• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.18 no.1
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• pp.22-25
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• 2007

Background and Objectives: Biomedical signals have been usually used for the diagnosis of the laryngeal function such as speech, electroglottograph(EGG), airflow and other signals. But, in most cases these signals were analysed separately. Here, we propose a new interchannel parameter Glottal Closure Delay Ratio(GCDR) which is estimated from speech and EGG measured simultaneously. Materials and Method: Speech and EGG signal were recorded simultaneously from 13 normal subjects, 39 patients. The patients' data included 16 polyps and 23 vocal folds palsy. Time difference between glottal closing instance on EGG and the first maximum peak on speech in a pitch period was calculated. Glottal closing instance was defined as the maximum peak on the first derivative of EGG signal(dEGG). Results: The standard deviation and jitter were calculated using 20-30 GCDRs extracted from each data, and they are significant different between normal and vocal fold paralysis group. Conclusion: The GCDR may be the first index reflecting speech and EGG characteristics and the perturbation of this parameter was significant different between normal and vocal fold paralysis group.

• Korean Journal of Food Science and Technology
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• v.9 no.1
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• pp.73-80
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• 1977

In order to detect the occurrence of aflatoxins in some suspecious Korean foodstuffs, 54 samples of Meju (a naturally inoculated soybean substrate for soy sauce and paste fermentation), 125 samples of Doenjang (a Korean-style fermented soybean paste), both produced at household level, and 31 samples of peanut were collected from 8 major cities of South Korea and subjected to assay by the official method of AOAC. The results were as follows: 1) Frequencies for the occurrence of aflatoxins in Meju, Doenjang and peanut were 7.4%(4/54), 8.8%(11/125) and none (0/31), respectively, in which Meju and Doenjang samples from Daegu and Busan showed the high ratio of the presence. 2) A Doenjang sample from Busan was found to contain the highest content of aflatoxins, of which $B_1,\;B_2,\;G_1\;and\;G_2$ were 66 ppb, 13 ppb, non-detectable and 5 ppb, respectively, while other samples detected were for $G_2$ only. 3) The identity of aflatoxin $B_1$ isolated from the Doenjang sample from Busan was confirmed by thin-layer chromatographic behavior, derivative formation and chicken embryo bioassay.

• Journal of the Korean Solar Energy Society
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• v.33 no.3
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• pp.42-50
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• 2013

Solar thermal reactor was studied for hydrogen production with a two step thermochemical cycle including T-R(Thermal Reduction) step and W-D(Water Decomposition) step. NiFe2O4 and Fe3O4 supported by monoclinic ZrO2 were used as a catalyst device and Ni powder was used for decreasing the T-R step reaction temperature. Maintaining a temperature level of about $1100^{\circ}C$ and $1400^{\circ}C$, for 2-step thermochemical reaction, is important for obtaining maximum performance of hydrogen production. The controller was designed for adjusting high temperature solar thermal energy heating the foam-device coated with nickel- ferrite powder. A Pill temperature control system was designed based on 2-step thermochemical reaction experiment data(measured concentrated solar radiation and the temperature of foam device during experiment). The cycle repeated 5 times, ferrite conversion rate are 4.49~29.97% and hydrogen production rate is 0.19~1.54mmol/g-ferrite. A temperature controller was designed for increasing the number of reaction cycles related with the amount of produced hydrogen.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.184-196
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• 2021

This paper proposes statistics adaptive linear regression-based object size prediction method for object detection. YOLOv2 and YOLOv3, which are typical deep learning-based object detection algorithms, designed the last layer of a network using statistics adaptive exponential regression model to predict the size of objects. However, an exponential regression model can propagate a high derivative of a loss function into all parameters in a network because of the property of an exponential function. We propose statistics adaptive linear regression layer to ease the gradient exploding problem of the exponential regression model. The proposed statistics adaptive linear regression model is used in the last layer of the network to predict the size of objects with statistics estimated from training dataset. We newly designed the network based on the YOLOv3tiny and it shows the higher performance compared to YOLOv3 tiny on the UFPR-ALPR dataset.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.331-341
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• 2018

Purpose: This study was conducted to develop a linear Proportional-Integral-Derivative (PID) control algorithm for the ascending and descending system of a rice transplanter and to analyze its response characteristics. Methods: A hydraulic model using a single-acting actuator, proportional valve and a PID control algorithm were developed for the ascending and descending system. The PID coefficients are tuned using the Ziegler-Nichols (Z-N) method and the characteristics of unit step response are analyzed to select the PID coefficients at various pump speeds. Results: Results showed that the performance of the PID controller was superior in any condition. It was found that the highest settling time and maximum overshoot were less than 0.210 s and 5%, respectively at all pump speed. It was determined that the steady state errors were 0% in all the cases. The lowest overshoot and settling time were calculated to be nearly 2.56% and 0.205 s, respectively at the pump rated speed (2650 rpm). Conclusions: The results indicated that the developed PID control algorithm would be feasible for the ascending and descending system of a rice transplanter. Finally, it would be helpful to plant the seedlings uniformly and improve the performance of the rice transplanter.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.186-191
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• 2019

In this paper, we propose a implementation approach of the high-speed feature extraction algorithm. The proposed method is based on the block type classification algorithm which reduces the computation time when target macro block is divided to smooth block type that has no image features. It is quantitatively identified that occurs at 29.5% of the total image using 200 standard test images with $64{\times}64$ macro block size. This means that within a standard test image containing various image information, 29.5% can reduce the complexity of the operation. When the proposed approach is applied to the Canny edge detection, the required latency of the edge detection can be completely eliminated, such as 2D derivative filter, gradient magnitude/direction computation, non-maximal suppression, adaptive threshold calculation, hysteresis thresholding. Also, it is expected that operation time of the feature detection can be reduced by applying block type classification algorithm to various feature extraction algorithms in this way.

• Journal of Biomedical and Translational Research
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• v.19 no.4
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• pp.130-139
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• 2018

This study aimed to analyze a high-performance liquid chromatography (HPLC) separation using a pentafluorophenyl column of parent drug hydroxychloroquine (HCQ) and its active metabolite, desethylhydroxchloroquine (DHCQ) applying to determine bioequivalence of two different formulations administered to patients. A rapid, simple, sensitive and specific liquid chromatography-tandem mass spectrometry (LC-MS/MS) method has been developed and validated for bioanalysis of HCQ and its metabolite DHCQ in human whole blood using deuterium derivative $hydroxychloroquine-D_4$ as an internal standard (IS). A triple-quadrupole mass spectrometer was operated using electrospray ionization in multiple reaction monitoring (MRM) mode. Sample preparation involves a two-step precipitation of protein techniques. The removed protein blood samples were chromatographed on a pentafluorophenyl (PFP) column ($50mm{\times}4.6mm$, $2.6{\mu}m$) with a mobile phase (ammonium formate solution containing dilute formic acid) in an isocratic mode at a flow rate of 0.45 mL/min. The standard curves were found to be linear in the range of 2 - 500 ng/mL for HCQ; 2 - 2,000 ng/mL for DHCQ in spite of lacking a highly sensitive MS spectrometry system. Results of intra- and inter-day precision and accuracy were within acceptable limits. A run time of 2.2 min for HCQ and 2.03 min for DHCQ in blood sample facilitated the analysis of more than 300 human whole blood samples per day. Taken together, we concluded that the assay developed herein represents a highly qualified technology for the quantification of HCQ in human whole blood for a parallel design bioequivalence study in a healthy male.

• Korean Journal of Agricultural Science
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• v.45 no.4
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• pp.823-837
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• 2018

The objective of this study was to evaluate the firmness and the sweetness index (SI) of tomatoes with a hyperspectral imaging (HSI) technique within the wavelength range of 1000 - 1550 nm. The hyperspectral images of 95 tomatoes were acquired with a push-broom hyperspectral reflectance imaging system, from which the mean spectra of each tomato were extracted from the regions of interest. The reference firmness and sweetness index of the same sample was measured and calibrated with their corresponding spectral data by partial least squares (PLS) regression with different preprocessing methods. The calibration model developed by PLS regression based on the Savitzky-Golay second-derivative preprocessed spectra resulted in a better performance for both the firmness and the SI of the tomatoes compared to models developed by other preprocessing methods. The correlation coefficients ($R_{pred}$) were 0.82, and 0.74 with a standard error of prediction of 0.86 N, and 0.63, respectively. Then, the feature wavelengths were identified using a model-based variable selection method, i.e., variable importance in projection, from the PLS regression analyses. Finally, chemical images were derived by applying the respective regression coefficients on the spectral image in a pixel-wise manner. The resulting chemical images provided detailed information on the firmness and the SI of the tomatoes. The results show that the proposed HSI technique has potential for rapid and non-destructive evaluation of firmness and the sweetness index of tomatoes.

• The korean journal of orthodontics
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• v.52 no.4
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• pp.268-277
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• 2022

Objective: Poor experience with Invisalign treatment affects patient compliance and, thus, treatment outcome. Knowing the potential discomfort level in advance can help orthodontists better prepare the patient to overcome the difficult stage. This study aimed to construct artificial neural networks (ANNs) to predict patient experience in the early stages of Invisalign treatment. Methods: In total, 196 patients were enrolled. Data collection included questionnaires on pain, anxiety, and quality of life (QoL). A four-layer fully connected multilayer perception with three backpropagations was constructed to predict patient experience of the treatment. The input data comprised 17 clinical features. The partial derivative method was used to calculate the relative contributions of each input in the ANNs. Results: The predictive success rates for pain, anxiety, and QoL were 87.7%, 93.4%, and 92.4%, respectively. ANNs for predicting pain, anxiety, and QoL yielded areas under the curve of 0.963, 0.992, and 0.982, respectively. The number of teeth with lingual attachments was the most important factor affecting the outcome of negative experience, followed by the number of lingual buttons and upper incisors with attachments. Conclusions: The constructed ANNs in this preliminary study show good accuracy in predicting patient experience (i.e., pain, anxiety, and QoL) of Invisalign treatment. Artificial intelligence system developed for predicting patient comfort has potential for clinical application to enhance patient compliance.

• Coupled systems mechanics
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• v.10 no.6
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• pp.509-520
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• 2021

Fiber-reinforced concrete (FRC) is a composite material where small fibers made from steel or polypropylene or similar material are embedded into concrete matrix. In a material model each constituent should be adequately described, especially the interface between the matrix and fibers that is determined with the 'bond-slip' law. 'Bond-slip' law describes relation between the force in a fiber and its displacement. Bond-slip relation is usually obtained from tension laboratory experiments where a fiber is pulled out from a matrix (concrete) block. However, theoretically bond-slip relation could be determined from bending experiments since in bending the fibers in FRC get pulled-out from the concrete matrix. We have performed specially designed laboratory experiments of three-point beam bending with an intention of using experimental data for determination of material parameters. In addition, we have formulated simple layered model for description of the behavior of beams in the three-point bending test. It is not possible to use this 'forward' beam model for extraction of material parameters so an inverse model has been devised. This model is a basis for formulation of an inverse model that could be used for parameter extraction from laboratory tests. The key assumption in the developed inverse solution procedure is that some values in the formulation are known and comprised in the experimental data. The procedure includes measured data and its derivative, the formulation is nonlinear and solution is obtained from an iterative procedure. The proposed method is numerically validated in the example at the end of the paper and it is demonstrated that material parameters could be successfully recovered from measured data.