• 한국치위생학회지
• /
• 제19권6호
• /
• pp.1099-1107
• /
• 2019

Objectives: The purpose of this study was to investigate the changes in pH in the oral cavity using the probiotic intake method. Methods: A total of 109 participants were enrolled and randomly assigned to three groups. Participants in the control group did not ingest powdered probiotics, those in experimental group 1 ingested powdered probiotics by dissolving them on the tongue, and those in experimental group 2 dissolved powdered probiotics on the tongue and rinsed with water. pH values were measured 5 times in all. The significance of each group was examined by the Kruskal-Wallis test. The trend over time was expressed as a graph with groupwise means and confidence intervals, considering repeated measurement data. Results: A significant difference was found between the control group and experimental group 1 at two time-points, i.e., immediately after intake and 3 min after ingestion. As a result of the time trend, the pH value of experimental group 2 was smaller than that of experimental group 1, compared to the control group. Conclusions: Studies have shown that taking probiotics with water may help reduce changes in oral pH. Probiotics should be aware of live bacteria and provide consumers with more detailed information on proper dosage and precautions.

• 융합정보논문지
• /
• 제11권7호
• /
• pp.31-38
• /
• 2021

본 논문은 감성 분석에 사용되는 RNN 모델의 최적화를 얻기 위한 성능분석을 위하여 하이퍼파라미터 튜닝에 따른 손실과 정확도의 추이를 관찰하여 모델과의 상관관계를 연구하였다. 연구 방법으로는 시퀀셜데이터를 처리하는데 가장 최적화된 LSTM과 Embedding layer로 히든레이어를 구성한 후, LSTM의 Unit과 Batch Size, Embedding Size를 튜닝하여 각각의 모델에 대한 손실과 정확도를 측정하였다. 측정 결과, 손실은 41.9%, 정확도는 11.4%의 차이를 나타내었고, 최적화 모델의 변화추이는 지속적으로 안정적인 그래프를 보여 하이퍼파라미터의 튜닝이 모델에 지대한 영향을 미침을 확인하였다. 또한 3가지 하이퍼파라미터 중 Embedding Size의 결정이 모델에 가장 큰 영향을 미침을 확인하였다. 향후 이 연구를 지속적으로 이어나가 모델이 최적의 하이퍼파라미터를 직접 찾아낼 수 있는 알고리즘에 대한 연구를 지속적으로 이어나갈 것이다.

• Proceedings of the National Institute of Ecology of the Republic of Korea
• /
• 제5권3호
• /
• pp.76-85
• /
• 2024

The purpose of this study is to propose a new method of analysis focusing on interconnections between species rather than traditional biodiversity analysis, which represents ecosystems in terms of species and individual counts such as species diversity and species richness. This new approach aims to enhance our understanding of ecosystem networks. Utilizing data from the 4th National Natural Environment Survey (2014-2018), the following eight taxonomic groups were targeted for our study: herbaceous plants, woody plants, butterflies, Passeriformes birds, mammals, reptiles & amphibians, freshwater fishes, and benthonic macroinvertebrates. A co-occurrence frequency analysis was conducted using nationwide data collected over five years. As a result, in all eight taxonomic groups, the degree value represented by a linear regression trend line showed a slope of 0.8 and the weighted degree value showed an exponential nonlinear curve trend line with a coefficient of determination (R²) exceeding 0.95. The average value of the clustering coefficient was also around 0.8, reminiscent of well-known social phenomena. Creating a combination set from the species list grouped by temporal information such as survey date and spatial information such as coordinates or grids is an easy approach to discern species distributed regionally and locally. Particularly, grouping by species or taxonomic groups to produce data such as co-occurrence frequency between survey points could allow us to discover spatial similarities based on species present. This analysis could overcome limitations of species data. Since there are no restrictions on time or space, data collected over a short period in a small area and long-term national-scale data can be analyzed through appropriate grouping. The co-occurrence frequency analysis enables us to measure how many species are associated with a single species and the frequency of associations among each species, which will greatly help us understand ecosystems that seem too complex to comprehend. Such connectivity data and graphs generated by the co-occurrence frequency analysis of species are expected to provide a wealth of information and insights not only to researchers, but also to those who observe, manage, and live within ecosystems.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2004년도 하계학술대회 논문집 C
• /
• pp.1930-1932
• /
• 2004

SOM(Self Organizing Map) algorithm which has some advantages such as data accumulation ability and the degradation trend trace ability was compared with conventionally used BP(Back Propagation) algorithm. For the purpose, partial discharge data were acquired and analysed from the artificial defects in GIS. As a result, basically the pattern recognition rate of BP algorithm was found out to be better than that of SOM algorithm. However, SOM algorithm showed a great on-site-applicability such as ability of suggesting new-pattern-possibility. Therefore, through increasing pattern recognition rate it is possible to apply SOM algorithm to partial discharge analysis. Also, for the image processing method it is required the normalization of the PRPDA graph. However, due to the normalization both BP and SOM algorithm have shown worse results, so that it is required further study to solve the problem.

• Journal of the Korean Data and Information Science Society
• /
• 제22권1호
• /
• pp.19-27
• /
• 2011

온라인 사용자에게 선택의 어려움을 줄여주고 사용의도를 높이기 위해 만들어진 것이 추천시스템이다. 추천시스템은 정보검색과 정보필터링을 용이하게 하고, 정보 과잉의 문제를 해결하는 데에 많은 도움을 주고 있다. 본 연구의 목적은 웹 상점을 이용하는 사용자들의 클릭스트림 데이터를 분석하여 데이터 인접성의 차이를 확인하고, 이를 통해 상품추천을 제안하고자 하는 데에 있다. 본 연구에서 제안하는 추천시스템의 성과를 검증하기 위하여 실험을 통해 알아본 결과, 추천시스템 적용 전보다 적용 후에 사용자들의 구매 의도는 높아졌고 탐색시간은 줄어들었다.

• 센서학회지
• /
• 제25권4호
• /
• pp.291-296
• /
• 2016

The ideal chemical sensor must show the similar result under the same condition for accurate measurement of gases regardless of time. However, the actual responses of chemical sensors have been shown the lacks of repeatability and reproducibility because of the drift which has been caused by aging and pollution of the sensor and the environment change such as temperature and humidity. If the problems are not properly taken into considerations, the stability and reliability of the system using chemical sensors would be decreased. In this paper, we analyzed the sensor's drift and applied the three different compensation methods(DWT( Discrete Wavelets Transform), Baseline Manipulation, Internal Normalization) for reducing the effects of the drift in order to improve the stability and the reliability of short term of the chemical sensors. And in order to compare the results of the methods, the standard deviation was used as a criterion. The sensor drift was analyzed by a trend line graph. We applied the three methods to the successive data measured for three days and compared the results. As a result of comparison, the standard deviation of DWT showed lowest value. (Before compensation: 7.1219, DWT: 1.3644, Baseline Manipulation: 2.5209, Internal Normalization: 3.1425).

• The Korean Journal of Ecology
• /
• 제13권2호
• /
• pp.83-92
• /
• 1990

Two-dimensional analysis provides a comprehensive description of the structure, scales of pattern and directional components in a spatial data set. In spectral analysisi, four functions are illustrated,; the autocorrelation, the periodogram, the R-spectrum and the $\theta$ -spectrum. The R-spectrum and $\theta$ -spectrum function respectively summarize the periodogram in term of scale of pattern and directional components. Sampling is measured in the Naejang National Park area where the Daphniphyllum trees grow. 320 contiguous (15$\times$15)m plots are located along the transect and density of all trees over DBH 3 cm recorded respectively. 12 species of vascular plant are recorded in this survey area. The trend surface of density of all plant are estimated using polynomial regression and are exhibited in 3-dimensional graph and density contour map. Transformation to the corresponding polar spectrum from the periodogram emphasized the directional components and the scales to pattern. R-spectrum corresponding to the scale of pattern of periodogram showed a large peak 15.47 in the interval 9$\theta$-spectrum corresponding to directional components have two peaks 8.28 and 11.05 in the interval $35^{\circ}\theta <45^{\circ}and 125^{\circ}\theta< <135^{\circ}, respectively. Programs to compute all the analyses described in this study was obtained from Dr. Ranshow and was translated to BASIC by the author.

• Journal of the Korean Data and Information Science Society
• /
• 제25권4호
• /
• pp.893-901
• /
• 2014

기업에서는 매달 제품이 일정 수량만큼 판매되고, 이들 중 일부는 반환 또는 클레임이 제기된다. 본 연구에서는 이러한 품질보증 반환 데이터의 반환율을 그래프상에 어떻게 타점할 것인지 먼저 살펴본다. 이어서 이런 데이터는 좌측 및 우측 중도중단 데이터의 결합으로 생각할 수 있으므로 이런 데이터에 대해 와이블 분포 등을 적합시켜 신뢰성분석을 실시해본다. 마지막으로 좌측 중도중단 데이터의 경우 구체적인 반환시기를 알 수 있다면 좌측 중도중단 데이터는 고장 데이터가 되어, 이제는 우측 중도중단만 남게 되는데, 이때에도 와이블분포 등을 적합시켜 신뢰성분석을 실시해보고자 한다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
• /
• pp.240-242
• /
• 2005

It is well known that the PD (Partial Discharge) signals are generated if insulators have some defects such as voids in electrical facility and various PD detection methods are developed for preventing electrical troubles. So, an interest for the PD signals is higher and higher according to the high concern for the defects detection method of the aging electrical facility. When the equipment to detect PD signals installed at site and it works, a lot of noises flow in the equipment from surrounding situation and it will be mixed with original PD waveform. So we can not get the desired PD waveform. Therefore, there are many trial to reject or suppress the noise from the PD signals from long times ago. The greater of them used the hardware such as bridge circuits and frequency filters to suppress the noise. This paper proposed a novel noise rejection method in acquired data from PD detection equipment. The noise has the irregular phase and higher signal level than real PD, and noise decision is performed after inspection of pulse distribution in ${\Phi}$-q-n graph of acquired data from PD detection equipments. By experimental results on high voltage electric equipments, it is shown that proposed method has good performance. It is expected that this noise rejection technology is useful in numeric calculation and trend management of PD level.

• 산업기술연구
• /
• 제33권A호
• /
• pp.31-39
• /
• 2013

There are two methods to make a distinction of deterioration of high-speed railway track. One is that an administrator checks for each attribute value of track induction data represented in graph and determines whether maintenance is needed or not. The other is that an administrator checks for monthly trend of attribute value of the corresponding section and determines whether maintenance is needed or not. But these methods have a weak point that it takes longer times to make decisions as the amount of track induction data increases. As a field of artificial intelligence, the method that a computer makes a distinction of deterioration of high-speed railway track automatically is based on machine learning. Types of machine learning algorism are classified into four type: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. This research uses supervised learning that analogizes a separating function form training data. The method suggested in this research uses SVM classifier which is a main type of supervised learning and shows higher efficiency binary classification problem. and it grasps the difference between two groups of data and makes a distinction of deterioration of high-speed railway track.