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

共 398 条记录，以下是261-270 订阅

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An Efficient and Accurate Object Detection algorithm And Its Application 5

An Efficient and Accurate Object Detection Algorithm And Its...

引用

IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Jiang Libiao Li Xiaojun South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Peoples R China

ISBN: (纸本)9781728143231;9781728143224

In order to solve the problem that the recognition accuracy and recognition speed of the current deep learning target detection algorithm are not compatible, an efficient and accurate object detection algorithm was proposed and applied to the inspection of small-sized body stamping parts of an auto parts enterprise. Firstly, image data sets of 10 small-sized automobile body stampings were produced. The transfer learning method was used to train YOLO V3, YOLO V3-tiny and the model proposed in this paper. Then, three models were used for small-sized body stamping parts detection and recognition experiment. The experimental results show that our model can detect 37 images per second in the same sample and test environment, which is 208.33% higher than the YOLO V3 model;the average detection precision is 96.50%, compared with The YOLO V3-tiny model is improved by 4.40%, and the average detection precision is improved up to 22.58% on a single smaller size object. This study can provide visual navigation support for the small-size body stamping robot automatic sorting system.

关键词： machine vision deep learning object detection transfer learning k-means clustering algorithm

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km-IFC: A Framework for Road Course Estimation Using Radar Detections 10

Km-IFC: A Framework for Road Course Estimation Using Radar D...

引用

10th IEEE International Conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Aaptha, B., V kadali, Phani Gupta, Saurabh Saju, Vinolia Continental Autonomous Mobil India Pvt Ltd Radar Percept Grp Bangalore Karnataka India

ISBN: (纸本)9798350385939;9798350385922

In recent years, the increasing demand for technological advances has driven the usage of state-of-the-art technologies like machine learning in automated systems. In this study, a novel framework, km-IFC, is presented that effectively estimates the course of the road using radar detections, and its results are discussed. It uses a combination of the k-means clustering algorithm for separation of data, the Isolation Forest algorithm for removal of outliers, and the least squares method to estimate the curve parameters of the roads' course. The estimated curve using radar data is compared with the vehicle-driven path (considered as ground truth). km-IFC achieved favorable results in both optimal and non-optimal scenarios, with almost negligible differences between the estimated curve and ground truth. Consequently, the estimated curve had an average difference of 0.365 meters and an overall confidence coefficient of 0.88, inclusive of all scenarios. Furthermore, the study conducted takes in raw sensor detection data and estimates the roads' course without relying on any external sensor outputs, thus making it very computationally efficient.

关键词： k-means clustering algorithm Isolation Forest algorithm Curve fitting Radar detections Road trajectory

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Dynamic Delegation Approach in the Electronic Evidence Storage of Cloud Platform

Dynamic Delegation Approach in the Electronic Evidence Stora...

引用

3rd International Conference on Applied Mechanics, Materials and Manufacturing (ICAMMM 2013)

作者： Wang, Yanfei Xie, Jun Xian, Zhengguang Capital Normal Univ Coll Informat Engn Beijing Peoples R China

ISBN: (纸本)9783037858882

The trustworthiness of the web resources is very important for secure cloud platform of electronic evidence storage calculation. In this paper, I analyzed the possibility of classes of delegation and trust level of authorization access. I used CGRS resource selection algorithm which is based on the k-means clustering algorithm in digital forensics analysis of cloud platform resources, and the algorithm is used to scalable environment for digital forensics analysis platform in substantive departments. The algorithm is proved effective and correct by the analysis and the experimental results.

关键词： Electronic evidence Cloud platform k-means clustering algorithm CGRS resource selection algorithm Dynamic Access Control

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Phase identification for product quality prediction in batch processes: application to industrial resin production

Phase identification for product quality prediction in batch...

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European Control Conference (ECC)

作者： Depalo, Andrea Barolo, Massimiliano Bezzo, Fabrizio Muradore, Riccardo Univ Verona Dept Comp Sci Altair Lab I-37100 Verona Italy Univ Padua Dept Ind Engn Comp Aided Proc Engn Lab CAPE Lab I-35100 Padua Italy

ISBN: (纸本)9783952426937

This paper addresses the phase identification problem in the development of a soft-sensor for quality variables prediction in batch processes. Batch processes are characterized by time-varying dynamic behavior. This means that input-output variable correlation is not constant during the whole process and so using a single statistical/deterministic model is not usually accurate enough to describe the plant behavior. The multi-phase approach overcomes this problem by grouping process data in different clusters according to their statistical structures. Each phase is then modeled independently using, e.g., the Partial Least Square (PLS) method. In this paper we propose a method to identify process phases based on the k-means clustering algorithm. The main advantages are that this method is generic, automatic and requires no a-priori knowledge of the process. The proposed methodology has been validated on data coming from an industrial plant for production of a resin.

关键词： batch processing (industrial) least mean squares methods product quality resins statistical analysis PLS method batch processes industrial resin production k-means clustering algorithm partial least square method phase identification problem product quality prediction soft-sensor time-varying dynamic behaviror Batch production systems Correlation Product design Quality assessment Resins Temperature measurement Yttrium

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Ant Colony Optimization for the k-means algorithm in Image Segmentation 10

Ant Colony Optimization for the K-means Algorithm in Image S...

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48th ACM Annual Southeast Regional Conference (SE)

作者： Hung, Chih-Cheng Sun, Mojia Southern Polytech State Univ 1100 S Marietta Pkwy Marietta GA 30060 USA

ISBN: (纸本)9781450300643

In this paper the ant colony optimization (ACO) is used in the k-means algorithm for improving the image segmentation. The learning mechanism of this algorithm is formulated by using the ACO meta-heuristic. As the pheromone dominates the exploration of ants for problem solutions, preliminary experiments on pheromone's update are reported. Two methods for defining and updating pheromone values are proposed and tested: one with the spatial coordinate distances and the other without using such a distance. The ACO improves the k-means algorithm by making it less dependent on the initial parameters.

关键词： k-means clustering algorithm Ant colony optimization Swarm Intelligence

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Study of obstacle information processing for unmanned underwater vehicle in unknown ocean environment

Study of obstacle information processing for unmanned underw...

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OCEANS Conference

作者： Zhang, Wei Wang, Xiufang Liang, Zhicheng Sun, Xixun Harbin Engn Univ Coll Automat Harbin 150001 Heilongjiang Pr Peoples R China

ISBN: (纸本)9781467397247

In ocean environment, to ensure the safety of navigation for unmanned underwater vehicle (UUV), it is the most important to obtain the position information and moving information of obstacles. In order to distinguish between static and dynamic obstacles, the detection of moving property is used to detect property of obstacles. Then, to meet the requirement of obstacle configurations, k-means clustering algorithm is put forward for clustering. Finally, least squares prediction is put forward for detecting position information of UUV, this method predicts the future position information of obstacles and the moving parameter, and solves the problem of lack of useful information for UUV. Simulations show that the proposed method could get the accurate position information and moving information of obstacles.

关键词： moving property detection unmanned underwater vehicle k-means clustering algorithm least squares prediction

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Improved Tabu Search algorithm for Solving the Vehicle Routing Problem with Soft Time Windows in B2C Environment 32

Improved Tabu Search Algorithm for Solving the Vehicle Routi...

引用

32nd Chinese Control And Decision Conference (CCDC)

作者： Li, Lin Chen, Ying Meng, Jiao Shenyang Aerosp Univ Coll Sci Shenyang 110136 Peoples R China

ISBN: (纸本)9781728158556

With the development of E-commerce, the B2C E-commerce develop rapidly. Customers put forward higher requirements for delivery speed and accitracy. This paper studied the vehicle routing problem with soft time windows in B2C environment. It improved the tabu search (TS) algorithm, and used k-means clustering algorithm to determine the initial solution to improve the algorithm's convergence speed. The algorithm was tested by using the data in literature [7]. The computing results were compared with those results in the literature [7] to verify the effectiveness of the algorithm.

关键词： B2C E-commerce Soft time window k-means clustering algorithm Tabu search

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Visual based laser speckle pattern recognition method for structural health monitoring

Visual based laser speckle pattern recognition method for st...

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Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems

作者： Park, kyeongtaek Torbol, Marco Ulsan Natl Inst Sci & Technol Sch Urban & Environm Engn 50 UNIST Gil Ulsan 44919 South Korea

ISBN: (数字)9781510608221

ISBN: (纸本)9781510608221

This study performed the system identification of a target structure by analyzing the laser speckle pattern taken by a camera. The laser speckle pattern is generated by the diffuse reflection of the laser beam on a rough surface of the target structure. The camera, equipped with a red filter, records the scattered speckle particles of the laser light in real time and the raw speckle image of the pixel data is fed to the graphic processing unit (GPU) in the system. The algorithm for laser speckle contrast analysis (LASCA) computes: the laser speckle contrast images and the laser speckle flow images. The k-mean clustering algorithm is used to classify the pixels in each frame and the clusters' centroids, which function as virtual sensors, track the displacement between different frames in time domain. The fast Fourier transform (FFT) and the frequency domain decomposition (FDD) compute the modal properties of the structure: natural frequencies and damping ratios. This study takes advantage of the large scale computational capability of GPU. The algorithm is written in Compute Unifies Device Architecture (CUDA C) that allows the processing of speckle images in real time.

关键词： System identification structural health monitoring speckle contrast imaging k-means clustering algorithm parallel computing

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Day-Ahead Solar Power Forecasting with Pattern Analysis and State Transition 3

Day-Ahead Solar Power Forecasting with Pattern Analysis and ...

引用

IEEE 3rd Global Power, Energy and Communication Conference (IEEE GPECOM)

作者： Hu, Yi-Liang Kun Shan Univ Green Energy Technol Res Ctr Tainan Taiwan

ISBN: (纸本)9781665435123

This study focuses on the day-ahead forecasting of the power generation of the grid-connected photovoltaic (PV) system. In the literature, most PV power forecasting models depends on the information from the numerical weather prediction (NWP). The errors of the weather prediction might cause the performance of the PV power forecasting model to decrease. Therefore, in this paper, the proposed day-ahead PV power forecasting model only utilizes the historical PV power generations as the input data. The proposed forecasting model is composed of two parts, pattern analysis and the state transition. The pattern analysis evaluates the similarity of the time series of the daily PV power generations based on Euclidean distance and group the time series with similar patterns by k- means time series clustering algorithm. Each cluster is regarded as one state. Then, the probabilities of the state transitions between two consecutive days are calculated. Finally, the expected PV power pattern of tomorrow is obtained as the forecasted PV power outputs. In this study, the proposed model is compared with the traditional feed-forward neural network ( FFNN). The results of the cast study demonstrate the proposed model leads to smaller errors in 10 sets of the testing data.

关键词： PV power day-ahead forecasting Euclidean distance k-means clustering algorithm state transition

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Research on Prediction of Missing Values Based on Multiple Models 39th

Research on Prediction of Missing Values Based on Multiple M...

引用

39th CCF National Conference on Computer Applications

作者： Wang, Yutang Gao, Erni Anhui Inst Informat Technol Wuhu Anhui Peoples R China

ISBN: (纸本)9789819796700;9789819796717

In the process of big data analysis, data integrity and consistency are often very important factors that affect the accuracy of analysis results. Therefore, before starting the analysis process, data cleaning work should be done on the collected data sources to ensure that subsequent analysis will not cause errors due to data anomalies. Therefore, maintaining data integrity is a very important task in data cleaning. One of the reasons for incomplete data is that the collected data contains missing values, which are caused by human negligence, instrument failure and other factors during the data collection process. The common methods for dealing with missing values are as follows: ignore the value group with missing values directly, or use the central tendency measurement (such as mean, median, etc.) of the missing value attribute to fill in the missing values. These methods may cause the loss of the original characteristics of the value group, which may affect the output of subsequent data analysis and application, and lead to incorrect results. To address this problem, this study uses machine learning methods to fill in missing values for a single field. We use data without missing values as training data, divide the data into multiple clusters using k-means clustering method to capture the hidden associations between data, and build prediction models for each cluster using multiple regression and artificial neural network. For missing values that need to be predicted, we first use kNN algorithm to find the cluster to which the data belongs, and then apply the model of that cluster to calculate the predicted value. The experiment proves that the multi-model filling method proposed in this study is better than the existing filling algorithms in terms of root mean square error.

关键词： Missing Value Multiple Regression Analysis Artificial Neural Network k-means clustering algorithm

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