检索结果-内蒙古大学图书馆

An improved pelican optimization-kernel extreme learning machine for highly accurate state of charge estimation of lithium-ion batteries in energy storage systems

引用

IONICS 2025年 1-17页

作者： Li, Sheng Wang, Shunli Cao, Wen Zhang, Liya Fernandez, Carlos Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Smart Energy Storage Inst Inner Mongolia 010000 Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen AB10 7GJ Scotland

The accurate estimation of the state of charge (SOC) of lithium-ion batteries is crucial for real-time monitoring and safety control. This paper proposes a novel method for estimating SOC by optimizing the kernel extreme learning machine (KELM) with a radial basis function (RBF) kernel using an enhanced pelican optimization algorithm (POA), termed TWCS-PO-KELM. This approach addresses the challenges of real-time estimation and low accuracy in conventional methods. This paper improves the basic POA by incorporating Tent chaotic mapping to diversify the initial population, a nonlinear inertia weight factor to improve local optimization, and a Cauchy variation alongside a sparrow alert mechanism to enhance the algorithm's robustness and optimization performance. The KELM model, based on the RBF kernel, enables efficient non-linear mapping of the input features, improving the accuracy of SOC estimation. Experimental results demonstrate that the TWCS-PO-KELM model offers superior SOC estimation with a mean absolute error (MAE) of 0.143%, root mean square error (RMSE) of 0.172%, and mean absolute percentage error (MAPE) of 1.344% under BBDST conditions, showcasing its strong tracking ability and robustness in comparison to other methods.

关键词： Lithium-ion battery State of charge pelican optimization algorithm Kernel extreme learning machine TWCS-PO-KELM model

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid pelican-particle swarm optimization algorithm (HPPSO) for global optimization problem

引用

INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2024年第8期15卷 3878-3893页

作者： Raj, Amit Punia, Parul Kumar, Pawan Cent Univ Haryana Dept Math Mahendergarh 123031 Haryana India

Particle Swarm optimization (PSO) has drawn attention due to its widespread use in scientific and engineering fields. However, it suffers from a major limitation which is its slow exploration capability leading to stagnation. To overcome this limitation, various algorithms have been hybridized to improve the exploration phase of PSO but still there is a need to improve it further. Keeping this in mind, this paper proposes a novel hybrid meta-heuristic algorithm called the Hybrid pelican-Particle Swarm optimization (HPPSO) for solving complex optimization problems. The purpose of hybridization is motivated by the excellent exploration capability of the pelican optimization algorithm (POA). The performance of the proposed HPPSO has been tested on 33 standard benchmark functions in MATLAB (R2023a). For evaluation, the obtained results of proposed HPPSO algorithm are compared with conventional PSO and POA along with other numerous hybridized algorithms of PSO (PSOGSA, HFPSO, PSOBOA, and PSOGWO). The results are analyzed statistically through convergence curves, boxplot and a non-parametric Wilcoxon signed rank test. These analyses show that the proposed HPPSO algorithm achieves a better optimum than other algorithms used in the present paper.

关键词： Exploration Exploitation Meta-heuristic algorithms Particle swarm optimization pelican optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

optimization of Power Prediction of BP Network with Improved pelican algorithm

Optimization of Power Prediction of BP Network with Improved...

引用

International Conference on Electronics, Electrical and Control System (EECS)

作者： Hui, Lichuan Li, Yao Zan, Lizhi Liaoning Tech Univ Sch Elect & Control Engn Huludao Liaoning Peoples R China

In view of the large fluctuation of photovoltaic output power affected by different weather, accurate prediction of photovoltaic output power is particularly important for the safe and stable operation of power system. Firstly, the pelican optimization algorithm (POA) is improved in the following three aspects : adding Circle chaotic map to make the population evenly distributed, introducing mutation factor to expand the search range of pelican when approaching prey, adding adaptive weight and firefly disturbance to avoid falling into local optimum in the water surface flight stage;then, in order to improve the prediction accuracy of BP algorithm, the improved pelican algorithm (IPOA) is used to optimize the weights and thresholds of BP neural network, and the IPOA-BP photovoltaic power prediction model is built to improve the accuracy of power prediction. Finally, this paper tests the prediction performance of IPOA-BP, POA-BP and basic BP power prediction models in sunny, cloudy and rainy days through experiments. The experimental results demonstrate that the IPOA-BP prediction model outperforms

关键词： Power forecasting BP neural networks pelican optimization algorithm Inertia weights Firefly disturbance

来源：评论

学校读者我要写书评

暂无评论

Optimized Transfer Learning With Hybrid Feature Extraction for Uterine Tissue Classification Using Histopathological Images

引用

MICROSCOPY RESEARCH AND TECHNIQUE 2025年第5期88卷 1582-1598页

作者： Patil, Veena I. Patil, Shobha R. Visvesvaraya Technol Univ Basaveshwar Engn Coll Dept Comp Sci & Engn Belagavi India BLDEAs VP Dr PG Halakatti Coll Engn & Technol Vijayapura 586103 India Visvesvaraya Technol Univ Basaveshwar Engn Coll Informat Sci & Engn Belagavi India

Endometrial cancer, termed uterine cancer, seriously affects female reproductive organs, and the analysis of histopathological images formed a golden standard for diagnosing this cancer. Sometimes, early detection of this disease is difficult because of the limited capability of modeling complicated relationships among histopathological images and their interpretations. Moreover, many previous methods do not effectively handle the cell appearance variations. Hence, this study develops a novel classification technique called transfer learning convolution neural network with artificial bald eagle optimization (TL-CNN with ABEO) for the classification of uterine tissue. Here, preprocessing is done by the median filter, followed by image enhancement by the multiple identities representation network (MIRNet). Moreover, pelican crow search optimization (PCSO) is used for adapting weights in MIRNet, where PCSO is generated by combining the crow search algorithm (CSA) and pelican optimization algorithm (POA). Then, segmentation quality assessment (SQA) helps in tissue segmentation, and deep convolutional neural network (DCNN) helps in parameter selection that is trained by fractional PCSO (FPCSO). Furthermore, feature extraction is done and, finally, cell classification is done by TL with CNN, which is trained by the proposed ABEO algorithm. Here, ABEO is newly developed by the integration of the bald eagle search (BES) algorithm and artificial hummingbird algorithm (AHA). Furthermore, ABEO + TL-CNN achieved a high accuracy of 89.59%, a sensitivity of 90.25%, and a specificity of 89.89% by utilizing the cancer image archive dataset.

关键词： artificial bald eagle optimization crow search optimization multiple identities representation network pelican optimization algorithm segmentation quality assessment

来源：评论

学校读者我要写书评

暂无评论

Drought prediction using advanced hybrid machine learning for arid and semi-arid environments

引用

KSCE JOURNAL OF CIVIL ENGINEERING 2025年第4期29卷

作者： Rezaei, Mohsen Moghaddam, Mehdi Azhdary Piri, Jamshid Azizyan, Gholamreza Shamsipour, Ali Akbar Univ Zabol Dept Civil Engn Zabol Iran Univ Sistan & Baluchestan Dept Civil Engn Zahedan Iran Univ Zabol Fac Water & Soil Dept Water Engn Zabol Iran Univ Tehran Fac Geog Dept Climatol Tehran Iran

Drought monitoring and forecasting are crucial for efficient water resources management, particularly semi-arid regions like South Baluchestan, a sub-basin in southeastern Iran known for its tropical fruit cultivation. This study aims to provide reliable predictions of the agricultural Standardized Precipitation Index (ASPI) ous time scales (1, 3, 6, and 12 months) using advanced hybrid machine learning models (ANN-POA, ANFIS-POA, and SVM-POA). Data from ten rain gauge and evaporation stations were utilized for this purpose. The dicate that the SVM-POA model outperformed both ANN-POA and ANFIS-POA in predicting ASPI drought Interestingly, increasing the time scale led to a decrease in the frequency of drought events, while simultaneously causing them to last longer. Additionally, the accuracy of all forecasting methods improved with a longer scale. A comprehensive evaluation of the models was conducted using six statistical indices (RMSE, MARE, NSE, WI, CI), along with visualizations such as clustered column charts, Taylor diagrams, and time-series plots. These findings highlight the potential of the SVM-POA model for short-term drought forecasting, significantly contribute to sustainable water resource management and support the cultivation of tropical in the arid and semi-arid South Baluchestan sub-basin.

关键词： Drought pelican optimization algorithm SVM ANFIS ANN Baluchestan

来源：评论

学校读者我要写书评

暂无评论

Energy-Aware Elastic Multi Controller Based SDN Control Plane for Efficient Load Balancing

引用

IETE JOURNAL OF RESEARCH 2025年

作者： Sivarajan, E. Jeyalakshmi, V. Anna Univ Dept Comp Sci Engn Chennai Tamil Nadu India Anna Univ Dept Elect & Commun Engn Chennai Tamil Nadu India

Software-defined networking (SDN) approach makes network management decisions independent of the control and data layers. Several controllers can manage various network segments to guarantee reliable and efficient operation due to the distributed SDN architecture, which efficiently handles contemporary SDN systems. Nevertheless, higher-load controllers are always explicitly regarded as overloaded controllers in distributed SDN, meaning that their load must be shifted to other controllers to prevent energy consumption and network latency. In this paper, a novel pelican Optimized Taylor series-based SDN load balancer (POT-SDN) algorithm is proposed, which reduces energy consumption and delay and improves the performance of multicore processors. The proposed POT-SDN method uses a novel pelican Optimized Taylor series (POT) algorithm for performing switch migration and balances the load efficiently. The efficacy of the developed POT-SDN method has been assessed using specific parameters such as energy consumption, Packet Loss Ratio (PLR), ARU, throughput, migration cost utilization and response time. Simulation findings demonstrate that POT-SDN network works better than current methods by reducing the Load Balancing (LB) controller time by about 87%, lowering the packet loss ratio by 49%, and raising the average throughput by 23%.

关键词： Load balancing pelican optimization algorithm POT-SDN framework Software-defined networking Taylor series

来源：评论

学校读者我要写书评

暂无评论

Synergistic Hierarchical Bi-Level optimization of Demand Response Programs and Smart Inverter Volt/Var Control for Maximizing Hosting Capacity of Power Systems With Renewable-Based Distributed Generators

引用

IEEE ACCESS 2025年 13卷 15390-15409页

作者： Zenhom, Zenhom M. Abdel Aleem, Shady H. E. Aboul Zahab, Essam Ali, Ziad M. Almalaq, Abdulaziz Boghdady, Tarek A. Ahram Canadian Univ Elect Power Engn Dept Giza 12451 Egypt Inst Aviat Engn & Technol Dept Elect Engn Giza 12658 Egypt Cairo Univ Elect Power Engn Dept Giza 12613 Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawasir Elect Engn Dept Wadi Ad Dawasir 11991 Saudi Arabia Univ Hail Coll Engn Dept Elect Engn Hail 55473 Saudi Arabia Buraydah Private Coll Engn & Informat Technol Coll Elect Engn Dept Buraydah 51418 Saudi Arabia

Renewable-based distributed generation (DG) offers economic and environmental benefits, but integrating it into the distribution network (DN) poses technical challenges. Hosting capacity (HC) defines the maximum amount of DG that can be safely integrated into the DN without violating technical constraints, with various methods available to enhance the DN and increase HC. This article proposes an optimization-based demand response (DR) program using dynamic pricing to enhance the HC. A hierarchical bi-level optimization framework is developed for the DR program, where the upper level optimizes the hosting capacity depending on the optimal load profiles optimized in the lower level. Additionally, DR is combined with smart inverters' (SIs) optimal Volt/VAR control to further improve the HC. The pelican optimization algorithm (POA) is employed to solve the optimization problem and is compared with four other optimization techniques, demonstrating its good performance. The proposed planning scheme is implemented on the IEEE 33-bus test system and a real DN consisting of 59 buses in Cairo, Egypt. The results show that applying DR alone can increase the average maximum HC value by 29.4%, while combining DR with optimal Volt/VAR control can achieve a 39.6% increase.

关键词： Voltage control optimization Tariffs Costs Silicon Capacitors Wind speed Water heating Reactive power control Heuristic algorithms Bi-level optimization demand response dynamic tariff hosting capacity metaheuristic algorithms optimization pelican optimization algorithm Volt/VAR control

来源：评论

学校读者我要写书评

暂无评论

An effectiveness of machine learning models for estimate the financial cost of assistive services to disability care in the Kingdom of Saudi Arabia

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-21页

作者： Algahtani, Obaid Almazah, Mohammed M. A. Alshormani, Farouq King Saud Univ Coll Sci Dept Math Riyadh 11451 Saudi Arabia King Khalid Univ Coll Sci & Arts Muhyil Dept Math Muhyil 61421 Saudi Arabia King Saud Univ Dept Math Coll Sci POB 2455 Riyadh 11451 Saudi Arabia King Salman Ctr Disabil Res Riyadh 11614 Saudi Arabia

As per the World Health Organization (WHO), the justifications of people with disabilities globally are restricted by physical and social barriers that exclude their full contribution to society. Constructed environment barriers can limit the availability of transportation, employment, goods and services, healthcare, and overall independent drive. The government of Saudi Arabia has applied programs and policies to enhance the quality of life for people with disabilities, including education, healthcare, and employment chances. Furthermore, they also take action to progress a few social guards that endorse public involvement and income-support plans for individuals with disabilities, besides efforts to uphold the cultural, social, political, and economic environment for accurate plans. Therefore, this study presents the Effectiveness of Machine Learning Models for estimating the Financial Cost of Assistive Services to Disability Care (EMLM-EFCASDC) technique in the KSA. The presented EMLM-EFCASDC technique mainly aims to develop a data-driven model that accurately predicts the cost of assistive services in disability care across the KSA. At first, the EMLM-EFCASDC approach utilizes Z-score normalization to preprocess the input data, ensuring that data variability is minimized for improved model accuracy. Next, an ensemble of machine learning (ML) models comprises three classifiers such as hybrid kernel extreme learning machine (HKELM), extreme gradient boosting (XGBoost), and support vector regression (SVR) for predicting the financial cost. Eventually, the modified pelican optimization algorithm (MPOA) is utilized to fine-tune the optimal hyperparameter of ensemble model parameters to achieve high predictive performance. An extensive range of simulation analyses are employed to ensure the enhanced performance of the EMLM-EFCASDC technique. The performance validation of the EMLM-EFCASDC method portrayed the least RMSLE value of 0.1154 on existing approaches in terms

关键词： Disability care Machine learning Financial cost Saudi Arabia pelican optimization algorithm Assistive services

来源：评论

学校读者我要写书评

暂无评论

Computer-aided diagnosis of Haematologic disorders detection based on spatial feature learning networks using blood cell images

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-20页

作者： Alsamri, Jamal Alqahtani, Hamed Al-Sharafi, Ali M. Darem, Abdulbasit A. Nazim, Khalid Sattar, Abdul Alshammeri, Menwa Alzahrani, Ahmad A. Obayya, Marwa Princess Nourah bint Abdulrahman Univ Coll Engn Dept Biomed Engn POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Coll Comp Sci Ctr Artificial Intelligence Dept Informat Syst Abha Saudi Arabia Univ Bisha Coll Comp & Informat Technol Dept Comp Sci & Artificial Intelligence Bisha 67714 Saudi Arabia Northern Border Univ Coll Sci Dept Comp Sci Ar Ar Saudi Arabia Majmaah Univ Coll Sci Dept Comp Sci & Informat Majmaah 11952 Saudi Arabia Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakaka Saudi Arabia Umm Al Qura Univ Coll Comp Dept Comp Sci & Artificial Intelligence Mecca Saudi Arabia

Analyzing biomedical images is vital in permitting the highest-performing imaging and numerous medical applications. Determining the analysis of the disease is an essential stage in handling the patients. Similarly, the statistical value of blood tests, the personal data of patients, and an expert estimation are necessary to diagnose a disease. With the growth of technology, patient-related information is attained rapidly and in big sizes. Currently, numerous physical methods exist to evaluate and forecast blood cancer utilizing the microscopic health information of white blood cell (WBC) images that are stable for prediction and cause many deaths. Machine learning (ML) and deep learning (DL) have aided the classification and collection of patterns in data, foremost in the growth of AI methods employed in numerous haematology fields. This study presents a novel Computer-Aided Diagnosis of Haematologic Disorders Detection Based on Spatial Feature Learning Networks with Hybrid Model (CADHDD-SFLNHM) approach using Blood Cell Images. The main aim of the CADHDD-SFLNHM approach is to enhance the detection and classification of haematologic disorders. At first, the Sobel filter (SF) technique is utilized for preprocessing to improve the quality of blood cell images. Additionally, the modified LeNet-5 model is used in the feature extractor process to capture the essential characteristics of blood cells relevant to disorder classification. The convolutional neural network and bi-directional gated recurrent unit with attention (CNN-BiGRU-A) method is employed to classify and detect haematologic disorders. Finally, the CADHDD-SFLNHM model implements the pelican optimization algorithm (POA) method to fine-tune the hyperparameters involved in the CNN-BiGRU-A method. The experimental result analysis of the CADHDD-SFLNHM model was accomplished using a benchmark database. The performance validation of the CADHDD-SFLNHM model portrayed a superior accuracy value of 97.91% over other

关键词： Haematologic disorders Computer-aided diagnosis Blood cell images Artificial Intelligence pelican optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Comparative study of a new semi-empirical model of the proton exchange membrane fuel cell for online prognostics applications

引用

ENERGY CONVERSION AND MANAGEMENT 2025年 331卷

作者： Perez, L. M. Jemei, Samir Boulon, Loic Ravey, Alexandre Kandidayeni, Mohsen Solano, Javier Univ Franche Comte FEMTO ST Inst CNRS FCLAB F-90000 Belfort France Univ Quebec Trois Rivieres Dept Elect & Comp Engn Hydrogen Res Inst Trois Rivieres PQ G8Z 4M3 Canada UTBM CNRS Inst FEMTO ST FCLAB F-90000 Belfort France European Inst Energy Res EIFER Emmy Noether Str 11 D-76131 Karlsruhe Germany

The prognostic of the proton exchange membrane fuel cell is a current topic of research. Consequently, the complexity of its degradation mechanisms has led to the development of semi-empirical models to improve predictive analysis. The accurate estimation of parameters for these models is a challenging task due to their multivariate, nonlinear, and complex characteristics. This work proposes anew semi-empirical model of the proton exchange membrane fuel cell and compares it with a widely used model in the literature. Unlike other similar studies, this comparison not only focuses on minimizing the sum of squared errors in relation to the experimental data but also evaluates the variation in the solution set and the computational effort involved. For both models, the unknown parameters are estimated using the recent pelican optimization algorithm. Four datasets are used to evaluate the development of the proposed model and the selected benchmark model. The first three datasets are open-access and well-recognized in academic literature, whereas the fourth dataset was obtained from a developed experimental test bench. The results show that the proposed model achieves high accuracy, with a mean absolute percentage error lower than 0.89% and the sum of squared errors below 0.9272 for all the studied scenarios. This model reduces parameter variation and decreases the relative standard deviation by over 12.7% compared to the utilized benchmark model for the first three datasets. Hence, the proposed model not only improves the precision of the estimated parameters without a notable increase in error but also reduces the computational load by at least 21.7% across all case studies.

关键词： Proton exchange membrane fuel cell Parameter estimation pelican optimization algorithm Model identification Prognostic and degradation Filter algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：