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Research on wheat impurity identification method based on terahertz imaging technology

SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2025年 326卷 125205页

作者： Li, Guangming Ge, Hongyi Jiang, Yuying Zhang, Yuan Jiang, Mengdie Wen, Xixi Sun, Qingcheng Henan Univ Technol Key Lab Grain Informat Proc & Control Minist Educ Zhengzhou 450001 Peoples R China Henan Prov Key Lab Grain Photoelect Detect & Contr Zhengzhou 450001 Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Peoples R China Henan Univ Technol Sch Artificial Intelligence & Big Data Zhengzhou 450001 Peoples R China

The traditional detection of impurities in wheat has difficulties such as low precision, time-consuming, and cumbersome, therefore, it is important to study the method of rapid and accurate detection of impurities in wheat for correctly assessing the quality grade of wheat. Terahertz (THz) technology has many superior properties such as transient, broadband, low-energy, and penetrating, which can realize rapid and nondestructive detection of wheat quality. In this study, a classification and recognition algorithm AHA-RetinaNet-X for wheat impurity terahertz images based on RetinaNet and artificial hummingbird algorithm (AHA) is proposed.A THz three-dimensional tomography imaging system is used to image wheat and its impurities, and two THz image datasets, respectively the wheat and impurity dataset for verifying the classification effect of wheat and impurities and the impurity dataset for verifying the classification effect of impurities. The experimental results show that the AHA-RetinaNet-X model outperforms other detection and classification models in terms of accuracy, F1score, precision, recall, and specificity, and is able to achieve 96.1%, 94.9%, 95.2%, 95.8%, 95.5%, 95.3%, and 93.3% for the wheat and impurity dataset and the impurity dataset, respectively, 95.6%, 96.3%, and 95.2%, and the mAP value of AHA-RetinaNet-X is also higher than the other models and can reach 92.1%. The combination of THz imaging technology and AHA-RetinaNet-X can realize the classification and identification of wheat and impurities, which provides a new method for the non-contact rapid nondestructive detection and identification of wheat and impurities, and also provides a reference for the research of the identification and detection methods of other substances.

关键词： Terahertz imaging Wheat impurities Target detection RetinaNet network artificial hummingbird algorithm

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Design of Optimal FOPI Controller for Two-Area Time-Delayed Load Frequency Control System with Demand Response

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2024年第7期19卷 4073-4085页

作者： Katipoglu, Deniz Soylu, Selim Aksaray Univ Dept Elect & Elect Engn Aksaray Turkiye

This study comprehensively investigates modeling and controller design for two-area load frequency control (LFC) system with demand response (DR) and time delays. An optimized fractional order proportional-integral (FOPI) controller is designed both to improve the stability of grid frequencies and to deal with the time delays. Using the stability boundary locus (SBL) method, the stability region for the FOPI controller parameter space is obtained. The boundaries of these stability regions are used in the optimization process of the FOPI controller parameters. A new objective function is used to minimize the integral time-weighted absolute error (ITAE) and time domain specifications such as overshoot (OS) and undershoot (US) of the frequency and tie-line power deviations. The parameters of the FOPI controller are optimized by artificial hummingbird algorithm (AHA). The simulation results are compared with gray wolf optimizer (GWO) and particle swarm optimization (PSO) tuned FOPI controllers. The AHA tuned FOPI (AHA-FOPI) controller gives very promising results, especially in terms of improving the transient response of the system by reducing the settling time (ST) and steady-state error (ess). Additionally, the effectiveness of the AHA-FOPI is demonstrated under increased time delay. It is evident from the simulation results that the AHA-FOPI controller has successfully improved the time domain characteristics of the LFC-DR system.

关键词： Load frequency control Demand response Stability region Fractional order PI artificial hummingbird algorithm Transient response

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Hybrid Optimized Deep Learning-Based Bacilli Segmentation and Infection-Level Identification of Tuberculosis Using Sputum Images

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND artificial INTELLIGENCE 2024年第1期38卷 2357017-2357017页

作者： Sathish, P. Preethi, D. Dominic, Clara Shanthi Kadiravan, G. NITTE Meenakshi Inst Technol Dept Master Comp Applicat Bangalore 560064 Karnataka India JAIN Deemed be Univ Dept Comp Sci & IT Bengaluru 560078 Karnataka India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram AP India

Presently, one of the foremost health issues and an extremely transferrable disease is Tuberculosis which is spreading worldwide. Tuberculosis is generally produced by mycobacterium tuberculosis and can cause death if it is not detected at premature stages. Therefore, a precise and efficient approach is essential for the identification of tuberculosis. The physical analysis of sputum smears through fluorescence microscopy for the detection process and also the treatment process is a subjective and difficult task. Thus, the hybrid optimization-driven deep learning approach is designed in this paper for infection-level identification of severity prediction of tuberculosis. Here, a sputum image is considered for performing tuberculosis detection and it is acquired from a database. For executing bacilli segmentation, the Deep Contour Aware Network (DCAN) model is used. In addition, DCAN is trained by the introduced Fractional artificial hummingbird algorithm (FAHA). Additionally, the LeNet scheme is applied to detect the infection level and severity detection. The LeNet is also trained using Coot-FAHA thereby;the system performance is highly increased. The devised optimization-enabled deep learning method achieved improved performance with precision, F1-score, and recall of 0.9326, 0.9386, and 0.9163, respectively.

关键词： Deep contour aware network pyramid histogram of oriented gradients artificial hummingbird algorithm fractional calculus

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FINE-TUNING MobileNetV3 WITH DIFFERENT WEIGHT OPTIMIZATION algorithmS FOR CLASSIFICATION OF DENOISED BLOOD CELL IMAGES USING CONVOLUTIONAL NEURAL NETWORK

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INTERNATIONAL JOURNAL FOR MULTISCALE COMPUTATIONAL ENGINEERING 2024年第6期22卷 65-93页

作者： Dhas, M. Mohana Singh, N. Suresh Manonmaniam Sundaranar Univ Annai Velankanni Coll Dept Comp Sci Tirunelveli 627012 Tamil Nadu India Malankara Catholic Coll Dept Comp Applicat Mariagri Kaliyakkavilai India

Breast cancer remains a formidable global health concern, underscoring the urgency for advanced diagnostic methodologies. This research presents a multifaceted framework aimed at significantly enhancing breast cancer diagnosis through innovative approaches in image processing and machine learning. The proposed framework encompasses several key contributions. Firstly, a robust denoising strategy is implemented using Convolutional Neural Network encoderdecoder architecture, augmented with data augmentation techniques. This addresses the challenge of vanishing gradients through enhanced Rectified Linear Units based Convolutional Neural Network, enhancing the model's generalization capability. Subsequent to denoising, feature extraction is performed utilizing a fine-tuned MobileNetV3 model. The model's performance is optimized through Modified Rectified Linear Units and NRMSProp approaches, effectively eliminating undesired features and improving overall efficiency. Crucially, a novel feature selection process is introduced, leveraging the artificial hummingbird algorithm based on Manta Ray Foraging Optimization algorithm. This algorithm selectively identifies essential features from breast cancer images, significantly elevating classification accuracy. To validate the proposed framework, a comprehensive evaluation is conducted, comparing its performance with a hybrid of five different metaheuristic algorithms, including Marine Predators algorithm, Tunicate Swarm algorithm, Manta Ray Foraging Optimization algorithm, Arithmetic Optimization algorithm, and Jelly Fish optimization algorithm. artificial hummingbird algorithm based on Manta Ray Foraging Optimization algorithm emerges as the most effective among these algorithms, showcasing superior performance. The evaluation utilized the Breast Cancer Histopathological Image Classification dataset, resulting in an impressive classification accuracy of 99.51% for the proposed model.

关键词： convolutional neural network artificial hummingbird algorithm marine predators algorithm MobileNetV3 improved ReLU

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Black-box modeling of ship maneuvering motion using multi-output least-squares support vector regression based on optimal mixed kernel function

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OCEAN ENGINEERING 2024年 293卷

作者： Jiang, Lichao Shang, Xiaobing Jin, Bao Zhang, Zhi Zhang, Wen Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China Shanghai Inst Aerosp Syst Engn Shanghai Peoples R China

Black-box modeling has been widely used to predict ship maneuvering motion. A novel black-box modeling approach using optimal mixed kernel multi-output least-squares support vector regression (MMLSSVR) based on the artificial hummingbird algorithm (AHA) is proposed in this paper. To improve the performance of the MMLSSVR model, a mixed kernel function that combines a radial basis function and a polynomial kernel function to capture both global and local properties is proposed. The optimal hyper-parameters of the mixed kernel function are determined by the AHA, which employs the linear combination of the mean square error and the determinant of error covariance as the objective function to balance between prediction accuracy and stability. The performance of the proposed method is evaluated using SR108 large container ship data. The experimental results demonstrate that the proposed MMLSSVR model achieves desirable prediction accuracy and strong generalizability for ship maneuvering motion modeling.

关键词： Black modeling Kernel function System identification artificial hummingbird algorithm Ship maneuvering motion

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Interconnected microgrids optimization via reconfiguration-based modular approach

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APPLIED ENERGY 2024年 375卷

作者： Ayrir, Wiam Helmi, Ahmed M. Ramadan, Haitham S. Ibn Tofail Univ Natl Sch Appl Sci Elect Engn Networks & Telecommun Syst Dept Kenitra 14000 Morocco Zagazig Univ Fac Engn Comp & Syst Engn Dept Zagazig 44519 Egypt Buraydah Private Coll Coll Engn & Informat Technol Comp Engn Dept Buraydah 51418 Saudi Arabia Zagazig Univ Elect Power & Machines Dept Zagazig 44519 Egypt Inst Int Stockage Hydrogene ISTHY F-90400 Meroux Mova Belfort Territo France

This paper addresses the reconfiguration of two link power distribution networks (PDNs) under two scenarios: horizontal and vertical interconnections. Recent metaheuristic algorithms such as the Slime Mould algorithm (SMA) and artificial hummingbird algorithm (AHA) are used for identifying the optimal tie switches (TSs) to insert within each PDN upon interconnection. Both PDNs, of 33 bus each, are horizontally or vertically interconnected. Simulation results, using MATLABTM, demonstrate significant improvements in both power loss reduction and voltage profile improvement. Specifically, after the insertion of the fifth TS, a reduction of over 28% and 39% in power losses is achieved in the horizontal and vertical scenarios, respectively. Additionally, the voltage magnitude (V V m vm) ) reaches a value of 0.9423 pu and 0.9481 pu, respectively. Furthermore, the results show that AHA outperforms other techniques to efficiently reconfigure interconnected PDNs.

关键词： Smart grids Distribution networks Metaheuristics artificial hummingbird algorithm Voltage improvement Power losses minimization

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The determination of the strongest attributes of high-performance concrete featuring innovative admixtures via optimal regression-based methodologies

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 883-893页

作者： Zhang, Lianchao Guo, Shizhong Qi, Jianwei Tangshan Maritime Inst Tangshan 063000 Hebei Peoples R China

The acquisition of an optimal mix design for high-performance concrete (HPC) is known to present significant challenges due to its inherently intricate nature. Therefore, implementing machine learning techniques can effectively streamline the process, resulting in more efficient use of resources and a reduction in labor intensity. The present study has employed the support vector regression (SVR) methodology to forecast the compressive strength (CS) of HPC. In addition, the present investigation furnished two meta-heuristic techniques for attaining appropriate and suitable outcomes. Specifically, these techniques encompass the artificial hummingbird algorithm (AHA) and Flow Direction algorithm (FDA). The hybridized approach within the SVR-AHA and SVR-FDA frameworks was achieved by integrating algorithms with the model. Moreover, several criteria indicators have been employed to identify the optimal hybrid model. About 170 experimental data sets have been used to validate the performance of the developed method, which the samples split into two stages, including training and testing. Consequently, the utilization of the AHA algorithm in conjunction with the SVR model led to an enhanced association model, as evidenced by the following outcomes: RMSE = 2.00 (MPa), R2 = 98.59%, WAPE = 0.114 (MPa), MAPE = 1.22 (MPa), and. MAE = 0.717 (MPa).

关键词： High-performance concrete Flow Direction algorithm artificial hummingbird algorithm Support vector regression Compressive strength

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Optimizing energy efficiency: predicting heating load with a machine learning approach and meta-heuristic algorithms

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 3993-4009页

作者： Feng, Yuan Baoji Univ Arts & Sci Baoji 721000 Shaanxi Peoples R China

This study responds to the urgent demand for accurate energy consumption forecasts and the strategic assessment of retrofit methodologies, aiming to achieve energy conservation and emissions reduction simultaneously. Amidst an escalating emphasis on energy-efficient practices, the research pioneers an integration of advanced optimization algorithms within a meticulous heating load (HL) prediction framework. Focused on the intricate domain of HL systems, this study leverages the Radial Basis Function (RBF) model, enhancing predictive precision through the integration of two meta-heuristic algorithms: the artificial hummingbird algorithm (AHA) and the Improved Grey Wolf Optimizer (IGWO). The validation process entails a comprehensive examination of HL data from diverse energy sources, subjected to stringent stability tests. This study introduces three distinctive models-RBF + AHA (RBAH), RBF + IGWO (RBIG), and an independent RBF model-each contributing unique insights for precise HL prediction. Remarkably, the RBAH model emerges as an exemplar, demonstrating an outstanding coefficient of correlation (R2) value of 0.996 and an exceptionally low root mean square error (RMSE) value of 0.605. These findings underscore the unparalleled predictive prowess of the RBAH model in forecasting heating load outcomes, marking it as a noteworthy advancement in the realm of energy prediction models. The study's contributions extend beyond the conventional, providing a robust framework and invaluable insights for advancing energy-efficient practices in building management, thereby addressing contemporary sustainability challenges.

关键词： Building energy Heating loud Radial basis function artificial hummingbird algorithm Improved grey wolf optimizer

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Estimation of pile-bearing capacity applying hybrid auto-regressed models based on regression decision tree

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 2225-2239页

作者： Li, Yunpeng Dong, Shenghuan Jian, Yingbin Mei, Yuqian Shijiazhuang Railway Vocat & Tech Coll Acad Affairs Off Shijiazhuang 050062 Peoples R China Shijiazhuang Railway Tech Coll Integrat Ctr Sci & Technol Ind & Educ Shijiazhuang 050062 Peoples R China Shijiazhuang Railway Vocat & Tech Coll Personnel Dept Shijiazhuang 050062 Peoples R China

The utilization of machine learning (ML) approaches for predicting pile bearing capacity (PBC) has been a focal point in various research endeavors. Nevertheless, the inherent limitations of some ML methods, specifically their challenges in identifying global minima and their dilatory convergence rate, constitute the principal drawbacks when employing this methodology. This present investigation was dedicated to the development of an ML-based predictive model that has been enriched through optimization algorithms. Models were developed employing a Regression Decision Tree (DT) along with two optimizers, including the artificial hummingbird algorithm (AHA) and Coot Optimization algorithm (COA). The inputs encompassed mean cohesion, mean friction angle, mean soil-specific weight, mean pile-soil friction angle, flap number, pile area, and pile length. At the same time, the model's output was defined as the ultimate bearing capacity of the piles. The relationship between input parameters and the output was determined. The outcomes of this study demonstrated a significant alignment between the PBC predictions generated by the Regression Decision Tree optimized with artificial hummingbird algorithm (DTAH) and the actual measured bearing capacities. The high coefficient of determination and low Root Mean Square Error values, specifically 0.998 and 89.29 (KN), respectively, for the testing datasets, underscore the value of employing optimized DT models as robust, efficient, and pragmatic tools for predicting PBC, offering substantial advantages in the field of civil and geotechnique engineering.

关键词： Pile bearing capacity Machine learning methods Regression Decision Tree Hybrid models artificial hummingbird algorithm Coot Optimization algorithm

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The Utilization of a Multi-Layer Perceptron Model for Estimation of the Heating Load

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第6期15卷 116-127页

作者： Chen, Ken Zhu, Wenyao Zhejiang Rongqe Technol Co Ltd Lishui 323000 Zhejiang Peoples R China Lishui Univ Dept Comp Sci & Technol Lishui 323000 Zhejiang Peoples R China

The growing significance of energy-efficient building management techniques has led to research that combines precise heating demand predictions with sophisticated optimization algorithms. This research seeks a comprehensive solution to enhance building energy efficiency, addressing the growing concern for sustainability and responsible resource use in contemporary research and practice. In this research endeavor, the complex topic of energy optimization within the complex domain of heating, ventilation, and air conditioning (HVAC) systems is being tackled with a combination of creative problem- solving techniques and thorough examination. The significance of accurate heating load forecasts for raising HVAC system efficiency and cutting expenses is emphasized in this study. It introduces innovative methods by combining two advanced optimization algorithms, the artificial hummingbird algorithm (AHA) and the Improved Arithmetic Optimization algorithm (IAOA), with the Multi-Layer Perceptron (MLP) model. The main objective is to improve heating load forecast accuracy and expedite HVAC system optimization procedures. This study emphasizes how important precise heating load forecasts are to attaining energy efficiency, cost savings, and the ultimate objective of encouraging environmental sustainability in building management. The assessments unequivocally illustrate that the MLAH (Multi-Layer Perceptron with artificial hummingbird algorithm) model in the second layer emerges as the most exceptional predictor. It attains an impressive maximum Coefficient of Determination (R2) 2 ) value of 0.998 during the testing phase, reflecting a remarkable explanatory capacity and displaying remarkably low Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values of 0.43 and 0.337, indicating minimal prediction discrepancies compared to alternative models.

关键词： Heating energy consumption heating load Multi- Layer Perceptron artificial hummingbird algorithm Improved Arithmetic Optimization algorithm

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