• Journal of the Economic Geographical Society of Korea
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• v.26 no.3
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• pp.223-237
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• 2023

The Seoul Onsu Industrial Complex, having been completed over 50 years ago, is an old industrial complex, with deteriorating infrastructure and factory buildings. Despite this, there's a current urban industrial agglomeration centered on the machinery industry in the Seoul Onsu Industrial Complex. This study aims to holistically analyze the physical deterioration of facilities in the aging industrial complex and the characteristics of industrial agglomeration to derive the identity of the Seoul Onsu Industrial Complex. Based on the research findings, the complex is seeing an enhanced urban industrial agglomeration due to the influx of small-scale businesses resulting from concentrated trade networks in the metropolitan area and plot subdivision, permission for noise-producing processes, and the ease of securing highly-skilled technicians. However, this agglomeration coexists with a weakening of the complex's production function, limited innovativeness of resident companies, and non-innovative features resulting from weakened competitiveness in the metropolitan machinery industry. In summary, the identity of the Seoul Onsu Industrial Complex is a 'Non-Innovative Urban Industry Agglomeration', an old industrial complex, witnessing non-innovative agglomeration based on a machinery industry network centered in the metropolitan area.

• Korean Chemical Engineering Research
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• v.62 no.1
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• pp.70-79
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• 2024

Raman analyzer is an analytical technique that utilizes the "Raman effect", which occurs when light is scattered by the inherent vibrations of molecules. It is used for molecular identification and composition analysis. In the natural gas industry, it is widely used in bunkering and tank lorry fields in addition to LNG export and import terminals. In this study, a LNG-specific Raman analyzer was installed and operated under actual field conditions to analyze the composition and principal properties (calorific value, reference density, etc.) of LNG. The measured LNG composition and calorific value were compared with those obtained by conventional gas chromatograph that are currently in operation and validated. The test results showed that the Raman analyzer provided rapid and stable measurements of LNG composition and calorific value. When comparing the calorific value, which serves as the basis for LNG transactions, with the results from conventional gas chromatograph, the Raman analyzer met the acceptable error criteria. Furthermore, the measurement results obtained in this study satisfied the accuracy criteria of relevant international standards (ASTM D7940-14) and demonstrated similar outcomes compared to large-scale international demonstration cases.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.130-145
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• 2023

The digital transformation of finance is accelerating, and digital banking has already become a major banking channel. Banks have traditionally placed importance on CRM(Customer Relationship Management) and have tried to retain their loyal customers, who contribute significantly to the bank, such as long-term transactions, holding accounts with a certain balance or more, and holding loans. In this situation, this study exploratorily analyzed the consumer behavior of digital banking deposits in a major bank of Korea(1,145 samples). Statistical analysis was performed using SPSS. The main findings of the study are summarized as follows. It was found that there were differences of consumer behavior in digital banking deposits by generation, and the MZ generation used digital banking more on holidays than other generations. As a result of analyzing the behavior of existing loyal customers and regular customers of digital banking deposit, there was a significant difference in both the amount and period of the deposit. It was confirmed that the existing loyal customers of the bank also engage in consumer behavior that contributes to the bank in digital banking. In addition, the interaction between the customer type and the date of sign up for the deposit period, which is the goal setting of financial consumers, it was found that there was a significant effect. This study empirically analyzed the consumer behavior of digital banking in a situation where decrease of bank branches and encounters with digital banking. The major concepts of the consumer behavior theory are Loyal Customer, Goal Pursuit, and Habit, which were confirmed in an example of digital banking. The results of this study can suggest practical implications for existing banks and Internet-only banks, including the importance of customer management in digital banking.

• Journal of the Korean association of regional geographers
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• v.5 no.1
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• pp.101-120
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• 1999

The purpose of this study is to analyze the impact of agricultural polices on the change of regional structure based on the specialization during the productivism period. Analysis are carried on through the comparison of distribution in 1950s and 1997. Since the 1950s, governmental policy has played a leading role in shaping the pattern of farming in Great Britain. The range of British measures have also been employed in an attempt to improve the efficiency of agriculture and raise farm income. Three fairly distinct phase can be identified in the developing relationship between government policies and British agriculture in the postwar period. In the 1st phase, The Agricultural Act of 1947 laid the foundations for agricultural productivism in Great Britain until membership of the EC. This was to be achieved through the system of price support and guaranteed prices and the means of a series of grants and subsidies. Guaranteed prices encouraged farmenrs to intensify production and specialize in either cereal farming or milk-beef enterprise. The former favoured eastern areas, whereas the latter favoured western areas. Various grants and subsidies were made available to farmers during this period, again as a way of increasing efficiency and farm incomes. Many policies, such as Calf Subsidy and the Ploughing Grant, Hill cow and Hill Sheep Schemes and the Hill Farming and Livestock Rearing Grant was provided. Some of these policies favoured western uplands, whilst the others was biased towards the Lake District. Concentration of farms occured especially in near the London Metropolitan Area and south part of Scotland. In the 2nd stage after the membership of EC, very high guaranteed price created a relatively risk-free environment, so farmers intensified production and levels of self-sufficiency for most agriculture risen considerably. As farmers were being paid high prices for as much as they could produce, the policy favoured areas of larger-scale farming in eastern Britain. As a result of increasing regional disparities in agriculture, the CAP became more geographically sensitive in 1975 with the setting up of the Less Favoured Areas(LFAs). But they are biased towards the larger farms, because such farms have more crops and/or livestock, but small farms with low incomes are in most need of support. Specialization of cereals such wheat and barely was occured, but these two cereal crops have experienced rather different trend since 1950s. Under the CAP, farmers have been paid higher guaranteed prices for wheat than for barely because of the relative shortage of wheat in the EC. And more barely were cultivated as feedstuffs for livestock by home-grown cereals. In the 1950s dairying was already declining in what was to become the arable areas of southern and eastern England. By the mid-1980s, the pastral core had maintained its dominance, but the pastoral periphery had easily surpassed arable England as the second most important dairying district. Pig farming had become increasingly concentrated in intensive units in the main cereal areas of eastern England. These results show that the measure of agricultural policy induced the concentration and specialization implicitly. Measures for increasing demand, reducing supply or raising farm incomes are favoured by large scale farming. And price support induced specialization of farming. And technology for specialization are diffused and induced geographical specialization. This is the process of change of regional structure through the specialization.