Franklin Open最新文献_第8页

Stability Analysis of Diabetes Mellitus Model in Neutrosophic Fuzzy Environment 中性模糊环境下糖尿病模型的稳定性分析

Franklin Open Pub Date : 2024-09-01 DOI: 10.1016/j.fraope.2024.100144

Ashish Acharya , Animesh Mahata , Manas Karak , Nikhilesh Sil , Supriya Mukherjee , Sankar Prasad Mondal , Banamali Roy

{"title":"Stability Analysis of Diabetes Mellitus Model in Neutrosophic Fuzzy Environment","authors":"Ashish Acharya , Animesh Mahata , Manas Karak , Nikhilesh Sil , Supriya Mukherjee , Sankar Prasad Mondal , Banamali Roy","doi":"10.1016/j.fraope.2024.100144","DOIUrl":"10.1016/j.fraope.2024.100144","url":null,"abstract":"<div><p>Neutrosophic differential equation (NDE) plays a vital role in mathematical modeling on uncertainty during last few years.It is established that NDE comprises a neutrosophic number whose membership function contains three parts like as truth function, indeterministic and falsity functions.In this paper, a diabetes mellitus model has been formulated in neutrosophic fuzzy environment to get more realistic mathematical model. The initial conditions of diabetes system are considered as trapezoidal neutrosophic fuzzy number. The concept of NDE via nuetrosophic generalised derivative of type-1 and type-2 has used in this article. This is the first article in which the stability analysis of diabetes model in neutrosophic environment is introduced. The existence of strong solution and weak neutrosophic solution of the proposed system are employed in the manuscript. Also, we have exposed the comparison of the suggested model between fuzzy environment and neutrosophic environment.The all results, formulas are confirmed graphically and numerically by MATLAB software.</p></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100144"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773186324000744/pdfft?md5=48c5f479ded7b390e168c0eca21195f2&pid=1-s2.0-S2773186324000744-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved YOLOv8 for small traffic sign detection under complex environmental conditions 改进 YOLOv8，在复杂环境条件下检测小型交通标志

Franklin Open Pub Date : 2024-09-01 DOI: 10.1016/j.fraope.2024.100167

Bin Ji, Jiafeng Xu, Yang Liu, Pengxiang Fan, Mengli Wang

{"title":"Improved YOLOv8 for small traffic sign detection under complex environmental conditions","authors":"Bin Ji, Jiafeng Xu, Yang Liu, Pengxiang Fan, Mengli Wang","doi":"10.1016/j.fraope.2024.100167","DOIUrl":"10.1016/j.fraope.2024.100167","url":null,"abstract":"<div><div>Propose an optimized and improved traffic sign detection model based on YOLOv8n, addressing the issues of low accuracy and inaccurate detection, especially in adverse weather conditions, observed in current traditional network models. We leverage existing techniques, including the BoTNet (Bottleneck Transformers for Visual Recognition) module to enhance image classification capabilities, the ODConv (Omni-dimensional Dynamic Convolution) module to supplement attention for improved accuracy, and the LSKA (Large Separable Kernel Attention) module to reduce memory and computational complexity while enhancing small object detection capabilities. Additionally, we employ the WIoU (Wise Intersection over Union) loss function to enhance the model’s generalization performance. Without additional preprocessing to simulate adverse weather conditions, our results on the TT100K dataset, including <span><math><mrow><mi>m</mi><mi>A</mi><mi>P</mi><mn>50</mn></mrow></math></span>, <span><math><mrow><mi>m</mi><mi>A</mi><mi>P</mi><mn>95</mn></mrow></math></span> (mAP is ’mean Average Precision’), and <span><math><mrow><mi>F</mi><mn>1</mn></mrow></math></span>, relative to the original YOLOv8n model, show improvements of 3%, 4%, and 2.5% in misty conditions and 1%, 2.6%, and 1.7% in dark conditions, respectively. On the GTSDB dataset, in misty conditions, the improvements are 5%, 4.2%, and 2.3%, and in dark conditions, the improvements are 3%, 6.6%, and 6%. When 30% of the training set is augmented with fog, the detection performance of the improved model is comparable to, or even exceeds, that of the YOLOv8n model trained with the entire fog-augmented training set. This comprehensive result highlights the significant superiority of our model over the comparative model, demonstrating its practical applicability.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100167"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extending boids for safety-critical search and rescue 为安全关键型搜救工作加长救生艇

Franklin Open Pub Date : 2024-09-01 DOI: 10.1016/j.fraope.2024.100160

Cole Hengstebeck, Peter Jamieson, Bryan Van Scoy

引用次数: 0

Design of a Yokeless double disc axial flux motor for light electric vehicles 设计用于轻型电动汽车的无磁双盘轴向磁通量电机

Franklin Open Pub Date : 2024-09-01 DOI: 10.1016/j.fraope.2024.100166

A.S. Augusto, Ricardo E. Caetano

{"title":"Design of a Yokeless double disc axial flux motor for light electric vehicles","authors":"A.S. Augusto, Ricardo E. Caetano","doi":"10.1016/j.fraope.2024.100166","DOIUrl":"10.1016/j.fraope.2024.100166","url":null,"abstract":"<div><div>One of the main challenges in designing electric vehicles is determining traction system performance, as engine power directly impacts battery size and the engine itself. Therefore, high power density, efficiency, and torque are all essential for vehicle autonomy and performance. This study proposes developing a dual-disc axial synchronous motor for Society of Automotive Engineers (SAE) Formula-type light vehicles. It addresses key motor design parameters such as current, terminal voltage, winding configuration, and reactances. The objective was to meet demands for specific vehicle dynamics, e.g., a wide RPM range, high torque, and dynamic performance. These parameters were validated using finite element simulations, which were then compared to a three-phase induction motor in a simulation software used by the Cheetah vehicle racing E-competition team. The motor designed here aims to provide superior dynamism, especially given the absence of reduction systems, which in turn reduces the losses associated with transmission ratios. This study not only details the dual-disc AFPM engine design process, but also validates it using finite element simulations, and presents justifications for improved dynamic performance and efficiency relative to an engine used in a Formula SAE competition. The side-by-side comparison clearly showed significant gains in speed, and significant reductions in lap time for the motor developed here.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100166"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An efficient local outlier detection approach using kernel density estimation 利用核密度估计的高效局部离群点检测方法

Franklin Open Pub Date : 2024-09-01 DOI: 10.1016/j.fraope.2024.100162

Rakhi, Bhupendra Gupta, Subir Singh Lamba

{"title":"An efficient local outlier detection approach using kernel density estimation","authors":"Rakhi, Bhupendra Gupta, Subir Singh Lamba","doi":"10.1016/j.fraope.2024.100162","DOIUrl":"10.1016/j.fraope.2024.100162","url":null,"abstract":"<div><div>In recent times, outlier detection has played a crucial role in computer networks, fraud detection, and many such applications. Despite adequate research initiatives addressing the topic of finding outliers in datasets, still faces numerous obstacles in establishing an appropriate approach for addressing specific applications of interest. The paper introduces an unsupervised outlier detection method, achieving robust local density estimation through the customization of a nonparametric kernel density evaluation. The identification of outliers involves comparing the local density of each data point with that of its neighbors. Additionally, the proposed method addresses the challenge of manually selecting the parameter for the size of the nearest neighborhood by assigning a predefined value to this parameter. With this predefined value of the parameter, the proposed method demonstrates efficient results, unlike other existing methods that require different values of this parameter for different datasets. To demonstrate the impact of this parameter and evaluate the performance of the proposed method, several assessments were done. The findings prove that the suggested method effectively detects local outliers.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100162"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DC-UNet: Looking for follicles in the ovarian ultrasound images DC-UNet：从卵巢超声图像中寻找卵泡

Franklin Open Pub Date : 2024-09-01 DOI: 10.1016/j.fraope.2024.100149

Manas Sarkar, Ardhendu Mandal, Anil Tudu

{"title":"DC-UNet: Looking for follicles in the ovarian ultrasound images","authors":"Manas Sarkar, Ardhendu Mandal, Anil Tudu","doi":"10.1016/j.fraope.2024.100149","DOIUrl":"10.1016/j.fraope.2024.100149","url":null,"abstract":"<div><p>The auspicious initiation of human reproduction starts by releasing the ovum through ovulation within the ovary. Ceaseless monitoring of the female reproductive organs has now become essential for combating fertility-related issues and for successful assisted reproduction. Cases of infertility and demands for assisted reproduction in our modern liberated society are rapidly increasing. External or Transvaginal ultrasound imaging of the ovary provides us with vital information about the number, size, and position of the follicles in the ovary and their cumulative response to biological stimuli. Manual screening of thousands of USG images having lacs of follicles is an extremely strenuous job and prone to humane error. This paper propounded a new deep-learning architecture named Double Contraction-UNet (DC-UNet) which makes follicle segmentation fully automatic. This model restructured the U-Net architecture by introducing two contracting paths to segment the follicular object with higher accuracy. The model was trained and tested on approximately forty two thousand annotated ovarian 2D ultrasonography images extracted from USOVA3D Training Set 1. The proposed model outperforms the other U-Net-based state-of-the-art models when trained and tested on the same dataset. The proposed model has achieved an accuracy rate of 97.82%, a precision rate of 97.54%, a Recall value of 94.34%, an F1 Score of 95.91%, a Dice Score of 0.76, and a Jaccard Similarity Index of 0.59.</p></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100149"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773186324000793/pdfft?md5=cd880a0b9110332141668316df826366&pid=1-s2.0-S2773186324000793-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142128827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An energy efficient routing establishment (EERE) mechanism for MANET-IoT security 面向城域网-物联网安全的节能路由建立（EERE）机制

Franklin Open Pub Date : 2024-09-01 DOI: 10.1016/j.fraope.2024.100150

V. Anjana Devi , Vithya Ganesan , V. Sri Anima Padmini , Shriman k.arun

{"title":"An energy efficient routing establishment (EERE) mechanism for MANET-IoT security","authors":"V. Anjana Devi , Vithya Ganesan , V. Sri Anima Padmini , Shriman k.arun","doi":"10.1016/j.fraope.2024.100150","DOIUrl":"10.1016/j.fraope.2024.100150","url":null,"abstract":"<div><p>Providing security to the Mobile Ad-hoc Network (MANET) integrated with the Internet of Things (IoT) is one of the most challenging and demanding tasks in improving the security of the network by detecting trust nodes in the path. High energy consumption, loss of data packets, and routing overheads are unsolvable issues in the existing security-aware routing methodologies. Energy Efficient Routing Establishment (EERE) scheme invokes hybrid whale positions integrated with the Flower Pollination Algorithm (WP-FPA) for optimal path allocation by calculating the energy level of nodes in the network. In addition, to ensure the security of the network, a trust evaluation factor has been performed to estimate the trust value for each participating node on the network by Aggregated Packet Control Trust Protocol (APCTP) . Security is improved by mitigating nodes in the path that are identified and it is eliminated by the trust evaluation factor and the node's neighboring information. The performance of the proposed security-based routing mechanisms is compared with metrics such as average residual energy, packet delivery ratio (PDR), packet delay, and network lifetime. The proposed EERE-APCTP mechanisms improve residual energy to 80% in stipulated node mobility. The packet delivery ratio is improved to 99% and network lifetime is increased to 265 milliseconds. Routing overhead is reduced to 0.6%.</p><p><em>Index Terms</em>— Whale Positions Integrated Flower Pollination Algorithm (WP-FPA), Mobile Ad-hoc security, Energy Efficient Routing Establishment (EERE), Trust Evaluation, MANET security, pollination optimization, Neighbor node feedback behavior.</p></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100150"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277318632400080X/pdfft?md5=6c8409bf1b3f4d64f1043766008010ac&pid=1-s2.0-S277318632400080X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fault-tolerant controller design with connectivity maintenance in fractional-order multi-agent systems using a super-twisting sliding mode algorithm 使用超扭曲滑模算法，在分数阶多代理系统中设计具有连通性维护功能的容错控制器

Franklin Open Pub Date : 2024-09-01 DOI: 10.1016/j.fraope.2024.100156

Farshid Aazam Manesh , Mahdi Pourgholi , Elham Amini Boroujeni

{"title":"Fault-tolerant controller design with connectivity maintenance in fractional-order multi-agent systems using a super-twisting sliding mode algorithm","authors":"Farshid Aazam Manesh , Mahdi Pourgholi , Elham Amini Boroujeni","doi":"10.1016/j.fraope.2024.100156","DOIUrl":"10.1016/j.fraope.2024.100156","url":null,"abstract":"<div><p>This article explores fault analysis in distributed fractional multi-agent systems, specifically addressing fault detection in nonlinear fractional-order multi-agent systems by introducing a sliding surface. To achieve fault detection, we employ a super-twisting sliding mode observer designed for accurate fault estimation within individual agents. Building on this, we present a new fractional-order sliding surface accompanied by a potential function, strategically designed to ensure connectivity maintenance. Drawing on the estimated fault and the fractional-order sliding surface, we propose a fault-tolerant controller. This controller operates within a finite time frame, simultaneously guaranteeing fault tolerance and connectivity maintenance. To demonstrate the effectiveness of our approach, we conduct simulations on three numerical examples. These examples represent heterogeneous systems with both directed and undirected graph topologies, showcasing the versatility and applicability of our proposed methodology.</p></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100156"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773186324000860/pdfft?md5=b18077dbc36c346f12814081d367e816&pid=1-s2.0-S2773186324000860-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of shifted weibull distribution parameters using optimization algorithms for optimal investment decisions making 利用优化算法估算移位威布尔分布参数，优化投资决策

Franklin Open Pub Date : 2024-09-01 DOI: 10.1016/j.fraope.2024.100152

Hamza Abubakar , Masnita Misiran , Amani Idris A. Sayed

{"title":"Estimation of shifted weibull distribution parameters using optimization algorithms for optimal investment decisions making","authors":"Hamza Abubakar , Masnita Misiran , Amani Idris A. Sayed","doi":"10.1016/j.fraope.2024.100152","DOIUrl":"10.1016/j.fraope.2024.100152","url":null,"abstract":"<div><p>This study examines the estimation of parameters for the Shifted Weibull Distribution (SWD) using several robust metaheuristic algorithms, with a focus on enhancing precision and reliability in investment data analysis. Utilizing investment return data from the Malaysian property sector, we evaluate the performance of five metaheuristic models: Election Algorithm (EA), Artificial Dragonfly Algorithm (ADA), Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The evaluation criteria include Bayesian Information Criterion (BIC), Root Mean Squared Error (RMSE), and accuracy. Results reveal that EA consistently outperforms other models, achieving the lowest BIC value of 147.2 and an impressive accuracy rate of 94.90% at a sample size of 1,000. The Genetic Algorithm (GA) shows the lowest RMSE of 0.99, indicating strong predictive performance. Tukey's HSD test highlights significant accuracy variations among the models, with EA and GA notably outperforming ACO and DE. However, RMSE and BIC metrics do not demonstrate clear variations among the models. These findings underscore the superior performance of the EA model in the context of SWD parameter estimation, making it the preferred choice for modeling investment return data. Future research should explore additional factors influencing model performance and validate these models with diverse real-world datasets to further enhance their applicability in financial decision-making.</p></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100152"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773186324000823/pdfft?md5=d7f79d25aefd2433b5b97d767eb84071&pid=1-s2.0-S2773186324000823-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning implementation using convolutional neural network in inorganic packaging waste sorting 利用卷积神经网络在无机包装废弃物分拣中实现深度学习

Franklin Open Pub Date : 2024-09-01 DOI: 10.1016/j.fraope.2024.100146

Pringgo Widyo Laksono, Anisa Anisa, Yusuf Priyandari

{"title":"Deep learning implementation using convolutional neural network in inorganic packaging waste sorting","authors":"Pringgo Widyo Laksono, Anisa Anisa, Yusuf Priyandari","doi":"10.1016/j.fraope.2024.100146","DOIUrl":"10.1016/j.fraope.2024.100146","url":null,"abstract":"<div><p>Municipal solid waste is a significant issue that causes environmental contamination. One of the most prevalent wastes that is difficult to decompose is waste from inorganic packaging. Inorganic packaging waste management can be done by sorting waste as the first step before going through subsequent processing. However, waste sorting is currently still difficult to do by human power in waste management facilities, so it is necessary to design a system that can assist the waste sorting process. This research aims to develop a model that can classify inorganic packaging at waste processing sites. To develop the model, we used five pre-trained Convolutional Neural Network (CNN) architectures, namely Xception, Inception V3, ResNet-50, Resnet-50 V2, and DenseNet-201. Then, the best architecture based on some metric performances will be tuned. The result displayed that the CNN model with Densenet 201 architecture, accompanied by tuning, achieved the best performance to classify the waste. The accuracy for the validation dataset is 95.31 %, the accuracy for the testing dataset is 95.6 %, precision is 0.96, recall is 0.96, and the F1-score is 0.96. The results of those performance metrics show that the model can predict the image of inorganic packaging waste well for further application to an automated waste sorting system.</p></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100146"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773186324000768/pdfft?md5=2854d37e60b253fbb6685f1ec8da16f6&pid=1-s2.0-S2773186324000768-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0