• Journal of the Korean Society of Food Science and Nutrition
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• v.28 no.6
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• pp.1304-1309
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• 1999

The respiratory characteristics and quality attributes of mature green mume fruits as influenced by modified atmosphere packaging(MAP) conditions during storage at 25oC for 8 days were investigated. The quality attributes of mume fruits were evaluated in terms of fresh weight loss, physiological injury and yellowing. The packaging materials used for MAP were low density polyethylene(LDPE) films with various different thicknesses. Yellowing and fresh weight loss of mume fruits were noticeably reduced by the packaging treatments with LDPE A and B. The physiological injury of the fruits during storage was found to be more severe in LDPE C than others. For LDPE A and B, the oxygen and carbon dioxide contents within the packages of Mume fruits maintained at the levels of 2~3% and 7~8%, respectively. With respect to visual quality, MAP prolonged the shelf life of the fruits much longer compared with the unsealed control. From the experimental results, it is suggested that the LDPE films with the gas trans mission rates of about 2,100 O2 ml/m2.day.atm and 6,700 CO2 ml/m2.day.atm would be proper for MAP of mature green mume fruits during storage at ambient temperature.

• Journal of KIISE
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• v.45 no.1
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• pp.22-29
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• 2018

Over the decades, neural networks have been successfully used in numerous applications from speech recognition to image classification. However, these neural networks cannot explain their results and one needs to know how and why a specific conclusion was drawn. Most studies focus on extracting binary rules from neural networks, which is often impractical to do, since data sets used for machine learning applications contain continuous values. To fill the gap, this paper presents an algorithm to extract logic rules from a trained neural network for data with continuous attributes. It uses hyperplane-based linear classifiers to extract rules with numeric values from trained weights between input and hidden layers and then combines these classifiers with binary rules learned from hidden and output layers to form non-linear classification rules. Experiments with different datasets show that the proposed approach can accurately extract logical rules for data with nonlinear continuous attributes.

• Journal of Korean Society of Forest Science
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• v.95 no.1
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• pp.12-22
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• 2006

Understanding the readability of texts in signs is necessary to enhance the communication effectiveness of the self-guided interpretive signs. This study compared signs' readability by different text attributes. A total of 1391 respondents participated in the questionnaire survey at the 'Neodeolgeong' area in Mudeung-Mountain Provincial Park during September-November of 2004. This study found that 'Hy Gyunmyungjo' in letter style, 'both-side' in letter justification, 190% (HWP 2002) in space between lines, 10 (HWP 2002) in space between letters, and 25 in the number of letters in a line showed the highest readability in text size 58 point, respectively. This study illustrated an example of an interpretive sign made up by combing the five text attributes which show the highest readability. This study also discussed the interpretive signs' text design and future research questions.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.608-615
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• 2006

This paper proposes a fuzzy model tree consisting of local linear models using fuzzy cluster for data modeling. First, cluster centers are calculated by fuzzy clustering method using all input and output attributes. And then, linear models are constructed at internal nodes with fuzzy membership values between centers and input attributes. The expansion of internal node is determined by comparing errors calculated in parent node with ones in child node, respectively. As a final step, data prediction is performed with a linear model having the highest fuzzy membership value between input attributes and cluster centers in leaf nodes. To show the effectiveness of the proposed method, we have applied our method to various dataset. Under various experiments, our proposed method shows better performance than conventional model tree and artificial neural networks.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.777-784
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• 2006

This paper proposes a c-fuzzy model tree using partial least square method to predict the Chlorophyll-a concentration in each zone. First, cluster centers are calculated by fuzzy clustering method using all input and output attributes. And then, each internal node is produced according to fuzzy membership values between centers and input attributes. Linear models are constructed by partial least square method considering input-output pairs remained in each internal node. The expansion of internal node is determined by comparing errors calculated in parent node with ones in child node, respectively. On the other hands, prediction is performed with a linear model haying the highest fuzzy membership value between input attributes and cluster centers in leaf nodes. To show the effectiveness of the proposed method, we have applied our method to water quality data set measured at several stations. Under various experiments, our proposed method shows better performance than conventional least square based model tree method.

• Korean Journal of Environment and Ecology
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• v.29 no.4
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• pp.615-625
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• 2015

This study examined the effects of wildlife or wildlife species on national park visitors' perception of place attributes and recreational experiences. Data were collected from 502 users of Seongsamjae Rest area and Nogodan Shelter area in Jirisan National Park and 173 college students during May-June 2014 using survey questionnaire. Some simulated photographs of water deer and Asiatic black bears were used for the college student survey. Overall, awareness about wildlife inhabiting in Jirisan National Park (AW) was not related with one's perception of place attributes (PPA) (i.e., crowdedness, naturalness, safety) and types of visitors' recreational experiences. Respondents with higher awareness about the presence of Asiatic black bear (AABB), however, tended to perceive Jirisan National Park as a place that provides 'wild' or 'natural' recreational opportunities compared to those with lower AABB. Differences in PPA (i.e., crowdedness, naturalness, safety) and types of recreational experiences were also found to be influenced by wildlife species. Respondents exposed to bear or water deer tended to perceive their recreational experiences as more 'wild'. Existence of wildlife in Jirisan National Park had a positive effect on the quality of visitors' recreational experiences. Different wildlife species showed different levels of effectiveness to quality enhancement of recreational experience. Some practical implications of the study were discussed from a managerial point of view.

• Proceedings of the KOR-KST Conference
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• 2007.11a
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• pp.249-255
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• 2007

• Proceedings of the Korean Society of Crop Science Conference
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• 1987.06a
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• pp.42-43
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• 1987

• Proceedings of the Korean Society of Environment and Ecology Conference
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• 2003.04a
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• pp.64-67
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• 2003

• The Journal of Korean Academic Society of Nursing Education
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• v.20 no.2
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• pp.276-287
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• 2014

Purpose: The purpose of this study was to explore the clear concept of organizational commitment for effective nursing personnel management. Method: This study was conducted using Walker & Avant's conceptual analysis framework(2005). Results: Organizational commitment was identified with six attributes: acknowledgment, acceptance, trust, sacrifice, attachment, regulation. Antecedents of organizational commitment were personal characteristics, perceived organizational support, empowerment, fair compensation, job satisfaction, job autonomy, organizational culture, and leadership. Consequences of organizational commitment were turnover intention, job performance and organizational citizenship behavior. Conclusion: Organization commitment is a core concept for understanding the management of nursing personnel. Appropriate instruments to operationalize the concept of organizational commitment including six attributes need to be developed.