• Bulletin of the Korean Chemical Society
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• v.34 no.3
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• pp.759-762
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• 2013

Four prominent features of Titan's haze are found within the '3.4-${\mu}m$' absorption to be uniform with recent vertically resolved Cassini/VIMS spectra. These are absorptions at 2998 $cm^{-1}$ (3.34 ${\mu}m$), 2968 $cm^{-1}$ (3.37 ${\mu}m$), 2927 $cm^{-1}$ (3.42 ${\mu}m$), and 2882 $cm^{-1}$ (3.47 ${\mu}m$). A detailed fitting suggests that the 2998 $cm^{-1}$ feature could originate from amorphous acetonitrile ($CH_3CN$) carrying about 25% of integrated optical depth; the remaining features, which account for 75% of the integrated optical depth, could arise from a distinct triplet (C-H stretching) structure of radiolyzed hydrocarbons. An additional feature was possibly evidenced at altitudes higher than 300 km and attributable to 'polymer-capped' methane ($CH_4$), significantly constraining the chemical composition of organic haze layers under Titan's active radiation field.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.5
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• pp.791-797
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• 2008

The officer of the deck(OOD) may sometimes have to carry out lookout as well as handling of auto pilot without a quartermaster at sea. The purpose of this paper is to develop the ship's auto pilot control module using speech recognition in order to reduce the potential risk of one man bridge system. The feature parameters predicting the OOD's intention was extracted from the sample wheel orders written in SMCP(IMO Standard Marine Communication Phrases). We designed a pre-recognition procedure which could make some candidate words using DTW(Dynamic Time Warping) algorithm, a post-recognition procedure which made a final decision from the candidate words using the feature parameters. To evaluate the effectiveness of these procedures the experiment was conducted with 500 wheel orders.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.06a
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• pp.317-318
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• 2013

International Hydrography Organization has published S-100 Universal Hydrographic Data Model to support use of various hydrographic data for navigational safety. In the S-100 standards, it is possible to manage hydrographic data and apply various application field by introducing the concept of registry and its register. In this study, the S-100 standard based product specification in the field of maritime safety is developed by designing application schema according to general feature model defined in the S-100 standard, and feature catalogue is produced through simple registry.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.155-167
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• 2007

There are four types of human beings according to the Sasang Typology, Oriental medical doctors frequently prescribe healthcare information and treatment depending on one's type, The feature ratios (Table 1) on the human face are the most important criterions to decide which type a patient is. In this paper, we proposed a system to extract these feature ratios from the people's side face, There are two challenges in acquiring the feature ratio: one that selecting representative features; the other, that detecting region of interest from human profile facial image effectively and calculating the feature ratio accurately. In our system, an adaptive color model is used to separate human side face from background, and the method based on geometrical model is designed for region of interest detection. Then we present the error analysis caused by image variation in terms of image size and head pose, To verify the efficiency of the system proposed in this paper, several experiments are conducted using about 173 korean's left side facial photographs. Experiment results shows that the accuracy of our system is increased 17,99% after we combine the front face features with the side face features, instead of using the front face features only.

• Smart Media Journal
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• v.1 no.1
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• pp.36-41
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• 2012

Feature selection is the important technique of selecting a subset of relevant features for building robust pattern recognition systems. Various methods have been studied for feature selection from sequential search algorithms to stochastic algorithms. In this work, we adopted a Quantum-inspired Genetic Algorithm (QGA) which is based on the concept and principles of quantum computing such as Q-bits and superposition of state for feature selection. The performance of QGA is compared to that of the Conventional Genetic Algorithm (CGA) with respect to the classification rates and the number of selected features. The experimental result using UCI data sets shows that QGA is superior to CGA.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.473-476
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• 2005

This paper is proposed a robust various feature parameters in noise. Feature parameter MFCC(Mel Frequency Cepstral Coefficient) used in conventional speech recognition shows good performance. But, parameter transformed feature space that uses PCA(Principal Component Analysis)and ICA(Independent Component Analysis) that is algorithm transformed parameter MFCC's feature space that use in old for more robust performance in noise is compared with the conventional parameter MFCC's performance. The result shows more superior performance than parameter and MFCC that feature parameter transformed by the result ICA is transformed by PCA.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2036-2038
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• 2001

In this paper, we propose the neural network based emotion recognition method for intelligently recognizing the human's emotion using vision system. In the proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). Also, we use R,G,B(red, green, blue) color image data and the gray image data to get the highly trust rate of feature point extraction. For this, we propose an algorithm to extract four feature points (eyebrow, eye, nose, mouth) from the face image acquired by the color CCD camera and find some feature vectors from those. And then we apply back-prapagation algorithm to the secondary feature vector(position and distance among the feature points). Finally, we show the practical application possibility of the proposed method.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.177-184
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• 2021

All over the world, people are affected by many chronic diseases and medical practitioners are working hard to find out the symptoms and remedies for the diseases. Many researchers focus on the feature detection of the disease and trying to get a better health recommendation system. It is necessary to detect the features automatically to provide the most relevant solution for the disease. This research gives the framework of Health Recommendation System (HRS) for identification of relevant and non-redundant features in the dataset for prediction and recommendation of diseases. This system consists of three phases such as Pre-processing, Feature Selection and Performance evaluation. It supports for handling of missing and noisy data using the proposed Imputation of missing data and noise detection based Pre-processing algorithm (IMDNDP). The selection of features from the pre-processed dataset is performed by proposed ensemble-based feature selection using an expert's knowledge (EFS-EK). It is very difficult to detect and monitor the diseases manually and also needs the expertise in the field so that process becomes time consuming. Finally, the prediction and recommendation can be done using Support Vector Machine (SVM) and rule-based approaches.

• Journal of Distribution Science
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• v.19 no.1
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• pp.75-87
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• 2021

Purpose: The objective of this research is to identify the core aspects of persuasive factors influencing consumer's experiential value towards using Augmented Reality (AR) try-on feature while shopping cosmetic products online. The conceptual framework of this study is adopted and integrated from the theoretical study on how narrative experience, media richness, and presence affect the formation of experiential value in the augmented reality interactive technology (ARIT) process. Research design, data and methodology: The sample (n = 550) were collected from online and offline questionnaires by using stratified random sampling and purposive sampling methods. Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were used to analyze the data to confirm goodness-of-fit of the model and hypothesis testing. Results: The results indicated that media richness induced higher experiential value (consumer ROI, playfulness, service excellence and aesthetics), followed by narrative experience and presence towards using AR try-on feature. Conclusions: Consumer's experiential value towards using AR try-on feature when shopping cosmetic products online rely on media richness, narrative experience and presence respectively. Therefore, marketing practitioners are recommended to develop the feature design and content to be more useful, authentic, user-friendly and entertaining to better connect and provide confidence to consumers when shopping cosmetics online.

• Korean Journal of Materials Research
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• v.33 no.4
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• pp.164-174
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• 2023

The bandgap characteristics of semiconductor materials are an important factor when utilizing semiconductor materials for various applications. In this study, based on data provided by AFLOW (Automatic-FLOW for Materials Discovery), the bandgap of a semiconductor material was predicted using only the material's compositional features. The compositional features were generated using the python module of 'Pymatgen' and 'Matminer'. Pearson's correlation coefficients (PCC) between the compositional features were calculated and those with a correlation coefficient value larger than 0.95 were removed in order to avoid overfitting. The bandgap prediction performance was compared using the metrics of R² score and root-mean-squared error. By predicting the bandgap with randomforest and xgboost as representatives of the ensemble algorithm, it was found that xgboost gave better results after cross-validation and hyper-parameter tuning. To investigate the effect of compositional feature selection on the bandgap prediction of the machine learning model, the prediction performance was studied according to the number of features based on feature importance methods. It was found that there were no significant changes in prediction performance beyond the appropriate feature. Furthermore, artificial neural networks were employed to compare the prediction performance by adjusting the number of features guided by the PCC values, resulting in the best R² score of 0.811. By comparing and analyzing the bandgap distribution and prediction performance according to the material group containing specific elements (F, N, Yb, Eu, Zn, B, Si, Ge, Fe Al), various information for material design was obtained.