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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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An Adaptive Fuzzy SIR Model for Real-Time Malware Spread Prediction in Industrial Internet of Things Networks

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IEEE Internet of Things Journal 2025年

作者： Zheng, Yan Na, Zhenyu Ji, Weidong Lu, Yang Harbin Normal University College of Computer and Information Engineering Harbin150010 China Northeast Forestry University College of Computer and Control Engineering Harbin150010 China

The Industrial Internet of Things (IIoT) networks serve as the foundational infrastructure for real-time communication and data exchange in smart manufacturing. Predicting the spread of malware within IIoT networks is particularly challenging due to uncertainties in infection and recovery rates, which are influenced by dynamic network conditions and device heterogeneity. In this paper, we propose an adaptive fuzzy SIR model that incorporates fuzzy logic and gradient descent optimization to address these uncertainties. Specifically, we integrate fuzzy logic with gradient descent, which introduces an adaptive mechanism to handle uncertain infection and recovery rates in real-time. This synergy ensures robust parameter tuning under fluctuating network states, significantly improving malware spread prediction. The proposed model dynamically adjusts infection and recovery rates using fuzzy differential equations and real-time data adaptation, enhancing prediction accuracy and resilience to network fluctuations. Experimental results demonstrate the model's advantages in improving predictive accuracy, convergence speed, and adaptability, making it a robust solution for securing IIoT networks in smart manufacturing. © 2014 IEEE.

关键词： Smart manufacturing

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Detuned Wireless Charging System with Constant Power Charging Ability and High-Misalignment Tolerance 7

Detuned Wireless Charging System with Constant Power Chargin...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： He, Jiafang Ma, Jun Dong, Shuai Li, Zhenjie Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology Harbin China Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798350359558

In practical applications, wireless charging systems (WCS) should solve unavoidable misalignment problems and realize stable output over a wide load range. Therefore, a detuned WCS with solid anti-misalignment capacity and constant power (CP) output over a wide load range using the overlapping coil magnetic coupler (OCMC) and Buck converter is proposed in this paper. First, the system structure is designed, and then the relationship between the mutual inductance, load resistance, and output power is investigated. Second, the detailed coupling characteristic of the optimized OCMC is analyzed. Finally, the closed-loop workflow of the WCS is described, and simulation results are given. © 2024 IEEE.

关键词： Inductance

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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Bipartite Formation control for Linear Multi-agent Systems with External Disturbances via Observer-Based Output Feedback 7th

Bipartite Formation Control for Linear Multi-agent Systems ...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Zhang, Pengyu Mao, Li Zhang, Wei Tang, Kai Laboratory of Intelligent Control and Robotics Shanghai University of Engineering Science 333 Longteng Road Shanghai China Computer Engineering College Dalian University of Technology 1 Guangning Road Dalian China

ISBN: (纸本)9789819733392

This article investigates the problem of bipartite formation control for a class of multi-agent systems composed of two sub-formations with an inverse connection. A distributed control algorithm with observer-based output feedback is proposed under directed signed topology and unknown external disturbances. Some sufficient conditions are established to achieve a bipartite time-varying formation with bounded tracking error. Furthermore, it is shown that the suggested observer-based formation control has good robustness. Finally, a simulation example is illustrated to show the feasibility and effectiveness of the presented control scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multi agent systems

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Parameter Design Method of the Magnetic Coupler for Wireless Charging System Using Metamodel of Optimal Prognosis 7

Parameter Design Method of the Magnetic Coupler for Wireless...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Ma, Jun He, Jiafang Li, Zhenjie Dong, Shuai Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology Harbin China Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798350359558

This paper addresses the parameter design problem of magnetic couplers and proposes a multi-objective optimization design method based on the Metamodel of Optimal Prognosis (MOP). The method involves mathematically fitting optimization objectives and design parameters using the optimal predictive metamodel, thereby establishing response surfaces for multi-objective optimization. By transforming finite element calculations of the magnetic coupler into numerical computations through fitting, the optimization process is accelerated. The parameters ultimately selected are verified through simulation, thus validating the effectiveness of the proposed optimization method. © 2024 IEEE.

关键词： Multiobjective optimization

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Research on detecting internal trunk-boring insects in living trees based on BP-UNet

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Nondestructive Testing and Evaluation 2025年

作者： Zhou, Hongwei Xia, Bo Liu, Guoyang Zhou, Hongju College of Computer and Control Engineering Northeast Forestry University Harbin China Electronic Engineering College Heilongjiang University Harbin China

The invasion of trunk-boring insects halts tree growth and causes tree death, making it a key factor in forest destruction. To address the issue of low detection accuracy due to the small size and high concealment of trunk-boring insects, a combined driving algorithm based on BP-UNet is proposed. The BP algorithm exhibits high sensitivity to relative permittivity, enabling the detection of smaller insects. By learning the implicit geometric features from the BP results, UNet can accurately reconstruct the size and shape of the insects. To demonstrate the performance of the proposed algorithm, the BP-UNet is compared with the BP and UNet algorithms, and its generalisation ability in different scenarios is tested. Experimental results show that the BP-UNet algorithm has good generalisation and achieves a detection accuracy of over 80% even in the presence of noise, providing quantitative images of trunk-boring insects within 1 second. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Invertebrates

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Chinese Journal of Electronics 2021年第5期30卷 876-884页

作者： QIAO Sibo PANG Shanchen WANG Min ZHAI Xue DAI Feng College of Computer Science and Technology China University of Petroleum College of Control Science and Engineering China University of Petroleum College of Information Science and Engineering Shandong University of Science and Technology

Popularity prediction of online video is widely used in many different scenarios. It can not only help video service providers to schedule video web sites,but also bring considerable profits on investment for both providers and advertisers if popularity of online video is predicted accurately. However, online video popularity prediction still cannot have a satisfactory result, due to the complexity of many crucial factors especially of video distribution network. In this article, we extract seven factors from huge amounts of data about user behavior,establishing a new multiple linear regression model to initially predict online video popularity. After that, a multichannel video popularity dynamic scheduling model is proposed to schedule videos on which channel and what time to be broadcast, according to its popularity predicted by multiple linear regression model, ensuring that maximum the sum value of online video popularity of each channel. Experimental results on dataset obtained from Sohu Video, a video service provider in China, and real-world video flow in Sohu Video demonstrate that the proposed model is robust and has promising performance in predicting online video popularity, which is helpful for video service providers to schedule videos on web sites effectively in the future.

关键词： regression analysis video communication Internet video signal processing Web sites real-world video flow online video popularity regression prediction model video service provider Sohu Video video distribution network user behavior video Web sites dynamic scheduling multichannel video popularity dynamic scheduling model multiple linear regression model

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High Speed Machining for Linear Paths Blended with G3 Continuous Pythagorean-Hodograph Curves

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Journal of Systems Science & Complexity 2022年第4期35卷 1586-1607页

作者： ZHAO Kai LI Shurong College of Control Science and Engineering China University of Petroleum(East China)Qingdao266580China College of Computer Science and Information Engineering Anyang Institute of TechnologyAnyang455000China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing100876China

Previously,many studies have illustrated corner blend problem with different parameter *** a few of them take a Pythagorean-hodograph(PH)curve as the transition arc,let alone corresponding real-time interpolation *** this paper,an integrated corner-transition mixing-interpolation-based scheme(ICMS)is proposed,considering transition error and machine tool ***,the ICMS smooths the sharp corners in a linear path through blending the linear path with G3 continuous PH transition *** obtain optimal PH transition curves globally,the problem of corner smoothing is formulated as an optimization problem with *** order to improve optimization efficiency,the transition error constraint is deduced analytically,so is the curvature extreme of each transition *** being blended with PH transition curves,a linear path has become a blend ***,the ICMS adopts a novel mixed interpolator to process this kind of blend curves by considering machine tool *** mixed interpolator can not only implement jerk-limited feedrate scheduling with critical points detection,but also realize self-switching of two interpolation ***,two patterns are machined with a carving platform based on *** l results show the effectiveness of ICMS.

关键词： Bézier control polygon corner transition jerk-limited scheduling mixed interpolation pythagorean-hodograph curve

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Exploring Gender Differences Based on Functional Magnetic Resonance Images by Deep Learning and Gradient-weighted Class Activation Mapping 16

Exploring Gender Differences Based on Functional Magnetic Re...

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16th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2023

作者： Luo, Jie Bin Chao Ma, Ji Wang, Yan Jiang Chen, Xue College of Control Science and Engineering Qingdao China Qingdao University The Institute for Digital Medicine and Computer-Assisted Surgery Qingdao China

ISBN: (纸本)9798350330755

Previous studies have shown gender differences in cognitive function and the prevalence of some neurological diseases. Understanding gender differences in functional connectivity (FC) will provide a new perspective for understanding these differences. We wanted to develop an effective model for the gender prediction and identify the major source of gender differences in functional connectivity. In this study we used FC generated from functional Magnetic Resonance Imaging (fMRI) from the Human Connectome Project in 1038 healthy subjects. We apply deep learning to test whether functional connectivity networks can effectively predict gender and identify the main sources of gender differences in FC. The accuracy of predicting gender by using the functional connectivity network has reached more than 90%, and we found that there are significant gender differences in the FC of interhemispheric brain areas and the FC in the right hemisphere, and in addition to the previously discovered default mode network (DMN), frontoparietal network (FPN), somatomotor network (SMN), there were gender differences in visual network (VIN), auditory network (AUN), and dorsal attention network (DAN) as well, and the gender differences within each resting-state network (RSN) were also large. Our results suggest that gender differences exist in functional connectivity networks and certain subnetworks can be focused on when studying gender-related cognitive functions and neurological disorders. © 2023 IEEE.

关键词： Magnetic resonance imaging

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