检索结果-内蒙古大学图书馆

4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Akanto, Tanjum Rahi Roy, Naruttam Kumar Dept. of Electrical and Electronic Engineering Khulna Bangladesh

ISBN: (纸本)9798331530938

Electrical fault detection and classification is crucial for the safety, stability and reliability of the power system. Accurate classification helps to identify fault types, enabling faster restoration of service. In this research work, a model is proposed based on support vector classification (SVC), with significantly higher evaluation metrics compared to other existing model for transmission line fault classification. After optimizing the hyperparameters of support vector classification (SVC), a 99.3% accuracy rate was achieved. A cross-comparison of the evaluation metrics for different algorithm has been presented to validate the selection of SVC over other algorithms. Also, the evaluation metrics of SVC, both before and after hyperparameter optimization, are compared and analyzed to justify the effectiveness of hyperparameter tuning in SVC. © 2025 IEEE.

关键词： Electrical Fault Hyperparameter Tuning Multiclass Classification Performance Metrics support vector classifier

来源：评论

学校读者我要写书评

暂无评论

MoSFPAD: An end-to-end ensemble of MobileNet and support vector classifier for fingerprint presentation attack detection

引用

COMPUTERS & SECURITY 2025年 148卷

作者： Rai, Anuj Dey, Somnath Patidar, Pradeep Rai, Prakhar Indian Inst Technol Indore Dept Comp Sci & Engn Indore 453552 Madhya Pradesh India

Automatic fingerprint recognition systems are the most extensively used systems for person authentication although they are vulnerable to Presentation attacks. Artificial artifacts created with the help of various materials are used to deceive these systems causing a threat to the security of fingerprint-based applications. This paper proposes a novel end-to-end model to detect fingerprint Presentation attacks. The proposed model incorporates MobileNet as a feature extractor and a support vector classifier as a classifier to detect presentation attacks in cross-material and cross-sensor paradigms. The feature extractor's parameters are learned with the loss generated by the support vector classifier. The proposed model eliminates the need for intermediary data preparation procedures, unlike other static hybrid architectures. The performance of the proposed model has been validated on benchmark LivDet 2011, 2013, 2015, 2017, and 2019 databases, and overall accuracy of 98.64%, 99.50%, 97.23%, 95.06%, and 95.20% are achieved on these databases, respectively. The performance of the proposed model is compared with state-of-the-art methods and is able to reduce the average classification error of 3.63%, 1.86%, 1.83%, 0.05%, 0.93% on LivDet 2011, 2013, 2015, 2017, and 2019 databases, respectively for same and cross material protocols in intra-sensor paradigm. The proposed method also reduced the average classification error of 1.59%, 1.41%, and 2.29% for LivDet 2011, 2013, and 2017 databases, respectively for the cross-sensor paradigm. It is evident from the results that the proposed method outperforms state-of-the-art methods in intra-sensor as well as cross-sensor paradigms in terms of average classification error.

关键词： Fingerprint Biometrics Presentation Attack Detection Hybrid Architecture support vector classifier Deep-learning

来源：评论

学校读者我要写书评

暂无评论

support vector classifier based on principal component analysis

引用

Journal of Systems Engineering and Electronics 2008年第1期19卷 184-190页

作者： Zheng Chunhong Jiao Licheng Li Yongzhao School of Electronic Engineering Xidian Univ. Xi'an 710071 P. R. China

support vector classifier （SVC） has the superior advantages for small sample learning problems with high dimensions, with especially better generalization ability. However there is some redundancy among the high dimensions of the original samples and the main features of the samples may be picked up first to improve the performance of SVC. A principal component analysis （PCA） is employed to reduce the feature dimensions of the original samples and the pre-selected main features efficiently, and an SVC is constructed in the selected feature space to improve the learning speed and identification rate of SVC. Furthermore, a heuristic genetic algorithm-based automatic model selection is proposed to determine the hyperparameters of SVC to evaluate the performance of the learning machines. Experiments performed on the Heart and Adult benchmark data sets demonstrate that the proposed PCA-based SVC not only reduces the test time drastically, but also improves the identify rates effectively.

关键词： support vector classifier principal component analysis feature selection genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Rockburst prediction in kimberlite with unsupervised learning method and support vector classifier

引用

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2019年 90卷 12-18页

作者： Pu, Yuanyuan Apel, Derek B. Xu, Huawei Univ Alberta Sch Min & Petr Engn Edmonton AB Canada

One of the most serious types of mining disasters in many countries, rockburst leads to injuries, deaths, and damages to facilities, which explains the need to study its prediction. However, due to highly non-linear relationship between the occurrence of rockburst and burst impact factors, traditional mechanism-based prediction methods cannot generate precise results. This paper employed a support vector classifier (SVC) to predict rockburst in kimberlite pipes at a diamond mine. We collected 246 groups of data based on real rockburst cases from all over the world as a supportive database. A novel dimensionality reduction method, t-SNE, helped to reduce relevance of original data attributes, and then, an unsupervised learning method (clustering) was adopted to relabel original data to determine relative intensity of these rockburst cases. After the processed prediction data was fed into the trained SVC model, the prediction results were obtained, which matched real rockburst cases that recently occurred at this mine. This data-driven prediction method can be easily conducted and does not rely on the discussion of rockburst mechanism, which has wide potential applications in rockburst prediction in engineering.

关键词： Rockburst prediction t-SNE Unsupervised learning support vector classifier

来源：评论

学校读者我要写书评

暂无评论

Switching impulse discharge voltage prediction of EHV and UHV transmission lines-tower air gaps by a support vector classifier

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第15期12卷 3711-3717页

作者： Qiu, Zhibin Ruan, Jiangjun Jin, Qi Wang, Xuezong Huang, Daochun Shu, Shengwen Wuhan Univ Sch Elect Engn Wuhan 430072 Hubei Peoples R China State Grid Fujian Elect Power Co Ltd Elect Power Res Inst Fuzhou 350007 Fujian Peoples R China

Discharge voltage prediction of practical air gaps in transmission projects is a long-sought goal and also a great challenge in high-voltage (HV) engineering. An approach combined electric field simulation, feature extraction and machine learning algorithm is presented in this study to predict the switching impulse discharge voltages of extra-HV (EHV) and ultra-HV (UHV) transmission lines-tower air gaps. Some features extracted from the electrostatic field distribution are used to characterise the air-gap configuration and taken as input parameters of a prediction model established by a support vector classifier (SVC). Three kinds of actual gap configurations in EHV and UHV transmission lines are taken as test samples to validate the validity of the SVC model. Trained by experimental data of rod-plane gaps and one of the engineering gap configurations, this model is able to predict the discharge voltages of the other two conductor-tower gaps with acceptable accuracy. The mean absolute percentage errors of the three prediction results are 6.84, 4.19 and 3.46%. This research demonstrates the feasibility of discharge voltage prediction for complicated engineering gaps, which is useful to reduce the costly full-scale tests and helpful to guide the external insulation design.

关键词： air gaps learning (artificial intelligence) feature extraction support vector machines discharges (electric) power transmission lines poles and towers prediction theory overhead line conductors power engineering computing feature extraction machine learning algorithm electrostatic field distribution support vector classifier engineering gap configurations conductor-tower gaps high-voltage engineering electric field simulation rod-plane gaps switching impulse discharge voltage prediction model HV engineering UHV transmission lines-tower air gaps EHV transmission lines-tower air gaps ultraHV transmission lines-tower air gaps extraHV transmission lines-tower air gaps SVC mean absolute percentage errors external insulation design

来源：评论

学校读者我要写书评

暂无评论

Typhoon-induced slope collapse assessment using a novel bee colony optimized support vector classifier

引用

NATURAL HAZARDS 2015年第3期78卷 1961-1978页

作者： Cheng, Min-Yuan Nhat-Duc Hoang Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn Keelung Taiwan Duy Tan Univ Inst Res & Dev Fac Civil Engn Da Nang Vietnam

This research proposes a novel bee colony optimized support vector classifier (BeeSVC) for predicting typhoon-induced slope collapses. The BeeSVC employs the support vector classifier (SVC) as a machine learning method to classify a slope into two classes: "stable slope" and "collapsed slope." Furthermore, the artificial bee colony algorithm is used as a metaheuristic to determine the hyper-parameters of the SVC appropriately. The contribution of the proposed method to the body of knowledge is multifold. First, the combined framework of the BeeSVC allows the assessment process to be operated automatically. Second, since the number of the "collapsed" class occupied more than 70 % of the historical cases, a repeated random sub-sampling procedure with the Student's t test is put forward to alleviate the class-imbalanced problem and reliably evaluate the model performance. Third, the mutual information between the input features and the slope performance is employed to reflect the contribution of each feature to the slope failure. Lastly, the superior performance has proved that the BeeSVC can be a very effective tool for decision-makers to forecast typhoon-induced slope collapses.

关键词： support vector classifier Artificial bee colony Machine learning Typhoon-induced slope collapse

来源：评论

学校读者我要写书评

暂无评论

Color-Base Damage Feature Enhanced support vector classifier for Monitoring Quake Image 7th

Color-Base Damage Feature Enhanced Support Vector Classifier...

引用

7th International Computational Color Imaging Workshop (CCIW)

作者： Yasuno, Takato Amakata, Masazumi Fujii, Junichiro Shimamoto, Yuri Yachiyo Engn Co Ltd Res Inst Infrastruct Paradigm Shift Taito Ku CS Tower5-20-8 Asakusabashi Tokyo 1118648 Japan

ISBN: (纸本)9783030139407;9783030139391

In recent times, significant natural disasters have affected our city lives. This includes the occurrence of large earthquakes that have devastated the city's infrastructure. During such times of crisis, it is important that emergency response times be as quick as possible to mitigate harm and loss. To achieve this, priority must be given to the various types of initial emergency response. Color image monitoring has the potential to prioritize responses. It uses multimode data resources such as openly sourced photos on social media, smartphone cameras, CCTV, and so forth. This paper presents a method to enhance the damaged color features extracted based on a pre-trained deep architecture DenseNet-201 in order to classify damage caused by several earthquakes, whose classifiers are Bayesian optimized to minimize the loss function with cross-validation. This method is applied to a case study of an earthquake image dataset. The study incorporates five target categories, namely bridge collapse, initial smoke and fire, road damage with accident risk to expand secondary loss for relevant users, tsunami damage, and non-disaster. Some advantages have been found when using color feature extraction for monitoring quake damage and further opportunities are remarked (189 words).

关键词： Quake image monitoring Color-base augmentation DenseNet-201 Damage feature enhancement support vector classifier

来源：评论

学校读者我要写书评

暂无评论

Smart Nursery for Smart Cities: Infant Sound Classification Based on Novel Features and support vector classifier 7

Smart Nursery for Smart Cities: Infant Sound Classification ...

引用

7th International Conference on Electrical and Electronics Engineering (ICEEE)

作者： Mahmoud, Ayyah Abdulhafith Alawadh, Intessar Nasser A. Latif, Ghazanfar Alghazo, Jaafar Prince Mohammad Bin Fahd Univ Comp Engn Dept Al Khobar Saudi Arabia Prince Mohammad Bin Fahd Univ Dept Comp Sci Al Khobar Saudi Arabia

ISBN: (纸本)9781728167886

In the age of smart cities, it is envisioned that most processes within the smart city context will be smart and automated. This includes smart houses, smart kitchens, etc. within this context, a need will arise for Smart nursery rooms. Within the smart nursery concept, the infant needs will need to be fulfilled automatically, in addition, to infant monitoring and safety. The motivation of this work is to design a smart cradle system for a smart nursery room that automates the functions of the cradle based on the infant's sounds. Therefore, in this paper, we propose an infant sound classification technique based on the support vector classifier (SVC) with Radial Basis Function (RBF) kernel using 18 extracted features of infant sounds. The proposed technique has been compared with two SVC kernel function, linear, and poly, as well as other classification algorithms including Decision Tree, Random Forest, and Gaussian Naive Bayes. As a result of comparing the confusion matrix, recall, F1 Score, accuracies, and precision values of various applied machine learning algorithms over-extracted features. SVC using RBF kernel function was found to be the most efficient model with an average accuracy of more than 96%. The proposed system outperforms all other systems proposed in the previous literature.

关键词： Baby crying Detection Smart Nursery Smart Cities support vector classifier Decision Tree Random Forest

来源：评论

学校读者我要写书评

暂无评论

Bearing Fault Diagnosis Using support vector classifier Based on Sine Cosine Algorithm 33

Bearing Fault Diagnosis Using Support Vector Classifier Base...

引用

33rd Chinese Control and Decision Conference (CCDC)

作者： Li, Sai Jiao, Rui Ding, Zhixia Wang, Liheng Ye, Xuan Wuhan Inst Technol Sch Elect & Informat Engn Wuhan 430205 Peoples R China CSSC Res Inst 722 Wuhan 430205 Peoples R China

ISBN: (纸本)9781665440899

In order to ensure the normal operation of rotating machinery, it is necessary and important to carry out fault diagnosis of rolling bearings. This paper proposes a fault diagnosis algorithm for rolling bearings, which is based on singular value decomposition with traditional empirical mode decomposition, and support vector classifier with sine cosine algorithm. First, empirical mode decomposition is utilized to characterize the complexity of vibration signals. Furthermore, singular value decomposition is applied to extract the fault feature. Subsequently, support vector classifier with sine cosine algorithm is proposed for fault recognition under various conditions. The performance of the proposed method has been verified by its successful application in rolling bearings experiments. Compared with the existing methods, this approach can detect bearings faults effectively, and improve the classification efficiency.

关键词： Fault diagnosis Rolling bearing support vector classifier Sine cosine algorithm

来源：评论

学校读者我要写书评

暂无评论

Identification of power quality disturbances based on improved TT transform and support vector classifier 8

Identification of power quality disturbances based on improv...

引用

8th IEEE International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia)

作者： Zhao, Luoyin Liu, Xiaobin Fu, Guangjie Northeast Petr Univ Coll Elect Informat Engn Daqing 163318 Peoples R China

ISBN: (纸本)9781509012107

Power quality disturbances (PQDs) identification is a crucial procedure in ameliorating the power quality performance for utilities and power consumers. The improved TT transform is proposed in this paper to extract the feature information of 5 typical power quality disturbance signals (e.g. voltage swell, sag, interruption, harmonics, oscillation). Two tuning parameters r and m are introduced in TT transform to improve the energy concentration and resolution as well. In accordance with the feature information, the categories of PQDs are identified intelligently by the support vector classifier (SVC). Ultimately, compared with back propagation (BP) network, the classification accuracy of SVC demonstrates preferable performance, which verifies the proposed method an elegant approach in PQDs identification.

关键词： Power quality identification S transform TT transform support vector classifier BP network

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：