• Korean Journal of Ichthyology
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• v.35 no.2
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• pp.121-128
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• 2023

Two specimens of Polymetme elongata (Phosichthyidae; Polymetme) were collected by a bottom trawl from both the southern coastal waters of Jejudo Island in October 2016 (70.65 mm SL) and Yellow Sea, Korea in August 2022 (54.04 mm SL). They are an elongated body with large mouth, 11 dorsal fin rays, 10 pectoral fin rays, 7 pelvic fin rays, 30~32 anal fin rays, 1 adipose fin, 18 gill rakers, 44 vertebrae, 1 preorbital photophore, 1~2 ventrolateral rows of photophores on body, 1 paired photophores in symphysis of lower jaw, 9 branchiostegal rays, 2 rows of 8 phorophores between pelvic fin origin and anal fin origin, and 1 row of 23 photophores thereafter. At the origin of anal fin, the second photophore is more higher than third photophore and the first photophore is most lowest. Our specimens differ from the other species of the same genus by the ratio of the head length to the standard length (1/5) and the gill rakers (18), absolutely. As this species is the first record of the family Phosichthyidae, genus Polymetme, and P. elongata in Korean waters, we suggest their new Korean names, "Gin-ael-tung-i-gwa", "Gin-ael-tung-i-sog", and "Gin-ael-tung-i", respectively.

• Journal of Korean Dental Science
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• v.16 no.2
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• pp.128-148
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• 2023

Dementia is an umbrella term that describes the loss of thinking, memory, attention, logical reasoning, and other mental abilities to the extent that it interferes with the activities of daily living. More than 50 million individuals worldwide live with dementia, which is expected to increase to 131 million by 2050. Recent research has shown that poor oral health increases the risk of dementia, while oral health declines with cognitive decline. In this narrative review, the literature was based on the "hypothesis" that dementia and oral health have a close relationship, and appropriate oral health and occlusal rehabilitation treatment can improve the quality of life of patients with dementia and prevent progression. We conducted a literature search in PubMed and Google Scholar databases, using the search terms "dementia," "major neurocognitive disorder," "dentition," "occlusion," "tooth loss," "dental prosthesis," "dental implant," and "occlusal rehabilitation" in the title field over the past 30 years. A total of 131 studies that scientifically addressed dementia, oral health, and/or oral rehabilitation were included. In a meta-analysis, the random effect model demonstrated significant tooth loss increasing the dementia risk 3.64-fold (pooled odds ratio=3.64, 95% confidence interval [2.50~5.32], P-value=0.0348). Tooth loss can be an important indicator of cognitive function decline. As the number of missing teeth increases, the risk of dementia increases. Loss of teeth can lead to a decrease in the ascending information to the brain and reduced masticatory ability, cerebral blood flow, and psychological atrophy. Oral microbiome dysbiosis and migration of key bacterial species to the brain can also cause dementia. Additionally, inflammation in the oral cavity affects the inflammatory response of the brain and the complete body. Conversely, proper oral hygiene management, the placement of dental implants or prostheses to replace lost teeth, and the restoration of masticatory function can inhibit symptom progression in patients with dementia. Therefore, improving oral health can prevent dementia progression and improve the quality of life of patients.

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• Journal of Platform Technology
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• v.11 no.6
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• pp.68-78
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• 2023

In this paper, the color distortion in images with haze would be analyzed. When haze is included in the scene, the color signal reflected in the scene is accompanied by color distortion due to the influence of transmittance according to the haze component. When the influence of haze is excluded by a conventional de-hazing method, the distortion of color tends to not be sufficiently resolved. Khoury et al. used the dark channel priority technique, a haze model mentioned in many studies, to determine the degree of color distortion. However, only the tendency of distortion such as color error values was confirmed, and specific color distortion analysis was not performed. This paper analyzes the characteristic of color distortion and proposes a restoration method that can reduce color distortion. Input images of databases used by Khoury et al. include Macbeth color checker, a standard color tool. Using Macbeth color checker's color values, color distortion according to changes in haze concentration was analyzed, and a new color distortion model was proposed through modeling. The proposed method is to obtain a mapping function using the change in chromaticity by step according to the change in haze concentration and the color of the ground truth. Since the form of color distortion varies from step to step in proportion to the haze concentration, it is necessary to obtain an integrated thought function that operates stably at all stages. In this paper, the improvement of color distortion through the proposed method was estimated based on the value of angular error, and it was verified that there was an improvement effect of about 15% compared to the conventional method.

• Clean Technology
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• v.26 no.2
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• pp.102-108
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• 2020

In recent years, packed column has been widely used in separation processes, such as absorption, desorption, distillation, and extraction, in the petrochemical, fine chemistry, and environmental industries. Packed column is used as a contacting facility for gas-liquid and liquid-liquid systems filled with random packed materials in the column. Packed column has various advantages such as low pressure drop, economical efficiency, thermally sensitive liquids, easy repairing restoration, and noxious gas treatment. The performance of a packed column is highly dependent on the maintenance of good gas and liquid distribution throughout a packed bed; thus, this is an important consideration in a design of packed column. In this study, hydraulic pressure drop, hold-up as a function of liquid load, and mass transfer in the air, air/water, and air-NH₃/water systems were studied to find the geometrical characteristic for raschig super-ring experiment dry pressure drop. Based on the results, design factors and operating conditions to handle noxious gases were obtained. The dry pressure drop of the random packing raschig super-ring was linearly increased as a function of gas capacity factor with various liquid loads in the Air/Water system. This result is lower than that of 35 mm Pall-ring, which is most commonly used in the industrial field. Also, it can be found that the hydraulic pressure drop of raschig super-ring is consistently increased by gas capacity factor with various liquid loads. When gas capacity factor with various liquid loads is increased from 1.855 to 2.323 kg^-1/2 m^-1/2 S^-1, hydraulic pressure drop increases around 17%. Finally, the liquid hold-up related to packing volume, which is a parameter of specific liquid load depending on gas capacity factor, shows consistent increase by around 3.84 kg^-1/2 m^-1/2 S^-1 of the gas capacity factor. However, liquid hold-up significantly increases above it.

• Journal of Korea Water Resources Association
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• v.47 no.5
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• pp.459-468
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• 2014

Groyne to control the direction and velocity of flow in rivers is generally installed for the purpose of protecting riverbanks or embankments from erosion caused by running water. In particular, as interest in river restoration and natural river improvement increases, groynes are proposed as a key hydraulic structure for local flow control and riparian habitat establishment. Groynes are installed mainly in groups rather than as individual structures. In case of groynes installed as a group, flow around the groynes change according to spacing in between the groynes. Therefore, groyne spacing is regarded as the most important factor in groyne design. This study aimed at examining changes of flows around and within the area of groynes that take place according to the spacing of groynes installed in order to propose the optimal spacing for upward groynes. To examine flow characteristics around groynes, this study looked at flows in main flow area and recirculation flow area separately. In main flow area, it examined the impact of flow velocity increasing as a result of conveyance reduction that is exerted on river bed stability in relation to changes in the maximum flow velocity according to installation spacing. As a factor causing impacts on scouring and sedimentation within the area of groynes, recirculation flow in the groyne area can lead problems concerning flow within the area and stability of embankment. As for recirculation area, an analysis was conducted on the scale of rotational flow and the flow around embankment that exerts impacts on stability of the embankment. In addition, a comparative analysis was carried with reference to changes of the central point of rotational flow that occur within the area of groynes. As a result of compositely examining the results, the appropriate installation spacing is proposed as min. four times-max. six times considering a decrease in flow velocity according to the installation of upward groynes, river bed stability and stability of embankments against counterflow within the area of groynes.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Korean Journal of Heritage: History & Science
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• v.57 no.2
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• pp.102-115
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• 2024

Painted Cultural heritage uses various materials such as paper, silk, wood, soil, and lime as a base layer to draw on using ink sticks and express lines or colors using various colorants. The importance of underdrawings is emphasized when it comes to replication and preservation, as they can reveal the original drawing. Investigations using infrared have been extensively conducted to detect underdrawings. However, there has been a paucity of research on the influence of underdrawing detection according to the base layer. In this study, the effect of the base layer materials on underdrawing detection in painted cultural heritage was confirmed using an infrared camera and hyperspectral camera (900 to 1700 nm). The study samples marked '檢' with ink below the color layer (cinnabar, orpiment, malachite, azurite, white lead, and red lead) by the base layer materials: Paper (Dakji, indigo/Dakji), silk (silk, silk/white lead), wood (celadonite/wood), soil (celadonite/soil), and lime. The difference in the effect on underdrawing detection was minimal for paper and silk, and no significant differences were found between Dakji and indigo/Dakji, or between silk and silk/white lead. However, we found that celadonite/wood, celadonite/soil, and lime have a significant impact on underdrawing detection. In particular, for wood and soil painted with celadonite, underdrawings were not detected for all six color layers. In the case of lime, it was found that all color layers except malachite had a more positive effect on underdrawing detection. The findings of this study will aid in selecting the appropriate method for underdrawing analysis in the restoration of painted cultural heritage.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2211-2221
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• 2013

During the four river restoration project, several weirs were constructed in the four rivers to prevent drought and flood, to improve water quality, and to manage water resources. However, due to the weir construction, bed changes are produced in the upstream channel of installed weirs because the incoming flow velocity is reduced and sediment transport capacity is also lowered. Especially, since the Haman Weir is located in the lowest downstream section among newly installed weirs in Nakdong River, bed change and sedimentation problems are expected due to the mild slope and reduced velocity. Therefore, numerical simulation was performed to analyze flow and bed changes in the upstream channel of Haman Weir and to evaluate quantitatively sediment control methods for bed stabilization using CCHE2D model. As a result of flow and bed change simulation after installation of Haman Weir, the flow velocity at the initial condition was faster than the final bed condition with the specific simulation time and it was represented that the locations where bed changes were great were identical for all modeling conditions of flow discharge. In case of 4.5 m of water level lowered from 5.0 m of the management water level at Haman Weir for bed stabilization, the flow velocity was generally faster than the case of the management water level and the continuous erosion was developed at the most narrow channel section as the applied discharge and simulation period were increased. The channel width extension at the most narrow channel section was proposed in this study to prevent and stabilize continuos bed erosion. As a result of numerical analysis, there was no bed erosion after channel width extension and it was presented that the channel geometry extension was effective for bed stabilization at Haman Weir.

• Korean Journal of Microbiology
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• v.47 no.4
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• pp.356-363
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• 2011

The accidental releases of total petroleum hydrocarbons (TPH) due to oil spills frequently ended up with soil and ground water pollution. TPH may be degraded through physicochemical and biological processes in the environment but with relatively slow rates. In this study an attempt has been made to develop an integrated chemical and biological treatment technology in order to establish an efficient and environment-friendly restoration technology for the TPH contaminated soils. A Fenton-like reaction was employed as a preceding chemical treatment process and a bioaugmentation process utilizing a diesel fuel degrader consortium was subsequently applied as a biological treatment process. An efficient chemical removal of TPH from soils occurred when the surfactant OP-10S (0.05%) and oxidants ($FeSO_4$ 4%, and $H_2O_2$ 5%) were used. Bioaugmentation of the degrader consortium into the soil slurry led to an increase in their population density at least two orders of magnitude, indicating a good survival of the degradative populations in the contaminated soils ($10^8-10^9$ CFU/g slurry). TPH removal efficiencies for the Fenton-treated soils increased by at least 57% when the soils were subjected to bioaugmentation of the degradative consortium. However, relatively lower TPH treatment efficiencies (79-83%) have been observed in the soils treated with Fenton and the degraders as opposed to the control (95%) that was left with no treatment. This appeared to be due to the presence of free radicals and other oxidative products generated during the Fenton treatment which might inhibit their degradation activity. The findings in this study will contribute to development of efficient bioremediation treatment technologies for TPH-contaminated soils and sediments in the environment.