• Journal of the Korean Society for Library and Information Science
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• v.55 no.1
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• pp.365-392
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• 2021

In order to assess the state of library and information science (LIS) research in Korea, the study analyzed bibliometric data of papers published in past 18 years in Korea Citation Index (KCI) and Social Science Citation Index (SSCI) journals. The analysis of study data, which consisted of 6,301 KCI journal papers with 26,474 citations and 86,727 SSCI journal papers with 1,196,961 citations from 2002 to 2020, involved comparison of research productivity and impact, collaboration trends, and key areas of research between domestic and international LIS scholars with normalizations by units of analysis for size differences. Even with size normalization, the study found a marked difference in citation patterns between domestic and international LIS research. Korean LIS authors were twice as productive as international LIS authors but a little over a half as impactful. The results also showed a much higher level of skewness in international research, where a fraction of top authors, institutions, and journals received a lion's share of citations. The trend of increasing co-authorship was much more pronounced among international publication, where the recent popularity of larger collaboration groups suggests multi-disciplinary and increasingly complex nature of modern LIS research in the world stage. The keyword analysis revealed a much more diverse subject area in international than domestic LIS research with a recent shift towards technology, such as big data, blockchain, and altmetrics. Keywords in SSCI journals also exhibited a less connection between popularity and impact than KCI keywords, where popular keywords did not necessarily correspond to impactful keywords.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.183-192
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• 2023

The remotely sensed data, such as satellite imagery and aerial photos, can be used to extract and detect some objects in the image through image interpretation and processing techniques. Significantly, the possibility for utilizing digital map updating and land monitoring has been increased through automatic object detection since spatial resolution of remotely sensed data has improved and technologies about deep learning have been developed. In this paper, we tried to extract plastic greenhouses into aerial orthophotos by using fully convolutional densely connected convolutional network (FC-DenseNet), one of the representative deep learning models for semantic segmentation. Then, a quantitative analysis of extraction results had performed. Using the farm map of the Ministry of Agriculture, Food and Rural Affairsin Korea, training data was generated by labeling plastic greenhouses into Damyang and Miryang areas. And then, FC-DenseNet was trained through a training dataset. To apply the deep learning model in the remotely sensed imagery, instance norm, which can maintain the spectral characteristics of bands, was used as normalization. In addition, optimal weights for each band were determined by adding attention modules in the deep learning model. In the experiments, it was found that a deep learning model can extract plastic greenhouses. These results can be applied to digital map updating of Farm-map and landcover maps.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• Korean Journal of Soil Science and Fertilizer
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• v.36 no.4
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• pp.200-209
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• 2003

Distribution coefficient ($K_d$) is an universal parameter estimating cadmium partition for a soil-water-crop system in agricultural lands. This study was performed to find some factors affecting soil-water partition coefficients for cadmium in some Korean soils. The distribution coefficients ($K_d$) of cadmium for the 15 series of agricultural soils were measured at quasi-steady state in the pH ranges from 2 to 11. The adsorption data of the selected soils showed a linear relationship between log $K_d$ and pH, which was well agreed with theoretically expected results ; $log\;K_d=0.6339pH+0.5532(r^2=0.70^{**})$. Normalization of the partition coefficients were performed in a range of pH 3.5 ~ 8.5 to minimize adverse effects of Al dissolution, cationic competition, and organic matter dissolution. The $K_d$-om, partition coefficients normalized for organic matter, improved this linearity to the pH of soils. The values of $K_d$-om measured from the field samples were significantly correlated with those of $K_d$ predicted from the sorption-edge experimental data ($r^2=0.68^{**}$).

• Journal of the Korean Geotechnical Society
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• v.19 no.5
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• pp.133-144
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• 2003

Horizontal consolidation characteristics of Busan marine clay were investigated by computing coefficient of horizontal consolidation from Piezocone data and comparing their results with those of standard consolidation test. It is well known that current prediction models of $c_h$ for high plastic soils have large uncertainties, and show a great difference between the predicted and the measured values. However, the spherical models and expanding cavity theory of Torstensson(1977), and Burns & Mayne(1998) based on modified Cam-Clay model with critical limit state concepts have relative reliability in estimating $c_h$ and good applicability in highly plasticity soils. In this paper, a normalization technique was used to evaluate $c_h$ using the Burns and Mayne's method based on the dissipation test, and their normalized consolidation curves give 0.015 of time factor($T_{50}$) when 50% degree of consolidation is completed. Comparison study using Piezocone data obtained at other similar ground site shows 1.5 times less systematicality than that of standard consolidation test, which indicates considerable approximation with the measured values because standard consolidation test gives the difference of three to few times compared with the measured values. In addition, design chart for estimating $c_h$ based on the chart from Robertson et al.(1992) and using the other method of the direct prediction from the of dissipation test was newly proposed. It is judged that new proposed chart is very applicable to Korean marine soils, especially in very high plastic soils.

• Journal of Korean Society for Geospatial Information Science
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• v.6 no.2 s.12
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• pp.105-117
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• 1998

The main object of this study was to prove the effectiveness of satellite Image data for extraction of the pine needle gall midge damaged area in the part of Kangwon-do area, and to present the detailed procedure of a digital image processing for extraction of those damaged area. The effectiveness of extraction of damaged area was improved by using the BRCT(Backwards Radiance Correction Transformation) with DEM for the normalization of topographic effects. The topographic surface analysis of the extracted damaged area revealed that the general damaged area was at south-west and south-east aspect with the slope of 31 to 38 degrees, the temperature of 21 to 25, and 23% to 39% of the highest altitude mountains. The new damaged area in which expanded area was at 27 to 30 degree of slope, the aspect of 46 to 180 degrees, the temperature of $11^{\circ}C\;to\;12^{\circ}C$ and 27% to 39% of the highest altitude mountains. The NDI(New Damaged Index) was developed using the environment factor and simple vegetation index.

• Applied Biological Chemistry
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• v.47 no.3
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• pp.300-306
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• 2004

Eight cation or anion-responsive polymer membranes were prepared by a casting procedure employing polyvinyl chloride, Bis (2-ethylhexyl)sebacate and each electroactive material in the ratio of 66 : 33 : 1. The resulting membranes were separately installed onto the sensitive area of the ionic electrodes to produce an 8-channel taste sensor array. The taste sensors of the array were connected to a high-input impedance amplifier and the amplified sensor signals were interfaced to a PC via an A/D converter. The taste evaluation system was applied to a discriminant analysis on six groups of marketed beverages like sikhye, sujunggwa, tangerine juice, ume juice, ionic drink and green tea. When the signal data from the sensor array were analyzed by principal component analysis after normalization, the 1st, 2nd and 3rd principal component explained most of the total data variance. The six groups of the analyzed beverages were discriminated well in the three dimensional principal component space. The half of the five groups of the analyzed beverages was also discriminated in the two dimensional principal component plane.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.27-37
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• 2008

Applications of 3D data increase with advancement of multimedia technique and contents, and it is necessary to manage and to retrieve for 3D data efficiently. In this paper, we propose a new method using the sliced shape which extracts efficiently a feature description for shape-based retrieval of 3D models. Since the feature descriptor of 3D model should be invariant to translation, rotation and scale for its model, normalization of models requires for 3D model retrieval system. This paper uses principal component analysis(PCA) method in order to normalize all the models. The proposed algorithm finds a direction of each axis by the PCA and creates orthogonal n planes in each axis. These planes are orthogonalized with each axis, and are used to extract sliced shape image. Sliced shape image is the 2D plane created by intersecting at between 3D model and these planes. The proposed feature descriptor is a distribution of Euclidean distances from center point of sliced shape image to its outline. A performed evaluation is used for average of the normalize modified retrieval rank(ANMRR) with a standard evaluation from MPEG-7. In our experimental results, we demonstrate that the proposed method is an efficient 3D model retrieval.

• Journal of The Korean Society of Integrative Medicine
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• v.9 no.4
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• pp.283-289
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• 2021

Purpose : The purpose of this study is to investigate the effect of mid frequency electrical stimulation during squat exercise on the muscle activity and muscle activity ratio of vastus medialis oblique and vastus lateralis, and to prepare scientific basic data for exercise intervention using mid frequency electrical stimulation. Methods : This study was conducted with students from University C located in Busan, and among a total of 123 subjects, 12 subjects who complained of knee joint dysfunction between 80 and 90 points using the Kujala patellofemoral score (KPS) were used. All subjects participated in the experiment for 3 days, and MVIC values were measured for normalization of muscle activity values on the first day. For the two days, participants participated in the experiment and performed squat exercise or squat exercise receiving mid-frequency electrical stimulation in random order. Measurements were taken in the squat position immediately after the squat exercise, and muscle activities of vastus medialis oblique and vastus lateralis were measured. The measured data were compared through the dependent t test, and the statistical significance level was set to .05. Results : According to the results of this study, in the case of applying mid-frequency electrical stimulation together in the ratio of vastus medialis oblique and vastus lateralis muscle activity during squat exercise, higher values were observed compared to the case of not applying mid-frequency electrical stimulation together, and statistically significant. Also, when mid-frequency electrical stimulation was applied to both vastus medialis oblique and vastus lateralis activities during the squat exercise, higher values were shown compared to the case where the mid-frequency electrical stimulation was not applied together, but there was no statistically significant difference. Conclusion : The results of this study reported that mid-frequency electrical stimulation provided to vastus medialis oblique increased the muscle activity ratio of vastus medialis oblique and vastus lateralis. Therefore, the improvement of coordination due to the enhancement of the activity of vastus medialis oblique through mid-frequency electrical stimulation will be more helpful in the treatment of patellofemoral pain syndrome patient. In addition, it is hoped that the electrical stimulation method applied to exercise will be widely used.

• Journal of the Korean Geotechnical Society
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• v.37 no.5
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• pp.47-63
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• 2021

Recently, the demand for three-dimensional (3D) underground maps from the perspective of digital twins and the demand for linkage utilization are increasing. However, the vastness of national geotechnical survey data and the uncertainty in applying geostatistical techniques pose challenges in modeling underground regional geotechnical characteristics. In this study, an optimal learning model based on multi-layer perceptron (MLP) was constructed for 3D subsurface lithological and geotechnical classification in Seoul, South Korea. First, the geotechnical layer and 3D spatial coordinates of each borehole dataset in the Seoul area were constructed as a geotechnical database according to a standardized format, and data pre-processing such as correction and normalization of missing values for machine learning was performed. An optimal fitting model was designed through hyperparameter optimization of the MLP model and model performance evaluation, such as precision and accuracy tests. Then, a 3D grid network locally assigning geotechnical layer classification was constructed by applying an MLP-based bet-fitting model for each unit lattice. The constructed 3D geotechnical layer map was evaluated by comparing the results of a geostatistical interpolation technique and the topsoil properties of the geological map.