• Resources Recycling
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• v.15 no.4 s.72
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• pp.19-26
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• 2006

The preparation of Ag nano-powder from aqueous silver nitrate solution, which would be available for the recycling of silver bearing wastes, was investigated by a reductive precipitation reaction using hydrazine hydrate as a reducing agent. Silver solution was prepared by dissolving silver nitrate with distilled water, and then the dispersant, Tamol NN8906 or Tween 20, was also mixed to avoid the agglomeration of particles during the reductive reaction followed by the addition of hydrazine hydrate to prepare Ag nano-particles. Ag particles obtained from the reduction reaction from silver solution were characterized using the particle size analyzer and TEM to determine the particle size distribution and morphology. It was found that about 100% excess of hydrazine hydrate was required to reduce completely silver ions in the solution. Ag powders with very narrow distribution could be obtained when Tamol NN8906 was used as the dispersant. In case of Tween 20, the particle size distribution showed typically the bimodal or multimodal distribution and the morphology of Ag particles was found to be irregular shape in both cases.

• Korean Journal of Medicinal Crop Science
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• v.12 no.5
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• pp.372-377
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• 2004

Scopolia parviflora Nakai, a rare and endangered species, is the sole plant producing tropane alkaloids (TA) among the Korean native species. In order to enhance TA productivity the SP72 root line was selected by screening 100 of root line, and the optimal culture media for root growth and TA production were investigated with the SP72 roots. Based on the several media, SH and 2B5 medium were determined as growth medium and White and NN medium as production medium. Among the four combinations of two-stage culture, 2BN (2B5 as growth medium plus NN as production medium) showed more enhanced root growth and TA production as compared with production media of White and NN medium and growth media of SH and 2B5 medium, respectively. However, bubble column bioreactor (BCB) cultures applying two-stage culture did not reveal the effective results despite of the each successful operation of two-stage culture in conical flasks and BCB cultures.

• Journal of Korean Society of Forest Science
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• v.101 no.3
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• pp.374-380
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• 2012

The k-Nearest Neighbor (kNN) technique is popularly applied to assess forest resources at the county level and to provide its spatial information by combining large area forest inventory data and remote sensing data. In this study, two approaches such as distance-weighting and stratification of training dataset, were compared to improve kNN-based forest growing stock estimates. When compared with five distance weights (0 to 2 by 0.5), the accuracy of kNN-based estimates was very similar ranged ${\pm}0.6m^3/ha$ in mean deviation. The training dataset were stratified by horizontal reference area (HRA) and forest cover type, which were applied by separately and combined. Even though the accuracy of estimates by combining forest cover type and HRA- 100 km was slightly improved, that by forest cover type was more efficient with sufficient number of training data. The mean of forest growing stock based kNN with HRA-100 and stratification by forest cover type when k=7 were somewhat underestimated ($5m^3/ha$) compared to statistical yearbook of forestry at 2011.

• Journal of the Korean Society for information Management
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• v.23 no.4 s.62
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• pp.277-294
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• 2006

This paper examines a level of categorization performance in a real-life collection of abstract articles in the fields of science and technology, and tests the optimal size of documents per category in a training set using a kNN classifier. The corpus is built by choosing categories that hold more than 2,556 documents first, and then 2,556 documents per category are randomly selected. It is further divided into eight subsets of different size of training documents : each set is randomly selected to build training documents ranging from 20 documents (Tr-20) to 2,000 documents (Tr-2000) per category. The categorization performances of the 8 subsets are compared. The average performance of the eight subsets is 30% in $F_1$ measure which is relatively poor compared to the findings of previous studies. The experimental results suggest that among the eight subsets the Tr-100 appears to be the most optimal size for training a km classifier In addition, the correctness of subject categories assigned to the training sets is probed by manually reclassifying the training sets in order to support the above conclusion by establishing a relation between and the correctness and categorization performance.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.9
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• pp.5007-5010
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• 2013

Heat shock proteins are molecular chaperones that may be constitutively present in cells protecting them from various stresses, such as extreme temperature, anoxia or chemical agents. Cervical cancer is the second most prevalent malignancy of women. In this study, we analyzed the expression of Hsp27 by immunohistochemistry in cervical intraepithelial lesions of Brazilian women, along with samples from non neoplasic lesions (NN). Cervical intraepithelial neoplasia I (CIN I), II (CIN II) and III (CIN III)/in situ carcinoma and squamous cell carcinoma (SCC) were included. Immunostaining was observed in 30 (100%) samples of NN, 46 (92%) in CIN I, 50 (100%) in CIN II, 52 (98.11%) in CIN III/CIS, and 46 (98.11%) in SCC. In group NN Hsp27 immunostaining was heterogeneous, more intense in basal and parabasal layers of the epithelium and less or absent in the intermediate and superficial layer. The majority of the samples of CIS and SCC presented strong staining in all epithelial layers. Metaplasic cells, when present, were strongly stained. In this study, Hsp27 protein was found to be commonly expressed in cervical epithelial cells.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.226-228
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• 2021

We propose a fall recognition system using the Pose Detection of Google ML kit using video data. Using the Pose detection algorithm, 33 three-dimensional feature points extracted from the body are used to recognize the fall. The algorithm that recognizes the fall by analyzing the extracted feature points uses k-NN. While passing through the normalization process in order not to be influenced in the size of the human body within the size of image and image, analyzing the relative movement of the feature points and the fall recognizes, thirteen of the thriteen test videos recognized the fall, showing an 100% success rate.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1247-1251
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• 2016

This study aims to investigate the effect of sampling frequency to the time domain and frequency domain analysis of pulse rate variability (PRV). Typical time domain variables - AVNN, SDNN, SDSD, RMSSD, NN50 count and pNN50 - and frequency domain variables - VLF, LF, HF, LF/HF, Total Power, nLF and nHF - were derived from 7 down-sampled (250 Hz, 100 Hz, 50 Hz, 25 Hz, 20 Hz, 15 Hz, 10 Hz) PRVs and compared with the result of heart rate variability of 10 kHz-sampled electrocardiogram. Result showed that every variable of time domain analysis of PRV was significant at 25 Hz or higher sampling frequency. Also, in frequency domain analysis, every variable of PRV was significant at 15 Hz or higher sampling frequency.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.109-117
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• 2017

The purpose of the study aimed to explore the possibility for natural preservative convergence materials by examining the antioxidant activity and antimicrobial effect of 19 wild plants in Jiri Mountain. Total polyphenols were higher in the order of CS(Camellia sinensis L., 87.9 mg GAE/ g), MP(Mentha piperascens Holmes., 85.1 mg), NN(Nelumbo nucifera Gaertn., 65.0 mg) and PD(Pinus densiflora Siebold & Zucc., 52.8 mg). Total flavonoids were high in NN(25.7 mg QUE / g) and MP (25.4 mg QUE / g). CS(58.1%), NN(47.9%), and MP(40.6%) showed high ABTS radical scavenging ability and the result was similar in DPPH radical scavenging ability. The extracts of HC(Hemerocallis coreana Nakai.), PD, and CO(Cornus officinalis Siebold et Zucc.) showed the highest inhibitory effect on the growth of E. coli. The extracts of PK(Pulsatilla koreana Nakai ex Nakai.), SC(Saururus chinensis Baill.), and MC(Smilax china L.) completely inhibited the proliferation of S. aureus, showing the possibility to be developed as natural preservatives and disinfectants.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.100-103
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• 2021

Most companies study why employees resign their jobs to prevent the outflow of excellent human resources. To obtain the data needed for the study, employees are interviewed or surveyed before resignation. However, it is difficult to get accurate results because employees do not want to express their opinions that may be disadvantageous to working in a survey. Meanwhile, according to the data released by the Korea Labor Institute, the greater the difference between the minimum level of education required by companies and the level of employees' academic background, the greater the tendency to resign jobs. Therefore, based on these data, in this study, we would like to predict whether employees will leave the company based on data such as major, education level and company type. We generate four kinds of resignation prediction models using Decision Tree, XGBoost, kNN and SVM, and compared their respective performance. As a result, we could identify various factors that were not covered in previous study. It is expected that the resignation prediction model help companies recognize employees who intend to leave the company in advance.

• Journal of Biomedical Engineering Research
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• v.37 no.2
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• pp.61-67
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• 2016

This paper presented a method for random forest based the arrhythmia classification using both heart rate (HR) and heart rate variability (HRV) features. We analyzed the MIT-BIH arrhythmia database which contains half-hour ECG recorded from 48 subjects. This study included not only the linear features but also non-linear features for the improvement of classification performance. We classified abnormal ECG using mean_NN (mean of heart rate), SD1/SD2 (geometrical feature of poincare HRV plot), SE (spectral entropy), pNN100 (percentage of a heart rate longer than 100 ms) affecting accurate classification among combined of linear and nonlinear features. We compared our proposed method with Neural Networks to evaluate the accuracy of the algorithm. When we used the features extracted from the HRV as an input variable for classifier, random forest used only the most contributed variable for classification unlike the neural networks. The characteristics of random forest enable the dimensionality reduction of the input variables, increase a efficiency of classifier and can be obtained faster, 11.1% higher accuracy than the neural networks.