• Steel and Composite Structures
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• v.46 no.1
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• pp.15-31
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• 2023

Organic solar cells utilized natural polymers to convert solar energy to electricity. The demands for green energy production and less disposal of toxic materials make them one of the interesting candidates for replacing conventional solar cells. However, the different aspects of their properties including mechanical strength and stability are not well recognized. Therefore, in the present study, we aim to explore the chaotic responses of these organic solar cells. In doing so, a specific type of organic solar cell constructed from layers of material with different thicknesses is considered to obtain vibrational and chaotic responses under different boundaries and initial conditions. A square plate structure is examined with first-order shear deformation theory to acquire the displacement field in the laminated structure. The bounding between different layers is considered to be perfect with no sliding and separation. On the other hand, nonlocal elasticity theory is engaged in incorporating the structural effects of the organic material into calculations. Hamilton's principle is adopted to obtain governing equations with regard to boundary conditions and mechanical loadings. The extracted equations of motion were solved using the perturbation method and differential quadrature approach. The results demonstrated the significant effect of relative glass layer thickness on the chaotic behavior of the structure with higher relative thickness leading to less chaotic responses. Moreover, a comprehensive parameter study is presented to examine the effects of nonlocality and relative thicknesses on the natural frequency of square organic solar cell structure.

• Advances in nano research
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• v.17 no.1
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• pp.61-73
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• 2024

This paper presents nonlinear vibration analysis of a composite cylindrical shell. The core of the shell is made of functionally graded (FG) porous materials and layers is fabricated of carbon nanotubes (CNTs) reinforced nanocomposites. To increase the accuracy of results, neutral surface position is considered. First-order shear deformation theory is used as displacement field to derive the basic relations of equation motions. In addition, von-Karman nonlinear strains are employed to account geometric nonlinearity and to enhance the results' precision, the exact position of the neutral surface is considered. To governing the partial equations of motion, the Hamilton's principle is used. To reduce the equation motions into a nonlinear motion equation, the Galerkin's approach is employed. After that the nonlinear motion equation is solved by multiple scales method. Effect of various parameters such as volume fraction and distribution of CNTs along the thickness directions, different patterns and efficiency coefficients of porous materials, geometric characteristics and initial conditions on nonlinear to linear ratio of frequency is investigated.

• Journal of Internet of Things and Convergence
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• v.6 no.2
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• pp.37-43
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• 2020

The paper proposed the method of calculating the latest approach angle that can reliably recognize multiple laser images even with the change in separation distance between screen and laser launch device. This method recognizes the angle of the laser pattern angle by using the distance of the laser pattern angle, and the angle extraction of the laser detects the laser image from the acquired image using the labeling algorithm, and performs the huff conversion to extract the angle of the straight line. The distance of the reference angle and angle of the laser image extracted using Euclidean distance among similarity scales is calculated, and the furnace is recognized using the calculated distance result value. Experiments with changing the separation distance to "200 cm to 400 cm" showed 100% recognition of individual strands at all separation distances. The experiment confirmed the reliability of the proposed method.

• Child Health Nursing Research
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• v.11 no.2
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• pp.196-202
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• 2005

Purpose: The purpose of this study was to analyze the factors affecting Self Care Behavior in Korean high school students. Method: Data were collected from November 9 to December 28, 2003. The participants in this study were 293 students(134 boys, 159 girls), recruited from two High School located in Seoul. Data collection was conducted through the use of Questionnaire that was modified by the investigator. The data were analyzed with descriptive statistics, Pearson Correlation coefficients and stepwise multiple regression. The SPSS win 10.0 program was used. Results: The mean of total item score the Self Care Behaviors scales was 3.37, which was slightly low. There was a significant correlation between Self Care Behavior, Hope, Social Support, and Self Care Agency(γ= .30 ～ .65, p<.001). Stepwise multiple regression analysis showed that Self Care Agency was a predictor of Self Care Behavior and accounted for 28% of the variance. Self Care Agency account for 31% of the variance in Self Care Behavior in the high school girls. Self Care Agency and Hope accounted for 27% of the variance in Self Care Behavior in High school boys. Conclusion: Self Care Agency accounted for variance in Self Care Behavior in these high school students. Therefore it is necessary to develop nursing interventions to increase Self Care Agency in high school students in order to increase the Self Care Behavior.

• Child Health Nursing Research
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• v.9 no.3
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• pp.323-331
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• 2003

Purpose: The purpose of this study is to analyze the factors affecting Deviant Behaviors of Korean High school Students. Method: Data was collected from October 8 to 31, 2002. The subjects for this study were 697 Korean High school Students(boys 347, girls 350), recruited from two High School located in Seoul. Data collection was conducted through the use of 6 Questionnaire that modified by the investigator. The data was analyzed by the SPSS win 10.0 program using Descriptive statistics, Pearson Correlation coefficient and stepwise multiple regression. Result: 1) The mean of total item score the Deviant Behaviors scales was 1.59, which was slightly low. 2) There was a significant correlation between Deviant Behaviors, Type A Personality, Aggression, Impulsivity, Stress and Social Support(γ= .11 ～ .65, p<.001), but It was no significant correlation Type A Personality and Stress(γ= -.01). 3) Stepwise multiple regression analysis showed that (1) Impulsivity, Social Support and Type A Personality were the predictors of Deviant Behaviors and account for 18.6％ of the variance in Deviant Behaviors. (2) Impulsivity account for 3.6％ of the variance in Deviant Behaviors in Subjects with a lower degree of score the Deviant Behaviors. Impulsivity and Social Support account for 23.2％ of the variance in Deviant Behaviors in subjects with higher degree of score the Deviant Behaviors. (3) Impulsivity account for 18.3％ of the variance in Deviant Behaviors in High school girls (n=350). Impulsivity and Social Support account for 20.1％ of the variance in Deviant Behaviors in High school boys (n=347). Conclusion: Impulsivity and Social Support account for Deviant Behaviors of High school Students. Therefore it is necessary to develop nursing intervention to reduce the level of Impulsivity, to increase the Social Support in order to decrease the Deviant Behaviors.

• Korean Journal of Remote Sensing
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• v.17 no.2
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• pp.183-188
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• 2001

The NOAA AVHRR remotely sensed SST data, collected by the National Fisheries Research and Development Institute (NFRDI), are analyzed in order to understand the spatial and temporal distributions of SST in the sea near korea. Our study is based on 10-day SST images during last 7 years (1991-1997). For a time series analysis of multiple SST images, all of images must be consistent exactly at the same position by adjusting the scales and positions of each SST image. We devised an algorithm which automatically detects cloud pixels from multiple SST images. The cloud detection algorithm is based on a physical constraint that SST anomalies in the ocean do not exceed certain limits (we used $\pm$3$^{\circ}C$ as a criterion of SST anomalies). The remotely sensed SST data are tuned by comparing remotely sensed data with observed SST at coastal stations. Seasonal variations of SST are studied by harmonic fit of SST normals at each pixel and the SST anomalies are studied by statistical method. It was found that the SST anomalies are rather persistent for one or two months. Utilizing the persistency of SST anomalies, we devised an algorithm for a prediction of future SST. In the Markov lprocess model of SST anomalies, autoregression coefficients of SST anomalies during a time elapse of 10 days are between 0.5 and 0.7. The developed algorithm with automatic cloud pixel detection and rediction of future SST is expected to be incorporated to the operational real time service of SST around Korea.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.132-142
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• 2014

In this paper, we propose a traffic lights detection method using visual attention and spot-lights detection. To detect traffic lights in city streets at day and night time, the proposed method is used the structural form of a traffic lights such as colors, intensity, shape, textures. In general, traffic lights are installed at a position to increase the visibility of the drivers. The proposed method detects the candidate traffic lights regions using the top-down visual saliency model and spot-lights detect models. The visual saliency and spot-lights regions are positions of its difference from the neighboring locations in multiple features and multiple scales. For detecting traffic lights, by not using a color thresholding method, the proposed method can be applied to urban environments of variety changes in illumination and night times.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.1-10
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• 2002

This paper presents the new template based human face recognition methods to improve the recognition performance against scale and in-plane rotation variations of face images. To enhance the recognition performance, the templates are generated by linear or nonlinear operation on multiple images including different scales and rotations of faces. As the invariant features to allow for scale and rotation variations of face images, we adopt the affine transformation, the log-polar mapping, and the log-polar image based FFT. The proposed recognition methods are evaluated in terms of the recognition rate and the processing time. Experimental results show that the proposed template based methods lead to higher recognition rate than the single image based one. The affine transformation based face recognition method shows marginally higher recognition rate than those of the log-polar mapping based method and the log-polar image based FFT, while, in the aspect of processing time, the log-polar mapping based method is the fastest one.

• Journal of Korean Traditional Oncology
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• v.24 no.2
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• pp.13-21
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• 2019

Objective : The purpose of this study is to report and observe effects of Traditional Korean Medicine (TKM) on stage IV metastatic breast cancer patient. Method : A right breast cancer patient diagnosed with metastatic lesions on liver, lung, spleen, multiple bones and skin on right breast April 2013. The patient received Herceptin + Docetaxel + Zometa from May 2013 and started to receive TKM since July 2013 to decrease side effects of chemotherapy. From December 2013 to November 2014, she had received Herceptin 18 times more. The clinical outcomes were measured by computed tomography, laboratory findings including tumor markers (CEA, CA15-3), liver function test (AST, ALT), and numeric rating scales (NRS). Results : After 6 years of TKM treatment combined with standard chemotherapy, tumor size was partially decreased in lung, liver, right pleura and spleen. Levels of tumor markers also showed decrease. There were no severe adverse events induced by TKM based on National Cancer Institute Common Terminology Criteria for Adverse Event (NCI-CTCAE) version 4.0. Conclusion : This case suggests that TKM combined with standard chemotherapy could be a promising method for treating metastatic stage IV breast cancer.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.319-330
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• 2006

A signal in real world usually composes of multiple signals having different scales of frequencies. For example sun-spot data is fluctuated over 11 year and 85 year. Economic data is supposed to be compound of seasonal component, cyclic component and long-term trend. Decomposition of the signal is one of the main topics in time series analysis. However when the signal is subject to nonstationarity, traditional time series analysis such as spectral analysis is not suitable. Huang et. at(1998) proposed data-adaptive method called empirical mode decomposition (EMD) . Due to its robustness to nonstationarity, EMD has been applied to various fields. Huang et. at, however, have not considered denoising when data is contaminated by error. In this paper we propose efficient denoising method utilizing cross-validation.