• Journal of Life Science
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• v.20 no.8
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• pp.1241-1248
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• 2010

The solvent extracts of Nelumbo nucifera G. were investigated for antioxidant activities, whitening and anti-wrinkle effects to apply as a functional ingredient in cosmetic products. For their industrial application, the cosmetic products were also prepared with advanced formulation techniques such as W/O/W multiple emulsion. Total phenolic and flavonoids contents increased in Nelumbo nucifera G.-Leaf (NN-L). The electron donating ability of Nelumbo nucifera G.-Flower (NN-F) or Nelumbo nucifera G.-Leaf (NN-L) extracts were above 85% at a concentration of 500 ppm. The superoxide dismutase (SOD)-like activity of Nelumbo nucifera G. (NN-L) extracts was about 60% at a concentration of 1,000 ppm. The xanthine oxidase inhibitory effect of NN-L extract was higher than that of NN-F and NN-S extracts. The tyrosinase inhibitory effect, which is related to skin-whitening, was 36% in NN-F at 1,000 ppm. For anti-wrinkle effect, the elastase inhibition activity of NN-L was about 30% at 1,000 ppm. The results of stability test showed that W/O/W multiple emulsion (ME) containing Nelumbo nucifera G. extracts. The electron donating ability of the ME containing NN-F and NN-L were about 60% at a concentration of 100 ppm. The superoxide dismutase (SOD)-like activity of the ME containing NN-L was 30% at 1,000 ppm. The tyrosinase inhibitory effect, which is related to skin-whitening, was 34% in the ME containing NN-F at 1,000 ppm. In anti-wrinkle effect, the elastase inhibition activity of the ME containing NN-L was about 55% at 1,000 ppm.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.70-72
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• 2005

데이터베이스 관리자는 효과적인 데이터베이스 관리를 위해 워크로드 특성을 잘 알아야 한다. 워크로드 특성은 데이터베이스 응용분야에 따라 다르며, 데이터베이스 환경에서 하나 이상의 응용 분야가 수행될 수 있다. 복합적인 데이터베이스 응용 분야 때문에, 관리자가 데이터베이스 시스템에서 발생하는 워크로드를 식별하기가 더욱 어려워졌다. 복합적인 데이터베이스 응용 분야의 효과적인 데이터베이스 관리를 수행하기 위해 워크로드를 식별할 수 있는 방법이 요구된다. 이를 위해, 본 연구는 TPC-C와 TPC-W 성능평가의 워크로드와 두 성능평가의 혼합된 워크로드들을 생성하여 워크로드 식별을 수행하였다. 워크로드 식별은 퍼지 k-NN 알고리즘을 수정하여 진행하였다. 수정된 k-NN 알고리즘은 혼합 비율에 따라 시험 워크로드 데이터와 훈련 워크로드 데이터간의 워크로드 식별 실험에 사용되었고, 분류를 위한 k-NN, 퍼지 k-NN, 분산 가중치 퍼지 k-NN 알고리즘의 결과와 비교되었다. 수정된 k-NN 알고리즘은 다른 알고리즘보다 k 인자에 따른 변동과 오차율이 감소하여 워크로드 식별에 더 적합함을 보였다. 본 논문의 결과는 복합된 데이터베이스 응용 분야의 특성을 보이는 데이터베이스 환경에서 워크로드 식별 정보를 창조하여 융통성 있는 튜닝 기법을 고려하는데 기여한다.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.260-262
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• 2004

Spatial database system provides many query types and most of them are required frequent disk I/O and much CPU time. k-NN search is to find k-th closest object from the query point and up to now, several k-NN search methods have been proposed. Among these, MINMAX distance method has an aim not to visit unnecessary node by applying pruning technique. But this method access more disk than necessary while pruning unnecessary node. In this paper, we propose new k-NN search algorithm based on density of object. With this method, we predict the radius to be expected to contain k-NN object using density of data set and search those objects within this radius and then adjust radius if failed. Experimental results show that this method outperforms the previous MINMAX distance method. This algorithm visit fewer disks than MINMAX method by the factor of maximum $22\%\;and\;average\;6\%.$

• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.1
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• pp.22-26
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• 1983

P+N and P+NN+ solar cells with the area of 3.36 $\textrm{cm}^2$ were fabricated by thermal diffusion. Under the light intensity of 100 mW/$\textrm{cm}^2$, total area(active area) conversion efficiency was 13.4%(14.7%) for P+N cell fabricated by 15 min boron predeposition at 94$0^{\circ}C$ and 20 min annealing at 80$0^{\circ}C$, and 14.3%(15.6%) for P+NN+ cell processed by 15 min boron predeposition at 94$0^{\circ}C$ and 50 min annealing at 80$0^{\circ}C$ after 20 min back phosphorus diffusion at 1,05$0^{\circ}C$. The minority carrier lifetime in bulk of P+NN+ cells was increased about 2~3 times comparing with P+N cells because of guttering and BSF effect due to back phosphorus doping. The methods used for efficiency improvement were AR coating, Ag electroplating, back doping and fine grid pattern as well as the control of front doping profile.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.217-227
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• 2022

In order to provide a location-based services regardless of indoor or outdoor space, it is important to provide position information of the terminal regardless of location. Among the wireless/mobile communication resources used for this purpose, Long Term Evolution (LTE) signal is a representative infrastructure that can overcome spatial limitations, but the positioning method based on the location of the base station has a disadvantage in that the accuracy is low. Therefore, a fingerprinting technique, which is a pattern recognition technology, has been widely used. The simplest yet widely applied algorithm among Fingerprint positioning technologies is k-Nearest Neighbors (kNN). However, in the kNN algorithm, it is difficult to find the optimal K value with the lowest positioning error for each location to be estimated, so it is generally fixed to an appropriate K value and used. Since the optimal K value cannot be applied to each estimated location, therefore, there is a problem in that the accuracy of the overall estimated location information is lowered. Considering this problem, this paper proposes a technique for adaptively varying the K value by using a Convolutional Neural Network (CNN) model among Artificial Neural Network (ANN) techniques. First, by using the signal information of the measured values obtained in the service area, an image is created according to the Physical Cell Identity (PCI) and Band combination, and an answer label for supervised learning is created. Then, the structure of the CNN is modeled to classify K values through the image information of the measurements. The performance of the proposed technique is verified based on actual data measured in the testbed. As a result, it can be seen that the proposed technique improves the positioning performance compared to using a fixed K value.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.653-657
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• 2004

In this study, PD(partial discharge) signals which occurrs at stator coil of traction Motor are acquired. these data are used for classifying the PD sources. W(Neural Network) has recently applied to classify the PB pattern. The PD data are used for the learning process to classify PD sources. The PD data come from normal specimen and defective specimens such as internal void discharges, slot discharges and surface discharges. PD distribution parameters are calculated from a set of the data, which is used to realize diagnostic algorithm. NN which applies distribution parameters is useful to classify the PD patterns of defective sources generating in stator coil of traction motor.

• Animal cells and systems
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• v.16 no.2
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• pp.121-126
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• 2012

In this study, a neural network (NN) modeling approach has been used to predict the mechanical and geometrical behaviors of mouse embryo cells. Two NN models have been implemented. In the first NN model dimple depth (w), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were used as inputs of the model while indentation force (f) was considered as output. In the second NN model, indentation force (f), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were considered as inputs of the model and dimple depth was predicted as the output of the model. In addition, sensitivity analysis has been carried out to investigate the influence of the significance of input parameters on the mechanical behavior of mouse embryos. Experimental data deduced by Fl$\ddot{u}$ckiger (2004) were collected to obtain training and test data for the NN. The results of these investigations show that the correlation values of the test and training data sets are between 0.9988 and 1.0000, and are in good agreement with the experimental observations.

• Computers and Concrete
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• v.30 no.2
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• pp.99-111
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• 2022

In this study, deep learning and k-Nearest Neighbor (kNN) models were used to estimate the sorptivity and freeze-thaw resistance of self-compacting mortars (SCMs) having binary and ternary blends of mineral admixtures. Twenty-five environment-friendly SCMs were designed as binary and ternary blends of fly ash (FA) and silica fume (SF) except for control mixture with only Portland cement (PC). The capillary water absorption and freeze-thaw resistance tests were conducted for 91 days. It was found that the use of SF with FA as ternary blends reduced sorptivity coefficient values compared to the use of FA as binary blends while the presence of FA with SF improved freeze-thaw resistance of SCMs with ternary blends. The input variables used the models for the estimation of sorptivity were defined as PC content, SF content, FA content, sand content, HRWRA, water/cementitious materials (W/C) and freeze-thaw cycles. The input variables used the models for the estimation of sorptivity were selected as PC content, SF content, FA content, sand content, HRWRA, W/C and predefined intervals of the sample in water. The deep learning and k-NN models estimated the durability factor of SCM with 94.43% and 92.55% accuracy and the sorptivity of SCM was estimated with 97.87% and 86.14% accuracy, respectively. This study found that deep learning model estimated the sorptivity and durability factor of SCMs having binary and ternary blends of mineral admixtures higher accuracy than k-NN model.

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1876-1878
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• 2003

The recognition of PD(Partial Discharge) phenomenon is useful for classification of defects. The distribution of stochastic parameters which consisted of those PD pulses data and pulses train can show discriminable characteristics of PD sources. But it is not sufficient to discriminate among to PD sources. In this paper, we suggests that classification method of PD source by NN(Neural Networks) are good tools for differentiate of those. The learning scheme of NN is (Back Propagation learning algorithm(BP).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1872-1879
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• 2022

Recently, with the development of artificial intelligence technology, research to use artificial intelligence to detect hacking attacks is being actively conducted. However, the fact that security data is a representative imbalanced data is recognized as a major obstacle in composing the learning data, which is the key to the development of artificial intelligence models. Therefore, in this paper, we propose a W-VAE oversampling technique that applies VAE, a deep learning generation model, to data extraction for oversampling, and sets the number of oversampling for each class through weight calculation using K-NN for sampling. In this paper, a total of five oversampling techniques such as ROS, SMOTE, and ADASYN were applied through NSL-KDD, an open network security dataset. The oversampling method proposed in this paper proved to be the most effective sampling method compared to the existing oversampling method through the F1-Score evaluation index.