检索结果-内蒙古大学图书馆

A New Comparative Approach Based on Features of Subcomponents and Machine Learning Algorithms to Detect and Classify Power Quality Disturbances

引用

Electric Power Components and Systems 2024年第8期52卷 1269-1292页

作者： Akkaya, Sıtkı Yüksek, Emre Akgün, Hasan Metehan Department of Electrical and Electronics Engineering of Faculty of Engineering and Natural Sciences Sivas University of Science and Technology Sivas Turkey Department of Computer Engineering of Faculty of Engineering and Natural Sciences Sivas University of Science and Technology Sivas Turkey Department of Aircraft Technology of Sivas Vocational School Sivas University of Science and Technology Sivas Turkey

Current measurement systems based on the IEEE-1159 standard have some limitations and robustness problems under noisy and fast-changing conditions. Besides, applying different methods for each Power Quality Disturbance (PQD) to every window is required but time-consuming and not feasible. Therefore, different kinds of two-stage methods, Detection and Classification (D&C), have been improved in many studies. Then, the required measurement can be performed to define disturbance. For this purpose, a new approach based on features of subcomponents with Machine Learning Algorithms (MLAs) to detect and classify PQDs is proposed. 21-class dataset including single and multiple PQDs under different noisy conditions was prepared randomly. Of this dataset, determined features were extracted and some of these were selected. Then, selected features were trained and tested with some MLAs in a workstation. Results obtained from comparative MLAs and the other classification methods show that the best MLA with related features is Random Forest with 96.97% while LightGBM, k-Nearest Neighbors, and XGBoost 96.85%, 96.73%, and 92.82% accuracy, respectively. Because the selected features, optimized parameters, and the related MLA were obtained by investigating for features provided from the PQDs in the whole parameter space, this approach brings the advantages of high accuracy, low D&C complexity, and computing load. © 2023 Taylor & Francis Group, LLC.

关键词： Power quality

来源：评论

学校读者我要写书评

暂无评论

Beyond p-y method:A review of artificial intelligence approaches for predicting lateral capacity of drilled shafts in clayey soils

引用

Journal of Rock Mechanics and Geotechnical engineering 2024年第9期16卷 3812-3840页

作者： M.E.Al-Atroush A.E.Aboelela Ezz El-Din Hemdan Civil and Environmental Engineering Program Engineering Management DepartmentCollege of EngineeringPrince Sultan UniversityRiyadhSaudi Arabia Department of Structural Engineering Faculty of EngineeringAin Shams UniversityCairoEgypt Structure and Materials Research Lab Prince Sultan UniversityRiyadhSaudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt

In 2023,pivotal advancements in artificial intelligence(AI)have significantly *** that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled *** study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these ***,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter *** extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this *** paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.

关键词： Laterally loaded drilled shaft load transfer and failure mechanisms Physics-informed neural networks(PINNs) P-y curves Artificial intelligence(AI) Dataset

来源：评论

学校读者我要写书评

暂无评论

Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

引用

Neutrosophic Sets and Systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

A Fuzzy Multi-Objective Framework for Energy Optimization and Reliable Routing in Wireless Sensor Networks via Particle Swarm Optimization

引用

computers, Materials & Continua 2025年第5期83卷 2773-2792页

作者： Medhat A.Tawfeek Ibrahim Alrashdi Madallah Alruwaili Fatma M.Talaat Department of Computer Science College of Computer and Information SciencesJouf UniversitySakaka72341AljoufSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72341AljoufSaudi Arabia Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33516Egypt Faculty of Computer Science&Engineering New Mansoura UniversityGamasa35712Egypt

Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past *** work has been put into its development in various aspects such as architectural attention,routing protocols,location exploration,time exploration,*** research aims to optimize routing protocols and address the challenges arising from conflicting objectives in WSN environments,such as balancing energy consumption,ensuring routing reliability,distributing network load,and selecting the shortest *** optimization techniques have shown success in achieving one or two objectives but struggle to achieve the right balance between multiple conflicting *** address this gap,this paper proposes an innovative approach that integrates Particle Swarm Optimization(PSO)with a fuzzy multi-objective *** proposed method uses fuzzy logic to effectively control multiple competing objectives to represent its major development beyond existing methods that only deal with one or two *** search efficiency is improved by particle swarm optimization(PSO)which overcomes the large computational requirements that serve as a major drawback of existing *** PSO algorithm is adapted for WSNs to optimize routing paths based on fuzzy multi-objective *** fuzzy logic framework uses predefined membership functions and rule-based reasoning to adjust routing *** adjustments influence PSO’s velocity updates,ensuring continuous adaptation under varying network *** proposed multi-objective PSO-fuzzy model is evaluated using NS-3 *** results show that the proposed model is capable of improving the network lifetime by 15.2%–22.4%,increasing the stabilization time by 18.7%–25.5%,and increasing the residual energy by 8.9%–16.2% compared to the state-of-the-art *** proposed model also achieves a 15%–24% reduction in load variance,demonstrating balanced routing and extended net

关键词： Wireless sensor networks particle swarm optimization fuzzy multi-objective framework routing stability

来源：评论

学校读者我要写书评

暂无评论

Surface Functionalized Plasmonic Sensors for Uric Acid Detection With Gold-Graphene Stacked Nanocomposites

引用

Photonic Sensors 2025年第1期15卷 122-137页

作者： Olabisi Abdullahi ONIFADE Dinie Dayana MOHAMAD AZRI Muhammad Hafiz ABU BAKAR Mohammed Thamer ALRESHEEDI Eng Khoon NG Mohd Adzir MAHDI Ahmad Shukri MUHAMMAD NOOR Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang 43400Malaysia Wireless and Photonics Research Centre of Excellence Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Department of Electrical Engineering College of EngineeringKing Saud UniversityRiyadh 11421Kingdom of SaudiArabia Department of Engineering University of CambridgeCambridge CB30FAThe United Kingdom Institute of Nanoscience and Nanotechnology(ION2) Universiti Putra MalaysiaSerdang 43400Malaysia

This study presented a surface-functionalized sensor probe using 3-aminopropyltriethoxysilane(APTES)self-assembled monolayers on a Kretschmann-configured plasmonic *** probe featured stacked nanocomposites of gold(via sputtering)and graphene quantum dots(GQD,via spin-coating)for highly sensitive and accurate uric acid(UA)detection within the physiological *** encompassed the field emission scanning electron microscopy for detailed imaging,energy-dispersive X-ray spectroscopy for elemental analysis,and Fourier transform infrared spectroscopy for molecular *** functionalization increased sensor sensitivity by 60.64%,achieving 0.0221°/(mg/dL)for the gold-GQD probe and 0.0355°/(mg/dL)for the gold-APTES-GQD probe,with linear correlation coefficients of 0.8249 and 0.8509,*** highest sensitivity was 0.0706°/(mg/dL),with a linear correlation coefficient of 0.993 and a low limit of detection of 0.2 mg/***,binding affinity increased dramatically,with the Langmuir constants of 14.29μM^(-1)for the gold-GQD probe and 0.0001μM^(-1)for the gold-APTES-GQD probe,representing a 142900-fold *** probe demonstrated notable reproducibility and repeatability with relative standard deviations of 0.166%and 0.013%,respectively,and exceptional temporal stability of 99.66%.These findings represented a transformative leap in plasmonic UA sensors,characterized by enhanced precision,reliability,sensitivity,and increased surface binding capacity,synergistically fostering unprecedented practicality.

关键词： Plasmon nanocomposite gold-graphene quantum dot uric acid,functionalize sensor

来源：评论

学校读者我要写书评

暂无评论

A new method for promoting the detection of breast cancer in thermograms: applying deep autoencoders for eliminating redundancies in parallel with preserving independent components

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第12期15卷 4085-4099页

作者： Shojaedini, Seyed Vahab Bahramzadeh, Bahram Biomedical Engineering Department Iranian Research Organization for Science & ampTechnology Tehran Iran Department of Computer Engineering Faculty of Engineering Islamic Azad University E-Campus Tehran Iran

Although thermography has been proposed over the past decade as an effective method for breast cancer diagnosis, the complexity of thermograms presents a significant obstacle, making their interpretation challenging. Recently, deep learning has emerged as a promising framework for analyzing breast thermograms. However, the performance of deep neural networks heavily relies on the volume of training images, and the limited number of labeled thermograms necessitates the generation of synthetic images for training. This article introduces a novel method that eliminates redundant or destructive information in synthetic thermograms while preserving essential and independent features using deep autoencoder concepts. Consequently, the proposed strategy enhances the representation of synthetic images for training deep networks, leading to improved breast cancer detection in thermograms. Evaluating the performance of the proposed method on the DMR-IR dataset shows a significant improvement in detecting cancerous breasts in thermograms compared to benchmark methods. Integrating this innovation into the basic model achieved an average accuracy of 92.3%, sensitivity of 93%, and specificity of 91.4%, surpassing the basic model’s 89.1%, 86%, and 92.5%, respectively. The proposed method demonstrated superior performance in mitigating overfitting, as evidenced by a smaller gap between training and validation curves, resulting in a 7% improvement in accuracy and a 3.2% increase in sensitivity. Although there was a slight decrease of 1.1% in specificity, it was outweighed by other parameter improvements. Additionally, the method led to a significant reduction in the dispersion range of test results, approximately six times less than alternative approaches. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Modelling and simulation of energy management of power-split hybrid electric vehicles using the discrete EVent system specification

引用

International Journal of Vehicle Systems Modelling and Testing 2024年第1期18卷 33-61页

作者： Maatoug, Abdelfettah Kharoubi, Sahraoui Maaskri, Moustafa Belalem, Ghalem Mahmoudi, Said Faculty of Applied Sciences Science and Technology Department University of Tiaret Tiaret14000 Algeria Faculty of Mathematics and Computer Science Computer Science Department University of Tiaret Tiaret14000 Algeria Faculty of Applied Sciences Electrical Engineering Department University of Tiaret Tiaret14000 Algeria Faculty of Exact and Applied Sciences Computer Science Department University of Oran 1 Ahmed Ben Bella Oran31000 Algeria Faculty of Engineering Computer Science Department University of Mons Mons7000 Belgium

This work provides a basis for studying energy management optimisation in power-split hybrid electric vehicles (PSHEVs) to reduce fuel consumption and increase powertrain efficiency by enforcing a strategy related to battery level and vehicle speed. Three modes were used to operate the vehicle to meet different situations related to available energy and desired speed. These are particularly abundance mode, ascetic mode and optimal mode. For this, a system was designed based on several sub-models written in the discrete EVent system (DEVS) specification. The vehicle's power train and control strategy models were created in the DEVSimPy environment, and a joint simulation was carried out from the composite sub models until the overall model is reached. Simulation results under several driving cycles showed that the proposed system improves the energy consumption of the vehicle in different operating modes of the engines and gives more opportunities to adopt electric motors in vehicle transmission. © 2024 Inderscience Enterprises Ltd.

关键词： Energy management

来源：评论

学校读者我要写书评

暂无评论

An Improved Farmland Fertility Algorithm with Hyper-Heuristic Approach for Solving Travelling Salesman Problem

引用

computer Modeling in engineering & sciences 2023年第6期135卷 1981-2006页

作者： Farhad Soleimanian Gharehchopogh Benyamin Abdollahzadeh Bahman Arasteh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmiaIran Department of Software Engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbulTurkey

Travelling Salesman Problem(TSP)is a discrete hybrid optimization problem considered *** aims to discover the shortest Hamilton route that visits each city precisely once and then returns to the starting point,making it the shortest route *** paper employed a Farmland Fertility Algorithm(FFA)inspired by agricultural land fertility and a hyper-heuristic technique based on the Modified Choice Function(MCF).The neighborhood search operator can use this strategy to automatically select the best heuristic method formaking the best ***-Kernighan(LK)local search has been incorporated to increase the efficiency and performance of this suggested approach.71 TSPLIB datasets have been compared with different algorithms to prove the proposed algorithm’s performance and *** results indicated that the proposed algorithm outperforms comparable methods of average mean computation time,average percentage deviation(PDav),and tour length.

关键词： Travelling salesman problem optimization farmland fertility optimization algorithm Lin-Kernighan

来源：评论

学校读者我要写书评

暂无评论

SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

引用

Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Mutual character dialogue generation with semi-supervised multitask learners and awareness

引用

International Journal of Information Technology (Singapore) 2024年第3期16卷 1357-1363页

作者： Ahrari Khalaf, Ayesheh Hassan Abdalla Hashim, Aisha Olowolayemo, Akeem Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia Department of Computer Science Faculty of Information and Communication Technology International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia

Consistent efforts have been ongoing to improve the friendliness and reliability of informal dialogue systems. However, most research focuses solely on mimicking human-like answers. Therefore, the interlocutors’ awareness features of the dialogue system are left unexplored. Meanwhile, cognitive science research reveals that awareness is a crucial indicator of an effective, high-quality informal conversation. This research aims to boost the quality of the conversational generation system by factoring in awareness of the interlocutors in the design and training of the dialogue system model. The Generative Pre-Trained Transformer-2 (GPT-2) model was implemented into the Persona Perception (P²) Bot to achieve the objectives of this study. This was to precisely develop model's understanding, P² Bot was implemented using a transmitter–receiver-based structure. The P² Bot leverages mutual persona awareness to improve the quality of customized dialogue generation. GPT-2 is a 1.5B parameter transformer model that produces state-of-the-art accuracy in a zero-shot setting on seven of the eight evaluated language modeling datasets. The observations of the proposed model on a sizable open-source dataset, PERSONA-CHAT, proved successful, with improvement above the state-of-the-art baselines in both automatic measures and human assessments. The model has achieved 82.2% accuracy on Hits@1 performance metrics in the original data and 68.8% on the revised data. On the human evaluation, the model scored an average of 2.66, pointing out that the responses provided were coherent and informative. A dialogue generation model with character and awareness which can communicate like an informative human expert was introduced. This study presents the submerging of GPT-2 model on a mutual persona perception dialogue generating model. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cognitive science Conversational agent Dialogue generation Generative Pre-trained Transformer 2 (GPT-2) Natural language understanding (NLU)

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：