• Applied Biological Chemistry
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• v.18 no.1
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• pp.16-22
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• 1975

This experiment was carried out to investigate the physiological characteristics of two original yeasts, 5-Y-5 and 6-Y-6, which selected from 24 Takju yeasts and three mutants, 30-24,30-81 and 40-27. induced from two original yeasts by the irradiation of UV light. The results were summarized as follows. 1) Alcohol tolerances of three mutants were decreased in some degree as compared with those of original yeasts. 2) Tolerances of lactic and citric acids of acid producing mutant 30-81, was increased than those of original yeasts. 3) In the case of using ammonium sulfate as a nitrogen source, two original yeasts and three mutants required Ca-pantothenate as a essential growth factor and four strains of yeasts except the mutant, 30-81, required biotin as a stimulated growth factor, When asparagine was used as a nitrogen source, two original yeasts and three mutants showed the same as above result but the stimulated effect of biotin was far less. 4) Propagation powers of the mutants were weaken than those of original yeasts, particular that of acid producing mutant, 30-81, was the weakest in the three mutants. 5) The optimum temperature for fermentation of original yeasts were $30^{\circ}C\;to\;35^{\circ}C$ but three mutants were $25^{\circ}C\;to\;30^{\circ}C$. 6) The optimum pH for fermentation of original yeasts were pH 5 to 6, and there is no appreciable difference between original yeasts and three mutants. The fermentation power of mutant,30-81, was decreased more rapidly than those of other mutants according to approach neutral. Three mutants were more sensible to heat than original yeasts. 7) Two original yeasts and three mutants were inhibited more over 20 percent of sugar for fermentation and three mutants were more sensible to sugar concentration than original yeasts.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

• The Korean Journal of Nuclear Medicine Technology
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• v.21 no.1
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• pp.39-43
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• 2017

Purpose Thyroglobulin (Tg) is a biologic marker of differentiated thyroid carcinoma (DTC), produced by normal thyroid tissue or thyroid cancer tissue. Therefore, the Tg values of DTC patients is the most specific indicator for judging whether recurrence occur or whether the remaining thyroid cancer is present. Thyroid cancer is currently the most common cancer in Korea, of which 90% is differentiated thyroid cancer. The number of patients with thyroid disease of this application also increased, and an accurate and prompt results are required. However, the incubation time of the Tg commonly takes about 24 hours in our hospital, and the result reporting time is delayed, and We could not satisfied with the requirements of clinical departments and patients. In order to fulfill these requirements, experiments were conducted by shortening the incubation time between company B's Kit currently in use and company C's Kit used in other hospitals. Through these experiments, we could perform the correlation with the original method and shortening method, and could find the optimum reaction time to satisfy the needs of the departments and the patients, and we will improve the competitiveness with the EIA examination. Materials and Methods In September 2016, we tested 65 patients company B's kit and company C's kit by three incubation ways. First method $37^{\circ}C$ shaking 2hr/2hr, Second method RT shaking 3hr/2hr, Third method 1hr/1hr shaking at $37^{\circ}C$. Fourth method RT shaking 3hr method which is the original method of Company C's Kit. Fifth method, the incubation time was shortened under room temperature shaking 2hr, Sixth method $37^{\circ}C$ shaking 2hr. And we performed and compared the correlation and coefficient of each methods. Results As a result of performing shortening method on company B currently in use, when comparing the Original method of company B kit, First method $37^{\circ}C$ shaking 2hr/2hr was less than Tg 1.0 ng/mL and the ratio of $R^2=0.5906$, above 1.0 ng/mL In the value, $R^2=0.9597$. Second method RT shaking 3hr/2hr was $R^2=0.7262$ less than value of 1.0 ng/mL, $R^2=0.9566$ above than value of 1.0 ng/mL. Third method $37^{\circ}C$ shaking 1hr/1hr was $R^2=0.7728$ less than value of 1.0 ng/mL, $R^2=0.8904$ above than value of 1.0 ng/mL. Forth, Company C's The original method, RT shaking 3hr was $R^2=0.7542$ less than value of 1.0 ng/mL, and $R^2=0.9711$ above than value of 1.0 ng/mL. Fifth method RT shaking 2hr was $R^2=0.5477$ less than value of 1.0 ng/mL, $R^2=0.9231$ above than value of 1.0 ng/mL. Sixth method $37^{\circ}C$ shaking 2hr showed $R^2=0.2848$ less than value of 1.0 ng/mL, $R^2=0.9028$ above than value of 1.0 ng/mL. Conclusion Samples with both values of 1.0 ng/mL or higher in both of the six methods showed relatively high correlation, but the correlation was relatively low less than value of 1.0 ng/mL. Especially, the $37^{\circ}C$ shaking 2hr method of company C showed a sharp fluctuation from the low concentration value of 1.0 ng/mL or less. Therefore, we are planning to continuously test the time, equipment, incubation temperature and so on for the room temperature shaking 2hr method and $37^{\circ}C$ shaking 1hr/1hr of company C which showed a relatively high correlation. After that, we can search for an appropriate shortening method through additional experiments such as recovery test, dilution test, sensitivity test, and provide more accurate and prompt results to the department of medical treatment, It is competitive with EIA test.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.30 no.2
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• pp.165-173
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• 1985

The objectives of the study were to investigate the effects of foliage clipping on photosynthesis and grain filling for branch and non branch types under the polyethylene film mulch and non mulch conditions in mono cropping and second cropping after barley in sesame (Sesamum indicum L.), and to improve poor grain filling at later flowering time utilizing these data. One thousand grain weight was more decreased in branch type than in non branch type, in polyethylene film mulch condition than in non mulch condition, and in second cropping after barley than in mono cropping by clipping lower part foliage. Twentyfive percent clipping of lower part foliage showed a little increase than no clipping. Matured grain rate also showed same tendency between branch and non branch type and between mono cropping and second cropping after barley as well as 1,000 grain weight except for polyethylene film mulch. Matured grain rate of 25% foliage clipping at 30 days after flowering in non branch type presented a little increase but decreased in branch type. Clipping of higher part leaves were so serious decrease of matured grain rate that higher part leaves at late maturing time have a major role in photosynthesis. Matured grain rate of foliage clipping at 10 days after flowering was decreased in all treatments. Chlorophyll content of higher part leaves at 50% lower part foliage clipping presented 39% increase compared to same positioned leaves of non treatment, and 66% increase by 50% higher part foliage clipping in lower part leaves. Photosynthetic activity was 58% more increased in 50% lower part foliage clipping than no clipping, but seriously decreased in 50% higher part foliage clipping. Therfore, photosynthates of remained lower part leaves could not only support their own demands, but also any contribution to translocation of photosynthates from source to sink at late maturing time. Harvest index was 28% increased in 25% lower part foliage clipping and 13% decreased in 50% higher part foliage clipping compared to no clipping. Leaf area was 48% increased in 50% lower part foliage clipping compared to the same positioned leaves of no clipping, and only 5% increased in higher part foliage clipping. Productivity by foliage clipping compared to non treatment, was highly decreased in branch type than in non branch type, in second cropping after barley than in mono cropping. Little difference was detected between polyethylene film mulch and non mulch conditions. Twenty five percentage of lower part foliage clipping on mono cropping of non branch type appeared 5% and 8% yield increase in each of polyethylene film mulch and non mulch conditions compared to no clipping, and all decreased in other treatments. Mean loss of productivity by foliage clipping at 10 days after flowering was serious than clipping at 30 days after flowering. As the result, contribution to photosynthesis of source at 10 days after flowering are larger than that at 30 days after flowering in sesame. Fifty percent lower part foliage clipping at 10 days after flowering showed so the most serious yield decrease that lower part leaves at that time were considered as the main role leaves for photosynthesis.