• Animal Bioscience
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• v.35 no.9
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• pp.1327-1339
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• 2022

Objective: Fat deposition in poultry is an important factor in production performance and meat quality research. miRNAs also play important roles in regulating adipocyte differentiation process. This study was to investigate the expression patterns of miRNAs in duck adipocytes after differentiation and explore the role of miR-214 in regulating carnitine palmitoyltransferases 2 (CPT2) gene expression during duck adipocyte differentiation. Methods: Successful systems for the isolation, culture, and induction of duck primary fat cells was developed in the experiment. Using Illumina next-generation sequencing, the miRNAs libraries of duck adipocytes were established. miRanda was used to predict differentially expressed (DE) miRNAs and their target genes. The expression patterns of miR-214 and CPT2 during the differentiation were verified by quantitative real-time polymerase chain reaction and western blot. Luciferase reporter assays were used to explore the specific regions of CPT2 targeted by miR-214. We used a miR-214 over-expression strategy in vitro to further investigate its effect on differentiation process and CPT2 gene transcription. Results: There were 481 miRNAs identified in duck adipocytes, included 57 DE miRNA candidates. And the 1,046 targets genes of DE miRNAs were mainly involved in p53 signaling, FoxO signaling, and fatty acid metabolism pathways. miR-214 and CPT2 showed contrasting expression patterns before and after differentiation, and they were selected for further research. The expression of miR-214 was decreased during the first 3 days of duck adipocytes differentiation, and then increased, while the expression of CPT2 increased both in the transcriptional and protein level. The luciferase assay suggested that miR-214 targets the 3'untranslated region of CPT2. Overexpression of miR-214 not only promoted the formation of lipid droplets but also decreased the protein abundance of CPT2. Conclusion: Current study reports the expression profile of miRNAs in duck adipocytes differentiated for 4 days. And miR-214 has been proved to have the regulator potential for fat deposition in duck.

• Asia pacific journal of information systems
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• v.19 no.3
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• pp.51-67
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• 2009

Developing an agile recommender system for nomadic users has been regarded as a promising application in mobile and ubiquitous settings. To increase the quality of personalized recommendation in terms of accuracy and elapsed time, estimating future context of the user in a correct way is highly crucial. Traditionally, time series analysis and Makovian process have been adopted for such forecasting. However, these methods are not adequate in predicting context data, only because most of context data are represented as nominal scale. To resolve these limitations, the alignment-prediction algorithm has been suggested for context prediction, especially for future context from the low-level context. Recently, an ontological approach has been proposed for guided context prediction without context history. However, due to variety of context information, acquiring sufficient context prediction knowledge a priori is not easy in most of service domains. Hence, the purpose of this paper is to propose a novel context prediction methodology, which does not require a priori knowledge, and to increase accuracy and decrease elapsed time for service response. To do so, we have newly developed pattern-based context prediction approach. First of ail, a set of individual rules is derived from each context attribute using context history. Then a pattern consisted of results from reasoning individual rules, is developed for pattern learning. If at least one context property matches, say R, then regard the pattern as right. If the pattern is new, add right pattern, set the value of mismatched properties = 0, freq = 1 and w(R, 1). Otherwise, increase the frequency of the matched right pattern by 1 and then set w(R,freq). After finishing training, if the frequency is greater than a threshold value, then save the right pattern in knowledge base. On the other hand, if at least one context property matches, say W, then regard the pattern as wrong. If the pattern is new, modify the result into wrong answer, add right pattern, and set frequency to 1 and w(W, 1). Or, increase the matched wrong pattern's frequency by 1 and then set w(W, freq). After finishing training, if the frequency value is greater than a threshold level, then save the wrong pattern on the knowledge basis. Then, context prediction is performed with combinatorial rules as follows: first, identify current context. Second, find matched patterns from right patterns. If there is no pattern matched, then find a matching pattern from wrong patterns. If a matching pattern is not found, then choose one context property whose predictability is higher than that of any other properties. To show the feasibility of the methodology proposed in this paper, we collected actual context history from the travelers who had visited the largest amusement park in Korea. As a result, 400 context records were collected in 2009. Then we randomly selected 70% of the records as training data. The rest were selected as testing data. To examine the performance of the methodology, prediction accuracy and elapsed time were chosen as measures. We compared the performance with case-based reasoning and voting methods. Through a simulation test, we conclude that our methodology is clearly better than CBR and voting methods in terms of accuracy and elapsed time. This shows that the methodology is relatively valid and scalable. As a second round of the experiment, we compared a full model to a partial model. A full model indicates that right and wrong patterns are used for reasoning the future context. On the other hand, a partial model means that the reasoning is performed only with right patterns, which is generally adopted in the legacy alignment-prediction method. It turned out that a full model is better than a partial model in terms of the accuracy while partial model is better when considering elapsed time. As a last experiment, we took into our consideration potential privacy problems that might arise among the users. To mediate such concern, we excluded such context properties as date of tour and user profiles such as gender and age. The outcome shows that preserving privacy is endurable. Contributions of this paper are as follows: First, academically, we have improved sequential matching methods to predict accuracy and service time by considering individual rules of each context property and learning from wrong patterns. Second, the proposed method is found to be quite effective for privacy preserving applications, which are frequently required by B2C context-aware services; the privacy preserving system applying the proposed method successfully can also decrease elapsed time. Hence, the method is very practical in establishing privacy preserving context-aware services. Our future research issues taking into account some limitations in this paper can be summarized as follows. First, user acceptance or usability will be tested with actual users in order to prove the value of the prototype system. Second, we will apply the proposed method to more general application domains as this paper focused on tourism in amusement park.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Journal of Industrial Convergence
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• v.22 no.2
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• pp.73-83
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• 2024

The purpose of this study is to diagnose various problems arising around social enterprises in the sport field from the perspective of the organization and derive necessary tasks and implications. In order to achieve the purpose of the study, the study was largely divided into three stages, and the results were derived. First, the main status and characteristics of social enterprises in the sport field were examined. The current status was analyzed focusing on aspects such as background and origin, legislation and policy, organizational goals, organizational structure and procedures, and organizational characteristics. Social enterprises in the sport sector were in their early stages, and the government's social enterprise policy goal tended to focus on increasing the number of social enterprises in a short period of time through financial input. In addition, it was found that most individual companies rely on government subsidy support due to insufficient profit generation capacity. In the second stage, we focused on the situational factors that affect the functional performance of social enterprises in the sport field. As a result of reviewing the value, ideology, technology, and history of the organization, which are situational factors, it was derived that when certified as a social enterprise in the sport field and supported by the central government or local governments, political control is strong to some extent and exposure to the market is not severe. In the last third step, tasks and implications were derived to form an appropriate organization for social enterprises in the sport field. After the social enterprise ecosystem in the sport sector has been established to some extent, it is necessary to gradually move from the current "government-type" organization to the "national enterprise" organization. This is true in light of the government's limited financial level, not in the short term, but in order for the organization of social enterprises in the sports sector to survive in the long term.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.100-101
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• 2012

The plasma damage free and room temperature processedthin film deposition technology is essential for realization of various next generation organic microelectronic devices such as flexible AMOLED display, flexible OLED lighting, and organic photovoltaic cells because characteristics of fragile organic materials in the plasma process and low glass transition temperatures (Tg) of polymer substrate. In case of directly deposition of metal oxide thin films (including transparent conductive oxide (TCO) and amorphous oxide semiconductor (AOS)) on the organic layers, plasma damages against to the organic materials is fatal. This damage is believed to be originated mainly from high energy energetic particles during the sputtering process such as negative oxygen ions, reflected neutrals by reflection of plasma background gas at the target surface, sputtered atoms, bulk plasma ions, and secondary electrons. To solve this problem, we developed the NBAS (Neutral Beam Assisted Sputtering) process as a plasma damage free and room temperature processed sputtering technology. As a result, electro-optical properties of NBAS processed ITO thin film showed resistivity of $4.0{\times}10^{-4}{\Omega}{\cdot}m$ and high transmittance (>90% at 550 nm) with nano- crystalline structure at room temperature process. Furthermore, in the experiment result of directly deposition of TCO top anode on the inverted structure OLED cell, it is verified that NBAS TCO deposition process does not damages to the underlying organic layers. In case of deposition of transparent conductive oxide (TCO) thin film on the plastic polymer substrate, the room temperature processed sputtering coating of high quality TCO thin film is required. During the sputtering process with higher density plasma, the energetic particles contribute self supplying of activation & crystallization energy without any additional heating and post-annealing and forminga high quality TCO thin film. However, negative oxygen ions which generated from sputteringtarget surface by electron attachment are accelerated to high energy by induced cathode self-bias. Thus the high energy negative oxygen ions can lead to critical physical bombardment damages to forming oxide thin film and this effect does not recover in room temperature process without post thermal annealing. To salve the inherent limitation of plasma sputtering, we have been developed the Magnetic Field Shielded Sputtering (MFSS) process as the high quality oxide thin film deposition process at room temperature. The MFSS process is effectively eliminate or suppress the negative oxygen ions bombardment damage by the plasma limiter which composed permanent magnet array. As a result, electro-optical properties of MFSS processed ITO thin film (resistivity $3.9{\times}10^{-4}{\Omega}{\cdot}cm$, transmittance 95% at 550 nm) have approachedthose of a high temperature DC magnetron sputtering (DMS) ITO thin film were. Also, AOS (a-IGZO) TFTs fabricated by MFSS process without higher temperature post annealing showed very comparable electrical performance with those by DMS process with $400^{\circ}C$ post annealing. They are important to note that the bombardment of a negative oxygen ion which is accelerated by dc self-bias during rf sputtering could degrade the electrical performance of ITO electrodes and a-IGZO TFTs. Finally, we found that reduction of damage from the high energy negative oxygen ions bombardment drives improvement of crystalline structure in the ITO thin film and suppression of the sub-gab states in a-IGZO semiconductor thin film. For realization of organic flexible electronic devices based on plastic substrates, gas barrier coatings are required to prevent the permeation of water and oxygen because organic materials are highly susceptible to water and oxygen. In particular, high efficiency flexible AMOLEDs needs an extremely low water vapor transition rate (WVTR) of $1{\times}10^{-6}gm^{-2}day^{-1}$. The key factor in high quality inorganic gas barrier formation for achieving the very low WVTR required (under ${\sim}10^{-6}gm^{-2}day^{-1}$) is the suppression of nano-sized defect sites and gas diffusion pathways among the grain boundaries. For formation of high quality single inorganic gas barrier layer, we developed high density nano-structured Al2O3 single gas barrier layer usinga NBAS process. The NBAS process can continuously change crystalline structures from an amorphous phase to a nano- crystalline phase with various grain sizes in a single inorganic thin film. As a result, the water vapor transmission rates (WVTR) of the NBAS processed $Al_2O_3$ gas barrier film have improved order of magnitude compared with that of conventional $Al_2O_3$ layers made by the RF magnetron sputteringprocess under the same sputtering conditions; the WVTR of the NBAS processed $Al_2O_3$ gas barrier film was about $5{\times}10^{-6}g/m^2/day$ by just single layer.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.149-171
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• 2020

The period at university is to make decision about getting an actual job. As our society develops rapidly and highly, jobs are diversified, subdivided, and specialized, and students' job preparation period is also getting longer and longer. This study analyzed the log data of college students to see how the various activities that college students experience inside and outside of school might have influences on employment. For this experiment, students' various activities were systematically classified, recorded as an activity data and were divided into six core competencies (Job reinforcement competency, Leadership & teamwork competency, Globalization competency, Organizational commitment competency, Job exploration competency, and Autonomous implementation competency). The effect of the six competency levels on the employment status (employed group, unemployed group) was analyzed. As a result of the analysis, it was confirmed that the difference in level between the employed group and the unemployed group was significant for all of the six competencies, so it was possible to infer that the activities at the school are significant for employment. Next, in order to analyze the impact of the six competencies on the qualitative performance of employment, we had ANOVA analysis after dividing the each competency level into 2 groups (low and high group), and creating 6 groups by the range of first annual salary. Students with high levels of globalization capability, job search capability, and autonomous implementation capability were also found to belong to a higher annual salary group. The theoretical contributions of this study are as follows. First, it connects the competencies that can be extracted from the school experience with the competencies in the Human Resource Management field and adds job search competencies and autonomous implementation competencies which are required for university students to have their own successful career & life. Second, we have conducted this analysis with the competency data measured form actual activity and result data collected from the interview and research. Third, it analyzed not only quantitative performance (employment rate) but also qualitative performance (annual salary level). The practical use of this study is as follows. First, it can be a guide when establishing career development plans for college students. It is necessary to prepare for a job that can express one's strengths based on an analysis of the world of work and job, rather than having a no-strategy, unbalanced, or accumulating excessive specifications competition. Second, the person in charge of experience design for college students, at an organizations such as schools, businesses, local governments, and governments, can refer to the six competencies suggested in this study to for the user-useful experiences design that may motivate more participation. By doing so, one event may bring mutual benefits for both event designers and students. Third, in the era of digital transformation, the government's policy manager who envisions the balanced development of the country can make a policy in the direction of achieving the curiosity and energy of college students together with the balanced development of the country. A lot of manpower is required to start up novel platform services that have not existed before or to digitize existing analog products, services and corporate culture. The activities of current digital-generation-college-students are not only catalysts in all industries, but also for very benefit and necessary for college students by themselves for their own successful career development.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.1
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• pp.98-106
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• 2009

This work proposes a 1280-RGB $\times$ 800-Dot 70.78mW 0.l3um CMOS LCD driver IC (LDI) for high-performance 16M-color low temperature poly silicon (LTPS) thin film transistor liquid crystal display (TFT-LCD) systems such as ultra mobile PC (UMPC) and mobile applications simultaneously requiring high resolution, low power, and small size at high speed. The proposed LDI optimizes power consumption and chip area at high resolution based on a resistor-string based architecture. The single column driver employing a 1:12 MUX architecture drives 12 channels simultaneously to minimize chip area. The implemented class-AB amplifier achieves a rail-to-rail operation with high gain and low power while minimizing the effect of offset and output deviations for high definition. The supply- and temperature-insensitive current reference is implemented on chip with a small number of MOS transistors. A slew enhancement technique applicable to next-generation source drivers, not implemented on this prototype chip, is proposed to reduce power consumption further. The prototype LDI implemented in a 0.13um CMOS technology demonstrates a measured settling time of source driver amplifiers within 1.016us and 1.072us during high-to-low and low-to-high transitions, respectively. The output voltage of source drivers shows a maximum deviation of 11mV. The LDI with an active die area of $12,203um{\times}1500um$ consumes 70.78mW at 1.5V/5.5V.

• Korean Journal of Food Science and Technology
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• v.50 no.5
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• pp.517-527
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• 2018

The current study investigated in vitro anti-diabetic and neuroprotective effects of the ethyl acetate fraction in Actinidia arguta sprouts (EFAS), on $H_2O_2$ and high glucose-induced cytotoxicity in human neuroblastoma MC-IXC cells. EFAS had high total phenolic and total flavonoid contents. An assessment of 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) radical scavenging activity of EFAS, as well as its potential for inhibiting malondialdehyde production, indicated that EFAS may possess significant antioxidant properties. EFAS exerted inhibitory effects on ${\alpha}-glucosidase$ via glycemic regulation which forms advanced glycation end products. In addition, EFAS exhibited significant acetylcholinesterase inhibitory effects. Moreover, EFAS displayed protective effects against $H_2O_2$ and high glucose-induced cell death, and inhibited the generation of reactive oxygen species in MC-IXC cells. Finally, the main physiological compound of EFAS was identified via high performance liquid chromatography as a rutin.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.11 s.353
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• pp.58-68
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• 2006

This work proposes a 10b 250MS/s $1.8mm^2$ 85mW 0.13um CMOS A/D Converter (ADC) for high-performance integrated systems such as next-generation DTV and WLAN simultaneously requiring low voltage, low power, and small area at high speed. The proposed 3-stage pipeline ADC minimizes chip area and power dissipation at the target resolution and sampling rate. The input SHA maintains 10b resolution with either gate-bootstrapped sampling switches or nominal CMOS sampling switches. The SHA and two MDACs based on a conventional 2-stage amplifier employ optimized trans-conductance ratios of two amplifier stages to achieve the required DC gain, bandwidth, and phase margin. The proposed signal insensitive 3-D fully symmetric capacitor layout reduces the device mismatch of two MDACs. The low-noise on-chip current and voltage references can choose optional off-chip voltage references. The prototype ADC is implemented in a 0.13um 1P8M CMOS process. The measured DNL and INL are within 0.24LSB and 0.35LSB while the ADC shows a maximum SNDR of 54dB and 48dB and a maximum SFDR of 67dB and 61dB at 200MS/s and 250MS/s, respectively. The ADC with an active die area of $1.8mm^2$ consumes 85mW at 250MS/s at a 1.2V supply.