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New Class Detection in Network Traffic Classification Using Confidence Information Embedded Cascade Structure

IEEE Transactions on Network Science and engineering 2025年第3期12卷 1692-1706页

作者： Lu, Haotian Dong, Yuning Wu, Zhiyuan Wei, Hua-Liang Lu, Guanming Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Nanjing210003 China The University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom

Network traffic classification plays an important role in network management. With continued emergence of new applications, classifiers need to deal with unknown classes in an open set environment. However, the available open set flow recognition methods cannot well balance the performance of new class detection and the fine-grained classification of known classes. Moreover, these methods could pursue high accuracy at the cost of the classification speed. To address these problems, this paper proposes an unknown network traffic detection method based on confidence (difference) and a cascade structure, by analyzing the confidence distributions of both the known and new classes. The proposed method works as follows. Firstly, it uses a cascade structure to detect new class samples (having high confidence) which are difficult to identify using existing methods;secondly, it employs the maximum confidence difference to classify the new and known classes. In order to better detect new classes with high confidence, an algorithm is designed to select the pseudo-negative samples from the unlabelled dataset with an adaptive threshold. The proposed method is evaluated on real-world datasets. The results show that compared with the state-of-the-art methods, the proposed method can significantly improve the overall accuracy and the classification latency is also greatly reduced. © 2013 IEEE.

关键词： Data accuracy

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Voltage and power control for distributed generation units in islanded microgrids 6th

Voltage and power control for distributed generation units i...

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6th International Conference on Renewable Energies Offshore, RENEW 2024

作者： Rouch, T. Bagheri M’zoughi, F. Aboutalebi, P. Ahmad, I. Garrido, A.J. Garrido, I. Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Faculty of Engineering of Bilbao University of the Basque Country Bilbao Spain

ISBN: (纸本)9781032905570

This article presents an optimized control strategy tailored for DC islanded microgrids, featuring a voltage controller based on mixed H2/H∞ state feedback using linear matrix inequalities (LMIs) and a power controller guided by droop characteristics. A polytopic modeling approach addresses system uncertainties and nonlinearities. The controller aims to stabilize the system, reject disturbances, equitably distribute power among distributed generation units (DGUs), and minimize energy consumption. Regional pole placement ensures welldamped transient responses. Simulation results demonstrate effective management of load perturbations and DGU disconnections, with a comparative analysis against an H∞ controller offering additional insights into performance. © 2025 The Author(s).

关键词： Microgrids

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Hybrid offshore wind turbines: control-oriented modeling approaches 6th

Hybrid offshore wind turbines: Control-oriented modeling app...

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6th International Conference on Renewable Energies Offshore, RENEW 2024

作者： Ahmad, I. M’zoughi, F. Aboutalebi, P. Garrido, I. Garrido, A.J. Automatic Control Group—ACG Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Engineering School of Bilbao—EIB/BIE Faculty of Engineering of Bilbao—EIB/BIE University of the Basque Country—UPV/EHU Bilbao Spain

ISBN: (纸本)9781032905570

In the pursuit of sustainable electricity generation from offshore wind and wave energy, the combination of Floating Offshore Wind Turbines (FOWTs) and Oscillating Water Columns (OWCs) has emerged as a promising solution to enhance the working lifespan of this kind of systems. This study introduces a control-focused modeling approach, using empirical data collected from the widely accepted FAST environment, to implement advanced control laws for these integrated platforms. Leveraging Long Short-Term Memory (LSTM) and Artificial Neural Networks (ANN), the research yields predictive models that effectively capture the platform pitch dynamics so as to optimize control strategies. By integrating advanced machine learning techniques, these approaches address the challenges of modeling floating offshore structures in dynamic environments. In this sense, the neuro-estimators developed in this study closely match the original plant results, offering researchers a straightforward way to implement complex control schemes that could hardly be implemented over the original non-linear system. © 2025 The Author(s).

关键词： Offshore wind turbines

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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System Optimal Routing and Distribution of Benefits on National Road Networks

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Transportation Research Procedia 2025年 82卷 2532-2545页

作者： Alexander Roocroft Giuliano Punzo Muhamad Azfar Ramli Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Institute of High Performance Computing A*STAR Singapore

Rerouting drivers from selfish route choices to system-optimal traffic patterns has the potential to improve the performance of existing infrastructure. Previous research has looked into assessing the potential of rerouting through the empirical price of anarchy, a measure of network efficiency. However, studies using real-world measurements have been limited by methodological accuracy and network size. Also, they have lacked understanding of the spatial distribution of benefits from rerouting and the relationship with marginal external cost road charges that can be used for implementation. In this article, we create an accurate data-driven traffic assignment model of England's Strategic Road Network. We use it to calculate the national price of anarchy, which is found to be almost 1 implying gains from rerouting at the national scale are minimal and smaller than in other studies. The results show the distribution of rerouting benefits varies strongly with different network zones and demand profiles. This did not match the distribution of marginal external cost charges. Some zones have noticeable benefits from rerouting although the overall network benefit is small, however, these zones do not coincide with where the largest road charges have to be applied for system-optimal rerouting. These results have implications for rerouting implementation.

关键词： Strategic Road Network Empirical Price of Anarchy National Traffic Assignment

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Locating Faults on Overhead Power Transmission Lines of 110 kV and Above Using Emergency Mode Parameters

Locating Faults on Overhead Power Transmission Lines of 110 ...

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International Youth Conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Dmitriy Sharygin Galina Filatova Andrey Yablokov Department of electric power systems automatic control Ivanovo State Power Engineering University (ISPU) Ivanovo Russia

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The article is devoted to the development of new fault location algorithms based on emergency mode parameters at instantaneous values on overhead power transmission lines of 110 kV and above. Increasing the accuracy of fault location algorithms is an important and urgent task. The methodological and simulation errors of fault location are directly related to the configuration of the power transmission line. The presence of parallel power transmission lines, mutual induction or branch substations affects the accuracy of fault location. In this work, the authors modernize their own previously developed fault location algorithms based on instantaneous values for complex power transmission line configurations. The developed fault location algorithms do not require additional corrections or means for processing primary signals and can be successfully applied when using data from digital in-strumental transformers. The studies of the algorithms were carried out on the simulation models of the overhead power lines in the Simulink/MATLAB software package. The remote fault location of the damage was carried out according to the parameters of the emergency mode. The assignment of parameters for the simulation modeling is carried out using the Monte Carlo method with subsequent statistical processing of the results and the construction of a justified inspection zone.

关键词： Instrument transformers Power transmission lines Accuracy Substations Voltage measurement Software packages Software algorithms Fault location Inspection Transmission line measurements

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Driver Behaviour Soft-Sensor based on Neurofuzzy systems and Weighted Projection on Principal Components

arXiv

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arXiv 2025年

作者： Escaño, Juan Manuel Ridao-Olivar, Miguel A. Ierardi, Carmelina Sánchez, Adolfo J. Rouzbehi, Kumars Department of Systems Engineering and Automatic Control Universidad de Sevilla Sevilla41092 Spain Department of Engineering Universidad Loyola Andalucía Sevilla41704 Spain

This work has as main objective the development of a soft-sensor to classify, in real time, the behaviors of drivers when they are at the controls of a vehicle. Efficient classification of drivers’ behavior while driving, using only the measurements of the sensors already incorporated in the vehicles and without the need to add extra hardware (smart phones, cameras, etc.), is a challenge. The main advantage of using only the data center signals of modern vehicles is economical. The classification of the driving behavior and the warning to the driver of dangerous behaviors without the need to add extra hardware (and their software) to the vehicle, would allow the direct integration of these classifiers into the current vehicles without incurring a greater cost in the manufacture of the vehicles and therefore be an added value. In this work, the classification is obtained based only on speed, acceleration and inertial measurements which are already present in many modern vehicles. The proposed algorithm is based on a structure made by several Neurofuzzy systems with the combination of projected data in components of various Principal Component Analysis. A comparison with several types of classical classifying algorithms has been made. © 2025, CC BY.

关键词： Smartphones

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Development of automatic Cartesian Robot Cannabis Seeder using LabVIEW

Development of Automatic Cartesian Robot Cannabis Seeder usi...

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International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering (ECTI-NCON)

作者： Rujipan Kosarat Keattipoom Sangwai Anan Wongjan Department of Software Engineering Faculty of Engineering Rajamangala University of Technology Lanna Chiangmai Thailand Department of Electronic Engineering and Automatic Control Systems Faculty of Engineering Rajamangala University of Technology Lanna Chiangmai Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This article describes the invention of autonomous cannabis seeding equipment to reduce contamination and planting time. The automated cannabis seeder employs an NI myRIO control board to operate a y-axis stepper motor attached to a ball screw to move along the breadth of each row of 14 seed trays. Soil drilling and burial Drill all holes at the same depth. This promotes seed germination and lowers human-seed germ contamination. These 7 soil drill heads and 7 seeders use 3D printers to design and shape the workpiece. An 800-watt air pump smokes the cannabis seeds. Linear electric motors function in two rows in the z-axis. The buffer reverses the drowning process from row 14 to the first row, starting from the 3rd row and continuing through the final series of steps until 14 rows in the y-axis are complete. The hardware controls all program activities in LabVIEW. We tested the automated cannabis seeding machine 100 times on average, sowing cannabis seeds into 98 seed trays and dropping 93 seeds at 94.80% efficiency in 10.38 minutes. A 16 percent faster machine than human work is available.

关键词： Technological innovation Motor drives Shape Soil Pulse width modulation Three-dimensional printing Seeds (agriculture) Printers Robots Contamination

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CONVERGENCE ANALYSIS OF LINEARIZED q PENALTY METHODS FOR NONCONVEX OPTIMIZATION WITH NONLINEAR EQUALITY CONSTRAINTS

arXiv

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arXiv 2025年

作者： El Bourkhissi, Lahcen Necoara, Ion Department of Automatic Control and Systems Engineering National University of Science and Technology Politehnica Bucharest Romania Department of Automatic Control and Systems Engineering National University of Science and Technology Politehnica Bucharest Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest050711 Romania

In this paper, we consider nonconvex optimization problems with nonlinear equality constraints. We assume that the objective function and the functional constraints are locally smooth. To solve this problem, we introduce a linearized q penalty based method, where q ∈ (1, 2] is the parameter defining the norm used in the construction of the penalty function. Our method involves linearizing the objective function and functional constraints in a Gauss-Newton fashion at the current iteration in the penalty formulation and introduces a quadratic regularization. This approach yields an easily solvable subproblem, whose solution becomes the next iterate. By using a novel dynamic rule for the choice of the regularization parameter, we establish that the iterates of our method converge to an ǫ-first-order solution in O(1/ǫ2+(q−1)/q) outer iterations. Finally, we put theory into practice and evaluate the performance of the proposed algorithm by making numerical comparisons with existing methods from literature. © 2025, CC BY.

关键词： Numerical methods

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