• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.73-83
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• 2017

This study was conducted to evaluate the chemical composition, digestibility, and energy value of 15 non-conventional feeds produced in South Korea as ruminant feeds. Three Hanwoo steers (body weight, $520{\pm}20.20kg$) fitted with a permanent rumen cannula and duodenal cannula were housed individually in tie-stall barns, followed by a 14-day adaptation period and 3-day experimental period. Chemical composition analysis, in situ nylon bag, and mobile bag technique were used as experiments. As a result of the chemical composition analysis offeeds, crude protein (CP) contentsofmalt meal, perilla meal, soy sauce cake, and soymilk residue were greater than 30%. As a result of the degradability characteristics analysis of feeds using an in situ nylon bag, rumen undegraded protein (RUP) contents of beet pulp, brewer's grain, coffee meal, malt meal, milo bran, perilla meal, ramen residue, and soymilk residue were greater than 50%. Analysis of total digestible nutrient (TDN) values of feeds using an in situ mobile bag showed that TDN values of beet pulp, brewer's grain, makgeolli residue, milo bran, perilla meal, ramen residue, rice bran, soy sauce cake, soybean curd cake, soymilk residue, and wheat bran weregreater than 50%. In summary, these non-conventional feeds have high potential value as good feed resources to replace formulated feeds or roughage. Therefore, the chemical composition, digestibility, and energy value of non-conventional feeds obtained from this study can be used as base data for the manufacture of ruminant total mixed ration (TMR) with improved feed efficiency, reduced feed costs, and reduction of environmental pollution.

• Analytical Science and Technology
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• v.24 no.3
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• pp.212-218
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• 2011

In this study, temporal stability of 5 reduced sulfur compounds (RSCs) including ($H_2S$, $CH_3SH$, DMS, $CS_2$, and DMDS) was investigated up to 30 days. To learn the temporal changes in RSC concentration levels, two types of long-term storage experiment were carried out by employing two different approaches for sample storing in Tedlar bag samplers. The first one named as a forward (F) storage method consists of preparing all samples in the beginning of experiment. All these samples were analyzed sequentially through time. The second approach named as a reversed (R) storage method was carried out by preparing each sample through time and by analyzing all of them in the last day. For these experiments, RSC standards were prepared at 10 ppb in 10 L Tedlar bag. The results of both methods were consistent enough to show a tendency of the concentration reduction through time. Moreover, the lightest RSC, $H_2S$ showed the most significant reduction of 84.8% at the end of experiment. To validate difference between these results, t-test was applied to the data obtained between the two methods at 90% significance level. According to t-test, the results of the two approaches were greatly distinguished from 3 RSCs ($H_2S$, $CH_3SH$, and DMDS). The results also indicated that the temporal reduction of RSC differs greatly between light ($H_2S$ and $CH_3SH$) and heavy RSCs (DMS, DMDS, and $CS_2$). The former generally exhibited much significant reduction through time due probably to their lower stability.

• Journal of Fashion Business
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• v.12 no.6
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• pp.93-106
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• 2008

The purpose of this study is to analyze design characteristics of Chanel bags by its type and shape, material, color, pattern, decorations in order to give information about foundation of the development of Korean fashion brand handbags and help suggest predict future handbag trends. The methods of this study are documentary research and demonstrative research. For the documentary research, mainly previous researches and fashion related data were used. For the demonstrative research, the total of 288 design photos of Chanel bag were selected from 2001 S/S season to 2008 F/W season fashion collections of firstview.com., style.com., and mode et mode. The results of this study are as follows; First, type and shape of bags are clutch(30%), flap(25%), shoulder(25%), others(10%), tote(5%), hobo(5%). Second, the material data shows that leather(46%), mixed(18%), fabric(17%), synthetics (10%), patent(3%), others(3%), and suede(2%). Third, the patterns are geometrical(27%), solid(25%), combination(24%), symbolic(11%), abstract(11%), nature(2%). Fourth, the colors are largely monochrome(73%) and multicolor(27%). In case of monochrome, achromatic color(50%) is more than chromatic color(23%). The chromatic color is consisted of YR(9%), Y(9%), R(5%), RP(4%), PB(2%), P(1%), GY(0.5%), BG(0.5%). The multicolor is consisted of similar coloration(12%), contrast coloration(10%), and accent coloration(5%). Fifth, the decoration data shows that metallic(53%), plain(23%), combination(22%), and handcrafted(1%).

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.39-47
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• 2017

The Internet have increased the number of digital web documents related to the history and traditions of Korean Culture. However, users who search for creators or materials related to traditional cultures are not able to get the information they want and the results are not enough. Document classification is required to access this effective information. In the past, document classification has been difficult to manually and manually classify documents, but it has recently been difficult to spend a lot of time and money. Therefore, this paper develops an automatic text classification model of traditional cultural contents based on the data of the Korean information culture field composed of systematic classifications of traditional cultural contents. This study applied TF-IDF model, Bag-of-Words model, and TF-IDF/Bag-of-Words combined model to extract word frequencies for 'Korea Traditional Culture' data. And we developed the automatic text classification model of traditional cultural contents using Support Vector Machine classification algorithm.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.163-170
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• 2019

Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.11-20
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• 2024

In this paper, we propose the BAG (Bit-width Aware Generator) and the Intermediate Layer Knowledge Distillation using Channel-wise Attention to reduce the knowledge gap between a quantized network, a full-precision network, and a generator in GDFQ (Generative Data-Free Quantization). Since the generator in GDFQ is only trained by the feedback from the full-precision network, the gap resulting in decreased capability due to low bit-width of the quantized network has no effect on training the generator. To alleviate this problem, BAG is quantized with same bit-width of the quantized network, and it can generate synthetic images, which are effectively used for training the quantized network. Typically, the knowledge gap between the quantized network and the full-precision network is also important. To resolve this, we compute channel-wise attention of outputs of convolutional layers, and minimize the loss function as the distance of them. As the result, the quantized network can learn which channels to focus on more from mimicking the full-precision network. To prove the efficiency of proposed methods, we quantize the network trained on CIFAR-100 with 3 bit-width weights and activations, and train it and the generator with our method. As the result, we achieve 56.14% Top-1 Accuracy and increase 3.4% higher accuracy compared to our baseline AdaDFQ.

• Fire Science and Engineering
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• v.28 no.6
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• pp.76-81
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• 2014

The purpose of this study was to comparative analysis of tidal volume and airway pressure after one-rescuer BVM, two-rescuer BVM, advanced airway devices with a Bag-valve mask using RespiTrainer. The data were obtained from June 2 to 10 in 2014. The collected data were analyzed using the SPSS WIN 18.0 program. The results showed that BVM ventilation using the endotracheal intubation produced higher mean tidal volume $497{\pm}78mL$, Two-rescuer ventilation $479{\pm}91mL$ One-rescuer ventilation $386{\pm}59mL$, King LTS-D $365{\pm}05mL$, Laryngeal mask airway (LMA) $351{\pm}35mL$. Peak airway pressure was higher in BVM ventilation using the endotracheal intubation. As a result, the study confirmed that the BVM Ventilation by endotracheal intubation and Two-rescuer BVM ventilation to one third the bag depth squeeze method is appropriate.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.10a
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• pp.76-80
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• 2003

A resource management system of for the paper-bag product company was made. The objective of the system is computering the hand writing management process to improve the performance of working process of the small company. For this, some programming and design strategies were made such as the simplification, ease to use, similarity with old system and object oriented concept. It is expected to reduce burdens and errors of hand writing and improve the efficiencies of small company.

• Journal of the Korean Society of Clothing and Textiles
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• v.19 no.6
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• pp.895-910
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• 1995

Journal of the Korean Society of Clothing and Textiles Vol. 19, No. 6 (1995) p. 895~910 The purpose of study was to estimate the consistency between brand image and shopping bag image, and also to find out the important factors which constructed the brand image. This study was conducted by means of a questionnaire survey of female students of moi or universities in Seoul. Frequency, percentage, mean, factor analysis, 1-test, ANOVA, Fisher's LSD, Coster analysis, MDS are used for data analysis. The result are as follows: 1) Brand image was devised into three factors: personality/modernity, nobility and usability. 2) The rate of consistency of brand image and shopping bag image was high in the brands of Benetton, System, Cresson, Be-art, Tomboy, Guess, Esprit, Anacapri, Mercoledi, Youngwoo in descending order. 3) The most important factor which represent the brand image was the choice of color. 4) This study found that shopping bags can be advertising media because they think that shopping bags played an important role as a walking advertising media on the street.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2093-2108
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• 2017

In this paper, we describe a novel method for recognizing human actions from different views via view knowledge transfer. Our approach is characterized by two aspects: 1) We propose a unsupervised topic transfer model (TTM) to model two view-dependent vocabularies, where the original bag of visual words (BoVW) representation can be transferred into a bag of topics (BoT) representation. The higher-level BoT features, which can be shared across views, can connect action models for different views. 2) Our features make it possible to obtain a discriminative model of action under one view and categorize actions in another view. We tested our approach on the IXMAS data set, and the results are promising, given such a simple approach. In addition, we also demonstrate a supervised topic transfer model (STTM), which can combine transfer feature learning and discriminative classifier learning into one framework.