• Journal of the Korea Organic Resources Recycling Association
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• v.25 no.2
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• pp.77-90
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• 2017

In order to promote recycling of animal and plant residues, it is necessary to prepare detailed statistics on the sources, generation amount and the state of disposal so that waste recycling companies and enterprises can obtain the information easily. Also, the recycling methods specified in the law should be appropriate. For this, the study reviewed the appropriateness of detailed classification of animal and plant residues and permitted recycling methods in the Enforcement Regulations of the Waste Management Act of Korea. For improvement of the detailed classification, the study conducted literature review on European and Japanese ones. Additionally, we visited slaughterhouses of livestock and poultry, vegetable oils manufacturing companies, starches and glucose or maltose manufacturing companies, which generate the waste and recycle the waste, to grasp the status of recycling in Korea. Based on the results, the study proposes improvement measures for the detailed classification and the permitted recycling types in the law.

• Journal of the Ergonomics Society of Korea
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• v.30 no.6
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• pp.705-713
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• 2011

Objective: The aim of this study is to compare results of emotion recognition by several algorithms which classify three different emotional states(happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. While three kinds of emotional stimuli were presented to participants, ANS responses(EDA, SKT, ECG, RESP, and PPG) as physiological signals were measured in twice first one for 60 seconds as the baseline and 60 to 90 seconds during emotional states. The obtained signals from the session of the baseline and of the emotional states were equally analyzed for 30 seconds. Participants rated their own feelings to emotional stimuli on emotional assessment scale after presentation of emotional stimuli. The emotion classification was analyzed by Linear Discriminant Analysis(LDA, SPSS 15.0), Support Vector Machine (SVM), and Multilayer perceptron(MLP) using difference value which subtracts baseline from emotional state. Results: The emotional stimuli had 96% validity and 5.8 point efficiency on average. There were significant differences of ANS responses among three emotions by statistical analysis. The result of LDA showed that an accuracy of classification in three different emotions was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study confirmed that the three emotions can be better classified by LDA using various physiological features than SVM and MLP. Further study may need to get this result to get more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms. Application: This could help get better chances to recognize various human emotions by using physiological signals as well as be applied on human-computer interaction system for recognizing human emotions.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.21-30
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• 2014

The use of induction motors has been recently increasing in a variety of industrial sites, and they play a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of induction motors in order to reduce economical damage caused by their faults. To early identify induction motor faults, this paper effectively estimates spectral envelopes of each induction motor fault by utilizing a linear prediction coding (LPC) analysis technique and an expectation maximization (EM) algorithm. Moreover, this paper classifies induction motor faults into their corresponding categories by calculating Mahalanobis distance using the estimated spectral envelopes and finding the minimum distance. Experimental results show that the proposed approach yields higher classification accuracies than the state-of-the-art conventional approach for both noiseless and noisy environments for identifying the induction motor faults.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.1-12
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• 2012

The agricultural area was classified into dry and paddy fields in this study using the near-infrared band of Landsat TM to extract land cover classes that need to the application of Expected Mean Concentration (EMC) in nonpoint source works. The accuracy of image classification of the land cover map from Landsat TM image showed 83.61% and 78.41% respectively by comparing with the large and middle scale land cover map of Ministry of Environment. As the result of Soil Conservation Service (SCS) Curve Number (CN) using the land cover map from image classification, Dongbok dam and Dongbok stream basin were analyzed high. Also Geymbaek water-gage and Bosunggang upstream basin showed high in the analysis of EMC of BOD, TN, TP by basin. And also Geymbaek water-gage and Bosunggang upstream basin showed high in the analysis of non-point source through coupling with direct runoff. Therefore these basins were selected with the main area for the management of nonpoint source.

• The Research Journal of the Costume Culture
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• v.11 no.6
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• pp.835-851
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• 2003

This study analyses patent information of apparel design using computer technology and researches the trend of patent application focused on International Patent Classification. In terms of trend by filling data, Patent application started first in 1974 and increased sharply in 1993 with 14 cases and increased to 25 cases in 2000. In case of Korea, they began somewhat late in 1996, but reached a similar level with the leading country in 2000. In terms of trend by applicant, Gerber Garment Technology, Inc. filed 7 cases TORAY IND INC, filed 6 cases Levi Strauss & Co. filed 4 cases, NEC HOME ELECTRONICS LTD filed 3 cases, TOYOBO CO LTD filed 3 cases. Japanese companies occupied 52% and United States's companies occupied 48%. In terms of trend by country, foreigner occupied 47% of the patents filed by United State. Japanese take up 10% of total patent of United States. Korean occupied 84% of total patent of Korea and foreigner, american occupied 16% of the patents filed by Korea. In regared to International Patent Classification, in the section level G filed 92 cases(53%). In class level, G06 marked the first place in United States, Japan, and Korea. In subclass level, G06F marksed the first place with 74 cases. G06T and A61B were regarded as the new technologies. The new technologies are representing the dimensions of garment or computer-rendered model, providing the virtual reality through the texture mapping, digital dressing room or virtual dressing, and performing or retriving display on a screen for the result of changing pattern ao dress design, The technologies of core patent are designing or producing custom manufactured item, providing or prealtering the data for pattern making and visually displaying, interactively generating or previewing of various articles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2304-2320
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• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.365-370
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• 2023

This paper aims to compare the performance of speech data classification into two groups, adult and elderly, based on the acoustic and linguistic characteristics that change due to aging, such as changes in respiratory patterns, phonation, pitch, frequency, and language expression ability. For acoustic features we used attributes related to the frequency, amplitude, and spectrum of speech voices. As for linguistic features, we extracted hidden state vector representations containing contextual information from the transcription of speech utterances using KoBERT, a Korean pre-trained language model that has shown excellent performance in natural language processing tasks. The classification performance of each model trained based on acoustic and linguistic features was evaluated, and the F1 scores of each model for the two classes, adult and elderly, were examined after address the class imbalance problem by down-sampling. The experimental results showed that using linguistic features provided better performance for classifying adult and elderly than using acoustic features, and even when the class proportions were equal, the classification performance for adult was higher than that for elderly.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6B
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• pp.615-625
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• 2008

In this study, classification of streams was conducted for Ji Stream, a tributary to the Geum River and Yo Stream, a tributary to the Seomjin River, and in addition, channel evolution model to the same streams was applied. The classification approaches suggested by Rosgen and Korea Institute of Construction Technology (KICT) were conducted. The channel evolution model suggested by Schumm et al. (1984) was applied. Based on the application results of Rosgen approach, Ji Stream and Yo stream show the characteristics of mountainous stream with pebbles. The application results of channel evolution model indicated that the current condition of Ji Stream and Yo Stream is a state of equilibrium, balancing the sediment supply and sediment transport capacity. The results of this study can be used as a fundamental data for water control project, river restoration and appropriate channel planning.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.57-66
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• 2023

In this study, we propose a novel research framework for the recommendation system that can estimate the user's emotional state and reflect it in the recommendation process by applying deep learning techniques and emotion AI (artificial intelligence). To this end, we build an emotion classification model that classifies each of the seven emotions of angry, disgust, fear, happy, sad, surprise, and neutral, respectively, and propose a model that can reflect this result in the recommendation process. However, in the general emotion classification data, the difference in distribution ratio between each label is large, so it may be difficult to expect generalized classification results. In this study, since the number of emotion data such as disgust in emotion image data is often insufficient, correction is made through augmentation. Lastly, we propose a method to reflect the emotion prediction model based on data through image augmentation in the recommendation systems.

• Journal of Korean Ophthalmic Optics Society
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• v.14 no.4
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• pp.53-57
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• 2009

Purpose: To study the relationship of N.P.C, heterophoria and near convergence and divergence by amount of the refractive error. Methods: All subjects have no ocular disease and their mean age is 22.7. All 39 subjects examined about refractive error, near point of convergence (NPC), heterophoria, near convergence and divergence. Results: Classified by low refractive state, middle refractive state, and high refractive state compared with the expected value of Morgan. NPC had been measured within low and middle refractive state eight all the 8cm. However, high refractive state measured 9.64 cm. Low and middle refractive state for the classification by near phoria. Near esophoria groups was smallest by near divergence and exophoria groups was smallest by near convergence. Conclusions: Near convergence were largest with esophoria while near divergence were largest with exophoria.