• Korean Journal of Child Studies
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• v.25 no.5
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• pp.193-208
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• 2004

This study purported to empirically examine joy expression and its cognitive and social contexts in children's play. The following question was asked: 1) What kind(s) of emotional expression(s) can be considered as a defining feature of play? 2) What cognitive/social play contexts are associated with joy expression. 30 children aged 3, 4, and 5 years were observed in terms of the length of each emotional expression at play/nonplay, and at cognitive/social play categories. The findings of this study showed that regardless of children's age and gender only joy expression could be considered as a defining feature of play, and that R&T play and chase games, or associative and cooperative social play were strongly related to joy expression. The findings were discussed in reference with existing assertions and perspectives, emphasizing the importance of joy expression in defining children's play despite of the predominance of interest expression in play. The findings were also discussed in reference with metacommunication functions and social construction of joy, considering cognitive/social contexts of joy. Implications for play researchers and practitioners were described in terms of developing playful learning strategies for childhood. Limitations of this study, and suggestions for further research were also provided.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.870-878
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• 2013

In this paper, we propose a novel fingerprint classification method to enhance the accuracy and efficiency of the fingerprint identification system, one of biometrics systems. According to the previous researches, fingerprints can be categorized into the several patterns based on their pattern of ridges and valleys. After construction of fingerprint database based on their patters, fingerprint classification approach can help to accelerate the fingerprint recognition. The reason is that classification methods reduce the size of the search space to the fingerprints of the same category before matching. First, we suggest a method to extract region of interest (ROI) which have real information about fingerprint from the image. And then we propose a feature extraction method which combines gray level co-occurrence matrix (GLCM) and wavelet features. Finally, we compare the performance of our proposed method with the existing method which use only GLCM as the feature of fingerprint by using the multi-layer perceptron and support vector machine.

• Journal of Fashion Business
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• v.5 no.1
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• pp.17-33
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• 2001

The purpose of this study is to offer the basic data for chinese men' clothing construction. This study analyzes characterization and classification of body types of the Chinese men with body measurement values. This researcher executed the body measurement of total 39 items on 414 chinese men in Beijing and Shanghai aged 20-49 years old and analyzed the data with methods of analysis of variance, factor analysis and cluster analysis using it as the study item. The results of this study can be summarized as follows; 1. As the result of comparative analysis of the body measurements by age group and birth region group in Beijing and Shanghai, the horizontal items such as the widths, depths, and girths increased with advancing ages, while heights decreased. 2. As the result of factor analysis on the items in Beijing and Shanghai, 5 factors on such as the first factor on the obesity of body, the second factor on the size of vertical of body, the third factor on the length of upper body, the forth factor on the width of the shoulder, the fifth factor on the degree of dropping shoulder were extracted. 3. As the result of classification based on the cluster analysis in Beijing, the body type were classified into 3 types. So, to see the feature of body form by types, type 1 was tallest, fattest type. type 2 was small stature, fat. type 3 was tall, thin. In Shanghai, he body type were classified into 3 types. So, to see the feature of body form by types, type 1 was tallest, fattest type. type 2 was small stature. type 3 was tall, thin.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.4
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• pp.1130-1147
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• 2014

Large size ships have a very flexible construction resulting in low resonance frequencies of the structural eigen-modes. This feature increases the dynamic response of the structure on short period waves (springing) and on impulsive wave loads (whipping). This dynamic response in its turn increases both the fatigue damage and the ultimate load on the structure; these aspects illustrate the importance of including the dynamic response into the design loads for these ship types. Experiments have been carried out using a segmented scaled model of a container ship in a Seakeeping Basin. This paper describes the development of the model for these experiments; the choice was made to divide the hull into six rigid segments connected with a flexible beam. In order to model the typical feature of the open structure of the containership that the shear center is well below the keel line of the vessel, the beam was built into the model as low as possible. The model was instrumented with accelerometers and rotation rate gyroscopes on each segment, relative wave height meters and pressure gauges in the bow area. The beam was instrumented with strain gauges to measure the internal loads at the position of each of the cuts. Experiments have been carried out in regular waves at different amplitudes for the same wave period and in long crested irregular waves for a matrix of wave heights and periods. The results of the experiments are compared to results of calculations with a linear model based on potential flow theory that includes the effects of the flexural modes. Some of the tests were repeated with additional links between the segments to increase the model rigidity by several orders of magnitude, in order to compare the loads between a rigid and a flexible model.

• 한국지형공간정보학회:학술대회논문집
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• 2002.11a
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• pp.117-122
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• 2002

Change detection is a useful technology that can be applied to various fields, taking temporal change information with the comparison and analysis among multi-temporal satellite images. Especially, change detection that utilizes high-resolution satellite imagery can be implemented to extract useful change information for many purposes, such as the environmental inspection, the circumstantial analysis of disaster damage, the inspection of illegal building, and the military use, which cannot be achieved by lower middle-resolution satellite imagery. However, because of the special characteristics that result from high-resolution satellite imagery, it cannot use a pixel-based method that is used for low-resolution satellite imagery. Therefore, it must be used a feature-based algorithm based on the geographical and morphological feature. This paper presents the system that builds the change map by digitizing the boundary of the changed object. In this system, we can make the change map using manual or semi-automatic digitizing through the user interface implemented with a floating window that enables to detect the sign of the change, such as the construction or dismantlement, more efficiently.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2143-2149
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• 2007

Visual Cortex Stimulator is among artificial retina prosthesis for blind man, is the method that stimulate the brain cell directly without processing the information from retina to visual cortex. In this paper, we propose image construction and recognition model that is similar to human visual processing by recognizing the feature data with orientation information, that is, the characteristics of visual cortex. Back propagation algorithm based on Delta-bar delta is used to recognize after extracting image feature by Kirsh edge detector. Various numerical patterns are used to analyze the performance of proposed method. In experiment, the proposed recognition model to extract image characteristics with the orientation of information from retinal cells to visual cortex makes a little difference in a recognition rate but shows that it is not sensitive in a variety of learning rates similar to human vision system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3242-3265
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• 2023

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

• Geomechanics and Engineering
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• v.38 no.5
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• pp.517-528
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• 2024

For numerous tunnelling projects implemented in urban areas due to limited space, it is crucial to take into account the interaction between the foundation, ground, and tunnel. In predicting the deformation of piled foundations and the ground during twin tunnel excavation, it is essential to consider various factors. Therefore, this study derived a prediction model for pile group settlement using machine learning to analyze the importance of various factors that determine the settlement of piled foundations during twin tunnelling. Laboratory model tests and numerical analysis were utilized as input data for machine learning. The influence of each independent variable on the prediction model was analyzed. Machine learning techniques such as data preprocessing, feature engineering, and hyperparameter tuning were used to improve the performance of the prediction model. Machine learning models, employing Random Forest (RF), eXtreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM, LGB) algorithms, demonstrate enhanced performance after hyperparameter tuning, particularly with LGB achieving an R² of 0.9782 and RMSE value of 0.0314. The feature importance in the prediction models was analyzed and P_N was the highest at 65.04% for RF, 64.81% for XGB, and P_CTC (distance between the center of piles) was the highest at 31.32% for LGB. SHAP was utilized for analyzing the impact of each variable. P_N (the number of piles) consistently exerted the most influence on the prediction of pile group settlement across all models. The results from both laboratory model tests and numerical analysis revealed a reduction in ground displacement with varying pillar spacing in twin tunnels. However, upon further investigation through machine learning with additional variables, it was found that the number of piles has the most significant impact on ground displacement. Nevertheless, as this study is based on laboratory model testing, further research considering real field conditions is necessary. This study contributes to a better understanding of the complex interactions inherent in twin tunnelling projects and provides a reliable tool for predicting pile group settlement in such scenarios.

• Explosives and Blasting
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• v.35 no.1
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• pp.43-52
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• 2017

Mountain and hill areas occupy by more than 70% in South Korea and Rock drilling should be applied in order to reduce noisy & vibration from massive civil engineering business such as road expansion, high-way construction, subway construction and construction of site renovation such as a newly-built & re-development of apartment, newly-built of high-rising building in downtown area. As Blasting noise & vibration such as vibration, noise, fly rock etc caused by blasting operation from large small scale construction occurs, neighboring residents who demand the compensation file a civil complaint so that the business reach a deadlock. As the excavation method for these areas, There are blasting of micro-vibration, mechanical excavation method(Rock splitter, Breaker etc), similar blasting method(plasma, gel fragmentation etc) to date. In this study, we are trying to find the feature & performance which get improved economic feasibility & construct ability through improving sympathetic detonation of New KINECKER-I used in blasting of micro-vibration & formulation and would provide convenience for use by introducing standard blasting pattern & construction method. Also, checked and confirmed all the blasting with connecting cap has been cleary detonated.

• Journal of the Korea Institute of Building Construction
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• v.17 no.6
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• pp.545-557
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• 2017

The study is about the efficient alternative to concrete surface in the field of visual inspection technology for deteriorated infrastructure. By combining industrial drones and deep learning based image analysis techniques with traditional visual inspection and research, we tried to reduce manpowers, time requirements and costs, and to overcome the height and dome structures. On board device mounted on drones is consisting of a high resolution camera for detecting cracks of more than 0.3 mm, a lidar sensor and a embeded image processor module. It was mounted on an industrial drones, took sample images of damage from the site specimen through automatic flight navigation. In addition, the damege parts of the site specimen was used to measure not only the width and length of cracks but white rust also, and tried up compare them with the final image analysis detected results. Using the image analysis techniques, the damages of 54ea sample images were analyzed by the segmentation - feature extraction - decision making process, and extracted the analysis parameters using supervised mode of the deep learning platform. The image analysis of newly added non-supervised 60ea image samples was performed based on the extracted parameters. The result presented in 90.5 % of the damage detection rate.