• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.1-10
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• 2022

Vegetables such as cabbage are greatly affected by natural disasters, so price fluctuations increase due to disasters such as heavy rain and disease, which affects the farm economy. Various efforts have been made to predict the price of agricultural products to solve this problem, but it is difficult to predict extreme price prediction fluctuations. In this study, cabbage prices were analyzed using the ensemble Voting technique, a method of determining the final prediction results through various classifiers by combining a single classifier. In addition, the results were compared with LSTM, a time series analysis method, and XGBoost and RandomForest, a boosting technique. Daily data was used for price data, and weather information and price index that affect cabbage prices were used. As a result of the study, the RMSE value showing the difference between the actual value and the predicted value is about 236. It is expected that this study can be used to select other time series analysis research models such as predicting agricultural product prices

• Journal of KIISE
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• v.42 no.3
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• pp.367-378
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• 2015

This study was directed at the design of a hybrid algorithm for competition relation extraction. Previous works on relation extraction have relied on various lexical and deep parsing indicators and mostly utilize only the machine learning method. We present a new algorithm integrating machine learning with various filtering methods. Some simple but useful features for competition relation extraction are also introduced, and an optimum feature set is proposed. The goal of this paper was to increase the precision of competition relation extraction by combining supervised learning with various filtering methods. Filtering methods were employed for classifying compete relation occurrence, using distance restriction for the filtering of feature pairs, and classifying whether or not the candidate entity pair is spam. For evaluation, a test set consisting of 2,565 sentences was examined. The proposed method was compared with the rule-based method and general relation extraction method. As a result, the rule-based method achieved positive precision of 0.812 and accuracy of 0.568, while the general relation extraction method achieved 0.612 and 0.563, respectively. The proposed system obtained positive precision of 0.922 and accuracy of 0.713. These results demonstrate that the developed method is effective for competition relation extraction.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.119-137
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• 2022

Recent recommendation system studies apply various deep learning models to represent user and item interactions better. One of the noteworthy studies is ONCF(Outer product-based Neural Collaborative Filtering) which builds a two-dimensional interaction map via outer product and employs CNN (Convolutional Neural Networks) to learn high-order correlations from the map. However, ONCF has limitations in recommendation performance due to the problems with CNN and the absence of side information. ONCF using CNN has an inductive bias problem that causes poor performances for data with a distribution that does not appear in the training data. This paper proposes to employ a Vision Transformer (ViT) instead of the vanilla CNN used in ONCF. The reason is that ViT showed better results than state-of-the-art CNN in many image classification cases. In addition, we propose a new architecture to reflect side information that ONCF did not consider. Unlike previous studies that reflect side information in a neural network using simple input combination methods, this study uses an independent auxiliary classifier to reflect side information more effectively in the recommender system. ONCF used a single latent vector for user and item, but in this study, a channel is constructed using multiple vectors to enable the model to learn more diverse expressions and to obtain an ensemble effect. The experiments showed our deep learning model improved performance in recommendation compared to ONCF.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.1
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• pp.1-9
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• 2013

This paper propose a human action recognition method that uses bag-of-features (BoF) based on CS-LBP (center-symmetric local binary pattern) and a spatial pyramid in addition to the random forest classifier. To construct the BoF, an image divided into dense regular grids and extract from each patch. A code word which is a visual vocabulary, is formed by k-means clustering of a random subset of patches. For enhanced action discrimination, local BoF histogram from three subdivided levels of a spatial pyramid is estimated, and a weighted BoF histogram is generated by concatenating the local histograms. For action classification, a random forest, which is an ensemble of decision trees, is built to model the distribution of each action class. The random forest combined with the weighted BoF histogram is successfully applied to Standford Action 40 including various human action images, and its classification performance is better than that of other methods. Furthermore, the proposed method allows action recognition to be performed in near real-time.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.192-197
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• 2012

With the advent of DNA microarray technologies, researches for disease diagnosis has been actively in progress. In typical experiments using microarray data, problems such as the large number of genes and the relatively small number of samples, the inherent measurement noise and the heterogeneity across different samples are the cause of the performance decrease. To overcome these problems, a new method using functional modules (e.g. signaling pathways) used as markers was proposed. They use the method using an activity of pathway summarizing values of a member gene's expression values. It showed better classification performance than the existing methods based on individual genes. The activity calculation, however, used in the method has some drawbacks such as a correlation between individual genes and each phenotype is ignored and characteristics of individual genes are removed. In this paper, we propose a method based on the ensemble classifier. It makes weak classifiers based on feature vectors using subsets of genes in selected pathways, and then infers the final classification result by combining the results of each weak classifier. In this process, we improved the performance by minimize the search space through a filtering process using gene-gene interaction information and the mRMR filter. We applied the proposed method to a classifying the lung cancer, it showed competitive classification performance compared to existing methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1111-1116
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• 2009

In this paper, we discuss distortion-tolerant pattern recognition using computational integral imaging reconstruction. Three-dimensional object information is captured by the integral imaging pick-up process. The captured information is numerically reconstructed at arbitrary depth-levels by averaging the corresponding pixels. We apply Fisher linear discriminant analysis combined with principal component analysis to computationally reconstructed images for the distortion-tolerant recognition. Fisher linear discriminant analysis maximizes the discrimination capability between classes and principal component analysis reduces the dimensionality with the minimum mean squared errors between the original and the restored images. The presented methods provide the promising results for the classification of out-of-plane rotated objects.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.6
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• pp.1060-1068
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• 2002

Compound continuously variable transmission(CVT) mechanisms are proposed, which can offer a backward mode, a geared neutral, an underdrive mode and an overdrive mode. They are composed of a 2K-H I type differential gear unit, a V-belt type continuously variable unit(CVU), a few friction clutches and gears, and not required of a starting device as a torque converter. Compound CVT mechanisms developed here present two distinct operating modes which are a power circulation mode and a power split mode. The transition of two modes takes place at the particular CVU speed ratio. For these CVT mechanisms, performance analysis related to speed ratio, power ratio and efficiency are executed and proven by experimental studies.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.799-802
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• 2002

Continuously variable transmission(CVT) mechanisms considered here combine the functions of a K-H-V type differential gear unit and a V-belt type continuously variable unit(CVU). As combining the functions of a K-H-V type differential gear unit and a V-belt type CVU, 24 different mechanisms are presented. Some useful theoretical formula related to speed ratio, power flow and efficiency are derived and analyzed. These mechanisms have many advantages which are the decrease of CVT size, the increase of overall efficiency, the extension of speed ratio range, and the generation of geared neutral.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.4
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• pp.170-178
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• 2004

Continuously variable transmission (CVT) mechanisms considered here are input coupled types that combine the functions of a 2K-H II type differential gear and a V-belt type continuously variable unit (CVU). For the 8 different mechanisms, 4 of them are power-circulation modes while the other 4 are power-split modes, various performance analysis (speed ratios, power flows, divisions of power transmission in a differential gear and a CVU, and theoretical efficiencies) are performed to vary design parameters. Experimental studies are executed to validate fundamental relations (speed ratios, power flows, efficiencies, occurrence of geared neutral). Some useful characteristics associated with performance also are discussed in the mechanisms.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.739-742
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• 1997

Continuously variable transmission(CVT) mechanisms are proposed, which can offer a backward mode, a geared neutral, an underdrive mode and an overdriver mode. They are not required of a starting device as a torque converter. CVT mechanisms developed here present two distinct operating modes which are a power circulation mode and a power split mode. The transition of two modes takes place at the particular CVU speed ratio. For these CVT mechanisms, performance analysis related to speed ratio, power ratio and theoretical efficiency are executed.