• Title/Summary/Keyword: mixed data set

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LIFETIME PRODUCTION PERFORMANCE OF HOLSTEIN FRIESIAN × SAHIWAL CROSSBREDS

  • Chaudhry, M.Z.;Shafiq, M.
    • Asian-Australasian Journal of Animal Sciences
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    • v.8 no.5
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    • pp.499-503
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    • 1995
  • The performance records of 410 Holstein Friesian crossbred cows belonging to seven genetic groups (Fl, 3/4, 1/4, 5/8, 3/8, triple cross and miscellaneous cross) maintained at Livestock Production Research Institute, Bahadurnagar, Okara were analyzed for various parameters of lifetime traits. For the analysis 2 data sets were made. Data set I included all the cows disposed off from the herd which have completed at least one lactation while for data set II performance traits for only first five lactations were considered. The data was analyzed by Mixed Model Least squares and Maximum Likelihood computer programme PC-I version. The least squares means ${\times}$ standard errors for data set I (periods are in days and milk yield is in litres) were $994.5{\pm}15.5$, $1,877.0{\pm}70.9$, $1,651.9{\pm}19.3$, $2,533.7{\pm}36.5$, $3,530.0{\pm}40.5$, $15,785.2{\pm}320.0$, $8.46{\pm}0.19$, $5.66{\pm}0.16$ and $3.79{\pm}0.08$, respectively for age at first calving (APC), Ist lactation milk yield (FLMY), productive life (PL), herd life (HL), total life (TL), lifetime milk yield (LTMY), milk yield per day of productive life (MY/PL), milk yield per day of herd life (MY/HL) and milk yield per day of total life (MY/TL). For data set II these values were $1,004.2{\pm}21.2$, $2,220.5{\pm}113.1$, $1,429.1{\pm}40.8$, $2,302.1{\pm}73.3$, $3,307.2{\pm}77.3$, $13,189.7{\pm}667.4$, $9.10{\pm}0.34$, $5.66{\pm}0.25$ and $4.02{\pm}0.18$ in the same order. For data set I the effect of year of first calving was significant for AFC, FLMY, PL, HL, LTMY and MY/PL. The season of Ist calving was significant only for MY/PL. The effect of genetic group was significant for AFC, FLMY, MY/PL and MY/TL while the effect of parity was significant for all the traits. For data set II the effect of year of Ist calving was significant only for AFC, FLMY and PL while the season of Ist calving was significant for FLMY and PL while the effect of genetic groups was significant for MY/HL only. The lifetime production performance is in general close to the various estimates reported in the literature.

A Kurtosis-based Algorithm for Blind Sources Separation Using the Cayley Transformation And Its Application to Multi-channel Electrogastrograms

  • Ohata, Masashi;Matsumoto, Takahiro;Shigematsu, Akio;Matsuoka, Kiyotoshi
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.471-471
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    • 2000
  • This paper presents a new kurtosis-based algorithm for blind separation of convolutively mixed source signals. The algorithm whitens the signals not only spatially but also temporally beforehand. A separator is built for the whitened signals and it exists in the set of para-unitary matrices. Since the set forms a curved manifold, it is hard to treat its elements. In order to avoid the difficulty, this paper introduces the Cayley transformation for the para-unitary matrices. The transformed matrix is referred to as para-skew-Hermitian matrix and the set of such matrices forms a linear space. In the set of all para-skew-Hermitian matrices, the kurtosis-based algorithm obtains a desired separator. This paper also shows the algorithm's application to electrogastrogram datum which are observed by 4 electrodes on subjects' abdomen around their stomachs. An electrogastrogram contains signals from a stomach and other organs. This paper obtains independent components by the algorithm and then extracts the signal corresponding to the stomach from the data.

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Optimization-Based Pattern Generation for LAD (최적화에 근거한 LAD의 패턴생성 기법)

  • Jang, In-Yong;Ryoo, Hong-Seo
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2005.10a
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    • pp.409-413
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    • 2005
  • The logical analysis of data(LAD) is an effective Boolean-logic based data mining tool. A critical step in analyzing data by LAD is the pattern generation stage where useful knowledge and hidden structural information in data is discovered in the form of patterns. A conventional method for pattern generation in LAD is based on term enumeration that renders the generation of higher degree patterns practically impossible. In this paper, we present a new optimization-based pattern generation methodology and propose two mathematical programming medels, a mixed 0-1 integer and linear programming(MILP) formulation and a well-studied set covering problem(SCP) formulation for the generation of optimal and heuristic patterns, respectively. With benchmark datasets, we demonstrate the effectiveness of our models by automatically generating with much ease patterns of high complexity that cannot be generated with the conventional approach.

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A longitudinal study for child aggression with Korea Welfare Panel Study data (한국복지패널 자료를 이용한 아동기 공격성에 대한 경시적 자료 분석)

  • Choi, Nayeon;Huh, Jib
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.6
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    • pp.1439-1447
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    • 2014
  • Most of literatures on Korean child aggression are based on using the cross-sectional data sets. Although there is a related study with a longitudinal data set, it is assumed that the data sets measured repeatedly in the longitudinal data are mutually independent. A longitudinal data analysis for Korean child aggression is then necessary. This study is to analyze the effect of child development outcomes including academic achievement, self-esteem, depression anxiety, delinquency, victimization by peers, abuse by parents and internet using time on child aggression with Korea Welfare Panel Study data observed three times between 2006 and 2012. Since Korea Welfare Panel Study data have missing values, the missing at random is assumed. The linear mixed effect model and the restricted maximum likelihood estimation are considered.

A study on intrusion detection performance improvement through imbalanced data processing (불균형 데이터 처리를 통한 침입탐지 성능향상에 관한 연구)

  • Jung, Il Ok;Ji, Jae-Won;Lee, Gyu-Hwan;Kim, Myo-Jeong
    • Convergence Security Journal
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    • v.21 no.3
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    • pp.57-66
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    • 2021
  • As the detection performance using deep learning and machine learning of the intrusion detection field has been verified, the cases of using it are increasing day by day. However, it is difficult to collect the data required for learning, and it is difficult to apply the machine learning performance to reality due to the imbalance of the collected data. Therefore, in this paper, A mixed sampling technique using t-SNE visualization for imbalanced data processing is proposed as a solution to this problem. To do this, separate fields according to characteristics for intrusion detection events, including payload. Extracts TF-IDF-based features for separated fields. After applying the mixed sampling technique based on the extracted features, a data set optimized for intrusion detection with imbalanced data is obtained through data visualization using t-SNE. Nine sampling techniques were applied through the open intrusion detection dataset CSIC2012, and it was verified that the proposed sampling technique improves detection performance through F-score and G-mean evaluation indicators.

Effects of Food Waste Mixed Organic Fertilizer Treatment on Growth and Yield of Capsicum annuum

  • Ho-Jun Gam;Yosep Kang;Eun-Jung Park;Seong-Heon Kim;Sang-Mo Kang;In-Jung Lee
    • Proceedings of the Korean Society of Crop Science Conference
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    • 2022.10a
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    • pp.109-109
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    • 2022
  • The global population is increasing every year, and the amount of food waste is also increasing. Direct landfilling of food waste has been prohibited since 2005, and in accordance with the London Convention in 2013, the discharge of livestock manure, sewage sludge, and food waste into the sea is prohibited. In the case of incineration to treat the discharged food waste, the heat point is lowered due to the moisture in the food waste itself, so fuel must be added. Therefore, this study was conducted to get basic data for setting the limit of application by investigating the growth and yield of crops after treating food waste dry powder mixed fertilizer (MF) on red pepper. In the experiment, continuous cultivation was carried out for two years in 2021 (1st year) and 2022 (2nd year). The treatment groups were set as Not Treatment (NT), Chemical Fertilizer (CF), Mixed Fertilizer (MF), Mixed Fertilizer×2 (MF×2). After harvest, crop growth and yield were investigated. As a result of the 1st years of growth survey, CF, MF, MF×2 show significant difference in shoot length compared to NT. About fresh weight and dry weight, CF show significant difference compared to NT. The 2nd years of growth survey, the shoot and root length, fresh weight did not show significant difference with NT. In case of dry weight, MF is significant increased compared to NT. As a result of the yield survey of the 1st year, all treatment groups did not show a significance in yield compared to the NT. In case of 2nd year, all treatment groups show significantly increased value compared to NT. The yield of MF was highest among the treatment groups. In the future, it is thought that it is necessary to quantitatively evaluate the effect of food waste dry powder mixed fertilizer through additional experiments and continuous cultivation, and to establish an appropriate amount of use and establishment of a manual based on this.

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Logistic Regression Type Small Area Estimations Based on Relative Error

  • Hwang, Hee-Jin;Shin, Key-Il
    • The Korean Journal of Applied Statistics
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    • v.24 no.3
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    • pp.445-453
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    • 2011
  • Almost all small area estimations are obtained by minimizing the mean squared error. Recently relative error prediction methods have been developed and adapted to small area estimation. Usually the estimators obtained by using relative error prediction is called a shrinkage estimator. Especially when data set consists of large range values, the shrinkage estimator is known as having good statistical properties and an easy interpretation. In this paper we study the shrinkage estimators based on logistic regression type estimators for small area estimation. Some simulation studies are performed and the Economically Active Population Survey data of 2005 is used for comparison.

Bayesian Approach for Software Reliability Models (소프트웨어 신뢰모형에 대한 베이지안 접근)

  • Choi, Ki-Heon
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.1
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    • pp.119-133
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    • 1999
  • A Markov Chain Monte Carlo method is developed to compute the software reliability model. We consider computation problem for determining of posterior distibution in Bayseian inference. Metropolis algorithms along with Gibbs sampling are proposed to preform the Bayesian inference of the Mixed model with record value statistics. For model determiniation, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions. A numerical example with simulated data set is given.

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Nonparametric Bayesian methods: a gentle introduction and overview

  • MacEachern, Steven N.
    • Communications for Statistical Applications and Methods
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    • v.23 no.6
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    • pp.445-466
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    • 2016
  • Nonparametric Bayesian methods have seen rapid and sustained growth over the past 25 years. We present a gentle introduction to the methods, motivating the methods through the twin perspectives of consistency and false consistency. We then step through the various constructions of the Dirichlet process, outline a number of the basic properties of this process and move on to the mixture of Dirichlet processes model, including a quick discussion of the computational methods used to fit the model. We touch on the main philosophies for nonparametric Bayesian data analysis and then reanalyze a famous data set. The reanalysis illustrates the concept of admissibility through a novel perturbation of the problem and data, showing the benefit of shrinkage estimation and the much greater benefit of nonparametric Bayesian modelling. We conclude with a too-brief survey of fancier nonparametric Bayesian methods.

Compact CNN Accelerator Chip Design with Optimized MAC And Pooling Layers (MAC과 Pooling Layer을 최적화시킨 소형 CNN 가속기 칩)

  • Son, Hyun-Wook;Lee, Dong-Yeong;Kim, HyungWon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.9
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    • pp.1158-1165
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    • 2021
  • This paper proposes a CNN accelerator which is optimized Pooling layer operation incorporated in Multiplication And Accumulation(MAC) to reduce the memory size. For optimizing memory and data path circuit, the quantized 8bit integer weights are used instead of 32bit floating-point weights for pre-training of MNIST data set. To reduce chip area, the proposed CNN model is reduced by a convolutional layer, a 4*4 Max Pooling, and two fully connected layers. And all the operations use specific MAC with approximation adders and multipliers. 94% of internal memory size reduction is achieved by simultaneously performing the convolution and the pooling operation in the proposed architecture. The proposed accelerator chip is designed by using TSMC65nmGP CMOS process. That has about half size of our previous paper, 0.8*0.9 = 0.72mm2. The presented CNN accelerator chip achieves 94% accuracy and 77us inference time per an MNIST image.