicates that the PLS-HD-EWMA approach outperforms all the other conventional methods, offering superior fault detection capabilities.

关键词： Training Sensitivity Fault detection Computational modeling Supervised learning Noise Safety Noise measurement Continuous-stirred tank reactor Monitoring

Approximate state estimation with large-scale sensor networks under event-based sensor scheduling strategy

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Approximate state estimation with large-scale sensor network...

IEEE Conference on Decision and control

作者： Xinhui Liu Meiqi Cheng Dawei Shi Ling Shi MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of $\mathscr{E}(\varepsilon)$-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement.

关键词： Protocols Event detection Scheduling algorithms Redundancy Approximation algorithms Real-time systems Sensors State estimation Observability Sensor arrays

Application of Machine Learning Models for Predicting Students' Performance in Mathematics: A K-Fold Approach

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Application of Machine Learning Models for Predicting Studen...

science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Felix Ale Ikpaya D. Ikpaya Ilesanmi Danyan Goodness O. Opateye Gregory Onwodi Olufemi O. Agboola Department of Engineering & Space Systems National Space Research & Development Agency Abuja Nigeria Ground Station & Mission Control National Space Research & Development Agency Abuja Nigeria Department of Mechanical & Mechatronics Engineering Achievers University Owo Nigeria Department of Computer Science National Open University of Nigeria Abuja Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Mathematics plays a pivotal role in science, technology and engineering. It is one of the compulsory subjects to gain admission into a higher institution in Nigeria. Considering its importance, students' performance has been a significant concern for most educators, government, parents and guardians. This study aims to predict students' performance in mathematics using the K-fold approach to support effective decision-making and policy implementation on mathematics. The primary dataset was collected from 192 senior secondary schools, and one mathematics student was randomly selected from five Senior Secondary Schools in Bwari Area Council, Abuja, and used as a sample. Students' Mathematics Achievement Test (SMAT) was used as the instrument to collect data. The data was processed and modelled using the Python Programming language. Thus, five machine learning models were trained and evaluated to overcome overfitting using four-, five---, and six-fold K-Fold cross-validation techniques. Five folds performed best for all the models except for the Support Vector Regressor, which performed best with six folds. The evaluation of the models showed that the Light Gradient Boosting Model outperformed others with a Mean Absolute Error of 12.642 and a Root Mean Square Error of 15.894. The models were significantly influenced by the students' personal study time, friends' assistance, and the parents' educational background. It is therefore recommended that academic researchers, educational authorities in Nigeria, and relevant stakeholders use and improve on this model to predict the students' scores and assist them in achieving great performance in mathematics.

关键词： Instruments Predictive models Mathematics Mathematical models Vectors Stakeholders Sustainable development

MD-SLAM: Multi-cue Direct SLAM

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arXiv 2022年

作者： Di Giammarino, Luca Brizi, Leonardo Guadagnino, Tiziano Stachniss, Cyrill Grisetti, Giorgio The Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Italy The University of Bonn Germany The Department of Engineering Science The University of Oxford United Kingdom

Simultaneous Localization and Mapping (SLAM) systems are fundamental building blocks for any autonomous robot navigating in unknown environments. The SLAM implementation heavily depends on the sensor modality employed on the mobile platform. For this reason, assumptions on the scene’s structure are often made to maximize estimation accuracy. This paper presents a novel direct 3D SLAM pipeline that works independently for RGB-D and LiDAR sensors. Building upon prior work on multi-cue photometric frame-to-frame alignment [4], our proposed approach provides an easy-to-extend and generic SLAM system. Our pipeline requires only minor adaptations within the projection model to handle different sensor modalities. We couple a position tracking system with an appearance-based relocalization mechanism that handles large loop closures. Loop closures are validated by the same direct registration algorithm used for odometry estimation. We present comparative experiments with state-of-the-art approaches on publicly available benchmarks using RGB-D cameras and 3D LiDARs. Our system performs well in heterogeneous datasets compared to other sensor-specific methods while making no assumptions about the environment. Finally, we release an open-source C++ implementation of our system. Copyright © 2022, The Authors. All rights reserved.

关键词： Pipelines

Autonomous driving of trucks in off-road environment

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arXiv 2023年

作者： Caldas, Kenny A.Q. Barbosa, Filipe M. Silva, Junior A.R. Santos, Tiago C. Gomes, Iago P. Rosero, Luis A. Wolf, Denis F. Grassi, Valdir Department of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos Brazil Division of Automatic Control Department of Electrical Engineering Linköping University Linköping Sweden Institute of Mathematics and Computer Science University of São Paulo São Carlos Brazil

Off-road driving operations can be a challenging environment for human conductors as they are subject to accidents, repetitive and tedious tasks, strong vibrations, which may affect their health in the long term. Therefore, they can benefit from a successful implementation of autonomous vehicle technology, improving safety, reducing labor costs and fuel consumption, and increasing operational efficiency. The main contribution of this paper is the experimental validation of a path tracking control strategy, composed of longitudinal and lateral controllers, on an off-road scenario with a fully-loaded heavy-duty truck. The longitudinal control strategy relies on a Non-Linear Model Predictive controller (NMPC), which considers the path geometry and simplified vehicle dynamics to compute a smooth and comfortable input velocity, without violating the imposed constraints. The lateral controller is based on a Robust Linear Quadratic Regulator (RLQR), which considers a vehicle model subject to parametric uncertainties to minimize its lateral displacement and heading error, as well as ensure stability. Experiments were carried out using a fully-loaded vehicle on unpaved roads in an open-pit mine. The truck followed the reference path within the imposed constraints, showing robustness and driving smoothness. © 2023, CC BY.

关键词： controllers

Explicitly Solvable Continuous-time Inference for Partially Observed Markov Processes

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arXiv 2023年

作者： Chen, Daniel Strang, Alexander G. Eckford, Andrew W. Thomas, Peter J. The Department of Mathematics Applied Mathematics and Statistics Department of Electrical Control and Systems Engineering Department of Computer and Data Science Department of Biology Case Western Reserve University ClevelandOH44106 United States Department of Statistics University of Chicago ChicagoIL60637 United States Department of Electrical Engineering and Computer Science York University TorontoONM3J 1P3 Canada

Many natural and engineered systems can be modeled as discrete state Markov processes. Often, only a subset of states are directly observable. Inferring the conditional probability that a system occupies a particular hidden state, given the partial observation, is a problem with broad application. In this paper, we introduce a continuous-time formulation of the sum-product algorithm, which is a well-known discrete-time method for finding the hidden states' conditional probabilities, given a set of finite, discrete-time observations. From our new formulation, we can explicitly solve for the conditional probability of occupying any state, given the transition rates and observations within a finite time window. We apply our algorithm to a realistic model of the cystic fibrosis transmembrane conductance regulator (CFTR) protein for exact inference of the conditional occupancy probability, given a finite time series of partial observations. Copyright © 2023, The Authors. All rights reserved.

关键词： Markov processes

Relay-projection microscopic telescopy

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Light(science & Applications) 2025年第4期14卷 1143-1155页

作者： Wenjun Yi Shuyue Zhu Meicheng Fu Nan Gu JunliQi Siyu Liu Mengjun Zhu Ping Wang Xin Chen Yi Zhang Hongyu Zhang Yao Xu Junyi Du Peng Xiong Zhaohua Dong Luobing Dong Qiong Liu Xiujian Li College of Science National University of Defense TechnologyChangshaChina Xi’an Satellite Control Center Xi’anChina School of Information and Electronics Engineering Hunan City UniversityYiyangChina College of Electronic Science National University of Defense TechnologyChangshaChina The School of Computer Science and Technology Xidian UniversityXi’anChina Senior Department of Otolaryngology Head and Neck Surgery the 6th Medical Center of Chinese PLA General HospitalChinese PLA Medical SchoolBeijingChina

The fundamental trade-off between spatial resolution and imaging distance poses a significant challenge for current imaging techniques,such as those used in modern biomedical diagnosis and remote ***,we introduce a new conceptual method for imaging dynamic amplitude-phase-mixed objects,termed relay-projection microscopic telescopy(rPMT),which fundamentally challenges conventional light collection techniques by employing non-line-ofsight light collection through square-law relay-projection *** successfully resolved tiny features measuring 2.76μm,22.10μm,and 35.08μm for objects positioned at distances of 1019.0 mm,26.4 m,and 96.0 m,respectively,from single-shot spatial power spectrum images captured on the relay screen;these results demonstrate that the resolution capabilities of rPMT significantly surpass the Abbe diffraction limit of the 25 mm-aperture camera lens at the respective distances,achieving resolution improvement factors of 7.9,25.4,and *** rPMT exhibits long-distance,wide-range,high-resolution imaging capabilities that exceed the diffraction limit of the camera lens and the focusing range limit,even when the objects are obscured by a scattering *** rPMT enables telescopic imaging from centimeters to beyond hundreds of meters with micrometer-scale resolution using simple devices,including a laser diode,a portable camera,and a diffusely reflecting *** contemporary high-resolution imaging techniques,our method does not require labeling reagents,wavefront modulation,synthetic receive aperture,or ptychography scanning,which significantly reduce the complexity of the imaging system and enhance the application *** method holds particular promise for in-vivo label-free dynamic biomedical microscopic imaging diagnosis and remote surveillance of small objects.

关键词： light collection techniques Imaging Distance Non Line Sight Relay Projection Microscopic Telescopy remote sensingherewe imaging techniquessuch biomedical diagnosis Spatial Resolution

Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

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arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy