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Analysis of Weighted Factors Influencing Submarine Cable Laying Depth Using Random Forest Method

APPLIED SCIENCES-BASEL 2024年第18期14卷 8364页

作者： Lyu, Chao Zhou, Xiaoqiang Liu, Shuang Shanghai Ocean Univ Coll Econ & Management Shanghai 201306 Peoples R China Shanghai Ocean Univ Coll Engn Sci & Technol Shanghai 201306 Peoples R China

This study addresses the limitations of traditional methods used to analyze factors influencing submarine cable burial depth and emphasizes the underutilization of cable construction data. To overcome these limitations, a machine learning-based model is proposed. The model utilizes cable construction data from the East China Sea to predict the weight of factors influencing cable burial depth. Pearson correlation analysis and principal component analysis are initially employed to eliminate feature correlations. The random forest method is then used to determine the weights of factors, followed by the construction of an optimized backpropagation (BP) neural network using the ISOA-BP hybrid optimization algorithm. The model's performance is compared with other machine learning algorithms, including support vector regression, decision tree, gradient decision tree, and the BP network before optimization. The results show that the random forest method effectively quantifies the impact of each factor, with water depth, cable length, deviation, geographic coordinates, and cable laying tension as the significant factors. The constructed ISOA-BP model achieves higher prediction accuracy than traditional algorithms, demonstrating its potential for quality control in cable laying construction and data-driven prediction of cable burial depth. This research provides valuable theoretical and practical implications in the field.

关键词： submarine power cable burial depth optimization algorithm machine learning

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A High-Accuracy Advanced Persistent Threat Detection Model: Integrating Convolutional Neural Networks with Kepler-Optimized Bidirectional Gated Recurrent Units

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ELECTRONICS 2025年第9期14卷 1772-1772页

作者： Hu, Guangwu Sun, Maoqi Zhang, Chaoqin Shenzhen Inst Informat Technol Sch Comp & Software Shenzhen 518029 Peoples R China Zhengzhou Univ Light Ind Coll Comp Sci & Technol Zhengzhou 450002 Peoples R China

Advanced Persistent Threat (APT) refers to a highly targeted, sophisticated, and prolonged form of cyberattack, typically directed at specific organizations or individuals. The primary objective of such attacks is the theft of sensitive information or the disruption of critical operations. APT attacks are characterized by their stealth and complexity, often resulting in significant economic losses. Furthermore, these attacks may lead to intelligence breaches, operational interruptions, and even jeopardize national security and political stability. Given the covert nature and extended durations of APT attacks, current detection solutions encounter challenges such as high detection difficulty and insufficient accuracy. To address these limitations, this paper proposes an innovative high-accuracy APT attack detection model, CNN-KOA-BiGRU, which integrates Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BiGRU), and the Kepler optimization algorithm (KOA). The model first utilizes CNN to extract spatial features from network traffic data, followed by the application of BiGRU to capture temporal dependencies and long-term memory, thereby forming comprehensive temporal features. Simultaneously, the Kepler optimization algorithm is employed to optimize the BiGRU network structure, achieving globally optimal feature weights and enhancing detection accuracy. Additionally, this study employs a combination of sampling techniques, including Synthetic Minority Over-sampling Technique (SMOTE) and Tomek links, to mitigate classification bias caused by dataset imbalance. Evaluation results on the CSE-CIC-IDS2018 experimental dataset demonstrate that the CNN-KOA-BiGRU model achieves superior performance in detecting APT attacks, with an average accuracy of 98.68%. This surpasses existing methods, including CNN (93.01%), CNN-BiGRU (97.77%), and Graph Convolutional Network (GCN) (95.96%) on the same dataset. Specifically, the proposed model demonstrates an acc

关键词： APT detection imbalanced dataset SMOTE-Tomek combined sampling optimization algorithm deep learning networks

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Robust transfer subspace learning based on low-rank and sparse representation for bearing fault diagnosis

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第6期35卷 066204-066204页

作者： Yu, Fuchao Xiu, Xianchao Li, Xinrong Liu, Jingjing Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligence & Syst Op Shenyang 110819 Peoples R China Shanghai Univ Sch Microelect Shanghai Key Lab Chips & Syst Intelligent Connect Shanghai 200444 Peoples R China

With the development of industrial intelligence, data-driven fault diagnosis plays an important role in prognostics and health management. However, there is usually a large amount of unlabeled data from different working conditions, making cross-domain fault diagnosis unstable and inflexible. To deal with this issue, we propose two novel transfer subspace learning methods based on the low-rank sparse representation (LRSR), called LRSR-G and LRSR-R. Specifically, LRSR-G integrates an additional matrix with LRSR to characterize the Gaussian noise for robustness, as well as capture global and local structures. Furthermore, LRSR-R adaptively learns the label matrix from samples instead of using the binary labeling matrix in LRSR-G, thus providing the possibility to improve the flexibility. In addition, we develop two efficient algorithms using the alternating direction method of multipliers to solve the proposed LRSR-G and LRSR-R. Extensive experiments are conducted on the Case Western Reserve University dataset and Jiangnan University (JNU) dataset. The results show that the proposed LRSR-G and LRSR-R perform better than the existing methods, while LRSR-R has more potential in cross-domain fault diagnosis tasks.

关键词： bearing fault diagnosis transfer subspace learning low-rank and sparse representation optimization algorithm

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optimization of a Solar Water Pumping System in Varying Weather Conditions by a New Hybrid Method Based on Fuzzy Logic and Incremental Conductance

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ENERGIES 2022年第22期15卷 8518-8518页

作者： Hilali, Abdelilah El Ouanjli, Najib Mahfoud, Said Al-Sumaiti, Ameena Saad Mossa, Mahmoud A. Moulay Ismail Univ Fac Sci Elect Commun Syst & Energy Optimizat Team Meknes 11201 Morocco Hassan First Univ Fac Sci & Technol Lab Mech Comp Elect & Telecommun Settat 26000 Morocco Sidi Mohamed Ben Abdellah Univ Higher Sch Technol Ind Technol & Serv Lab Fes 30000 Morocco Khalifa Univ Adv Power & Energy Ctr Dept Elect & Comp Engn Abu Dhabi 127788 U Arab Emirates Minia Univ Fac Engn Elect Engn Dept Al Minya 61111 Egypt

The present work consists of developing a new hybrid FL-INC optimization algorithm for the solar water pumping system (SWPS) through a SEPIC converter whose objective is to improve these performances. This technique is based on the combination of the fuzzy logic of artificial intelligence and the incremental conductance (INC) technique. Indeed, the introduction of fuzzy logic to the INC algorithm allows the extraction of a maximum amount of power and an improvement in the efficiency of the SWPS. The performance of the system through the SEPIC converter is compared with those of the direct coupling to show the interest of the indirect coupling, which requires an adaptation stage driven by an optimal control algorithm. In addition, a comparative analysis between the proposed hybrid algorithm and the conventional optimization techniques, namely, P&O and INC Modified (M-INC), was carried out to confirm improvements related to the SWPS in terms of efficiency, tracking speed, power quality, tracking of the maximum power point under different weather changes, and pumped water flow.

关键词： solar water pumping system FL-INC SEPIC converter MPPT optimization algorithm centrifugal water pump

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Design optimization of linear arrays and time-modulated antenna arrays using meta-heuristics approach

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2022年第5期35卷

作者： Poddar, Suman Paul, Prajat Chakraborty, Avishek Ram, Gopi Mandal, Durbadal NIT Durgapur Dept ECE Durgapur W Bengal India SR Univ Dept ECE Warangal 506371 Telangana India NIT Warangal Dept ECE Warangal Telangana India

This article presents an approach to design a non-uniformly excited linear array antenna (LAA) and an alternate solution to LAAs in the form of time-modulated linear arrays (TMLA) for optimum far-field radiation pattern synthesis. The performance analysis of both the antenna arrays is studied, and the advantages and limitations of the proposed design approaches are presented with numerical outcomes. The far-field radiation pattern is optimized to reduce the interference by simultaneously lowering the sidelobe levels and increasing the directivities of LAAs and TMLAs. A recently proposed meta-heuristics computation technique called whale optimization algorithm (WOA) is employed to get the optimal results. The current excitation amplitude and the inter-element spacing of the 16-element LAA are optimized with WOA to attain the desired results. However, due to the large dynamic range ratios (DRRs) of excitation weights, array feed network complexities are increased, which restricts the design of typical LAAs. TMLAs can be regarded as an alternative solution where the problem of large DRRs as well as the design complexities of LAAs are eliminated. The concept of 'time' is vital in time-modulation as it plays the role of an added degree of freedom realized by periodically turning ON and OFF the radio-frequency switches with an electronic control circuit to achieve an enhanced array radiation pattern. WOA is utilized to determine the optimal switch-ON time periods for each array element and the optimal inter-element spacing of a 16-element TMLA. A set of numerical results validates the advantages of TMLAs over LAAs, and the limitations of TMLAs with respect to LAAs are also indicated. The performance of both the arrays is then compared with other published literature results to establish the superiority of the proposed approach.

关键词： linear array antenna meta-heuristics optimization algorithm pattern synthesis sidelobe levels time-modulation

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Modeling and Control of Irrational Fractional-Order System and Its Block Diagram Simulation 34

Modeling and Control of Irrational Fractional-Order System a...

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34th Chinese Control and Decision Conference (CCDC)

作者： Chen, Lanfeng Xue, Dingyu Cui, Xinshu Shenyang Normal Univ Coll Phys Sci & Technol Shenyang 110034 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)9781665478960

The use of fractional-order to describe an actual system can better reveal its essential characteristics and its behavior. The description and analysis of an irrational fractional-order system are more complicated than that of a rational fractional-order system. To solve this difficulty, this paper studies its system identification and controller design for the characteristics of such systems. Firstly, based on the frequency domain response data of these systems, combined with the optimization algorithm to realize the rationalization of them, the curves fitting are used to illustrate the correctness and effectiveness of the identification algorithm. Then, based on the identified rational models, the optimum controllers are designed. Step response comparison shows that the performance of fractional-order controllers is superior to that of traditional integer-order controllers. Finally, through the block diagram implementation, the effectiveness and correctness of the fractional-order controller design are explained.

关键词： irrational fractional-order system frequency domain response data optimization algorithm optimum controllers block diagram

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A quantum inspired differential evolution algorithm with multiple mutation strategies 21

A quantum inspired differential evolution algorithm with mul...

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21st IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Liu, Jie Qin, XingSheng Jiang, F. Guangxi Normal Univ Coll Elect & Engn Guilin Peoples R China Univ Technol Sydney NSW Australia

ISBN: (纸本)9781665494250

The advent of the digital age and the internet has recently seen a corresponding increase in security concerns. Intrusion detection systems are one of the crucial factors to consider in today's digital world. This paper proposes a metaheuristic algorithm based on quantum differential evolution with multiple strategies. This algorithm proposes a new differential mutation strategy approach to improve the search capability and convergence speed. Then, a quantum rotation gate is used to perform the secondary evolution of the population. Finally, various benchmark functions are chosen to demonstrate the optimization ability of the algorithm. The experimental results show that the model outperforms differential evolution and quantum differential evolution. And has better optimization capability, efficiency and stability. This evolutionary technique plays an important role in identifying network intrusions and security attacks.

关键词： quantum inspired differential evolution algorithm mutation strategy network security rotation gate optimization algorithm

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optimization-based reference calculation for Modular Multilevel Converters in balanced and unbalanced network conditions 23

Optimization-based reference calculation for Modular Multile...

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23rd European Conference on Power Electronics and Applications (EPE ECCE Europe)

作者： Spier, Daniel Westerman Prieto-Araujo, Eduardo Lopez-Mestre, Joaquim Gomis-Bellmunt, Oriol Control Intelligent Energia Coop CINERGIA Carrer Can Baletes 7 Argentona 08310 Spain Univ Politecn Cataluna CITCEA UPC Avinguda Diagonal 647 Barcelona 08028 Spain

ISBN: (纸本)9789075815375

The paper addresses an optimization-based algorithm to calculate the references of the Modular Multi-level Converter (MMC) during normal and constrained scenarios (when the prioritization of quantities is required). The optimization problem prioritizes to satisfy the positive- and negative-sequence active and reactive current set-points demanded by the Transmission System Operator (TSO) through the corresponding grid code. If the TSO's requirements are achieved, the algorithm minimizes the arm impedances losses. Otherwise, it attempts to reduce the error between the current components and the TSO's current set-points. The optimization-based current reference calculation is derived based on the steady-state equations of the MMC, considering the maximum currents that can flow through the MMC's arms, the maximum and minimum arm applied voltages and the maximum sub-module capacitor's voltage. Simulation in the time-domain have been conducted and the results indicate that this method can be potentially employed to calculate the converter's references during both normal and faulted conditions.

关键词： Modular Multilevel Converters (MMC) HVDC optimization algorithm

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Aerodynamic Shape optimization for Delaying Dynamic Stall of Airfoils Using Cokriging Regression

Aerodynamic Shape Optimization for Delaying Dynamic Stall of...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Raul, Vishal Leifsson, Leifur Iowa State Univ Dept Aerosp Engn Ames IA 50011 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

The dynamic stall phenomenon produces adverse aerodynamic loading, which negatively affects the structural strength and life of aerodynamic systems. Aerodynamic shape optimization (ASO) provides a practical approach for delaying and mitigating dynamic stall characteristics without the addition of an auxiliary system. A typical ASO investigation requires multiple evaluations of accurate but time-consuming computational fluid dynamics (CFD) simulations. In the case of dynamic stall, unsteady CFD simulations are required for airfoil shape evaluation;combining it with high-dimensions of airfoil shape parameterization renders the ASO investigation computationally costly. In this study, metamodel-based optimization (MBO) is proposed using the multifidelity modeling (MFM) technique to efficiently conduct ASO investigation for computationally expensive dynamic stall cases. MFM methods combine data from accurate high-fidelity (HF) simulations and fast low-fidelity (LF) simulations to provide accurate and fast predictions. In particular, Cokriging regression is used for approximating the objective and constraint functions. The airfoil shape is parameterized using six PARSEC parameters. The objective and constraint functions are evaluated for a sinusoidally oscillating airfoil with the unsteady Reynolds-averaged Navier-Stokes equations at a Reynolds number of 135,000, Mach number of 0.1, and reduced frequency of 0.05. The initial metamodel is generated using 220 LF and 20 HF samples. The metamodel is then sequentially refined using the expected improvement infill criteria and validated with the normalized root mean square error. The refined metamodel is utilized for finding the optimal design. The optimal airfoil shape shows higher thickness, larger leading-edge radius, and an aft camber compared to baseline (NACA 0012). The optimal shape delays the dynamic stall occurrence by 3 degrees and reduces the peak aerodynamic coefficients. The performance of the MFM method is also co

关键词： Aerodynamic Shape optimization NACA 0012 Computational Fluid Dynamics Reynolds Averaged Navier Stokes Airfoil Geometry Leading Edges Lift Coefficient Richardson Extrapolation Vortices optimization algorithm

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Application of grey system theory to phosphorite sinter process: From modeling to control

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ASIAN JOURNAL OF CONTROL 2021年第1期23卷 13-22页

作者： Toktassynova, Nigina Fourati, Hassen Suleimenov, Batyrbek Satbayev Univ Dept Automat & Control Alma Ata Kazakhstan Univ Grenoble Alpes GIPSA Lab F-38400 Grenoble France

The sintering process of phosphorite ore occurs with a large amount of return caused by untimely process control. The control task of phosphorite ore sintering is to regulate the parameters of the process to obtain a high quality sinter. The parameter clearly responsible for sinter quality is the temperature in the wind box. Therefore, in order to solve the control task, it is necessary to predict the highest temperature of the charge (also known as the burn through point (BTP)). In this paper, the theory of grey systems is used as a predictive model, which makes it possible to obtain an adequate model that uses a small number of initial samples of real temperature data. Based on the grey model GMC(1,n) a new optimal model is presented, which is constructed by using optimization algorithm. Optimal model predicts the BTP, and to establish an optimal regulation, a control synthesis is carried out through an optimization of the prediction according to the "particle swarm" algorithm.

关键词： burn through point control structure grey system optimization algorithm predictive model sinter process

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