• Proceedings of the Materials Research Society of Korea Conference
• /
• 2012.05a
• /
• pp.68.1-68.1
• /
• 2012

The best part of graphene is - charge-carriers in it are mass less particles which move in near relativistic speeds. Comparing to other materials, electrons in graphene travel much faster - at speeds of $10^8cm/s$. A graphene sheet is pure enough to ensure that electrons can travel a fair distance before colliding. Electronic devices few nanometers long that would be able to transmit charge at breath taking speeds for a fraction of power compared to present day CMOS transistors. Many researches try to check a possibility to make it a perfect replacement for silicon based devices. Graphene has shown high potential to be used as interconnects in the field of high frequency electrical devices. With all those advantages of graphene, we demonstrate characteristics of electrical and optical properties of graphene such as the effect of graphene geometry on the microwave properties using the measurements of S-parameter in range of 500 MHz - 40 GHz at room temperature condition. We confirm that impedance and resistance decrease with increasing the number of graphene layer and w/L ratio. This result shows proper geometry of graphene to be used as high frequency interconnects. This study also presents the optical properties of graphene oxide (GO), which were deposited in different substrate, or influenced by oxygen plasma, were confirmed using different characterization techniques. 4-6 layers of the polycrystalline GO layers, which were confirmed by High resolution transmission electron microscopy (HRTEM) and electron diffraction analysis, were shown short range order of crystallization by the substrate as well as interlayer effect with an increase in interplanar spacing, which can be attributed to the presence of oxygen functional groups on its layers. X-ray photoelectron Spectroscopy (XPS) and Raman spectroscopy confirms the presence of the $sp^2$ and $sp^3$ hybridization due to the disordered crystal structures of the carbon atoms results from oxidation, and Fourier Transform Infrared spectroscopy (FTIR) and XPS analysis shows the changes in oxygen functional groups with nature of substrate. Moreover, the photoluminescent (PL) peak emission wavelength varies with substrate and the broad energy level distribution produces excitation dependent PL emission in a broad wavelength ranging from 400 to 650 nm. The structural and optical properties of oxygen plasma treated GO films for possible optoelectronic applications were also investigated using various characterization techniques. HRTEM and electron diffraction analysis confirmed that the oxygen plasma treatment results short range order crystallization in GO films with an increase in interplanar spacing, which can be attributed to the presence of oxygen functional groups. In addition, Electron energy loss spectroscopy (EELS) and Raman spectroscopy confirms the presence of the $sp^2$ and $sp^3$ hybridization due to the disordered crystal structures of the carbon atoms results from oxidation and XPS analysis shows that epoxy pairs convert to more stable C=O and O-C=O groups with oxygen plasma treatment. The broad energy level distribution resulting from the broad size distribution of the $sp^2$ clusters produces excitation dependent PL emission in a broad wavelength range from 400 to 650 nm. Our results suggest that substrate influenced, or oxygen treatment GO has higher potential for future optoelectronic devices by its various optical properties and visible PL emission.

• Polymer(Korea)
• /
• v.27 no.3
• /
• pp.249-254
• /
• 2003

The variation of chemical structure and physical properties of crosslinked polyethylene (XLPE) during thermal aging process was investigated. The formation of carbonyl functional group resulting from thermal oxidation reaction of XLPE was monitored using X-ray photoelectron spectroscopy and near infrared (NIR) spectroscopy. It was observed that the intensity of carbonyl peak observed at 1715 nm linearly increased with aging time in NIR spectroscopy. The linear relationship between NIR peak absorbance and aging time confirmed that NIR spectroscopy might be used as a proper tool for monitoring the aging process of polymeric materials. Also the formation of crosslinks during the aging process was monitored using thermal mechanical analysis, stress-strain test, and Shore hardness test. The change in the physical properties, such as the increase in the glass transition temperature from 110 to 132$^{\circ}C$, the decrease in the strain from 265 to 110%, as well as the increase in the shore D hardness from 32 to 50, was observed during the aging process.

• Korean Journal of Agricultural Science
• /
• v.41 no.1
• /
• pp.59-63
• /
• 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.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1612-1612
• /
• 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.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1062-1062
• /
• 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.

• Composites Research
• /
• v.15 no.2
• /
• pp.40-47
• /
• 2002

The di-functional epoxy resins, i.e., diglycidylether of bisphenol A(DGEBA) and diglycidylethere of bisphenol F(DGEBF) were initiated by cationic catalyst, i.e., benzylquinoxalinium hexafluoroantimonate(BQH) using electron-beam(EB) technique. And the effect of structure of DGEBA and DGEBF on thermal stabilities and cure behaviors was investigated. According to the experimental results, the decomposed activation energy based on Horowitz-Metzger method was higher in the case of DGEBA, but intergral procedural decomposition temperature(IPDT) of DGEBA was lower than DGEBF. This could be interpreted in terms of high crosslink density resulted from hydroxyl bond of DGEBF backbone. It was confirmed in increasing the hydroxyl band at $7000\;cm^{-1}$ and $5235\;cm^{-1}$ using near-infrared spectroscopy(NIRS).

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.43 no.3
• /
• pp.425-430
• /
• 2014

Foods contain various nutrients such as carbohydrates, protein, oil, vitamins, and minerals. Among them, carbohydrates, protein, and oil are the main constituents of foods. Usually, these constituents are analyzed by the Kjeldahl and Soxhlet method and so on. However, these analytical methods are complex, costly, and time-consuming. Thus, this study aimed to rapidly and effectively analyze carbohydrate, protein, and oil contents with near-infrared reflectance spectroscopy (NIRS). A total of 517 food samples were measured within the wavelength range of 400 to 2,500 nm. Exactly 412 food calibration samples and 162 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of carbohydrates, the most accurate equation was obtained under 1, 4, 5, 1 (1st derivative, 4 nm gap, 5 points smoothing, and 1 point second smoothing) math treatment conditions using the weighted MSC (multiplicative scatter correction) scatter correction method with MPLS (modified partial least square) regression. In the case of protein and oil, the best equation were obtained under 2, 5, 5, 3 and 1, 1, 1, 1 conditions, respectively, using standard MSC and standard normal variate only scatter correction methods with MPLS regression. Calibrations of these NIRS equations showed a very high coefficient of determination in calibration ($R^2$: carbohydrates, 0.971; protein, 0.974; oil, 0.937) and low standard error of calibration (carbohydrates, 4.066; protein, 1.080; oil, 1.890). Optimal equation conditions were applied to a validation set of 162 samples. Validation results of these NIRS equations showed a very high coefficient of determination in prediction ($r^2$: carbohydrates, 0.987; protein, 0.970; oil, 0.947) and low standard error of prediction (carbohydrates, 2.515; protein, 1.144; oil, 1.370). Therefore, these NIRS equations can be applicable for determination of carbohydrates, proteins, and oil contents in various foods.

• Journal of Biomedical Engineering Research
• /
• v.42 no.6
• /
• pp.268-276
• /
• 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.

• The Journal of Internal Korean Medicine
• /
• v.43 no.5
• /
• pp.1018-1028
• /
• 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.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.8
• /
• pp.1-7
• /
• 2022

With the development of artificial intelligence, efforts to incorporate neuroscience mining with AI have increased. Neuroscience mining, also known as NSM, expands on this concept by combining computational neuroscience and business analytics. Using fNIRS (functional near-infrared spectroscopy)-based experiment dataset, we have investigated the potential of NSM in the context of the BPSC (business problem-solving creativity) prediction. Although BPSC is regarded as an essential business differentiator and a difficult cognitive resource to imitate, measuring it is a challenging task. In the context of NSM, appropriate methods for assessing and predicting BPSC are still in their infancy. In this sense, we propose a novel NSM method that systematically combines CNN, BiLSTM, and attention network for the sake of enhancing the BPSC prediction performance significantly. We utilized a dataset containing over 150 thousand fNIRS-measured data points to evaluate the validity of our proposed NSM method. Empirical evidence demonstrates that the proposed NSM method reveals the most robust performance when compared to benchmarking methods.