• Journal of Korean Academic Society of Home Health Care Nursing
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• v.4
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• pp.5-22
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• 1997

The purpose of the study was to development an information delivery system for the home nursing service, to demonstrate and to evaluate the efficiency of it. The period of research conduct was from September 1996 to August 31, 1997. At the 1st stage to achieve the purpose, Firstly Assessment tool for the patients with cerebral vascular disease who have the first priority of HNS among the patients with various health problems at home was developed through literature review. Secondly, after identification of patient nursing problem by the home care nurse with the assessment tool, the patient's classification system developed by Park (1988) that was 128 nursing activities under 6 categories was used to identify the home care nurse's activities of the patient with CAV at home. The research team had several workshops with 5 clinical nurse experts to refine it. At last 110 nursing activities under 11 categories for the patients with CVA were derived. At the second stage, algorithms were developed to connect 110 nursing activities with the patient nursing problems identified by assessment tool. The computerizing process of the algorithms is as follows: These algorithms are realized with the computer program by use of the software engineering technique. The development is made by the prototyping method, which is the requirement analysis of the software specifications. The basic features of the usability, compatibility, adaptability and maintainability are taken into consideration. Particular emphasis is given to the efficient construction of the database. To enhance the database efficiency and to establish the structural cohesion, the data field is categorized with the weight of relevance to the particular disease. This approach permits the easy adaptability when numerous diseases are applied in the future. In paralleled with this, the expandability and maintainability is stressed through out the program development, which leads to the modular concept. However since the disease to be applied is increased in number as the project progress and since they are interrelated and coupled each other, the expand ability as well as maintainability should be considered with a big priority. Furthermore, since the system is to be synthesized with other medical systems in the future, these properties are very important. The prototype developed in this project is to be evaluated through the stage of system testing. There are various evaluation metrics such as cohesion, coupling and adaptability so on. But unfortunately, direct measurement of these metrics are very difficult, and accordingly, analytical and quantitative evaluations are almost impossible. Therefore, instead of the analytical evaluation, the experimental evaluation is to be applied through the test run by various users. This system testing will provide the viewpoint analysis of the user's level, and the detail and additional requirement specifications arising from user's real situation will be feedback into the system modeling. Also. the degree of freedom of the input and output will be improved, and the hardware limitation will be investigated. Upon the refining, the prototype system will be used as a design template. and will be used to develop the more extensive system. In detail. the relevant modules will be developed for the various diseases, and the module will be integrated by the macroscopic design process focusing on the inter modularity, generality of the database. and compatibility with other systems. The Home care Evaluation System is comprised of three main modules of : (1) General information on a patient, (2) General health status of a patient, and (3) Cerebrovascular disease patient. The general health status module has five sub modules of physical measurement, vitality, nursing, pharmaceutical description and emotional/cognition ability. The CVA patient module is divided into ten sub modules such as subjective sense, consciousness, memory and language pattern so on. The typical sub modules are described in appendix 3.

• Journal of Korean Society of Forest Science
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• v.105 no.2
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• pp.247-252
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• 2016

This study was conducted to evaluate the technical efficiency of Annual Chestnut production in South Korea. In this study, technical efficiency is the maximum possible production for which a certain amount of costs is inputted. For analysis on the technical efficiency we used output-oriented BCC Model, and then we analyzed correlation among input costs, production, gross income, net income, and market price per unit in order to determine the cause of variation in the technical efficiency. As study materials, we used statistics for the forestry production costs for 7 years from 2008 to 2014. The study results showed that the maximum possible production and actual production in 2008, 2009, and 2010 were 1,568 kg, 1,745 kg, and 1,534 kg by hectares in the order which were the same values. Consequently, the technical efficiency of those was all evaluated as 1.00. On the other hand, actual production from 2011 to 2014 was 1,270 kg 1,047 kg, 1,258 kg, and 1,488 kg by hectares in the order and the maximum possible production was 1,524 kg, 1,467 kg, 1,635 kg, and 1,637 kg by hectares in the analysis. From those values, the technical efficiency was evaluated in the following order:0.83, 0.71, 0.75, 0.91. The lowest value of the technical efficiency was 0.71 in 2012, and the values of this increased gradually since 2013. It is indicated that the cause of variation in the technical efficiency was related to the relationship between production and market price, and there was a negative correlation with r = -0.821 (p<0.05). The level of maximum available production per unit area was between 1,488kg in lower limit and 1,745 kg in upper limit, and the average was turned out as 1,548 kg.

• Journal of Gifted/Talented Education
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• v.11 no.3
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• pp.131-153
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• 2001

The importance of identifying gifted children during early childhood is becoming recognized. Nonetheless, most researchers preferred to study the primary and secondary levels where children are already and more clearly demonstrating what talents they have, and where more reliable predictions of gifted may be made. Comparatively lisle work has been done in this area. When we identify giftedness during early childhood, we have to consider the potential of the young children rather than on actual achievement. Giftedness during early childhood is still developing and less stable than that of older children and this prevents us from making firm and accurate predictions based on children's actual achievement. Dynamic assessment, based on Vygotsky's concept of the zone of proximal development(ZPD), suggests a new idea in the way the gifted young children are identified. In light of dynamic assessment, for identifying the potential giftedness of young children. we need to involve measuring both unassisted and assisted performance. Dynamic assessment usually consists of a test-intervene-retest format that focuses attention on the improvement in child performance when an adult provides mediated assistance on how to master the testing task. The advantages of the dynamic assessment are as follows: First, the dynamic assessment approach can provide a useful means for assessing young gifted child who have not demonstrated high ability on traditional identification method. Second, the dynamic assessment approach can assess the learning process of young children. Third, the dynamic assessment can lead an individualized education by the early identification of young gifted children. Fourth, the dynamic assessment can be a more accurate predictor of potential by linking diagnosis and instruction. Thus, it can make us provide an educational treatment effectively for young gifted children.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• The korean journal of orthodontics
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• v.27 no.5 s.64
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• pp.815-824
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• 1997

The purpose of this study is to develop the basic algorithm for the finite element method modeling of individual malocclusions. Usually, a great deal of time is spent in preprocessing. To reduce the time required, we developed a standardized procedure for measuring the position of each tooth and a program to automatically preprocess. The following procedures were carried to complete this study. 1. Twenty-eight teeth morphologies were constructed three-dimensionally for the finite element analysis and saved as separate files. 2. Standard brackets were attached so that the FA points coincide with the center of the brackets. 3. The study model of a patient was made. 4. Using the study model, the crown inclination, angulation, and the vertical distance from the tip of a tooth was measured by using specially designed tools. 5. The arch form was determined from a picture of the model with an image processing technique. 6. The measured data were input as a rotational matrix. 7. The program provides an output file containing the necessary information about the three-dimensional position of teeth, which is applicable to several finite element programs commonly used. The program for a basic algorithm was made with Turbo-C and the subsequent outfile was applied to ANSYS. This standardized model measuring procedure and the program reduce the time required, especially for preprocessing and can be applied to other malocclusions easily.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.5
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• pp.705-715
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• 2010

The adoption of carbon foot print system is being activated mostly in the developed countries as one of the long-term response towards tightened up regulations and standards on carbon emission in the agricultural sector. The Korean Ministry of Environment excluded the primary agricultural products from the carbon foot print system due to lack of LCI (life cycle inventory) database in agriculture. Therefore, the research on and establishment of LCI database in the agriculture for adoption of carbon foot print system is urgent. Development of LCA (life cycle assessment) methodology for application of LCA to agricultural environment in Korea is also very important. Application of LCA methodology to agricultural environment in Korea is an early stage. Therefore, this study was carried out to find out the effect of lettuce cultivation on agricultural environment by establishing LCA methodology. Data collection of agricultural input and output for establishing LCI was carried out by collecting statistical data and documents on income from agro and livestock products prepared by RDA. LCA methodology for agriculture was reviewed by investigating LCA methodology and LCA applications of foreign countries. Results based on 1 kg of lettuce production showed that inputs including N, P, organic fertilizers, compound fertilizers and crop protectants were the main sources of major emission factor during lettuce cropping process. The amount of inputs considering the amount of active ingredients was required to estimate the actual quantity of the inputs used. Major emissions due to agricultural activities were $N_2O$ (emission to air) and ${NO_3}^-$/${PO_4}^-$ (emission to water) from fertilizers, organic compounds from pesticides and air pollutants from fossil fuel combustion in using agricultural machines. The softwares for LCIA (life cycle impact assessment) and LCA used in Korea are 'PASS' and 'TOTAL' which have been developed by the Ministry of Knowledge Economy and the Ministry of Environment. However, the models used for the softwares are the ones developed in foreign countries. In the future, development of models and optimization of factors for characterization, normalization and weighting suitable to Korean agricultural environment need to be done for more precise LCA analysis in the agricultural area.

• Korean Journal of Agricultural Science
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• v.8 no.1
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• pp.90-96
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• 1981

To design more efficient agricultural machinery, the accurately measuring system among many other factors is essential. A light-beam oscillographic recorder is generally used in measuring dynamic strain but it is not compatible with the extremely high speed measuring system such as 1,000 m/s, also is susceptable to damage due to vibration while using the system in field. The recorder used light sensitive paper for strip chart recording. The reading and analysis of data from the strip charts is very cumbersome, errorneous and time consuming. A microcomputer was interfaced with A/D converter, microcomputer program was developed for measuring, system calibration was done and the strain generated from a cantilever beam vibrator was measured. The results are summarized as follows. 1. Microcomputer program was developed to perform strain measuring of agricultural machine elements and could be controled freely the measuring intervals, no. of channels and no. of data. The maximum measuring speed was $62{\mu}s$. 2. Calibration the system was performed with triangle wave generated from a function generator and checked by an oscilloscope. The sampled data were processed using HP 3000 minicomputer of Chungnam National University computer center the graphical results were triangle same as input wave and so the system have been out of phase distorsion and amplitude distorsion. 3. The strain generated from a cantilever beam vibrator which has free vibration period of 0.019 second were measured by the system controlled to have l.0 ms of time interval and its computer output showing vibration curve which is well filted to theoretical value. 4. Using microcomputer on measuring the strain of agricultural machine elements could not only save analyzing time and recording papers but also get excellent adaptation to field experiment, especially in measurement requiring high speed and good precision.

• Journal of Agricultural Extension & Community Development
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• v.21 no.4
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• pp.1093-1124
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• 2014

Studies on the role of agricultural science colleges are mostly divided into agricultural production, which is the primary function of agriculture, and other functions, which have recently begun to be emphasized as a result of social needs. With the green revolution and the aging of the farming population, there is a strong view that the role of agricultural science colleges should remain as it is. However, agriculture is expanding in terms of concept and content by converging with other industries not traditionally associated with agricultural production. Thus, the fields that now need to form part of agricultural science knowledge are becoming more detailed and expansive. The government's perception remains at the level of merely fostering farmers. This was evident in a survey on the employment rate, a factor used to evaluate colleges, in which the role of agricultural science colleges was limited to fostering farmers. Agro- industry fields, other than agriculturalists, include general industries in which the academic fields of agricultural science are combined with other academic fields. Thus, even when someone is employed in an industry that requires background knowledge of agricultural science, there is often a perception that he or she is employed in a field that is irrelevant to the major. This study examines the role of agricultural science colleges in agriculture and farm villages by focusing on the employment of graduates of these colleges within agro-industry. We categorize academic research on agricultural science into 16 fields, based on the medium level of the National Standard Science and Technology Classification Codes. Then, we categorize the employment fields into 168 fields, based on the small classification level of the inter-industry relations classification. Thus, we investigate 220 departments of 37 colleges, nationwide. Our findings show that the average employment rate of graduates of agricultural science colleges is 69.0%. Furthermore, 33.0% of all employees work in agro-industry fields that require background knowledge in agricultural science, which is one out of three job seekers. Then, 3.6% of employees work in business startups in agro-industry. The aforementioned government survey showed that only 0.1% of all college graduates in Korea were employed as agriculturalists in 2013. However, our results showed that 13.3% of graduates were working as agriculturalists, which is significantly different to the results of the government survey. These results confirm that agricultural science colleges contribute greatly to the employment of graduates, including farmers, agro-industry, and business startups in agro-industry fields.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.3
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• pp.1-16
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• 2012

Under the international business circumstance, global supply chain management is considered a vital strategic challenge to small and medium-sized enterprises(SMEs) suffering from deficient resources and capabilities to exploit overseas markets comparing with large corporations. That is because they can expand their business domains into overseas markets by establishing strategic alliances with global supply chain partners. Although a wide range of previous researches have emphasized the cooperative networks in the chain, most are ignoring the importance of developing relational characteristics such as trust and reciprocity with the partners. Besides, verifying the relational factors influencing firms' export performances, some studies proposed different and inconsistent factors. According to the social capital theory, which is the social quality and networks facilitating close cooperation of inter-individual and inter-organization, provides the integrated view to identify the relational characteristics in the aspects of network, trust and reciprocal norm. Meanwhile, a number of researchers shows that global entrepreneurship is the internal and intangible resource necessary to promote SMEs' internationalization. Upon closer examination, however, they cannot explain clearly its influencing mechanism in the inter-firm cooperative relationships. This study is to verify the effect of social capital accumulated within global supply chain on SMEs' qualitative and quantitative export performance. In addition, we shed new light on global entrepreneurship expected to be concerned with the formation of social capital and the enhancement of export performances. For this purpose, the questionnaires, developed through literature review, were collected from 192 Korean SMEs affiliated in Korean Medium Industries Association and Global Chief Executive Officer's Club focusing on their memberships' international business. As a result of multi-regression analysis, the social capital - network, trust and reciprocal norm shared with global supply chain partner - as well as global entrepreneurship - innovativeness, proactiveness and risk-taking - have positive effect on SMEs' export performances. Also global entrepreneurship affects positively social capital which has mediating effect partially in the relationship between global entrepreneurship and performances. These results means that there is a structural process - global entrepreneurship(input), social capital(output), and export performances(outcome). In other words, a firm should consistently invest in and develop the social capital with global supply chain partners in order to achieve common goals, establish strategic collaborations and obtain long-term export performances. Furthermore, it is required to foster the global entrepreneurship in an organization so as to build up the social capital. More detailed practical issues and discussion are made in the conclusion.