Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.
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.
Korea's first exploratory tuna fishing was done with a used longliner in 1957. Then the commercial fishing has been made steady headway since the 1960's and grown up to one of major tuna fishing countries in 1970's. The tuna fishing aimed primarily at acquiring foreign currency, then tuna was exported directly from the overseas fishing base. Tuna, however, has been gradually favored by Koreans as high-proteined foods according to the growth of GNP since the 1970's. In 1980, the canned tuna began to be produced and sold at home. And so the demand of raw tuna for cannaries has steeply increased not only for home but also for abroad, and stimulated the development of tuna purse seine fishery. The author carried out a study on the development of tuna purse seine fishery in Korea and countries concerned-the United States and Japan-because it is recognized to be significant for the further development of this fishery. Just as purse seining was originated in the United States, so tuna purse seining was also pioneered by Californian fishermen in the west coastal waters of the United States (Eastern Pacific Ocean). They started to produce the canned tuna in the early 1900's, and the demand for raw tuna began to be increased rapidly. In those days, tuna was mostly caught by pole-and-line, but the catch amount was far away from the demand. To satisfy this demand, they began to try out fishing tuna by the use of purse seine which had been born in the eastern waters in the 1820's and applied to catch white fishes in the western waters of the United States in those days. Even though their trial was technically successful through severe trial and error, a new problem was raised on the management of tuna resource and the preservation of porpoise which was occassionally caught with tuna. Then the Inter-American Tropical Tuna Commission (IATTC) was established by countries neighboring to the United States in 1950 and they set up the Commission's Yellowfin Regulatory Area (CYRA) and regulated the annual quota for yellowfin. Then, American owners tried to send their seiners to the Western African waters to expand the fishing ground in 1967 and to the Centeral-Western Pacfic in 1974, and the fishing ground was widely expanded. The number of the United States' purse seiners amounted to about 150 in 1980, but the enthusiasm was gradually cooled thereafter and the number of seiner was decreased to 67 in 1986. The landing of tuna by purse seiners in the United States after 1980 maintains 200 thousands M/T or so with a little increase despite the decreasing of domestic seiners. This shows that the landing by foreign seiners are increasing, compared with the landing by domestic seiners are decreasing. In Japan, even though purse seining was introduced in 1880, they had fished tuna by longline and pole-and -line until the tuna purse seining was introduced from the United States again. In the 1960's, Japanese tuna seiners made the exploratory fishing in the South-western Pacific and West African waters with a limited success. In 1971, the government-funded research center "JARMRAC" conducted the exploratory fishing which extended to the Central American waters, the Asia-Pacific Region and the South-western Pacific. It had also much difficulties, till they improved the fishing gear adaptable to the new fishing condition in the South-western Pacific. Japanese government has begun to licence 32 single seiners and 7 group seiners since 1980 and their standard has lasted up to now. The catch in the Pacific Islands Region amounted to 160 thousands M/T in 1986. Korea's tuna purse seine fishery was originated in 1971 by Jedong Industrial Co., Ltd. with three used tuna purse seiners purchased from the United States, and they began to fish in the Eastern Pacific, but failed owing to the superannuation of vessel and the infancy of fishing technique. The second challenge was done by Dongwon Industrial Co., Ltd. in 1979, with one used seiner purchased from the United States, and started to fish in the Eastern Pacific. Even though the first trial was almost unsuccessful but they could obtain the noticeable success by removing the vessel to the South-western Pacific in 1980. This success stimulated the Korean entherprisers to take part in this fishery, and the number of Korean tuna purse seiners has been increased rapidly in accordance with the increased demand for raw tuna. The number of vessels actually at work amounted to 36 in 1990 and they operate in the South-western Pacific. The annual catch of tuna by purse seiners amounted to 170 thousands M/T in 1990 and ranked to one of the major tuna purse seining countries in the world.
As business incubation centers (BICs) have been operating for more than 10 years in Korea, many early stage startups tend to use the services provided by the incubating centers. BICs in Korea have accumulated the knowledge and experience in the past ten years and their services have been considerably improved. The business incubating service has three facets : (1) business infrastructure service, (2) direct service, and (3) indirect service. The mission of BICs is to provide the early stage entrepreneurs with the incubating service in a limited period time to help them grow strong enough to survive the fierce competition after graduating from the incubation. However, the incubating services sometimes fail to foster the independence of new startup companies, and raise the dependence of many companies on BICs. Thus, the dependence on BICs is a very important factor to understand the survival of the incubated startup companies after graduation from BICs. The purpose of this study is to identify the main factors that influence the firm's dependence on BICs and to characterize the relationships among the identified factors. The business incubating service is a core construct of this study. It includes various activities and resources, such as offering the physical facilities, legal service, and connecting them with outside organizations. These services are extensive and take various forms. They are provided by BICs directly or indirectly. Past studies have identified various incubating services and classify them in different ways. Based on the past studies, we classify the business incubating service into three categories as mentioned above : (1) business infrastructure support, (2) direct support, and (3) networking support. The business infrastructure support is to provide the essential resources to start the business, such as physical facilities. The direct support is to offer the business resources available in the BICs, such as human, technical, and administrational resources. Finally, the indirect service was to support the resource in the outside of business incubation center. Dependence is generally defined as the degree to which a client firm needs the resources provided by the service provider in order to achieve its goals. Dependence is generated when a firm recognizes the benefits of interacting with its counterpart. Hence, the more positive outcomes a firm derives from its relationship with the partner, the more dependent on the partner the firm must inevitably become. In business incubating, as a resident firm is incubated in longer period, we can predict that her dependence on BICs would be stronger. In order to foster the independence of the incubated firms, BICs have to be able to manipulate the provision of their services to control the firms' dependence on BICs. Based on the above discussion, the research model for relationships between dependence and its affecting factors was developed. We surveyed the companies residing in BICs to test our research model. The instrument of our study was modified, in part, on the basis of previous relevant studies. For the purposes of testing reliability and validity, preliminary testing was conducted with firms that were residing in BICs and incubated by the BICs in the region of Gwangju and Jeonnam. The questionnaire was modified in accordance with the pre-test feedback. We mailed to all of the firms that had been incubated by the BICs with the help of business incubating managers of each BIC. The survey was conducted over a three week period. Gifts (of approximately ₩10,000 value) were offered to all actively participating respondents. The incubating period was reported by the business incubating managers, and it was transformed using natural logarithms. A total of 180 firms participated in the survey. However, we excluded 4 cases due to a lack of consistency using reversed items in the answers of the companies, and 176 cases were used for the analysis. We acknowledge that 176 samples may not be sufficient to conduct regression analyses with 5 research variables in our study. Each variable was measured through multiple items. We conducted an exploratory factor analysis to assess their unidimensionality. In an effort to test the construct validity of the instruments, a principal component factor analysis was conducted with Varimax rotation. The items correspond well to each singular factor, demonstrating a high degree of convergent validity. As the factor loadings for a variable (or factor) are higher than the factor loadings for the other variables, the instrument's discriminant validity is shown to be clear. Each factor was extracted as expected, which explained 70.97, 66.321, and 52.97 percent, respectively, of the total variance each with eigen values greater than 1.000. The internal consistency reliability of the variables was evaluated by computing Cronbach's alphas. The Cronbach's alpha values of the variables, which ranged from 0.717 to 0.950, were all securely over 0.700, which is satisfactory. The reliability and validity of the research variables are all, therefore, considered acceptable. The effects of dependence were assessed using a regression analysis. The Pearson correlations were calculated for the variables, measured by interval or ratio scales. Potential multicollinearity among the antecedents was evaluated prior to the multiple regression analysis, as some of the variables were significantly correlated with others (e.g., direct service and indirect service). Although several variables show the evidence of significant correlations, their tolerance values range between 0.334 and 0.613, thereby demonstrating that multicollinearity is not a likely threat to the parameter estimates. Checking some basic assumptions for the regression analyses, we decided to conduct multiple regression analyses and moderated regression analyses to test the given hypotheses. The results of the regression analyses indicate that the regression model is significant at p < 0.001 (F = 44.260), and that the predictors of the research model explain 42.6 percent of the total variance. Hypotheses 1, 2, and 3 address the relationships between the dependence of the incubated firms and the business incubating services. Business infrastructure service, direct service, and indirect service are all significantly related with dependence (β = 0.300, p < 0.001; β = 0.230, p < 0.001; β = 0.226, p < 0.001), thus supporting Hypotheses 1, 2, and 3. When the incubating period is the moderator and dependence is the dependent variable, the addition of the interaction terms with the antecedents to the regression equation yielded a significant increase in R2 (F change = 2.789, p < 0.05). In particular, direct service and indirect service exert different effects on dependence. Hence, the results support Hypotheses 5 and 6. This study provides several strategies and specific calls to action for BICs, based on our empirical findings. Business infrastructure service has more effect on the firm's dependence than the other two services. The introduction of an additional high charge rate for a graduated but allowed to stay in the BIC is a basic and legitimate condition for the BIC to control the firm's dependence. We detected the differential effects of direct and indirect services on the firm's dependence. The firms with long incubating period are more sensitive to indirect service positively, and more sensitive to direct service negatively, when assessing their levels of dependence. This implies that BICs must develop a strategy on the basis of a firm's incubating period. Last but not least, it would be valuable to discover other important variables that influence the firm's dependence in the future studies. Moreover, future studies to explain the independence of startup companies in BICs would also be valuable.