• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.8 no.1
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• pp.50-56
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• 1997

The objective of this study was to compare the family environment and developmental history of three groups of children classified by their temperament. The parents of 484 Korean children aged between 3 and 7 years completed the Korean version of Parental Temperamental Questionnaire developed by Thomas and Chess and Developmental Questionnaire created by Division of Child and Adolescent Psychiatry, Samsung Medical Center. After clustering these children into 5 temperamental groups according to the method proposed by Fullard et al, 98 Easy, 36 Difficult, and 21 Slow-To-Warm-Up children were included in the analysis. Statistically meaningful differences observed among three groups were as follow：1) Marital conflict of parents was more frequent in Difficult and Slow-To-Warm-Up children than in Easy children. 2) Parentchild conflict was more frequent in Difficult children than in Easy children. 3) Conflict among siblings was more frequent in Difficult children than in Easy children. 4) Average monthly income of family was less in Difficult children than in Easy children. 5) Toilet training was achieved later in Difficult children than in Easy children. 6) Motor development was slower, between 2 and 5 years old, in Slow-To-Warm-Up children than in Easy children. 7) Fear of stranger started earlier in Slow-To-Warm-Up children than in easy children. 8) Physical health was poorer in Difficult and Slow-To-Warm-Up children than in easy children. The findings indicate that Difficult child or Slow-To-Warm-Up child group have unfavorable family environment, different developmental milestone and poorer physical health in comparison with Easy child group.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.26 no.6
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• pp.519-528
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• 1993

The biodegradation experiment, the TOD analysis and the element analysis for dispersant, Bunker-C and dispersant/Bunker-C oil mixtures were conducted for the purposes of evaluating the biodegradability of dispersnat/Bunker-C oil mixtures and studying the consumption of dissolved oxygen with relation to biodegradation in the seawater. The results of biodegradation experiment showed the mixtures with $1:10{\sim}5:10$ mix ratios of dispersant to 4mg/l of Bunker-C oil to be $0.34{\sim}2.06mg/l$ of $BOD_5$ and to be $1.05{\sim}5.47mg/l$ of $BOD_{20}$ in natural seawater. The results of TOD analysis showed 1mg of Bunker-C oil to be 3.16mg of TOD. The results of element analysis showed the contents of carbon and hydrogen to be $87.3\%\;and\;11.5\%$ for Bunker-C oil, respectively, but nitrogen element was not detected in Bunker-C oil. The biodegradability of dispersant/Bunker-C oil mixture shown as the ratio of $BOD_5$/TOD was increased from $3\%\;to\;11\%$ as a mix ratio of dispersant to 4mg/l of Bunker-C oil changed from 1:10 to 5:10, and the mixtures were found to belong in the organic matter group of low-biodegradability. The deoxygenation rates($K_1$) and ultimate oxygen demands($L_o$) obtained through the biodegration experiment and Thomas slope method were found to be $0.072{\sim}0.097/day$ and $1.113{\sim}6.746mg/l$ for the mixtures with $1:10{\sim}5:10$ mix ratios of dispersant to 4mg/l of Bunker-C oil, respectively. The ultimate oxygen demand of mixture was increased as a mix ratio of dispersant to Bunker-C oil changed from 1:10 to 10:5. This means that the more dispersants are applied to the sea for Bunker-C oil cleanup, the more decreases the dissolved oxygen level in the seawater.

• Journal of the Korean Wood Science and Technology
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• v.37 no.4
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• pp.397-405
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• 2009

In this study, we investigated the potential of producing bioethanol from Liriodendron tulipifera by using oxalic acid pretreatment. Amounts of fermentable sugars, mostly xylose and glucose, in the liquid fraction (hydrolysate) was $40.22g/{\ell}$ after the biomass was pretreated with 0.037 g/g of oxalic acid for 20 minutes at $160^{\circ}C$. Production amounts of ethanol was $8.6g/{\ell}$ from the 72 hours of simultaneous saccharification and fermentation (SSF) on solid fraction of the pretreated sample. At the same condition, when the reaction time increased to 40 minutes, $32.66g/{\ell}$ of fermentable sugars in the hydrolysate and $9.5g/{\ell}$ of ethanol was produced from the process of pretreatment and SSF. As a result of analyzing the fermentation inhibitors, such as acetic acid, 5-HMF, furfural and total phenolic compounds, as the reaction time increased, the amount of the fermentation inhibitors in the hydrolysate increased. Production of the fermentation inhibitors was more affected by initial concentration of oxalic acid rather than reaction time. $3.39{\sim}5.78g/{\ell}$ of acetic acid was produced by pretreatment with 0.013 g/g of oxalic acid, and the amount of furfural produced by decomposition of xylose was 2~3 times higher than the amount of 5-HMF produced by decomposition of glucose. All the hydrolysates contained more than $5g/{\ell}$ of total phenols considered as the degradation product of lignin. Therefore, by analyzing the amount of fermentable sugars and fermentation inhibitors in the hydrolysate, and producing ethanol from SSF of solid fraction of the pretreated sample, the biomass pretreated with 0.037 g/g of oxalic acid for 20 minutes at $160^{\circ}C$ can be expected to produce the most ethanol.

• Korean Journal of Remote Sensing
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• v.8 no.1
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• pp.27-43
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• 1992

The purpose of this study is to detect and evaluate the change of urban vegetation of Seoul during 1980s. Large areas covered with agricultural crops or forests were converted to residential and commercial areas, roads, schools, sports complexes, etc. There were also widespreas concerns on the deterioration of the quality of urban vegetation due to severe air pollution, overcrowding of nature parks, and idling of farm lands by land speculators. The image used for this study were MSS(Oct. 4, 1979) and TM(Apr. 26, 1990). The Green Vegetation Index of Kauth & Thomas(1976) was for the analysis. The GVI were resampled with 75$\times$75m grids and overlaid with the jurisdictional boundaries of 22 districts of Seoul. The results were reclassified to 6 classes, class 6 representing grids with the most vigorous vegetation or the best vegetation improvement in 1980s. The finding of this study can be summarized as follows : First, the most vigorous vigorous vegetation, in terms of GVI, of the 1979 image can be found at paddy fields located on alluvial near Han River. Broad-leaf forests located on hilly terrains have higher GVI than conifers located on the upper-parts of mountains. The average GVI of the northern part and southern part of Han River are 3.56 and 3.74, respectively. The main reason why the southern part has higher GVI is that there are more prime agricultural lands. Districts of Kangseo, Yangcheon, and Songpa have the highest percentage of grids of GVI class 6, and the percentages are 3.55 %, 3.47 %, and 2.69 %, respectively. Second, the most vigorous vegetation of the 1990 image can be found at the grass lands of the Yongsan golf club and the Sungsu horse racing track. The GVI of farm lands is lower than forest because most agricultural crops are at the early stage of growing season when the TM image was taken. The size of built-up area is much larger than of 1979. On the other hand, vegetation patches surrounded by developed area become smaller and have stronger contrast to surrounding area. The average GVI of the northern part and southern part of Han River are 3.57 and 3.51, respectively. The main reason why the southern part has lower GVI is the at more large-scale urban development projects were carried out in there during 1980s. Districts of Tobong, Nowon, and Seocho have the highest percentage of class 6, and the perecentages are 16.58 %, 10.14 %, and 8.50% respectively. Third, the change of urban vegetation in Seoul during 1980s are significant. Grids of GVI change classes 1 and 2, which represent severe vegetation loss, occupy 15.97% of Seoul. Three districts which lost the most vegetation are Yangcheon, Kangseo, and Songpa, where the percentages of GVI class 1 are 13.42%, 13.39% and 9.06%, respectively. The worst deterioration was mainly caused by residential developments. On the other hand, the vegetation of some part of Seoul improved in this period. Grids of GVI change classes 5 and 6 occupy 9.83 % of Seoul. Distircts of Jung, Yongsan, and Kangnam have the highest percentage of grids with GVI change classes 5 and 6, and their percentages are 22.31%, 19.17%, and 13.66%, respectively. The improvement of vegetation occurred in two areas. Forest vegetation is generally improving despite of concerns based on air pollution and heavy use by recreationists. Vegetation in open spaces established in riverside parks, large residential areas, and major public facilities are also improving.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.110-122
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• 2010

The emergence of the field of organizational buying behavior in the mid-1960’s with the publication of Industrial Buying and Creative Marketing (1967) set the stage for a new paradigm of thinking about how business was conducted in markets other than those serving ultimate consumers. Whether it is "industrial marketing" or "business-to-business marketing" (B-to-B), organizational buying behavior remains the core differentiating characteristic of this domain of marketing. This paper explores the impact of several dynamic factors that have influenced how organizations relate to one another in a rapidly increasing interdependence, which in turn can impact organizational buying behavior. The paper also raises the question of whether or not the major conceptual models of organizational buying behavior in an interdependent world are still relevant to guide research and managerial thinking, in this dynamic business environment. The paper is structured to explore three questions related to organizational interdependencies: 1. What are the factors and trends driving the emergence of organizational interdependencies? 2. Will the major conceptual models of organizational buying behavior that have developed over the past half century be applicable in a world of interdependent organizations? 3. What are the implications of organizational interdependencies on the research and practice of organizational buying behavior? Consideration of the factors and trends driving organizational interdependencies revealed five critical drivers in the relationships among organizations that can impact their purchasing behavior: Accelerating Globalization, Flattening Networks of Organizations, Disrupting Value Chains, Intensifying Government Involvement, and Continuously Fragmenting Customer Needs. These five interlinked drivers of interdependency and their underlying technological advances can alter the relationships within and among organizations that buy products and services to remain competitive in their markets. Viewed in the context of a customer driven marketing strategy, these forces affect three levels of strategy development: (1) evolving customer needs, (2) the resulting product/service/solution offerings to meet these needs, and (3) the organization competencies and processes required to develop and implement the offerings to meet needs. The five drivers of interdependency among organizations do not necessarily operate independently in their impact on how organizations buy. They can interact with each other and become even more potent in their impact on organizational buying behavior. For example, accelerating globalization may influence the emergence of additional networks that further disrupt traditional value chain relationships, thereby changing how organizations purchase products and services. Increased government involvement in business operations in one country may increase costs of doing business and therefore drive firms to seek low cost sources in emerging markets in other countries. This can reduce employment opportunitiesn one country and increase them in another, further accelerating the pace of globalization. The second major question in the paper is what impact these drivers of interdependencies have had on the core conceptual models of organizational buying behavior. Consider the three enduring conceptual models developed in the Industrial Buying and Creative Marketing and Organizational Buying Behavior books: the organizational buying process, the buying center, and the buying situation. A review of these core models of organizational buying behavior, as originally conceptualized, shows they are still valid and not likely to change with the increasingly intense drivers of interdependency among organizations. What will change however is the way in which buyers and sellers interact under conditions of interdependency. For example, increased interdependencies can lead to increased opportunities for collaboration as well as conflict between buying and selling organizations, thereby changing aspects of the buying process. In addition, the importance of communication processes between and among organizations will increase as the role of trust becomes an important criterion for a successful buying relationship. The third question in the paper explored consequences and implications of these interdependencies on organizational buying behavior for practice and research. The following are considered in the paper: the need to increase understanding of network influences on organizational buying behavior, the need to increase understanding of the role of trust and value among organizational participants, the need to improve understanding of how to manage organizational buying in networked environments, the need to increase understanding of customer needs in the value network, and the need to increase understanding of the impact of emerging new business models on organizational buying behavior. In many ways, these needs deriving from increased organizational interdependencies are an extension of the conceptual tradition in organizational buying behavior. In 1977, Nicosia and Wind suggested a focus on inter-organizational over intra-organizational perspectives, a trend that has received considerable momentum since the 1990's. Likewise for managers to survive in an increasingly interdependent world, they will need to better understand the complexities of how organizations relate to one another. The transition from an inter-organizational to an interdependent perspective has begun, and must continue so as to develop an improved understanding of these important relationships. A shift to such an interdependent network perspective may require many academicians and practitioners to fundamentally challenge and change the mental models underlying their business and organizational buying behavior models. The focus can no longer be only on the dyadic relations of the buying organization and the selling organization but should involve all the related members of the network, including the network of customers, developers, and other suppliers and intermediaries. Consider for example the numerous partner networks initiated by SAP which involves over 9000 companies and over a million participants. This evolving, complex, and uncertain reality of interdependencies and dynamic networks requires reconsideration of how purchase decisions are made; as a result they should be the focus of the next phase of research and theory building among academics and the focus of practical models and experiments undertaken by practitioners. The hope is that such research will take place, not in the isolation of the ivory tower, nor in the confines of the business world, but rather, by increased collaboration of academics and practitioners. In conclusion, the consideration of increased interdependence among organizations revealed the continued relevance of the fundamental models of organizational buying behavior. However to increase the value of these models in an interdependent world, academics and practitioners should improve their understanding of (1) network influences, (2) how to better manage these influences, (3) the role of trust and value among organizational participants, (4) the evolution of customer needs in the value network, and (5) the impact of emerging new business models on organizational buying behavior. To accomplish this, greater collaboration between industry and academia is needed to advance our understanding of organizational buying behavior in an interdependent world.