• Asia pacific journal of information systems
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• v.1 no.1
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• pp.63-86
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• 1991

In this paper, our concern is the artificial neural network-based patten classification, when can resolve the difficulties in the Autoregressive Moving Average(ARMA) model identification problem To effectively classify a time series into an approriate ARMA model, we adopt the Multi-layered Backpropagation Network (MLBPN) as a pattern classifier, and Extended Sample Autocorrelation Function (ESACF) as a feature extractor. To improve the classification power of MLBPN's we suggest an integrated neural network system which consists of an AR Network and many small-sized MA Networks. The output of AR Network which will gives the MA order. A step-by-step training strategy is also suggested so that the learned MLBPN's can effectively ESACF patterns contaminated by the high level of noises. The experiment with the artificially generated test data and real world data showed the promising results. Our approach, combined with a statistical parameter estimation method, will provide a way to the automation of ARMA modeling.

• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.946-950
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• 2010

Many sentiment categorization systems based on machine learning methods use morphological analyzers in order to extract linguistic features from sentences. However, the morphological analyzers do not generally perform well in a customer review domain because online customer reviews include many spacing errors and spelling errors. These low performances of the underlying systems lead to performance decreases of the sentiment categorization systems. To resolve this problem, we propose a feature extraction method based on simple longest matching of Eojeol (a Korean spacing unit) and phoneme patterns. The two kinds of patterns are automatically constructed from a large amount of POS (part-of-speech) tagged corpus. Eojeol patterns consist of Eojeols including content words such as nouns and verbs. Phoneme patterns consist of leading consonant and vowel pairs of predicate words such as verbs and adjectives because spelling errors seldom occur in leading consonants and vowels. To evaluate the proposed method, we implemented a sentiment categorization system using a SVM (Support Vector Machine) as a machine learner. In the experiment with Korean customer reviews, the sentiment categorization system using the proposed method outperformed that using a morphological analyzer as a feature extractor.

• Culinary science and hospitality research
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• v.14 no.4
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• pp.283-291
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• 2008

The present study is purposed to suggest accurate guidelines for developing standardized chicken meat stock containing salt, and to develop a product for mass production of uniform quality achieved by applying High Pressure Extraction Cooking(HPEC) using a high.pressure extractor. Through this study, we examined water contents, ash contents, salinity, turbidity and mineral contents of chicken meat stock according to the addition of salt. The ash contents increased with the increase of the addition of salt, but the water contents decreased with the increase of the addition of salt. Salinity increased with the increase of the addition of salt. Turbidity decreased with the increase of the addition of salt, and difference in turbidity according to the addition of salt was regular. Among mineral contents, Na showed the highest content, which was believed to be because of the addition of salt, and it was followed by K and P. The results of this study show that the mineral contents in the stock were different according to the addition of salt, but they were neither proportional to the addition of salt nor showed a regular pattern.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.21-26
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• 2003

A new method to realize 3-dimensional object pattern recognition system using Fourier-based feature extractor has been proposed. The procedure to obtain the invariant feature vector is as follows ; A closed surface is generated by tracing the surface of object using the 3-dimensional polar coordinate. The centroidal distances between object's geometrical center and each closed surface points are calculated. The distance vector is translation invariant. The distance vector is normalized, so the result is scale invariant. The Fourier spectrum of each normalized distance vector is calculated, and the spectrum is rotation invariant. The Fourier-based feature generating from above procedure completely eliminates the effect of variations in translation, scale, and rotation of 3-dimensional object with closed-surface. The experimental results show that the proposed method has a high accuracy.

• Analytical Science and Technology
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• v.14 no.1
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• pp.88-93
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• 2001

To measure the levels of dioxins in food selling at local markets, meat was analyzed by high resolution gas chromatography/high resolution ass spectrometry (HRGC/HRMS). The food samples were obtained from 5 large cities of Seoul, Chunchon, Daejon, Kwangju and Busan in Korea. All the samples were minced and extracted with Soxhlet extractor for 18 hours. After extraction, extracts were cleaned up by sulfuric acid impregnated silica gel, purified on a series of silica gel, alumina, carbon column chromatography and then analyzed by HRGC/HRMS. The contaminated levels were calculated as the TEQs by multiplying with the corresponding WHO-TEFs for each congeners. The overall recoveries were ranged from 80% to 153% and the limit of detection was about 0.01 ppt at S/N>3. The levels of PCDD/Fs for beef, pork and chicken were 0.018, 0.008 and <0.001 pgTEQ/g, respectively. In addition, the levels of non-ortho-co-planar PCBs for beef, pork and chicken were 0.008, 0.002 and 0.001 pgTEQ/g, respectively. Among food samples analyzed, chicken showed the lowest level of dioxin-like compounds. Regarding congener pattern, OCDD and PCB #77 were the highest contributing congeners.