• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.109-117
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• 2023

Schedule delays present a major risk factor, as they can adversely affect construction projects, such as through increasing construction costs, claims from a client, and/or a decrease in construction quality due to trims to stages to catch up on lost time. Risk management has been conducted according to the importance and priority of schedule delay risk, but quantification of risk on the depth of schedule delay tends to be inadequate due to limitations in data collection. Therefore, this research used the BERT (Bidirectional Encoder Representations from Transformers) language model to convert the contents of aconstruction log, which comprised unstructured data, into WBS (Work Breakdown Structure)-based structured data, and to form a model of classification and quantification of risk. A process was applied to eight highway construction sites, and 75 cases of rain schedule delay risk were obtained from 8 out of 39 detailed work kinds. Through a K-S test, a significant probability distribution was derived for fourkinds of work, and the risk impact was compared. The process presented in this study can be used to derive various schedule delay risks in construction projects and to quantify their depth.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.69-90
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• 2023

The purpose of this study is to evaluate the service attributes and consumer reactions of telemedicine apps in South Korea and visualize their differentiation by constructing positioning maps. We crawled 23,219 user reviews of 6 major telemedicine apps in Korea from the Google Play store. Topics were derived by BERTopic modeling, and sentiment scores for each topic were calculated through KoBERT sentiment analysis. As a result, five service characteristics in the application attribute category and three in the medical service category were derived. Based on this, a two-dimensional positioning map was constructed through principal component analysis. This study proposes an objective service evaluation method based on text mining, which has implications. In sum, this study combines empirical statistical methods and text mining techniques based on user review texts of telemedicine apps. It presents a system of service attribute elicitation, sentiment analysis, and product positioning. This can serve as an effective way to objectively diagnose the service quality and consumer responses of telemedicine applications.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.165-184
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• 2023

Recently, Deep learning analysis of unstructured text data using language models, such as Google's BERT and OpenAI's GPT has shown remarkable results in various applications. Most language models are used to learn generalized linguistic information from pre-training data and then update their weights for downstream tasks through a fine-tuning process. However, some concerns have been raised that privacy may be violated in the process of using these language models, i.e., data privacy may be violated when data owner provides large amounts of data to the model owner to perform fine-tuning of the language model. Conversely, when the model owner discloses the entire model to the data owner, the structure and weights of the model are disclosed, which may violate the privacy of the model. The concept of offsite tuning has been recently proposed to perform fine-tuning of language models while protecting privacy in such situations. But the study has a limitation that it does not provide a concrete way to apply the proposed methodology to text classification models. In this study, we propose a concrete method to apply offsite tuning with an additional classifier to protect the privacy of the model and data when performing multi-classification fine-tuning on Korean documents. To evaluate the performance of the proposed methodology, we conducted experiments on about 200,000 Korean documents from five major fields, ICT, electrical, electronic, mechanical, and medical, provided by AIHub, and found that the proposed plug-in model outperforms the zero-shot model and the offsite model in terms of classification accuracy.

• Journal of Service Research and Studies
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• v.14 no.2
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• pp.18-36
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• 2024

As interest in ESG has been increased, it is easy to find papers that empirically study that a company's ESG activities have a positive impact on the company's performance. However, research on what ESG activities companies should actually engage in is relatively lacking. Accordingly, this study systematically classifies ESG activities of companies and seeks to provide insight to companies seeking to plan new ESG activities. This study analyzes how Chinese manufacturing companies perform ESG activities based on their dynamic capabilities in the global economy and how they differ in their activities. This study used the ESG annual reports of 151 Chinese manufacturing listed companies on the Shanghai & Shenzhen Stock Exchange and ESG indicators of China Securities Index Company (CSI) as data. This study focused on the following three research questions. The first is to determine whether there are any differences in ESG activities between companies with high ESG scores (TOP-25) and companies with low ESG scores (BOT-25), and the second is to determine whether there are any changes in ESG activities over a 10-year period (2010-2019), focusing only on companies with high ESG scores. The results showed that there was a significant difference in ESG activities between high and low ESG scorers, while tracking the year-to-year change in activities of the top-25 companies did not show any difference in ESG activities. In the third study, social network analysis was conducted on the keywords of E/S/G. Through the co-concurrence matrix technique, we visualized the ESG activities of companies in a four-quadrant graph and set the direction for ESG activities based on this.

• Information Systems Review
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• v.25 no.2
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• pp.181-203
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• 2023

With the fierce competition of companies for the attraction of outstanding individuals, job satisfaction of employees has been of importance. In this circumstance, many companies try to invest in job satisfaction improvement by finding employees' everyday experiences and difficulties. However, due to a lack of understanding of the employee experience, their investments are not paying off. This study examined the relationship between employee experience and job satisfaction using employee reviews and company ratings from Glassdoor, one of the largest employee communities worldwide. We use text mining techniques such as K-means clustering and LDA topic-based sentiment analysis to extract key experience factors by job level, and DistilBERT sentiment analysis to measure the sentiment score of each employee experience factor. The drawn employee experience factors and each sentiment score were analyzed quantitatively, and thereby relations between each employee experience factor and job satisfaction were analyzed. As a result, this study found that there is a significant difference between the workplace experiences of managers and general employees. In addition, employee experiences that affect job satisfaction also differed between positions, such as customer relationship and autonomy, which did not affect the satisfaction of managers. This study used text mining and quantitative modeling method based on theory of work adjustment so as to find and verify main factors of employee experience, and thus expanded research literature. In addition, the results of this study are applicable to the personnel management strategy for improving employees' job satisfaction, and are expected to improve corporate productivity ultimately.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.577-586
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• 2024

As new technologies are developed, new security threats such as the emergence of AI technologies that create ransomware are also increasing. New security equipment such as XDR has been developed to cope with these security threats, but when using various security equipment together rather than a single security equipment environment, there is a difficulty in creating numerous regular expressions for identifying and classifying essential data. To solve this problem, this paper proposes a method of identifying essential information for identifying threat information by introducing artificial intelligence-based entity name recognition technology in various security equipment usage environments. After analyzing the security equipment log data to select essential information, the storage format of information and the tag list for utilizing artificial intelligence were defined, and the method of identifying and extracting essential data is proposed through entity name recognition technology using artificial intelligence. As a result of various security equipment log data and 23 tag-based entity name recognition tests, the weight average of f1-score for each tag is 0.44 for Bi-LSTM-CRF and 0.99 for BERT-CRF. In the future, we plan to study the process of integrating the regular expression-based threat information identification and extraction method and artificial intelligence-based threat information and apply the process based on new data.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.58 no.2
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• pp.119-127
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• 2013

The interaction between Agrobacterium and soybean has been studied at the transcriptome level but not at the metabolic level. However, it is necessary to investigate the difference in metabolites between susceptible and non-susceptible cultivars for high efficiency transformation. We investigated the difference in metabolites from sonicated soybean cotyledons of Korean cultivars and Bert cultivar. To identify difference in metabolites, sonicated extracts were analysed by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS). The soybean cultivars were classified by susceptibility using green fluorescent protein expression. We found a difference in metabolites between the high susceptible and low susceptible cultivars. The FT-ICR/MS experimental m/z data of different metabolites were compared with theoretical m/z in KNApSAcK database. The candidate list was made using KNApSAcK and focused on phenolic compounds. These candidate metabolites are speculated to influence factors in the interaction. This list of candidates may be useful to investigate the interaction between Agrobacterium and plants to increase transformation efficiency.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.14 no.3
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• pp.171-178
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• 1981

The growth of Sebastes alutus was studied by scale reading to check the change of growth rate at the early stage of life. Lee's phenomenon was recognized on the scale measurements except thc first ring radius. No evidence was found to support the change of growth rate at early stage. Von Bert-alanffy's growth equation was estimated with the back-calculated fork lengths, $1_t=357.8(1-e^{-0.6124(t+1.8566)}),\;and\;W_{\infty}=784.4g$

• Journal of the Korea Computer Graphics Society
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• v.10 no.2
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• pp.27-34
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• 2004

본 논문에서는 스윕에 기반하여 인체 형상을 모델링하고 변형하는 방법을 제시한다. 본 방법은 다각형 메쉬 형태로 주어진 3차원 인체 형상을 스윕 기반의 형상 구조로 재구성하여, 형상을 모델링하고 변형한다. 인체 형상의 팔, 다리, 몸통 등 각 부분을 근사하는 스윕 곡면을 생성하고 다각형 메쉬 상의 꼭지점들을 인접한 스윕 곡면과 연결하며, 스윕 곡면이 만나는 팔, 다리와 몸통 사이에서는 꼭지점마다 연결된 스윕 단면들을 블렌딩한다. 이를 통해 스윕을 제어하여 이와 연결된 인체 형상의 자연스러운 변형을 얻어낼 수 있다. 본 논문에서는 몇 개의 애니메이션 예들을 통하여 제시한 인체 변형 방법이 자연스러운 인체 동작 생성에 효과적임을 보인다. 본 논문의 결과들은 스윕 기반의 인체 형상 변형 방법이 실시간에 상당히 사실적이고 자연스러운 형상 변형이 가능함을 보여주어, 캐릭터 skinning 방법으로서 적절한 대안임을 보여준다.

• International Journal of Automotive Technology
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• v.3 no.2
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• pp.63-70
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• 2002

Future on-board vehicle control systems can be further improved through new types of mechatronic systems. In particular, these systems' capacities for interaction enhance safety, comfort and economic viability. The Automotive Engineering Department (fzd) of darmstadt University of Technology is engaged in research of the mechatronic vehicle corner, which consists of three subsystems: sensor tire, electrically actuated wheel brake and smart suspension. By intercommunication of these three systems, the brake controller receives direct, fast and permanent information about dynamic events in the tire contact area provided by the tire sensor as valuable control input. This allows to control operation conditions of each wheel brake. The information provided by the tire sensor for example help to distinguish between staightline driving and cornering as well as to determine $\mu$-split conditions. In conjunction with current information of dynamic wheel loads, tire pressures and friction tyre/road, the ideal brake force distribution can be achieved. Alike through integration of adaptive suspension bushings, elastokinematic behaviour and wheel positions can be adapted to manoeuver-oriented requirements.