• KIEAE Journal
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• v.16 no.3
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• pp.13-23
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• 2016

Purpose: In this paper, based on the design drawings of Naju Agricultural Technology Learning Center by focusing on features of New-Hanok applied technology in the design and construction process of New-Hanok Type Public buildings by looking at the characteristics of the whole building is planned to be presented in the future development direction and value of public architecture applied to public buildings. Method: we first review the Phase 1 study results of technology development and application technology to look at the type and characteristics of the technologies applied in Naju Hanok Experience Agriculture Learning Center. As construction designs through the consultation suggestions reflect changes were seven times. By analyzing the changes in the basic design and conduct design in the process were organized for the new technologies applied and whether the application of existing technology hanok. Result: (1) Complements the shortcomings of technology and R & D to offer an alternative to the reinforcement was omitted modify the construction method or irrational process. (2) Implementation of a technique aiming to apply new-hanok workability and economic efficiency is based on a combination of the modern construction techniques and materials. (3) The use of modern materials to the extent that can assist in the purpose and function of the building are to be accommodated. (4) There is sufficient historical study and design plan for establishing identity is necessary in order to reflect the history and tradition of new-hanok public buildings.

• Smart Structures and Systems
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• v.31 no.4
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• pp.365-381
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• 2023

The existing vision-based techniques for inspection and condition assessment of civil infrastructure are mostly manual and consequently time-consuming, expensive, subjective, and risky. As a viable alternative, researchers in the past resorted to deep learning-based autonomous damage detection algorithms for expedited post-disaster reconnaissance of structures. Although a number of automatic damage detection algorithms have been proposed, the scarcity of labeled training data remains a major concern. To address this issue, this study proposed a semi-supervised learning (SSL) framework based on consistency regularization and cross-supervision. Image data from post-earthquake reconnaissance, that contains cracks, spalling, and exposed rebars are used to evaluate the proposed solution. Experiments are carried out under different data partition protocols, and it is shown that the proposed SSL method can make use of unlabeled images to enhance the segmentation performance when limited amount of ground truth labels are provided. This study also proposes DeepLab-AASPP and modified versions of U-Net++ based on channel-wise attention mechanism to better segment the components and damage areas from images of reinforced concrete buildings. The channel-wise attention mechanism can effectively improve the performance of the network by dynamically scaling the feature maps so that the networks can focus on more informative feature maps in the concatenation layer. The proposed DeepLab-AASPP achieves the best performance on component segmentation and damage state segmentation tasks with mIoU scores of 0.9850 and 0.7032, respectively. For crack, spalling, and rebar segmentation tasks, modified U-Net++ obtains the best performance with Igou scores (excluding the background pixels) of 0.5449, 0.9375, and 0.5018, respectively. The proposed architectures win the second place in IC-SHM2021 competition in all five tasks of Project 2.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.27-35
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• 2018

This paper is a study to classify malicious applications in Android environment. And studying the threat and behavioral analysis of malicious Android applications. In addition, malicious apps classified by machine learning were performed as experiments. Android behavior analysis can use dynamic analysis tools. Through this tool, API Calls, Runtime Log, System Resource, and Network information for the application can be extracted. We redefined the properties extracted for machine learning and evaluated the results of machine learning classification by verifying between the overall features and the main features. The results show that key features have been improved by 1~4% over the full feature set. Especially, SVM classifier improved by 10%. From these results, we found that the application of the key features as a key feature was more effective in the performance of the classification algorithm than in the use of the overall features. It was also identified as important to select meaningful features from the data sets.

• Journal for History of Mathematics
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• v.21 no.1
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• pp.79-96
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• 2008

In early 21st century, universities in Korea has been asked the new roles according to the changes of educational and social environment. With Korea's NURI and Brain Korea 21 project support, some chosen research oriented universities now should produce "teacher of teachers". We look 100 years back America's mathematics and see many resemblances between the status of US mathematics at that time and the current status of Korean mathematics, and find some answer for that. E. H. Moore had produced many good research mathematicians through his laboratory teaching techniques. R. L. Moore was his third PhD students. He developed his Texas/Moore method. In this article, we analyze what R. L. Moore had done through his American School of Topology and Moore method. We consider the meaning that early University of Texas case gives us in PBL(Problem Based Learning) process.

• Communications of Mathematical Education
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• v.21 no.4
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• pp.621-646
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• 2007

In our new learning environment, we were asked to change our teaching method in our Linear Algebra class. In mathematics class, we could use several math-softwares such as MATHEMATICA, MATLAB, MAPLE, Drive etc.. MATLAB was quite well fit with our Linear Algebra class. In this paper we introduce an efficient way of delivery on important concepts in linear algebra by using well-known MATLAB/ATLAST M-files which we downloded from http://www.umassd.edu/specialprograms/atlast/.

• Physical Therapy Rehabilitation Science
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• v.13 no.1
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• pp.106-112
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• 2024

Objective: This study aimed to evaluate the validity of a depth camera-based system for monitoring physical function, assessing its feasibility for accurately monitoring activities of daily living. Design: A cross-sectional study. Methods: Twenty-three participants were enlisted to perform fifteen activities of daily living within a living laboratory designed to simulate a home environment. Activities were monitored using a depth camera system capable of classifying actions into standing, sitting, and lying down, with a conventional video camera employed for activity recording. The duration of each activity, as measured by the system, was compared to direct observations made by a physical therapist which were analyzed using a motion analysis software. The association between these two measurement approaches was assessed through correlation analysis, coefficient of determination, intraclass correlation coefficient (ICC), and Bland-Altman plots. Results: Our findings indicated that standing activities exhibited the highest correlation (r=0.847) between the system measurements and physical therapist observations, followed by sitting (r=0.817) and lying down (r=0.734), which demonstrated lower correlations. However, the ICC and Bland-Altman plots revealed notable variances between the two measurement methods, particularly for activities involving lying down. Conclusions: In this study, the depth camera-based physical monitoring system showed promise feasibility in distinguishing standing, sitting, and lying down activities at home environments. However, the current study also underlined some necessities of enhancements in capturing lying down activities.

• 대한공업교육학회지
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• v.42 no.2
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• pp.108-122
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• 2017

This research paper is to examine James Watt who led the 1st industrial revolution successfully. His great work was called monumental achievement in the human history of civilization. Here, we looked over the Watts' educational environment during his infant, juvenile, and adolescence period and also, his learning attitude about his own field through literature review. The basic infra of soft and hard wares for the industrial revolution through the process of R & D on new developing steam engine resulted from the very industrial revolution and its R & D environment were to be investigated. The useful information and knowledge from this process of the research are able to give an appropriate educational guidance to bring up the development of creativity in schooling systems. And also a lesson from the past could be used to provide the desirable direction for the 4th industrial revolution which is just begun to start now. The main results from this study are as follows; First, Watts' parents positively guided him onto the technology of manual field because they recognized their son was interested in technology field. The parents' attitude stimulated and guided his sons' self-development, had been equal to the aims of education. Second, Watt made a chance of making friendships with professors of Glasgow University. He spontaneously had done self-directed learning for getting knowledge and technology, and thus he became an expert of practical engineer and theorist. Third, the Lunar society, which was jumping over one's social position in their society of the 18th century through new thinking way, leading new ages had been very good R & D social infra for Watt to open and connect new advanced level of science and technology in his age. This society provided a study environment fields for their members to exchange their ideas of scientific curiosity and freely inquiry, technology informations. They had discussed and understood the issues to be occurred in their own fields and accumulated necessary knowledge for problem-solving, respectively. Such as this R & D system environment will be also considered in the modern research group. Fourth, the entrepreneur such as Boulton, who understand technology and grasp its value in future, is needed. The system of 'grue of management' will support the researcher with financial support, which is necessary in R & D. And the researcher like Watt who takes pleasure in technology itself and study eagerly in his field without financial problems, that is, 'grue of technical expert' is essential when leading to success in the industrial revolution.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.411-418
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• 2022

Incorrectly loaded containers can easily knock down by strong winds. Container collapse accidents can lead to material damage and paralysis of the port system. In this paper, We propose a deep learning-based container loading state and accident risk detection technique. Using Darknet-based YOLO, the container load status identifies in real-time through corner casting on the top and bottom of the container, and the risk of accidents notifies the manager. We present criteria for classifying container alignment states and select efficient learning algorithms based on inference speed, classification accuracy, detection accuracy, and FPS in real embedded devices in the same environment. The study found that YOLOv4 had a weaker inference speed and performance of FPS than YOLOv3, but showed strong performance in classification accuracy and detection accuracy.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.