• Journal of the Korea Organic Resources Recycling Association
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• v.31 no.3
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• pp.55-64
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• 2023

This batch experiment was conducted to investigate the patterns of major plant nutrients in the leachates from the soil that was incorporated with rice hull biochar adjusted pH with dry fish powder utilizing rice hull biochar for loading the soil microorganisms. The rice hull biochar adjusted pH between 6.0 and 7.0, and the mixture ratio of rice hull biochar and dry fish powder was 4:6. The treatments consisted of three; the soil incorporated with rice hull biochar non-adjusted pH with dry fish powder as control (RB + DF), the soil incorporated with rice hull biochar adjusted pH by pyroligneous acid solution and dry fish powder (RBP+DF), and the soil incorporated with rice hull biochar adjusted pH by citric acid solution and dry fish powder (RBC+DF). NH₄-N, NO₃-N, PO₄-P, and K concentrations in the leachates were analyzed during incubation. The accumulated NH₄-N and PO₄-P concentrations in the leachates from the RBC+DF treatment were the highest during leaching periods. The highest accumulated NO₃-N and K concentrations in the leachates from the RBP+DF treatment were observed. It observed that NH₄-N and PO₄-P were more released in the adjusted citric acid solution, but NO₃-N and K were less released than those in the pyroligneous acid solution due to their low absorption capacity. Furthermore, it is necessary to investigate crop growth responses to the soil incorporated with adjusted pH rice hull biochar and dry fish powder for loading soil microorganisms.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.17 no.2
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• pp.23-42
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• 2006

This is a study of the digital archives of Culturecontent.com where 'Cultural Archetype Contents' are currently in service. One of the major purposes of our study is to point out problems in the current system and eventually propose improvements to the digital archives. The government launched a four-year project for developing the cultural archetype content sources and establishing its related business with the hope of enhancing the nation's competitiveness. More specifically, the project focuses on the production of source materials of cultural archetype contents in the subjects of Korea's history. tradition, everyday life. arts and general geographical books. In addition, through this project, the government also intends to establish a proper distribution system of digitalized culture contents and to control copyright issues. This paper analyzes the digital archives system that stores the culture content data that have been produced from 2002 to 2005 and evaluates the current system's weaknesses and strengths. The summary of our findings is as follows. First. the digital archives system does not contain a semantic search engine and therefore its full function is 1agged. Second, similar data is not classified into the same categories but into the different ones, thereby confusing and inconveniencing users. Users who want to find source materials could be disappointed by the current distributive system. Our paper suggests a better system of digital archives with text mining technology which consists of five significant intelligent process-keyword searches, summarization, clustering, classification and topic tracking. Our paper endeavors to develop the best technical environment for preserving and using culture contents data. With the new digitalized upgraded settings, users of culture contents data will discover a world of new knowledge. The technology we introduce in this paper will lead to the highest achievable digital intelligence through a new framework.

• 한국노년학
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• v.27 no.2
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• pp.459-472
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• 2007

This research has its purpose of developing a tool to assess the needs of the Korean elderly at home population and to provide adequate services by evaluating their physical, psychological, and socio-environmental aspects. This developed tool is composed of two hundred questions and has the advantage of combined physical, psychological and social environmental situation assessment of the elderly at home. The tool also contains not only the objective view of the professionals, but also the subjective appeals of the elderly at home population so that it can reflect their substantial desires. The assessment tool was developed over 21 months from July, 2004 to March, 2006 and this period can be divided into three different stages. In the first stage, collecting of questions for the desire-focused assessment of the elderly by literature investigation and researching foreign source materials was carried out, and this ultimately developed assessment tool was applied to the long-term care insurance pilot project in the second stage. In this process, we revised some insufficiencies of this tool after we applied to elderly of 250 from the pilot project and other 200 elderly from this research team. For the last stage, the tool was completed by using inquiries of the focused group and the group of professionals to ensure its reliability and validity. In the process of developing the tool, the total of 200 questions under 13 subcategories was selected. The 13 subcategories are basic information, subjective appeals, information of the main helper, use of services, house environment, condition of health, condition of rehabilitation, daily living(ADL, IADL, defecation, assistance), social maintenance, behavioral disability, medical health, living habits, and strength. This tool is on the purpose to assess thoroughly the desires that the elderly at home population has and to provide the best service they need.

• Korean small business review
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• v.31 no.1
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• pp.73-88
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• 2009

This study examines the characteristics of 24 Korean hidden champions such as key success factors, core competences, strategic problems, and desirable future directions. The study categorized them into 8 types with Danny Miller's four trajectories and top manager's decision making style(rationality and passion). Danny Miller argued in his book, Icarus paradox, that outstanding firms will extend their orientations until they reach dangerous extremes and their momentum will result in common trajectories of decline. He suggested four very common success types: Craftsmen, Builders, Pioneers, Salesmen. He also suggested common trajectories of decline:Focusing(from Craftsmen to Tinkers), Venturing(from Builders to Imperialists), Inventing(from Pioneers to Escapists), Decoupling(from Salesmen to Drifts). In Korea, successful startups appear to possess three kinds of drive: Technology-drive, Vision-drive, Market-drive. Successful technology-driven firms tend to grow as craftsmen or pioneers. Successful vision-driven and market-driven ones tend to grow as builders and salesmen respectively. Korean top managers or founders seem to have two kinds of decision making style: Passion-based and Rationality-bases. Passion-based(passionate) entrepreneurs are biased towards action or proactiveness in competing and getting things done. Rationality- based ones tend to emphasis the effort devoted to scanning and analysing information to better understand a company's threats, opportunities and options. Consequently this study suggested 4*2 types of Korean hidden champions: (1) passionate craftsmen, (2) rational craftsmen, (3) passionate builders, (4) rational builders, (5) passionate pioneers, (6) rational pioneers, (7) passionate salesmen, (8) rational salesmen. These 8 type firms showed different success stories and appeared to possess different trajectories of decline. These hidden champions have acquired competitive advantage within domestic or globally niche markets in spite of the weak market power and lack of internal resources. They have maintained their sustainable competitiveness by utilizing three types of growth strategy; (1) penetrating into the global market, (2) exploring new service market, (3) occupying the domestic market. According to the types of growth strategy, these firms showed different financial outcomes and possessed different issues for maintaining their competitiveness. This study found that Korean hidden champions were facing serious challenges from the transforming economic structure these days and possessed the decline potential from their success momentum or self-complacence. It argues that they need to take a new growth engine not to decline in the turbulent environment. It also discusses how firms overcome the economic crisis and find a new business area in promising industries for the future. It summarized the recent policy of Korean government called as "Green Growth" and discussed how small firms utilize such benefits and supports from the government. Other implications for firm strategies and governmental policies were discussed.

• Journal of Practical Agriculture & Fisheries Research
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• v.20 no.1
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• pp.163-174
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• 2018

Rice cultivation is immensely affected by many climatic factors including temperature, precipitation, etc, and imbalanced climatic conditions negatively affect the growth of rice. In this study, we investigated the effects of different agroclimatic zones of Chungnam Province on rice quality and examined the correlations between climatic characteristics and rice yield components. Average temperatures and rainfall were higher in 'Western Sobaek Inland' than those in the 'South Western coastal zone, and precipitation records showed a wide variation among counties due to typhoons during the examined periods. The average accumulative temperature affecting the magnitude of production during reproductive growth periods was higher in "Cheon-An", "Gong-Ju", "Yeon-Gi (Se-Jong)", "Bo-Ryeong", and "Dang-Jin" counties than those in other counties. The plant height was higher in 'Western Sobaek Inland' counties such as "Yeon-Gi(Se-Jong)" and "Cheon-An", and 'Southern Charyeong Plain' counties such as "Cheong-Yang", "Dang-Jin", and "A-San", than those in other counties. The number of tillers during the 40 days after rice transplantation in "Seo-Cheon" and "Bo-Ryeong" counties increased compared to other counties. This result was relevant to the fact that the date of rice transplantation in those counties was 3 to 4 days later than those in other counties of Chung-Nam Province. The average yield (milled rice basis) was the highest in 'Western Sobaek Inland' zone, showing 3,756 kg ha^-1, followed by 'Southern Charyeong Plain' zone showing 3,621kg ha^-1, and was the lowest in 'South Western coastal zone by 3,315kg ha^-1. "Yeon-Gi(Se-Jong)" and "Dang-Jin" counties showed the highest yields of 4,100kg ha^-1. "Seo-San", "Seo-Cheon", and "Tae-An" counties were relatively lower yields of 3,240~3,280kg ha^-1 in comparison of other counties.

• Journal of Venture Innovation
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• v.6 no.1
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• pp.181-198
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• 2023

This study aims to analyze the influence of organizational support perception of workers in medical institutions on intrinsic job motivation, and to check whether there is significance in the mediating effect of work-family conflict and work-family balance factors in this process. The results of empirical analysis through the questionnaire are as follows. First, it was confirmed that organizational support recognition had a significant positive effect on work-family balance as well as intrinsic job motivation, and work-family balance had a significant positive effect on intrinsic job motivation. Second, it was confirmed that organizational support recognition had a significant negative effect on work-family conflict, but work-family conflict had no significant influence on intrinsic job motivation. Third, in order to reduce job stress for medical institution workers, it is necessary to reduce job intensity, assign appropriate workload for ability. And in order to improve manpower operation and job efficiency, Job training and staffing in the right place are needed. Fourth, in order to improve positive organizational support perception and intrinsic job motivation, It is necessary to induce long-term service by providing support and institutional devices to increase attachment to the current job and recognize organizational problems as their own problems with various incentive systems. The limitations of this study and future research directions are as follows. First, it is believed that an expanded analysis of medical institution workers nationwide by region, gender, medical institution, academic, and income will not only provide more valuable results, but also evaluate the quality of medical services. Second, it is necessary to reflect the impact of the work-life balance support system on each employee depending on the environmental uncertainty or degree of competition in the hospital to which medical institution workers belong. Third, organizational support perception will be recognized differently depending on organizational culture and organizational type, and organizational size and work characteristics, working years, and work types, so it is necessary to reflect this. Fourth, it is necessary to analyze various new personnel management techniques such as hospital's organizational structure, job design, organizational support method, motivational approach, and personnel evaluation method in line with the recent change in the government's medical institution policy and the global business environment. It is also considered important to analyze by reflecting recent and near future medical trends.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.37-50
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• 2023

Venture capital invests the necessary capital and supports management and technology in promising small and medium-sized venture companies in the early stages of start-up with promising technology and excellent manpower. It plays a role as a key player in the venture ecosystem that realizes profits by collecting the investment through various means after growth. Venture capital's job is to recruit various investors(LPs) to invest in small and medium-sized venture companies with growth potential through the formation of venture investment funds, and to collect investment as companies grow, distribute and reinvest. The main tasks of venture capitalists, which play the most important role in venture investment, are finding promising companies, corporate analysis and evaluation, investment screening, follow-up management, and investment recovery. Venture capital's success indicators are fund formation and return on investment, and venture capitalists are rewarded with annual salary, performance-based incentive, and promotion with work performance such as investment, exit, and fund formation. Compared to the recent rapidly growing venture investment market, investment manpower is insufficient, and venture capital is making great efforts to foster manpower and establish infrastructure and systems for long-term service, but research has been conducted mainly from a quantitative perspective. Accordingly, this study aims to empirically analyzed the impact of investment experience, delegation of authority, job fit, and peer relationships on fund formation and return on investment according to the characteristics of the venture capital industry. The results of these empirical studies suggested that future venture capital needs a job environment and manpower operation strategy so that venture capitalists with high job fit and investment experience can work for a long time.

• Research in Plant Disease
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• v.30 no.2
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• pp.139-147
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• 2024

Erwinia amylovora is a gram-negative plant pathogen that causes fire blight in apple and pear trees, resulting in significant damage worldwide. In this study, we monitored the emergence of fire blight from 2019 to 2023 to determine the emergence patterns and the factors affecting the outbreak of the disease. As a result of the 5-year survey on the emergence of fire blight, a total of 2,029 cases have emerged, mostly in apple trees of 1,378 cases (67.9%) followed by 645 cases (31.8%) in pear trees, and from quince, hawthorn, and mountain ash trees. Fire blight appeared in specific areas of Gyeonggi, Chungnam, Gangwon, and Chungbuk provinces in 2019, but spread to Andong and Yesan in 2021, Muju and Bonghwa in 2023. In 2020 and 2021, there were 744 and 618 cases of fire blight outbreaks, respectively, compared to other years (188-245 cases/year). Notably, 914 of these cases were observed in apple trees from May to July, with 667 cases reported in Chungju and Jecheon. The incidence of fire blight was positively correlated with the daily maximum temperatures and rainy days in January and February, as well as the rainy days in May and June. The average age of the diseased pear trees was 25 years, higher than the 10-year average age of the apple trees. This study provides fundamental information to understand the status and factors affecting the fire blight emergence in Korea. Prevention measures should be established through continuous analysis of the status of fire blight.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.