• Journal of Environmental Health Sciences
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• v.10 no.1
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• pp.87-92
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• 1984

Extraction method of ginseng saponin by Extrelut column was studied as compared with that by phase separation. The results obtained were as follows: 1. Extraction time consumed by Extrelut column was 2 ~ 3 hours and much shorter as compared with that by phase separation. 2. Recovery rates Of ginsenoside by Extrelut column method were 97.8 ~ 106.1% and much higher as compared with those by phase separation method.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.361-364
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• 2008

It is well known that combining spatial and spectral information can improve land use classification from satellite imagery. High spatial resolution classification has a limitation when only using the spectral information due to the complex spatial arrangement of features and spectral heterogeneity within each class. Therefore, extracting the spatial information is one of the most important steps in high resolution satellite image classification. In this paper, we propose a new spatial feature extraction method. The extracted features are integrated with spectral bands to improve overall classification accuracy. The classification is achieved by applying a Support Vector Machines classifier. In order to evaluate the proposed feature extraction method, we applied our approach to KOMPSAT-2 data and compared the result with the other methods.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.41 no.2
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• pp.59-65
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• 2015

The extraction of third molars is a common task carried out at dental/surgery clinics. Postoperative pain is one of the two most common complications of this surgery, along with dry socket. Knowledge of the frequent risk factors of this complication is useful in determining high-risk patients, planning treatment, and preparing the patients mentally. Since the risk factors for postoperative pain have never been summarized before while the risk factors for dry socket have been highly debated, this report summarizes the literature regarding the common predictors of postextraction pain. Except for surgical difficulty and the surgeon's experience, the influences of other risk factors (age, gender and oral contraceptive use) were rather inconclusive. The case of a female gender or oral contraceptive effect might mainly be associated with estrogen levels (when it comes to dry socket), which can differ considerably from case to case. Improvement in and unification of statistical and diagnostic methods seem necessary. In addition, each risk factor was actually a combination of various independent variables, which should instead be targeted in more comprehensive studies.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.13-20
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• 2016

Biometric authentication for account-based mobile payment continues to gain attention because of improvements on sensors that can collect biometric information. We propose an enhanced method for mobile payment security based on biometric authentication. In this mobile payment system, the communication between the user and the relying party is based on public key infrastructure. This method secures both the key and the biometric template in the user side using fuzzy vault biometric cryptosystems, which is based on non-random chaff point generator. In this paper, we consider an important process for the common fuzzy vault system, that is, the feature extraction method. We evaluate various feature extraction methods to enhance the accurate performance of the system.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.525-525
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• 2000

In this paper, we present recursive algorithms for state space model identification using subspace extraction via Schur complement. It is shown that an estimate of the extended observability matrix can be obtained by subspace extraction via Schur complement. A relationship between the least squares residual and the Schur complement matrix obtained from input-output data is shown, and the recursive algorithms for the subspace-based state-space model identification (4SID) methods are developed. We also proposed the above algorithm for an instrumental variable (IV) based 4SID method. Finally, a numerical example of the application of the algorithms is illustrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4573-4584
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• 2015

The traditional feature extraction algorithms for recognition of communication signals can hardly realize the balance between computational complexity and signals' interclass gathered degrees. They can hardly achieve high recognition rate at low SNR conditions. To solve this problem, a novel feature extraction algorithm based on Holder coefficient was proposed, which has the advantages of low computational complexity and good interclass gathered degree even at low SNR conditions. In this research, the selection methods of parameters and distribution properties of the extracted features regarding Holder coefficient theory were firstly explored, and then interval gray relation algorithm with improved adaptive weight was adopted to verify the effectiveness of the extracted features. Compared with traditional algorithms, the proposed algorithm can more accurately recognize signals at low SNR conditions. Simulation results show that Holder coefficient based features are stable and have good interclass gathered degree, and interval gray relation classifier with adaptive weight can achieve the recognition rate up to 87% even at the SNR of -5dB.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.2
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• pp.31-38
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• 2011

Skin color is a very important information for an automatic face recognition. In this paper, we proposed a skin region extraction method using the MLP(Multi-Layer Perceptron) and skin color model. We use the adaptive lighting compensation technique for improved performance of skin region extraction. Also, using an preprocessing filter, normally large areas of easily distinct non-skin pixels, are eliminated from further processing. Experimental results show that the proposed method has better performance than the conventional methods, and reduces processing time by 31~49% on average.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.231-240
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• 2001

Conventional expert systems has been criticized due to its lack of capability to adapt to the changing decision-making environments. In literature, many methods have been proposed to make expert systems more environment-adaptive by incorporating fuzzy logic and neural networks. The objective of this paper is to propose a new approach to building a self-evolving expert system inference mechanism by integrating fuzzy neural network and fuzzy rule extraction technique. The main recipe of our proposed approach is to fuzzify the training data, train them by a fuzzy neural network, extract a set of fuzzy rules from the trained network, organize a knowledge base, and refine the fuzzy rules by applying a pruning algorithm when the decision-making environments are detected to be changed significantly. To prove the validity, we tested our proposed self-evolving expert systems inference mechanism by using the bankruptcy data, and compared its results with the conventional neural network. Non-parametric statistical analysis of the experimental results showed that our proposed approach is valid significantly.

• Plant Resources
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• v.6 no.3
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• pp.221-226
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• 2003

To improve industrial scale extraction method for extraction of icariin from Epimedium koreanum Nakai, the yields under different extracting conditions such as solvent, temperature, duration and solvent to plant material weight ratio were compared. Regarding extracting solution, highest extracts and icariin yield could be achieved when 10% EtOH was used. In case of plant material to extracting solvent ratio, no significant differences could be observed from 1/10 to 1/50, indicating 1/10 was the most efficient. Extracting temperature significantly affected extracts and icariin yields in that 9$0^{\circ}C$ increased the collected extracts and icariin contents up to 29.6% and 0.76%, respectively, compared to 27.2%, 0.33% at 7$0^{\circ}C$. The yield of extracts was less dependent upon extracting temperature compared to icariin yield. Regarding extraction time, 4 hr and 6 hr resulted in high extracts and icariin yield, respectively. We found extracting Epimedium koreanum Nakai in 10 times volume of 10% EtOH for 4 and 6 hr at 9$0^{\circ}C$ seem to be relatively efficient methods for extracts and icariin, respectively.

• ETRI Journal
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• v.41 no.6
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• pp.750-759
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• 2019

Presently, the extraction of hand-crafted features is still the dominant method in radar emitter recognition. To solve the complicated problems of selection and updation of empirical features, we present a novel automatic feature extraction structure based on deep learning. In particular, a convolutional neural network (CNN) is adopted to extract high-level abstract representations from the time-frequency images of emitter signals. Thus, the redundant process of designing discriminative features can be avoided. Furthermore, to address the performance degradation of a single platform, we propose the construction of an ensemble learning-based architecture for multi-platform fusion recognition. Experimental results indicate that the proposed algorithms are feasible and effective, and they outperform other typical feature extraction and fusion recognition methods in terms of accuracy. Moreover, the proposed structure could be extended to other prevalent ensemble learning alternatives.