• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1121-1121
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• 2001

A previous study (Berzaghi et al., 2001) evaluated the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural networks (ANN) on the prediction of the chemical composition of forages, using a large NIR database. The study used forage samples (n=25,977) from Australia, Europe (Belgium, Germany, Italy and Sweden) and North America (Canada and U.S.A) with reference values for moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected using 10 different Foss NIR Systems instruments, only some of which had been standardized to one master instrument. The aim of the present study was to evaluate the behaviour of these different calibration methods when predicting the same samples measured on different instruments. Twenty-two sealed samples of different kind of forages were measured in duplicate on seven instruments (one master and six slaves). Three sets of near infrared spectra (1100 to 2500nm) were created. The first set consisted of the spectra in their original form (unstandardized); the second set was created using a single sample standardization (Clone1); the third was created using a multiple sample procedure (Clone6). WinISI software (Infrasoft International Inc., Port Mathilda, PA, USA) was used to perform both types of standardization, Clone1 is just a photometric offset between a “master” instrument and the “slave” instrument. Clone6 modifies both the X-axis through a wavelength adjustment and the Y-axis through a simple regression wavelength by wavelength. The Clone1 procedure used one sample spectrally close to the centre of the population. The six samples used in Clone 6 were selected to cover the range of spectral variation in the sample set. The remaining fifteen samples were used to evaluate the performances of the different models. The predicted values for dry matter, protein and neutral detergent fibre from the master Instrument were considered as “reference Y values” when computing the statistics RMSEP, SEPC, R, Bias, Slope, mean GH (global Mahalanobis distance) and mean NH (neighbourhood Mahalanobis distance) for the 6 slave instruments. From the results we conclude that i) all the calibration techniques gave satisfactory results after standardization. Without standardization the predicted data from the slaves would have required slope and bias correction to produce acceptable statistics. ii) Standardization reduced the errors for all calibration methods and parameters tested, reducing not only systematic biases but also random errors. iii) Standardization removed slope effects that were significantly different from 1.0 in most of the cases. iv) Clone1 and Clone6 gave similar results except for NDF where Clone6 gave better RMSEP values than Clone1. v) GH and NH were reduced by half even with very large data sets including unstandardized spectra.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.1-6
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• 2019

Neural networks have been used to predict the direction of stock index movement from past data. The conventional research that predicts the upward or downward movement of the stock index predicts a rise or fall even with small changes in the index. It is highly likely that losses will occur when trading ETFs by use of the prediction. In this paper, a neural network model that predicts the movement direction of the daily KOrea composite Stock Price Index (KOSPI) to reduce ETF trading losses and earn more than a certain amount per trading is presented. The proposed model has outputs that represent rising (change rate in index ${\geq}{\alpha}$), falling (change rate ${\leq}-{\alpha}$) and neutral ($-{\alpha}$ change rate < ${\alpha}$). If the forecast is rising, buy the Leveraged Exchange Traded Fund (ETF); if it is falling, buy the inverse ETF. The hit ratio (HR) of PNN1 implemented in this paper is 0.720 and 0.616 in the learning and the evaluation respectively. ETF trading yields a yield of 8.386 to 16.324 %. The proposed models show the better ETF trading success rate and yield than the neural network models predicting KOSPI.

• Clean Technology
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• v.29 no.2
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• pp.151-159
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• 2023

The realization of carbon neutrality requires cooperation from various stakeholders and the utilization of a governance system. The criteria for participating members are crucial for the successful operation of governance, and it is especially necessary for experts who can provide scientific advice for policy implementation to share a framework for successful consensus. In this study, governance model theory and model structure, governance common success factors by case, and the application of governance cases in the climate and environmental sector of Seoul, were investigated and analyzed to derive common success factors in order to present the activity standards of the science and technology experts participating in governance. The study of the model theory suggested that the model structure is commonly composed of a basic condition-process-result structure, and it was confirmed that common success factors can be derived at the process stage which is the activity period of members. Through the case study of common success factors, overlapping factors were found to be reliability, accountability, transparency, networks, and related factors. The validity of the common success factors was verified using the analysis results of satisfaction survey data from Seoul Governance Committee participants. The results confirmed that reliability was the most valuable factor followed by networks, transparency, and responsibility, and it was found that the related factors were appropriately derived. The findings of this study are expected to be used as an activity factor for science and technology experts to increase the acceptability and effectiveness of carbon-neutral policies in the future.

• Cartoon and Animation Studies
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• s.44
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• pp.285-306
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• 2016

With the increase use of smartphones, users can consume contents such as webtoon, webnovel and TV drama directly provided by the producers. In this Direct-to-Consumer era, webdrama services from the portal websites are increasing rapidly. Webdramas such as , , and can be analyzed in real time using responses such as unique users, likes, and comments. The analyses used in this research were Social Media Big Data Mining Method and Opinion Mining Method. Specific key words from webdrama can be extracted and viewers positive, neutral or negative emotion can be predicted from the words. The analyses of popular webdramas showed that the established K-Pop Idol member appearance and servicing portal site greatly influence the views, traffics, comments, and likes. Also, 'Mobile TV' proved the effectiveness as another platform other than television. Mobile targeted contents and robust business models still to be developed and identified. Overcoming these few tasks, Korea will be proven to be a webdrama content powerhouse.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5B
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• pp.414-426
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• 2003

XML is being used to describe components and applications in a vendor and language neutral. Therefore it already has a role in distributed system. XML is also being used as a data interchange format between components and applications in loosely coupled large-scale application. Until now, policy is described for specific applications and devices. Its use has been very limited. In current network management system, we can only invoke predefined operations and actions using policy-based network management. The main motivation for the recent interests in policy-based networks is to support dynamic adaptability of behavior by changing policy without recoding or stopping system. For these reasons we present the use of the XML for describing the policy and PIB(Policy Information Base) in COPS-PR. It improves flexibility and interoperability among heterogeneous network systems. It also can add new functionality into network components. In this paper, we propose a dynamically extensible network management architecture using XML-based PIB.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.78-83
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• 2019

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically abnormal sound on machines using the acoustic emission by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose here an automatic fault diagnosis method of hand drills using discrete wavelet transform(DWT) and pattern recognition techniques such as artificial neural networks(ANN). We first conduct a filtering analysis based on DWT. The power spectral density(PSD) is performed on the wavelet subband except for the highest and lowest low frequency subband. The PSD of the wavelet coefficients are extracted as our features for classifier based on ANN the pattern recognition part. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.845-854
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• 2016

Sentiment analysis or opinion mining is a technique of text mining employed to identify subjective information or opinions of an individual from documents in blogs, reviews, articles, or social networks. In the literature, only a problem of binary classification of ratings based on review texts in an online review. However, because there can be positive or negative reviews as well as neutral reviews, a multi-class classification will be more appropriate than the binary classification. To this end, we consider the multi-class classification of ratings based on review texts. In the preprocessing stage, we extract words related with ratings using chi-square statistic. Then the extracted words are used as input variables to multi-class classifiers such as support vector machines and proportional odds model to compare their predictive performances.

• Journal of Korea Water Resources Association
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• v.36 no.1
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• pp.117-128
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• 2003

The objectives of this study are to develop the total maximum daily loads simulation system, TOLOS that is capable of estimating annual nonpoint source pollution from small watersheds, to monitor the hydrology and water quality of the Balkan HP#6 watershed, and to validate TOLOS with the field data. TOLOS consists of three subsystems: the input data processor based on a geographic information system, the models, and the post processor. Land use pattern at the tested watershed was classified from the Landsat TM data using the artificial neutral network model that adopts an error back propagation algorithm. Paddy field components were added to SWAT model to simulate water balance at irrigated paddy blocks. SWAT model parameters were obtained from the GIS data base, and additional parameters calibrated with field data. TOLOS was then tested with ungauged conditions. The simulated runoff was reasonably good as compared with the observed data. And simulated water quality parameters appear to be reasonably comparable to the field data.

• Cross-Cultural Studies
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• v.29
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• pp.25-53
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• 2012

The attitude of observer in the painting of history is to exclude a prejudice and a subjective view of an artist and to introduce a photograph, which is a record of objectivity, in the process of painting. Its ultimate intent is to redescribe the fact of an event's image intactly without any prejudice and to represent the event as a proven evidence that it was. The representation of history based on fact had already been conceived in imagination of renowned artists such as Francisco Goya or $Th{\acute{e}}odore$ $G{\acute{e}}ricault$ even before cameras were invented. What they portrayed was their own truth of reality which is gained through their observation, not a history that have corresponded to political ideologies, for all reliance on a limited tool of representation, painting. Furthermore, history was necessary for 19th century impressionism artists to be represented under proven fact in a neutral perspective excluding all subjective prejudice, not based on the representation with imagination. Edouard Manet in particular reconstited an instant moment on the basis of real proof of photograph without personal prejudice or opinion as if today's photojournalism. The catastrophic series by Andy Warhol and the photographic painting by Gerhard Richter show another role of painting in the realm of art, each of them implying information distortion and abuse by current media and intentional deformation toward history as Manet's painting of history. Today, the representation of an historical event that we experience in the era of the Internet and social networks having a great deal of information already came to be the exclusive property of the cutting edge mass media. Nevertheless, the attitude of observer which is realistic and contemplative in the realm of art is the crucial point in terms of artists' act as ever.

• Journal of IKEEE
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• v.26 no.3
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• pp.355-363
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• 2022

Korea is pursuing a plan to switch and expand energy sources with a focus on renewable energy with the goal of becoming carbon neutral by 2050. As the instability of energy supply increases due to the intermittent nature of renewable energy, accurate prediction of the amount of renewable energy generation is becoming more important. Therefore, the government has opened a small-scale power brokerage market and is implementing a system that pays settlements according to the accuracy of renewable energy prediction. In this paper, a prediction model was implemented using a statistical model and an artificial intelligence model for the prediction of solar power generation. In addition, the results of prediction accuracy were compared and analyzed, and the revenue from the settlement amount of the renewable energy generation forecasting system was estimated.