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SI-EDTL: Swarm intelligence ensemble deep transfer learning for multiple vehicle detection in UAV images

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第5期34卷

作者： Darehnaei, Zeinab Ghasemi Shokouhifar, Mohammad Yazdanjouei, Hossein Fatemi, Seyed Mohammad Jalal Rastegar Islamic Azad Univ Coll Engn Dept Elect Engn Saveh Branch Saveh Iran Shahid Beheshti Univ Dept Elect & Comp Engn Tehran Iran Urmia Univ Microelect Res Lab Orumiyeh Iran

This article proposes a swarm intelligence ensemble deep transfer learning (named SI-EDTL) for multiple vehicle detection in unmanned aerial vehicle (UAV) images. This method is based on Faster regional-based convolutional neural networks (Faster R-CNN), in which, a set of region proposals are extracted using region proposal network (RPN), and then, CNN is used to mine highly descriptive features of these windows to classify regions. We use three Faster R-CNNs as feature extractors (InceptionV3, ResNet50, and GoogLeNet) that have already pre-trained on ImageNet data, combined with five transfer classifiers (KNN, SVM, MLP, C4.5 Decision Tree, and Naive Bayes). As a result, 15 different base learners are trained through deep transfer learning on a UAV dataset to classify the region proposals into multiple vehicles (car, van, truck, and bus). We combine these 15 base learners through a weighted averaging aggregation into four vehicle classes or no vehicle (background). Hyperparameters of the ensemble model are tuned using whale optimization algorithm, to achieve the best trade-off between total accuracy, precision, and recall. The proposed SI-EDTL model has been successfully developed using parallel processing in MATLAB R2020b. Experimental results on AU-AIR dataset of UAV images demonstrate the superiority of the SI-EDTL model against existing techniques.

关键词： deep transfer learning ensemble learning Faster R-CNN unmanned aerial vehicle vehicle detection whale optimization algorithm

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A Temperature Compensation Method for aSix-Axis Force/Torque Sensor Utilizing Ensemble hWOA-LSSVM Based on Improved Trimmed Bagging

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SENSORS 2022年第13期22卷 4809-4809页

作者： Li, Xuhao Gao, Lifu Cao, Huibin Sun, Yuxiang Jiang, Man Zhang, Yue Anhui Univ Inst Phys Sci Hefei 230093 Peoples R China Anhui Univ Inst Informat Technol Hefei 230093 Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Sch Sci Isl Hefei 230026 Peoples R China

The performance of a six-axis force/torque sensor (F/T sensor) severely decreased when working in an extreme environment due to its sensitivity to ambient temperature. This paper puts forward an ensemble temperature compensation method based on the whale optimization algorithm (WOA) tuning the least-square support vector machine (LSSVM) and trimmed bagging. To be specific, the stimulated annealing algorithm (SA) was hybridized to the WOA to solve the local entrapment problem, and an adaptive trimming strategy is proposed to obtain the optimal trim portion for the trimmed bagging. In addition, inverse quote error (invQE) and cross-validation are employed to estimate the fitness better in training process. The maximum absolute measurement error caused by temperature decreased from 3.34% to 3.9 x 10(-3)% of full scale after being compensated by the proposed method. The analyses of experiments illustrate the ensemble hWOA-LSSVM based on improved trimmed bagging improves the precision and stability of F/T sensors and possesses the strengths of local search ability and better adaptability.

关键词： temperature compensation six-axis force/torque sensor whale optimization algorithm bagging least square support vector machine

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A transient electromagnetic signal denoising method based on an improved variational mode decomposition algorithm

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MEASUREMENT 2021年 184卷 109815-109815页

作者： Feng, Guorui Wei, Huiru Qi, Tingye Pei, Xiaoming Wang, Hong Taiyuan Univ Technol Coll Min Engn Taiyuan Peoples R China Res Ctr Green Min Engn Technol Shanxi Prov Taiyuan Peoples R China Shanxi Feida Technol Co Ltd Jinzhong Peoples R China

Transient electromagnetic method (TEM) is often disturbed by surrounding noise in field measurement, which leads to low resolution of data and affects accurate positioning of geological anomalies. In order to solve this problem, an improved variational mode decomposition (VMD) algorithm based on whale optimization algorithm (WOA) is proposed. Firstly, the WOA is used to quickly obtain the optimal parameter combination of the decomposition number K and the penalty factor alpha of the VMD algorithm. Then, the improved VMD is used to adaptively decompose the signal. Finally, the Bhattacharyya distance algorithm is used to identify the effective mode and noise mode to accurately reconstruct the signal. The simulation comparison test shows that the proposed method has better performance in the noise processing of TEM signals. In addition, in the field experiment, this method successfully filters out the noise modes in the signal, which greatly improves the accuracy of data interpretation and verifies the effectiveness of the algorithm.

关键词： Transient electromagnetic signal Electromagnetic noise Variational mode decomposition Parameter optimization whale optimization algorithm

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Enhancing Economic Efficiency: Analyzing Transformer Life-Cycle Costs in Power Grids

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ENERGIES 2024年第3期17卷 606页

作者： Gui, Fangxu Chen, Heng Zhao, Xinyue Pan, Peiyuan Xin, Cheng Jiang, Xue North China Elect Power Univ Sch Energy Power & Mech Engn Beijing 102200 Peoples R China State Grid Econ & Tech Res Inst Co Beijing 102200 Peoples R China State Grid Liaoning Elect Power Co Econ & Tech Res Inst Shenyang 110000 Peoples R China

The transformer is a fundamental piece of equipment for power grids. The analysis and optimization of their life-cycle costs are of great importance to reinforce the economic efficiency of electrical networks. This paper constructs a comprehensive transformer life-cycle cost (LCC) model by fusing life-cycle cost theory with relevant transformer expenditure. It proceeds to examine the life-cycle cost aspects of the transformer, delving into its cost dynamics under various influencing factors, establishing interconnections between these factors and analyzing the cost relationship. By employing MATLAB software (Matlab 2021a) along with the whale optimization algorithm (WOA) this paper optimizes the objective function. Through this, it establishes the LCC model for 20 power transformers, obtaining the optimal objective function curve and the maximum value for LCC optimization of the transformer. Unlike previous research, this study adds a detailed analysis of several factors that influence LCC. At the same time, it develops a more complete, scientific and rational LCC optimization model. An illustrative example validates the model and the superiority of the whale optimization algorithm. The algorithm not only shows its scientific basis and superiority, but also serves as a guiding mechanism for LCC management in transformer engineering practices. Ultimately, it emerges as a fundamental tool to improve the efficiency of power grid asset management.

关键词： transformer life-cycle cost whale optimization algorithm cost proportion analysis cost impact factors

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On-Orbit Calibration and Wet Tropospheric Correction of HY-2C Correction Microwave Radiometer

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REMOTE SENSING 2023年第14期15卷 3633-3633页

作者： Zheng, Xiaomeng Zhang, Dehai Zhao, Jin Jiang, Maofei Chinese Acad Sci Natl Space Sci Ctr Key Lab Microwave Remote Sensing Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100040 Peoples R China

HY-2C is the third satellite in China's ocean dynamic environment satellite series, and carries a correction microwave radiometer (CMR) to correct the wet tropospheric path delay for the aligned radar altimeter. To effectively use the brightness temperatures (T-B) of CMR to retrieve path delay, an on-orbit calibration effort is required. In this study, an antenna pattern correction (APC) method and a neural network method are used to perform an on-orbit calibration for CMR's antenna temperatures and a model based on the whale optimization algorithm (WOA), Levenberg-Marquardt (LM) algorithm, and Back-Propagation neural network (WOA-LM-BP) has been proposed to retrieve the wet tropospheric correction (WTC) of CMR. The on-orbit calibration results, compared with the simulated brightness temperatures calculated by the radiative transfer model (RTM), have shown that compared with the APC method, the neural network method can almost eliminate the latitude variation, and the total bias and standard deviation of the on-orbit calibrated T-B at all channels have obviously decreased. The retrieved WTC results also have shown that the retrieved WTC of CMR has a good agreement with the corresponding ones from the model-derived WTC and Jason-3.

关键词： microwave radiometer on-orbit calibration wet tropospheric correction whale optimization algorithm Back-Propagation neural network

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Hybrid machine learning based energy policy and management in the renewable-based microgrids considering hybrid electric vehicle charging demand

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 128卷 106702-106702页

作者： Lei, Ming Mohammadi, Mojtaba Wuhan Univ Sci & Technol Sch Art & Design Wuhan 430030 Hubei Peoples R China Shiraz Univ Technol Dept Elect Engn Shiraz Iran

In this paper, a secured energy management architecture for both islanded and grid-connected operation modes of renewable hybrid AC-DC microgrids considering various renewable energy sources, distributed power generation units, energy storage, and plug-in hybrid electric vehicles (PHEV) is proposed. In this architecture, whale optimization algorithm (WOA) is employed to minimize the operation cost of the grid and also an intrusion detection system (IDS) based on the sequential hypothesis testing (SHT) approach is introduced to detect identity-based cyber-attacks (i.e. Sybil attack, masquerading attack) on the wireless-based advanced metering infrastructures (AMI). The proposed IDS makes use of the received signal strength (RSS) value to distinguish different signal sources and detect cyber-attacks. The feasibility and performance of the proposed architecture are tested on a practical hybrid microgrid, which is constructed based on the IEEE 33-bus test system. the results are provided for both islanded and grid-connected operation modes.

关键词： hybrid AC-DC microgrid Optimal energy management whale optimization algorithm Sequential hypothesis testing Security

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Aquifer flow parameter estimation using coupled meshless methods and metaheuristic algorithms

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ENVIRONMENTAL MODELLING & SOFTWARE 2024年 177卷

作者： Das, Sanjukta Eldho, T. I. Indian Inst Technol Dept Civil Engn Mumbai 400076 India

A reliable analysis of various groundwater problems requires accurate input of aquifer parameters. However, field measurement of such parameters is tedious and expensive. Inverse modelling by Simulation-optimization (SO) resolves this limitation. In this study, the unknown transmissivity of confined aquifers is estimated using SO models. Three simulation models of strong, weak and hybrid categories of meshless methods, namely, Radial Point Collocation Method (RPCM), Meshless Local Petrov Galerkin (MLPG) and Meshless Weak Strong (MWS) form, are coupled with metaheuristic algorithms of Differential Evolution (DE), Particle Swarm optimization (PSO) and whale optimization algorithm (WOA), resulting in nine SO models. Five of these nine models are novel SO models and first-time application of WOA for groundwater flow parameter estimation. The application of models to heterogeneous hypothetical and complex field-type aquifers prove that solutions are similar to true transmissivities. This study provides an insight into the selection of suitable SO model based on available resources and requirements.

关键词： Simulation optimization model Groundwater flow Meshless model Aquifer parameter estimation Meshless weak strong whale optimization algorithm

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An embedded feature selection approach for depression classification using short text sequences

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Priya, S. Kavi Karthika, K. Pon Mepco Schlenk Engn Coll Dept Comp Sci & Engn Sivakasi 626005 Tamil Nadu India

Depression has become a serious mental health issue worldwide, particularly due to the rise of the Global Pandemic. Identifying depression of an individual from short texts shared in social media is a challenging task. The present work aims to select the optimal feature subset for classifying short texts for depression detection. By performing feature selection, it is possible to eliminate redundant and noisy features in high-dimensional datasets with small sample sizes. This can prevent the "curse of dimensionality" and enhance the effectiveness of classification algorithms. However, current feature selection methods often focus on optimizing classification or clustering performance, while neglecting the stability of the selected features. This can lead to unstable results and make it challenging to identify meaningful and interpretable features. This paper introduces a novel embedded feature selection approach named Statistical Relevance Class Frequency based on whale optimization algorithm (SRCF-WOA) for selecting feature subsets from short texts in social media. The proposed methodology extracts both the unigram features and composite features to capture the semantic and structural information. chi *** (Chi-squared relevance class frequency) filter approach is applied to rank the extracted features to signify the importance of the features. WOA is adapted to retrieve the optimal subset of features with low-dimensional space using its high exploration and high exploitation capability. In the evaluation process, four benchmark short text datasets and two classifiers are used. The comparison shows that the proposed embedded feature selection method outperforms other algorithms in terms of accuracy and F beta scores(beta = 0.5, 1, and 2). The sensitivity analysis is carried out to check the robustness and stability of the proposed method. The findings indicate that the SRCF-WOA surpasses other methods on the majority of datasets, achieving the maximum classificati

关键词： Depression detection Composite feature selection Dimensionality reduction whale optimization algorithm Text classification

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A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification

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SENSORS 2021年第16期21卷 5571-5571页

作者： Mandal, Moumita Singh, Pawan Kumar Ijaz, Muhammad Fazal Shafi, Jana Sarkar, Ram Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Jadavpur Univ Dept Informat Technol Kolkata 700106 India Sejong Univ Dept Intelligent Mechatron Engn Seoul 05006 South Korea Prince Sattam Bin Abdul Aziz Univ Coll Arts & Sci Dept Comp Sci Wadi Ad Dwasir 11991 Saudi Arabia

In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods-Mutual Information, ReliefF, Chi Square, and Xvariance-and then each feature from the union set was assessed by three classification algorithms-support vector machine, naive Bayes, and k-nearest neighbors-and an average accuracy was calculated. The features with higher accuracy were selected to obtain a preliminary subset of optimal features. In the second stage, Pearson correlation was used to discard highly correlated features. In these two stages, XGBoost classification algorithm was applied to obtain the most contributing features that, in turn, provide the best optimal subset. Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy. We evaluated the proposed feature selection framework on four publicly available disease datasets taken from the UCI machine learning repository, namely, arrhythmia, leukemia, DLBCL, and prostate cancer. Our obtained results confirm that the proposed method can perform better than many state-of-the-art methods and can detect important features as well.

关键词： feature selection filter method wrapper method whale optimization algorithm arrhythmia disease classification cancer dataset

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A Hybrid Forecasting Model for Short-Term Power Load Based on Sample Entropy, Two-Phase Decomposition and whale algorithm Optimized Support Vector Regression

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IEEE ACCESS 2020年 8卷 166907-166921页

作者： Li, Wenwu Shi, Qiang Sibtain, Muhammad Li, Dan Mbanze, Daniel Eliote China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China China Three Gorges Univ Hubei Key Lab Cascaded Hydropower Stat Operat & C Yichang 443002 Peoples R China Higher Educ Polytech Inst Elect Engn Dept Tete 2304 Mozambique

To improve the accuracy and reliability of short-term power load forecasting and reduce the difficulty caused by load volatility and non-linearity, a hybrid forecasting model (CEEMDAN-SE-VMD-PSR-WOA-SVR) is proposed. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to generate multiple intrinsic modal functions (IMF) by decomposing the historical power load series. Then the sample entropy (SE) of each IMF is calculated to quantitatively evaluate the corresponding complexity. Afterward, variational mode decomposition (VMD) is adopted to achieve secondary decomposition for the component with the maximum sample entropy. Subsequently, the phase space reconstruction (PSR) is applied to reconstruct each IMF into a high-dimensional feature space matrix, which is formed as the input of support vector regression (SVR). Finally, SVR optimized by whale optimization algorithm (WOA) is used for the prediction, where the predicted values of all IMFs are accumulated to obtain the final prediction results. The experimental result demonstrates that the proposed hybrid model can effectively decompose the load series with non-linear characteristic and provide more accurate forecasting results by comparing the other models.

关键词： Load modeling Predictive models Load forecasting Prediction algorithms Mathematical model Analytical models Forecasting Short-term power load forecasting two-phase decomposition sample entropy whale optimization algorithm support vector regression

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