• Title/Summary/Keyword: K-means algorithm

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Design of Multiple Model Fuzzy Predictors using Data Preprocessing and its Application (데이터 전처리를 이용한 다중 모델 퍼지 예측기의 설계 및 응용)

  • Bang, Young-Keun;Lee, Chul-Heui
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.1
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    • pp.173-180
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    • 2009
  • It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

Co-registration of Multiple Postmortem Brain Slices to Corresponding MRIs Using Voxel Similarity Measures and Slice-to-Volume Transformation

  • Kim Tae-Seong
    • Journal of Biomedical Engineering Research
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    • v.26 no.4
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    • pp.231-241
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    • 2005
  • New methods to register multiple hemispheric slices of the postmortem brain to anatomically corresponding in-vivo MRI slices within a 3D volumetric MRI are presented. Gel-embedding and fiducial markers are used to reduce geometrical distortions in the postmortem brain volume. The registration algorithm relies on a recursive extraction of warped MRI slices from the reference MRI volume using a modified non-linear polynomial transformation until matching slices are found. Eight different voxel similarity measures are tested to get the best co-registration cost and the results show that combination of two different similarity measures shows the best performance. After validating the implementation and approach through simulation studies, the presented methods are applied to real data. The results demonstrate the feasibility and practicability of the presented co­registration methods, thus providing a means of MR signal analysis and histological examination of tissue lesions via co­registered images of postmortem brain slices and their corresponding MRI sections. With this approach, it is possible to investigate the pathology of a disease through both routinely acquired MRls and postmortem brain slices, thus improving the understanding of the pathological substrates and their progression.

Active Vibration Control of Structure Using Active Tuned Mass Damper and Modified PPF Controller (능동동조질량감쇠기와 수정 PPF 제어기를 이용한 구조물의 능동진동제어)

  • Kim, Ki-Young;Kwak, Moon-K.
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.18 no.2
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    • pp.224-230
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    • 2008
  • This paper is concerned with the active vibration control of building structure by means of the active tuned mass damper and the modified positive position feedback controller. To this end, one-degree-of-freedom spring-mass-damper system equipped with ATMD is considered. The stability condition for the addressed system when applying the proposed PPF controller is derived by Routh-Hurwitz stability criterion. The stability condition shows that the modified PPF controller is absolutely stable if the controller gain is positive. so that the modified PPF controller can be used without difficulty. Theoretical study shows that the modified PPF controller can effectively suppress vibrations as the original PPF controller does in smart structure applications. To investigate the validity of the modified PPF controller, a simple experimental structure with an ATMD system driven by DC motor was built. The modified PPF control algorithm was implemented on Atmel 128 microcontroller. The experimental result shows that the modified PPF controller can also suppress vibrations for the real structure.

Disambiguation of Author Names Using Co-citation (동시인용정보를 이용한 동명이인 저자의 중의성 해소)

  • Kang, In-Su
    • Journal of Information Management
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    • v.42 no.3
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    • pp.167-186
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    • 2011
  • Co-citation means that two or more studies are cited together by a later study. This paper deals with the relationship between co-citation and author disambiguation. Author disambiguation is to cluster same-name author instances into real-world individuals. Co-citation may influence author disambiguation in terms that two or more related research works performed by the same person may be co-cited by some later studies. This article describes automated steps to gather co-citation information from Google scholar, and proposes a new clustering algorithm to effectively integrate co-citation information with other author disambiguation features. Experiments showed that co-citation helps to improve the performance of author disambiguation.

Performance Test of APIS, DELOS Algorithm using Paramics (Paramics를 이용한 APID, DELOS평가)

  • Nam, Doohee
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.13 no.4
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    • pp.61-66
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    • 2013
  • The central core of the Traffic Management System is an Incident Management System. Whole approach has been component-orientated, with a secondary emphasis being placed on the traffic characteristics at the sites. The first action taken during the development process was the selection of the required data for each components within the existing infrastructure of Algeria freeway system. After review and analysis of existing incident detection methodologies, Paramics was utilized to test the performance of APID, DELOS algorithms. The existing system of Algeria freeway was tested in a different configuration at different sections of freeway, thereby increasing the validity and scope of the overall findings. The incident detection module has been performed according to predefined system validation specifications. The Paramics simulation was done with the use of synchronous analysis, thereby providing a means for testing the incident detection module.

Prediction System Design based on An Interval Type-2 Fuzzy Logic System using HCBKA (HCBKA를 이용한 Interval Type-2 퍼지 논리시스템 기반 예측 시스템 설계)

  • Bang, Young-Keun;Lee, Chul-Heui
    • Journal of Industrial Technology
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    • v.30 no.A
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    • pp.111-117
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    • 2010
  • To improve the performance of the prediction system, the system should reflect well the uncertainty of nonlinear data. Thus, this paper presents multiple prediction systems based on Type-2 fuzzy sets. To construct each prediction system, an Interval Type-2 TSK Fuzzy Logic System and difference data were used, because, in general, it has been known that the Type-2 Fuzzy Logic System can deal with the uncertainty of nonlinear data better than the Type-1 Fuzzy Logic System, and the difference data can provide more steady information than that of original data. Also, to improve each rule base of the fuzzy prediction systems, the HCBKA (Hierarchical Correlation Based K-means clustering Algorithm) was applied because it can consider correlationship and statistical characteristics between data at a time. Subsequently, to alleviate complexity of the proposed prediction system, a system selection method was used. Finally, this paper analyzed and compared the performances between the Type-1 prediction system and the Interval Type-2 prediction system using simulations of three typical time series examples.

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[Retracted]Hot Spot Analysis of Tourist Attractions Based on Stay Point Spatial Clustering

  • Liao, Yifan
    • Journal of Information Processing Systems
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    • v.16 no.4
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    • pp.750-759
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    • 2020
  • The wide application of various integrated location-based services (LBS social) and tourism application (app) has generated a large amount of trajectory space data. The trajectory data are used to identify popular tourist attractions with high density of tourists, and they are of great significance to smart service and emergency management of scenic spots. A hot spot analysis method is proposed, based on spatial clustering of trajectory stop points. The DBSCAN algorithm is studied with fast clustering speed, noise processing and clustering of arbitrary shapes in space. The shortage of parameters is manually selected, and an improved method is proposed to adaptively determine parameters based on statistical distribution characteristics of data. DBSCAN clustering analysis and contrast experiments are carried out for three different datasets of artificial synthetic two-dimensional dataset, four-dimensional Iris real dataset and scenic track retention point. The experiment results show that the method can automatically generate reasonable clustering division, and it is superior to traditional algorithms such as DBSCAN and k-means. Finally, based on the spatial clustering results of the trajectory stay points, the Getis-Ord Gi* hotspot analysis and mapping are conducted in ArcGIS software. The hot spots of different tourist attractions are classified according to the analysis results, and the distribution of popular scenic spots is determined with the actual heat of the scenic spots.

EDGE: An Enticing Deceptive-content GEnerator as Defensive Deception

  • Li, Huanruo;Guo, Yunfei;Huo, Shumin;Ding, Yuehang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.5
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    • pp.1891-1908
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    • 2021
  • Cyber deception defense mitigates Advanced Persistent Threats (APTs) with deploying deceptive entities, such as the Honeyfile. The Honeyfile distracts attackers from valuable digital documents and attracts unauthorized access by deliberately exposing fake content. The effectiveness of distraction and trap lies in the enticement of fake content. However, existing studies on the Honeyfile focus less on this perspective. In this work, we seek to improve the enticement of fake text content through enhancing its readability, indistinguishability, and believability. Hence, an enticing deceptive-content generator, EDGE, is presented. The EDGE is constructed with three steps: extracting key concepts with a semantics-aware K-means clustering algorithm, searching for candidate deceptive concepts within the Word2Vec model, and generating deceptive text content under the Integrated Readability Index (IR). Furthermore, the readability and believability performance analyses are undertaken. The experimental results show that EDGE generates indistinguishable deceptive text content without decreasing readability. In all, EDGE proves effective to generate enticing deceptive text content as deception defense against APTs.

Method for Estimating Intramuscular Fat Percentage of Hanwoo(Korean Traditional Cattle) Using Convolutional Neural Networks in Ultrasound Images

  • Kim, Sang Hyun
    • International journal of advanced smart convergence
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    • v.10 no.1
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    • pp.105-116
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    • 2021
  • In order to preserve the seeds of excellent Hanwoo(Korean traditional cattle) and secure quality competitiveness in the infinite competition with foreign imported beef, production of high-quality Hanwoo beef is absolutely necessary. %IMF (Intramuscular Fat Percentage) is one of the most important factors in evaluating the value of high-quality meat, although standards vary according to food culture and industrial conditions by country. Therefore, it is required to develop a %IMF estimation algorithm suitable for Hanwoo. In this study, we proposed a method of estimating %IMF of Hanwoo using CNN in ultrasound images. First, the proposed method classified the chemically measured %IMF into 10 classes using k-means clustering method to apply CNN. Next, ROI images were obtained at regular intervals from each ultrasound image and used for CNN training and estimation. The proposed CNN model is composed of three stages of convolution layer and fully connected layer. As a result of the experiment, it was confirmed that the %IMF of Hanwoo was estimated with an accuracy of 98.2%. The correlation coefficient between the estimated %IMF and the real %IMF by the proposed method is 0.97, which is about 10% better than the 0.88 of the previous method.

A Study on Image Analysis for Determination of Wear Area in Accelerated Durability Test (가속내구시험 마모영역 판별에 대한 이미지 분석 연구)

  • Cheon, Min-Woo;Lee, Chul-Hee
    • Tribology and Lubricants
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    • v.38 no.4
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    • pp.128-135
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    • 2022
  • In the product development process, the reliability of the product can be secured through durability tests. However, since the durability test method is expensive and time consuming, a method to save time and money by utilizing virtual product development (VPD) is required. However, research on the accuracy of the results of virtual product development is required. In this paper, an accelerated durability test was designed and conducted using a planetary gear decelerator. And an analysis model under the same conditions was created and simulated. To correlate the results of the experiment with the results of the analytical model, created a model that can discriminate the wear region using one of the data mining methods, the k-means algorithm method and HSV (Hue, Saturation, Value). The wear area is compared by counting the number of pixels defined as wear through a discrimination model. A similar ratio was calculated by comparing the pixel ratio of the area determined as wear in the entire area. It showed a similar ratio of about 70%, and it is necessary to improve the discrimination method.