• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.59-69
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• 2010

Green or an environmental consciousness has been a major issue for businesses and government offices, as well as consumers, worldwide. In response to this movement, the Korean government announced, in the early 2000s, the era of "Green Growth" as a way to encourage green-related business activities. The Korean fashion industry, in various levels of involvement, presents diverse eco-friendly products as a part of the green movement. These apparel products include organic products and recycled clothing. For these companies to be successful, they need information about who are the consumers who consider green issues (e.g., environmental sustainability) as part of their personal values when making a decision for product purchase, use, and disposal. These consumers can be considered as eco-sumers. Previous studies have examined consumers' purchase intention for or with eco-friendly products. In addition, studies have examined influential factors used to identify the eco-sumers or green consumers. However, limited attention was paid to eco-sumers' disposal or recycling behavior of clothes in comparison with their green product purchases. Clothing disposal behaviors are ways that consumer can get rid of unused clothing and in clue temporarily lending the item or permanently eliminating the item by "handing down" (e.g., giving it to a younger sibling), donating, exchanging, selling, or simply throwing it away. Accordingly, examining purchasing behaviors of eco-friendly fashion items in conjunction with clothing disposal behaviors should improve understanding of a consumer's clothing consumption behavior from the environmental perspective. The purpose of this exploratory study is to provide descriptive information about Korean eco-sumers who have ecologically-favorable lifestyles and behaviors when buying and disposing of clothes. The objectives of this study are to (a) categorize Koreans on the basis of clothing disposal behaviors; (b) investigate the differences in demographics, lifestyles, and clothing consumption values among segments; and (c) compare the purchase intention of eco-friendly fashion items and influential factors among segments. A self-administered questionnaire was developed based on previous studies. The questionnaire included 10 items of clothing disposal behavior, 22 items of LOHAS (Lifestyles of Health and Sustainability) characteristics, and 19 items of consumption values, measured by five-point Likert-type scales. In addition, the purchase intention of two eco-friendly fashion items and 11 attributes of each item were measured by seven-point Likert type scales. Two polyester fleece pullovers, made from fabric created from recycled bottles with the PET identification code, were selected from one Korean brand and one US imported brand among outdoor sportswear brands. A brief description of each product with a color picture was provided in the survey. Demographic variables (i.e., gender, age, marital status, education level, income, occupation) were also included. The data were collected through a professional web survey agency during May 2009. A total of 600 final usable questionnaires were analyzed. The age of respondents ranged from 20 to 49 years old with a mean age of 34 years. Fifty percent of the respondents were males and about 58% were married, and 62% reported having earned university degrees. Principal components factor analysis with varimax rotation was used to identify the underlying dimensions of the clothing disposal behavior scale, and three factors were generated (i.e., reselling behavior, donating behavior, non-recycling behavior). To categorize the respondents on the basis of clothing disposal behaviors, k-mean cluster analysis was used, and three segments were obtained. These consumer segments were labeled as 'Resale Group', 'Donation Group', and 'Non-Recycling Group.' The classification results indicated approximately 98 percent of the original cases were correctly classified. With respect to demographic characteristics among the three segments, significant differences were found in gender, marital status, occupation, and age. LOHAS characteristics were reduced into the following five factors: self-satisfaction, family orientation, health concern, environmental concern, and voluntary service. Significant differences were found in the LOHAS factors among the three clusters. Resale Group and Donation Group showed a similar predisposition to LOHAS issues while the Non-Recycling Group presented the lowest mean scores on the LOHAS factors compared to the other segments. The Resale and Donation Groups described themselves as enjoying or being satisfied with their lives and spending spare-time with family. In addition, these two groups cared about health and organic foods, and tried to conserve energy and resources. Principal components factor analysis generated clothing consumption values into the following three factors: personal values, social value, and practical value. The ANOVA test with the factors showed differences primarily between the Resale Group and the other two groups. The Resale Group was more concerned about personal value and social value than the other segments. In contrast, the Non-Recycling Group presented the higher level of social value than did Donation Group. In a comparison of the intention to purchase eco-friendly products, the Resale Group showed the highest mean score on intent to purchase Product A. On the other hand, the Donation Group presented the highest intention to purchase for Product B among segments. In addition, the mean scores indicated that the Korean product (Product B) was more preferable for purchase than the U.S. product (Product A). Stepwise regression analysis was used to identify the influence of product attributes on the purchase intention of eco product. With respect to Product A, design, price and contribution to environmental preservation were significant to predict purchase intention for the Resale Group, while price and compatibility with my image factors were significant for the Donation Group. For the Non-Recycling Group, design, price compatibility with the factors of my image, participation to eco campaign, and contribution to environmental preservation were significant. Price appropriateness was significant for each of the three clusters. With respect to Product B, design, price and compatibility with my image factors were important, but different attributes were associated significantly with purchase intention for each of the three groups. The influence of LOHAS characteristics and clothing consumption values on intention to purchase Products A and B were also examined. The LOHAS factor of health concern and the personal value factor were significant in the relationships with the purchase intention; however, the explanatory powers were low in the three segments. Findings showed that each group as classified by clothing disposal behaviors showed differences in the attributes of a product, personal values, and the LOHAS characteristics that influenced their purchase intention of eco-friendly products. Findings would enable organizations to understand eco-friendly behavior and to design appropriate strategic decisions to appeal eco-sumers.

• Journal of Food Hygiene and Safety
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• v.26 no.4
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• pp.417-423
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• 2011

Salted Cabbage products purchased from different companies at 4 different districts in South Korea were detected in this study. Cabbage and salt are the main materials for kimchi manufacture. The results of general bacteria contaminated in the samples were $1.4{\times}10^5$, $6.4{\times}10^5$, $1.7{\times}10^7$, $3.6{\times}10^7$ CFU/g in cabbage and $2.7{\times}10^3$ CFU/g in salt, respectively. The results of coliforms were detected as $2.4{\times}10^4$ CFU/g, and there was no Escherichia coli in any sample. Staphylococcus aureus was detected in cabbage as $9.9{\times}10^2$, $8.0{\times}10^1$, and $3.0{\times}10^3$ CFU/g, Bacillus cereus was also found in cabbage as $4.1{\times}10^3$ and $1.0{\times}10^1$ CFU/g. The results of Campylobacter jejuni and Vibrio paraheamolyticus were $2.4{\times}10^6$ and $1.0{\times}10^4$ CFU/g in cabbage, respectively. $1.0{\times}10^3$ CFU/g for Yersinia enterocolitica was determined in salt. In case of Listeria monocytogenes, the results were $1.5{\times}10^1$, $1.1{\times}10^2$, and $4.5{\times}10^1$ CFU/g in cabbage. Total batcteria ranged from $1.4{\times}10^1$ to $4.4{\times}10^5$ CFU/g were detected in salting solution, from $1.5{\times}10^4$ to $1.2{\times}10^8$ CFU/g in dehydrated salted-cabbage, from $9.4{\times}10^4{\sim}1.3{\times}10^8$ CFU/g in minced salted-cabbage. The results of E. coli in samples from different companies were different from one to anther. The results of the contamination of S. aureus and B. cereus showed positive in salting solution and dehydrated salted-cabbage at a portion of companies. V. paraheamolyticus was detected in salting solution. The contamination of Y. enterocolitica ranged from $9.5{\times}10^2$ to $1.8{\times}10^3$ CFU/g in salting solution, from $1.7{\times}10^1$ to $2.7{\times}10^2$ CFU/g in dehydrated salted-cabbage, from $1.2{\times}10^2$ to $1.3{\times}10^8$ CFU/g in minced salted-cabbage. The contamination of L. monocytogenes ranged from $8.0{\times}10^2$ to $1.7{\times}10^4$ CFU/g in salting solution, from $2.8{\times}10^2$ to $1.2{\times}10^4$ CFU/g in dehydrated salted-cabbage. During the manufacture processing of Kim chi, microorganisms were detected in cabbages salted in different concentrations of salt solution at 8%, 10%, 12% and 15% for 5-20 hours. As the results, $3.5{\times}10^5-1.7{\times}10^6$, $3.4{\times}10^5-2.5{\times}10^6$, $5.4{\times}10^5-2.3{\times}10^6$, $4.0{\times}10^5-2.3{\times}10^6$ CFU/g were detected for E. coli in samples at different treatment conditions. $1.9{\times}10^4-4.1{\times}10^4$, $4.1{\times}10^3-2.8{\times}10^4$, $1.5{\times}10^3-7.8{\times}10^3$, $2.2{\times}10^4-6.6{\times}10^4$ CFU/g were detected for S. aureus in samples at different treatment conditions. Salmonella typhimurium was detected in salted cabbage with various salt concentration after salting for 5 hrs, the result ranged from $2.5{\times}10^5$ to $3.8{\times}10^6$ CFU/g, and change of microorganism was the smallest in salted cabbage under the concentration of salting solution at 10% for 15 hours. The cabbage salted in 10% salting solution for 15 hours were washed with water for 2 and 3 times, with chlorine for 3 times, and with acetic acid for 3 times. E. coli was detected in the samples washed with water for 2 and 3 times, washed with chlorine for 3 times. The contamination of S. aureus was $3.0{\times}10^5$ CFU/g in the samples washed with water for 2 times, $5.6{\times}10^3$ CFU/g in the samples washed with acetic acid for 3 times, $3.6{\times}10^5$ CFU/g in the samples washed with water for 3 times and same amount in the samples washed with chlorine for 3 times. According to the results, the contamination of S. aureus was $5.6{\times}10^3$ CFU/g lower in samples washed with chlorine and acetic acid than that in samples washed with water. In case of S. typhimurium, it has been detected in samples washed with water and chlorine, $3.0{\times}10^1$ CFU/g as the lowest concentration among all the samples was measured in the samples washed with acetic acid for 3 times.

• Journal of Korean Society of Forest Science
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• v.25 no.1
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• pp.13-47
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• 1975

Among the many species of bamboo, it is well known that the dwarf-type is widely distributed in the tropical regions, and the slender type in temperated zone. In the temperated zone the trees have extensively differentiated into one hundred species in 50 genera. In many oriental countries, the bamboo wood is being used as a material for construction and for the manufacture of technical instruments. The bamboo shoot is also regarded as a good and delicious edible resource. Moreover, recent medical investigation verifies that the sap of certain species of the bamboo is an antibiotic effect against cancer. Fortunately, it is very easy to propagate the bamboo trees by using cutting from southeastern Asian countries. This important resource can further be used as a significant source of pulp, which is becoming increasingly important. The classification system of this significant resource has not been completely established to date, even though its importance has been emphasized. Initiated by Canlevon Linne in the 18th century, a classification method concerning the morphological characteristics of flowers was the first step in developing a classification. But it was not an easy task to accomplish, because this type of classification system is based on the sexual organs in bamboo trees. Because the bamboo has a long life cycle of 60-120 years and classification according to this method was very difficult as the materials for the classification are not abundant and some species have changed, even though many references related to the morphological classification of bamboo trees are available nowadays. So, the certification of bamboo trees according to the morphological classification system is not reasonable for us. Consequently, the classification system of bamboo trees on the basis of endomorphological characteristics was initiated by Chinese-born Liese. And classification method based on the morphological characteristics of the vascular bundle was developed by Grosser. These classification methods are fundamentally related to Holltum's classification method, which stressed the morphology of the ovary. The author investigated to re-establish a new classification method based on the vascular sheath. Twenty-six species in 11 genera which originated from Formosa where used in the study. The results obtained from the investigation were somewhat coordinated with those of Crosser. Many difficulties were found in distinguishing the species of Bambusa and Dendrocalamus. These two species were critically differentiated under the new classification system, which is based on the existence of a separated vascular bundle sheath in the bamboo. According to these results, it is recommended that Babusa divided into two groups by placing it into either subspecies or the lower categories. This recommendation is supported by the observation that the evolutional pattern of the bamboo thunk which is from outward to inward. It is also supported by the viewpoint that the fundamental hypothesis in evolution is from simple to complex. There remained many problems to be solved through more critical examination by comparing the results to those of the classification based on the sexual organs method. The author observed the figure of the cross-sectional area of vascular trunk of bamboo tree and compared the results with those of Grosser and Liese, i.e. A, $B_1$, $B_2$, C, and D groups in classification. Group A and $B_2$ were in accordance with the results of those scholars, while group D showed many differences, Grosser and Liese divided bamboo into "g" type and "h" type according to the vascular bundle type; and they included Dendrocalamus and Bambusa in Group D without considering the type of vascular bundle sheath. However, the results obtained by the author showed that Dendrocalamus and Bambusa are differentiated from each other. By considering another group, "i" identified according to the existence of separated vascular bundle sheath. Bambusa showed to have a separated vascular bundle sheath while Dendrocalamus does not have a separated vascular bundle sheath. Moreover, Bambusa showed peculiar characteristics in the figure of vascular development, i.e., one with an inward vascular bundle sheath and the other with a bivascular bundle sheath (inward and outward). In conclusion, the bamboo species used in this experiment were classified in group D, without any separated vascular bundle sheath, and in group E, with a vascular bundle sheath. Group E was divided into two groups, i.e., and group $E_1$, with bivascular sheath, and group $E_2$, with only an inward vascular sheath. Therefore, the Bambusa in group D as described by Grosser and Liese was included in group E. Dendrocalamus seemed to be the middle group between group $E_l$ and group $E_2$ under this classification system which is summarized as follows: Phyllostachys-type: Group A - Phyllostachys, Chymonobambus, Arundinaria, Pseudosasa, Pleioblastus, Yashania Pome-type: Group $B_2$ - Schizostachyum, Melocanna Hemp-type: Group D - Dendrocalamu Bambu-type: Group $E_1$ - Bambusa ghi.