• International conference on construction engineering and project management
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• 2015.10a
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• pp.637-638
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• 2015

There has been growing interests in life-cycle project management in the construction industry. A lot of attention is given to Building Information Modeling (BIM) which stores and uses a variety of construction information for the life cycle of project management. However, due to the additional workload arising from BIM, its expected effects versus its input costs are still under discussion in practice. As an attempt to address this issue, one of previous studies suggested an automated linking process by developing Standard Classification Numbering System (SCNS) and Geometry Breakdown Structure (GBS) to enhance the efficiency of integration process of BIM objects, cost, and schedule. Though SCNS and GBS facilitates identifying all different dataset, making object sets and linking schedule activities still needs to be manually done without having an automated tool. In this context, the purpose of this paper is to develop and validate a fully automated integration system for 3D-objects, cost, and schedule. A prototype system for single family homes (Hanok) was developed and tested in order to verify its efficiency.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.378-385
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• 2023

Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.

• BMB Reports
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• v.57 no.3
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• pp.125-134
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• 2024

Whole-genome doubling (WGD), characterized by the duplication of an entire set of chromosomes, is commonly observed in various tumors, occurring in approximately 30-40% of patients with different cancer types. The effect of WGD on tumorigenesis varies depending on the context, either promoting or suppressing tumor progression. Recent advances in genomic technologies and large-scale clinical investigations have led to the identification of the complex patterns of genomic alterations underlying WGD and their functional consequences on tumorigenesis progression and prognosis. Our comprehensive review aims to summarize the causes and effects of WGD on tumorigenesis, highlighting its dualistic influence on cancer cells. We then introduce recent findings on WGD-associated molecular signatures and genetic aberrations and a novel subtype related to WGD. Finally, we discuss the clinical implications of WGD in cancer subtype classification and future therapeutic interventions. Overall, a comprehensive understanding of WGD in cancer biology is crucial to unraveling its complex role in tumorigenesis and identifying novel therapeutic strategies.

• Annual Conference of KIPS
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• 2024.05a
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• pp.294-297
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• 2024

Malware detection has become increasingly critical with the proliferation of end devices. To improve detection rates and efficiency, the research focus in malware detection has shifted towards leveraging machine learning and deep learning approaches. This shift is particularly relevant in the context of the widespread adoption of end devices, including smartphones, Internet of Things devices, and personal computers. Machine learning techniques are employed to train models on extensive datasets and evaluate various features, while deep learning algorithms have been extensively utilized to achieve these objectives. In this research, we introduce TabNet, a novel architecture designed for deep learning with tabular data, specifically tailored for enhancing malware detection techniques. Furthermore, the Synthetic Minority Over-Sampling Technique is utilized in this work to counteract the challenges posed by imbalanced datasets in machine learning. SMOTE efficiently balances class distributions, thereby improving model performance and classification accuracy. Our study demonstrates that SMOTE can effectively neutralize class imbalance bias, resulting in more dependable and precise machine learning models.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.423-430
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• 2024

The task of vision safety monitoring in construction environments presents a formidable challenge, owing to the dynamic and heterogeneous nature of these settings. Despite the advancements in artificial intelligence, the nuanced analysis of small or tiny personal protective equipment (PPE) remains a complex endeavor. In response to this challenge, this paper introduces an innovative safety monitoring system, specifically designed to enhance the safety monitoring of working both at ground level and at elevated heights. This novel system integrates a suite of sophisticated technologies: instance segmentation, shape classification, object tracking, a visualization report, and a real-time notification module. Collectively, these components coalesce to deliver a safety monitoring solution, ensuring a higher standard of protection for construction workers. The experimental results…..

• Journal of Science and Technology Studies
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• v.2 no.1 s.3
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• pp.1-38
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• 2002

Recently KISTEP(Korea Institute of Science and Technology Evaluation and Planning) held an open forum for examining 'the Manuscript of National Science and Technology Standard Taxonomic System'. I submitted my opinion letter to the forum because I thought that the matter of setting the standard taxonomic system for Science and Technology is so closely related to the research concern of STS that it needs checking from the viewpoint of STS. This paper primarily focuses on making a criticism of and constructing an alternative to the mamuscript, but it goes so far as to ground the matter upon the STS viewpoint. I propose that we interpret an open forum related to science and technology as an example case of the community of inquiry. Further I try, standing in the context of learning to form a model of doing STS interdisciplinary research. In the context of decision I point out the problem with the 'scale' principle involved in categrizing criteria of the taxonomic system and argue that the problem leads to omitting STS from National Science and Technology Standard Taxonomic System although STS takes up science and technology themselves as its research concern proper. In the context or teaming I seek to set up a typical case study or STS. One of the typical STS research tasks is trying to construct a positive alternative to as well as make a criticism of a given suggestion, for clearer alternatives will, in him, provoke sharper criticisms or safer acceptances. I hope that the model in this paper will exemplify such an alternating procedure of criticism and acceptance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.519-522
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• 2017

In this paper, we propose an algorithm for an alternative method using the clustering algorithm in a system that needs classification to extract individual user context information. In the conventional user classification system, the user has to input his own information. In this paper, we will research and develop a system applying a clustering algorithm which can extract user 's perceived information applying the improved algorithm for user management base. Generally, the algorithm that distinguishes users with the same data makes sure that recorded information matches the newly entered information, and then responds accordingly. However, it is troublesome to manually input information of the new user. Therefore, in this paper, we propose a method to distinguish users by using the clustering algorithm based on the analyzed data from the working memory in the accumulated system without directly inputting the user information. The study shows that the management method applied to the applied algorithm is more adaptive in environments where the number of people is different from that of the existing system (as a subjective observer test method).

• Journal of the Korean earth science society
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• v.42 no.2
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• pp.221-244
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• 2021

To examine the limitations of the the Blosser's (1973) 'Question Category System for Science', which is mainly applied to research in science education in Korea, we analyzed 30 hours of class conversation in a small group program (for first-year middle school students) with a researcher participating as a teacher. When classified according to Blosser's (1973) classification system, distinguishing between 'open and closed questions' was difficult. Questions with the same content were classified into different types depending on their context, whereas some questions could not be classified appropriately. Additionally, higher-level questions (open questions) were not more effective than lower-level questions (closed questions) in improving students' thinking ability or participation in class. The questionnaire's effect differed depending on the information provided by the teacher before questioning, and in many cases, previous question influenced the next questions. However, in the science education questionnaire research based on Blosser's (1973) classification, which is mainly conducted in Korea, each individual question is classified according to the cognitive level, disregarding the influence of context and prior and subsequent questions and the quality of instructions is evaluated by the frequency of higher level questioning. The results of this study indicate that the method of evaluating instruction quality based on the frequency of high-level questioning, which is currently conducted in domestic science education inquiry research, should be avoided.

• Asia Marketing Journal
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• v.10 no.2
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• pp.125-151
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• 2008

Both companies and consumers are highly interested in on-line customer reviews which enable consumers to share their experience and knowledge about products. In this study, after classifying real reviews into context units and deriving categories, we analyzed differences between categories based on channel(manufacturers' homepage/ shopping mall), product attribute(search/experience) and price(high/low). The method to derive categories is based on roughly adopting constructs of ACSI model and elaborate and repetitive classification of real reviews. We set up the classification category with 3 levels. Level 1 consists of product and service, level 2 consists of function, design, price, purchase motive, suggestion/user-tip and recommendation/repurchase in product and AS/up-grade and delivery/others in service and level 3 is composed of details of level 2 of category. We could find remarkable differences between channels in all 8 items of level 2 of category. As the number of context units in homepage is more than in shopping mall, we found reviews in homepage is more concrete. Moreover, overall satisfaction in review was higher at homepage's. Also, in product attribute dimension, we found different patterns of reviews in design, purchase motive, suggestion/user-tip, recommendation/repurchase, AS/up-grade and delivery/others and no difference in overall customer's satisfaction. In price dimension, we found differences between high and low price in design, price and AS/up-grade and no difference in overall customer's satisfaction.

• Journal of Korean Public Health Nursing
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• v.30 no.1
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• pp.149-163
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• 2016

Purpose: The aim of this study was to assess the readability and suitability of printed educational materials related to metabolic syndrome in South Korea. Methods: Data were collected on 15 educational materials on metabolic syndrome from public health centers in Seoul. The 9 Graded Korean Vocabulary Classification and Korean version of SAM (Suitability Assessment of Materials) were used for the readability evaluation and the suitability evaluation respectively. Results: Overall average of the readability was 3.0th grade level. The percentage of 1st to 4th grade words was 79.4%. The printed educational materials on metabolic syndrome were written according to recommended reading levels. In suitability assessment, 2 out of 15 materials(13.3%) were scored as superior, 12 materials(80.0%) were scored as adequate and only 1 (6.7%) was scored as inadequate. The total average score of suitability was adequate. However, there are limitations in "summary and review" and "context is given first" due to limited writing pages. Conclusion: Readability and suitability of educational materials for metabolic syndrome were evaluated as adequate level. However, future health educational materials should be evaluated for readability via different factors including length of sentences, numbers of sentences, and structure of sentences. In addition, for easier understanding and motivation of readers, materials should use summary & review, context and proper interaction.