• Current Optics and Photonics
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• 제1권4호
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• pp.412-420
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• 2017

Multinucleated bone resorptive osteoclasts differentiate from bone marrow-derived monocyte/macrophage precursor cells. During osteoclast differentiation, mononuclear pre-osteoclasts change their morphology and biochemical characteristics. In this study, Raman spectroscopy with multivariate techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to extract biochemical information related to various cellular events during osteoclastogenesis. This technique allowed for label-free and noninvasive monitoring of differentiating cells, and clearly discriminated four different time points during osteoclast differentiation. The Raman band intensity showed significant time-dependent changes that increased up to day 4. The results of Raman spectroscopy agreed with results from atomic force microscopy (AFM) and tartrate-resistant acid phosphatase (TRAP) staining, a conventional biological assay. Under AFM, normal spindle-like mononuclear pre-osteoclasts became round and smaller at day 2 after treatment with a receptor activator of nuclear $factor-{\kappa}B$ ligand and they formed multinucleated giant cells at day 4. Thus, Raman spectroscopy, in combination with PCA-LDA, may be useful for noninvasive label-free quality assessment of cell status during osteoclast differentiation, enabling more efficient optimization of the bioprocesses.

• Korean Chemical Engineering Research
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• 제46권2호
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• pp.233-247
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• 2008

공정 모니터링 기술은 공정 내에서 일어나는 예상치 못한 조업변화 및 이상을 조기에 감지하고 조업 이상에 영향을 끼친 근본 원인을 밝혀내어 제거해 줌으로써 공정의 안정적인 조업과 양질의 제품생산의 기반을 제공하여 준다. 데이터에 기반한 통계적 공정 모니터링 방법은 양질의 공정 데이터만 주어진다면 통계적 처리를 접목하여 비교적 쉽게 모니터링을 할 수 있고 공정의 데이터 분석에 이용할 수 있는 도구를 얻을 수 있다는 장점이 있다. 그러나 실제 공정에서는 비선형성, non-Gaussianity, 다중 운전모드, 공정상태변화로 인해 기존의 다변량 통계적 방법을 이용한 공정 모니터링 기법은 비효율적이거나, 공정 감시 성능의 저하, 종종 신뢰할 수 없는 결과를 야기한다. 이러한 경우 기존의 방법으로는 더이상 공정을 정확히 감시할 수 없기 때문에 최근에 많은 새로운 방법들이 개발 되었다. 본 총설에서는 이러한 단점을 보안하기 위해 최근 주목할 만한 연구결과인 공정 비선형성을 고려한 커널주성분분석(kernel principle component analysis) 모니터링 기법, 주성분분석 모델 조합을 이용한 다중모델(mixture model) 모니터링 기법, 공정 변화를 고려한 적응모델(adaptive model) 모니터링 기법, 그리고 센서 이상진단과 보정의 이론과 응용결과에 대하여 소개한다.

• 제어로봇시스템학회논문지
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• 제22권4호
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• pp.276-280
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• 2016

Due to the increasing interest in safety and consistent product quality over a past few decades, demand for effective quality monitoring and safe operation in the modern industry has propelled research into statistical based fault detection and diagnosis methods. This paper describes the application of Hotelling $T^2$ index based Principal Component Analysis (PCA) method for fault detection and diagnosis in industrial processes. Multivariate statistical process control techniques are now widely used for performance monitoring and fault detection. Conventional methods such as PCA are suitable only for steady state processes. These conventional projection methods causes false alarms or missing data for the systems with transient values of processes. These issues significantly compromise the reliability of the monitoring systems. In this paper, a reliable method is used to overcome false alarms occur due to varying process conditions and missing data problems in transient states. This monitoring method is implemented and validated experimentally along with matlab. Experimental results proved the credibility of this fault detection method for both the steady state and transient operations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.18-18
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• 1996

This paper reviews some of the most prominent and promising areas of chemical process control both in relations to batch and continuous processes. These areas include the modeling, optimization, control and monitoring of chemical processes and entire plants. Most of these areas explicitly utilize a model of the process. For this purpose the types of models used are examined in some detail. These types of models are categorized in knowledge-driven and datadriven classes. In the areas of modeling and optimization, attention is paid to batch reactors using the Tendency Modeling approach. These Tendency models consist of data- and knowledge-driven components and are often called Gray or Hybrid models. In the case of continuous processes, emphasis is placed in the closed-loop identification of a state space model and their use in Model Predictive Control nonlinear processes, such as the Fluidized Catalytic Cracking process. The effective monitoring of multivariate process is examined through the use of statistical charts obtained by the use of Principal Component Analysis (PMC). Static and dynamic charts account for the cross and auto-correlation of the substantial number of variables measured on-line. Centralized and de-centralized chart also aim in isolating the source of process disturbances so that they can be eliminated. Even though significant progress has been made during the last decade, the challenges for the next ten years are substantial. Present progress is strongly influenced by the economical benefits industry is deriving from the use of these advanced techniques. Future progress will be further catalyzed from the harmonious collaboration of University and Industrial researchers.

• 한국지하수토양환경학회지:지하수토양환경
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• 제19권5호
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• pp.18-25
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• 2014

Traditional investigation procedures of soil and groundwater contamination are followed by soil gas sampling, soil sampling, groundwater sampling, establishment of monitoring wells, and groundwater monitoring. It often takes several weeks to obtain the analysis reports, and sometimes, it needs supplemental sampling and analysis to delineate the polluted area. Laser induced fluorescence (LIF) system is designed for the detection of free-phase petroleum pollutants, and it is suitable for on-site real-time site investigation when coupling with a direct push testing tool. Petroleum products always contain polycyclic aromatic hydrocarbon (PAH) compounds possessing fluorescence characteristics that make them detectable through LIF detection. In this study, LIF spectroscopy of 5 major fuel products was conducted to establish the databank of LIF fluorescence characteristic spectra, including gasoline, diesel, jet fuel, marine fuel and low-sulfur fuel. Multivariate statistical tools were also applied to distinguish LIF fluorescence characteristic spectra among the mixtures of selected fuel products. This study successfully demonstrated the feasibility of identifying fuel species based on LIF characteristic fluorescence spectra, also LIF seemed to be uncovered its powerful ability of tracing underground petroleum leakages.

• ALGAE
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• 제34권2호
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• pp.141-151
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• 2019

The 1982-83, 1986-87, 1991-92, and 1997-98 El $Ni{\tilde{n}}o$-Southern Oscillations (ENSOs) were compared with regards to their strength and timing in the tropical Pacific Ocean, changes in ocean temperature and wave intensity, and their impacts to giant kelp populations in the Northeast Pacific. The Multivariate ENSO Index, oceanographic data, and kelp abundance data all show that the 1982-83 and 1997-98 ENSOs were stronger and resulted in greater losses of giant kelp than the 1986-87 and 1991-92 ENSOs, but that the 1982-83 and 1997-98 ENSOs differed with regard to the arrival of destructive waves relative to when the ocean waters warmed and cooled. The 1982-83 ENSO was more destructive to the giant kelp populations in central California, USA than the 1997-98 ENSO, but the 1997-98 ENSO was more destructive to the giant kelp in southern California. These events appeared similarly destructive to the populations in Baja California, Mexico. Recovery of the kelp populations also varied among the two strong ENSOs due to the ocean conditions following each ENSO. In southern and Baja California, recovery was slow following the 1982-83 ENSO, while recovery was more rapid following the 1997-98 ENSO. Unfortunately, the monitoring programs used to evaluate the kelp populations stopped shortly after the 1997-98 ENSO, resulting in a lack of data for comparisons with the more recent weak ENSOs that occurred between 2002 and 2010, or with the strong ENSO that occurred in 2014-2016. This supports the need for continued long-term monitoring programs to better understand how climate anomalies impact coastal ecosystems.

• Journal of the Korean Data and Information Science Society
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• 제14권4호
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• pp.1045-1053
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• 2003

Markovian EWMA control chart for simultaneously monitoring mean vector of the several correlated quality characteristics is investigated. Properties of multivariate Shewhart chart and EWMA chart are evaluated for matched FSI (fixed sampling interval) and VSI(variable sampling interval) scheme. We obtained VSI EWMA chart is more efficient than Shewhart chart for small or moderate shifts. And, we obtained stablized numerical results with Markov chain method when the number of transient state is greater than 100.

• 한국가스학회지
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• 제18권3호
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• pp.53-59
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• 2014

본 연구에서는 비정상상태 운전을 기본으로 하는 CNG 충전소를 대상으로 다변량 통계분석방법 중의 하나인 다차원의 대용량 데이터 처리에 적합한 주성분분석(PCA) 기법을 사용하여 실시간 이상감지 및 진단이 가능한 모니터링 시스템을 제안하였다. CNG 충전소로부터 매초 간격으로 수집되는 7개의 압력센서 데이터와 5개의 온도센서 데이터의 주요 경향을 나타내는 변수들의 조합으로 주성분이라 불리는 새로운 특성변수들을 산출하고, 분산의 분포를 통해 특성변수의 계산으로부터 모델을 구축하였다. 모니터링은 구축된 모델을 통해 운전 중의 실시간 데이터를 반영하여 진행된다. 시스템 검증 및 정확성을 개선하기 위해 모니터링 테스트를 수행한 결과, 정상상태의 모든 데이터를 정상으로 판단하였고, 이상 데이터의 성공적인 검출 시 관련 변수를 추적하여 비정상 원인을 찾아낼 수 있었다.

• 한국지능시스템학회논문지
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• 제13권4호
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• pp.386-390
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• 2003

데이터마이닝은 많은 양의 데이터로부터 의사결정에 유용한 패턴을 발견하는 과정으로서 최근 경영 및 공학 분야의 폭넓은 영역에서 많은 관심을 모으고 있다. 어떤 그룹을 여러 하위그룹으로 분류해내는 일은 데이터마이닝의 주요 내용 중 하나이다. 의사결정나무로 알려진 트리기반 기법은 그러한 분류모형을 수립하는 데 효율적인 방안을 제공한다 트리학습에 있어서 우선적인 관건은 목표변수에 의해 측정되는 노드불순도를 최소화하는 것이다. 하지만 공정관측, 마케팅과학, 임상분석 등과 같은 문제에서는 여러 목표변수를 동시에 고려해야 하는 상황이 쉽게 등장하는 데, 본 논문의 목적은 이처럼 다변량 목표변수를 갖는 데이터셋에서 활용할 수 있는 노드불순도 측정방안을 제시하는 데 있다. 아울러 수치 예를 이용하여 적용결과에 대해 논의한다.

• Journal of Ecology and Environment
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• 제35권4호
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• pp.279-289
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• 2012

This study conducted a comparative analysis of benthic macroinvertebrate communities in the Fosu and Benya lagoons in Ghana, based on the anthropogenic effect on the two lagoons. Salinity, oxygen, temperature, conductivity, turbidity and pH were measured, invertebrate richness and species densities were determined. The AZTI Marine Biotic Index (AMBI) and multivariate statistics were used to determine the different responses of fauna to pollution. The fauna were categorized into five ecological groups based on the degree of tolerance of the different species to pollution: disturbance-sensitive species; disturbance-indifferent species, disturbance-tolerant species, second-order opportunistic species; and first-order opportunistic species. The Fosu Lagoon supported more pollution tolerant species, whereas the Benya Lagoon had more species that were sensitive to organic enrichment under relatively unpolluted conditions. Chironomus sp., which is adapted to virtually anoxic conditions, was the most abundant in the Fosu Lagoon whereas Nemertea sp. was the most abundant in the Benya Lagoon. The numerical and relative abundance (%) of all 7 taxa in the Fosu Lagoon was 1,359 and 92.35%, respectively. The numerical and relative abundance (%) of all 34 taxa in the Benya Lagoon was 2,459 and 87.52%, respectively. Expectedly, the level of dissolved oxygen in the less saline Fosu Lagoon was higher than that in the more saline Benya Lagoon. The reduced photoperiod and photosynthetic activities of aquatic plants might account for this trend. There is a need to implement comprehensive monitoring and management initiatives for sustaining the ecological health of coastal lagoons in Ghana in order to support the many people that depend upon these ecosystems for their livelihood.