• Journal of the Korean Society of Food Science and Nutrition
• /
• v.43 no.8
• /
• pp.1270-1277
• /
• 2014

This study investigated the quality characteristics of instant rice noodles manufactured with broken rice flour as an application of rice-processed products. We examined the physicochemical characteristics of common rice flour (CRF), broken rice flour (BRF), and CRF mixed with BRF (CBRF). Futhermore, instant rice noodles were manufactured with these three types of rice flour, and their quality and sensory characteristics were also investigated. Damaged starch content and water-binding capacity of rice flour were highest in BRF. Particle size of rice flour was significantly different among the three types. RVA pasting viscosities of BRF and CBRF were higher than that of CRF. Volume after cooking of instant rice noodles increased in rice noodles made with broken rice flour (BRN). Turbidity and cooking loss of BRN were higher than those of common rice noodles (CON). For texture properties, CON displayed the highest hardness, adhesiveness, and chewiness. In the sensory evaluation, springiness and overall acceptability values of CON were significantly higher than those of other rice noodle types (BRN and CBRN). In conclusion, BRN showed increased cooking loss and turbidity with reduced texture and overall acceptability values. The results of this study suggest that added amount of CRF may significantly increase the overall quality of instant rice noodles prepared with BRF.

• Journal of The Korean Association For Science Education
• /
• v.33 no.2
• /
• pp.425-443
• /
• 2013

We developed problem-solving type inquiry learning programs reflecting scientists' research process and analyzed the activities of science-gifted high school students, and the understanding and the effects of the programs after implementation in class. For this study, twelve science-gifted students in the 10th grade participated in the program, which consisted of three different modules - making a cycloidal pendulum, surface growth, and synchronization using metronomes. Diet Cola Test (DCT) was used to find out the effect on the improvement of the ability to design experiments by comparing pre/post scores, with a survey and an interview being conducted after the class. Each module consisted of a series of processes such as questioning the phenomenon scientifically, designing experiments to find solutions, and doing activities to solve the problems. These enable students to experience problem-solving type research process through the program class. According to this analysis, most students were likely to understand the characteristics of problem-solving type inquiry learning programs reflecting the scientists' research process. According to the students, there are some differences between this program class and existing school class. The differences are: 'explaining phenomenon scientifically,' 'designing experiments for themselves,' and 'repeating the experiments several times.' During the class students have to think continuously, design several experiments, and carry them out to solve the problems they found at first. Then finally, they were able to solve the problems. While repeating this kind of activities they have been able to experience the scientists' research process. Also, they showed a positive attitude toward the scientists' research by understanding problem-solving type research process. These problem-solving type inquiry learning programs seem to have positive effects on students in designing experiments and offering the opportunity for critical argumentation on the causes of the phenomena. The results of comparing pre/post scores for DCT revealed that almost every student has improved his/her ability to design experiments. Students who were accustomed to following teacher's instructions have had difficulty in designing the experiments for themselves at the beginning of the class, but gradually, they become used to doing it through the class and finally were able to do it systematically.

• Tuberculosis and Respiratory Diseases
• /
• v.45 no.6
• /
• pp.1277-1283
• /
• 1998

Background : Surgical tracheostomy(ST) is usually performed by surgeons in operating room. For a patient with mechanical ventilation, however, transportation to operating room for ST could be dangerous for patients. In addition, ST is often delayed due to unavailability of operating room or surgeon. Percutaneous dilatational tracheostomy(PDT), although novel in Korea, is gaining popularity as a bedside procedure in the hospitals of western countries. We evaluated the technical ease and safety of PDT in comparison with ST. Method : Thirty-eight patients in medical intensive care unit (ICU) who were either under mechanical ventilation for more than 7 days or required airway protection, were randomly assigned to ST(18 patients) or PDT(20 patients). Between two groups, there was no significant clinical difference except that female to male ratio was higher in the ST group. ST was performed by second year residents of the department of otolaryngology while PDT was performed by third grade medical resident and pulmonologist under bronchoscopic guide using Ciaglia Percutaneous Tracheostomy Set(Cook Critical Care, Bloomington, USA) in medical ICU. The following factors were compared between two groups : number of delayed cases after the decision for tracheostomy, procedural time, complications related to tracheostomy. Results : Delayed cases were 11 in ST group and 3 in PDT group (P<0.05). Procedural time was significantly shorter in PDT group ($15.6{\pm}7.1min$) than in ST group ($29.1{\pm}11.6min$, P<0.0001). Complications related to tracheostomy occurred in 5 cases in ST group : accidental decannulation (1), subcutaneous emphysema (2) and minor bleeding (2), and in 4 cases in PDT group : minor bleeding (2), subcutaneous emphysema (1) and premature extubation (1) (P>0.05). Conclusion : Since percutaneous dilatational tracheostomy was easy to practice and its complications were not different from surgical tracheostomy, PDT can be a useful bedside procedure for mechanically ventilated patients.

• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.29-45
• /
• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of agriculture & life science
• /
• v.46 no.2
• /
• pp.163-168
• /
• 2012

The objective of this study is to quantify the levels of ammonia and hydrogen sulfide inmechanically ventilated slurry-pit swine house according to pig's growth stage and seasonal condition. Mean concentrations of ammonia and hydrogen sulfide in the housing room of gestation/farrowing pigs were 5.60 (${\pm}2.48$) ppm and 178.4 (${\pm}204.8$) ppb in spring, 2.51 (${\pm}3.08$) ppm and 86.6 (${\pm}112.5$) ppb in summer, 4.96 (${\pm}2.84$) ppm and 182.3 (${\pm}242.6$) ppb in autumn, and 6.82 (${\pm}3.42$) ppm and 206.3 (${\pm}356.8$) ppb in winter, respectively. Mean concentrations of ammonia and hydrogen sulfide in the housing room of nursery pigs were 7.18 (${\pm}3.26$) ppm and 486.0 (${\pm}190.2$) ppb in spring, 4.23 (${\pm}2.95$) ppm and 206.4 (${\pm}186.9$) ppb in summer, 7.02 (${\pm}2.65$) ppm and 465.4 (${\pm}156.8$) ppb in autumn, and 9.25 (${\pm}3.68$) ppm and 618.4 (${\pm}298.3$) ppb in winter, respectively. Mean concentrations of ammonia and hydrogen sulfide in the housing room of growing/fattening pigs were 9.26 (${\pm}3.02$) ppm and 604.4 (${\pm}186.8$) ppb in spring, 6.78 (${\pm}3.88$) ppm and 312.5 (${\pm}215.4$) ppb in summer, 9.34 (${\pm}2.14$) ppm and 578.2 (${\pm}248.1$) ppb in autumn, and 14.65 (${\pm}3.15$) ppm and 825.3 (${\pm}316.9$) ppb in winter, respectively. As a result, mean concentrations of ammonia and hydrogen sulfide in terms of pig's growth stage were highest in growing/fattening housing room followed by nursery housing room and gestation/farrowing housing room (p<0.05). The swine house showed the highest levels of ammonia and hydrogen sulfide in winter followed by spring, autumn and summer. However, there was no significant difference of ammonia and hydrogen sulfide among seasons (p>0.05).

• FLOWER RESEARCH JOURNAL
• /
• v.19 no.1
• /
• pp.55-63
• /
• 2011

This study was carried out to develop new cultivars of Hibiscus species from crosses between introduced interspecific hybrids and cultivars in Hibiscus species. Fruit setting of interspecific crosses of Hibiscus strains was less than 10% and the number of seeds in the fruit was also in low level. Three individuals of specific flower and leaf characteristics were selected from crosses between introduced interspecific hybrid, 'Fujimusme'(♀), and H. syriacus 'Namwon'(♂) in 2004. A new strain, Hibiscus ${\times}$ 'W-26', was selected from the crossing of interspecific hybrid, 'Fujimusme'(♀), and H. syriacus 'Namwon'(♂), which had white flower and narrow separated petal. Hibiscus ${\times}$ 'WRB-2' was selected from the crossing of interspecific hybrid, 'Fujimusme'(♀), and H. syriacus 'Namwon'(♂), which had white flower and blue eye spot. Hibiscus ${\times}$ 'R-141' was selected from crosses between introduced interspecific hybrid, 'Shichisai'(♀) and H. syriacus 'Namwon'(♂), which had large flowers over 13 cm diameter and revealed tall tree type. Hibiscus ${\times}$ 'R-142' was selected from the crossing of interspecific hybrid, 'Shichisai'(♀), and H. syriacus 'Namwon'(♂), which had large flowers over 13 cm diameter and revealed tall tree type. The characteristics were succeded after grafting. Flower of 'R-142' had reddish violet color with red eye spot, whereas its parent had blue and purple flowers.

• Journal of Bio-Environment Control
• /
• v.28 no.4
• /
• pp.461-470
• /
• 2019

This study was carried out to quantify the drought stress in grafted watermelon seedlings non-destructively by using chlorophyll fluorescence (CF) imaging technique rather than the visual judgment. Six-day old watermelon seedlings were grown under uniform irrigation for 3 days, and then given drought stress. Afterward, the sensor for the measurement of water content in plug tray cell unit was used to classify the drought-stress level into nine groups from D1 (53.0%, sufficient moisture state) to D9 (15.7%, extremely dry stress), and the 16 CF parameters were measured. In addition, re-irrigation was performed on the drought stressed seedlings(D5 - D9) to determine the growth and photosynthesis recovery level, which was not confirmed by visual judgment. The kinetic curve patterns of CF in three different drought stressed seedling groups were found to be different for the early detection of drought stress. All the 16 CF parameters decreased continuously with exposure to drought stress and drastically decreased from D5 (32.1%) where the visual judgment was possible. The fluorescence decline ratio (Rfd_Lss) started to decrease from the initial drought stress level (D5 - D6), and the Maximum PSII quantum yield (Fv/Fm) was significantly decreased in the later extreme drought stress range (D7 - D9) by re-irrigation recovery test. Thus, Rfd_Lss and Fv/Fm parameters were finally selected as potent indicators of growth and photosynthesis recovery in the initial and later stages of drought stress. Also, to the differences in the numerical values of the individual chlorophyll fluorescence parameters, the drought stress level was intuitively confirmed through the image. These results indicate that Rfd and Fv/Fm can be considered as potential CF parameters for the detection of low and extremely high drought stress, respectively. Furthermore, Fv/Fm can be considered as the best CF parameters for recovery at re-irrigation.

• Food Engineering Progress
• /
• v.15 no.1
• /
• pp.48-55
• /
• 2011

In this research, the near-infrared absorption from 1,100-2,300 nm was used to measure the content of capsaicinoids in the red-pepper powder by using the Acousto-optic tunable filters (AOTF) spectrometer with sample plate and sample rotating unit. Non-spicy red-pepper samples from one location (Younggwang-gun. Korea) were mixed with spicy one (var. Chungyang) to make samples separated by particle size (below 0.425 mm, 0.425-0.71 mm, and 0.71- 1.4 mm). The Partial Least Squares Regression (PLSR) model to predict the capsaicinoid content on particle sizes was developed with measured spectra by AOTF spectrometer and used to analyze the amount of capsaicinoids by HPLC. The PLSR Model of red-pepper powder of below 0.425 mm, 0.425-0.71 mm, and 0.71-1.4 mm with cross validation had ${R_V}^2$ = 0.948-0.979 and Standard Error of Prediction (SEP) = 6.56-7.94 mg%. The prediction error of smaller particle size of red-pepper powder was low. The best PLSR model was found in pretreatment of Range Normalization, Standard Normal Variate, and 1st Derivatives of red-pepper powder of below 1.4 mm with cross validation, having ${R_V}^2$ = 0.959 and SEP = 8.82 mg%.

• Journal of Life Science
• /
• v.29 no.7
• /
• pp.800-808
• /
• 2019

Charcot-Marie-Tooth disease (CMT) is a group of rare peripheral neuropathies characterized by progressive muscle weakness and atrophy and areflexia in the upper and lower extremities. The most common subtype of CMT is CMT1A, which is caused by a tandem duplication of the PMP22 gene in the 17p12 region. Patients with CMT1A show a loose genotype-phenotype correlation, which suggests the existence of secondary genetic or association factors. Recently, polymorphisms of rs71428439 (n.83A>G) and rs2292832 (n.86T>C) in the MIR149 have been reported to be associated with late onset and mild phenotypic CMT1A severity. The aim of this study was to examine the intrafamilial heterogeneities of clinical phenotypes according to the genotypes of these two SNPs in MIR149. For this study, we selected 6 large CMT1A families who showed a wide range of phenotypic variation. This study suggested that both SNPs were related to the onset age and severity in the dominant model. In particular, the AG+GG (n.83A>G) and TC+CC genotypes (n.86T>C) were associated to late onset and mild symptoms. Motor nerve conduction velocity (MNCV) was not related to the MIR149 genotypes. These results were consistent with the previous studies. Therefore, we suggest that the rs71428439 and rs2292832 variants in MIR149 may serve as genetic modifiers of CMT1A intrafamilial phenotypic heterogeneity, as they have a role in the unrelated patients. This is the first study to show an association using large families with variable clinical CMT1A phenotypes. The results will be helpful in the molecular diagnosis and treatment of patients with CMT1A.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.43-61
• /
• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.