• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.1
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• pp.683-692
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• 2009

On-site remote sensing technique by using single lens reflex(SLR) digital camera will be expected as the useful tool for the vegetation measurement field such as a crop growth management, the monitoring of revegetation slope and the evaluation of environment. We reviewed the availability of the vegetation measurement using a digital camera which is sailed for general-purpose. As a result, we could analysis relationship with the illuminance of image plane and incidence energy of multitemporal observation images by doing gamma correction and exposure compensation. And also, we proposed the model formulas for the correction of influences of capturing angle and illuminance. In addition, we obtained high correlation of normalized difference vegetation index(NDVI) between digital camera and spectral photometer.

• Journal of Navigation and Port Research
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• v.43 no.6
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• pp.469-474
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• 2019

One of the world's major resources is crude oil, the most fundamental part of the industry. There is no place that does not use crude oil. Petroleum refining products and chemical production industrial products are produced through nearby petrochemical complexes and ports after importing crude oil. There would be a possible relationship among the petrochemical complexes and nearby regional ports working with liquid cargoes. To confirm these relations, Ulsan Port, Daesan Port, and Yeosu Gwangyang Port were selected for this study. A Vector Auto Regressive model using time series data was applied. A Unit Root Test was performed. The relationship was confirmed through the Granger and Toda Yamamoto Causality Test.

• Journal of Korean Society for Quality Management
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• v.48 no.3
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• pp.511-520
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• 2020

Purpose: The purpose of this study was to devise an accurate machine learning model for predicting silica concentrations following the addition of impurities, through time series analysis of mining data. Methods: The mining data were preprocessed and subjected to time series analysis using the machine learning model. Through correlation analysis, valid variables were selected and meaningless variables were excluded. To reflect changes over time, dependent variables at baseline were treated as independent variables at later time points. The relationship between independent variables and the dependent variable after n point was subjected to Pearson correlation analysis. Results: The correlation (R2) was strongest after 3 hours, which was adopted as a dependent variable. According to root mean square error (RMSE) data, the proposed method was superior to the other machine learning methods. The XGboost algorithm showed the best predictive performance. Conclusion: This study is important given the current lack of machine learning studies pertaining to the domestic mining industry. In addition, using time series analysis in mining data will show further improvement. Before establishing a predictive model for the proposed method, predictions should be made using data with time series characteristics. After doing this work, it should also improve prediction accuracy in other domains.

• Journal of Korea Multimedia Society
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• v.4 no.6
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• pp.518-523
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• 2001

This paper proposes the new method which is adopted in extracting character features and recognizing characters using fractal dimension of the Chaos theory which highly recolonizes a minute difference with strange attractor created from Henon system. This paper implements a high precision character recognition system. firstly, it gets features of mesh, projection and cross distance feature from character images. And their feature is converted into data of time series. Then using modified Henon system suggested in this paper, each characters attractor about standard Korean Character, KSC 5601 is reconstructed. Secondly, in order to analyze the Chaotic degree of each characters attractor, it gets last features of character image after calculating box-counting Dimension, Natural Measure, Information Bit, Information Dimension which are meant fractal dimension. An experimental result shows 97.49% character classification rates for 2350 Korean characters using proposed method in this paper.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.6860-6868
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• 2015

In this study, we consider a problem of forecasting daily city gas demand of Korea. Forecasting daily gas demand is a daily routine for gas provider, and gas demand needs to be forecasted accurately in order to guarantee secure gas supply. In this study, we analyze the time series of city gas demand in several ways. Data analysis shows that primary factors affecting the city gas demand include the demand of previous day, temperature, day of week, and so on. Incorporating these factors, we developed a multiple linear regression model. Also, we devised a sampling procedure that selectively collects the past data considering the characteristics of the city gas demand. Test results on real data exhibit that the MAPE (Mean Absolute Percentage Error) obtained by the proposed method is about 2.22%, which amounts to 7% of the relative improvement ratio when compared with the existing method in the literature.

• Journal of Information Technology Applications and Management
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• v.14 no.4
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• pp.43-59
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• 2007

The purpose of the study was to examine the effectiveness of Neuro Feedback training by observing the pre and post brainwave measurement results of about 50 (experimental group 25. comparative group 25) subjects who have shown psychological difficulties in studying. attention deficit, and personalities. The study took place at Neuro Feedback training Center B. in between the months of July 2006 and May 2007. The methodology involved in the study included the Coloring Analysis Program of the Brain Quotient Test. As the brain waves are adjusted by timeseries linear analysis. the brain function quotients can reflect the functional states of the brain. Through the test, three parameters relaxation, attention and concentration-were initially measured for one minute each and the lowest parameter out of the three was selected as the training mode or improvement target. The training took place two or three times a week. for about 40 to 60 minutes per session. Because the clients have come to the training center at different times. the researcher sampled the results of only those who had attended more than 30 training sessions. The tool used to measure the psychological reaction was POMS (Profile of Mood State). while the tool used to measure the emotional and attention-deficit characteristics was the Amen Clinic ADD Type questionnaire. Hypothesis testing included t-test. The result of the study showed the Theta: SMR ratio of (left)p = .013. (right) p = .019. The result also confirmed the differences of both ATQ(left) p = .011. (right)p = .030 and SQ(left) p = .017. (right) p = .022. The result confirmed of emotional p = .000. attention-deficit characteristics p = .000. The result of the study suggest Neuro Feedback technique's possibility in positively affecting the subjects' mental state and attention-deficit characteristics.

• The KIPS Transactions:PartD
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• v.19D no.1
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• pp.113-120
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• 2012

Today, Internet users can easily create and share the digital contents with others through various online content sharing services such as YouTube. So, many portal sites are flooded with lots of user created contents (UCC) in various media such as texts and videos. Estimating popularity of UCC is a crucial concern to both users and the site administrators. This paper proposes a method to predict the popularity of Internet articles, a kind of UCC, using the dynamics of the online contents themselves. To analyze the dynamics, we regarded the access counts of Internet posts as the popularity of them and analyzed the variation of the access counts. We derived a model to predict the popularity of a post represented by the time series of access counts, which is based on an exponential function. According to the experimental results, the difference between the actual access counts and the predicted ones is not more than 10 for 20,532 posts, which cover about 90.7% of the test set.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.3
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• pp.66-77
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• 2014

UTIS(Urban Traffic Information System) directly collects link travel time in urban area by using probe vehicles. Therefore it can estimate more accurate link travel speed compared to other traffic detection systems. However, UTIS includes some missing data caused by the lack of probe vehicles and RSEs on road network, system failures, and other factors. In this study, we suggest a new model, based on k-NN algorithm, for imputing missing data to provide more accurate travel time information. New imputation model is an adaptive k-NN which can flexibly adjust the number of nearest neighbors(NN) depending on the distribution of candidate objects. The evaluation result indicates that the new model successfully imputed missing speed data and significantly reduced the imputation error as compared with other models(ARIMA and etc). We have a plan to use the new imputation model improving traffic information service by applying UTIS Central Traffic Information Center.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.8
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• pp.534-546
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• 2009

Query by humming is an effective and intuitive querying mechanism when a user wants to find a song without knowing lyrics. The query by humming system takes a user-hummed melody as input, compares it with melodies in a music database, and returns top-k similar melodies to the input. In this paper, we propose a novel algebra for query by humming, and design and implement a real query by humming system called HummingBase by exploiting the algebra. By analyzing existing similarity search techniques, we derive 10 core operators for the algebra. By using the well-defined algebra, we can easily implement such a system in a extensible and modular way. With two case studies, we show that the proposed algebra can easily represent the query processing processes of existing query-by-humming systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.713-719
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• 2009

In this paper, we develope a water demand forecasting algorithm using AR(Auto-regressive) and MLP(Multi-layer perceptron). To show effectiveness of the proposed method, we analyzed characteristics of time-series data collected in "A" purification plant at Jeon-Buk province during 2007-2008, and then performed the proposed method with various input factors selected through various analyses. As noted in experimental results, the performance of three types model such as multi-regressive, AR(Auto-regressive), and AR+MLP(Auto-regressive + Multi-layer perceptron) show 5.1%, 3.8%, and 3.6% with respect to MAPE(Mean Absolute Percentage Error), respectively. Thus, it is noted that the proposed method can be used to predict short-term water demand for the efficient operation of a water purification plant.