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Network Security Situation Prediction Model Based on VMD Decomposition and DWOA Optimized BiGRU-ATTN Neural Network

IEEE ACCESS 2023年 11卷 129507-129535页

作者： Zhang, Shengcai Fu, Qiming An, Dezhi Gansu Univ Polit Sci & Law Sch Cyber Secur Lanzhou 730070 Peoples R China

The widespread adoption of Internet-of-Things (IoT) devices has resulted in a comprehensive transformation of human life. However, the network security challenges posed by the IoT devices have become increasingly severe, necessitating the implementation of effective security mechanisms. Network security situational awareness enables an effective network state prediction for better formulation of network security defense strategies. Existing network security situational prediction methods are typically constrained by situational sequence data, especially those sequences with a high degree of non-stationarity, leading to unstable predictions and low performance. Moreover, in real-world application scenarios, the network security situational sequences are often highly non-stationary. To address these challenges, we introduce a novel hybrid prediction model named Variational Mode Decomposition (VMD) - Dynamic whale optimization Algorithm (DWOA) - Bidirectional Gated Recurrent Unit (BiGRU) - Attention Mechanism (ATTN). The proposed model integrates VMD, BiGRU, ATTN, and DWOA. Initially, network security situational awareness sequences are processed using VMD to decompose them into a series of subsequences, thus reducing the non-stationarity of the original sequences. Subsequently, an enhanced DWOA optimization algorithm is introduced for tuning the hyperparameters of the BiGRU-ATTN network. Ultimately, BiGRU-ATTN is employed to predict each of these subsequences, which are then aggregated to yield the final network security situational prediction value. When compared with several existing methods on public network security datasets, the proposed VMD-DWOA-BiGRU-ATTN method demonstrated an improvement in the R<^>2 values ranging from 6.34% to 52.61%. These results substantiate that the model significantly enhances predictive performance.

关键词： Network security Predictive models Security Time series analysis Data models Prediction algorithms Internet of Things Bidirectional control whale optimization algorithms Attention mechanism bi-directional gated recurrent unit dynamic whale optimization algorithm network security situation prediction variational mode decomposition

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Prediction of Converter Tapping Weight Based on Principal Component Analysis-whale optimization Algorithm-Backpropagation Algorithm Neural Network Model

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STEEL RESEARCH INTERNATIONAL 2024年第3期95卷

作者： Xue, Chao Xu, Weili Bao, Yanping Zhao, Lihua Liu, Xin Zheng, Ruixuan Univ Sci & Technol Beijing State Key Lab Adv Met Beijing 100083 Peoples R China Jianlong Xilin Iron & Steel Co Ltd Technol Ctr Yichun 153000 Peoples R China Univ Sci & Technol Beijing Sch Met & Ecol Engn Beijing 100083 Peoples R China

Tapping weight is an important parameter in converter blowing process, wherein precisely predicting the quantity of steel required in an alloy baking converter can effectively guide the requirement of alloy ingredients. In practical production, the main approach is empirical estimation, despite its low accuracy. Employing a general neural network model for prediction requires to gather the converter blowing parameters and endpoint temperature measurement sampling parameters as the model inputs. However, this data cannot be obtained until the blowing process reaches its endpoint, rendering it impractical for alloy batching that requires advance preparation for baking. In this study, a principal component analysis-whale optimization algorithm-backpropagation algorithm (PCA-WOA-BP) neural network tapping prediction model is developed using raw material parameters available before alloy baking as input. This model is integrated into the intelligent alloy reduction model of a factory. The model achieves a regression coefficient R2 of 0.823, with 98.53% of furnaces having a prediction error of less than 2 t. The tapping weight of 20 consecutive heats in actual production is predicted;the error range is less than 80% within 1 t, and the converter tapping weight is predicted accurately. By using the collected molten iron and scrap information of the converter through the adjusted principal component analysis-whale optimization algorithm-backpropagation algorithm tapping weight prediction model, a very accurate prediction value of the converter tapping weight can be obtained in the early stage of converter *** (c) 2024 WILEY-VCH GmbH

关键词： backpropagation algorithm neural networks data analyses industrial applications tapping weights whale optimization algorithms

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A Business Model for Resource Sharing in Cell-Free UAVs-Assisted Wireless Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第8期71卷 8839-8852页

作者： Tun, Yan Kyaw Park, Yu Min Tra Huong Thi Le Han, Zhu Hong, Choong Seon Kyung Hee Univ Dept Comp Sci & Engn Yongin 17104 South Korea Kyung Hee Univ Elect & Informat Convergence Engn Dept Yongin 17104 South Korea Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA

Unmanned aerial vehicles (UAVs) are widely deployed to enhance the wireless network capacity and to provide communication services to mobile users beyond the infrastructure coverage. Recently, with the help of a promising technology called network virtualization, multiple service providers (SPs) can share the infrastructures and wireless resources owned by the mobile network operators (MNOs). Then, they provide specific services to their mobile users using the resources obtained from MNOs. However, wireless resource sharing among SPs is challenging as each SP wants to maximize their utility/profit selfishly while satisfying the QoS requirement of their mobile users. Therefore, in this paper, we propose joint users association and wireless resource sharing problem in the cell-free UAVs-assisted wireless networks with the objective of maximizing the total network utility of the SPs while ensuring QoS constraints of their mobile users and the resource constraints of the UAVs deployed by MNOs. To solve the proposed mixed-integer non-convex problem, we decompose the proposed problem into two subproblems: users association, and resource sharing problems. Then, a two-sided matching algorithm is deployed in order to solve users association problem. We further deploy the whale optimization and Lagrangian relaxation algorithms to solve the resource sharing problem. Finally, extensive numerical results are provided in order to show the effectiveness of our proposed algorithm.

关键词： Resource management Wireless networks Virtualization Quality of service Games whale optimization algorithms Interference Two-sided matching game whale optimization Lagrangian relaxation mobile network operators (MNOs) unmanned aerial vehicles (UAVs) service providers (SPs) user association resource sharing

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Application of WOA Tuned Type-2 FLC for LFC of Two Area Power System With RFB and Solar Park Considering TCPS in Interline

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IEEE ACCESS 2022年 10卷 112007-112018页

作者： Soliman, Ahmed Mohammed Attiya Eldin, Mostafa Bahaa Mehanna, Mohammed Ahmed Al Azhar Univ Fac Engn Dept Elect Engn Cairo 1427 Egypt Elswedy Elect Ind Mech Elect & Plumbing MEP Tech Off New Cairo 11853 Egypt

In this article, interval type-2 fuzzy logic controller (IT2FLC) has been proposed as secondary load frequency controller (LFC) for hybrid two area power system instead of type-1 fuzzy logic controller (T1FLC) and PID based controllers. The investigated power system integrates both conventional and renewable power resources like large scale solar parks. These large scale solar parks in addition to demand load changes increases load frequency control problems due to their continuous and severe output power variation. In order to reduce the effect of solar irradiance variation on the power system, another type-2 fuzzy logic controller has been proposed to control the output for the solar park during cloudy days instead of maximum power point trackers. As one of the best energy storage systems utilized in modern power systems, Reduction oxidation flow battery (RFB) has been integrated in the investigated power system to act as fast active power source which absorbs and discharges power during disturbances caused by generation or demand changes. Power flow controller like thyristor controlled phase shifter (TCPS) has been proposed in this paper to control tie-line power shared between generating areas during system disturbances. In order to enhance the dynamic performance for the proposed controller, meta-heuristic nature inspired optimization algorithm like whale optimization algorithm (WOA) has been proposed for proposed controller gains off line tuning. The superiority of the proposed IT2FLC controller against T1FLC controller has been investigated by simulating their performance during severe demand load changes and solar irradiance variations, while WOA enhanced the dynamic performance of the proposed controller compared to other optimization algorithms like particle swarm optimization (PSO) and grey wolf optimization (GWO).

关键词： Power system stability Power generation whale optimization algorithms Uncertainty Renewable energy sources Linguistics Heuristic algorithms Flexible AC transmission systems Solar energy Fuzzy logic Area control error (ACE) automatic generation control (AGC) flexible alternating current transmission system (FACTS) interval type-2 fuzzy logic system (IT2FLC) load frequency control (LFC) redox flow battery (RFB) solar park thyristor controlled phase shifter (TCPS) whale optimization algorithm (WOA)

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An Exact Zoeppritz Based Prestack Inversion Using whale optimization Particle Filter Algorithm Under Bayesian Framework

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷

作者： Tang, Jing Li, Peng Huang, Xuri Lai, Qiang Wang, Ding Southwest Petr Univ Sch Geosci & Technol Chengdu Peoples R China Southwest Petr Univ Sch Geosci & Technol State Key Lab Oil & Gas Reservoir Geol & Exploitat Chengdu Peoples R China PetroChina Southwest Oil & Gasfield Co Chengdu Peoples R China Shaoxing Univ Key Lab Rock Mech & Geohazards Zhejiang Prov Shaoxing Peoples R China

Conventional amplitude versus offset (AVO) inversion methods are mainly based on various Zoeppritz approximations. The assumptions of small contrast and linear relationship lead to the most inversion methods being difficult to have high inversion accuracy. In this article, the exact Zoeppritz equation is used to establish the prestack inversion method under the Bayesian framework. It integrates multisource information to generate posterior distributions of P-, S-wave velocity and density. In the Bayesian theory, the prior model works as the regularization term which has a strong effect on the inversion results. The strategy to obtain a relatively accurate prior model can improve the inversion accuracy. Therefore, an exact Zoeppritz equation based nonlinear AVO inversion algorithm combing whale optimization particle filtering (WOPF) is proposed. The WOPF method can generate a relatively stable and accurate initial model for the Bayesian prestack inversion. We validate the new method through two synthetic models and field data. Comparisons are made with the conventional linear and nonlinear AVO inversion methods. The results show that the proposed method can provide much more accurate inverted elastic parameters in different geological conditions.

关键词： Mathematical models whale optimization algorithms Bayes methods whales Particle filters Fluids Filtering Amplitude versus offset (AVO) exact Zoeppritz equation particle filter (PF) whale optimization particle filter (WOPF) algorithm

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Wild Horse Optimizer-Based Spiral Updating for Feature Selection

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IEEE ACCESS 2022年 10卷 106258-106274页

作者： Ewees, Ahmed A. Ismail, Fatma H. Ghoniem, Rania M. Univ Bisha Coll Comp & Informat Technol Dept Informat Syst Bisha 61922 Saudi Arabia Damietta Univ Dept Comp Dumyat 34517 Egypt Misr Int Univ Fac Comp Sci Cairo 11341 Egypt Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 11671 Saudi Arabia

Feature selection (FS) is a vital and challenging process in several domains, including data mining, data clustering, text mining, education, biology, medicine, public health, machine learning, image processing, and so on. The greedy and comprehensive algorithm methods cannot identify the best subset amid the rising number of features. Thus, swarm-based algorithms are becoming more popular for identifying the best group of features. This study relies on the spiral-updated position of the whale optimization Algorithm (WOA) to propose an improved version of the Wild Horse Optimizer (WHO). This improvement enhances the WHO's ability to update solutions and explore various possibilities in the search domain. The proposed method (WHOW) was assessed using two experiments to confirm the efficacy of the improved optimizer. The first experiment was global optimization using CEC 2019 benchmark functions, whereas the second was an FS by testing 20 benchmark datasets. The results obtained using the proposed WHOW method were compared to some popular algorithms over the benchmark datasets of global optimization and FS. The experimental results reflect the superiority of WHOW in the solutions to different optimization problems and its ability to select prominent features over most benchmark datasets. These results are due to the implementation of bubble nets in the WHO using spiral movement, which promotes flexibility and performance.

关键词： Convergence Behavioral sciences Statistics Social factors Benchmark testing Metaheuristics whale optimization algorithms Feature extraction Wild horse optimizer whale optimization algorithm spiral updating position global optimization feature selection

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whale optimization Algorithm-Based Parallel Computing for Accelerating Misalignment Estimation of Reflective Fourier Ptychography Microscopy

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2023年第1期15卷 1页

作者： Pham, Van Huan Chon, Byong Hyuk Ahn, Hee Kyung Korea Res Inst Stand & Sci Adv Instrumentat Inst Opt Imaging & Metrol Team Daejeon 34113 South Korea

Reflective Fourier ptychographic microscopy has much potential for industrial surface inspection due to the ability to overcome the physical limits of the numerical aperture of the optical microscopy. However, the time cost for misalignment calibration and Fourier ptychography (FP) recovery has been a big issue for industrial applications which require fast output. Here, we introduce a misalignment estimation method which is accelerated through the whale optimization algorithm by running in parallel on Central Processing Units (CPUs), named pWOA, to reduce computing time. The proposed method shows more accurate and faster calibration compared to other population-based algorithms, including the parallel genetic algorithm and the parallel particle swarm optimization, and much faster than that of the exhaustive search both in simulations and in real experiments. In addition, this cost-effective technique can address global non-convex optimization problems with heavy loss functions including reflective FP.

关键词： Light emitting diodes optimization whale optimization algorithms Statistics Sociology Parallel processing Mirrors Microscopy technologies for computing other imaging technique

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Indoor Visible Light Positioning Based on Improved whale optimization Method With Min-Max Algorithm

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Liu, Ren Liang, Zhonghua Wang, Zhenyu Li, Wei Changan Univ Sch Informat Engn Dept Commun Engn Xian 710064 Peoples R China

Recently, the Min-Max algorithm has been widely used in various indoor positioning systems due to its simplicity and robustness. In this article, an improved whale optimization algorithm (IWOA) associated with the Min-Max algorithm is proposed to improve the positioning accuracy concerning the original Min-Max algorithm for visible light positioning (VLP) systems. In the proposed algorithm, the three-dimensional (3-D) region of interest (RoI) affiliated with the target node (TN) is first obtained using the original Min-Max algorithm, and then the IWOA is employed to find the accurate position of the TN in the 3-D RoI with the lowest fitness value. In software simulations, the signal-to-noise ratio (SNR) value of the VLP system is 15 dB and the standard deviation of measured distance noise is 1 m. Our software simulation results show that in the case of two-dimensional (2-D) positioning, the averaged positioning error (APE) of the proposed IWOA-Min-Max algorithm is decreased by 84.6%, 52.1%, 1.9%, and 19.93%, respectively, for the original Min-Max algorithm, the extended Min-Max algorithms, the whale optimization algorithm (WOA), and the least square estimation (LSE). Furthermore, in the case of 3-D positioning, it is also reduced by 81.7%, 75.4%, 8.5%, and 63.9%, respectively, compared with that of the above four existing algorithms. Finally, hardware-in-the-loop simulation (HILS) results are also provided to verify the effectiveness of the proposed IWOA-Min-Max algorithm.

关键词： Statistics Sociology Three-dimensional displays whale optimization algorithms Partitioning algorithms Estimation LED lamps Improved whale optimization algorithm (IWOA) min-max visible light positioning (VLP)

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Research on 3D Localization Technology Based on Hybrid Signal Models and Adaptive Dynamic Weight whale optimization Algorithm

Research on 3D Localization Technology Based on Hybrid Signa...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Zhengxin Li Yiyang Du Jingchen Ma Chengcheng Jin Zhen Zhang Bijie Power Supply Bureau Guizhou Power Grid Limited Liability Company Bijie China School of Computer and Data Engineering Zhejiang University of Technology Ningbo Institute Ningbo China School of Energy Systems Lappeenranta University of Technology Lappeenranta Finland School of Computer Science and Technology Shenyang Institute of Engineering Shenyang China Jiulan Academy Nanchang University Nanchang China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This paper investigates a 3D localization technology based on hybrid signal models and the Adaptive Dynamic Weight whale optimization Algorithm (AWOA). By analyzing the physical characteristics of five types of opportunity signals (TOA, TDOA, DFD, AOA, and RSS), mathematical models are constructed to derive the position information. It is found that at least four signal sources are required for accurate localization using TOA and RSS methods, while TDOA and AOA methods need at least three signal sources. DFD requires at least three signal sources to determine the target's direction and velocity. To enhance localization accuracy, a hybrid localization model based on the least squares method is proposed and optimized in real-time using the AWOA. This algorithm, inspired by the hunting behavior of whales, effectively addresses localization errors in non-line-ofsight (NLOS) environments. Experimental results demonstrate that the technology can accurately track the target's trajectory in real-time over a 10-second interval, with the final localization error within an acceptable range. This study provides an effective solution for high-precision 3D localization in complex environments.

关键词： Location awareness Adaptation models Solid modeling Three-dimensional displays Accuracy Heuristic algorithms whale optimization algorithms Real-time systems Robustness optimization

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Improved whale optimization Algorithm for VSG Control

Improved Whale Optimization Algorithm for VSG Control

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Electrical Automation and Artificial Intelligence (ICEAAI), International Conference on

作者： Mingliang Xia Xihuai Wang Shanghai Maritime University School of Logistics Engineering Shanghai China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

With the increasing seriousness of energy problems and the extensive use of new energy sources, how to ensure that the new energy power generation system has good grid-connected stability has become a new research hotspot. Virtual synchronous generator (VSG) technology introduces the operating characteristics of synchronous generator (SG) on the basis of grid-connected inverter, so that it has the same rotor inertia and damping. However, the design of VSG system parameters is complicated, and the traditional whale optimization (WOA) algorithm often fails to achieve the best optimization effect. To address this problem, a mathematical model of the VSG system is established, and based on the WOA algorithm, a mixed-strategy improved whale optimization (IWOA) algorithm is proposed. By comparing the simulation results under the environment of grid voltage and frequency fluctuation, it is proved that the IWOA algorithm is more suitable for VSG grid stabilization.

关键词： Voltage fluctuations Simulation Rotors Power system stability Synchronous generators Stability analysis whale optimization algorithms Particle swarm optimization optimization Power generation

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