• School Mathematics
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• v.18 no.3
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• pp.527-539
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• 2016

A word 'is' in "A parallelogram is trapezoid." is ambiguous and very rich when it comes to its meaning. In this paper, 'is' as in everyday language will be identified as semantic primes that can be interpreted in different ways depending on context and situation, and meanings of 'is' in mathematics will be discussed separately. Focusing on 'identity', 'is' will be reinterpreted in the view of equivalence relation and van Hieles' work. 'Is', as a mathematical sign, is thought to have a significant importance in producing mathematical ideas meaningfully.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.745-745
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• 2002

Both geophysical and geospatial data provide important information in the establishment of the optimal telecommunication systems especially in the mobile telecommunication environment. The objective of this study is to utilize geophysical properties and geospatial information in the analysis of the telecommunication environment through point-to-point wave property modeling. Geophysical properties associated with wave propagation parameters of the earth surface were analyzed based on hierarchical land classification using Landsat ETM＋ and IKONOS images. Three-dimensional geospatial information was obtained by processing stereo aerial images. The results show that the accurate geospatial information and reliable geosphysical property of the surface improve the prediction of receiving power of the receivers located near corners of the buildings where diffractions occur. The wave property model developed from accurate telecommunication environment could be applied to optimal cell planning and delay time analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.369-372
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• 2018

최근 지식경영에 있어 특허를 통한 지식재산권 확보는 기업 운영에 큰 영향을 주는 요소이다. 성공적인 특허 확보를 위해서, 먼저 변화하는 특허 분류 제계를 이해하고, 방대한 특허 정보 데이터를 빠르고 신속하게 특허 분류 체계에 따라 분류화 시킬 필요가 있다. 본 연구에서는 머신 러닝 기술 중에서도 계층적 주의 네트워크를 활용하여 특허 자료의 초록을 학습시켜 분류를 할 수 있는 방법을 제안한다. 그리고 본 연구에서는 제안된 계층적 주의 네트워크의 성능을 검증하기 위해 수정된 입력데이터와 다른 워드 임베딩을 활용하여 진행하였다. 이를 통하여 특허 문서 분류에 활용하려는 계층적 주의 네트워크의 성능과 특허 문서 분류 활용화 방안을 보여주고자 한다. 본 연구의 결과는 많은 기업 지식경영에서 실용적으로 활용할 수 있도록 지식경영 연구자, 기업의 관리자 및 실무자에게 유용한 특허분류기법에 관한 이론적 실무적 활용 방안을 제시한다.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1305-1316
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• 2018

Graphical user interface(GUI) has a very important role to interact with software users. However, designing and coding of GUI are tedious and pain taking processes. In many studies, the researchers are trying to convert GUI elements or widgets to code or describe formally their structures by help of domain knowledge of stochastic methods. In this paper, we propose the GUI elements detection approach based on object detection strategy using deep neural networks(DNN). Object detection with DNN is the approach that integrates localization and classification techniques. From the experimental result, if we selected the appropriate object detection model, the results can be used for automatic code generation from the sketch or capture images. The successful GUI elements detection can describe the objects as hierarchical structures of elements and transform their information to appropriate code by object description translator that will be studied at future.

• The Korean Journal of Health Service Management
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• v.13 no.3
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• pp.13-26
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• 2019

Objectives: To investigate whether medical institutions can prevent accidents by analyzing the root cause of a medical accident and identifying the tendencies. Methods: A total of 345 medical cases were used for the RCA(Root Cause Analysis). The root causes were classified using the SHELL model. The suitability of the model was confirmed by SPSS's MDPREF and Euclidean distance. An SPSS20.0 hierarchical regression analysis was used as an influencing factor on the degree of injury resulting from medical accidents. Results: The SHELL model was suitable for classification. The rates of accident causes were LS49%, L34%, LL10.2%, LE3.7%, LH2.3%. The order in which the degree of a patient's injury was affected were: Risk Threshold (${\beta}=.180$), Time (${\beta}=.175$), Surgical stage (${\beta}=-.166$), Do not use procedure (${\beta}=.147$). Conclusions: Health care institutions should remove priorities through system improvement and training. For patients' safety, the five factors of the SHELL model should be managed in harmony.

• Journal of Applied Biological Chemistry
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• v.64 no.2
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• pp.127-131
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• 2021

Perilla frutescens var. crispa (Pfc), a herb belonging to the mint family (Lamiaceae), has been used for medicinal and aromatic purposes. In the present study, we analyzed the variation in the chemical composition of essential oils (EOs) obtained from five different genotypes of Pfc collected from different regions. Based on principal component analysis (PCA) and hierarchical cluster analysis (HCA), we identified three groups: PA type containing perillaldehyde, PP type containing dillapiole, and 2-acetylfuran type. To assess the correlation between EO components and antimicrobial activities, we compared classification results generated by PCA and HCA based on antimicrobial activity values. The findings suggested that the major compounds obtained from EOs of Pfc are responsible for their antimicrobial activities. Chemotypes of Pfc plants are essentially qualitative traits that are important for breeders. The present findings provide potential information for breeding Pfc as an antimicrobial agent.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1540-1551
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• 2020

Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.

• Journal of Korea Trade
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• v.23 no.4
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• pp.103-114
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• 2019

Purpose - This paper investigates the structure of exports across countries in terms of value added. Exports value added is examined under two categories, domestic and overseas. Using a statistical classification method by distance based on these two value added categories, this paper estimates the similarity of exports value added across countries including Korea. Design/methodology - The model of study is to employ a generalized distance function and then derive the Manhattan and Euclidean distances. The paper also performs cluster analysis using the Partitioning Around Medoids (PAM) and hierarchical methods to classify the 44 sample countries considered in this study. Findings - Our main findings are as follows. The 44 countries can be classified under 5 groups by their domestic and overseas value added in exports. Korea has a sandwich global value chains (GVCs) position between Japan, China, and Taiwan in the East Asian region. Originality/value - Existing papers point out the double counting problem of trade statistics as the intermediate goods trade across borders increases. This paper addresses the double counting problem by using the World Input-Output Table. The paper shows the need to explore the similarity of value added in exports structure across countries and investigate the GVCs position and role of each country.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.443-452
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• 2020

Worker's awkward postures and unreasonable physical load can be corrected by monitoring construction activities, thereby increasing the safety and productivity of construction workers and projects. However, manual identification is time-consuming and contains high human variance. In this regard, an automated activity recognition system based on inertial measurement unit can help in rapidly and precisely collecting motion data. With the acceleration data, the machine learning algorithm will be used to train classifiers for automatically categorizing activities. However, input acceleration data are extracted either from designed experiments or simple construction work in previous studies. Thus, collected data series are discontinuous and activity categories are insufficient for real construction circumstances. This study aims to collect acceleration data during long-term continuous work in a construction project and validate the feasibility of activity recognition algorithm with the continuous motion data. The data collection covers two different workers performing formwork at the same site. An accelerator, as well as portable camera, is attached to the worker during the entire working session for simultaneously recording motion data and working activity. The supervised machine learning-based models are trained to classify activity in hierarchical levels, which reaches a 96.9% testing accuracy of recognizing rest and work and 85.6% testing accuracy of identifying stationary, traveling, and rebar installation actions.

• Research in Mathematical Education
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• v.26 no.3
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• pp.235-252
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• 2023

We investigated the classification of learner groups for students' mathematical modeling competency and analyzed the characteristics in each profile group for each country and variable using PISA 2012 data from six countries. With a perspective on measuring sub-competency, we applied the latent profile analysis method to student achievement for mathematical modeling variables - Formulate, Employ, Interpret. The findings showed the presence of 4-6 profile groups, with the variables exhibiting high and low achievement within each profile group varying by country, and a hierarchical structure was observed in the profile group distribution in all countries, interestingly, the Formulate variable showed the largest difference between high-achieving and low-achieving profile groups. These results have significant implications. Comparison by country, variable, and profile group can provide valuable insights into understanding the various characteristics of students' mathematical modeling competency. The Formulate variable could serve as the most suitable predictor of a student's profile group and the score range of other variables. We suggest further studies to gain more detailed insights into mathematical modeling competency with different cultural contexts.