• Journal of Information Processing Systems
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• v.17 no.5
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• pp.1020-1033
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• 2021

Nowadays, online or mobile social network services (SNS) are very popular and widely spread in our society and daily lives to instantly share, disseminate, and search information. In particular, SNS such as YouTube, Flickr, Facebook, and Amazon allow users to upload billions of images or videos and also provide a number of multimedia information to users. Information retrieval in multimedia-rich SNS is very useful but challenging task. Content-based media retrieval (CBMR) is the process of obtaining the relevant image or video objects for a given query from a collection of information sources. However, CBMR suffers from the dimensionality curse due to inherent high dimensionality features of media data. This paper investigates the effectiveness of the kernel trick in CBMR, specifically, the kernel principal component analysis (KPCA) for dimensionality reduction. KPCA is a nonlinear extension of linear principal component analysis (LPCA) to discovering nonlinear embeddings using the kernel trick. The fundamental idea of KPCA is mapping the input data into a highdimensional feature space through a nonlinear kernel function and then computing the principal components on that mapped space. This paper investigates the potential of KPCA in CBMR for feature extraction or dimensionality reduction. Using the Gaussian kernel in our experiments, we compute the principal components of an image dataset in the transformed space and then we use them as new feature dimensions for the image dataset. Moreover, KPCA can be applied to other many domains including CBMR, where LPCA has been used to extract features and where the nonlinear extension would be effective. Our results from extensive experiments demonstrate that the potential of KPCA is very encouraging compared with LPCA in CBMR.

• Journal of Astronomy and Space Sciences
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• v.38 no.3
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• pp.157-164
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• 2021

We describe a method for the in-orbit calibration of body-mounted magnetometers based on the CHAOS-7 geomagnetic field model. The code is designed to find the true calibration parameters autonomously by using only the onboard magnetometer data and the corresponding CHAOS outputs. As the model output and satellite data have different coordinate systems, they are first transformed to a Star Tracker Coordinate (STC). Then, non-linear optimization processes are run to minimize the differences between the CHAOS-7 model and satellite data in the STC. The process finally searches out a suite of calibration parameters that can maximize the model-data agreement. These parameters include the instrument gain, offset, axis orthogonality, and Euler rotation matrices between the magnetometer frame and the STC. To validate the performance of the Python code, we first produce pseudo satellite data by convoluting CHAOS-7 model outputs with a prescribed set of the 'true' calibration parameters. Then, we let the code autonomously undistort the pseudo satellite data through optimization processes, which ultimately track down the initially prescribed calibration parameters. The reconstructed parameters are in good agreement with the prescribed (true) ones, which demonstrates that the code can be used for actual instrument data calibration. This study is performed using Python 3.8.5, NumPy 1.19.2, SciPy 1.6, AstroPy 4.2, SpacePy 0.2.1, and ChaosmagPy 0.5 including the CHAOS-7.6 geomagnetic field model. This code will be utilized for processing NextSat-1 and Small scale magNetospheric and Ionospheric Plasma Experiment (SNIPE) data in the future.

• Journal of Sensor Science and Technology
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• v.30 no.5
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• pp.320-325
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• 2021

Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) methods are conventional analytical methods to analyze water quality. Both of these methods are technically indirect measurement methods, require complicated preconditions, and are time-consuming. On the other hand, the total organic carbon (TOC) method is a direct and fast measurement method which is more intuitive and accurate than the BOD and COD methods. However, general TOC analysis methods involve complicated processes and high power consumption owing to the process of phase transition from liquid to gas by a high-temperature heater. Furthermore, periodic consumables are also required for the removal of inorganic carbon (IC). Titanium dioxide (TiO₂) is one of the most suitable photocatalysts for simple processes. Its usage involves low power consumption because it only reacts with the organic carbon (OC) without the requirement of any other reagents and extra processes. We investigated a TiO₂ photocatalyst-based TOC analysis for simple and affordable products. TiO₂-coated fiber substrate maintained under carbon included water was exposed to ultraviolet (UV) radiation of wavelength 365 nm. This method is suitable for the real-time monitoring of water pollution because of its fast reaction time. Its linear property is also sufficient to match the real value.

• Journal of the Korean Society of Physical Medicine
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• v.16 no.2
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• pp.45-52
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• 2021

PURPOSE: This study examined the imaging procedure of pectoralis minor muscle thickness and assessed the intra- and inter-rater reliability of the muscle thickness measured by two raters using rehabilitative ultrasound imaging (RUSI) in healthy individuals. METHODS: Fifteen participants (aged 21 - 28, seven females, and eight males) were involved in the study. The primary rater palpated the coracoid process and the fourth rib, defined as the width of the index finger lateral to the sternum to avoid breast tissues, and lined the two landmarks. The second examiner checked 1 / 3 (1st point) and 1 / 2 (2nd point) of the line length as measurement points. The two raters obtained right side muscle images of the participants at a standardized sitting position using RUSI with a 7.5 MHz linear transducer at 40mm depth. For intra-rater reliability, the principal rater took three images per point and tried to take one more with an interval. For the inter-rater reliability, the other rater performed the same tasks as the principal rater on the same day. The reliability was analyzed using the intra-class correlation coefficient (ICC), the standard error of the measurement (SEM), and Bland and Altman plots. RESULTS: The reliability at all points was excellent for the same rater (ICC_3,1 = .973 - .978, SEM = .042 - .046), and between raters (ICC_2,1 = .939 - .959, SEM = .059 - .097). CONCLUSION: These findings show that the RUSI could be reliable for examining the pectoralis minor muscle thickness in healthy individuals at all measurement sites.

• Current Optics and Photonics
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• v.5 no.4
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• pp.450-457
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• 2021

Upconversion (UC) properties of Y₂O₃:Ho³⁺/Yb³⁺ spherical particles synthesized by spray pyrolysis were investigated by changing the dopant concentration and calcination temperature. Citric acid (CA), ethylene glycol (EG) and N, N-dimethylformamide (DMF) were used to control the microstructure of Y₂O₃:Ho³⁺/Yb³⁺ particles. In terms of achieving the highest UC green emission intensity, the optimal concentrations of Ho³⁺ and Yb³⁺ were found to be 0.3% and 3.0%, respectively. In addition, the UC intensity of Y₂O₃:Ho³⁺/Yb³⁺ showed a linear relationship with the crystallite size. The use of organic additives allows Y₂O₃:Ho³⁺/Yb³⁺ particles to have a spherical and dense structure, resulting in significantly reducing the surface area while maintaining high crystallinity. As a result, the UC emission intensity of Y₂O₃:Ho³⁺/Yb³⁺ particles having a dense structure showed the UC emission intensity about 3.8 times higher than that of hollow particles prepared without organic additives. From those results, when Y₂O₃:Ho³⁺/Yb³⁺ particles are prepared by the spray pyrolysis process, the use of the CA/EG/DMF mixtures as organic additives has been suggested as an effective way to substantially increase the UC emission intensity.

• Journal of the Korea Society for Simulation
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• v.30 no.4
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• pp.1-8
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• 2021

Deep learning is one of the most widely accepted methods for the forecasting of time series data which have the complexity and non-linear behavior. In this paper, we investigate the modification of a state-of-art WaveNet deep learning architecture and walk forward validation (WFV) in order to forecast electric power consumption data 24-hour-ahead. WaveNet originally designed for raw audio uses 1D dilated causal convolution for long-term information. First of all, we propose a modified version of WaveNet which activates real numbers instead of coded integers. Second, this paper provides with the training process with tuning of major hyper-parameters (i.e., input length, batch size, number of WaveNet blocks, dilation rates, and learning rate scheduler). Finally, performance evaluation results show that the prediction methodology based on WFV performs better than on the traditional holdout validation.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.104-110
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• 2021

A novel selective oxidation process has been developed for low source/drain (S/D) series resistance of the fin channel metal oxide semiconductor field effect transistor (MOSFET). Using this technique, the selective oxidation fin-channel MOSFET (SoxFET) has the gate-all-around structure and gradually enhanced S/D extension regions. The SoxFET demonstrated over 70% reduction in S/D series resistance compared to the control device. Moreover, it was found that the SoxFET behaved better in performance, not only a higher drive current but also higher transconductances with suppressing subthreshold swing and drain induced barrier lowering (DIBL) characteristics, than the control device. The saturation current, threshold voltage, peak linear transconductance, peak saturation transconductance, subthreshold swing, and DIBL for the fabricated SoxFET are 305 ㎂/㎛, 0.33 V, 13.5 𝜇S, 76.4 𝜇S, 78 mV/dec, and 62 mV/V, respectively.

• Korean Journal of Breeding Science
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• v.42 no.5
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• pp.481-487
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• 2010

The ripening behavior of three apple cultivars, 'Tsugaru', 'Hongro' and 'Fuji' was distinctive and the involvement of POLYGALACTURONASE(PG) in the fruit softening process was confirmed to be ethylene dependent. Fruit softening is genetically coordinated by the action of several cell wall enzymes, including PG which depolymerizes cell wall pectin. Also, loss of firmness is associated with increasing of the ripening hormone, ethylene. In this work, climacteric ripening of three apple cultivars, Tsugaru, Hongro and Fuji, producing different ethylene levels and ripening responses, was examined. Correspondingly in Fuji, a linear and basal ethylene level was observed over the entire period of measurements, and Tsugaru and Hongro displayed a typical climacteric rise in ethylene production. Transcript accumulation of genes involved in ethylene biosynthesis (MdACS3 and MdACO1) and MdPG1 was studied in Tsugaru, Hongro and Fuji cultivars. Expression of MdACO1 transcripts was shown in all three ripened apple fruits. However, the MdACS3 and MdPG1 were transcribed differently in these cultivars. Comparing the MdPG1 of 'Tsugaru', 'Hongro' and 'Fuji', structural difference was discovered by genomic Southern analysis. Overall results pointed out that MdACS3 and MdPG1 play an important role in regulation of fruit ripening in apple cultivar.

• Journal of Ginseng Research
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• v.43 no.1
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• pp.27-37
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• 2019

Background: The structural conversions in ginsenosides induced by steaming or heating or acidic condition could improve red ginseng bioactivities significantly. In this paper, the chemical transformations of red American ginseng from fresh Panax quinquefolium L. under steaming were investigated, and the possible mechanisms were discussed. Methods: A method with reversed-phase high-performance liquid chromatography coupled with linear ion trap mass spectrometry ($HPLC-MS^n$)-equipped electrospray ionization ion source was developed for structural analysis and quantitation of ginsenosides in dried and red American ginseng. Results: In total, 59 ginsenosides of protopanaxadiol, protopanaxatriol, oleanane, and ocotillol types were identified in American ginseng before and after steaming process by matching the molecular weight and/or comparing $MS^n$ fragmentation with that of standards and/or known published compounds, and some of them were determined to be disappeared or newly generated under different steaming time and temperature. The specific fragments of each aglycone-type ginsenosides were determined as well as aglycone hydrated and dehydrated ones. The mechanisms were deduced as hydrolysis, hydration, dehydration, and isomerization of neutral and acidic ginsenosides. Furthermore, the relative peak areas of detected compounds were calculated based on peak areas ratio. Conclusion: The multicomponent assessment of American ginseng was conducted by $HPLC-MS^n$. The result is expected to provide possibility for holistic evaluation of the processing procedures of red American ginseng and a scientific basis for the usage of American ginseng in prescription.

• Applied Chemistry for Engineering
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• v.30 no.4
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• pp.509-511
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• 2019

Hydrazine derivatives used in fine chemicals, pharmaceuticals and cosmetics can be synthesized by reduction reaction from diazonium derivatives. The reduction method using $SnCl_2$ facilitates the reaction conversion, but the use of $SnCl_2$ is limited when residual heavy metals are issued in the final product. For the conversion of protected (2-hydroxyphenyl)diazonium derivatives into its hydrazine derivatives in Ramalin preparation process, the reduction method was developed by using $NaHSO_3$. In this study, the effect of steric hindrance according to the protected 2-hydroxygroupinphenyldiazonium derivatives was found, and linear C1~C5 alkyl groups for the hydroxy protection were preferable during the diazonium reduction reaction. Considering the economical efficiency and industrial production for the preparation of Ramalin, a variety of protecting groups were investigated. As a result, 2-(allyloxyphenyl)hydrazine was obtained with 85% yield and 99.7% purity when the hydroxy protecting group was used as an allyl group that could be easily deprotected.