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检索条件"主题词=k-Means clustering algorithm"

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Research of Flexible Load Analysis of Distribution Network Based on Big Data 4

Research of Flexible Load Analysis of Distribution Network B...

引用

4th IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Sun RongQi Xiao XueQuan Zhou Feng Zhou YuShan State Grid XuZhou Power Supply Co Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9781728114101

Aiming at the problem of insufficient computing power in the analysis of flexible load under the condition of massive user data in distribution network, a flexible load analysis method based on big data is proposed. Firstly, the data fusion and feature extraction of the massive user data in distribution network are carried out to realize the preprocessing of the original data. Then, on the basis of MapReduce parallel computing model, the k-means algorithm is used to cluster the load characteristic data and calculate the industry flexibility coefficient and adjustable load. According to the results of load flexibility analysis based on the historical data of a power supply company in a certain city, this method can make statistics on load flexibility (load adjustable space) from industry dimension and time dimension, judge the current load flexibility level, and combine external data and temperature data to carry out load flexibility analysis. According to historical data, the adjustable load level of a certain area, a certain industry and a certain period in the future is predicted.

关键词： big data MapReduce model k-means clustering algorithm Load flexibility

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Research on the Application of YOLOv3-Tiny algorithm in the Detection of Circuit Experiment Equipment 2021

Research on the Application of YOLOv3-Tiny Algorithm in the ...

引用

2nd International Conference on Artificial Intelligence and Information Systems (ICAIIS )

作者： Liang, Mingju Wang, Zhenguo Wang, Yang Guangdong Shunde Innovat Design Inst Foshan Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou Peoples R China

ISBN: (纸本)9781450390200

In the physical circuit experiment teaching of junior high school, the equipment detection link before the experiment can be completed by the target detection algorithm based on embedded devices. In order to solve the problems of slow detection speed and false detection and missed detection in the traditional target detection algorithm on embedded devices, a real-time target detection model based on YOLOv3-Tiny is proposed. By data augmentation of self-made circuit experimental equipment data set, the problem of electronic equipment category imbalance is solved. k-means clustering algorithm is used to obtain the optimal Anchor size and number of circuit experimental equipment data sets, and the parameters of YOLOv3-Tiny algorithm are improved. The test results show that the improved YOLOv3-Tiny algorithm can achieve a detection accuracy of 94.61% and a detection frame rate of 39 frame/s on the NPU processor of the embedded device Rk3399Pro development board.

关键词： Target Detection YOLOv3-Tiny embedded NPU processor k-means clustering algorithm

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Application of Neural Networks in Early Detection and Diagnosis of Parkinson's Disease

Application of Neural Networks in Early Detection and Diagno...

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International Conference on Cyber and it Service Management (CITSM)

作者： Olanrewaju, Rashidah Funke Sahari, Nur Syarafina Musa, Aibinu A. Hakiem, Nashrul Int Islamic Univ Malaysia Dept Elect & Comp Engn Fac Engn Kuala Lumpur Malaysia Fed Univ Technol Minna Minna Niger State Nigeria UIN Syarif Hidayatullah Jakarta Fac Sci & Technol Dept Informat Engn South Tangerang Indonesia

ISBN: (纸本)9781479979738

Parkinson's disease (PD) is a chronic neurological progressive disorder caused by lack of the chemical dopamine in the brain. Up to today, there is still no cure or prevention for PD, and usually the disease worsens gradually over time. However, this disease can be controlled with some treatment, especially in the early stage. Hence, this study proposes a method in early detection and diagnosis of PD by using the Multilayer Feedforward Neural Network (MLFNN) with Back-propagation (BP) algorithm. This MLFNN with BP algorithm is simulated using MATLAB software. The dataset information used in this study was taken from the Oxford Parkinson's Disease Detection Dataset. The output of the network is classified into healthy or PD by using k-means clustering algorithm. The performance of this classifier was evaluated based on the three parameters;sensitivity, specificity and accuracy. The result shows that network can be used in diagnosis and detection of PD due to the good performance, which is 83.3% for sensitivity, 63.6% for specificity, and 80% for accuracy.

关键词： backpropagation diseases medical computing medical disorders multilayer perceptrons neurophysiology pattern clustering BP algorithm MLFNN Matlab software Oxford Parkinson's disease detection dataset PD detection PD diagnosis PD output Parkinson's disease detection Parkinson's disease diagnosis accuracy parameter back-propagation algorithm chemical dopamine chronic neurological progressive disorder healthy output k-means clustering algorithm multilayer feedforward neural network performance evaluation sensitivity parameter specificity parameter Accuracy Classification algorithms clustering algorithms Diseases Neural networks Neurons Sensitivity Multilayer feedforward neural network MATLAB back propagation maladjustment Parkinson Disease clustering algorithms medical computing medical disorders Neural network Multilayer perceptrons Sorting algorithm Neurophysiology Cluster Analysis Performance Evaluation Early Diagnosis

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Power Missing Data Filling Based on Improved k-means algorithm and RBF Neural Network 4th

Power Missing Data Filling Based on Improved k-Means Algorit...

引用

4th International Conference on Cloud Computing and Security (ICCCS)

作者： Shi, Zhan Li, Xingnan Su, Zhuo Guangdong Grid Corp Power Dispatching Control Ctr Guangzhou 510600 Guangdong Peoples R China

ISBN: (纸本)9783030000189;9783030000172

Power data mainly comes from power generation, transmission, consumption, scheduling and statistics. However, in the process of power data acquisition, problems such as data missing seriously affect the further analysis. In this paper, we propose a missing data filling method based on improved k-means clustering and Radial Basis Function neural network (kM-RBF) to solve the problem of missing power data. Firstly, the data samples are clustered by k-means, and the clustering results are used as the parameters of RBF neural network. The RBF neural network is trained with the complete data samples, and then the missing values are predicted. In order to verify the effectiveness of the algorithm, we have chosen the power consumption and power generation metadata of each province in China for analysis and simulated the absence of data. Simulation results show that the kM-RBF can obtain higher accuracy of missing data filling.

关键词： k-means clustering algorithm Missing data filling Power system data Self-adaptive weights Radial basis function neural network

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A Method of Estimating Transmission Line Parameters Using Cloud Computing Based on Distributed Intelligence 17

A Method of Estimating Transmission Line Parameters Using Cl...

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17th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Sun, Yuefeng Gao, Zhengnan Hu, Shubo Sun, Hui Su, Anlong Wang, Shunjiang Gao, kai Ge, Weichun Dalian Univ Technol Dalian Peoples R China State Grid Liaoning Elect Power Supply Co Ltd Shenyang Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Shenyang Inst Comp Technol Shenyang Peoples R China

ISBN: (纸本)9781538633601

The popularization and application of PMU measurement devices in power system provides real-time data monitoring tools for power grid operators. However, the measuring time interval of measuring devices is extremely short. The processing and analysis of the big data generated by measuring devices presents new requirements for the power system, and brings new challenges to the operators. In this paper, the method of parameter estimation of transmission line using cloud computing based on distributed intelligence is studied in depth. An efficient solution aim at processing the big data is given. The k-means clustering algorithm is used to fit the actual situation of the transmission line parameters under the temperature and humidity micro-meteorology. A new way of the mass data application is provided in this paper. The experimental example proves that the cloud computing model based on distributed intelligence can greatly improve the computational efficiency and save the computing time. In addition, the parameters of the transmission line in micro-meteorology conform to the actual operation of the power grid, and early warning can be provided to operators when the real-time operating parameters change suddenly.

关键词： big data cloud computing parameter estimation distributed intelligence k-means clustering algorithm

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k-means based path optimization for smart warehouse system

K-Means based path optimization for smart warehouse system

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Wang, Qiuyang Bi, Shuhui Zhuang, Wenling Shen, Tao Yan, Xuehua Univ Jinan Sch Elect Engn Jinan 250022 Peoples R China Jinan Vocat Coll 5518 Ivyou Rd Jinan Shandong Peoples R China

ISBN: (纸本)9781665417525

In order to improve picking efficiency in the "Part-to-picker" warehouse system, the path optimization for smart warehousing of small commodity is investigated by combing with item classification in this paper. First of all, a mathematical model of the global environment for a small commodity warehouse with a robot that can pick multiple items at one time is established by using grid method. Secondly, the order picking strategy is studied from the perspective of items classification based on k-means clustering algorithm, and the rule of cluster items is defined. Then, the ant colony optimization is used to plan the optimal path for picking up goods based on the item classification. Finally, the effectiveness of the proposed approach was demonstrated through simulations, compared with the traditional order-based sequential picking strategy, the modified picking strategy which fusing item clustering and path planning could improve the system's outbound efficiency about 28.1%.

关键词： k-means clustering algorithm ant colony optimization path planning

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Cluster Analysis of Trip Purpose Based on Residents' Travel Characteristic 7

Cluster Analysis of Trip Purpose Based on Residents' Travel ...

引用

IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)

作者： Wei, Huang Lei, Gong Qin, Luo Tian, Lei Shenzhen Technol Univ Coll Urban Transportat & Logist Shenzhen Peoples R China

ISBN: (纸本)9781665460071

In the era of big data and artificial intelligence, individual generated traffic data has gradually become the key data source for extracting personal behavior information and travel purpose prediction. Based on the Person Trip survey of residents' travel in Tokyo, this paper uses Spearman correlation coefficient to explore the correlation between residents' travel attribute patterns. In the situation of unsupervised learning, k-means clustering algorithm is used to analyze the differences of travel purpose caused by various attributes of residents' travel. This paper explores the possible reasons for the low accuracy of travel purpose prediction by supervised machine learning methods. Through the visual display of clustering results, it puts forward guiding suggestions for the method system of obtaining residents' travel patterns based on traffic big data, which is of great significance to the development of new technologies and academic theories in the field of urban transportation.

关键词： Resident trip pattern Trip purpose Correlation analysis k-means clustering algorithm

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Effective Multipath Mitigation Methods for RTk in Urban Environments 9th

Effective Multipath Mitigation Methods for RTK in Urban Envi...

引用

9th China Satellite Navigation Conference (CSNC) - Location, Time of Augmentation

作者： Lin, kai-qin Deng, Zhong-liang Yin, Lu Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (纸本)9789811300295;9789811300288

RTk is one of the most precise positioning technologies, which has been widely used in many applications. However, in an urban area, GNSS receiver easily receive multipath signals caused by signal reflection and diffraction by both line-of-sight (LOS) and non-line-of-sight (NLOS) satellites. Multipath signals degrade the phase observation quality and makes it difficult to detect integer ambiguities in RTk positioning. This paper proposes two methods to mitigate multipath effect. In the first method, a least square equation is constructed by using the relationship between the classical satellite elevation angle noise model and the signal-to-noise ratio (SNR) noise model. The least square residual is used to determine NLOS and LOS satellites based on the k-means clustering algorithm. The second method mitigates LOS multipath signal based on the consistency check between prediction residuals and prediction covariance of Extended kalman Filter. Practical experiences show that the proposed tow methods can able mitigate multipath effect and increase the availability of integer ambiguity resolution.

关键词： RTk positionings Multipath effect Least square residual k-means clustering algorithm

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Exploring Users' Preference on Mobile Based on Customer Features 32

Exploring Users' Preference on Mobile Based on Customer Feat...

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32nd International Conference on Conceptual Modeling (ER)

作者： Yu, Gang Wang, Zhiyan Xue, Jiang S China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Guangdong Peoples R China

ISBN: (纸本)9783319141398;9783319141381

Users' mobile model chosen can indicate their telecommunication network demand and consumption habit. The study of users' preference on mobile model has count for much meaning for mobile operators' business strategy and mobile model product sale. This paper looks for customer features as the basis for users' preference judgment from the basic features such as customer attributes, consumption and service usage, that most related to mobile model characteristics, using a genetic algorithm iteration method and k-means clustering algorithm. Our method performs well on how to judge user's preference on mobile model.

关键词： telecommunication customer feature feature selection mobile model characteristic k-means clustering algorithm Adaptive genetic algorithm

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Research on Customer Credit Demand Forecasting Based on Big Data Analysis 18

Research on Customer Credit Demand Forecasting Based on Big ...

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2nd International Conference on Computer Science and Application Engineering (CSAE)

作者： Zhang, Han Wang, kuisheng Xian Shiyou Univ POB 710065 Xian Shaanxi Peoples R China

ISBN: (纸本)9781450365123

With(1) the rapid development of the Internet, Internet credit business has emerged and the process is now booming. As a result, there is a problem of predicting credit demand of users. Therefore, we propose a method of using big data analysis to forecast the credit demand of users in this paper, which is used to reduce the risk of credit business and improve the utilization of funds.

关键词： Data analysis credit demand ReliefF feature extraction algorithm k-means clustering algorithm GBDT (Gradient Boosting Decision Tree) model

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