• Smart Structures and Systems
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• v.5 no.2
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• pp.153-171
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• 2009

In this paper, the analytical fault detection and diagnosis (FDD) is presented using model-based and signal-based methodology with wavelet analysis on signals obtained from sensors and sensor networks. In the model-based FDD, we present the modeling of contact interface found in soft materials, including the biomedical contacts. Fingerprint analysis and signal-based FDD are also presented with an experimental framework consisting of a mechanical pneumatic system typically found in manufacturing automation. This diagnosis system focuses on the signal-based approach which employs multi-resolution wavelet decomposition of various sensor signals such as pressure, flow rate, etc., to determine leak configuration. Pattern recognition technique and analytical vectorized maps are developed to diagnose an unknown leakage based on the established FDD information using the affine mapping. Experimental studies and analysis are presented to illustrate the FDD methodology. Both model-based and wavelet-based FDD applied in contact interface and manufacturing automation have implication towards better quality of life by applying theory and practice to understand how effective diagnosis can be made using intelligent FDD. As an illustration, a model-based contact surface technology an benefit the diabetes with the detection of abnormal contact patterns that may result in ulceration if not detected and treated in time, thus, improving the quality of life of the patients. Ultimately, effective diagnosis using FDD with wavelet analysis, whether it is employed in biomedical applications or manufacturing automation, can have impacts on improving our quality of life.

• Journal of the East Asian Society of Dietary Life
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• v.19 no.6
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• pp.1009-1017
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• 2009

This study was performed to analyze the preferences and actual use patterns of university students (96 males and 187 females) for bakeries in the Gyeongju area of Korea. A total of 283 questionnaires were used for the examination and statistical analyses were completed using SPSS Win (14.0) by descriptive analysis and $x^2$-tests. The most favored bakery products were prepared items such as sandwiches and toast. Most of the respondents (92.9%) typically used bread for snacks, and the main places of purchase were well-known bakery shops (38.5%) in which females preferred well-known shops more than males. In addition, the respondents liked milk (79.9%) and jam (39.7%) as the beverage and food, respectively, to eat with bread. When choosing bread, the main selection point was taste (80.2%) and the cost per person per visit was usually 1,000~5,000 won (63.3%). The consumption frequency rate revealed that 49.1% of the students consumed bread as a snack, while 24.8% consumed bread with other foods 1~2 times a week. In the case of purchasing bread as a snack, females had more purchases than males (p<0.05). Students who lived in their own home (p<0.001) with a commute time to school greater than 30 minutes (p<0.001) had the highest number of bread purchases as a snack. The most important point for bread purchase was hygiene (4.60). Overall, for the development of bakeries in the Gyeongju area it seems imperative to address the bakery shop environment, including such aspects as hygiene, price, and new bread product development for students.

• Journal of Digital Contents Society
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• v.19 no.5
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• pp.995-1002
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• 2018

The study was intended to identify the factors that affect pediatric nurses' perception toward child abuse and their reporting attitude. The report rate was very low, only 3 (9.1%) of respondents. Most of them did not report, 30(90.9%), and the main causes of non-reporting were not-serious or lack of evidence. The recognition and experience of child abuse depending on the characteristics of nurses in pediatric wards showed a significant difference according to their academic background (X2=16.52, p=.011). The results of the review of the differences in the reported attitudes of child abuse nurses showed a significant difference in the age of nurses in the pediatric ward (X2=13.64, p=.034). Nurses in the pediatric ward are required to develop intervention programs and tools for assessing child abuse, and to provide education and systems for the prevention of child abuse, which will make it necessary for the universal reporting of cases against child abuse and the prevention of child abuse.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1
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• pp.87-92
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• 1996

Transient signals in underwater show several characterisrics, that is, short duration, strong nonstationarity, various types of transient sources, which make it difficult to analyze and classify transient signals. In this paper, the feature vector extraction method for transient SOMAR signals is discussed by applying digital signal processing methods to the analysis of transient signals. A feature vector extraction methods using wavelet transform, which enable us to obtain better recognition rate than automatic classification using the classical method, are proposed. It is confirmed by simulation that the proposed method using wavelet transform performs better than the classical method even with smaller number of feature vectors. Especially, the feature vector extraction method using PR-QMF wavelet transform with the Daubechies coefficients is shown to perform well in noisy environment with easy implementation.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.223-230
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• 2001

Since self-organizing map (SOM) preserves the topology of ordering in input spaces and trains itself by unsupervised algorithm, it is Llsed in many areas. However, SOM has a shortcoming: structure cannot be easily detcrmined without many trials-and-errors. Structure-adaptive self-orgnizing map (SASOM) which can adapt its structure as well as its weights overcome the shortcoming of self-organizing map: SASOM makes use of structure adaptation capability to place the nodes of prototype vectors into the pattern space accurately so as to make the decision boundmies as close to the class boundaries as possible. In this scheme, the initialization of weights of newly adapted nodes is important. This paper proposes a method which optimizes SASOM with genetic algorithm (GA) to determines the weight vector of newly split node. The leanling algorithm is a hybrid of unsupervised learning method and supervised learning method using LVQ algorithm. This proposed method not only shows higher performance than SASOM in terms of recognition rate and variation, but also preserves the topological order of input patterns well. Experiments with 2D pattern space data and handwritten digit database show that the proposed method is promising.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.4
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• pp.17-22
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• 2020

Behavior-based authentication technology, which is currently being researched a lot, requires a long extraction of a lot of data to increase the recognition rate of authentication compared to other authentication technologies. This paper uses the touch sensor and the gyroscope embedded in the smartphone in the Android environment to measure five times to the user to use only the minimum data that is essential among the behavior feature data used in the behavior-based authentication study. By requesting, a total of six behavior feature data were collected by touching the five touch screen, and the mean value was calculated from the changes in data during the next touch measurement to measure the cosine similarity between the value and the measured value. After generating the allowable range of cosine similarity by performing, we propose a user behavior based authentication method that compares the cosine similarity value of the authentication attempt data. Through this paper, we succeeded in demonstrating high performance from the first EER of 37.6% to the final EER of 1.9% by adjusting the threshold applied to the cosine similarity authentication range even in a small number of feature data and experimenter environments.

• Proceedings of the Korea Association of Information Systems Conference
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• 2000.11a
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• pp.1.2-24
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• 2000

The field of neural networks has been a recent surge in activity as a result of progress in developments of efficient training algorithms. For this reason, and coupled with the widespread availability of powerful personal computer hardware for running simulations of networks, there is increasing focus on the potential benefits this field can offer. The neural network may be viewed as an advanced pattern recognition technique and can be applied in many areas such as financial time series forecasting, medical diagnostic expert system and etc.. The intention of this study is to build and implement one simple artificial neural networks hereinafter called ANN. For this purpose, some literature survey was undertaken to understand the structures and algorithms of ANN theoretically. Based on the review of theories about ANN, the system adopted 3-layer back propagation algorithms as its learning algorithm to simulate one case of medical diagnostic model. The adopted ANN algorithm was performed in PC by using turbo PASCAL and many input parameters such as the numbers of layers, the numbers of nodes, the number of cycles for learning, learning rate and momentum term. The system output more or less successful results which nearly agree with goals we assumed. However, the system has some limitations such as the simplicity of the programming structure and the range of parameters it can dealing with. But, this study is useful for understanding general algorithms and applications of ANN system and can be expanded for further refinement for more complex ANN algorithms.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.1-18
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• 2017

Recently, identification of the extremely stressed condition of children is an essential skill for real-time recognition of a dangerous situation because incidents of children have been dramatically increased. In this paper, therefore, we present a model based on machine learning techniques for stress status identification of a child by using bio-signals such as voice and heart rate that are major factors for presenting a child's emotion. In addition, a smart band for collecting such bio-signals and a mobile application for monitoring child's stress status are also suggested. Specifically, the proposed method utilizes stress patterns of children that are obtained in advance for the purpose of training stress status identification model. Then, the model is used to predict the current stress status for a child and is designed based on conventional machine learning algorithms. The experiment results conducted by using a real-world dataset showed that the possibility of automated detection of a child's stress status with a satisfactory level of accuracy. Furthermore, the research results are expected to be used for preventing child's dangerous situations.

• Korean journal of food and cookery science
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• v.16 no.2
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• pp.143-150
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• 2000

A survey was carried out from 143 cooks working at the deluxe hotels in Seoul to obtain the information on the working environment, characteristics, and the satisfaction of the cooks to improve the culinary art training program and the working environment. The results were as follows: Most of the cooks(58.7%) worked for 8-9 hours/day and 51.7% of the cooks earned 1-1.5 million won in a month. The cooks had average 1.6 licenses per person, however, their licenses accorded with their work place only with 85.3%. Subjects were unsatisfied with the pay(55.2%), but 42.7% of the cooks hoped to work in their current work place. If they could transfer to other work place, they wished to run a restaurant of their own. The unsatisfaction rate was high among the cooks worked for 10-15 years, and 19.2% of them was unsatisfied with the environment of work place and 11.5% was in promotion. The longer the working period of the cook, the higher the recognition of the culinary skill. Also, the more frequently transfer to other places, the higher the self-estimation in their skills. Subjects answered that the most important factors for good cooking are the good taste and hygiene. The quality of ingredients was recognized more important among the cooks worked for longer period. The greatest hindrance for the improvement of cooking skill was indicated as insufficient knowledge among the cooks worked under one year, lack of confidence among those worked for 2-5 years, and authoritarianism of seniors for 6-15 years. They answered that the most important qualification for cook is the sincere attitude. The cooks with under 5 years of experience indicated experience and studying attitude and the ones with over 6 years of experience culinary skill as the important factors for cooks. The important factors for promotion was pointed out as culinary skill and human relationship.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.432-441
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• 2002

Though many services offer useful information on internet, computer users are not so familiar with such services that they need an assistant system to use the services easily In the case of web sites, for example, the operators answer the users e-mail questions, but the increasing number of users makes it hard to answer the questions efficiently. In this paper, we propose an assistant system which responds to the users questions automatically and helps them browse the Hanmail Net FAQ (Frequently Asked Question) conceptually. This system uses two-level self-organizing map (SOM): the keyword clustering SOM and document classification SOM. The keyword clustering SOM reduces a variable length question to a normalized vector and the document classification SOM classifies the question into an answer class. Experiments on the 2,206 e-mail question data collected for a month from the Hanmail net show that this system is able to find the correct answers with the recognition rate of 95% and also the browsing based on the map is conceptual and efficient.