• Title/Summary/Keyword: mixture 모델

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Depth Map Pre-processing using Gaussian Mixture Model and Mean Shift Filter (혼합 가우시안 모델과 민쉬프트 필터를 이용한 깊이 맵 부호화 전처리 기법)

  • Park, Sung-Hee;Yoo, Ji-Sang
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.15 no.5
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    • pp.1155-1163
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    • 2011
  • In this paper, we propose a new pre-processing algorithm applied to depth map to improve the coding efficiency. Now, 3DV/FTV group in the MPEG is working for standard of 3DVC(3D video coding), but compression method for depth map images are not confirmed yet. In the proposed algorithm, after dividing the histogram distribution of a given depth map by EM clustering method based on GMM, we classify the depth map into several layered images. Then, we apply different mean shift filter to each classified image according to the existence of background or foreground in it. In other words, we try to maximize the coding efficiency while keeping the boundary of each object and taking average operation toward inner field of the boundary. The experiments are performed with many test images and the results show that the proposed algorithm achieves bits reduction of 19% ~ 20% and computation time is also reduced.

Adjustment of Korean Birth Weight Data (한국 신생아의 출생체중 데이터 보정)

  • Shin, Hyungsik
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.21 no.2
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    • pp.259-264
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    • 2017
  • Birth weight of a new born baby provides very important information in evaluating many clinical issues such as fetal growth restriction. This paper analyzes birth weight data of babies born in Korea from 2011 to 2013, and it shows that there is a biologically implausible distribution of birth weights in the data. This implies that some errors may be generated in the data collection process. In particular, this paper analyzes the relationship between gestational period and birth weight, and it is shown that the birth weight data mostly of gestational periods from 28 to 32 weeks have noticeable errors. Therefore, this paper employs the finite Gaussian mixture model to classify the collected data points into two classes: non-corrupted and corrupted. After the classification the paper removes data points that have been predicted to be corrupted. This adjustment scheme provides more natural and medically plausible percentile values of birth weights for all the gestational periods.

Infrared Image Segmentation by Extracting and Merging Region of Interest (관심영역 추출과 통합에 의한 적외선 영상 분할)

  • Yeom, Seokwon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.26 no.6
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    • pp.493-497
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    • 2016
  • Infrared (IR) imaging is capable of detecting targets that are not visible at night, thus it has been widely used for the security and defense system. However, the quality of the IR image is often degraded by low resolution and noise corruption. This paper addresses target segmentation with the IR image. Multiple regions of interest (ROI) are extracted by the multi-level segmentation and targets are segmented from the individual ROI. Each level of the multi-level segmentation is composed of a k-means clustering algorithm an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering algorithm initializes the parameters of the Gaussian mixture model (GMM) and the EM algorithm iteratively estimates those parameters. Each pixel is assigned to one of clusters during the decision. This paper proposes the selection and the merging of the extracted ROIs. ROI regions are selectively merged in order to include the overlapped ROI windows. In the experiments, the proposed method is tested on an IR image capturing two pedestrians at night. The performance is compared with conventional methods showing that the proposed method outperforms others.

Thermo-fluid Dynamic Analysis through a Numerical Simulation of Canister (수치 모사를 통한 사출관 내부의 열유동 해석)

  • Kim, Hyun muk;Bae, Seong hun;Park, Cheol hyeon;Jeon, Hyeok soo;Kim, Jeong Soo
    • Journal of the Korean Society of Propulsion Engineers
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    • v.21 no.1
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    • pp.72-83
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    • 2017
  • A thermo-fluid dynamic analysis was performed through the numerical simulation of a missile canister. Calculation was made in a fixed analytical volume and fully evaporated water was used as a coolant. To analyze the interaction among the hot gas, coolant, and mixture flow, Realizable $k-{\varepsilon}$ turbulence and VOF(Volume Of Fluid) model were chosen and parametric study was performed with the change of coolant flow rate. It could be found that the pressure on the canister top nonlinearly increased with the increase of coolant flow rate. Temperature and coolant distribution were closely related to the flow behavior in canister. Temperature on the canister bottom indicated a decrease being proportional to coolant flow rate in early times but after a specific time, the temperature increased with the tendency being reversed. In addition, the early part of temperature showed a fluctuating phenomenon because of the overall circulatory flow of mixture gas.

Machine Learning Framework for Predicting Voids in the Mineral Aggregation in Asphalt Mixtures (아스팔트 혼합물의 골재 간극률 예측을 위한 기계학습 프레임워크)

  • Hyemin Park;Ilho Na;Hyunhwan Kim;Bongjun Ji
    • Journal of the Korean Geosynthetics Society
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    • v.23 no.1
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    • pp.17-25
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    • 2024
  • The Voids in the Mineral Aggregate (VMA) within asphalt mixtures play a crucial role in defining the mixture's structural integrity, durability, and resistance to environmental factors. Accurate prediction and optimization of VMA are essential for enhancing the performance and longevity of asphalt pavements, particularly in varying climatic and environmental conditions. This study introduces a novel machine learning framework leveraging ensemble machine learning model for predicting VMA in asphalt mixtures. By analyzing a comprehensive set of variables, including aggregate size distribution, binder content, and compaction levels, our framework offers a more precise prediction of VMA than traditional single-model approaches. The use of advanced machine learning techniques not only surpasses the accuracy of conventional empirical methods but also significantly reduces the reliance on extensive laboratory testing. Our findings highlight the effectiveness of a data-driven approach in the field of asphalt mixture design, showcasing a path toward more efficient and sustainable pavement engineering practices. This research contributes to the advancement of predictive modeling in construction materials, offering valuable insights for the design and optimization of asphalt mixtures with optimal void characteristics.

Optimization of Ingredients for the Preparation of Chinese Quince (Chaenomelis sinensis) Jam by Mixture Design (모과잼 제조시 혼합물 실험계획법에 의한 재료 혼합비율의 최적화)

  • Lee, Eun-Young;Jang, Myung-Sook
    • Journal of the Korean Society of Food Science and Nutrition
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    • v.38 no.7
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    • pp.935-945
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    • 2009
  • This study was performed to find the optimum ratio of ingredients in the Chinese quince jam. The experiment was designed according to the D-optimal design of mixture design, which included 14 experimental points with 4 replicates for three independent variables (Chinese quince paste $45{\sim}60%$, pectin $1.5{\sim}4.5%$, sugar $45.5{\sim}63.5%$). A mathematical analytical tool was employed for the optimization of typical ingredients. The canonical form and trace plot showed the influence of each ingredient in the mixture against final product. By use of F-test, sweetness, pH, L, b, ${\Delta}E$, and firmness were expressed by a linear model, while the spreadmeter value, a, and sensory characteristics (appearance, color, smell, taste, and overall acceptability) were by a quadratic model. The optimum formulations by numerical and graphical method were similar: Chinese quince paste 54.48%, pectin 2.45%, and sugar 53.07%. Optimum ingredient formulation is expected to improve use of Chinese quince and contribute to commercialization of high quality Chinese quince jam.

Scream Sound Detection Based on Universal Background Model Under Various Sound Environments (다양한 소리 환경에서 UBM 기반의 비명 소리 검출)

  • Chung, Yong-Joo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.12 no.3
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    • pp.485-492
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    • 2017
  • GMM has been one of the most popular methods for scream sound detection. In the conventional GMM, the whole training data is divided into scream sound and non-scream sound, and the GMM is trained for each of them in the training process. Motivated by the idea that the process of scream sound detection is very similar to that of speaker recognition, the UBM which has been used quite successfully in speaker recognition, is proposed for use in scream sound detection in this study. We could find that UBM shows better performance than the traditional GMM from the experimental results.

Growth model for Pichia stipitis growing on sugar mixtures (혼합당에서의 Pichia stipitis의 생육 모델)

  • 이유석;권윤중변유량
    • KSBB Journal
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    • v.7 no.4
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    • pp.265-270
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    • 1992
  • Low cost fermentation substrates frequently contain a mixture of carbon sources including hexoses, pentoses and disaccharides. Fermentation of such mixtures requires an understanding of how each of these substrates is utilized. During batch culture of Pichia stipitis CBS 5776 on sugar mixtures, glucose causes catabolite repression of xylose and cellobiose utilization. Also, glucose causes a permanent repression of xylose utilization as evidenced by reduced growth rates during the xylose phase of glucose/xylose fermentation. The growth model for multiple substrates is developed based on a cyclic AMP mediated catabolite repression mechanism and this model adequately described the growth and ethanol production from sugar mixtures.

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An Analytical Study on the Condensation of Submerged Vapor Jets in Subcooled Liquids (과냉수에서의 증기응축제트에 대한 해석적 연구)

  • 김기웅;이계복;김환열
    • Journal of Energy Engineering
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    • v.8 no.2
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    • pp.333-340
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    • 1999
  • A numerical study of turbulent condensing vapor jet submerged in subcooled liquids has been conducted. A physical model of the process is presented employing the locally homogeneous flow approximation of two phase flow in conjunction with a $\kappa$-$\varepsilon$-g model of turbulence properties. In this model the turbulence is represented by differential equations for its kinetic energy and dissipation. A differential equation for the concentration fluctuations is solved and a clipped normal probability distribution function is proposed for the mixture fraction. Effects of steam mass flux, pool temperature and nozzle internal diameter on the condensing vapor jet are also analyzed. The model is evaluated using existing data for turbulent condensing vapor jets. The agreement between the predictions and the available experimental data is good.

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Development of Fatigue Performance Model of Asphalt Concrete using Dissipate Energy

  • Kim, Nak-Seok
    • Journal of the Korean Society of Hazard Mitigation
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    • v.10 no.3
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    • pp.39-43
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    • 2010
  • The main objective of this research is to develop a mechanistic performance predictive model for fatigue cracking of asphalt-aggregate mixtures. Controlled-stress diametral fatigue tests were performed to characterize fatigue cracking of asphalt-aggregate mixtures. Performance prediction model for fatigue cracking was developed using the internal damage ratio (IDR) growth method. In the IDR growth method, the general concepts of the dissipated energy, the reference tensile strain, the threshold tensile strain, and the strain shift factor were introduced. The source of the dissipated energy in the fatigue test is from the intrinsic viscoelastic material property of an asphalt concrete mixture and the damage growth within the asphalt concrete specimen. In controlled-stress mode test, the dissipated energy is gradually increased with an increasing number of load applications.