• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.29 no.2
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• pp.87-94
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• 2023

Fresh bakery products are widely consumed worldwide and therefore particular requirements for their quality characteristics have been established. The shelf life of bakery products is mainly subjected to microbial spoilage and staling. This study investigated the optimum conditions of modified atmosphere packaging (MAP) application to extend the shelf life of the bakery products. The gas conditions of the headspace in 'Baumkuchen' cake were 0, 30, 70, and 100% CO₂ concentrations and stored at 30℃ for 5 days. The bakery samples were evaluated weight loss, hardness, color change, pH and total aerobic bacteria, yeast and molds count throughout the storage period. Values of the weight loss and hardness were increased over the storage period, meanwhile pH was significantly decreased. However, no significant color changes were observed during storage. It was also found no significant difference between the different gas treatments. Total aerobic bacteria count of the stored samples after day 5 was increased by 6.94 log CFU/g in the air filled package, compared to 6.20 log CFU/g in the 100% CO₂ filled package and 6.02 log CFU/g in the 70% CO₂ filled package. Yeast and molds count were 3.65 log CFU/g in air filled package, 2.66 log CFU/g in 100% CO₂ filled package, 2.64 log CFU/g in 70% CO₂ filled package, 2.86 log CFU/g in 30% CO₂ filled package and 3.31 log CFU/g in 100% N₂ filled package on day 2. In conclusion, it was shown that 70% and 100% CO₂ treatments in the package were effective to reduce microbial growth.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Current Research on Agriculture and Life Sciences
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• v.29
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• pp.83-89
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• 2011

Various functional and useful components in Portulaca oleracea were extracted with ethanol and the optimum solvent conditions were set by monitoring of response surface methodology(RSM). A central composite design for optimization was applied to investigate the effects of the three independent variables of extraction temperature, ethanol concentration, and extraction time, on dependent variables including total phenolics, electron-donating ability, brown clolor and total flavonoids of Portulaca oleracea. The content of total phenol was essentially unaffected by extraction time or extraction temperature, but it was highly influenced by ethanol concentration. The maximum total phenol content was 31.70mg/mL obtained at 45.84% of ethanol concentration, $79.66^{\circ}C$, and after 2.67hr of extraction. Electron-donating ability (EDA) was affected by ethanol concentration and the maximum EDA was 74.67mg/mL at 52.95% ethanol concentration, $52.33^{\circ}C$ and 4.84hr of extration time. The browning color was rarely affected by extraction time but, it was highly influenced by ethanol concentration and extraction temperature. The maximum extent of browning color was obtained at 97.75% of ethanol concentraion, $65.88^{\circ}C$ and 2.93hr of extraction time. The content of total flavonoid was significantly influenced by extraction time, and the maximum total flavonoid level was 58.28mg/mL obtained at 96.62% ethanol concentration, $61.87^{\circ}C$ after 3.70hr of extraction. As a result, The optimal conditions for effective extraction were predicted as follows, 70.3% of ethanol concentration, $62.1^{\circ}C$ of extraction temperature and 3.3hr of extraction time.

• Current Research on Agriculture and Life Sciences
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• v.30 no.2
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• pp.68-75
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• 2012

This study was conducted to monitor the yields of useful substances extracted from Inonotus obliquus. Optimization of ultrasonic-assisted extraction process was carried out by using response surface methodology under different extraction conditions. A central composite design was applied to investigate the effects of independent variables such as extraction time ($X_1$), ethanol concentration ($X_2$) and extraction temperature ($X_3$) on dependent variables such as soluble solid yield ($Y_1$), total phenol contents ($Y_2$), total flavonoid contents ($Y_3$) and browning color($Y_4$). Soluble solid yield was affected by ethanol concentration and extraction temperature. The maximum soluble solid yield was 18.02% at 20.47 min ($X_1$), 42.85% ($X_2$) and $69.57^{\circ}C$ ($X_3$) in saddle point. Total phenol contents were highly affected by ethanol concentration and extraction temperature. The maximum total phenol contents were 71.57mg GAE/g at 21.60min ($X_1$), 45.19% ($X_2$), $69.68^{\circ}C$ ($X_3$). The electron donating ability was affected by extraction temperature and extraction time. Total flavonoid contents were affected by only extraction temperature. The maximum total flavonoid contents were 35.98 mg RE/g at 22.53min ($X_1$), 46.37% ($X_2$), $69.56^{\circ}C$ ($X_3$) in saddle point. The browning color was highly affected by extraction time, ethanol concentration and extraction temperature. The maximum browning color was at 22.00 min ($X_1$), 46.89% ($X_2$), $69.71^{\circ}C$ ($X_3$) in saddle point. As a result, the optimum extraction conditions were predicted; extraction time of 21.50 min, ethanol concentration of 44.87% and extraction temperature of $69.635^{\circ}C$.

• Journal of the Korean Society of Radiology
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• v.5 no.4
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• pp.189-194
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• 2011

This study used magnetic resonance diffusion tensor imaging (DTI) to quantitatively analyze the neural fiber tractography according to the age of normal corpus callosum and to evaluate of usefulness. The research was intended for the applicants of 60 persons that was in a good state of health with not brain or other disease. The test parameters were TR: 6650 ms, TE: 66 ms, FA: $90^{\circ}$, NEX: 2, thickness: 2 mm, no gap, FOV: 220 mm, b-value: $800s/mm^2$, sense factor: 2, acquisition matrix size: $2{\times}2{\times}2mm^3$, and the test time was 3 minutes 46 seconds. The evaluation method was constructed the color-cored FA map include to the skull vertex from the skull base in scan range. We set up the five ROI of corpus callosum of genu, anterior-mid body, posterior-mid body, isthmus, and splenium, tracking, respectively, and to quantitatively measured the length of neural fiber. As a result, the length of neural fiber, for the corpus callosum of genu was 20's: $61.8{\pm}6.8$, 30's: $63.9{\pm}3.8$, 40's: $65.5{\pm}6.4$, 50's: $57.8{\pm}6.0$, 60's: $58.9{\pm}4.5$, more than 70's: $54.1{\pm}8.1mm$, for the anterior-mid body was 20's: $54.8{\pm}8.8$, 30's: $58.5{\pm}7.9$, 40's: $54.8{\pm}7.8$, 50's: $56.1{\pm}10.2$, 60's: $48.5{\pm}6.2$, more than 70's: $48.6{\pm}8.3mm$, for the posterior-mid body was 20's: $72.7{\pm}9.1$, 30's: $61.6{\pm}9.1$, 40's: $60.9{\pm}10.5$, 50's: $61.4{\pm}11.7$, 60's: $54.9{\pm}10.0$, more than 70's: $53.1{\pm}10.5mm$, for the isthmus was 20's: $71.5{\pm}17.4$, 30's: $74.1{\pm}14.9$, 40's: $73.6{\pm}14.2$, 50's: $66.3{\pm}12.9$, 60's: $56.5{\pm}11.2$, more than 70's: $56.8{\pm}11.3mm$, and for the splenium was 20's: $82.6{\pm}6.8$, 30's: $86.9{\pm}6.4$, 40's: $83.1{\pm}7.1$, 50's: $81.5{\pm}7.4$, 60's: $78.6{\pm}6.0$, more than 70's: $80.55{\pm}8.6mm$. The length of neural fiber for normal corpus callosum were statistically significant in the genu(P=0.001), posterior-mid body(P=0.009), and istumus(P=0.012) of corpus callosum. In order of age, the length of neural fiber increased from 30s to 40s, as one grows older tended to decrease. For this reason, the nerve cells of brain could be confirmed through the neural fiber tractography to progress actively in middle age.

• Journal of Bio-Environment Control
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• v.30 no.1
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• pp.77-84
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• 2021

This study attempted to find a way to maintain the quality of mixing baby wild leaf vegetables with existing baby leaf vegetables in various ratios. The crops for mixing baby leaf vegetables were Peucedanum japoincum Thunberg and Ligularia stenocephala, as wild vegetables, and red romaine, which is widely used in young leafy vegetables. The mixing ratio of red romaine and wild vegetables was red romaine 0: mantilla oil 5: L. stenocephala ratio 5 (R0: P5: L5), red romaine 3.3: P. japoincum 3.3: L. stenocephala ratio 3.3 (R3.3: P3.3: L3.3), red romaine 5: P. japoincum 2.5: L. stenocephala 2.5 (R5: P2.5: L2.5), red romaine 8: P. japoincum 1: L. stenocephala 1 (R8: P1: L1), red romaine 10: P. japoincum 0: L. stenocephala 0 (R10: P0: L0). All treatments were packaged in OTR (oxygen transmittance) 10,000 cc m-2·day-1·atm-1 film and stored for 27 days at 2℃/85% RH conditions. Fresh weight, carbon dioxide, oxygen, and ethylene concentrations of the baby leaf packages were examined approximately every 3 days, and visual quality, chlorophyll content, and chromaticity were examined on the 27th day of storage. The oxygen and carbon dioxide concentration in the packages were affected by the respiration rate of the crop. As the mixing ratio of lettuce, which had a low respiration rate, increased, the oxygen concentration in the packages was higher and the carbon dioxide concentration was lower. Oxygen concentration decreased significantly after 15 days, but was remained above 16%, and on the contrary, carbon dioxide concentration was kept at 1-4% until the 15th, and then gradually increased to 2-5% on the 27th day. The concentration of ethylene was maintained at 3-6 µL·L-1 until the end of storage (27th day). Visual quality score measured at the end of storage was slightly less than 3.0, which is the limit of marketability of all treatments. Although there was no significant difference, the chlorophyll content (SPAD) of red romaine and P. japoincum were most similar with an initial value in R8:P1:1 treatment, and L. stenocephala was higher value in R8:P1:L1 and R5:P2.5:L2.5 treatments at the end of storage. The leaf color (L∗, a∗, b∗, chroma) of the three crops at end of storage compared with the heat map showed the least change in the R5:P2.5:L2.5 and R8:P1:L1 treatments at the end of storage. Among them, R8:P1:L1 treatment maintained the highest chlorophyll content, the second lowest ethylene concentration, and adequate carbon dioxide concentration of 2-3%. Therefore, it is judged that the mixed ratio of red romaine 8: P. japoincum 1: L. stenocephala 1 (R8: P1: L1) is most suitable for the mixed package of baby leaf vegetables of these three crops.

• Journal of Wetlands Research
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• v.17 no.3
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• pp.251-263
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• 2015

This study suggested a novel approach of estimating the optimal probability density function (OPDF) of the annual maximum rainfall time series (AMRT) combining the L-moment ratio diagram and the geographical information system. This study also reported several interesting geographical characteristics of the AMRT in Korea. To achieve this purpose, this study determined the OPDF of the AMRT with the duration of 1-, 3-, 6-, 12-, and 24-hours using the method of L-moment ratio diagram for each of the 67 rain gages in Korea. Then, a map with the Thiessen polygons of the 67 rain gages colored differently according the different type of the OPDF, was produced to analyze the spatial trend of the OPDF. In addition, this study produced the color maps which show the fitness of a given probability density function to represent the AMRT. The study found that (1) both L-skewness and L-kurtosis of the AMRT have clear geographical trends, which means that the extreme rainfall events are highly influenced by geography; (2) the impact of the altitude on these two rainfall statistics is greater for the mountaneous region than for the non-mountaneous region. In the mountaneous region, the areas with higher altitude are more likely to experience the less-frequent and strong rainfall events than the areas with lower altitude; (3) The most representative OPDFs of Korea except for the Southern edge are Generalized Extreme Value distribution and the Generalized Logistic distribution. The AMRT of southern edge of Korea was best represented by the Generalized Pareto distribution.

• Science of Emotion and Sensibility
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• v.19 no.3
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• pp.3-14
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• 2016

This study is aimed to measure the physical colorimetric property according to three conditions, natural dyestuffs (Gardenia, Sappan wood, Lac, Gardenia blue, Mugwort, and Indigo), fabric types (cotton, silk), and presence of mordant (without, with), and then to evaluate the psychological sensibility. Also, to perform analysis of variance (ANOVA) to find out the differences of physical properties according to the three natural dyeing conditions, and to analyze the relationship between physical property and psychological property by Pearson's correlation analysis and then suggest the prediction model by regression analysis using SPSS program (ver. 21.0). Finally, to propose a certain sensibility image map of naturally dyed fabrics, MDS (Multidimensional Scaling) was used, and as a result, Gardenia dyed fabrics having the color sensibilities such as 'hard' and 'heavy' were suggested to evoke masculine image, and to evoke feminine image, Sappan wood and Lac having 'bright', 'transparent', 'soft' and 'light' sensibilities were suggested. Natural image might be induced by using 'subdued' Mugwort dyed fabrics, and active image might be induced by using 'showy' Indigo dyed fabric.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.253-262
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• 2016

Here, we estimated daily Photosynthetically Available Radiation (PAR) from Geostationary Ocean Colour Imager (GOCI) and compared it with daily PAR derived from polar-orbiting MODIS images. GOCI-based PAR was also validated with in-situ measurements from ocean research station, Socheongcho. GOCI PAR showed similar patterns with in-situ measurements for both the clear-sky and cloudy day, whereas MODIS PAR showed irregular patterns at cloudy conditions in some areas where PAR could not be derived due to the clouds of sunglint. GOCI PAR had shown a constant difference with the in-situ measurements, which was corrected using the in-situ measurements obtained on the days of clear-sky conditions at Socheongcho station. After the corrections, GOCI PAR showed a good agreement excepting on the days with so thick cloud that the sensor was optically saturated. This study revealed that GOCI can estimate effectively the daily PAR with its advantages of acquiring data more frequently, eight times a day at an hourly interval in daytime, than other polar orbit ocean colour satellites, which can reduce the uncertainties induced by the existence and movement of the cloud and insufficient images to map the daily PAR at the seas around Korean peninsula.