• 제목/요약/키워드: functional near infrared spectroscopy

검색결과 39건 처리시간 0.03초

가교 폴리에틸렌의 열노화에 따른 구조와 물성의 변화 (The Variation of Structure and Physical Properties of XLPE during Thermal Aging Process)

  • 이미영;김철환;구철수;김복렬;이영관
    • 폴리머
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    • 제27권3호
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    • pp.249-254
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    • 2003
  • 가교 폴리에틸렌을 열을 가하여 노화시킨 후 구조 및 물성의 변화를 조사하였다. 가교 폴리에틸렌의 열적 산화 반응으로 카보닐 그룹이 형성됨을 X-선 광전자 분광법과 근적외선 분광법을 이용하여 확인하였다. 노화 시간이 길어질수록 1715 nm에서 관찰되는 카보닐 피이크가 정량적으로 증가하는 것을 근적외선 분광법을 이용하여 관찰하였다. 열화 시간에 따른 흡수 피이크의 선형적인 관계로부터 근적외선 분광법이 고분자 재료의 열화 과정을 감시하는데 적합한 방법임을 확인할 수 있었다. 또한 노화에 의한 가교 결합의 발생과 그에 따른 물리적 성질의 변화를 TMA, 응력-변형 시험, 쇼어 경도 측정 방법을 이용하여 관찰하였다. 노화가 진행됨에 따라 유리 전이 온도가 110에서 132$^{\circ}C$로 증가함을 관찰하였으며, 인장률은 265에서 110%로 점차 감소하고 쇼어 D 경도는 32에서 50으로 크게 증가하는 것을 관찰할 수 있었다.

정밀농업을 위한 토양의 실시간 이화학 성분 분석 기술 개발 (Development of real-time chemical properties analysis technique in paddy soil for precision farming)

  • 윤현웅;최창현;김용주;홍순중
    • 농업과학연구
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    • 제41권1호
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    • pp.59-63
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    • 2014
  • Precision farming aims at reduced environmental impacts with increased productivity. Soils are multi-functional media in which air, water and biota occur together and form an essential part of the landscape with a fundamental role in the environment. The requirement for herbicides and fertilizers can vary within a field in response to spatial differences in soil properties. Near infrared (NIR) spectroscopy is widely used today as a nondestructive analytical technique which is capable of determining a number of physio-chemical parameters. The objectives of this study were to develop optimal models to predict chemical properties of paddy soils by visible and NIR reflectance spectra. Total of 60 soil samples were collected in spring from 20 paddy fields within central regions in Korea. Reflectance spectra, moisture contents, pH, total nitrogen (N), organic matter, available phosphate ($P_2O_5$) of soil samples were measured. The reflectance spectra were measured in wavelength ranges of 400-2,500 nm with 2 nm interval. The method of partial least square (PLS) analysis was used to determine the soil properties. The PLS analyses showed good correlation between predicted and measured chemical properties of paddy soils in the wavelength range of 1,800-2,400 nm. Especially, it showed better performance than the previous results which used the entire wavelength range of the spectrophotometer, without considering the optimal wavelength of each soil properties.

The importance of NIR spectroscopy in the estimation of nutritional quality of grains for ruminants

  • Flinn, Peter C.
    • 한국근적외분광분석학회:학술대회논문집
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    • 한국근적외분광분석학회 2001년도 NIR-2001
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    • pp.1612-1612
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    • 2001
  • The production of grain for export and domestic use is one of Australia's most important agricultural industries, and the NIR technique has been used extensively over many years for the routine monitoring of grain quality, particularly moisture and protein content. Because most Australian grain is intended for human food production, the determinants of grain quality for livestock feed, apart from protein, have been largely ignored. However the increasing use of grain for feeding to pigs, poultry, beef cattle and dairy cows has led to an important national research project entitled “Premium Grains for Livestock”. Two of the objectives of this project are to determine the compositional and functional characteristics of grains which influence their nutritional quality for the various classes of livestock, and to adopt rapid and objective analytical tests for these quality criteria. NIR has been used in this project firstly to identify a set of grain samples from a large population of breeders' lines which showed a wide spectral variation, and hence a potentially wide variation in nutritional value. The selected samples were not only subjected to an extensive array of chemical, physical and in vitro analyses, but also were grown out to produce sufficient quantities of grain to feed to animals in vivo studies. Additional grains were also strategically selected from farms in order to include the effect of weather damage, such as rain, drought and frost. In this study to date, NIR calibrations have been derived or attempted, on both ground and whole grains, for in vivo dry matter digestibility (DMD), pepsin-cellulase dry matter disappearance, protein, fat, acid detergent fibre, neutral detergent fibre, starch, in sacco DMD and in vitro assays to simulate starch digestion in the lumen and small intestine. Results so far indicate high calibration accuracy for chemical components (SECV 0.3 to 2.6%) and very promising statistics for in vivo DMD (SECV 1.8, $R^2$ 0.93, SD 7.0, range 61.9 to 92.3, n=60). There appears to be some potential for NIR to estimate some in vitro properties, depending upon the accuracy of reference methods and appropriate sample populations. Current work is in progress to extend the range of grains with in vivo DMD values (a very laborious and expensive process) and to increase the robustness of the various NIR calibrations, with the aim of implementing uniform testing procedures for nutritional value of grains throughout Australia.

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CHANGING THE ANIMAL WORLD WITH NIR : SMALL STEPS OR GIANT LEAPS\ulcorner

  • Flinn, Peter C.
    • 한국근적외분광분석학회:학술대회논문집
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    • 한국근적외분광분석학회 2001년도 NIR-2001
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    • pp.1062-1062
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    • 2001
  • The concept of “precision agriculture” or “site-specific farming” is usually confined to the fields of soil science, crop science and agronomy. However, because plants grow in soil, animals eat plants, and humans eat animal products, it could be argued (perhaps with some poetic licence) that the fields of feed quality, animal nutrition and animal production should also be considered in this context. NIR spectroscopy has proved over the last 20 years that it can provide a firm foundation for quality measurement across all of these fields, and with the continuing developments in instrumentation, computer capacity and software, is now a major cog in the wheel of precision agriculture. There have been a few giant leaps and a lot of small steps in the impact of NIR on the animal world. These have not been confined to the amazing advances in hardware and software, although would not have occurred without them. Rapid testing of forages, grains and mixed feeds by NIR for nutritional value to livestock is now commonplace in commercial laboratories world-wide. This would never have been possible without the pioneering work done by the USDA NIR Forage Research Network in the 1980's, following the landmark paper of Norris et al. in 1976. The advent of calibration transfer between instruments, algorithms which utilize huge databases for calibration and prediction, and the ability to directly scan whole grains and fresh forages can also be considered as major steps, if not leaps. More adventurous NIR applications have emerged in animal nutrition, with emphasis on estimating the functional properties of feeds, such as in vivo digestibility, voluntary intake, protein degradability and in vitro assays to simulate starch digestion. The potential to monitor the diets of grazing animals by using faecal NIR spectra is also now being realized. NIR measurements on animal carcasses and even live animals have also been attempted, with varying degrees of success, The use of discriminant analysis in these fields is proving a useful tool. The latest giant leap is likely to be the advent of relatively low-cost, portable and ultra-fast diode array NIR instruments, which can be used “on-site” and also be fitted to forage or grain harvesters. The fodder and livestock industries are no longer satisfied with what we once thought was revolutionary: a 2-3 day laboratory turnaround for fred quality testing. This means that the instrument needs to be taken to the samples rather than vice versa. Considerable research is underway in this area, but the challenge of calibration transfer and maintenance of instrument networks of this type remains. The animal world is currently facing its biggest challenges ever; animal welfare, alleged effects of animal products on human health, environmental and economic issues are difficult enough, but the current calamities of BSE and foot and mouth disease are “the last straw” NIR will not of course solve all these problems, but is already proving useful in some of these areas and will continue to do so.

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전자선 경화를 이용한 에폭시 수지의 열안정성과 경화동력학에 관한 연구 (Studies on Thermal Stability and Cure Behavior of Epoxy Resins using Electron-beam Curing Technique)

  • 박수진;허건영;이재락
    • Composites Research
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    • 제15권2호
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    • pp.40-47
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    • 2002
  • 이관능성 에폭시 수지인 DGEBA와 DGEBF를 양이온 촉매인 BQH를 사용하여 전자선(electron-beam) 경화 기술에 의해 경화하였다. 그리고 수지의 구조적 차이가 열안정성과 경화동력학에 미치는 영향을 연구하였다. 실험적 결과에 의하면, Horowitz-Metzger 법에 의한 분해 활성화 에너지는 DGEBA의 경우가 높았지만 적분 열분해 온도(IPDT)는 DGEBA가 DGEBF 보다 낮았다. 이것은 DGEBF 주사슬의 수소 결합으로 인해 가교밀도가 높아졌기 때문인 것으로 사료되며, 근적외선 분광기(NIRS)를 사용하여 $5235\;cm^{-1}$$7000\;cm^{-1}$에서의 hydroxyl band의 증가로 확인하였다.

근적외 분광분석법을 이용한 국내 유통 식품 함유 탄수화물, 단백질 및 지방의 정량 분석 (Quantitative Analysis of Carbohydrate, Protein, and Oil Contents of Korean Foods Using Near-Infrared Reflectance Spectroscopy)

  • 송이슬;김영학;김기쁨;안경근;황영선;강인규;윤성원;이준수;신기용;이우영;조영숙;정명근
    • 한국식품영양과학회지
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    • 제43권3호
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    • pp.425-430
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    • 2014
  • 식품의 3대 영양소인 탄수화물, 단백질 및 지방의 일반적인 분석 방법은 Kjeldahl 및 Soxhlet 시험법과 같은 기존의 화학 분석 방법으로 분석하였다. 그러나 이러한 분석 방법은 시료의 전처리 과정이 필요하고 많은 비용과 분석 시간이 소모되며 복잡한 추출과정을 거친다는 단점이 있다. 따라서 본 연구에서는 국내 유통 식품 및 농산물 자원에 함유된 탄수화물, 단백질 및 지방의 함량을 근적외 분광분석법(near-infrared reflectance spectroscopy, NIRS)으로 신속하고 정확하게 동시에 측정할 수 있는 방법을 검토하였다. 분석시료는 517종의 다양한 식품 시료를 예측모델 개발용(calibration set) 412종과 예견치 분석용(validation set) 162종으로 구분하여 사용하였다. 기존의 화학 분석 방법에 의해 측정된 성분들의 분석 결과와 근적외 스펙트럼 데이터간의 상관관계를 조사하여 각 성분별 예측모델을 검토하였으며, 변형부분최소자승법(MPLS) 및 다양한 수처리와 산란보정을 이용한 결과, 탄수화물, 단백질 및 지방의 산란방식은 각각 weighted MSC, standard MSC 및 SNV only로 수처리는 각각 1차 미분(1st derivative, 4 nm gap, 5 points smoothing, 1 point second smoothing), 2차 미분(2, 5, 5, 3) 및 1차 미분(1, 1, 1, 1)을 적용하여 예측모델을 검토한 결과 $R^2$값이 0.971, 0.974 및 0.937로 높고 SEC값은 4.066, 1.080 및 1.890으로 낮은 최적의 예측모델을 개발하였다. 세 성분의 최적 예측모델에 의한 상관도와 잔차 히스토그램을 검토한 결과 세 성분 모두 근적외 분광분석법 예측모델로 적합함을 확인할 수 있었으며, 최적의 예측모델을 미지의 식품 시료 162종에 적용한 결과, 탄수화물, 단백질 및 지방의 $r^2$(SEP)값은 0.987(2.515), 0.970(1.144) 및 0.947(1.370)로 $r^2$값은 높으며 SEP값은 낮은 양호한 양상을 나타내었다. 그러나 지방의 결정계수($R^2$, $r^2$)값은 탄수화물, 단백질에 비해 다소 낮은 양상을 나타내므로 추후 식품 검체에 적용 시 탄수화물 및 단백질 성분에 비해 예측결과의 정확성이 다소 낮을 수 있다고 판단되어진다. 이상의 결과에서 전처리 단계에서 복잡한 추출과정, 많은 비용소모, 분석시간 및 고도의 분석기술을 요하는 기존 습식 화학분석 방법의 단점을 보완하고자 검토되었던 근적외 분광분석법은 다량의 식품분석 시료를 분석하기에는 매우 효율적이라고 생각되며, 이런 점들을 고려해 보면 근적외 분광분석 예측모델들은 추후에 미지 식품시료에 함유된 탄수화물, 단백질 및 지방의 기존 분석법을 대체하여 편리하고 빠르게 함량을 예측 가능할 것으로 판단된다.

Elastic net 기반 특징 선택을 적용한 fNIRS 기반 뇌-컴퓨터 인터페이스 데이터셋 분류 정확도 평가 (Assessment of Classification Accuracy of fNIRS-Based Brain-computer Interface Dataset Employing Elastic Net-Based Feature Selection)

  • 신재영
    • 대한의용생체공학회:의공학회지
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    • 제42권6호
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    • pp.268-276
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    • 2021
  • Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

침 및 운동 치료로 호전된 파킨슨병 환자 1례에 대한 증례보고 (A Case Report of a Patient with Parkinson's Disease Treated with Acupuncture and Exercise Therapy)

  • 박미소;박상수;이승현;허왕정;유호룡
    • 대한한방내과학회지
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    • 제43권5호
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    • pp.1018-1028
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    • 2022
  • Objectives: Parkinson's disease is characterized by progressive, irreversible damage to dopamine neurons in the substantia nigra pars compacta, as well as motor and non-motor symptoms. This disease currently has no dependable disease-modifying treatment. In this paper, we describe the treatment of a 67-year-old female with Parkinson's disease using acupuncture and exercise therapy. Case Presentation: Clinical symptoms and the United Kingdom Parkinson's Disease Society Brain Bank Diagnostic Criteria were used to diagnose the patient with Parkinson's disease. Over a 12-week period, the patient visited a Korean medicine hospital 18 times and was treated with acupuncture and exercise therapy in addition to anti-Parkinson's drugs. Before and after treatment, clinical examinations were performed using tools such as the Unified Parkinson's Disease Rating Scale, Fall Efficacy Scale, Parkinson's Disease Questionnaire, Berg Balance Scale, and Non-Motor Symptoms Scale. Furthermore, functional near-infrared spectroscopy was used to assess cortical hemodynamics. All clinical examination results improved after 12 weeks of intervention. In particular, improvements on the Total Unified Parkinson's Disease Rating Scale and Part III of this scale demonstrated large, clinically important differences. Conclusion: This case suggests that combining acupuncture and exercise therapy could produce an effective treatment for Parkinson's disease patients.

Applying a Novel Neuroscience Mining (NSM) Method to fNIRS Dataset for Predicting the Business Problem Solving Creativity: Emphasis on Combining CNN, BiLSTM, and Attention Network

  • Kim, Kyu Sung;Kim, Min Gyeong;Lee, Kun Chang
    • 한국컴퓨터정보학회논문지
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    • 제27권8호
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    • pp.1-7
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    • 2022
  • 인공지능 기술이 발달하면서 뉴로사이언스 마이닝(NSM: NeuroScience Mining)과 AI를 접목하려는 시도가 증가하고 있다. 나아가 NSM은 뉴로사이언스와 비즈니스 애널리틱스의 결합으로 인해 연구범위가 확장되고 있다. 본 연구에서는 fNIRS 실험을 통해 확보한 뉴로 데이터를 분석하여 비즈니스 문제 해결 창의성(BPSC: business problem-solving creativity)을 예측하고 이를 통해 NSM의 잠재력을 조사한다. BPSC는 비즈니스에서 차별성을 가지게 하는 중요한 요소이지만, 인지적 자원의 하나인 BPSC의 측정 및 예측에는 한계가 존재한다. 본 논문에서는 BPSC 예측 성능을 높이는 방안으로 CNN, BiLSTM 그리고 어텐션 네트워크를 결합한 새로운 NSM 기법을 제안한다. 제안된 NSM 기법을 15만 개 이상의 fNIRS 데이터를 활용하여 유효성을 입증하였다. 연구 결과, 본 논문에서 제안하는 NSM 방법이 벤치마킹한 알고리즘(CNN, BiLSTM)에 비하여 우수한 성능을 가지는 것으로 나타났다.