• Proceedings of the KOR-BRONCHOESO Conference
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• 1993.05a
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• pp.97-97
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• 1993

Primary tumor arises infrequently in the parotid gland and generally, only about 20 to 40 percent of which prove to be malignant. They are characterized by histopathologic diversity, slow tumor growth, significant proportion of patients who have received previous treatment elsewhere. We have reviewed retrospectively 101 cases of parotid gland tumors which were treated for the recent eight years (1985-1992), Non-neoplastic tumor-like lesions were all excluded.

• Food Science and Preservation
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• v.18 no.4
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• pp.442-458
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• 2011

Among Cucubitaceae, melon (Cucumis melo) is one of the most diversified fruits, with various forms, sizes, pulps, and peel colors, In addition, it is a commercially important crop because of its high sweetness, deep flavor, and abundant juice. In the species, there are both climacteric and non-climacteric melons depending on the respiration and ethylene production patterns after harvest. Ethylene is also considered a crucial hormone for determining sex expression, Phytohormones other than ethylene interact and regulate ripening, There are some indices that can be used to evaluate the optimum harvest maturity. The harvest time can be estimated after the pollination time, which is the most commonly used method of determining the harvest maturity of the fruit. Besides the physiological aspects, the biochemical alterations, including those of sweetness, firmness, flavor, color, and rind, contribute to the overall fruit quality. These changes can be categorized based on the ethylene-dependent and ethylene-independent phenomena due to the ethylene-suppressed transgenic melon. After harvest, the fruits are precooled to $10^{\circ}C$ to reduce the field heat, after which they are sized and packed. The fruits can be treated with hot water ($60^{\circ}C$ for 60 min) to prevent the softening of the enzyme activity and microorganisms, and with calcium to maintain their firmness. 1-methylenecyclopropene (1-MCP) treatment also maintains their storability by inhibiting respiration and ethylene production. The shelf life of melon is very short even under cold storage, like other cucurbits, and it is prone to obtaining chilling injury under $10^{\circ}C$. In South Korea, low-temperature ($10^{\circ}C$) storage is known to be the best storage condition for the fruit. For long-time transport, CA storage is a good method of maintaining the quality of the fruit by reducing the respiration and ethylene. For fresh-cut processing, washing with a sanitizing agent and packing with plastic-film processing are needed, and low-temperature storage is necessary. The consumer need and demand for fresh-cut melon are growing, but preserving the quality of fresh-cut melon is more challenging than preserving the quality of the whole fruit.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Proceedings of the Korean Sanitation Conference
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• 2005.12a
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• pp.76-97
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• 2005

As the recent change of multiformity and taste in clination in eating habit culture is yearly in creasing foods used oil and fats. Because the frying food is especially important snack , it's safty is very essential. In order to know the safty and harmfulness of frying oil and fats. The 20 kinds samples were purchased chicken fried food shops around the north of seoul and kyunggi. The acid value, iodine value, peroxide value, TBA value, fatty acid, carbonyl value, and smoke point of deep fat fried oils were analyzed. Results of analyzed, A company of deep fat frying oil showed stability state and C company and B company of deep fat frying oil is acidification to turned. But D company of deep fat frying oil showed quite a bit acidification progressived of used hydrogenated oil.

• Korean Journal of Soil Science and Fertilizer
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• v.36 no.5
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• pp.333-343
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• 2003

This experiment was carried out to improve the utilization of liquid pig manure (LPM) for rice at the two textures of valley soil in 2000 and 2001. The soil textures were coarse loamy and fine loamy in Sachon and Jisan series, respectively. Treatments consisted of no fertilized plot, chemical fertilized plot, LPM 150%, LPM 100%, LPM 100%+NK (top dressing) 30%, LPM 70%+NK 30%, LPM 50%+NK 50% plot. LPM was applied as basal fertilizer compare to nitrogen of chemical fertilized plot. Total N contents in the LPM were 6.0 and $4.5g\;kg^{-1}$ in 2000 and 2001, respectively. After the experiment, P and K contents of soils were not difference between chemical and LPM application plots. But heavy metal contents in soils were slightly higher in LPM application plots than in chemical fertilized plot. Immediately after LPM application, ammonia gas content was $18mg\;kg^{-1}$ in LPM 150% plot, but it was $3mg\;kg^{-1}$ in LPM 50% plot. Two days after LPM application, ammonia gas content was 3 times higher in coarse loamy than in fine loamy soil. After rotary tillage, ammonia gas was not detected at all LPM treatments. This result suggests that rotary tillage can reduce the nasty smell of LPM quickly. Inorganic nitrogen, $NO_3$ and $NH_4$, contents in water of paddy was higher at coarse loamy soil from rice transplanting to tillering stage. After that season, inorganic nitrogen contents of water were not different according to soil texture and treatments. Content of $NH_4-N$ in soil solution was higher at LPM plots than chemical fertilizer plot. Total nitrogen contents in rice plant after harvesting were higher at chemical fertilization plot than LPM application plot, but K contents showed an opposite tendency. Rice yield was decreased only in LPM plots at two soil textures. But yield was not significantly difference between chemical fertilizer and LPM+top dressing plots at coarse loamy soil and increased 5% at LPM 50%+NK 50% plot at fine loamy soil in 2001.

• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.341-354
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• 2021

A large part of Gapyeong is occupied by Korean pine (Pinus koraiensis) and Japanese larch (Larix kaempferi) plantations. Abies holophylla stands are scattered throughout Gapyeong, but little information on their distribution is available. This study explored the potential of succession from planted species to native A. holophylla in plantations. Trees were inventoried and regeneration of A. holoplhylla and stand management history were examined in Korean pine, Japanese larch, and A. holophylla-dominated stands. The importance percentage of A. holophylla was the highest among species with a range of 36.1% to 79.1% in all stands and the density of A. holophylla in understory (DBH <2 cm or <1.3 m height) ranged from 50 to 5,820 trees ha^-1. Non-metric multidimensional scaling classified stands into four types, AN, AP, AM, and P. The AN type showed a reverse J-shape DBH distribution, which was similar to that in natural A. holophylla stands. Both AP and AM types included Korean pine plantations with A. holophylla seed trees within stands. For AP, A. holophylla competed with planted species in overstory and deciduous broadleaved species in understory. The AM type was once thinned from below, thus stem density in the mid DBH classes was lower than upper or lower DBH classes. The P type consisted of plantations without A. holophylla seed trees. However, understory regeneration of A. holophylla was abundant through seed supply from A. holophylla in adjacent stands. Plantations with A. holophylla seed trees within or in adjacent stands showed vigorous natural regeneration of A. holophylla, highlighting the potential for succession from planted species to native A. holophylla in the Gapyeong area. Further studies can help develop techniques to restore plantations to native species-dominated natural stands using ecological succession.

• Journal of Wetlands Research
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• v.21 no.1
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• pp.34-42
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• 2019

Baseflow which is one of the unmeasurable components of streamflow and slowly flows through underground is important for water resource management. Despite various separation methods from researches preceded, it is difficult to find a significant separation method for baseflow separation. This study applied the MRC method and developed the improved approach to separate baseflow from total streamflow hydrograph. Previous researchers utilized the whole streamflow data of study period at once to derive synthetic MRCs causing unreliable results. This study has been proceeded with total nine areas with gauging stations. Each three areas are selected from 3 domestic major watersheds. Tool for drawing MRC had been used to draw MRCs of each area. First, synthetic MRC for whole period and two other MRCs were drawn following two different criteria. Two criteria were set by different conditions, one is flow condition and the other is seasonality. The whole streamflow was classified according to seasonality and flow conditions, and MRCs had been drawn with a specialized program. The MRCs for flow conditions had low R2 and similar trend to recession segments. On the other hand, the seasonal MRCs were eligible for the baseflow separation that properly reflects the seasonal variability of baseflow. Comparing two methods of assuming MRC for baseflow separation, seasonal MRC was more effective for relieving overestimating tendency of synthetic MRC. Flow condition MRCs had a large distribution of the flow and this means accurate MRC could not be found. Baseflow separation using seasonal MRC is showing more reliability than the other one, however if certain technique added up to the flow condition MRC method to stabilize distribution of the streamflow, the flow conditions method could secure reliability as much as seasonal MRC method.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.623-634
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• 2022

Due to river maintenance projects such as the creation of hydrophilic areas around rivers and the Four Rivers Project, the flow characteristics of rivers are continuously changing, and the risk of water quality accidents due to the inflow of various pollutants is increasing. In the event of a water quality accident, it is necessary to minimize the effect on the downstream side by predicting the concentration and arrival time of pollutants in consideration of the flow characteristics of the river. In order to track the behavior of these pollutants, it is necessary to calculate the diffusion coefficient and dispersion coefficient for each section of the river. Among them, the dispersion coefficient is used to analyze the diffusion range of soluble pollutants. Existing experimental research cases for tracking the behavior of pollutants require a lot of manpower and cost, and it is difficult to obtain spatially high-resolution data due to limited equipment operation. Recently, research on tracking contaminants using RGB drones has been conducted, but RGB images also have a limitation in that spectral information is limitedly collected. In this study, to supplement the limitations of existing studies, a hyperspectral sensor was mounted on a remote sensing platform using a drone to collect temporally and spatially higher-resolution data than conventional contact measurement. Using the collected spatio-temporal hyperspectral images, the tracer concentration was calculated and the transverse dispersion coefficient was derived. It is expected that by overcoming the limitations of the drone platform through future research and upgrading the dispersion coefficient calculation technology, it will be possible to detect various pollutants leaking into the water system, and to detect changes in various water quality items and river factors.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.