• Journal of Korean Society of Forest Science
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• v.104 no.4
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• pp.558-563
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• 2015

The effects of pre-treatment methods (water cooling, water cooling+ozone, precooling+microbubble, water cooling+ozone+microbubble) after harvest on the quality of 'Mipung' chestnut were studied. Changes in quality of chestnut were greater precooling treatments effect than washing treatments. But, decaying rate and total microorganism were significantly differences among treatments. The decaying rate after 12 weeks storage was highest at 20.0% in non-treatments and lowest at 3.3% in water cooling+ozone and water cooling+ozone+microbubble treatments. The total microorganism immediately after washing treatments was in the order non-treatments (4.4 log CFU/g) > water cooling treatments (4.0 log CFU/g) > water cooling+ozone+microbubble treatments (3.5 log CFU/g) > water cooling+ozone treatments (3.4 log CFU/g) > water cooling+microbubble treatments (3.3 log CFU/g), and after 12 weeks storage was increased within 4.7 to 5.9 log CFU/g. Thus, the washing treatments, especially ozone treatments, extended the shelf-life of the 'Mipung' chestnut by inhibiting the decaying.

• Journal of Food Hygiene and Safety
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• v.32 no.6
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• pp.485-492
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• 2017

This study investigated to determine the microbial contamination levels of strawberries at harvest and distribution stages and to suggest a control measure for reducing the microbial contamination of strawberries by replacing worker's gloves used at harvest and distribution stages. According to the monitoring results, the contamination levels of total aerobic bacteria (TAB) were in the order of soil ($7.12{\pm}0.61{\log}_{10}CFU/g$), gloves ($6.06{\pm}1.80{\log}_{10}CFU/cm^2$), strawberry ($3.28{\times}0.98{\log}_{10}CFU/g$), and water ($3.08{\pm}0.55{\log}_{10}CFU/g$) at harvest stage. TAB of strawberry at was harvest stage reduced from $3.28{\pm}0.98{\log}_{10}CFU/g$ to $1.85{\pm}0.21{\log}_{10}CFU/g$ and $2.6{\pm}0.30{\log}_{10}CFU/g$ at cold and room temperature storage, respectively. By the replacement of worker's gloves and distribution temperature, TAB levels of the strawberries were significantly reduced when compared to those of the strawberries treated without replacement of worker's gloves and distributed at room temperature. For reusing the replaced gloves, washing with a commercial disinfectant, clorox, was effective to reduce microorganisms contaminated on the worker's gloves. These results demonstrated that appropriate replacement of gloves at the harvest and distribution stages is an effective method for reducing microbial contamination of fresh strawberries.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.949-956
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• 2007

This paper describes the design and implementation of the database which is used in Personal Life Log Media system. The database contains information about media that capture personal experiences and enables the user to retrieve the media in a user friendly ways. The implementation of the database design is done by managing video data, which captures user's personal experiences, with its spatial and temporal information. The database enables the user to retrieve the video by mentioning where and/or when the video has been taken.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.453-454
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• 2017

The travellers using social media leave their location history in the form of trajectories. These trajectories can be bridged for acquiring information, required for future recommendation for the future travelers, who are new to that location, providing all sort of information. In this paper, we propose a personalized travel path recommendation scheme based on social life log. By taking advantage of two kinds of social media such as travelogue and community contributed photos, the proposed scheme can not only be personalized to user's travel interest but also be able to recommend a travel path rather than individual Points of Interest (POIs). It also maps both user's and routes' textual descriptions to the topical package space to get user topical package model and route topical package model (i.e., topical interest, cost, time and season).

• Journal of the East Asian Society of Dietary Life
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• v.16 no.5
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• pp.559-563
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• 2006

This research was performed to investigate the potential use of Capsella bursa-pastoris (L.) as an ingredient to improve the biological function and flavor of kimchi. The quality characteristics were studied for kimchi with or without Capsella bursa-pastoris (L.) Medicus (CBM kimchi) during fermentation for 25 days at $10^{\circ}C$. The pH changes of CBM kimchi were slower than those of control but did not show any significant difference after addition of Capsella bursa-pastoris (L.) Medicus during fermentation. The titratable acidity of CBM kimchi was higher than that of control. The log number of total bacteria was lower about 1.5 $log_{10}$ cycle in CBM kimchi than in control during fermentation. However, lactic acid bacteria did not show significant difference between CMB kimchi and control. Sensory qualities of kimchi such as color, taste, flavor and overall acceptability were significantly improved by addition of 3% Capsella bursa-pastoris (L.) Medicus(p<0.05).

• Korean Journal of Food Science and Technology
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• v.46 no.1
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• pp.108-114
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• 2014

To analyze the hazards associated with cucumber and hot pepper cultivation areas, a total of 72 samples were obtained and tested to detect the presence of biological (sanitary indicative, pathogenic bacteria and fungi) and chemical hazards (heavy metals and pesticide residues). The levels of sanitary indicative bacteria (aerobic plate counts and coliforms) and fungi were ND-7.2 and ND-4.8 log CFU/(g, mL, hand, or $100cm^2$) in cucumber cultivation areas, and ND-6.8 and 0.4-5.3 log CFU/(g, mL, hand, or $100cm^2$) in hot pepper cultivation areas. More specifically, the soil of hot pepper cultivation areas was contaminated with coliforms at a maximum level of 5.6 log CFU/g. Staphylococcus aureus was detected only in glove samples at a level of 1.4 log CFU/$100cm^2$ and Bacillus cereus was detected in the majority of samples at a level of ND-4.8 log CFU/(g, mL, hand, or $100cm^2$). Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. were not detected. Heavy metal (Zn, Cu, Ni, Pb, and Hg) chemical hazards were detected at levels lower than the regulation limit. Residual insecticides were not detected in cucumbers; however, hexaconazole was detected at a level of 0.016 mg/kg (maximum residue limit: 0.3 mg/kg) in hot peppers.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.2
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• pp.1-10
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• 2013

In this paper, make a study decision problem called an optimal release policies after testing a software system in development phase and transfer it to the user. In the course of correcting or modifying the software, finite failure non-homogeneous Poisson process model, presented and was proposed release policies of the life distribution, half-logistic and log-logistic distributions model which used to an area of reliability because of various shape and scale parameter. In this paper, discuss optimal software release policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement. In a numerical example, the parameter estimation using maximum likelihood estimation of failure time data make out, and software optimal release time was estimated.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.4
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• pp.460-469
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• 2008

Process mining aims at mining valuable information from process execution results (called "event logs"). Even though process mining techniques have proven to be a valuable tool, the mining results from real process logs are usually too complex to interpret. The main cause that leads to complex models is the diversity of process logs. To address this issue, this paper proposes a trace clustering approach that splits a process log into homogeneous subsets and applies existing process mining techniques to each subset. Based on log profiles from a process log, the approach uses existing clustering techniques to derive clusters. Our approach are implemented in ProM framework. To illustrate this, a real-life case study is also presented.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.1-10
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• 2012

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The finite failure nonhomogeneous Poisson process models presented and the life distribution applied exponential and log shaped type hazard function. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than autonomous errors-detected factor that is generally efficient model could be confirmed. This paper, a failure data analysis of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error and coefficient of determination.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.27-39
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• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.