• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2483-2504
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• 2023

Most existing low-light enhancement algorithms either use a large number of training parameters or lack generalization to real-world scenarios. This paper presents a novel lightweight and robust pixel-wise polynomial approximation-based deep network for low-light image enhancement. For mapping the low-light image to the enhanced image, pixel-wise higher-order polynomials are employed. A deep convolution network is used to estimate the coefficients of these higher-order polynomials. The proposed network uses multiple branches to estimate pixel values based on different receptive fields. With a smaller receptive field, the first branch enhanced local features, the second and third branches focused on medium-level features, and the last branch enhanced global features. The low-light image is downsampled by the factor of 2b-1 (b is the branch number) and fed as input to each branch. After combining the outputs of each branch, the final enhanced image is obtained. A comprehensive evaluation of our proposed network on six publicly available no-reference test datasets shows that it outperforms state-of-the-art methods on both quantitative and qualitative measures.

• Journal of The Korean Association For Science Education
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• v.22 no.2
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• pp.299-313
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• 2002

A web-based instruction program for the enriched course under the 7th Revised National Curriculum of Biology in Korea was developed and the application effects to learners were analyzed. For the development of the web-based instruction program, five topics of biology from the enriched courses through 7th to 10th grades in the middle and high school science textbooks were selected and modulated with interrogative sentences. Each topic of programs was divided into four activity sections according to the learners' activity procedures supplemented with explanations and evaluations. Each activity was hyper-linked to multi-layers and animations. Further, a virtual experiment was also developed and an evaluation section designed by Java Script was attached. Among five topics, one topic of 'Reproduction and development' at 9th grade level was selected to examine the effects on students' learning. Among 247 9th grade students in the research subject school, only 67 students were able to accessible to ultra-thin Internet cables with their computers at home and they became an experimental group. A control group was assigned to those who are similar level of school science achievement to the experiment group and did not use the web-based program. It was found that most of 9th grade students are able to use Internet at home, however, they do not prefer to use Internet for homework or task project. Rather, most of students used Internet for e-mail or information navigation. Students used internet to solve problems of science and perceived the benefits of Internet for science learning. However, there are not many students to utilize Internet for science homework or task project. Students expressed that they do not prefer to use a web-based learning program for science learning due to lack of interests in science. The effects on students who studied with this program appeared to be significantly high compared to those who did not study with this program. Students who studied with this program positively evaluated this program, in particular, they enjoyed animation effect and virtual experiments. It was concluded that a web-based program for science learning should be developed and distributed through Internet in an attractive and interesting format for students. It was also concluded that various web-based programs for science learning with animation effect and virtual experiments should be developed to increase students' interests in science as well as to improve students' science achievements.

• Journal of IKEEE
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• v.15 no.4
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• pp.354-362
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• 2011

One of the unsolved problems in Artificial Neural Networks is related to the capacity of a neural network. This paper presents a CoreNet which has a multi-leveled input and a multi-leveled output as a 2-layered artificial neural network. I have suggested an equation for calculating the capacity of the CoreNet, which has a p-leveled input and a q-leveled output, as $a_{p,q}=\frac{1}{2}p(p-1)q^2-\frac{1}{2}(p-2)(3p-1)q+(p-1)(p-2)$. With an odd value of p and an even value of q, (p-1)(p-2)(q-2)/2 needs to be subtracted further from the above equation. The simulation model 1(3)-1(6) has 3 levels of an input and 6 levels of an output with no hidden layer. The simulation result of this model gives, out of 216 possible functions, 80 convergences for the number of implementable function using the cot(x) input leveling method. I have also shown that, from the simulation result, the two diverged functions become implementable by precalculating the weight values. The simulation result and the precalculation of the weight values give the same result as the above equation in the total number of implementable functions.

• Journal of The Korean Association For Science Education
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• v.37 no.3
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• pp.507-521
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• 2017

The purpose of this study is to investigate a meaningful path model among the components of students' positive experiences about science and science learning to understand the interactive relationships among different variables of affective domains. Positive Experiences about Science (PES) means whole experiences that have positive effects on students' affectional achievement related with science learning, which consists of science academic emotion, science-related self-concept, science learning motivation, science-related attitude, and science-related career aspiration. We conducted an online survey with 1,841 students consisting of 4th, 6th, 8th, and 10th graders from 17 provinces and cities using Test for Indicators of Positive Experiences about Science (TIPES). To explore the structural relationships among variables, we selected and analyzed an optimal structural equation model and then conducted multigroup analyses among groups. According to the analysis of the structural equation model, 'positive as well as negative science academic emotion' has effects on science learning motivation, science-related attitude, and science-related career aspiration via science-related self-concept. According to the independent t-test results for TIPES scores by participants' characteristics, there were statistically significant differences in the average scores of five sub-components of PES depending on gender, school-level, school location, and participation in science-related activities. According to the multi-group analysis results, the difference of path coefficients by gender and school-level were statistically significant, whereas the difference of path coefficients by school location and participation were not significant. Discussed in the conclusion are the implications of this research for science education research and ways to help students' affectional achievement related with science learning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.6 no.2
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• pp.149-158
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• 2005

In this paper, we have designed and implemented a portable e-tongue (electronic tongue) system using MACS (multi array chemical sensor) and PDA. The system embedded in PDA has merits such as comfortable user interface and data transfer by internet from on-site to remote computer. MACS was made up 7 electrodes (${NH_4}^+$, $Na^+$, $Cl^-$, ${NO_3}^-$, $K^+$, $Ca^{2+}$, $Na^+$, pH) and a reference electrode. For learning the system, we adapted the Levenberg-Marquardt algorithm based on the back-propagation, which could iteratively learned the pre-determined standard patterns, in e-tongue system. Conclusionally, the relationship between the standard patterns and unknown pattern can be easily analyzed. The e-tongue was applied to whiskeys and cognac (one high level whisky, one low level whiskey, two cognac) and 2 sample whiskeys for each standard patterns and unknown patterns. The relationship between the standard patterns and unknown patterns can be easily analyzed.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.275-292
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• 2022

In order to detect climate changes using satellite imagery, the GCOS (Global Climate Observing System) defines requirements such as spatio-temporal resolution, stability by the time change, and uncertainty. Due to limitation of GK-2A sensor performance, the level-2 products can not satisfy the requirement, especially for spatial resolution. In this paper, we found the optimal pan-sharpening algorithm for GK-2A products. The six pan-sharpening methods included in CS (Component Substitution), MRA (Multi-Resolution Analysis), VO (Variational Optimization), and DL (Deep Learning) were used. In the case of DL, the synthesis property based method was used to generate training dataset. The process of synthesis property is that pan-sharpening model is applied with Pan (Panchromatic) and MS (Multispectral) images with reduced spatial resolution, and fused image is compared with the original MS image. In the synthesis property based method, fused image with desire level for user can be produced only when the geometric characteristics between the PAN with reduced spatial resolution and MS image are similar. However, since the dissimilarity exists, RD (Random Down-sampling) was additionally used as a way to minimize it. Among the pan-sharpening methods, PSGAN was applied with RD (PSGAN_RD). The fused images are qualitatively and quantitatively validated with consistency property and the synthesis property. As validation result, the GSA algorithm performs well in the evaluation index representing spatial characteristics. In the case of spectral characteristics, the PSGAN_RD has the best accuracy with the original MS image. Therefore, in consideration of spatial and spectral characteristics of fused image, we found that PSGAN_RD is suitable for GK-2A products.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.117-126
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• 2020

A maritime object detection system is an intelligent assistance system to maritime autonomous surface ship(MASS). It detects automatically floating debris, which has a clash risk with objects in the surrounding water and used to be checked by a captain with a naked eye, at a similar level of accuracy to the human check method. It is used to detect objects around a ship. In the past, they were detected with information gathered from radars or sonar devices. With the development of artificial intelligence technology, intelligent CCTV installed in a ship are used to detect various types of floating debris on the course of sailing. If the speed of processing video data slows down due to the various requirements and complexity of MASS, however, there is no guarantee for safety as well as smooth service support. Trying to solve this issue, this study conducted research on the minimization of computation volumes for video data and the increased speed of data processing to detect maritime objects. Unlike previous studies that used the Hough transform algorithm to find the horizon and secure the areas of interest for the concerned objects, the present study proposed a new method of optimizing a binarization algorithm and finding areas whose locations were similar to actual objects in order to improve the speed. A maritime object detection system was materialized based on deep learning CNN to demonstrate the usefulness of the proposed method and assess the performance of the algorithm. The proposed algorithm performed at a speed that was 4 times faster than the old method while keeping the detection accuracy of the old method.

• Journal of Technology Innovation
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• v.20 no.2
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• pp.161-187
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• 2012

Recently, environmentally friendly technology are becoming important due to reconsideration about climate change and environmental pollution. In addition, as well as technical skills and social interaction through an analysis of the nonlinear transition management and policy implementation are emerging. This study of the development of photovoltaic industry in Korea 10 years analyze with strategic niche management (SNM) based on the theoretical and multi-layered perspective (MLP) is used as the analytical framework. Choose the gerverment-support project for niche technology, through a process of quantifying and alnalyze the phase transition to Regime with the numerical method and policy vision, learning effects, and network that key elements of SNM, MLP. Through the analysis of the photovoltaic industry technology-commercialization phase was investigated. This conventional overall and step-by-step model for technical management is proposed to replace exiting linear and narrow method and through the case study its validity was confirmed.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1739-1756
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• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.