• 한국해양학회지
• /
• v.23 no.2
• /
• pp.70-75
• /
• 1988

In order to set up a predictive model for an effective spat collection of ark shells, Anadara broughtonii, the survival rate and the time required for each developmental stage of planktonic larvae were investigated during the period from July 1 to October 30 in 1974, in one of the main ark shell seed collection areas, Chinhae Bay, in the southern part of Korea. The advent of D-shaped larvae ca. $94.3{\times}72.7{\mu}m$ long had three peaks during the surveyed period: August 25, August 31 and September 9, umbo-shaped larvae ca. $141.6{\times}108.4{\mu}m$ and full grown larvae ca. $269.3{\times}221.7{\mu}m$ long also showed three peaks: September 6, September 12 and September 20 for the former, and September 20, September 25 and October 5 for the latter, respectively. About 11 to 12 days was required for D-shaped larvae to develop to umbo-shaped stage. At this intermorphological stage, the daily survival rate was 0.93 with a total survival rate of 45% for the stage. The time required for umbo-shaped larvae to develop to full grown larvae varied from 13 to 15 days with a daily survival rate of 0.93 and with a total survival rate of 36% in the period. Twenty-five to twenty-six days were required for each peak group of the D-shaped larvae to reach a full grown stage, and their total survival rate was 16% during this developmental stage.

• Korea journal of population studies
• /
• v.11 no.1
• /
• pp.204-229
• /
• 1988

This paper documents and discusses trends and differentials in youth's participation in the labor force and employment. Youth in this study is defined asthe young aged 15-29. Youth passes through a series of life-course transitions,which include school completion own family formation(marriage and childbirth) .mandatory service in the army (by males) , and their economic activities are affectedby those life-course events. Accordingly we show how and to what extent youth'slabor force participation and employment varies with age and how the age patternhas changed over time.Throughout the 1980's and 1990's, youth's labor force participation showeddifferent trends by age group Labor fDrce participation rate of the 15-19 agedsteeply decreased, while that of the 25-29 steadily increased during the twodecades, the rate fsr the 20-24 aged showing not much variation. The former is dueto the increased rate of school enrollment among the age group, while the lattercould be attributed, in part, to the young women s increased and more steadyparticipation in the labor force over time.While labor force participation could be considered as a result of one's choicesand preferences, employment opportunities are more or less restricted by labormarket structure and institutions . This study documents how the structuralconstraints have interacted with individual and group attributes to differentiateemployment opportunities between individuals (educational background) and groups(especially sex diffrences) . One of the most salient feature of youth's em[ploymentstructure is the recent high unemployment rate of the college graduates. We discusshow that is related to the'credential society'in which one's educational credentials and it's social status play major role in determining who gets what in terms of job opportunities. Also is discussed the discordance between school and labor marketsupply and demand system, which is apparent in the prolonged oversupply of thecollege graduates, which is due to the consistently high rate of college entranceobserved since the early 1980's. Theoretically the job market for college graduates isviewed not as the'neoclassical'wage competition market but as job competition market in which one's (good) job opportunity is determined by one s position in thejob queue, which is in turn heavily dependent on from which college one get shis/her college degree as well as one's sex.

• Journal of the Korean Society for information Management
• /
• v.38 no.3
• /
• pp.23-39
• /
• 2021

This study aims to explore research trends in Blockchain studies in South Korea using dynamic topic modeling and network analysis. To achieve this goal, we conducted the university & institute collaboration network analysis, the keyword co-occurrence network analysis, and times series topic analysis using dynamic topic modeling. Through the university & institute collaboration network analysis, we found major universities such as Soongsil University, Soonchunhyang University, Korea University, Korea Advanced Institute of Science and Technology (KAIST) and major institutes such as Ministry of National Defense, Korea Railroad Research Institute, Samil PricewaterhouseCoopers, Electronics and Telecommunications Research Institute that led collaborative research. Next, through the analysis of the keyword co-occurrence network, we found major research keywords including virtual assets (Cryptocurrency, Bitcoin, Ethereum, Virtual currency), blockchain technology (Distributed ledger, Distributed ledger technology), finance (Smart contract), and information security (Security, privacy, Personal information). Smart contracts showed the highest scores in all network centrality measures showing its importance in the field. Finally, through the time series topic analysis, we identified five major topics including blockchain technology, blockchain ecosystem, blockchain application 1 (trade, online voting, real estate), blockchain application 2 (food, tourism, distribution, media), and blockchain application 3 (economy, finance). Changes of topics were also investigated by exploring proportions of representative keywords for each topic. The study is the first of its kind to attempt to conduct university & institute collaboration networks analysis and dynamic topic modeling-based times series topic analysis for exploring research trends in Blockchain studies in South Korea. Our results can be used by government agencies, universities, and research institutes to develop effective strategies of promoting university & institutes collaboration and interdisciplinary research in the field.

• Korean Journal of Heritage: History & Science
• /
• v.54 no.2
• /
• pp.240-253
• /
• 2021

Various decipherment technologies, including manual rubbing, have been continuously applied to the Goguryeo Stele in Chungju. However, an official document for advanced decipherment is required as there remain characters to be deciphered. This study focuses on interdisciplinary digital decipherment using digital visualization results based on reflectance transformation imaging and three-dimensional scanning. On that basis, the joint decipherment document in 2019 is classified into eight types according to character changes from 1979 to 2000, and decipherment achievements are discussed. Important outcomes of the joint decipherment document include a new interpretation of four existing characters and the identification of 28 new characters. Additionally, 68 characters on the front face and 20 on the left face, which are listed differently in previous decipherment documents, are determined to be a single character through a consensus process. Compared to the previous decipherment document, the "decision" character is increased by a total of 89 characters (22.6%), and the "different opinion" character is decreased by 126 characters (32.0%). Thus, this digital decipherment greatly contributes to the reexamination of the Goguryeo Stele in Chungju and complements the previous decipherment document through reflectance transformation imaging and three-dimensional scanning. However, continuous research is necessary to enhance decipherment rates, since 123 characters (31.3%) are yet to receive decipherment. In the future, decipherment advancement regarding the Goguryeo Stele in Chungju should be based on convergence research between the humanities and science. Furthermore, it seems that researchers must make constant efforts to apply new imaging analysis technology and to develop customized technology for decipherment.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.4
• /
• pp.693-704
• /
• 2021

This study empirically verified the effect of strategic orientation on information technology(IT) competency and corporate performance for organizational members engaged in small and medium-sized enterprises (SMEs). In the research model proposed in this study, strategic orientation affects corporate performance, and IT competency is used as a mediating variable in this process. For this study, a survey was conducted on organizational members working in small and medium-sized manufacturers located in Ulsan Metropolitan City. A total of 320 questionnaires were distributed, and 277 copies were used in this study. The collected data were statistically analyzed using SPSS 24.0. The research results are as follows: First, customer orientation, market orientation, and technology orientation of strategic orientation were found to have a positive (+) effect on both information technology knowledge and information technology operation of IT competency. And it was found that both customer orientation and technology orientation of strategic orientation only affects the information technology infrastructure of IT competency. Second, it was found that customer orientation and technology orientation of strategic orientation had a positive (+) effect on corporate performance, but market orientation had no effect on corporate performance. Third, it was found that information technology knowledge, information technology operation, and information technology infrastructure of IT competency had a positive (+) effect on corporate performance. Fourth, as a result of examining the mediating effect of information technology competency between strategic orientation and corporate performance, information technology knowledge, information technology operation, and information technology infrastructure of IT capability were found to have a partial mediating effect between customer orientation and technology orientation of strategic orientation and corporate performance. These research results suggest that in today's fourth industrial revolution era, customer-oriented and technology-oriented management strategies should be established to improve the competitive advantage and performance of small and medium-sized enterprises(SMEs) in the supply chain with large enterprises, and at the same time information technology capabilities such as information technology knowledge, information technology operation, and information technology infrastructure should be strengthened.

• Membrane Journal
• /
• v.32 no.4
• /
• pp.235-252
• /
• 2022

Forward osmotic pressure-free reverse osmosis (Δ𝜋=0 RO) was invented in 2013. The first patent (US 9,950,297 B2) was registered on April 18, 2018. The "Osmotic Pressure of Concentrated Solutions" in JACS (1908) by G.N. Lewis of MIT was used for the estimation. The Chang's RO system differs from conventional RO (C-RO) in that two-chamber system of osmotic pressure equalizer and a low-pressure RO system while C-RO is based on a single chamber. Chang claimed that all aqueous solutions, including salt water, regardless of its osmotic pressure can be separated into water and salt. The second patent (US 10.953.367B2, March 23, 2021) showed that a low-pressure reverse osmosis is possible for 3.0% input at Δ𝜋 of 10 to 12 bar. Singularity ZERO reverse osmosis from his third patent (Korea patent 10-22322755, US-PCT/KR202003595) for a 3.0% NaCl input, 50% more water recovery, use of 1/3 RO membrane area, and 1/5th of theoretical energy. These numbers come from Chang's laboratory experiments and theoretical analysis. Relative residence time (RRT) of feed and OE chambers makes Δ𝜋 to zero or negative by recycling enriched feed flow. The construction cost by S-ZERO was estimated to be around 50~60% of the current RO system.

• Journal of Intelligence and Information Systems
• /
• v.21 no.4
• /
• pp.111-131
• /
• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.1-25
• /
• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.