• Clean Technology
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• v.18 no.1
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• pp.31-37
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• 2012

Biomass has become a major feedstock for bioenergy and other bio-based products because of its renewability and environmental benefits. Various researches have been done in the prediction of crucial characteristics of biomass, including the active utilization of spectroscopy data. Near infrared (NIR) spectroscopy has been widely used because of its attractive features: it's non-destructive and cost-effective producing fast and reliable analysis results. This work developed the multivariate statistical scheme for predicting weight loss profiles based on the utilization of NIR spectra data measured for six lignocellulosic biomass types. Wavelet analysis was used as a compression tool to suppress irrelevant noise and to select features or wavelengths that better explain NIR data. The developed scheme was demonstrated using real NIR data sets, in which different prediction models were evaluated in terms of prediction performance. In addition, the benefits of using right pretreatment of NIR spectra were also given. In our case, it turned out that compression of high-dimensional NIR spectra by wavelet and then PLS modeling yielded more reliable prediction results without handling full set of noisy data. This work showed that the developed scheme can be easily applied for rapid analysis of biomass.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.127-133
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• 2021

This study studies the effect of social welfare service quality on facility reuse for users of social welfare facilities. The purpose of this study is to study the effect of the parallel triple mediation effect of facility image user loyalty and facility satisfaction. To this end 219 users of social welfare facilities were analyzed using SPSS 23.0 for Windows and Process macro. As a result, first the variables of service quality facility image user loyalty facility satisfaction and facility reuse all formed significant positive relationships. Second facility image user loyalty and facility satisfaction had multiple mediating effects. This study presented a new model using multivariate in response to social welfare facilities in pandemic situations.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.2
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• pp.89-96
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• 2017

This study analyzed the characteristics of water quality and the correlation between Chl-a and water quality factors among four weirs located in the middle and downstream of Nakdong River for five years. The concentration of nutrients and Chl-a from DS to CH was higher than that of GG, which is considered to be due to the influx of Kumho River located at upstream of DS. There was a significant correlationship between Chl-a and most of the water quality factors for all season data. Based on the comparison results between all season data and summer season data, a negative correlation between Chl-a and nutrients ($PO_4-P$, $NH_3-N$) was increased. Based on analysis on summer in 2015 with relatively low precipitation and high algal blooms, the correlation between Chl-a and $PO_4-P$ at all sites were increased. Therefore phosphorus is an important factor in the river on summer season. And PCA results showed the first factor was classified as T-N, $NO_3-N$ for all seasons, and the first factor was classified as T-P, $PO_4-P$ for summer seasons. Consequently, the middle and downstream of Nakdong River were most affected by nutrients, especially it was affected by phosphorous pollutants rather than nitrogen pollutants during summer seasons.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.538-547
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• 2022

In this study, the population and community structure of Berchemia berchemiaefolia stands located at Mt. Naeyon (Gyeongbuk, Korea) were quantified, and multivariate analysis was done to determine the correlations between vegetation group types and environmental factors and to have reference data for the conservation and restoration of this species. In total, there were 164 B. berchemiaefolia trees in Mt. Naeyon. The average DBH of the trees was 24.5 cm, forming a normal distribution. It rarely appeared in an understory vegetation height of 3 m. About37.1% of the trees were branched. B. berchemiaefolia stands were classified into two groups: B. berchemiaefolia-Quercus serrata community and B. berchemiaefolia-Carpinus laxiflora community. Canopy gap, organic matter, exchangeable Ca, and cation exchange capacity were the major site characteristics affecting the distribution pattern of the stands. Currently, B. berchemiaefolia trees dominate in Mt. Naeyon, but depending on different habitat positions, the species was in a natural successional stage to C. laxiflora or C. cordata, which is a shade-tolerant species.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.11
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• pp.641-649
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• 2017

In the study, the water quality of the Imjin River basin with pollutants of changing characteristics it was determined through statistical analysis, correlation analysis, principle component and factor analysis, and cluster analysis. Among all analyzed data points, the average water quality concentration at the Sincheon 3 site shows high levels of BOD 13.4 mg/L, COD 19.9 mg/L, T-N 11.145 mg/L, T-P 0.336 mg/L, TOC 14.2 mg/L, indicating that Sincheon basin requires intersive water quality management out of the entire drainage basin. The correlational analysis of comprehensive water quality data shows statistically significant correlation between COD, TOC, BOD, T-N water quality factors, as well as finding of high correlation between organic and nutrients. The principal component analysis show that 2 main components being extracted at 81.221% from the measuring station's entire data, while seasonal data show 3 main components being extracted at 96.241%. Factor analysis of the entire data set and the seasonal data identify BOD, COD, T-N, T-P, TOC as the common factors influencing water quality. The spatial and temporal cluster analysis showed 4 groups and 3 groups, respectively, according to seasonal characteristics and land use. By analysing the water quality factors for the Imjin River basins over an 8 year period, with consideration to the spatial and temporal characteristics, this study will become the fundamental analytic data that will help understand the future changes of water quality in the Imjin River basin.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.27-32
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• 1997

Continual monitoring of abnormal operating conditions i a key issue in maintaining high product quality and safe operation, since the undetected process abnormality may lead to the undesirable operations, finally producing low quality products, or breakdown of equipment. The statistical projection method recently highlighted has the advantage of easily building reference model with the historical measurement data in the statistically in-control state and not requiring any detailed mathematical model or knowledge-base of process. As the complexity of process increases, however, we have more measurement variables and recycle streams. This situation may not only result in the frequent occurrence of process Perturbation, but make it difficult to pinpoint trouble-making causes or at most assignable source unit due to the confusing candidates. Consequently, an ad hoc skill to monitor and diagnose in plat-wide scale is needed. In this paper, we propose a hierarchical plant-wide monitoring methodology based on hierarchical decomposition and principal component analysis for handling the complexity and interactions among process units. This have the effect of preventing special events in a specific sub-block from propagating to other sub-blocks or at least delaying the transfer of undesired state, and so make it possible to quickly detect and diagnose the process malfunctions. To prove the performance of the proposed methodology, we simulate the Tennessee Eastman benchmark process which is operated continuously with 41 measurement variables of five major units. Simulation results have shown that the proposed methodology offers a fast and reliable monitoring and diagnosis for a large scale chemical plant.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2001.11a
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• pp.43-43
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• 2001

원료의 저급화와 공정의 폐쇄화가 급속히 진행되고 있는 현재의 국내 제지산업 현황을 고려할 때 고품질의 제품을 저렴하게 생산하기 위해서는 화학첨가제의 효과적 활용기술 확립과 이를 통한 초지공정 wet end의 성공적 제어기술이 요청된다. 특히 환경보전과 환 경관련 규제에 부응하기 위하여 초지공정의 무방류화가 점차 가속화되고 있는 현 시점에 서 wet end의 효율적 관리를 위해서는 그 동안 개방형 wet end에서 적용되던 개념의 공 정관리의 한계를 극복할 수 있는 기술 개발이 필요하다. 공정의 폐쇄화에 따른 습부화학적 문제를 해결하기 위해서는 백수의 재활용에 따른 지료 화학적 특성을 평가하고 고분자전해질의 거동을 분석해야 한다. 보류향상제 혹은 탈수촉 진제로서 첨가된 고분자전해질은 그 기능을 수행하기 위해서는 섬유에 흡착이 이루어져 야 하므로 백수로 제조된 지료 내에서 고분자전해질의 흡착 특성을 파악하는 것은 습부에 서 일어나는 현상들에 대한 이해 및 효율적인 공정제어를 위한 첫 단계라 할 수 있다. 본 연구에서는 실험실적으로 제조된 백수를 이용하여 조성된 지료 내에서 양이온성 구 아 검의 흡착현상을 분석하였다. 본 연구에서는 폐쇄화의 기준으로 폐쇄화 정도(Level of C Closure ; LC)에 따라 실험을 수행하였다. 여기에서 LC란 총 사용된 물의 양에 대한 지료조성 시 사용되는 백수의 양을 백분율로 나타낸 것이다. 양이온성 구아 검의 흡착을 평가하기 위해 PhenoVsulfuric acid spectrophotometric method를 이용하여 펄프 슬 러리 상등액에 존재하는 미흡착된 양이온성 구아 검의 함량을 측정하였고. 양이온성 구아 검 이 섬유상에 흡착하였을 때 나타나는 섬유의 S-potential 변화와 펄프 상등액의 양이 온 요구량 변화를 평가하였으며 이들의 상관관계를 분석하였다.축력으로 변형시키는데 비해 도침은 단순히 압축 압력만을 종이에 가하는 것이 다르다고 볼 수 있는데, 라 이너지와 백상지가 같은 조건하에서 왜 이러한 큰 차이를 보이는 이유를 아직 알수 없다.해 동일한 공정 데이터들올 이용하여 보편적으로 사용하는 통계기법 중의 하나인 주성분회귀분석을 실시하였다. 주성분 분석은 여러 개의 반응변수에 대하여 얻어진 다변량 자료의 다차원적인 변 수들을 축소, 요약하는 차원의 단순화와 더불어 서로 상관되어있는 반응변수들 상호간 의 복잡한 구조를 분석하는 기법이다. 본 발표에서는 공정 자료를 활용하여 인공신경망 과 주성분분석을 통해 공정 트러블의 발생에 영향 하는 인자들을 보다 현실적으로 추 정하고, 그 대책을 모색함으로써 이를 최소화할 수 있는 방안을 소개하고자 한다.금 빛 용사 둥과 같은 표면처리를 할 경우임의 소재 표면에 도금 및 용 사에 용이한 재료를 오버레이용접시킨 후 표면처리를 함으로써 보다 고품질의 표면층을 얻기위한 시도가 이루어지고 있다. 따라서 국내, 외의 오버레이 용접기술의 적용현황 및 대표적인 적용사례, 오버레이 용접기술 및 용접재료의 개발현황 둥을 중심으로 살펴봄으로서 아직 국내에서는 널리 알려지지 않은 본 기 술의 활용을 넓이고자 한다. within minimum time from beginning of the shutdown.및 12.36%, $101{\sim}200$일의 경우 12.78% 및 12.44%, 201일 이상의 경우 13.17% 및 11.30%로 201일 이상의 유기의 경우에만 대조구와 삭제 구간에 유의적인(p<0.05) 차이를 나타내었다.는 담수(淡水)에서 10%o의 해수(海水)로 이주된지 14일(日) 이후에 신장(腎臟)에서 수축된 것으로 나타났다. 30%o의 해수(海水)에 적응(適應)된 틸라피아의 평균 신사구체(腎絲球體)의 면적은 담수

• Journal of the Korean Institute of Gas
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• v.18 no.3
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• pp.53-59
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• 2014

In this study, we suggest a system to build the monitoring model for compressed natural gas (CNG) stations, operated in only non-stationary modes, and perform the real-time monitoring and the abnormality diagnosis using principal component analysis (PCA) that is suitable for processing large amounts of multi-dimensional data among multivariate statistical analysis methods. We build the model by the calculation of the new characteristic variables, called as the major components, finding the factors representing the trend of process operation, or a combination of variables among 7 pressure sensor data and 5 temperature sensor data collected from a CNG station at every second. The real-time monitoring is performed reflecting the data of process operation measured in real-time against the built model. As a result of conducting the test of monitoring in order to improve the accuracy of the system and verification, all data in the normal operation were distinguished as normal. The cause of abnormality could be refined, when abnormality was detected successfully, by tracking the variables out of the score plot.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1171-1180
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• 2015

Multidimensional scaling (MDS) is a graphical technique of multivariate analysis to display dissimilarities among individuals into low-dimensional space. We often have two kinds of MDS which are metric MDS and non-metric MDS. Metric MDS can be applied to quantitative data; however, we need additional information about variables because it only shows relationships among individuals. Gower (1992) proposed a method that can represent variable information using trajectories of the pseudo-points for quantitative variables on the metric MDS space. We will call his method a 'replacement method'. However, the trajectory can not be represented even though metric MDS can be applied to binary data when we apply his method to binary data. Therefore, we propose a method to represent information of binary variables using pseudo-points called a 'partition method'. The proposed method partitions pseudo-points, accounting both the rate of zeroes and ones. Our metric MDS using the proposed partition method can show the relationship between individuals and variables for binary data.

• Ecology and Resilient Infrastructure
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• v.7 no.2
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• pp.97-113
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• 2020

Soil is the foundation of human life and the basis for food security. Considering this it is prioritized in the UN's Sustainable Development Goals (SDG). Therefore, research on soil resilience in the agricultural environment is crucial for sound and sustainable soil management, especially in highly uncertain and unpredictable conditions. Soil resilience is defined in different ways by several researchers; however, its definition typically includes the concepts of recovery and resistance to stress. The physical, chemical, and biological characteristics of soils that are used to assess the soil resilience, i.e., the response of soil to various types of stress are summarized in this study. In addition, various statistical processing techniques and quantification methods are summarized considering the wide spatial and temporal scope of soil resilience research. Several soil resilience studies typically conduct the following five steps: (1) soil and site selection (2) stress (independent variable) setting (3) soil characteristics and indicator (dependent variable) setting (4) performing various spatiotemporal scale experiments (5) statistical analysis. The previous and present studies present a general introduction of soil resilience, based on which, further practical research considering domestic agricultural environment should be conducted. The extensive range of soil resilience measurements will require collaboration between researchers in various fields.