检索结果-内蒙古大学图书馆

Prediction and control of Hopf bifurcation in a large-scale PV grid-connected system based on an optimised support vector machine

引用

JOURNAL OF ENGINEERING-JOE 2017年第14期2017卷 2666-2671页

作者： Li, Sheng Wei, Zhinong Ma, Yanan Cheng, Jielei Hohai Univ Coll Energy & Elect Engn Nanjing Peoples R China Nanjing Inst Technol Sch Elect Power Engn Nanjing Peoples R China

The unstable Hopf bifurcation (UHB) of a power system generally precedes the saddle-node bifurcation, so the UHB point can be the system critical point of voltage collapse. However, after the large-scale photovoltaic (PV) power station is connected to the system, the fluctuation characteristic of PV output can lead to the change of UHB point position and the evaluation process of system voltage stability is also affected. Therefore, according to the feature that the UHB point has the minimum value of hopf bifurcation index (HBI) index, a method for predicting the equilibrium HBI value is proposed based on an optimised support vector machine (svm) algorithm (including particle swarm optimisation svm and genetic algorithm optimisation svm). Once the HBI prediction value of the current operating point (the equilibrium point) is less than or equal to the preset HBI alert value, it is possible to take an emergency load-shedding measure to remove a small amount of load, and the position of UHB point under maximum PV output is used to set the amount of load-shedding, so the oscillatory collapse of the load bus voltage caused by a sudden increase of PV output or load fluctuation can be avoided. Taking a large-scale PV power station access to the classic three-node system as an example, the prediction and control method is verified. Simulation result shows that the method is simple and practical, as well as suitable for on-line prediction and control of UHB point in a PV grid-connected system, which is able to improve the prevention and emergency control capability of the system voltage stability.

关键词： photovoltaic power systems load shedding bifurcation power grids support vector machines power engineering computing particle swarm optimisation genetic algorithms power system dynamic stability power generation control large-scale PV power station control method online prediction emergency control capability load fluctuation load bus voltage oscillatory collapse emergency load-shedding measure equilibrium point current operating point HBI prediction value particle swarm optimisation genetic algorithm optimisation svm algorithm HBI index system voltage stability evaluation process UHB point position large-scale photovoltaic power station saddle-node bifurcation voltage collapse power system unstable Hopf bifurcation optimised support vector machine large-scale PV grid-connected system

来源：评论

学校读者我要写书评

暂无评论

Computer Software and Hardware Fault Diagnosis Method Based on svm

International Core Journal of Engineering

引用

International Core Journal of Engineering 2020年第12期6卷 447-453页

作者： Chuyan Dai

With the rapid development of information technology and the popularity of the Internet in the world, the status of computer in daily life and work is becoming more and more important. However, as a highly integrated electronic information equipment, the computer has high precision, and the probability of failure is very high. When there is a problem, the general staff can handle it, but it is difficult to repair because of the lack of computer knowledge. Based on the above background, the purpose of this paper is to discuss the fault diagnosis method of computer software and hardware based on svm. In view of the shortcomings of artificial diagnosis method of laboratory computer hardware fault, such as strong subjective factors, large error and high failure rate, a fault diagnosis method based on svm algorithm is proposed. In this paper, svm algorithm and maximum likelihood estimation algorithm are used to analyze the construction of fault diagnosis model in detail. Taking one or more hardware failures in the process of computer operation as an example, the svm algorithm diagnosis reasoning is carried out. The effectiveness of the method is verified by comparing the simulation results. The results show that compared with the artificial fault diagnosis method, the efficiency of svm algorithm is improved by about 20%, so the prediction method of svm algorithm plays a certain role in reducing the failure rate of computer software and hardware, and it is feasible.

关键词： svm algorithm Computer Failure Fault Diagnosis Diagnosis Prediction rate

来源：评论

学校读者我要写书评

暂无评论

KNN and svm Classification for Chainsaw Sound Identification in the Forest Areas

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第12期10卷 531-536页

作者： Gnamele, N'tcho Assoukpou Jean Ouattara, Yelakan Berenger Kobea, Toka Arsene Baudoin, Genevieve Laheurte, Jean-Marc UFHB Lab Signaux & Syst Abidjan Cote Ivoire UFHB Lab Phys Atmosphere & Mecan Fluides Abidjan Cote Ivoire UPEM ESIEE PARIS Marne La Vallee France UPEM ESYCOM FRE 2028 Marne La Vallee France

We present in this paper a comparative study of two classifiers, namely, svm (support vector machine) and KNN (K-Nearest Neighbors), which we combine to MFCC (Mel-Frequency Cepstral Coefficients) in order to make possible the detection of chainsaw's sounds in a forest environment. Optimization's calculation of the relevant characteristics of the sounds recorded in the forest and the judicious choice of the key parameters of the classifiers allows us to obtain a true positive rate of 95.63% for the svm-LOG-KERNEL and 94.02% for the KNN. The svm-LOG-KERNEL classifier offers a better classification result and a processing time 30 times faster than KNN.

关键词： KNN algorithm svm algorithm MFCC sound recognition forest monitoring machine learning

来源：评论

学校读者我要写书评

暂无评论

Forecasting Stock Market Movements Using Various Kernel Functions in Support Vector Machine 16

Forecasting Stock Market Movements Using Various Kernel Func...

引用

International Conference on Advances in Information Communication Technology and Computing

作者： Upadhyay, Ved Prakash Panwar, Subhash Merugu, Ramchander Panchariya, Ravindra Govt Engn Coll Bikaner Rajasthan India Mahatma Gandhi Univ Nalgonda Telangana India

ISBN: (纸本)9781450342131

In stock market forecasting achieving good prediction accuracy is always been a highly challenging task for researchers and financial analyst. Forecasting stock market needs to deal with the most volatile, non-parametric and non-linear data sets. Also there are various factors that may affect the growth of stock market. So in order to make a good stock market forecasting system we need to use all the parameters that may affect the market volatility. Support Vectors Machine (svm) have been found to be one of most efficient machine learning algorithm in modeling stock market prices and movements. Researchers are using these classification algorithms for so many years and have got a good predictive accuracy. Here in our research we have used svm algorithm to making prediction for CNX NIFTY index value. In our experiment we have compared prediction accuracy for various Kernel Types of svm.

关键词： svm algorithm stock market index value kernel types

来源：评论

学校读者我要写书评

暂无评论

Space Vector Direct Torque Control for Five-level Open-end winding Induction Motor Drive to suppress Harmonic Spikes

Space Vector Direct Torque Control for Five-level Open-end w...

引用

IEEE Region 10 Symposium on Technologies for Smart Cities (TENSYMP)

作者： Vinod, B. R. Shiny, G. Baiju, M. R. CET Power Elect Res Lab Dept E&C Trivandrum Kerala India

ISBN: (纸本)9781509062553

The work suggests a space vector modulated direct torque control method to suppress the harmonic spikes. The control circuit of the presented drive utilizes a three phase five level (5-level) inverter and an induction motor configured in open-end winding (OEW) topology. The 5-level inverter is realized using two 3-level inverters and each one is connected to two open ends of an induction motor (IM). The scheme uses 60 degree coordinate system to minimize calculation complexity of the modulation algorithm. The space vector modulation (svm) used in this scheme makes use of a fractal based approach for the identification of sectors and switching voltage space vectors. The hardware outputs of the prescribed work were compared with lower level inverter fed DTC drives. The speed controlling potential of the prescribed method is verified through experimental results.

关键词： direct torque control svm algorithm fractal approach open end winding induction motor

来源：评论

学校读者我要写书评

暂无评论

Interpolation of Missing Data of Magnetic Flux Leakage in Oil Pipeline Based on Improved Supporting Vector Machine

Interpolation of Missing Data of Magnetic Flux Leakage in Oi...

引用

International Conference on Computer Systems, Electronics and Control (ICCSEC)

作者： Jiang, Lin Yang, Jinqi Hong, Xiaowei Zheng, Li Lu, Danyu Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Shenyang Highlight Technol Co Ltd Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9781538635735

In the internal detection of submarine pipeline, the data exported from magnetic flux leakage detector may exist some missing data. In order to get accurate data, the magnetic flux leakage data should be preprocessed. The significant part of data preprocessing is to discriminate the missing data, and then to compensate these true values reasonably and effectively. In this paper, svm algorithm is used in the process of single-channel interpolation to random missing data firstly. Secondly, the svm traversal algorithm is proposed to achieve the interpolation of whole random missing data block. In order to improve the interpolation accuracy, an improved svm algorithm is then introduced. The svm traversal is carried out by using the axial data and the radial data respectively. Then based on the no missing values in the random missing data block, the least squares method is used to obtain the weight for interpolation of statistical missing data. Lastly, the real data exported from magnetic flux leakage detector is used to simulate. The interpolation results are compared with the BP neural network. The results show that this method is more feasible and effective.

关键词： svm algorithm Data interpolation MFL Texting

来源：评论

学校读者我要写书评

暂无评论

Aquatic Plant Functional Type Spectral Characteristics Analysis and Comparison Using Multi-temporal and Multi-sensor Remote Sensing over the Poyang Lake Wetland, China

Aquatic Plant Functional Type Spectral Characteristics Analy...

引用

18th International Conference on Geoinformatics

作者： Wang, Lin Gong, Peng Dronova, Iryna Inst Remote Sensing Applicat Chinese Acad Sci & B State Key Lab Remote Sensing Sci Beijing Peoples R China Univ Calif Berkeley Dept Environm Sci Policy & Management Div Ecosyst Sci Berkeley CA 94720 USA

ISBN: (纸本)9781424473021

In systems with strong seasonal difference in vegetation structure and appearance, multi-temporal imagery can be particularly useful for community-and species-level discrimination. And, since the availability of past data for one source of time series images may be limited, so we need to develop multi-temporal and multi-source method for wetland ecosystem monitoring. To perform this type of analysis, the image spectral characteristics comparison between different aquatic macrophytes and different sensors should be studied firstly. We used TM images, Beijing-1 images and HJ-1 images for this analysis and based on the determination of aquatic plant functional types (PFTs). The objectives of this study were: (1) single-sensor single-date aquatic PFT analysis;(2) multi-source single-date diagnostic spectral characteristics analysis and comparison for different aquatic PFTs;(3) multi-source multi-temporal diagnostic spectral characteristics analysis for different aquatic PFTs. From this analysis we found that: (1) For the single-date TM data, the diagnostic spectral band and indexes are Band 2, 4, 5, NDVI, and MNDWI;the best temporal for discriminating different Nonpersistent Emergent Wetland PFTs are in low water level periods, and water infilling and subsiding periods for seasonal submerged and floating aquatic macrophyte. Multi-spectral Decision Tree classification method lead the more good results for most of PFTs;(2) the same type of aquatic PFTs have similar and comparable reflectance characteristics between multi-sensor optical data which could satisfy the time series analysis by compensating more available past images;(3) phenological curves and relative canopy moisture curves extracted from time series remote sensing images provide important information for distinguish different PFTs.

关键词： TSNDVI image TSNDWI image Beijing-1 small satellite data HJ-1 satellite data TM data Decision Tree model svm algorithm

来源：评论

学校读者我要写书评

暂无评论

Learner modeling in cloud computing

引用

EDUCATION AND INFORMATION TECHNOLOGIES 2020年第6期25卷 5581-5599页

作者： Ghallabi, Sameh Essalmi, Fathi Jemni, Mohamed Kinshuk Res Lab Technol Informat & Commun & Elect Engn La Tunis Tunisia Arab League Educ Cultural & Sci Org ICT Dept Tunis Tunisia Univ North Texas Coll Informat Denton TX 76203 USA

With the emergence of technology, the personalization of e-learning systems is enhanced. These systems use a set of parameters for personalizing courses. However, in literature, these parameters are not based on classification and optimization algorithms to implement them in the cloud. Cloud computing is a new model of computing where standard and virtualized resources are provided as a service through the Internet. This paper proposes an approach that allows learner modeling in the cloud where these parameters are integrated. The suggested approach is based on the support vector machine algorithm, which analyzes the learners' traces to find the best classification of learners through selected parameters with a low cost. An experimentation is conducted to validate this approach. This experimentation is based on the produced traces for learner modeling. The obtained results show that this approach represents the learner model with low operation costs compared to classic systems (no cloud).

关键词： Learner modeling personalized learning systems learners' traces personalization parameters svm algorithm Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Implementation of Android-based Fall-detecting System 13

Implementation of Android-based Fall-detecting System

引用

13th IEEE International Conference on Signal Processing (ICSP)

作者： Jia, Huiyu Li, Meihui Ning, Yunkun Liang, Shengyun Li, Huiqi Zhao, Guoru Acad Sci Chinese Shenzhen Key Lab Low Cost Healthcare Shenzhen 518055 Peoples R China Acad Sci Chinese Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Wuhan Res Inst Post & Telecommun Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781509013456

Accidental falls are the main factors that endanger the health of elderly people in modern society. Timely and effective fall detection and alarm can reduce the risk of falls. With the increasing of the aging society, the design and development of the fall detection system which is portable, accurate and real-time, has gradually become one of the most urgent needs of the community. With the rapid development of mobile Internet, smart phones as another kind of wearable device have become essential products of people's life. Smart phone is portable and flexible, which can be built in various sensors. It can be used to monitor human motion data. In order to detect falls promptly and minimize the damage to the aged, a fall detection system based on Android phones was designed and implemented and a fall detection algorithm for phones was proposed in the paper. In this way, the elderly can detect falls without other equipment at any time. Meanwhile, the system integrated the phone system function greatly, provided a good platform for human-computer interaction in the detection, and gave remote alarm to help the elderly get timely rescue.

关键词： fall detection svm algorithm Decision Tree

来源：评论

学校读者我要写书评

暂无评论

Robust Vehicle Classification Using Sound Signals and Attention Module

Robust Vehicle Classification Using Sound Signals and Attent...

引用

International Conference on Smart Transportation and City Engineering

作者： Chi, Guowei Xu, Zijun Sowah, Edmund Li, Weiqing Nanjing Univ Sci & Technol Coll Comp Sci & Technol Nanjing Peoples R China Nanjing Univ Sci & Technol Coll Elect & Opt Engn Nanjing Peoples R China

ISBN: (数字)9781510649767

ISBN: (纸本)9781510649767;9781510649750

Classification of vehicles in urban areas is an important part of transport and traffic management. To improve the performance of car classification, many Intelligent Transport Systems (ITS) have been developed to accomplish this tedious task. Unfortunately, most of these systems are inaccurate in vehicle detection and/or classification. This paper proposes a new car classification algorithm based on vehicle acoustic signals, which adds an attention module to the original BP neural network, highlighting features in the time and frequency domain information that are more capable of distinguishing between vehicle classes, and providing better robustness in real-world environments. To evaluate the performance of the proposed algorithm on the vehicle classification task, we compare the method with the traditional svm algorithm and the BP neural network without the attention mechanism Experimental results show that the method using vehicle acoustic features combined with an attention module achieves a classification accuracy of 89.34%, which is a 12.22% improvement over the traditional svm method, and a 5.23% improvement compared to the BP neural network method without the attention module.

关键词： vehicle acoustic signals BP neural network svm 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：