• The Journal of Korean Physical Therapy
• /
• v.36 no.1
• /
• pp.39-44
• /
• 2024

Purpose: Cybersickness is a type of motion sickness induced by virtual reality (VR) or augmented reality (AR) environments that presents symptoms including nausea, dizziness, and headaches. This study aimed to investigate how cathodal transcranial direct current stimulation (tDCS) alleviates motion sickness symptoms and modulates brain activity in individuals experiencing cybersickness after exposure to a VR environment. Methods: This study was performed on two groups of healthy adults with cybersickness symptoms. Subjects were randomly assigned to receive either cathodal tDCS intervention or sham tDCS intervention. Brain activity during VR stimulation was measured by 38-channel functional near-infrared spectroscopy (fNIRS). tDCS was administered to the right temporoparietal junction (TPJ) for 20 minutes at an intensity of 2mA, and the severity of cybersickness was assessed pre- and post-intervention using a simulator sickness questionnaire (SSQ). Result: Following the experiment, cybersickness symptoms in subjects who received cathodal tDCS intervention were reduced based on SSQ scores, whereas those who received sham tDCS showed no significant change. fNIRS analysis revealed that tDCS significantly diminished cortical activity in subjects with high activity in temporal and parietal lobes, whereas high cortical activity was maintained in these regions after intervention in subjects who received sham tDCS. Conclusion: These findings suggest that cathodal tDCS applied to the right TPJ region in young adults experiencing cybersickness effectively reduces motion sickness induced by VR environments.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.39 no.1
• /
• pp.7-14
• /
• 1994

This study was conducted to establish the rapid evaluation method of chemical components of rice grain on the basis of non-destructive method. A near-infrared reflectance spectroscopic(NIRS) method was utilized, for the determination of amylose, protein, magnesium, and potassium content of rice. A multiple linear regression analysis for the data obtained by standard laboratory methods and NIRS method was carried out to make a calibration. The standard error of prediction for amylose, protein, magneisum and potassium content were 0.88%, 0.28%, 12.62mg and 10.79mg, respectively. It was concluded that the NlRS method can be useful the rapid determination of amylose, protein, magnesium and potassium content instead of the existing laboratory method.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.3113-3113
• /
• 2001

Some two years ago our company undertook a project on manufacturing network rationalization to maximize competitiveness through continuous improvement in manufacturing efficiency. One key outcome was the recognition of the benefits that could be derived from timely application of new technology or novel use of existing technologies and even more importantly the need to develop company wide strategies to maximize the impact of such applications. As a direct result an exercise was undertaken to identify the ten most promising technologies from a list of literally hundreds seen as having the capability of making a rapid impact on the manufacturing initiative. One of the outcomes of this exercise was the identification of Near Infrared Spectroscopy as a pivotal technology for improving process understanding, performance, and control to deliver consistent product quality cost effectively with broad applicability across our product range. While NIR had been in use in targeted areas on some of our sites for some years our new challenge was to develop a strategy to extend NIRs application, initially over 17 manufacturing sites, while concurrently expanding the NIR skill base company wide to ensure that the return on initial investment could be further maximized as shared applications across the remaining sites as required. This presentation will provide an overview of how life cycle based user requirement specifications were developed covering: ㆍSpectrophotometers ㆍSample interfaces ㆍSoftware ㆍEquipment and Software qualification ㆍCalibration transfer ㆍ Ease of developing effective user interfaces and control for applications transferred to a production area ㆍUser training ㆍWorld wide support The presentation will also describe the process adopted for vendor selection to ensure maximum utilization of the existing company wide NIR skill base and its future development to expedite applications of the technology in development, quality control and production areas.

• Applied Biological Chemistry
• /
• v.41 no.3
• /
• pp.234-239
• /
• 1998

In non-destructive evaluation of free acid content in apples by near- infrared spectroscopy(NIRS), browning and heat treatment of squeezed apple juice affected to the accuracy but titrable alkali concentration did not. The free acid content in apples after harvest was able to determine using different apples in harvest time for calibration making. The result of MLR, multiple correlation coefficient(R) was 0.77 and standard error of prediction(SEP) was 0.03%. The free acid content in apples during storage was able to determine using calibration equation established with stored apples, R was 0.90 and SEP was ca. 0.04%. The prediction accuracy by LAIR was not sufficient for use of quantitative analysis of free acid content in apple, but classification of law and high level in acid content was supposed to be applicable.

• 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.

• Analytical Science and Technology
• /
• v.23 no.3
• /
• pp.304-311
• /
• 2010

Near Infrared Spectroscopy (NIRs) has been applied for rapid and nondestructive paper measurement by replacing the current destructive method to the property of paper. Current standard methods for the property of paper were pH, moisture, breaking length, and folding endurance, which data were compared with spectrum of FT-NIR spectrometer. Various paper products such as copy, envelope, white, newspaper, as well as old paper produced around 1960~1980 were used as the sample. The correlation ($R^2$) and standard error of prediction (SEP) results for breaking length, folding endurance, moisture and pH are $R^2$=0.914, SEP=0.508, $R^2$=0.926, SEP=0.281, $R^2$=0.941, SEP=0.931, pH $R^2$=0.949, SEP= -0.0631, respectively. This result show that NIRs can be applied to practical application for nondestructive analysis of paper records materials.

• Journal of Korea Multimedia Society
• /
• v.21 no.5
• /
• pp.609-616
• /
• 2018

In this paper, we designed a multimodal bio-signal measurement system to observe changes in the brain nervous system and vascular system during sleep. Changes in the nervous system and the cerebral blood flow system in the brain during sleep induce a unique correlation between the changes in the nervous system and the blood flow system. Therefore, it is necessary to simultaneously observe changes in the brain nervous system and changes in the blood flow system to observe the sleep state. To measure the change of the nervous system, EEG, EOG and EMG signal used for the sleep stage analysis were designed. We designed a system for measuring cerebral blood flow changes using functional near-infrared spectroscopy. Among the various imaging methods to measure blood flow and metabolism, it is easy to measure simultaneously with EEG signal and it can be easily designed for miniaturization of equipment. The sleep stage was analyzed by the measured data, and the change of the cerebral blood flow was confirmed by the change of the sleep stage.

• Korean Journal of Plant Resources
• /
• v.30 no.1
• /
• pp.38-49
• /
• 2017

The objective of this research was to develop Near-Infrared Reflectance Spectroscopy (NIRS) model for amylose and protein contents analysis of large accessions of rice germplasm. A total of 511 accessions of rice germplasm were obtained from National Agrobiodiversity Center to make calibration equation. The accessions were measured by NIRS for both brown and milled brown rice which was additionally assayed by iodine and Kjeldahl method for amylose and crude protein contents. The range of amylose and protein content in milled brown rice were 6.15-32.25% and 4.72-14.81%, respectively. The correlation coefficient ($R^2$), standard error of calibration (SEC) and slope of brown rice were 0.906, 1.741, 0.995 in amylose and 0.941, 0.276, 1.011 in protein, respectively, whereas $R^2$, SEC and slope of milled brown rice values were 0.956, 1.159, 1.001 in amylose and 0.982, 0.164, 1.003 in protein, respectively. Validation results of this NIRS equation showed a high coefficient determination in prediction for amylose (0.962) and protein (0.986), and also low standard error in prediction (SEP) for amylose (2.349) and protein (0.415). These results suggest that NIRS equation model should be practically applied for determination of amylose and crude protein contents in large accessions of rice germplasm.

• 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.

• Journal of Animal Environmental Science
• /
• v.18 no.sup
• /
• pp.81-90
• /
• 2012

This study was evaluated high end research grade Near infrared spectrophotometer (NIRS) to low end popular field grade multiple Near infrared spectrophotometer (NIRS) for rapid analysis at forage quality at sight with 241 samples of Italian ryegrass silage during 3 years collected whole country for evaluate accuracy and precision between instruments. Firstly collected and build database high end research grade NIRS using with Unity Scientific Model 2500X (650 nm~2,500 nm) then trim and fit to low end popular field grade NIRS with Unity Scientific Model 1400 (1,400 nm~2,400 nm) then build and create calibration, transfer calibration with special transfer algorithm. The result between instruments was 0.000%~0.343% differences, rapidly analysis for chemical constituents, NDF, ADF, and crude protein, crude ash and fermentation parameter such as moisture, pH and lactic acid, finally forage quality parameter, TDN, DMI, RFV within 5 minutes at sight and the result equivalent with laboratory data. Nevertheless during 3 years collected samples for build calibration was organic samples that make differentiate by local or yearly bases etc. This strongly suggest population evaluation technique needed and constantly update calibration and maintenance calibration to proper handling database accumulation and spread out by knowledgable control laboratory analysis and reflect calibration update such as powerful control center needed for long lasting usage of forage analysis with NIRS at sight. Especially the agriculture products such as forage will continuously changes that made easily find out the changes and update routinely, if not near future NIRS was worthless due to those changes. Many research related NIRS was shortly study not long term study that made not well using NIRS, so the system needed check simple and instantly using with local language supported signal methods Global Distance (GD) and Neighbour Distance (ND) algorithm. Finally the multiple popular field grades instruments should be the same results not only between research grade instruments but also between multiple popular field grade instruments that needed easily transfer calibration and maintenance between instruments via internet networking techniques.