• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.21-35
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• 2023

Expectations surrounding generative AI technology and its profound ramifications are sweeping across various industrial domains. Given the anticipated pivotal role of the startup ecosystem in the utilization and advancement of generative AI technology, it is imperative to cultivate a deeper comprehension of the present state and distinctive attributes characterizing venture capital (VC) investments within this domain. The current investigation delves into South Korea's landscape of VC investment deals and prognosticates the projected VC investments by juxtaposing these against the United States, the frontrunner in the generative AI industry and its associated ecosystem. For analytical purposes, a compilation of 286 investment deals originating from 117 U.S. generative AI startups spanning the period from 2008 to 2023, as well as 144 investment deals from 42 South Korean generative AI startups covering the years 2011 to 2023, was amassed to construct new datasets. The outcomes of this endeavor reveal an upward trajectory in the count of VC investment deals within both the U.S. and South Korea during recent years. Predominantly, these deals have been concentrated within the early-stage investment realm. Noteworthy disparities between the two nations have also come to light. Specifically, in the U.S., in contrast to South Korea, the quantum of recent VC deals has escalated, marking an augmentation ranging from 285% to 488% in the corresponding developmental stage. While the interval between disparate investment stages demonstrated a slight elongation in South Korea relative to the U.S., this discrepancy did not achieve statistical significance. Furthermore, the proportion of VC investments channeled into generative AI enterprises, relative to the aggregate number of deals, exhibited a higher quotient in South Korea compared to the U.S. Upon a comprehensive sectoral breakdown of generative AI, it was discerned that within the U.S., 59.2% of total deals were concentrated in the text and model sectors, whereas in South Korea, 61.9% of deals centered around the video, image, and chat sectors. Through forecasting, the anticipated VC investments in South Korea from 2023 to 2029 were derived via four distinct models, culminating in an estimated average requirement of 3.4 trillion Korean won (ranging from at least 2.408 trillion won to a maximum of 5.919 trillion won). This research bears pragmatic significance as it methodically dissects VC investments within the generative AI domain across both the U.S. and South Korea, culminating in the presentation of an estimated VC investment projection for the latter. Furthermore, its academic significance lies in laying the groundwork for prospective scholarly inquiries by dissecting the current landscape of generative AI VC investments, a sphere that has hitherto remained void of rigorous academic investigation supported by empirical data. Additionally, the study introduces two innovative methodologies for the prediction of VC investment sums. Upon broader integration, application, and refinement of these methodologies within diverse academic explorations, they stand poised to enhance the prognosticative capacity pertaining to VC investment costs.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.2
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• pp.51-63
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• 2024

While the government policy to fully adopt BIM in the construction sector is being implemented, the construction and utilization of landscape BIM models are facing challenges due to problems such as limitations in BIM authoring tools, difficulties in modeling natural materials, and a shortage in BIM content including libraries. In particular, plants, fundamental design elements in the field of landscape architecture, must be included in BIM models, yet they are often omitted during the modeling process, or necessary information is not included, which further compromises the quality of the BIM data. This study aimed to contribute to the construction and utilization of landscape BIM models by developing a plant library that complies with BIM standards and is applicable to the landscape industry. The plant library of trees and shrubs was developed in Revit by modeling 3D shapes and collecting attribute items. The geometric information is simplified to express the unique characteristics of each plant species at LOD200, LOD300, and LOD350 levels. The attribute information includes properties on plant species identification, such as species name, specifications, and quantity estimation, as well as ecological attributes and environmental performance information, totaling 24 items. The names of the files were given so that the hierarchy of an object in the landscape field could be revealed and the object name could classify the plant itself. Its usability was examined by building a landscape BIM model of an apartment complex. The result showed that the plant library facilitated the construction process of the landscape BIM model. It was also confirmed that the library was properly operated in the basic utilization of the BIM model, such as 2D documentation, quantity takeoff, and design review. However, the library lacked ground cover, and had limitations in those variables such as the environmental performance of plants because various databases for some materials have not yet been established. Further efforts are needed to develop BIM modeling tools, techniques, and various databases for natural materials. Moreover, entities and systems responsible for creating, managing, distributing, and disseminating BIM libraries must be established.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.109-122
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• 2016

Recently, big data analysis has developed into a field of interest to individuals and non-experts as well as companies and professionals. Accordingly, it is utilized for marketing and social problem solving by analyzing the data currently opened or collected directly. In Korea, various companies and individuals are challenging big data analysis, but it is difficult from the initial stage of analysis due to limitation of big data disclosure and collection difficulties. Nowadays, the system improvement for big data activation and big data disclosure services are variously carried out in Korea and abroad, and services for opening public data such as domestic government 3.0 (data.go.kr) are mainly implemented. In addition to the efforts made by the government, services that share data held by corporations or individuals are running, but it is difficult to find useful data because of the lack of shared data. In addition, big data traffic problems can occur because it is necessary to download and examine the entire data in order to grasp the attributes and simple information about the shared data. Therefore, We need for a new system for big data processing and utilization. First, big data pre-analysis technology is needed as a way to solve big data sharing problem. Pre-analysis is a concept proposed in this paper in order to solve the problem of sharing big data, and it means to provide users with the results generated by pre-analyzing the data in advance. Through preliminary analysis, it is possible to improve the usability of big data by providing information that can grasp the properties and characteristics of big data when the data user searches for big data. In addition, by sharing the summary data or sample data generated through the pre-analysis, it is possible to solve the security problem that may occur when the original data is disclosed, thereby enabling the big data sharing between the data provider and the data user. Second, it is necessary to quickly generate appropriate preprocessing results according to the level of disclosure or network status of raw data and to provide the results to users through big data distribution processing using spark. Third, in order to solve the problem of big traffic, the system monitors the traffic of the network in real time. When preprocessing the data requested by the user, preprocessing to a size available in the current network and transmitting it to the user is required so that no big traffic occurs. In this paper, we present various data sizes according to the level of disclosure through pre - analysis. This method is expected to show a low traffic volume when compared with the conventional method of sharing only raw data in a large number of systems. In this paper, we describe how to solve problems that occur when big data is released and used, and to help facilitate sharing and analysis. The client-server model uses SPARK for fast analysis and processing of user requests. Server Agent and a Client Agent, each of which is deployed on the Server and Client side. The Server Agent is a necessary agent for the data provider and performs preliminary analysis of big data to generate Data Descriptor with information of Sample Data, Summary Data, and Raw Data. In addition, it performs fast and efficient big data preprocessing through big data distribution processing and continuously monitors network traffic. The Client Agent is an agent placed on the data user side. It can search the big data through the Data Descriptor which is the result of the pre-analysis and can quickly search the data. The desired data can be requested from the server to download the big data according to the level of disclosure. It separates the Server Agent and the client agent when the data provider publishes the data for data to be used by the user. In particular, we focus on the Big Data Sharing, Distributed Big Data Processing, Big Traffic problem, and construct the detailed module of the client - server model and present the design method of each module. The system designed on the basis of the proposed model, the user who acquires the data analyzes the data in the desired direction or preprocesses the new data. By analyzing the newly processed data through the server agent, the data user changes its role as the data provider. The data provider can also obtain useful statistical information from the Data Descriptor of the data it discloses and become a data user to perform new analysis using the sample data. In this way, raw data is processed and processed big data is utilized by the user, thereby forming a natural shared environment. The role of data provider and data user is not distinguished, and provides an ideal shared service that enables everyone to be a provider and a user. The client-server model solves the problem of sharing big data and provides a free sharing environment to securely big data disclosure and provides an ideal shared service to easily find big data.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• Science of Emotion and Sensibility
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• v.15 no.2
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• pp.297-306
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• 2012

In this study we investigated emotional characteristics of various whites which have slightly different nuances to suggest guidelines that help designers to select appropriate colors when designing white based products. The study involved three different procedures. In experiment 1, we selected 20 emotional words through a survey (N=30) among 60 words, which we picked from literature review that was thought to be appropriate to evaluate product colors. In experiment 2, we evaluated the emotional characteristics of 13 basic colors from the I.R.I Hue & Tone 120 system (N=30) using previously selected emotional words, to find relative emotional positions of white in comparison to other colors. Based on the ratings, factor analysis was conducted and consequently four factors were extracted: flamboyant, elegant, clear, and soft. Accordingly, the emotional characteristics of the 13 colors were profiled and compared with those of white. Finally, in experiment 3, we conducted an evaluation of emotional characteristics on 25 whites with different nuances facilitating the four factors obtained in experiment 2. The color stimuli used in experiments were measured in terms of CIE 1976 $L^*a^*b^*$, and regression analysis was performed in order to predict the emotional characteristics through the L, a, and b values of a color, as long as that is perceived as a white. Throughout three empirical studies, we observed three overruling tendencies : First, there are four important factors when evaluating product color - flamboyant, elegance, clearness and softness; second, white is dominantly the most elegant in comparison to other colors; third, the emotional factors of the study were affected by some combinations of attributes of colors rather than by all three-hue, saturation and brightness. In addition, the equations derived from the regression analysis in experiment 3, it is expected that designers may predict the emotional distinction between nuances of white.

• Journal of Korean Academy of Nursing Administration
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• v.4 no.2
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• pp.307-319
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• 1998

This study is an empirical investigation and study on the measurement of nursing service quality as perceived by patients. A series of H1. H2. H3 alternative hypotheses were tested using a sample of 250 patients in Taegu City. Korea. HI hypothese were tested for application of five component of service quality (SERVQlTAL and SE RPERF : tangiblity. reliability. responsiveness. accessibility. understandability) in Taegu area Hospitals. Validity test - the five components of service quality were rearranged into two components of service quality (personal factor. nonpersonal factor). Although SERVQUAL was verified in USA. application for five components of service quality in Korea indicated that it need more analytical studies. Nobody can deny the fact that the recent growth of the nursing service quality is one of the most important driving forces of hospital management. In many hospitals. the nursing quality charges more than 50% of the medical service quality. As a result. many hospital managers should be enormous interests in the investment potentiality of the nursing service. However. doesn't many researchers invest their time and effort on the research of the quality control in nursing service. Nursing service management is the process to satisfy customer's desires and expectations through the various service activities. Presently nursing service are being faced with three Common tasks of improving quality of nursing service. competitively differential advantage and productivity because of quantitative expansion of Nursing service. Such a phenomenon is also found in our medical service industry. resulting from increasing demands for medical service owing to national medical insurance policy and consumer's attitude change emphasizing prevention of illness. excessiveness of medical facilities in large cities and increasing medical lawsuits due to influence of consumerism. Therefore. under such circumstances. this research on nursing service is conducted from nursing managements to improve the nursing service quality problems faced by medical institutions. The results of this theoretical/empirical research are as follows: 1. Nursing service Quality is regarded as patients' perceived quality and evaluated on the basis (5 dimension) of technical and functional quality. 2. Nursing service Quality is a concept of patients evaluation on the measurable multi-dimensions intrinsic and extrinsic attributes of service. 3. Nursing service Quality is conceptually defined as the difference between the perceived service and the expected service. 4. Korean consumers trend to evaluate nursing service quality based on such dimensions as responsiveness and reliability. understandability. accessibility. tangibility. 5. After analyzing whether or not there are some differences in respective medical institution. it was found that there are significant difference on understandability. reliability. communicability. courtesy. competence. 6. After analyzing the difference between the expected nursing service and the nursing perceived service, it was found that the expected nursing service is higher than the perceived service in every medical institution. 7. HI hypothesis was tested with regard to the validity test between SERVQUAL and SERVPERF in nursing service quality. The result of validity test between SERVQUAL and SERVPERF was found to have differential result. That is the R2 of SERVPERF is higher than that of SERVQUAL. Therefore. HI was verified in nursing management. H2. H3 hypotheses were tested whether or not the nursing service quality and patient satisfaction is the preceding variable. The result of H2 hypothes is that the nursing service quality is the preceding variable of patient satisfaction and the patient satisfaction is that of revisiting intention. After analyzing whether or not there is any differences on the demographic variable of five nursing service quality factor. it was found that there are statistically significant differences on communicability and courtesy at the sex. understand ability. accessibility and tangibility at the age. understandability at the academic background respectively.

• Journal of dental hygiene science
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• v.3 no.2
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• pp.59-66
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• 2003

As a basic research material to make more efficient healthcare and health promotion for dental hygienists, this study intends to determine which factors may affect their awareness of occupational diseases. For this sake, this study attempted to investigate into a variety of literatures and data, and applied a questionnaire survey to 160 dental hygienists for about 5 months(from June to October 2003), who were all employed in domestic dental clinics or offices. As a result of analysis, this study can be concluded as follows: (1) The result of analyzing how dental hygienists recognized their occupational diseases showed that 'stiffness in muscle, neck or shoulder due to intensive use of specific physical regions' and 'stress resulting from the attributes of each task' reached the highest awareness(90.6%) of all, which was followed by 'lower eyesight due to detailed or even sophisticated tasks for many hours(65%)' and 'symptoms of ruptured disk due to standing tasks as dental hygienists do for many hours(62.5%).' (2) The result of analyzing how dental hygienists recognize their occupational diseases showed that their awareness averaged 7.28 points out of 14 points, which implies that their awareness of occupational diseases is not very high. Meanwhile, the result of examining how they recognize detrimental or harmful properties of given materials against human body showed that a majority of total respondents(74.4%) regarded given materials as detrimental to human body. (3) The result of examining how dental hygienists recognize their occupational diseases showed that a majority of total respondents(91.9%) identified their own occupational diseases. Many of total respondents(41.9%) pointed out that environmental improvement around workshop in each clinic or office should be foremost prerequisite to health and welfare for dental hygienists. Next, 34.4% of total respondents pointed out the necessity to perform in-house health diagnosis and examination on a regular basis, and 13.1% of total respondents thought it necessary to carry on healthcare education into harmful properties in the aspect of dental materiology, respectively.

• Korean Journal of Ecology and Environment
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• v.43 no.3
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• pp.428-436
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• 2010

This study was to evaluate water quality characteristics and ecological health using a mulimetric fish model in Taehwa-River watershed during May~September 2009. The ecological health assessments were based on the Index of Biological Integrity (IBI) using fish community and the multimetric model of Qualitative Habitat Evaluation Index (QHEI). For the study, the models of IBI and QHEI were modified as 8 and 11 metric attributes, respectively. We also analyzed spatial patterns of chemical water quality over the period of 2000~2009, using the water chemistry dataset, obtained from the Ministry of Environment, Korea. Values of BOD and COD averaged $1.7\;mg\;L^{-1}$ (scope: $0.1{\sim}31.8\;mg\;L^{-1}$) and $3.6\;mg\;L^{-1}$ (scope: $0.4{\sim}33\;mg\;L^{-1}$), respectively during the study. Total nitrogen (TN) and total phosphorus (TP) averaged $2.8\;mg\;L^{-1}$ and $96.8\;{\mu}g\;L^{-1}$, respectively, indicating an eutrophic-hypertrophic state. Also, TN and TP showed longitudinal increases toward the downriver reach. In the watershed, QHEI values varied from 67.5 (fair condition) to 164.5 (good condition) by the criteria of US EPA (1993). There was a abruptly decreasing tendency from T9 site in the QHEI values. According to 1st and 2nd surveys of Taewha River, multimetric model values of IBI was averaged 26.1 (n=14) with "good" condition (B) and the spatial variation was evident. Our results suggest that the mainstream sites was getting worse health condition along the river gradient due to inputs of the point and non-point sources from the urban (Ulsan city). Overall, dataset of IBI, QHEI, and water chemistry indicated that the ecological river health showed a downriver decline and the pattern was closely associated with habitat degradations and chemical pollutions as the waters pass through the urban region.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.1
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• pp.132-137
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• 2010

This study was performed to investigate the effect of apple and maltitol as ingredients on the quality of Jochung, a grain syrup. Four kinds of Jochung products were prepared from steamed-rice, apple juice, heated-apple sarcocarp (at $70^{\circ}C$, 60 min), and a mixture (sarcocarp : maltitol＝5:1, w/w) by saccharifying (at $55^{\circ}C$, 8 hrs) with a malt (100 g/500 g rice), mixing the ingredients (steamed-rice : ingredient＝5:5, w/w), filtering, and heating the filtrate (at $95^{\circ}C$, 2 hrs): product (A) with apple juice added before saccharified, product (B) with apple juice added after saccharified, product (C) with heated-apple sarcocarp added after saccharified, and product (D) with the mixture added after saccharified. The product (D) had the lowest pH value ($4.60\pm0.01$) of any other products. The contents of reducing sugar and total phenolic compound were the highest in the product (A) among all the products, which comprised $68.10\pm6.71$% and $7.36\pm0.85$ mg/g, respectively, resulting in good quality. The solidity and the dextrose equivalence had the highest value in the product (B) and the product (C), respectively. The malic acid content ($4.10\pm0.02$%) of the product (D) was the highest of any other organic acids identified by HPLC. Hunter L, a, and b values of the product (D) were the highest compared to other products. In sensory evaluation, the product (A) had generally higher score in all sensory attributes. It was concluded from the chemical and sensory evaluation that adding the apple juice before saccharified might be an effective method for manufacturing good quality rice-Jochung.