• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1295-1303
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• 2022

As the role of water distribution networks (WDNs) becomes more important, identifying abnormal events (e.g., pipe burst) rapidly and accurately is required. Since existing approaches such as field equipment-based detection methods have several limitations, model-based methods (e.g., machine learning based detection model) that identify abnormal events using hydraulic simulation models have been developed. However, no previous work has examined the impact of data uncertainties on the results. Thus, this study compares the effects of measurement error-induced pressure data uncertainty in WDNs. An artificial neural network (ANN) is used to predict nodal pressures and measurement errors are generated by using cumulative density function inverse sampling method that follows Gaussian distribution. Total of nine conditions (3 input datasets × 3 output datasets) are considered in the ANN model to investigate the impact of measurement error size on the prediction results. The results have shown that higher data uncertainty decreased ANN model's prediction accuracy. Also, the measurement error of output data had more impact on the model performance than input data that for a same measurement error size on the input and output data, the prediction accuracy was 72.25% and 38.61%, respectively. Thus, to increase ANN models prediction performance, reducing the magnitude of measurement errors of the output pressure node is considered to be more important than input node.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.17 no.2
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• pp.131-140
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• 2006

Objectives : This study was conducted to investigate the deficits of executive function in children with ADHD, Asperger's Disorder(AD), and teaming disorder (LD), and to identify the differential characteristics of executive function deficits among three groups. Methods : The clinical group consisted of 46 children between the ages of 7 and 15 (16 ADHD, 16 LD, 14 AD). Neuropsychological tests for measuring cognitive function, attention and executive function were individually administered to children, and their performance scores were calculated based on the age norm for each test. Results : There was no significant difference in FSIQ, VIQ, and PIQ among the three groups. However, the AD group tended to show higher scores on the subtests of Information, Vocabulary and Digit Span, and lower score on Comprehension subtest than the ADHD and LD groups, while the LD group tended to show the lowest scores on the Information and Vocabulary subtests. On ADS, the ADHD group showed the highest omission and commission errors. All groups showed poor performances belonging to below 25 percentile ranks on executive function tests when compared to the age norms of normative group. The number of completed category on WCST was the smallest in the ADHD group, while the working memory score was the lowest in the LD group. Conclusion : These results suggest that ADHD, LD, and AD children have executive function deficit in common. However, the specific deficit areas in executive function are different for each group.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.13 no.1
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• pp.85-92
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• 2002

Objectives：This study was conducted to compare the memory function among the attention deficit/hyperactivity disorder(ADHD), the learning disorder(LD) and the comorbidity disorder(ADHD+LD) groups. Methods：Thirty-four children(11 ADHD, 5 LD, 9 ADHD+LD, and 8 Psychiatric control) were individually assessed using the KEDI-WISC and Memoty Assessment Scale(MAS), and then the results of those test were analyzed. Results：In memory test, all of three group showed lower performances than control group. The comorbidity, the LD and the ADHD group showed lower scores in almost subtests of MAS respectively. The good performance in memory test was significantly correlated with the types of memory strategy and error response children used during testing. Discussion：The clinical utility of the memory test like MAS was discussed in terms of differential diagnosis for ADHD, LD and ADHD+LD children.

• Journal of Ginseng Research
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• v.32 no.4
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• pp.341-346
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• 2008

In the present study, we assessed the effects of white ginseng and red ginseng extract on the learning and memory impairments induced by scopolamine. The cognition-enhancing effect of ginseng extracts was investigated using the Morris water maze and Y-maze test. Drug-induced amnesia was induced by treating animals with scopolamine (2 mg/kg, i.p.), an antagonist of muscarinic acetylcholine (ACh) receptor. Tacrine was used a positive control. Ginseng extract (200 mg/kg, p.o.), tacrine (10 mg/kg, p.o.) administration significantly reduced the escape latency during training in the Morris water maze (p<0.05). At the probe trial session, scopolamine significantly increased the escape latency on day 5 in comparison with control (p<0.01). The effect of ginseng extracts on spontaneous alternation in Y-maze was similar to that of scopolamine treated group. In addition, numbers of arm entries were similar in all experimental groups. Moreover, red ginseng extract significantly inhibited acetylcholinesterase activity in the cortex and serum (p<0.05). Brain ACh contents of ginseng extract treated groups increased more than that of scopolamine group, which did not show statistically significant. These results suggest that ginseng extract may be useful for the treatment of cognitive impairment.

• Journal of Industrial Convergence
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• v.17 no.2
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• pp.29-34
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• 2019

The purpose of this study is to analyze the achievements of employment support by the government support specialization project. The data used in this study are based on the comparison of 5 - year employment support field and the operation results of the program until 2014-2018. The results of the study are as follows. First, the overall employment rate of the university has been continuously increased. Especially, the employment rate of the specialization department has been higher than the employment rate of the non - specialization department. Second, as a result of the analysis of the employment capacity strengthening index and the learning capacity strengthening index, it showed a steady increase in each year and contributed to the cultivation of customized talents required by the local society and the national industry. Third, as a result of analyzing the satisfaction of students who are business users, it was confirmed that the business reflecting the demands of the consumers was realized. Fourth, the continuous improvement of the business and the reflux have made the infrastructure of the employment support project more advanced and the system of supporting employment of the university systematically established. In conclusion, the result of the employment support project according to the specialization program showed excellent results and it is necessary to complement theses results when establishing related business plan in the future.

• Knowledge Management Research
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• v.22 no.2
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• pp.1-17
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• 2021

In this study, we predicted the bitcoin prices of Bithum and Coinbase, a leading exchange in Korea and USA, using ARIMA and Recurrent Neural Networks(RNNs). And we used news articles from each country to suggest a separated RNN model. The suggested model identifies the datasets based on the changing trend of prices in the training data, and then applies time series prediction technique(RNNs) to create multiple models. Then we used daily news data to create a term-based dictionary for each trend change point. We explored trend change points in the test data using the daily news keyword data of testset and term-based dictionary, and apply a matching model to produce prediction results. With this approach we obtained higher accuracy than the model which predicted price by applying just time series prediction technique. This study presents that the limitations of the time series prediction techniques could be overcome by exploring trend change points using news data and various time series prediction techniques with text mining techniques could be applied to improve the performance of the model in the further research.

• 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.

• Korean Journal of School Psychology
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• v.16 no.3
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• pp.401-431
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• 2019

This study explored the lived experiences of high school students who experienced a norm-referenced evaluation with the Naesin system. The participants were 15 high school students who resided in the areas of Seoul, Gyeonggi, and Busan. This study adopted a phenomenological research method, which is developed by Giorgi. The study resulted in 370 meaning units, 71 summaries of meaning units, 26 sub-constituents, and 9 constituents. The results showed that participants experienced their relationships with classmates were centered around competitions and they experienced the sense of repeated frustration with their academic goals. Participants perceived that their personal values equated with their academic rankings and they anticipated academic rankings becoming their future social rankings. Low rankings with good performance, learning for exam, and unfair treatment in school made them realize that the education is not for learning but for differentiating students. Participants have found the ways to know better about reality and self-regulated their thoughts and emotions. Further, this study identified resilient aspects of the participants such as support from parents and teachers and hopeful thoughts. This study discussed the meaning of the findings and implications of the findings.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.127-137
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• 2024

Physical models, which are often used for water resource management, are difficult to build and operate with input data and may involve the subjective views of users. In recent years, research using data-driven models such as machine learning has been actively conducted to compensate for these problems in the field of water resources, and in this study, an artificial neural network was used to simulate long-term rainfall runoff in the Osipcheon watershed in Samcheok-si, Gangwon-do. For this purpose, three input data groups (meteorological observations, daily precipitation and potential evapotranspiration, and daily precipitation - potential evapotranspiration) were constructed from meteorological data, and the results of training the LSTM (Long Short-term Memory) artificial neural network model were compared and analyzed. As a result, the performance of LSTM-Model 1 using only meteorological observations was the highest, and six LSTM-MLP ensemble models with MLP artificial neural networks were built to simulate long-term runoff in the Fifty Thousand Watershed. The comparison between the LSTM and LSTM-MLP models showed that both models had generally similar results, but the MAE, MSE, and RMSE of LSTM-MLP were reduced compared to LSTM, especially in the low-flow part. As the results of LSTM-MLP show an improvement in the low-flow part, it is judged that in the future, in addition to the LSTM-MLP model, various ensemble models such as CNN can be used to build physical models and create sulfur curves in large basins that take a long time to run and unmeasured basins that lack input data.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.