• Asian-Australasian Journal of Animal Sciences
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• v.28 no.7
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• pp.926-935
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• 2015

The efficiency of genome-wide association analysis (GWAS) depends on power of detection for quantitative trait loci (QTL) and precision for QTL mapping. In this study, three different strategies for GWAS were applied to detect QTL for carcass quality traits in the Korean cattle, Hanwoo; a linkage disequilibrium single locus regression method (LDRM), a combined linkage and linkage disequilibrium analysis (LDLA) and a $BayesC{\pi}$ approach. The phenotypes of 486 steers were collected for weaning weight (WWT), yearling weight (YWT), carcass weight (CWT), backfat thickness (BFT), longissimus dorsi muscle area, and marbling score (Marb). Also the genotype data for the steers and their sires were scored with the Illumina bovine 50K single nucleotide polymorphism (SNP) chips. For the two former GWAS methods, threshold values were set at false discovery rate <0.01 on a chromosome-wide level, while a cut-off threshold value was set in the latter model, such that the top five windows, each of which comprised 10 adjacent SNPs, were chosen with significant variation for the phenotype. Four major additive QTL from these three methods had high concordance found in 64.1 to 64.9Mb for Bos taurus autosome (BTA) 7 for WWT, 24.3 to 25.4Mb for BTA14 for CWT, 0.5 to 1.5Mb for BTA6 for BFT and 26.3 to 33.4Mb for BTA29 for BFT. Several candidate genes (i.e. glutamate receptor, ionotropic, ampa 1 [GRIA1], family with sequence similarity 110, member B [FAM110B], and thymocyte selection-associated high mobility group box [TOX]) may be identified close to these QTL. Our result suggests that the use of different linkage disequilibrium mapping approaches can provide more reliable chromosome regions to further pinpoint DNA makers or causative genes in these regions.

• Journal of Microbiology and Biotechnology
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• v.18 no.2
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• pp.270-282
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• 2008

The yeast Saccharomyces cerevisiae has defense mechanisms identical to higher eukaryotes. It offers the potential for genome-wide experimental approaches owing to its smaller genome size and the availability of the complete sequence. It therefore represents an ideal eukaryotic model for studying cellular redox control and oxidative stress responses. S. cerevisiae Yap1 is a well-known transcription factor that is required for $H_2O_2$-dependent stress responses. Yap1 is involved in various signaling pathways in an oxidative stress response. The Gpx3 (Orp1/PHGpx3) protein is one of the factors related to these signaling pathways. It plays the role of a transducer that transfers the hydroperoxide signal to Yap1. In this study, using extensive proteomic and bioinformatics analyses, the function of the Gpx3 protein in an adaptive response against oxidative stress was investigated in wild-type, gpx3-deletion mutant, and gpx3-deletion mutant overexpressing Gpx3 protein strains. We identified 30 proteins that are related to the Gpx3-dependent oxidative stress responses and 17 proteins that are changed in a Gpx3-dependent manner regardless of oxidative stress. As expected, $H_2O_2$-responsive Gpx3-dependent proteins include a number of antioxidants related with cell rescue and defense. In addition, they contain a variety of proteins related to energy and carbohydrate metabolism, transcription, and protein fate. Based upon the experimental results, it is suggested that Gpx3-dependent stress adaptive response includes the regulation of genes related to the capacity to detoxify oxidants and repair oxidative stress-induced damages affected by Yap1 as well as metabolism and protein fate independent from Yap1.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1491-1500
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• 2008

This paper proposes an intelligent surveillance system to recognize suspicious patterns of the human behavior by using the Hidden Markov Model. First, the method finds foot area of the human by motion detection algorithm from image sequence of the surveillance camera. Then, these foot locus form observation series of features to learn the HMM. The feature that is position of the human foot is changed to each code that corresponds to a specific label among 16 local partitions of image region. Therefore, specific moving patterns formed by the foot locus are the series of the label numbers. The Baum-Welch algorithm of the HMM learns each suspicious and specific pattern to classify the human behaviors. To recognize the inputted human behavior pattern in a test image, the probabilistic comparison between the learned pattern of the HMM and foot series to be tested decides the categorization of the test pattern. The experimental results show that the method can be applied to detect a suspicious person prowling in corridor.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.6
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• pp.69-73
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• 2009

Network Virtual Environments (NVEs) is a virtual world where users exchanges messages via network connection. A limited visibility sphere called area of interest (AOI) is used to reduce the load created by the interactions between users. VON-Forwarding model is proposed as an effective methods to reduce network bandwidth in P2P network environment. Vorocast and Fibocast originated from Von-forwarding resolves the problems to receive the same messages repeatly. In this paper, We proposed an Extended Volocast scheme to improve the problem not to get consistency except a limited area near to the center of AOI. The proposed scheme maintains the consistency about the broad area into AOI by adjusting geometrical series $2^X$. We perform simulation experiments to show that the proposed scheme provide better performance compared to the other schemes.

• Information Systems Review
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• v.7 no.1
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• pp.195-217
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• 2005

Individual recognizes knowledge sharing as a transaction action. This transaction occurring in knowledge sharing is considered as a special and complicated transaction derived from employee's relationship rather than a economic transaction. In addition, It is important that knowledge sharing among individuals is established through a closed interrelationship with situation. In this point of view, knowledge sharing can be explained by a social exchange relationship. Therefore, there are two study's purpose as follows. First, The study draws factors affecting to knowledge sharing in the view of social exchange theory. The study reviews factors that are presented at previous social exchange theories and affecting to knowledge sharing focused on organization contingency traits, relationship traits, and individuals traits among individuals in an organization. Second, even though trust and organization involvement is resulted in above affecting factors, most previous studies are mainly examined as the same level to other factors affecting to knowledge sharing. Thus, this paper focused that the above factors affect to trust and organization involvement that affect to knowledge sharing intention. That is, this study presents that when affecting factors mediate trust and involvement, there is a knowledge sharing intention for creating organization knowledge. For the study, 160 government employees are administered for the survey so that the research model and hypothesis are developed. Empirical study shows that in public organizations knowledge sharing affects to relationship traits factors and individuals traits affects trust and organization involvement. Also, it is examined that trust and organization involvement affecting to knowledge sharing intention in such a sequence.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.4
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• pp.185-194
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• 2013

Abstract- In distant-talking environments, speech recognition performance degrades significantly due to noise and reverberation. Recent work of Michael L. Selzer shows that in microphone array speech recognition, the word error rate can be significantly reduced by adapting the beamformer weights to generate a sequence of features which maximizes the likelihood of the correct hypothesis. In this approach, called Likelihood Maximizing Beamforming algorithm (Limabeam), one of the method to implement this Limabeam is an UnSupervised Limabeam(USL) that can improve recognition performance in any situation of environment. From our investigation for this USL, we could see that because the performance of optimization depends strongly on the transcription output of the first recognition step, the output become unstable and this may lead lower performance. In order to improve recognition performance of USL, some post-filter techniques can be employed to obtain more correct transcription output of the first step. In this work, as a post-filtering technique for first recognition step of USL, we propose to add a Wiener-Filter combined with Feature Weighted Malahanobis Distance to improve recognition performance. We also suggest an alternative way to implement Limabeam algorithm for Hidden Markov Network (HM-Net) speech recognizer for efficient implementation. Speech recognition experiments performed in real distant-talking environment confirm the efficacy of Limabeam algorithm in HM-Net speech recognition system and also confirm the improved performance by the proposed method.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2019.10a
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• pp.32-32
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• 2019

The aim of this study was to provide basic guidelines for conservation and management of endangered plants in the national parks of Korea. Iris dichotoma Pall. (Iridaceae), which is a popular garden plant, is considered a second-class endangered species by Korean government and it is listed as a EN (Endangered) species in Red Data Book of Korea. We analyzed ecological conditions of I. dichotoma habitats based on vegetation properties and soil characteristics. This species which is known to inhabit in grassland adjacent to the ocean of lowlands slope and its population was located at an elevation of 8 m to 11 m. In the study sites, the mean of soil organic matter, total nitrogen and soil pH were 6.16%, 0.234% and 5.39 respectively. Additionally, the genetic variation and structure of three populations were assessed using ISSR (Inter Simple Sequence Repeat) markers. The genetic diversity of I. dichotoma (P = 59.46%, H = 0.206, S = 0.310) at the species level was relatively high. Analysis of molecular variance (AMOVA) showed 82.1% of the total genetic diversity was occurred in within populations and 17.9% variation among populations. Lastly, we developed predicted distribution model based on climate and topographic factors by applying SDMs (Species Distribution Models). Consequently, current status of I. dichotoma habitats is limited with natural factors such as the increase of the coverage rate of the herbs due to ecological succession. Therefore, it is essential to establish in situ and ex situ conservation strategies for protecting natural habitats and to require exploring potential and alternative habitats for reintroduction.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.1-8
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• 2019

This paper carried out a study to reduce damage from jellyfish whose population has increased due to global warming. The emergence of jellyfish on the beach could result in casualties from jellyfish stings and economic losses from closures. This paper confirmed from the preceding studies that the pattern of jellyfish's appearance is predictable through machine learning. This paper is an extension of The prediction model of emergence of Busan coastal jellyfish using SVM. In this paper, we used deep neural network to expand from the existing methods of predicting the existence of jellyfish to the classification by index. Due to the limitations of the small amount of data collected, the 84.57% prediction accuracy limit was sought to be resolved through data expansion using bootstraping. The expanded data showed about 7% higher performance than the original data, and about 6% better performance compared to the transfer learning. Finally, we used the test data to confirm the prediction performance of jellyfish appearance. As a result, although it has been confirmed that jellyfish emergence binary classification can be predicted with high accuracy, predictions through indexation have not produced meaningful results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.115-123
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• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

• Smart Media Journal
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• v.12 no.9
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• pp.19-27
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• 2023

Since morphemes are the smallest unit of meaning in Korean, it is necessary to develop an accurate morphemes analyzer to improve the performance of the Korean language model. However, most existing analyzers present morpheme analysis results by learning word unit tokens as input values. However, since Korean words are consist of postpositions and affixes that are attached to the root, even if they have the same root, the meaning tends to change due to the postpositions or affixes. Therefore, learning morphemes using word unit tokens can lead to misclassification of postposition or affixes. In this paper, we use morpheme-level tokens to grasp the inherent meaning in Korean sentences and propose a morpheme analyzer based on a sequence generation method using Transformer. In addition, a user dictionary is constructed based on corpus data to solve the out - of-vocabulary problem. During the experiment, the morpheme and morpheme tags printed by each morpheme analyzer were compared with the correct answer data, and the experiment proved that the morpheme analyzer presented in this paper performed better than the existing morpheme analyzer.