• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.6 no.2
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• pp.81-92
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• 2001

According to analyses of high-resolution seismic profiles (air gun, sparker, and SBP) and a deep-drill core(YSDP 105) in the mid-eastern Yellow Sea, stratigraphic and geoacoustic models have been established and seismo-acoustic modeling has been fulfilled using ray tracing of finite element method. Stratigraphic model reflects seismo-, litho-, and chrono-stratigraphic sequences formed under a significant influence of Quaternary glacio-eustatic sea-level fluctuations. Each sequence consists of terrestrial to very-shallow-marine coarse-grained lowstand systems tract and tidal fine-grained transgressive to highstand systems tract. Based on mean grain-size data (121 samples) of the drill core, bulk density and P-wave velocity of depositional units have been inferred and extrapolated down to a depth of the recovery using the Hamilton's regression equations. As goo-acoustic parameters, the 121 pairs of bulk density and P-wave velocity have been averaged on each unit of the stratigraphic model. As a result of computer ray-tracing simulation of the subsurface strata, we have found that there are complex ray paths and many acoustic-shadow zones owing to the presence of irregular layer boundaries and low-velocity layers.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Asia pacific journal of information systems
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• v.24 no.3
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• pp.255-279
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• 2014

It is not too much to say that smartphones have become an essential part of our lives due to their versatility. Nevertheless, they still have less overall capabilities than their desktop counterparts. Specifically, they have small screens and low resolutions, which make their applications difficult to have a usable interface. To account for these limitations, the interface of smartphone applications should be designed carefully and properly. Good interface design to any application is critical. However, a comprehensive information systems (IS) literature review found that there has been little research on the user interface design of smartphone applications. More specifically, there has been little empirical evidence and understanding about how metaphors, an imaginative way of describing objects and concepts, in the user interface of smartphone applications affect users' intention to use the applications. Thereby, the research goals of this study are to examine 1) the effects of the metaphors in the user interface of smartphone applications on the interaction between users and applications and 2) the effects of mediating variables including the interaction between users and applications, users' beliefs and attitudes, on users' intention to use the applications. A survey was conducted to collect data. University students and practitioners participated in the survey. A 24-item questionnaire was developed on a 5-point Likert-type scale. The measurement items were mostly adapted from the previous studies in the IS literature and modified to fit the context of this study. First, a principal component factor analysis was performed to explore the inter-relationships among a set of variables. The analysis showed that most of the items loaded quite strongly on the six components. The analysis also revealed the six components with eigenvalues exceeding 1, explaining a total of 70.7 per cent of the variance. The reliabilities of the items were also checked. Most Cronbach alpha values were above 0.8, so the scales were considered reliable. In sum, the results of the analysis support the decision to retain the six factors for further investigation. Next, the structural model was analyzed with AMOS structural equation modeling. The values of GFI, AGFI, NFI, TLI, CFI, and RMSEA were checked. The values showed that the research model considerably have a good fit in general. Next, the convergent and discriminant validities of all constructs were examined. The values for the standardized regression weights and critical ration (CR) indicated sufficient convergent validity for all constructs. In addition, the square root of the average variance extracted (AVE) of each construct was compared with its correlations with all other constructs. The results supported discriminant validity for all constructs. In sum, the results of analysis demonstrated adequate convergent and discriminant validities for all constructs. Finally, path coefficients between the variables were examined. Methphor was found to have an impact on interaction (${\beta}$ = .457, p = .000). There were also significant effects of the interaction on perceived usefulness (${\beta}$ = .273, p = .000) and ease of use (${\beta}$ = .405, p = .000). User attitude was significantly influenced by these two beliefs, perceived usefulness (${\beta}$ = .386, p = .000) and ease of use (${\beta}$ = .347, p = .000) respectively. Further, the results of analysis found that users' intention to use smartphone applications was significantly influenced by user attitude (${\beta}$ = .567, p = .000). Based upon the analyses, all hypotheses were supported. This study found that the metaphors used in the interface of smartphone applications affect not only the interaction between users and applications, but also users' intention to use the applications through the mediating variables, perceived usefulness and ease of use. These findings imply that if the metaphors used in the user interface of application are easy enough to understand for smartphone users, then the application can be perceived useful and easy to use, which in turn make users to have an intention to use the application. In conclusion, this study contributed not only to validate and extend Technology Acceptance Model (TAM) partially, but also to develop the construct of metaphor in smartphone settings. However, since a single empirical study cannot be enough to validate the findings, some limitations should be considered.

• Food Science and Preservation
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• v.19 no.5
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• pp.633-638
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• 2012

The aim of this study was to investigate the growth of aerobic bacteria in fresh-cut salad during short-term temperature abuse ($4{\sim}30^{\circ}C$temperature for 1, 2, and 3 h) for 72 h and to develop predictive models for the growth of total viable cells (TVC) based on Predictive food microbiology (PFM). The tool that was used, Pathogen Modeling program (PMP 7.0), predicts the growth of Aeromonas hydrophila (broth Culture, aerobic) at pH 5.6, NaCl 2.5%, and sodium nitrite 150 ppm for 72 h. Linear models through linear regression analysis; DMFit program were created based on the results obtained at 5, 10, 20, and $30^{\circ}C$ for 72 h ($r^2$ >0.9). Secondary models for the growth rate and lag time, as a function of storage temperature, were developed using the polynomial model. The initial contamination level of fresh-cut salad was 5.6 log CFU/mL of TVC during 72 h storage, and the growth rate of TVC was shown to be 0.020~1.083 CFU/mL/h ($r^2$ >0.9). Also, the growth tendency of TVC was similar to that of PMP (grow rate: 0.017~0.235 CFU/mL/h; $r^2=0.994{\sim}1.000$). The predicted shelf life with PMP was 24.1~626.5 h, and the estimated shelf life of the fresh-cut salads with short-term temperature abuse was 15.6~31.1 h. The predicted shelf life was more than two times the observed one. This result indicates a 'fail safe' model. It can be taken to a ludicrous extreme by adopting a model that always predicts that a pathogenic microorganism will grow even under conditions so strict as to be actually impossible.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.34 no.6
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• pp.643-651
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• 2001

Paddlewheel-driven circulation in a culture pond has been simulated based on the depth integrated 2 dimensional hydrodynamic model. Acceleration by paddlewheel is expressed as shaft force divided by water mass discharged by paddlewheel blades. The model has been calibrated and applied to culture ponds as following steps:- i) The model predicted velocities at every 10 m along longitudinal direction from the paddlewheel. The model was calibrated comparing the results with the measured values at mass correction factor $\alpha$ and dimensionless eddy viscosity constant $\gamma$, respectively, in a range $15\~20$ and 6. ii) Wind shear stress was simulated under conditions of direction $0^{\circ}C,\;90^{\circ}C\;and\;180^{\circ}C$ and speed 0.0, 2.5, 5.0 and 7.5 m/s. Change rate of current speed was <$1\%$ at wind in parallel or opposite direction to the paddlewheel-driven jet flow, while $4\%$ at orthogonal angle. iii) The model was then applied to 2 culture ponds located at the Western coast of Korea. The measured and predicted currents for the ponds were compared using the regression analysis. Analysis of flow direction and speed showed correlation coefficients 0.8928 and 0.6782 in pond A, 0.8539 and 0.7071 in pond B, respectively. Hence, the model is concluded to accurately predict circulation driven by paddlewheel such that it can be a useful tool to provide pond management strategy relating to paddlewheel operation and water quality.

• The Journal of Information Systems
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• v.25 no.1
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• pp.1-23
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• 2016

Purpose Self-congruity deals with the effect of symbolic value-expressive attributes on consumer decision and behavior, which is the theoretical foundation of the "non-utilitarian destination positioning". Functional congruity refers to utilitarian evaluation of a product or service by consumers. In addition, recent years, social network services, especially mobile social network services have created many opportunities for e-WOM communication that enables consumers to share personal consumption related information anywhere at any time. Moreover, self-construal is a hot and popular topic that has been discussed in the field of modem psychology as well as in marketing area. This study aims to examine the moderating effect of self-construal on the relationship between self-congruity, functional congruity and tourists' positive electronic word of mouth (e-WOM). Design/methodology/approach In order to verify the hypotheses, we developed a questionnaire with 32 survey items. We measured all the items on a five-point Likert-type scale. We used Sojump.com to collect questionnaire and gathered 218 responses from whom have visited Korea before. After a pilot test, we analyzed the main survey data by using SPSS 20.0 and AMOS 18.0, and employed structural equation modeling to test the hypotheses. We first estimated the measurement model for its overall fit, reliability and validity through a confirmatory factor analysis and used common method bias test to make sure that whether measures are affected by common-method variance. Then we tested the hypotheses through the structural model and used regression analysis to measure moderating effect of self-construal. Findings The results reveal that the effect of self-congruity on tourists' positive e-WOM is stronger for tourists with an independent self-construal compared with those with interdependent self-construal. Moreover, it shows that the effect of functional congruity on tourists' positive e-WOM becomes salient when tourists' self-construal is primed to be interdependent rather than independent. We expect that the results of this study can provide important implications for academic and practical perspective.

• Journal of dental hygiene science
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• v.10 no.5
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• pp.307-314
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• 2010

This study aimed to measure such features of emotional responses perceived by students as learning climate, department living stress, and perceived helplessness to analyze their effects on major satisfaction among students at the department of dental hygiene; to do this, a survey was conducted with 431 students, regardless of college year, who were at the department of dental hygiene in four colleges in Gyeonggi Province, Daejeon, and Chungcheong Province. An existing emotion scale which went through the generalization process was used to draw a multiple model in the combination form in order to collect emotional factors affecting college students' satisfaction with their major, which had existed as a hypothetical proposition, and make overall interpretation of relevance through the explainable, predictable modeling process by measuring emotional factors and phenomenal description of the level of general perception. The results showed that major satisfaction was very significantly affected by emotional features among students at the department of dental hygiene, which needs to be treated as an important factor to enhance expertise related to major learning and improve students' living.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Journal of Distribution Research
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• v.17 no.4
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• pp.55-80
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• 2012

I. Introduction Developing the relationships between companies is very important issue to ensure a competitive advantage in today's business environment (Bleeke & Ernst 1991; Mohr & Spekman 1994; Powell 1990). Partnerships between companies are based on having same goals, pursuing mutual understanding, and having a professional level of interdependence. By having such a partnerships and cooperative efforts between companies, they will achieve efficiency and effectiveness of their business (Mohr and Spekman, 1994). However, it is difficult to expect these ideal results only in the B2B corporate transaction. According to agency theory which is the well-accepted theory in various fields of business strategy, organization, and marketing, the two independent companies have fundamentally different corporate purposes. Also there is a higher chance of developing opportunism and conflict due to natures of human(organization), such as self-interest, bounded rationality, risk aversion, and environment factor as imbalance of information (Eisenhardt 1989). That is, especially partnerships between principal(or buyer) and agent(or supplier) of companies within supply chain, the business contract itself will not provide competitive advantage. But managing partnership between companies is the key to success. Therefore, managing partnership between manufacturer and supplier, and finding causes of conflict are essential to improve B2B performance. In conclusion, based on prior researches and Agency theory, this study will clarify how business hazards cause conflicts on supply chain and then identify how developed conflicts have been managed by two control mechanisms. II. Research model III. Method In order to validate our research model, this study gathered questionnaires from small and medium sized enterprises(SMEs). In Korea, SMEs mean the firms whose employee is under 300 and capital is under 8 billion won(about 7.2 million dollar). We asked the manufacturer's perception about the relationship with the biggest supplier, and our key informants are denied to a person responsible for buying(ex)CEO, executives, managers of purchasing department, and so on). In detail, we contact by telephone to our initial sample(about 1,200 firms) and introduce our research motivation and send our questionnaires by e-mail, mail, and direct survey. Finally we received 361 data and eliminate 32 inappropriate questionnaires. We use 329 manufactures' data on analysis. The purpose of this study is to identify the anticipant role of business hazard (environmental dynamism, asset specificity) and investigate the moderating effect of control mechanism(formal control, social control) on conflict-performance relationship. To find out moderating effect of control methods, we need to compare the regression weight between low versus. high group(about level of exercised control methods). Therefore we choose the structural equation modeling method that is proper to do multi-group analysis. The data analysis is performed by AMOS 17.0 software, and model fits are good statically (CMIN/DF=1.982, p<.000, CFI=.936, IFI=.937, RMSEA=.056). IV. Result V. Discussion Results show that the higher environmental dynamism and asset specificity(on particular supplier) buyer(manufacturer) has, the more B2B conflict exists. And this conflict affect relationship quality and financial outcomes negatively. In addition, social control and formal control could weaken the negative effect of conflict on relationship quality significantly. However, unlikely to assure conflict resolution effect of control mechanisms on relationship quality, financial outcomes are changed by neither social control nor formal control. We could explain this results with the characteristics of our sample, SMEs(Small and Medium sized Enterprises). Financial outcomes of these SMEs(manufacturer or principal) are affected by their customer(usually major company) more easily than their supplier(or agent). And, in recent few years, most of companies have suffered from financial problems because of global economic recession. It means that it is hard to evaluate the contribution of supplier(agent). Therefore we also support the suggestion of Gladstein(1984), Poppo & Zenger(2002) that relational performance variable can capture the focal outcomes of relationship(exchange) better than financial performance variable. This study has some implications that it tests the sources of conflict and investigates the effect of resolution methods of B2B conflict empirically. And, especially, it finds out the significant moderating effect of formal control which past B2B management studies have ignored in Korea.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.