• Current Optics and Photonics
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• v.1 no.2
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• pp.107-112
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• 2017

An adaptive optics system can be simulated or analyzed to predict its closed-loop performance. However, this type of prediction based on various assumptions can occasionally produce outcomes which are far from actual experience. Thus, every adaptive optics system is desired to be tested in a closed loop on an optical test bench before its application to a telescope. In the close-loop test bench, we need an atmospheric simulator that simulates atmospheric disturbances, mostly in phase, in terms of spatial and temporal behavior. We report the development of an atmospheric turbulence simulator consisting of two point sources, a commercially available deformable mirror with a $12{\times}12$ actuator array, and two random phase plates. The simulator generates an atmospherically distorted single or binary star with varying stellar magnitudes and angular separations. We conduct a simulation of a binary star by optically combining two point sources mounted on independent precision stages. The light intensity of each source (an LED with a pin hole) is adjustable to the corresponding stellar magnitude, while its angular separation is precisely adjusted by moving the corresponding stage. First, the atmospheric phase disturbance at a single instance, i.e., a phase screen, is generated via a computer simulation based on the thin-layer Kolmogorov atmospheric model and its temporal evolution is predicted based on the frozen flow hypothesis. The deformable mirror is then continuously best-fitted to the time-sequenced phase screens based on the least square method. Similarly, we also implement another simulation by rotating two random phase plates which were manufactured to have atmospheric-disturbance-like residual aberrations. This later method is limited in its ability to simulate atmospheric disturbances, but it is easy and inexpensive to implement. With these two methods, individually or in unison, we can simulate typical atmospheric disturbances observed at the Bohyun Observatory in South Korea, which corresponds to an area from 7 to 15 cm with regard to the Fried parameter at a telescope pupil plane of 500 nm.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.1
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• pp.138-145
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• 2007

Wireless transmission system using the multipath channel is affected ISI due to the delay spread. So we use a decision feedback equalizer which consist of decision part and feedback filter for remove the ISI effectively. In this paper, we propose a improved adaptive decision feedback equalizer to mitigate ISI effectively. The proposed adaptive decision feedback equalizer is construct by using soft decision device and hierarchical feedback filter based on MMSE sub-optimal equalizer using the LMS algorithm. Soft decision device mitigate the error propagation in feedback filter by incorrectly detected decision symbol and feedback filter which is divided two step independently mitigate the ISI by using a adaptive algorithm. As a result this structure shows better performance than conventional decision feedback equalizer by mitigating the error propagation in filter cause incorrectly detecting symbol. and we get the MSE more rapidly by using larger step-size due to reduce the number of feedback filter tap. In computer simulation, we compare the bit error rate performance of proposed decision feedback equalizer with conventional one on the S-V channel model for UWB system.

• Journal of Korean Society of Steel Construction
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• v.20 no.1
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• pp.21-31
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• 2008

response surface method (RSM) is widely used to evaluate th e extremely smal probability of ocurence or toanalyze the reliability of very complicated structures. Althoug h Monte-Carlo Simulation (MCS) technique can evaluate any system, the procesing time of MCS dependson the reciprocal num ber of the probability of failure. The stochastic finite element method could solve thislimitation. However, it is limit ed to the specific program, in which the mean and coeficient o f random variables are programed by a perturbation or by a weigh ted integral method. Therefore, it is not aplicable when erequisite programing. In a few number of stage analyses, RSM can construct a regresion model from the response of the c omplicated structural system, thus, saving time and efort significantly. However, the acuracy of RSM depends on the dist ance of the axial points and on the linearity of the limit stat e functions. To improve the convergence in exact solution regardl es of the linearity limit of state functions, an improved adaptive response surface method is developed. The analyzed res ults have ben verified using linear and quadratic forms of response surface functions in two examples. As a result, the be st combination of the improved RSM techniques is determined and programed in a numerical code. The developed linear adapti ve weighted response surface method (LAW-RSM) shows the closest converged reliability indices, compared with quadratic form or non-adaptive or non-weighted RSMs.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.4
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• pp.65-77
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• 2019

Adolescents reach the game-use states of adaptive and maladaptive by the absorption to computer game. Authors claimed that the two states are commonly related with the time of game-use, and the degree of them are distinctive according to adolescent individuals, specifically their self-control propensity. Authors proposed a conceptual research model that Big Five personality types predict their self-control which moderates the relationships from game use-time to the maladaptive and adaptive states. The data to test its validity and reliability had been sampled 999 Korean students in elementary school, middle school, and high school. Resultingly, the openness and conscientiousness of the adolescents affected positively on the self-control, which moderated negatively the relationship from the game use time to the maladaptive use state, but the positive moderation on the relationships from game use time to adpative state was not significant. These results mean that we could apply teenager's Big Five personality type and their self-control traits as a tool for preventing teens from the overuse state like addiction.

• The Korean Journal of Coaching Psychology
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• v.7 no.3
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• pp.81-107
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• 2023

The purpose of this study was to identify psychological factors that influence psychological well-being in middle-aged adults(40-65 years old). In particular, we aimed to determine whether mentalization, the ability to clarify one's internal experiences, and adaptive cognitive emotional regulation have a dual mediating effect on the relationship between insecure adult attachment(anxious, avoidant) and psychological well-being in middle-aged adults. To address the research questions, structural equation modeling was conducted using Jamovi 2.2.5 statistical program to analyze survey responses from 317 middle-aged adults (117 males and 200 females) who voluntarily participated through mobile and offline surveys. The results of the study are as follows. First, in the structural equation model, the simple mediating effect of mentalization on the relationship between insecure adult attachment(anxious and avoidant) and psychological well-being in middle-aged adults was not significant. Second, the simple mediating effect of adaptive cognitive emotion regulation on the relationship between insecure adult attachment and psychological well-being was significant only for anxious attachment. Third, the relationship between insecure adult attachment and psychological well-being was fully mediated by mentalization and adaptive cognitive emotion regulation for anxious attachment, but partially mediated for avoidant attachment. These findings help provide a theoretical framework for developing programs to increase psychological well-being among middle-aged adults, a growing segment of society.

• The Journal of Society for e-Business Studies
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• v.21 no.3
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• pp.101-128
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• 2016

To improve competitiveness & performance for salesmen of small & medium IT company, this study aims not only to inspect how value orientation, leadership & justice make effects for Organizational Citizenship Behavior & Business Corporate Performance & but also to explore the role of adaptive selling practices as parameter. To support the study, the data collected from 314 employees in sales roles at more than 200 IT companies was processed via. regression analysis method. The research model of study lies at identification of 'the Effects of Value Orientation, Leadership, & Justice of/Posed by the Salesmen of a IT Company on Organizational Citizenship Behavior & Corporate Performance' based on the phenomena of unfair sales strategies rampantly being taken for short-term profits & survivals despite of the value of upholding business ethics to realize long-term, sustainable growth of a business of company. The hypotheses of this study are formulated as follows. First, value orientation, leadership, & justice shall have effects on organizational citizenship behavior & Corporate performance. Second, adaptive selling practices shall function as the parameters between the independent & dependent variables. The analysis results on the research, undertaken with verification of parametric effects, confirm the following: 1. Value orientation imposes positive (+) effects on adaptive selling practices which impose positive (+) impacts on organizational citizenship behavior & Corporate performance. 2. Adaptive selling practices function as a full parameter between value orientation & organizational citizenship behavior whilst functioning as a partial parameter between value orientation & Corporate performance. 3. Leadership imposes positive (+) effects on adaptive selling practices which impose positive (+) effects on organizational citizenship behavior & Corporate performance. 4. Adaptive selling practices function as a partial parameter between leadership & organizational citizenship behavior whilst functioning as a full parameter between leadership & Corporate performance. Therefore, this study is concluded that establishing & executing sales strategies in consideration of value orientation & fairness is of extreme importance for IT companies to realize & maintain their sustainable corporate management, & last but not least, it is necessary for IT companies to proactively introduce & provide educational systems for their salesmen thus to help them to uphold & sustain ethics & values of the business.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.411-425
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• 2021

Accelerated failure time (AFT) model represents a linear relationship between the log-survival time and covariates. We are interested in the inference of covariate's effect affecting the variation of survival times in the AFT model. Thus, we need to model the variance as well as the mean of survival times. We call the resulting model mean and variance AFT (MV-AFT) model. In this paper, we propose a variable selection procedure of regression parameters of mean and variance in MV-AFT model using penalized likelihood function. For the variable selection, we study four penalty functions, i.e. least absolute shrinkage and selection operator (LASSO), adaptive lasso (ALASSO), smoothly clipped absolute deviation (SCAD) and hierarchical likelihood (HL). With this procedure we can select important covariates and estimate the regression parameters at the same time. The performance of the proposed method is evaluated using simulation studies. The proposed method is illustrated with a clinical example dataset.

• Journal of the Korea Computer Graphics Society
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• v.15 no.3
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• pp.1-9
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• 2009

Traditional computer simulation uses only traditional input and output devices. With the recent emergence of haptic techniques, which can give users kinetic and tactile feedback, the field of computer simulation is diversifying. In particular, as the virtual-reality-based surgical simulation has been recognized as an effective training tool in medical education, the practical virtual simulation of surgery becomes a stimulating new research area. The surgical simulation framework should represent the realistic properties of human organ for the high immersion of a user interaction with a virtual object. The framework should make proper both haptic and visual feedback for high immersed virtual environment. However, one model may not be suitable to simulate both haptic and visual feedback because the perceptive channels of two feedbacks are different from each other and the system requirements are also different. Therefore, we separated two models to simulate haptic and visual feedback independently but at the same time. We propose an adaptive mass-spring method as a multi-modal simulation technique to synchronize those two separated models and present a framework for a dual model of simulation that can realistically simulate the behavior of the soft, pliable human body, along with haptic feedback from the user's interaction.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1931-1942
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• 2021

As the utility of an optical satellite image with a high spatial resolution (i.e., fine-scale) has been emphasized, recently, various studies of the land surface monitoring using those have been widely carried out. However, the usefulness of fine-scale satellite images is limited because those are acquired at a low temporal resolution. To compensate for this limitation, the spatiotemporal data fusion can be applied to generate a synthetic image with a high spatio-temporal resolution by fusing multiple satellite images with different spatial and temporal resolutions. Since the spatio-temporal data fusion models have been developed for mid or low spatial resolution satellite images in the previous studies, it is necessary to evaluate the applicability of the developed models to the satellite images with a high spatial resolution. For this, this study evaluated the applicability of the developed spatio-temporal fusion models for KOMPSAT-3A and Sentinel-2 images. Here, an Enhanced Spatial and Temporal Adaptive Fusion Model (ESTARFM) and Spatial Time-series Geostatistical Deconvolution/Fusion Model (STGDFM), which use the different information for prediction, were applied. As a result of this study, it was found that the prediction performance of STGDFM, which combines temporally continuous reflectance values, was better than that of ESTARFM. Particularly, the prediction performance of STGDFM was significantly improved when it is difficult to simultaneously acquire KOMPSAT and Sentinel-2 images at a same date due to the low temporal resolution of KOMPSAT images. From the results of this study, it was confirmed that STGDFM, which has relatively better prediction performance by combining continuous temporal information, can compensate for the limitation to the low revisit time of fine-scale satellite images.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.71-86
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• 2022

The water quality index (WQI) has been widely used to evaluate marine water quality. The WQI in Korea is categorized into five classes by marine environmental standards. But, the WQI calculation on huge datasets is a very complex and time-consuming process. In this regard, the current study proposed machine learning (ML) based models to predict WQI class by using water quality datasets. Sihwa Lake, one of specially-managed coastal zone, was selected as a modeling site. In this study, adaptive boosting (AdaBoost) and tree-based pipeline optimization (TPOT) algorithms were used to train models and each model performance was evaluated by metrics (accuracy, precision, F1, and Log loss) on classification. Before training, the feature importance and sensitivity analysis were conducted to find out the best input combination for each algorithm. The results proved that the bottom dissolved oxygen (DO_Bot) was the most important variable affecting model performance. Conversely, surface dissolved inorganic nitrogen (DIN_Sur) and dissolved inorganic phosphorus (DIP_Sur) had weaker effects on the prediction of WQI class. In addition, the performance varied over features including stations, seasons, and WQI classes by comparing spatio-temporal and class sensitivities of each best model. In conclusion, the modeling results showed that the TPOT algorithm has better performance rather than the AdaBoost algorithm without considering feature selection. Moreover, the WQI class for unknown water quality datasets could be surely predicted using the TPOT model trained with satisfactory training datasets.