• Tuberculosis and Respiratory Diseases
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• v.44 no.1
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• pp.69-84
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• 1997

Background : Although the overall prognosis of patients with lung cancer is poor, highly effective treatment exists for the small subset of patients with early lung cancer(carcinoma in situ/micro- invasive cancer). But very few patients have benefit from them because these lesions are difficult to detect and localize with conventional white-light bronchoscopy. To overcome this problem, a Lung Imaging Fluorescence Endoscopic device(LIFE) was developed to detect and clearly delineate the exact location and extent of premalignant and early lung cancer lesions using differences in tissue autofluorescence. Purpose : The purpose of this study was to determine the difference of sensitivity and specificity in detecting dysplasia and carcinoma between fluorescence imaging and conventional white light bronchoscopy. Material and Methods : 35 patients (16 with abnormal chest X-ray, 2 with positive sputum study, 2 with undiagnosed pleural effusion, 15 with respiratory symptom) have been examined by LIFE imaging system. After a white light bronchoscopy, the patients were submitted to fluorescence bronchoscopy and the findings of both examinations have been classified in 3 categories(class I, II, III). From of all class n and III sites, 79 biopsy specimens have been collected for histologic examination: a comparison between histologic results and white light or fluorescence bronchoscopy has been performed for assessing sensitivity and specificity of the two methods. Results : 1) Total 79 sires in 35 patients were examined. Histology demonstrated 8 normal mucosa, 21 hyperplasia, 23 dysplasia, and 27 microinvasive and invasive carcinoma. 2) The sensitivity of white light or fluorescence bronchoscopy in detecting dysplasia was 60.9% and 82.6%, respectively. 3) The results of this study showed 70.3 % sensitivity for microinvasive or invasive carcinoma with LIFE system, versus 100% sensitivity for white light in 27 cases of carcinoma. The false negative study of LIFE system was 8 cases(3 adenocarcinoma and 5 small cell carcinoma), which were infiltrated in submucosal area and had normal epithelium. Conclusion : To improve the ability 10 diagnose and stage more accurately, fluorescence imaging may become an important adjunct to conventional bronchoscopic examination because of its high detection rate of premalignant and malignant epithelial lesion. But. it has limitation to detect in submucosal infiltrating carcinoma.

• Journal of the East Asian Society of Dietary Life
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• v.21 no.5
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• pp.625-636
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• 2011

The purpose of this study was to evaluate the relationship between bone health and sodium intake in female university students using a dish frequency questionnaire (DFQ 125), anthropometric checkups, food records for 3 days, and ultrasound measurement of calcaneus bone mineral density. Subjects were divided into two groups: normal (n=196) and osteopenia (n=52). There were no significant differences in age or height between the two groups. The average weight, body mass index, and body fat in the normal group were significantly higher than in the osteopenia group. The sodium intake of DFQ was positively correlated with the sodium intake of 3 days of dietary records (p=0.0003). There were no significant differences in the sodium intake between the two groups from DFQ. The dishes were ranked by sodium intake: kimchies were 17.68%, noodles and mandu were 16.36%, stews were 13.69%, main dishes such as meat, egg, and beans were 11.47%, and fish and shellfish were 11.07%. The frequency of eating noodles and mandu (p=0.0116), stews (p=0.0008), kimchies (p=0.0482), fish and shellfish (p=0.0362), vegetables (p=0.0064) and seasoning (p=0.0347) were negatively associated with bone mineral density. Bone health was not significantly different with increasing quartiles of sodium intake. As excessive sodium intakes may indirectly affect bone mineral density, these results suggest that to prevent osteoporosis, university students needed to be more educated about diets containing less sodium through nutrition education programs.

• Horticultural Science & Technology
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• v.32 no.1
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• pp.53-59
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• 2014

This study aimed to determine an appropriate cooling timing in the root zone for lowering substrate temperature and its effect on physiological response of sweet pepper (Capsicum annum L. 'Orange glory') grown on coir substrate in summer, from the July 16 to October 15, 2012. Daily temperature of substrate, root activity, leaf water potential, first flowering date, and the number of fruits were measured by circulating cool water through a XL pipe in the root zone during either all day (all-day) or only night time (5 p.m. to 3 a.m.; night) from the July 23 to September 23, 2012. For comparison, no cooling (control) was also applied. Between the $23^{rd}$ of July and $31^{st}$ of August (hot temperature period), daily average temperatures in substrates were $25.6^{\circ}C$, $26.1^{\circ}C$, and $29.1^{\circ}C$ for the all-day and night treatment, and control respectively. About 1.8 to $5^{\circ}C$ lower substrate temperature was observed in both treatments compared to that of control. In sunny day ($600-700 W{\cdot}m^{-2}{\cdot}s^{-1}$), the highest temperature of substrate was measured between 4 p.m. and 5 p.m. under both the all-day and night treatments, whereas it was measured between 7 p.m. and 8 p.m. under the control. Substrate temperatures during the day (6 a.m. to 8 p.m.) and night (8 p.m. to 6 a.m.) differed depending on the treatments. During the day and night, averaged substrate temperature was lower about $3.3^{\circ}C$ and $4.0^{\circ}C$ for the all-day, and $2.1^{\circ}C$ and $3.4^{\circ}C$ for the night treatment, compared to that of control. In the all-day and night treatment, the TD [TD = temperature of (control)] was greater in bottom than that of other regions of the substrate. Between the day and night, no different TD values were observed under the all-day treatment, whereas under the night treatment there was difference with the greatest degree in the bottom of the substrate. During the hot temperature period, total numbers of days when substrate temperature was over $25^{\circ}C$ were 40, 23 and 27 days for the control, all-day, and night treatment, respectively, and the effect of lowering substrate temperature was therefore 42.5% and 32.5% for the all-day and night treatment, respectively, compared to that for the control. Root activity and leaf water potential of plants grown under the all-day treatment were significantly higher than those under the night treatment. The first flowering date in the all-day treatment was similar to that in the night treatment, but 4-5 day faster than in the control. Also, the number of fruits in both treatments was significantly higher than that in the control. However, there was no effect of root zone cooling on eliminating delay in fruiting caused by excessively higher air temperature (> $30^{\circ}C$), although the substrate temperature was reduced $18^{\circ}C$ to $5^{\circ}C$. These results suggest that the method of cooling root zone temperature need to be incorporated into the lowering growing temperature for growth and fruit set of health paprika.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.7 no.1
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• pp.77-91
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• 1996

In order to elucidate the biological etiology and the effects of comorbidity on biological variables in tic disorders, plasma serotonin (5-hydroxlfryptamine, 5-HT) and 5-hydroxy- indoleacetic acid (5-HIAA) we.e measured in 87 tic disorders and 30 control subjects. The 87 tic disorder were composed of 45 Tourette's disorder(TS), 22 chronic motor tic disorders (CMT) and 20 transient tic disorders (TTD). Among these patients,43 patients were pure tic disorder (PT), 28 subject also had attention deficit hyperactivity disorder (T+ADHD) and 16 subjects had obsessive compulsive disorders (T+ OCD) as comorbid disorders. The results are summarized as follows : 1) Plasma 5-HT levels showed significant positive correlations with plasma 5-HIAA levels (Pennon r=0.77, p<0.05). 2) Plasma 5-HT and 5-HIAA levels showed no significant correlation with age in tic disorders. 3) Plasma 5-HIAA and 5-HT levels showed no significant correlations with age in control subjects. 4) There was significant difference in plasma 5-HT levels among TS, CMT, TTD and control groups (ANOVA F=34.48, df=3, 113, p<0.01), and post-hoc test using Scheffe method showed significant differences between control and TS, control and CMT, control and ITD groups. But, post-hoc test showed no significant differences between TS and CMT, TS and TTD, CMT and TTD groups. 5) There was significant difference in plasma 5-HIAA levels among TS, CMT, TTD and control groups (ANOVA F=26.48, df=3, 113, p<0.01), and post-hoc test using Scheffe method showed significant differences between control and TS, control and CMT, control and TTD groups. But, post-hoc test showed no significant differences between TS and CMT, TS and TTD, CMT and TID groups.f) There was significant difference in plasma 5-HT and 5-HIAA levels among PT, T+ADHD, T+OCD and contol groups (ANOVA 5-HT, F=37.59, df=3, 113, p<0.01, 5-HIAA, F=27.37, df=3, 113, p<0.01), and post-hoc test using Scheffe method showed signiscant differences between control and PT, control and T+ADHD and control and T+OCB. But, post-hoc test showed no significant differences between PT and T+ADHD, PT and T+ OCD and T+ADHD and T+ OCD. These results show that decreased 5-HT and 5-HIAA levels may play a role in the genesis of tic disorders, but these findings have no significant correlations with the severity of tic disorders. And the comorbid disorders of tics may have minimal effects on the biochemical abnormalities. Future studies must be focused on the effects of serotonin agonists and antagonists on tic disorders and molecular biological methodology may enhance to elucidate the mechanisms of these abnormal findings.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.165-183
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• 2016

Over the past decade, there has been a rapid diffusion of technological devices and a rising number of various devices, resulting in an escalation of virtual reality technology. Technological market has rapidly been changed from smartphone to wearable devices based on virtual reality. Virtual reality can make users feel real situation through sensing interaction, voice, motion capture and so on. Facebook.com, Google, Samsung, LG, Sony and so on have investigated developing platform of virtual reality. the pricing of virtual reality devices also had decreased into 30% from their launched period. Thus market infrastructure in virtual reality have rapidly been developed to crease marketplace. However, most consumers recognize that virtual reality is not ease to purchase or use. That could not lead consumers to positive attitude for devices and purchase the related devices in the early market. Through previous studies related to virtual reality, there are few studies focusing on why the devices for virtual reality stayed in early stage in adoption & diffusion context in the market. Almost previous studies considered the reasons of hard adoption for innovative products in the viewpoints of Typology of Innovation Resistance, MIR(Management of Innovation Resistant), UTAUT & UTAUT2. However, product-based antecedents also important to increase user intention to purchase and use products in the technological market. In this study, we focus on user acceptance and resistance for increasing purchase and usage promotions of wearable devices related to virtual reality based on headgear products like Galaxy Gear. Especially, we added a variables like attitude confidence as a dimension for user resistance. The research questions of this study are follows. First, how attitude confidence and innovativeness resistance affect user intention to use? Second, What factors related to content and brand contexts can affect user intention to use? This research collected data from the participants who have experiences using virtual rality headgears aged between 20s to 50s located in South Korea. In order to collect data, this study used a pilot test and through making face-to-face interviews on three specialists, face validity and content validity were evaluated for the questionnaire validity. Cleansing the data, we dropped some outliers and data of irrelevant papers. Totally, 156 responses were used for testing the suggested hypotheses. Through collecting data, demographics and the relationships among variables were analyzed through conducting structural equation modeling by PLS. The data showed that the sex of respondents who have experience using social commerce sites (male=86(55.1%), female=70(44.9%). The ages of respondents are mostly from 20s (74.4%) to 30s (16.7%). 126 respondents (80.8%) have used virtual reality devices. The results of our model estimation are as follows. With the exception of Hypothesis 1 and 7, which deals with the two relationships between brand awareness to attitude confidence, and quality of content to perceived enjoyment, all of our hypotheses were supported. In compliance with our hypotheses, perceived ease of use (H2) and use innovativeness (H3) were supported with its positively influence for the attitude confidence. This finding indicates that the more ease of use and innovativeness for devices increased, the more users' attitude confidence increased. Perceived price (H4), enjoyment (H5), Quantity of contents (H6) significantly increase user resistance. However, perceived price positively affect user innovativeness resistance meanwhile perceived enjoyment and quantity of contents negatively affect user innovativeness resistance. In addition, aesthetic exterior (H6) was also positively associated with perceived price (p<0.01). Also projection quality (H8) can increase perceived enjoyment (p<0.05). Finally, attitude confidence (H10) increased user intention to use virtual reality devices. however user resistance (H11) negatively affect user intention to use virtual reality devices. The findings of this study show that attitude confidence and user innovativeness resistance differently influence customer intention for using virtual reality devices. There are two distinct characteristic of attitude confidence: perceived ease of use and user innovativeness. This study identified the antecedents of different roles of perceived price (aesthetic exterior) and perceived enjoyment (quality of contents & projection quality). The findings indicated that brand awareness and quality of contents for virtual reality is not formed within virtual reality market yet. Therefore, firms should developed brand awareness for their product in the virtual market to increase market share.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.45-69
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• 2016

Recently, sentiment analysis using open Internet data is actively performed for various purposes. As online Internet communication channels become popular, companies try to capture public sentiment of them from online open information sources. This research is conducted for the purpose of analyzing pulbic sentiment of Korean Top-10 companies using a multi-categorical sentiment lexicon. Whereas existing researches related to public sentiment measurement based on big data approach classify sentiment into dimensions, this research classifies public sentiment into multiple categories. Dimensional sentiment structure has been commonly applied in sentiment analysis of various applications, because it is academically proven, and has a clear advantage of capturing degree of sentiment and interrelation of each dimension. However, the dimensional structure is not effective when measuring public sentiment because human sentiment is too complex to be divided into few dimensions. In addition, special training is needed for ordinary people to express their feeling into dimensional structure. People do not divide their sentiment into dimensions, nor do they need psychological training when they feel. People would not express their feeling in the way of dimensional structure like positive/negative or active/passive; rather they express theirs in the way of categorical sentiment like sadness, rage, happiness and so on. That is, categorial approach of sentiment analysis is more natural than dimensional approach. Accordingly, this research suggests multi-categorical sentiment structure as an alternative way to measure social sentiment from the point of the public. Multi-categorical sentiment structure classifies sentiments following the way that ordinary people do although there are possibility to contain some subjectiveness. In this research, nine categories: 'Sadness', 'Anger', 'Happiness', 'Disgust', 'Surprise', 'Fear', 'Interest', 'Boredom' and 'Pain' are used as multi-categorical sentiment structure. To capture public sentiment of Korean Top-10 companies, Internet news data of the companies are collected over the past 25 months from a representative Korean portal site. Based on the sentiment words extracted from previous researches, we have created a sentiment lexicon, and analyzed the frequency of the words coming up within the news data. The frequency of each sentiment category was calculated as a ratio out of the total sentiment words to make ranks of distributions. Sentiment comparison among top-4 companies, which are 'Samsung', 'Hyundai', 'SK', and 'LG', were separately visualized. As a next step, the research tested hypothesis to prove the usefulness of the multi-categorical sentiment lexicon. It tested how effective categorial sentiment can be used as relative comparison index in cross sectional and time series analysis. To test the effectiveness of the sentiment lexicon as cross sectional comparison index, pair-wise t-test and Duncan test were conducted. Two pairs of companies, 'Samsung' and 'Hanjin', 'SK' and 'Hanjin' were chosen to compare whether each categorical sentiment is significantly different in pair-wise t-test. Since category 'Sadness' has the largest vocabularies, it is chosen to figure out whether the subgroups of the companies are significantly different in Duncan test. It is proved that five sentiment categories of Samsung and Hanjin and four sentiment categories of SK and Hanjin are different significantly. In category 'Sadness', it has been figured out that there were six subgroups that are significantly different. To test the effectiveness of the sentiment lexicon as time series comparison index, 'nut rage' incident of Hanjin is selected as an example case. Term frequency of sentiment words of the month when the incident happened and term frequency of the one month before the event are compared. Sentiment categories was redivided into positive/negative sentiment, and it is tried to figure out whether the event actually has some negative impact on public sentiment of the company. The difference in each category was visualized, moreover the variation of word list of sentiment 'Rage' was shown to be more concrete. As a result, there was huge before-and-after difference of sentiment that ordinary people feel to the company. Both hypotheses have turned out to be statistically significant, and therefore sentiment analysis in business area using multi-categorical sentiment lexicons has persuasive power. This research implies that categorical sentiment analysis can be used as an alternative method to supplement dimensional sentiment analysis when figuring out public sentiment in business environment.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.71-88
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• 2017

Language models were originally developed for speech recognition and language processing. Using a set of example sentences, a language model predicts the next word or character based on sequential input data. N-gram models have been widely used but this model cannot model the correlation between the input units efficiently since it is a probabilistic model which are based on the frequency of each unit in the training set. Recently, as the deep learning algorithm has been developed, a recurrent neural network (RNN) model and a long short-term memory (LSTM) model have been widely used for the neural language model (Ahn, 2016; Kim et al., 2016; Lee et al., 2016). These models can reflect dependency between the objects that are entered sequentially into the model (Gers and Schmidhuber, 2001; Mikolov et al., 2010; Sundermeyer et al., 2012). In order to learning the neural language model, texts need to be decomposed into words or morphemes. Since, however, a training set of sentences includes a huge number of words or morphemes in general, the size of dictionary is very large and so it increases model complexity. In addition, word-level or morpheme-level models are able to generate vocabularies only which are contained in the training set. Furthermore, with highly morphological languages such as Turkish, Hungarian, Russian, Finnish or Korean, morpheme analyzers have more chance to cause errors in decomposition process (Lankinen et al., 2016). Therefore, this paper proposes a phoneme-level language model for Korean language based on LSTM models. A phoneme such as a vowel or a consonant is the smallest unit that comprises Korean texts. We construct the language model using three or four LSTM layers. Each model was trained using Stochastic Gradient Algorithm and more advanced optimization algorithms such as Adagrad, RMSprop, Adadelta, Adam, Adamax, and Nadam. Simulation study was done with Old Testament texts using a deep learning package Keras based the Theano. After pre-processing the texts, the dataset included 74 of unique characters including vowels, consonants, and punctuation marks. Then we constructed an input vector with 20 consecutive characters and an output with a following 21st character. Finally, total 1,023,411 sets of input-output vectors were included in the dataset and we divided them into training, validation, testsets with proportion 70:15:15. All the simulation were conducted on a system equipped with an Intel Xeon CPU (16 cores) and a NVIDIA GeForce GTX 1080 GPU. We compared the loss function evaluated for the validation set, the perplexity evaluated for the test set, and the time to be taken for training each model. As a result, all the optimization algorithms but the stochastic gradient algorithm showed similar validation loss and perplexity, which are clearly superior to those of the stochastic gradient algorithm. The stochastic gradient algorithm took the longest time to be trained for both 3- and 4-LSTM models. On average, the 4-LSTM layer model took 69% longer training time than the 3-LSTM layer model. However, the validation loss and perplexity were not improved significantly or became even worse for specific conditions. On the other hand, when comparing the automatically generated sentences, the 4-LSTM layer model tended to generate the sentences which are closer to the natural language than the 3-LSTM model. Although there were slight differences in the completeness of the generated sentences between the models, the sentence generation performance was quite satisfactory in any simulation conditions: they generated only legitimate Korean letters and the use of postposition and the conjugation of verbs were almost perfect in the sense of grammar. The results of this study are expected to be widely used for the processing of Korean language in the field of language processing and speech recognition, which are the basis of artificial intelligence systems.

• Korean Journal of Soil Science and Fertilizer
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• v.1 no.1
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• pp.13-26
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• 1968

To establish the most efficient method of nitrogen and phosphorus fertilization in paddy rice a series of internationally coordinated research using $N^{15}\;P^{32}$ isotopes were carried out from the year 1962 through 1967, supervised by the Joint FAO/IAEA Division of Atomic Energy in Agriculture under the auspicies of FAO, UN. In Korea College of Agriculture, Seoul National University had been participated in the Coordinated Research Programme from the year 1963 through the last year, arid the results obtained through the six years' researches are summarized as follows: 1. In the application of superphosphate broadcasting or placement at the surface of paddy as basal dressing was most efficient. 2. Split or late application of superphosphate did not affect the grain yield of the rice, but its efficiency in the rice plant uptake was less than in the case of basal dressing of whole amounts. 3. The contents of available soil phoshorus of the experimental sites in Korea were above 60 ppm, and the efficiency of phosphorus utilization of the rice from the fertilizer was approximately 10 per cent. The grain yield response of the rice to phosphorus application in Korea was rather small comparing to those of other countries. 4. The nitrogen uptake of the rice plants from the fertilizer was most efficient when the fertilizer was applied at the time of ear prime growth. However, the most efficient utilization of nitrogen did not necessarily accompany the maximum yield of the rice and basic application of adequate amount of nitrogen was required to secure proper number of panicles. 5. In the application of nitrogenous fertilizer shallow placement at 5 cm depth was most efficient. The effect of split application of nitrogen was not so noticeable. It seemed due to the fact that total application amount of nitrogen, 60 kg N/ha, was smaller than that of usual dosage in Korea. 6. The efficiency of nitrogen utilization of the rice from the fertilizer was about 40 percent in Korea, and the yield reponse of the rice plant to nitrogen application was remarkable comparing to those of other countries, showing the marked differences in the fertilizer efficiency and grain yield according to the application method. 7. The nitrogen uptake of the rice plant was not likely affected by the time and the rate of phosphorus application whereas the efficiency of phosphorus utilization was affected to some degree by the method of nitrogen application.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.69-93
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• 2017

The development of ICT has leaded the diversification and changes of supplies and demands in markets. It also caused the creations of a variety of values which are differentiated from those in the existing market. Therefore, a new-type market is created, which can include multi-value chains which are from ICT-based created markets as well as the existing markets. We defined the platform as the new-type market. In the platform, the multi-value chains can be coexisted with multi-values. In true market, when a new-type value chain entered into an existing market, it is general that it can be conflicted with the existing value chain in the market. The conflicted problem among multi-value chains in a market is caused by the sharing of limited market resources like suppliers, consumers, services or products among the value chains. In other words, if there are multi-value chains in the platform, then it is possible to have conflictions, overlapping, creations or losses of values among the value chains. To solve the problem, we introduce coexistence factors to reduce the conflictions to reach market equilibrium in the platform. In the other hand, it is possible to lead the creations of differentiated values from the existing market and to augment the total market values in the platform. In the early era of ICT development, ICT was introduced for improvement of efficiency and effectiveness of the value chains in the existing market. However, according to the changed role of ICT from the supporter to the promotor of the market, ICT became to lead the variations of the value chains and creations of various values in the markets. For instance, Uber Taxi created a new value chain with ICT-based new-type service or products with new resources like new suppliers and consumers. When Uber and Traditional Taxi services are playing at the same time in Taxi service platform, it is possible to create values or make conflictions among values between the new and old value chains. In this research, like Uber and traditional taxi services, if there are conflictions among the multi-value chains, then it is necessary to minimize the conflictions in the platform for the coexistence of multi-value chains which can create the value-added values in the platform. So, it is important to predict and discuss the possible conflicted problems between new and old value chains. The confliction should be solved to reach market equilibrium with multi-value chains in the platform. That is, we discuss the possibility of the coexistence of multi-value chains in the platform which are comprised of a variety of suppliers and customers. To do this, especially we are focusing on the healthcare markets. Nowadays healthcare markets are popularized in global market as well as domestic. Therefore, there are a lot of and a variety of healthcare services like Traditional-, Tele-, or Intelligent- healthcare services and so on. It shows that there are multi-suppliers, -consumers and -services as components of each different value chain in the same platform. The platform can be shared by different values that are created or overlapped by confliction and loss of values in the value chains. In this research, as was said, we focused on the healthcare services to show if a platform can be shared by different value chains like traditional-, tele-healthcare and intelligent-healthcare services and products. Additionally, we try to show if it is possible to increase the value of each value chain as well as the total value of the platform. As the result, it is possible to increase of each value of each value chain as well as the total value in the platform. Finally, we propose a coexistence model to overcome such problems and showed the possibility of coexistence between the value chains through experimentation.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.