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Bolstering IoT security with IoT device type Identification using optimized Variational Autoencoder Wasserstein Generative Adversarial Network

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NETWORK-COMPUTATION IN NEURAL SYSTEMS 2024年第3期35卷 278-299页

作者： Sankar, Jothi Shri Dhatchnamurthy, Saravanan Mary, X. Anitha Gupta, Keerat Kumar SIMATS Saveetha Sch Engn Dept Comp Sci & Engn Chennai 602105 Tamil Nadu India Sathyabama Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India Karunya Inst Technol & Sci Dept Robot Engn Coimbatore India Graph Era Deemed Univ Dept Civil Engn Dehra Dun Uttarakhand India

Due to the massive growth in Internet of Things (IoT) devices, it is necessary to properly identify, authorize, and protect against attacks the devices connected to the particular network. In this manuscript, IoT Device Type Identification based on Variational Auto Encoder Wasserstein Generative Adversarial Network optimized with pelican optimization algorithm (IoT-DTI-VAWGAN-POA) is proposed for Prolonging IoT Security. The proposed technique comprises three phases, such as data collection, feature extraction, and IoT device type detection. Initially, real network traffic dataset is gathered by distinct IoT device types, like baby monitor, security camera, etc. For feature extraction phase, the network traffic feature vector comprises packet sizes, Mean, Variance, Kurtosis derived by Adaptive and concise empirical wavelet transforms. Then, the extracting features are supplied to VAWGAN is used to identify the IoT devices as known or unknown. Then pelican optimization algorithm (POA) is considered to optimize the weight factors of VAWGAN for better IoT device type identification. The proposed IoT-DTI-VAWGAN-POA method is implemented in Python and proficiency is examined under the performance metrics, like accuracy, precision, f-measure, sensitivity, Error rate, computational complexity, and RoC. It provides 33.41%, 32.01%, and 31.65% higher accuracy, and 44.78%, 43.24%, and 48.98% lower error rate compared to the existing methods.

关键词： Adaptive and concise empirical wavelet transforms Internet of things pelican optimization algorithm Variational Auto Encoder Wasserstein Generative Adversarial Network

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Hybrid Energy Storage Power Allocation Method for Smoothing Wind Power Fluctuations 2

Hybrid Energy Storage Power Allocation Method for Smoothing ...

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IEEE 2nd International Conference on Power Science and Technology (ICPST)

作者： Liu, Muyang Chen, Yutian Pi, Shengyuan Chen, Junru Zhong, Weilin Xinjiang Univ Sch Elect Engn Urumqi Peoples R China

ISBN: (纸本)9798350349047;9798350349030

The volatility and randomness of wind power can seriously threaten the safe and stable operation of the power grid, and a hybrid energy storage system composed of batteries and supercapacitors can be configured to more effectively realize the fluctuation suppression of wind farms. In this paper, a hybrid energy storage power allocation method based on parameter optimized variational mode decomposition is proposed for hybrid energy storage system to suppress wind power fluctuations. Firstly, the grid-connected power of wind power and the reference power of hybrid energy storage in line with the national grid-connected standard are obtained by the adaptive sliding average algorithm. The improved pelican optimization algorithm is used to determine the optimal value combination of the number of modes and the quadratic penalty factor in the variational mode decomposition. The parameter-optimized variational mode decomposition is used to decompose the total hybrid energy storage power and complete the hybrid energy storage system power allocation. The example results show that the proposed method effectively suppresses wind power fluctuation, overcomes the subjectivity of conventional variational mode decomposition parameter setting, reduces the phenomenon of modal aliasing, and can realize the reasonable allocation of hybrid energy storage system power and improve the safety of the system.

关键词： hybrid energy storage pelican optimization algorithm power allocation variational modal decomposition

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An Improved Differential Evolution algorithm for Parameter Extraction of Photovoltaic Models

An Improved Differential Evolution Algorithm for Parameter E...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Chowlur Revanna, Jai Keerthy Mahitha, Dilli. Jyothsna, Kalakonda Mahendar, A. Manjula, M. Harrisburg University of Science and Technology Information Systems Engineering and Management Harrisburg United States Malla Reddy Engineering College for Women Autonomous Coimbatore India Vaageshwari College of Engineering Department of Electronics and Communication Engineering Karimnagar India Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

In photovoltaic systems, extracting parameters from generated current-voltage data is critical for simulating, controlling, and optimizing their performance. While there are several strategies for accomplishing this task, but they all have limitations. This work provides an improved adaptive differential evolution approach for solar system parameter extraction. A crossover rate sorting mechanism is developed in this system that allocates different crossover rates to individuals based on their fitness, allowing better-performing individuals to have a greater impact on the following generation. A pelican optimization strategy used to speed up convergence and balance exploration and exploitation. The performance of the proposed method is validated by extracting characteristics from several solar models, including single diode, double diode, and photovoltaic modules. As a result, the proposed method is a viable and efficient option for parameter extraction in photovoltaic models. © 2023 IEEE.

关键词： Crossover Rate Sorting Method Differential Evolution Exploration pelican optimization algorithm Photovoltaic systems

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Exponential gannet firefly optimization algorithm enabled deep learning for diabetic retinopathy detection

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartA期87卷

作者： Prabhakar, Telagarapu Rao, T. V. Madhusudhana Maram, Balajee Chigurukota, Dhanunjayarao GMR Inst Technol Dept ECE Vizianagaram 532127 Andhra Pradesh India Vignans Inst Informat Technol Dept Comp Sci & Engn Visakhapatnam India Chandigarh Univ Dept Comp Sci & Engn Mohali 140055 Punjab India Gokaraju Lailavathi Womens Engn Coll Dept CSE Hyderabad India

The diabetes complication which causes various damage to the human eye lead to complete blindness is called diabetic retinopathy. The investigation of the optimization-based Deep Learning (DL) approach is introduced for the detection of diabetic retinopathy using fundus images. Here, the fundus images are pre-processed initially using a median filter and Region of Interest (RoI) extraction, to remove the noise in the image. U-Net is used for lesion segmentation and trained using the introduced Gannet pelican optimization algorithm (GPOA) to identify various types of lesions where GPOA is the integration of the Gannet optimization algorithm (GOA) and pelican optimization algorithm (POA). The data augmentation process is carried out using flipping, rotation, shearing, cropping, and translation of fundus images, and the data-augmented fundus image is allowed for a feature extraction process where the image and vector-based features of fundus images are extracted. In addition, Deep Q Network (DQN) is used for the detection of diabetic retinopathy and is trained using the introduced Exponential Gannet pelican optimization algorithm (EGFOA). The EGFOA is the combination of Exponentially Weighted Moving Average (EWMA), Gannet optimization algorithm (GOA), and Firefly optimization algorithm (FFA). Experimental outcomes achieved a maximum of 91.6% of accuracy, 92.2% of sensitivity, and 91.9% of specificity.

关键词： Diabetic retinopathy Gannet optimization algorithm Firefly optimization algorithm pelican optimization algorithm RoI extraction Lesion segmentation

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Behavioral-level modelling of GaN HEMT large signal based on pelican-Gaussian process regression algorithm

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MICROELECTRONICS JOURNAL 2024年 143卷

作者： Cai, Haiyi Zhang, Jincan Wang, Shaowei Liu, Min Zhang, Juwei Henan Univ Sci & Technol Coll Informat Engn Luoyang 471023 Peoples R China

In this paper, a novel technology named pelican-Gaussian process regression machine learning algorithm is proposed for modelling the large-signal characteristics of Gallium Nitride High Electron Mobility Transistors (GaN HEMT). Hyperparameter optimization in traditional Gaussian process regression algorithms tends to fall into local optimums and is overly dependent on the initial values. In order to solve this problem, the pelican optimization algorithm is introduced to optimize the hyperparameters in Gaussian process regression algorithms in the article. The pelican optimization algorithm is able to make the global exploration and local search ability of the algorithm be effectively balanced by helping particles to escape from the local optimal position. The I-V characteristics, output power, power gain, power gain efficiency and small-signal S-parameters of GaN HEMT devices are used to verify the effectiveness of the proposed algorithm. The experimental results show that higher fitting accuracy and generalization ability is found in the improved GPR whose hyperparameters are optimized by the pelican optimization algorithm.

关键词： GaN HEMT Large -signal model Gaussian process regression pelican optimization algorithm Hyperparameters

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Bearing Fault Diagnosis Based on IPOA-VMD and SSA-HKELM

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IEEE ACCESS 2024年 12卷 190295-190309页

作者： Chang, Baoxian Zhao, Xing Guo, Dawei Zhao, Siyu Fei, Jiyou Li, Hua Liu, Xiaodong Dalian Jiaotong Univ Coll Zhan Tianyou Dalian 116028 Peoples R China Jinzhou Med Univ Coll Food & Hlth Jinzhou 121000 Peoples R China

This study presents a novel fault diagnosis approach for rolling bearings that integrates the Improved pelican optimization algorithm (IPOA) for optimizing Variational Mode Decomposition (VMD) and the Sparrow Search algorithm (SSA) for optimizing the Hybrid Kernel Extreme Learning Machine (HKELM). The method aims to overcome challenges such as weak early fault signals and the complexities in extracting fault characteristics that often result in subpar fault classification outcomes. A novel comprehensive indicator is introduced as the fitness function during the parameter selection phase of IPOA. By utilizing IPOA, the optimal combination of VMD's parameters, including the mode component K and penalty factor alpha , is determined. Signal decomposition via VMD yields a set of Intrinsic Mode Functions (IMF). The Kolmogorov-Smirnov Distance (KSD) is employed as a measure to assess the correlation between each IMF component and the original signal. Subsequently, the KSD values of the IMFs are calculated to identify the optimal IMF components, with their Multi-scale Range Entropy (MRE) computed as a distinguishing feature. Lastly, the HKELM, enhanced through SSA optimization, is employed for the training and classification of rolling bearing faults, with the reliability and efficacy of the proposed methodology validated through simulation and empirical data.

关键词： optimization Fault diagnosis Kernel Entropy Classification algorithms Rolling bearings Linear programming Feature extraction Signal processing algorithms Computational modeling pelican optimization algorithm variational mode decomposition sparrow search algorithm hybrid kernel extreme learning machine

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Prediction method of PM2.5 concentration based on decomposition and integration

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MEASUREMENT 2023年第1期216卷

作者： Yang, Hong Wang, Wenqian Li, Guohui Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Shaanxi Peoples R China

With the acceleration of urbanization leading to a general decrease in air quality, accurate PM2.5 concentration prediction is of the utmost practical meaning for the control and prevention of air pollution in the region. Therefore, a new hybrid prediction model for PM2.5 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), approximate entropy (ApEn), variational mode decomposition optimized by capuchin search algorithm (CVMD), long short-term memory optimized by pelican optimization algorithm (POA-LSTM) and error correction (EC), named CEEMDAN-ApEn-CVMD-POA-LSTM-EC, is proposed. First, CEEMDAN is used to acquire a limited amount of intrinsic mode functions (IMFs). Second, calculate ApEn value for each IMF component, and divide each IMF component into high-complexity and low-complexity components by the size of ApEn values. Third, variational mode decomposition optimized by capuchin search algorithm (CVMD), named CVMD, is proposed. CVMD is used as a secondary decomposition method to further decompose high-complexity components adaptively into a finite number of IMFs. Fourth, long short-term memory optimized by pelican optimization algorithm, named POA-LSTM, is proposed. POA-LSTM predicts all IMF components, and the results of their predictions are combined to generate the original prediction results. Final, error sequence is decomposed and predicted again by the EC module CVMD-POA-LSTM to obtain prediction results of error sequence, and final prediction results are acquired by combining original prediction results and prediction results of error sequence. The datasets in Beijing, Shanghai, and Xi'an were selected for simulation experiments to demonstrate the superiority of the proposed model. Taking Beijing as an example, RMSE, MAE, MAPE and R2 values are 1.9947, 1.5577, 0.1157 and 0.9947, which are superior to other comparison models and have the best performance.

关键词： PM2 5 concentration prediction Secondary decomposition Capuchin search algorithm pelican optimization algorithm Long short-term memory Error correction

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Enhancing MPPT efficiency in PV systems under partial shading: A hybrid POA&PO approach for rapid and accurate energy harvesting

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 162卷

作者： Wang, Hao Li, Lin Ye, Haoshen Zhao, Weiwei Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China Univ Birmingham Birmingham Ctr Energy Storage BCES Birmingham B15 2TT England Univ Birmingham Sch Chem Engn Birmingham B15 2TT England

Partial shading in operational environments introduces multiple peaks in the output characteristics of photovoltaic (PV) systems, presenting significant challenges to energy harvesting. This study introduces a novel meta-heuristic algorithm, termed POA&PO, which aims to address the maximum power point tracking (MPPT) issues in PV systems. The algorithm capitalizes on the global search capability of the POA method to quickly pinpoint the range with the maximum power, followed by the fast convergence of the PO method to ensure both rapidity and accuracy of the solution. Extensive simulation tests, conducted in MATLAB/SIMULINK, have demonstrated the efficacy of the POA&PO algorithm, achieving an average tracking efficiency of 99.97 % with a convergence time of 0.3 s in step response tests;under the EN50530 test standard, the algorithm also showed sustained and stable tracking of ramp signals. Moreover, practical testing utilizing a new, low-cost indoor PV simulator confirmed the algorithm's high performance under controlled conditions, yielding an average tracking efficiency of 97.03 % and a convergence time of 0.18 s. This paper highlights the capacity of the developed algorithm to reliably, accurately, and swiftly achieve high energy transfer efficiency. Additionally, the innovative and economical experimental testing methods employed are emphasized, contributing to the practical applicability and cost-effectiveness of the proposed solution.

关键词： pelican optimization algorithm Maximum power point tracking Photovoltaic systems

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Research on rice leaf area index estimation based on fusion of texture and spectral information

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年第1期211卷

作者： Yuan, Wangshu Meng, Ying Li, Yan Ji, Zeguang Kong, Qingming Gao, Rui Su, Zhongbin Northeast Agr Univ Inst Elect & Informat Harbin 150030 Peoples R China

Leaf Area Index (LAI) is one of the indicators used to measure the growth status of rice fields. Rapid, accurate, and large-scale monitoring of LAI plays an important role in ensuring stable grain yield increase. In recent years, the spectral saturation problem and the parameter adjustment problem of machine learning algorithms have become the main limitations to improve the accuracy of LAI estimation. High-resolution Unmanned Aerial Vehicles (UAVs) images contain not only rich spectral information, but also texture information reflecting the crop canopy structure. Therefore, in order to fully understand the role of spectral information and texture information fusion in rice LAI estimation, this study used the hyperspectral sensor carried by the UAVs to obtain the spectral images of rice canopy of different varieties and different growth stages. Rice canopy reflectance and 8 basic texture features based on Gray-level Co-occurrence Matrix (GLCM) were extracted from hyperspectral images to calculate vegetation indexs (VIs) and combined texture features. Normalized difference texture index (NDTI), Non-linear texture index (NLTI), Enhanced vegetation texture index (EVTI), and Modified triangular texture index (MTTI) were calculated using two and three GLMC-based texture features to explore the effect of combinations of different basic texture features on LAI sensitivity. Two rice LAI estimation models were developed for single spectral indicators and combined with texture indicators, respectively. The results show that: (1) After preprocessing and feature band screening, the optimal spectral band, vegetation index, and trilateral parameters were obtained. When the combined spectral parameters (SP) of the three were used as the only input to the model, R2 showed an increasing trend throughout the growth period. The best results were achieved using the support vector regression (SVR) combined with the pelican optimization algorithm (POA) in the pre jointing stage: R2= 0.8

关键词： Rice Leaf area index Hyperspectral image UAVs Textural features pelican optimization algorithm

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Renewable energy utilization and stability through dynamic grid connection strategy and AI-driven solution approach

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JOURNAL OF ENERGY STORAGE 2024年 95卷

作者： Gao, Jin-Tian Tang, Yu Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equipm Tianjin 300130 Peoples R China

The connection of renewable energy sources such as wind and solar power into the power grid can significantly reduce both costs and pollution emissions. However, the variability, volatility, and anti -peak regulation characteristics of renewable energy pose significant challenges for power system dispatch. This paper proposes a hybrid economic emission dispatch model (HDEED) for wind-solar-thermal-storage systems, with operational cost and pollution emission as objective functions. The study aims to develop optimal grid -connection strategies for clean energy by utilizing the energy -shifting capability of energy storage systems. This includes strategies based on optimal load fluctuation and optimal operation income for new energy stations. A generalized load fluctuation coefficient is proposed to assess load fluctuations after wind and solar energy integration, comparing and analyzing the performance of energy storage power stations with varying capacities. In terms of algorithm development, the paper proposes the pelican optimization algorithm with a clustering strategy (POA-CS), specifically tailored to address the complexities of economic emission scheduling. The effectiveness of the proposed strategy and algorithm is validated using an enhanced IEEE -39 bus test system. Results indicate that the generalized load fluctuation coefficient under the optimal grid -connected strategy based on load fluctuation is 21% lower than that of direct grid connection of wind power and photovoltaic, leading to a significant reduction in net load fluctuation. Furthermore, under the optimal grid -connected strategy based on the operation income of new energy stations, the revenue of these plants increased by 22.40% compared to direct grid connections of wind power and photovoltaic systems. The POA-CS algorithm demonstrates superior performance, continuity, and smoothness in obtaining the Pareto optimal boundary under consistent testing conditions.

关键词： Energy storage systems Hybrid economic emission dispatch Optimal scheduling Renewable energy Optimal grid connection pelican optimization algorithm

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