• Korean Journal of Culture and Social Issue
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• v.25 no.1
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• pp.55-77
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• 2019

Negative experience related to work-family multiple roles has been associated with internalizing problems in mothers. In particular, employed mothers with preschool children report high degree of stress. As such, the need to examine potential factors that may explain and alleviate such difficulties has been emphasized. The purpose of this study was thus to examine the mediating effect of sociotropy on the relationship between negative work-family multiple roles and internalizing problems in employed mothers with preschool children. The Negative Experience of Work-Family Multiple Roles Scale, Adult Self Report (ASR) Scale, and Personal Style Inventory-II (PSI-II), were completed by 208 employed mothers with preschool children through an online survey. The results indicated that the direct effect of negative experience of work-family multiple roles on internalizing problems was statistically significant and the indirect effect of sociotropy in this relationship was significant. These findings suggest that sociotropy in employed mothers may indirectly explain internalizing problems related to multiple roles. The implications of sociotropy in negative experience of work-family multiple roles and internalizing problems are discussed.

• The Journal of the Korea Contents Association
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• v.17 no.4
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• pp.606-620
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• 2017

The purpose of this study was to develop and validate a Children's Play Disposition Scale(CPDS) which could be used to evaluate children's play patterns and preferences. The participants of this study were parents of 437 5-7-year-old children (age range from 51months to 106months). Preliminary items were developed through a review of relevant research, multiple intelligence theory and scales, confirmation of item adequacy and content validity. After the content validity was confirmed by experts, these items were edited down to a final list of 27 items representing 6 factors identified by exploratory factor analysis. The 6 factors of the scale consists of initiative, linguistic activity, logical-mathematical activity, art and craft, physical activity, and sensitivity respectively. Concurrent validity was established by using correlations between each factor of the CPDS and sub-factors and the total scores of Multiple Intelligence Checklist for preschoolers (Multiple Intelligence Institute Co., Ltd, 2008) and Multiple Intelligence Checklist for elementary schoolers (Multiple Intelligence Institute Co., Ltd, 2007). In addition, the reliability of each factor, as measured by Cronbach's ${\alpha}$, ranged from .53 to .79. The CPDS provides the developmental and educational information for strengthening children's developmental forte and for supporting children's developmental weakness. This scale can be used on developing children's play contents and guiding play methods in the future.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.349-352
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• 2000

영상으로부터 의미있는 객체를 영역화하기 위하여, 움직임에 의한 시간적 정보를 이용하거나, 형태학적(Morphological) 기법과 같이 공간적 정보를 이용하는 방법이 있다. 그러나, 단지 시간적 정보나 공간적 정보만을 이용하는 방법은 그 한계를 가지고 있으며, 본 논문에서는 시공간 정보를 이용하여 분할하는 방법을 채택하였다. 시간적 분할에서는, 두 프레임에서 움직임 정보를 찾아내었던 기존 방법을 보완하여 연속되는 세 프레임을 사용하도록 하였다. 이렇게 하면 움직임이 미세한 영상에 대해서도 객체를 분리해 낼 가능성을 높일 수 있게 된다. 공간적 분할시에는, Watershed 알고리즘을 이용하는 형태학적 분할(Morphological Segmentation)[1][2]을 하게 되는데, 전처리 과정의 단일척도경사(Monoscale Gradient) 대신 다중척도 경사(Multiscale Gradient)[3][4]를 사용하여 미세한 경사는 누그러뜨리고 에지 부분의 경사만을 강조하게 하였다. 또한 개선된 Watershed 알고리즘을 제안하여 기존의 Watershed 알고리즘의 과분할 문제를 보완하였다.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.7
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• pp.1-14
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• 1999

In the present study, the flame factor which primarily influence the simulation accuracy of the combustion process in a gasoline engine was modeled as a nonlinear function of turbulent intensity to laminar flame speed ratio. Multi-length-scale production rate model for turbulent kinetic energy equation was introduced to consider the different length scales of the swirling and tumbling motions in cylinder on the production rte of turbulent kinetic energy. By7 introducing the multi-length-scale production rate model for the turbulent kinetic energy equation, the predictions of turbulent burning velocity , cylinder pressure, mass burning rate and engine performance of a gasoline engine can much be improved.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.393-406
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• 2018

The depression and anxiety are the most popular mental disorders and not easy to distinguish because of their lots of similarities in the diagnostic criteria, related theories, and clinical symptoms. In this article, we developed the affect scale for distinguishable diagnosis, utilized the relationships between the Positive and Negative affect, and the depression and anxiety. We made up the seed scale of the items which selected by partial correlation, and set the scoring up by multiple regression method. The Multi-dimentional affect scale is reliable and working similarly as the scales used before, but less correlated to each other. We conclude that the affect scale achieved the diagnosis for distinguish between depression and anxiety. Our suggestions for the further study are to redeem the cultural differences, modify by the elaborate methods, and validate by the actual clinical data.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.386-388
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• 2000

에너지 차분 흉부 단순 X선 화상으로부터 폐종류 음영을 검출하기 위한 필터를 예측해서 그 성능을 평가하기 위한 방법을 제안한다. 더욱이 그 평가방법을 이용해서 기존에 제안된 필터인 다중 해상도 ▽2G 필터의 평가를 행한다. 방사선과 전문의의 진단보조 혹은 총합자동진단시스템의 구성요소로서 필터가 발휘한 역할을 고려한 후, 필터가 만족해야 할 조건 및 그 조건을 만족한 경우에 있어서 몇가지 성능평가 척도를 명확히 한다. 제안한 평가방법을 통해서 다중 해상도 필터가 단일 해상도 필터에 비해 높은 성능을 나타내고 있음을 명확히 한다.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.327-328
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• 2007

에너지 흉부 단순 X선 영상으로부터 폐 종류 음영을 검출하기 위한 필터를 예측해서 성능좋게 평가하기 위한 방법을 제안한다. 더욱이 그 평가방법을 이용해서 기존에 제안된 다중 해상도 ${\nabla}^{2}G$ 필터의 평가를 행한다. 전문의의 진단보조 혹은 총합자동진단시스템의 구성요소로서 필터가 수행한 역할을 고려한 후, 필터가 만족해야만 하는 조건 및 그 조건을 만족한 경우에 있어서 몇가지 성능평가 척도를 명확히 한다. 제안한 평가방법을 통해서 다중해상도 필터가 단일해상도 필터에 비해 좋은 성능을 나타내고 있음을 명확히 한다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.33-42
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• 2022

In this paper, we proposed a deep learning model based on multi-scale dilated convolution feature fusion for the segmentation of black ice region in road image to send black ice warning to drivers in real time. In the proposed multi-scale dilated convolution feature fusion network, different dilated ratio convolutions are connected in parallel in the encoder blocks, and different dilated ratios are used in different resolution feature maps, and multi-layer feature information are fused together. The multi-scale dilated convolution feature fusion improves the performance by diversifying and expending the receptive field of the network and by preserving detailed space information and enhancing the effectiveness of diated convolutions. The performance of the proposed network model was gradually improved with the increase of the number of dilated convolution branch. The mIoU value of the proposed method is 96.46%, which was higher than the existing networks such as U-Net, FCN, PSPNet, ENet, LinkNet. The parameter was 1,858K, which was 6 times smaller than the existing LinkNet model. From the experimental results of Jetson Nano, the FPS of the proposed method was 3.63, which can realize segmentation of black ice field in real time.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.1-7
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• 2021

In this paper, we propose a feature selection technique for multi-label classification. Many existing feature selection techniques have selected features by calculating the relation between features and labels such as a mutual information scale. However, since the mutual information measure requires a joint probability, it is difficult to calculate the joint probability from an actual premise feature set. Therefore, it has the disadvantage that only a few features can be calculated and only local optimization is possible. Away from this regional optimization problem, we propose a feature selection technique that constructs a low-rank space in the entire given feature space and selects features with sparsity. To this end, we designed a regression-based objective function using Nuclear norm, and proposed an algorithm of gradient descent method to solve the optimization problem of this objective function. Based on the results of multi-label classification experiments on four data and three multi-label classification performance, the proposed methodology showed better performance than the existing feature selection technique. In addition, it was showed by experimental results that the performance change is insensitive even to the parameter value change of the proposed objective function.

• Journal of the Korea Computer Industry Society
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• v.2 no.3
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• pp.377-388
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• 2001

Software metrics provide effective methods for characterizing software. Metrics have traditionally been composed through the definition of an equation, but this approach restricted within a full understanding of every interrelationships among the parameters. This paper use fuzzy logic system that is capable of uniformly approximating any nonlinear function and applying cognitive psychology theory. First of all, we extract multiple regression equation from the factors of 12 software complexity metrics collected from Java programs. We apply cognitive psychology theory in program volume factor, and then measure program volume complexity to execute fuzzy learning. This approach is sound, thus serving as the groundwork for further exploration into the analysis and design of software metrics.