Franklin Open最新文献

{"title":"A hue-preserving unsharp masking with adjustable saturation using logarithmic ratio operations","authors":"Mashiho Mukaida ,&nbsp;Aoi Kamehara ,&nbsp;Takanori Koga ,&nbsp;Noriaki Suetake","doi":"10.1016/j.fraope.2025.100280","DOIUrl":"10.1016/j.fraope.2025.100280","url":null,"abstract":"<div><div>Unsharp masking is a typical image sharpening technique. When this technique is applied to a color image, each RGB component is processed independently and the color image is reconstructed from the outputs obtained for each component. In this case, the hue of the resultant image is significantly different from that of the input image. In addition, when the sharpening parameter is large, clipping processing is required for the output pixel values that exceed the displayable color gamut, which may cause a loss of detailed patterns. To address these problems in image sharpening, this study proposes a hue-preserving unsharp masking method with adjustable saturation. The proposed method first applies an unsharp masking technique with logarithmic ratio operations to each RGB component of the input image to obtain a sharpened image with a reduced loss of detailed patterns. The sharpened image is then approximated by a linear transformation that satisfies a conditional equation for hue preservation in the RGB color space. If the approximated pixel values are out of gamut, they are modified to be in their corresponding equi-hue planes of the RGB color space while preserving the lightness values of the sharpened image. The effectiveness and validity of the proposed method are demonstrated by comparing it with previous methods using various images. Specifically, the proposed method outperformed the comparison methods in ARNIQA, the latest non-reference image quality assessment, with a value of 0.5539. Furthermore, detailed quantitative evaluation indices such as hue preservation (HD), sharpening effect (GRVE), ratio of black and white skips (CR), and saturation index (CN) were comparable to those of the other methods.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100280"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New insight to multi-order Mittag-Leffler stability and Lyapunov theorems for random initialization time fractional order systems","authors":"Bichitra Kumar Lenka","doi":"10.1016/j.fraope.2025.100282","DOIUrl":"10.1016/j.fraope.2025.100282","url":null,"abstract":"<div><div>We consider fractional order systems associated with different orders and random initialization time placed on a real number line. We introduce a new concept of multi-order Mittag-Leffler stability and formulate new proofs to Lyapunov stability theorems for random initialization time fractional order systems. The new theorems give way to measuring fractional derivatives of scalar Lyapunov functions and enable a pathway to estimate decay associated with trajectories of such systems. A few examples that deal with applications of interest have been discussed.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100282"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms","authors":"Mohd Herwan Sulaiman ,&nbsp;Zuriani Mustaffa ,&nbsp;Ahmad Salihin Samsudin ,&nbsp;Amir Izzani Mohamed ,&nbsp;Mohd Mawardi Saari","doi":"10.1016/j.fraope.2025.100293","DOIUrl":"10.1016/j.fraope.2025.100293","url":null,"abstract":"<div><div>State of Charge (SoC) estimation plays a crucial role in battery management systems for electric vehicles, directly impacting their operational efficiency and reliability. This study presents a hybrid approach combining the CatBoost algorithm with metaheuristic optimization techniques to enhance SoC estimation accuracy and robustness. The methodology was validated using an extensive dataset collected from 72 real-world driving trips of a BMW i3 (60 Ah), comprising 1053,910 instances of battery and vehicle operation metrics. A comprehensive data preprocessing pipeline was implemented, including missing value treatment, outlier removal, and feature normalization using Min-Max scaling. Three distinct metaheuristic algorithms were investigated: Barnacles Mating Optimizer (BMO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Whale Optimization Algorithm (WOA), each integrated with CatBoost to optimize critical parameters including learning rate, tree depth, regularization, and bagging temperature. Experimental results demonstrate that the BMO<img>CatBoost approach achieved superior performance with best-case metrics of RMSE = 6.1031, MAE = 4.1303, and R² = 0.8211, outperforming both PSO<img>CatBoost, GA-CatBoost, and WOA-CatBoost implementations. The framework's effectiveness was validated through rigorous testing, establishing its potential for real-world electric vehicle applications. This research contributes to the advancement of battery management technology, offering promising implications for electric vehicle energy management and broader energy storage applications.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100293"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing high voltage gain interleaved boost converters for PV and wind systems using hybrid deep learning with bitterling fish and secretary bird algorithms","authors":"G.Veera Sankara Reddy,&nbsp;S. Vijayaraj","doi":"10.1016/j.fraope.2025.100291","DOIUrl":"10.1016/j.fraope.2025.100291","url":null,"abstract":"<div><div>The integration of renewable energy sources such as photovoltaic (PV) and wind systems demands high-efficiency, high-voltage gain power conversion architectures. However, interleaved boost converters, while suitable for such applications, face challenges in balancing complexity, scalability, cost, and dynamic environmental variability. This study introduces a series of novel intelligent control frameworks to overcome these limitations and improve overall system performance. Firstly, a Neuro-LSTM BitterSec Optimization Network (NL-BSONet) is proposed to enhance the efficiency of high-voltage gain interleaved boost converters while minimizing system complexity. This hybrid approach leverages neural networks and LSTM-based learning for real-time optimization, offering improved scalability and lower switching losses. To address power quality issues caused by fluctuating irradiance and wind speeds, the study introduces the Adaptive Neuro-Deep Reinforcement Learning Bitterling Optimizer (AN-DRLBO). This model integrates Deep Reinforcement Learning (DRL) for adaptive energy conversion, Adaptive Neural Networks (ANN) for real-time system stabilization, and Bitterling Fish Optimization (BFO) for robust performance under transient conditions. Furthermore, due to the difficulty in achieving optimal control parameters under variable environmental conditions, an Adaptive LSTM-Encoded Secretary Optimization Network (AL-SONet) is developed. This framework employs Long Short-Term Memory (LSTM) networks for predictive control, Autoencoder-based Optimization (AEO) for feature extraction and simplification, and Secretary Bird Optimization (SBO) for dynamic parameter tuning. The proposed architectures demonstrate superior performance, achieving 97 % energy conversion efficiency, a voltage gain of 32.5 dB, and minimal output ripple, thereby ensuring stable and efficient integration of renewable energy sources. This research contributes a comprehensive and adaptive control solution for next-generation renewable energy systems.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100291"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing model errors in UAV altitude control using compensator","authors":"Gilang Nugraha Putu Pratama ,&nbsp;Alfian Maarif ,&nbsp;Iswanto Iswanto ,&nbsp;Evi Wahyu Pratiwi","doi":"10.1016/j.fraope.2025.100279","DOIUrl":"10.1016/j.fraope.2025.100279","url":null,"abstract":"<div><div>The control of UAV quadrotors remains a compelling topic in control engineering, largely due to their nonlinear dynamics and complex behavior. Effective altitude control requires a well-designed controller capable of maintaining stability and performance. However, standard controllers may not suffice when confronted with parametric uncertainties or time delays, necessitating the addition of compensators to ensure robust performance. This study proposes a model error compensator designed to mitigate errors arising from parametric uncertainties using the Particle Swarm Optimization (PSO) algorithm. Simulation results confirm that the proposed compensator effectively reduces response variations caused by uncertainties and time delays, demonstrating its potential to enhance the reliability of altitude control in UAV quadrotors.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100279"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced multi-objective Evolutionary Mating Algorithm with improved crowding distance and Levy flight for optimizing comfort index and energy consumption in smart buildings","authors":"Muhammad Naim Bin Nordin ,&nbsp;Mohd Herwan Sulaiman ,&nbsp;Nor Farizan Zakaria ,&nbsp;Zuriani Mustaffa","doi":"10.1016/j.fraope.2025.100286","DOIUrl":"10.1016/j.fraope.2025.100286","url":null,"abstract":"<div><div>This paper introduces a novel Multi-Objective Evolutionary Mating Algorithm (MOEMA) designed to address the inherent challenges of optimizing comfort index and energy consumption in smart building systems. While current Evolutionary Mating Algorithms (EMA) primarily focus on single-objective optimization and rely on weighted functions for handling multiple objectives, such approaches prove impractical for the complex trade-offs between comfort index and energy efficiency. The proposed MOEMA enhances the original EMA framework through two key innovations: an improved crowding distance function inspired by the Non-dominated Sorting Genetic Algorithm (NSGA) to enhance solution diversity and selection pressure, and the integration of Levy flight mechanics to improve exploration efficiency by balancing local and global searches. These enhancements enable MOEMA to effectively navigate complex multi-objective landscapes, leading to more diverse and well-converged Pareto-optimal solutions. The algorithm's performance is thoroughly assessed using the chosen benchmark functions and validated through practical applications in smart building environments. It simultaneously optimizes various comfort parameters, including temperature, illuminance, and air quality, while minimizing energy consumption and maximizing the comfort index. Comparative analysis against established algorithms, like NSGA-II demonstrates MOEMA's effectiveness in achieving superior solution diversity and convergence characteristics. The results indicate that MOEMA offers a robust framework for handling the complex balance between the smart building's comfort index and energy usage where it achieves 0.03 % better at comfort index and with 10.65 % lower energy consumption than NSGA-II. It contributing to the broader fields of building automation and sustainable development while aligning with Industry 4.0 initiatives.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100286"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust finite-time almost passivity and practical finite-time stability of uncertain switched nonlinear systems","authors":"Xuelian Gao ,&nbsp;Hongbo Pang ,&nbsp;Chensong Li","doi":"10.1016/j.fraope.2025.100288","DOIUrl":"10.1016/j.fraope.2025.100288","url":null,"abstract":"<div><div>In this paper, the robust finite-time almost passivity and practical finite-time stability of a class of switched nonlinear systems with the structural uncertainties are investigated. First, multiple storage functions are adopted to characterize robust finite-time almost passivity property of uncertain switched nonlinear systems, which only requires each subsystem to be passive outside a ball on its active interval. Then, based on the proposed almost passivity concept, practical finite-time stability is obtained. Second, a switching law dependent on states is adopted to make the system robust finite-time almost passive. Third, robust finite-time almost passification is achieved by designing a switching law dependent on the states and a set of robust controllers. The advantage of the designed switching law is that the adjacent storage functions are allowed to increase at each switching point. Finally, an example is provided to verify the validity of the results.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100288"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fighting typhoid fever: Modeling antibiotic resistance and antibiotic switching","authors":"Maria M. Shaale ,&nbsp;Josiah Mushanyu ,&nbsp;Farai Nyabadza ,&nbsp;Samuel M. Nuugulu","doi":"10.1016/j.fraope.2025.100274","DOIUrl":"10.1016/j.fraope.2025.100274","url":null,"abstract":"<div><div>Typhoid fever continues to be a major public health concern, particularly in developing countries where the sanitation infrastructure is inadequate. The rise in resistance to typhoid drugs has made treatment increasingly challenging, resulting in longer recovery times and continued transmission of the disease within households and communities. This growing resistance underscores the urgent need for improved treatment strategies and public health intervention. In this study, we presented a mathematical model of typhoid fever that incorporates antibiotic resistance and the implementation of antibiotic switching as a control strategy. The model considers individuals infected with typhoid antibiotic sensitive strains and typhoid antibiotic resistant strain. The effects of antibiotic switching, which involves transitioning patients between different antibiotics, are modeled to study its impact on the prevalence of resistant and sensitive strains. The model is analyzed and the model reproduction number, <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>, is found to be the sum of two reproduction numbers <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>r</mi></mrow></msub></math></span> representing the contribution of the sensitive and resistant strains, respectively. The stability analysis indicates that the disease-free equilibrium is stable when the model reproduction number is less than one, suggesting the possibility of eradicating the disease under effective control measures. In contrast, the endemic equilibrium remains stable when the reproduction number exceeds one, indicating persistent infection levels. Sensitivity analysis is performed to identify critical parameters that influence the persistence of typhoid in the population. Numerical simulations are performed to support the theoretical findings. The results obtained demonstrate that antibiotic switching can reduce the prevalence of resistant and sensitive strains and overall infection levels, highlighting their potential as an effective strategy to manage antibiotic resistance in typhoid fever.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100274"},"PeriodicalIF":0.0,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the efficacy and site-specific performance of machine learning approaches: A comprehensive review of autism detection models","authors":"Deblina Mazumder Setu ,&nbsp;Tania Islam ,&nbsp;Md Maklachur Rahman ,&nbsp;Samrat Kumar Dey ,&nbsp;Tazizur Rahman","doi":"10.1016/j.fraope.2025.100275","DOIUrl":"10.1016/j.fraope.2025.100275","url":null,"abstract":"<div><div>As autism diagnoses rise globally, it is important to find a better approach for early and effective prediction. The primary objectives are to identify the models that provide the optimum balance of accuracy while taking age and data type considerations into account, as well as to identify shortcomings and recommend future directions. This study investigates the efficacy of various computational models in early autism detection, analyzing 22 distinct studies. From them, 18 studies are based on 14 popular machine learning (ML) models to identify the most effective prediction methods. And four of them are more progressive, sophisticated methods including the convolutional neural network (CNN) model, diagnostic autism spectrum disorder (DASD) strategy, Ensemble Diagnosis Methodology (EKNN), and Self-Organizing Maps (SOM). Some existing study find out that Gradient Boosting, Extreme Gradient Boosting (XGBoost), DecisionTree (DT), RandomForest (RF), and Light Gradient-Boosting Machine (LGB) demonstrated maximum accuracy scores of 100<span><math><mtext>%</mtext></math></span>, while AdaBoost (AB), Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) achieved accuracies of 100<span><math><mtext>%</mtext></math></span>, 100<span><math><mtext>%</mtext></math></span>, 96<span><math><mtext>%</mtext></math></span>, and 96<span><math><mtext>%</mtext></math></span>, respectively. In contrast to the most recent model, sophisticated CNN obtained 99.39<span><math><mtext>%</mtext></math></span> accuracy. For ML models, LR requires less processing time compared to others with high accuracy, making it a suitable choice for efficiency-driven applications, while CNN is optimal for neuroimaging-based autism detection. This study also suggests that the choice of model for autism prediction should be based on specific requirements of accuracy and processing time. This study contributes to the field by providing a comprehensive evaluation of current methodologies, guiding future researchers toward more precise and efficient early autism detection strategies.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100275"},"PeriodicalIF":0.0,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the impact of prevention strategies of a fractional order malaria model using Adam–Bashforth approach","authors":"Agnes Adom-Konadu ,&nbsp;Albert Lanor Sackitey ,&nbsp;Joshua Kiddy K. Asamoah ,&nbsp;Martin Anokye ,&nbsp;Emmanuel Donkor","doi":"10.1016/j.fraope.2025.100277","DOIUrl":"10.1016/j.fraope.2025.100277","url":null,"abstract":"<div><div>Even though there is a malaria vaccine for children under-five, malaria continues to be one of the deadly diseases in Sub-Sahara Africa. Nonetheless, there are varieties of preventive measures that, when properly used, can serve as a sort of vaccination and aid in the eradication process. The proportion of persons who must follow the preventive measures (<span><math><mi>π</mi></math></span>) is crucial in the battle to eradicate malaria. In this study, we provide a mathematical model of Caputo fractional order that captures the dynamics of malaria transmission with an emphasis on preventive measures. For the analysis of the model’s solution, the fixed point theorem is utilized to determine the existence and uniqueness of the solution with Ulam–Hyers stability. It has been observed that increasing <span><math><mi>π</mi></math></span> reduces the infected human and vector population. It was proven that a closed community may eventually control or possibly eradicate malaria by reducing both transmission rates and increasing preventive rates. Also, if the preventive strategies campaign is intensified and more than 50% of the human population in contiguous communities in the region acting in concert implement these, then a marked reduction should be seen in the infected vector population leading to a complete eradication of malaria in the region. In order to find the numerical trajectories of the caputo fractional order, the Adam–Bashforth approach scheme is used.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100277"},"PeriodicalIF":0.0,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}