• Journal of Distribution Research
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• v.13 no.1
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• pp.1-33
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• 2008

As the interest in the business-to-business(B2B) electronic commerce is increasing, many companies are participating in the B2B e-Marketplaces. The e-Marketplace is defined as the virtual market that many players take part in to transact. The e-Marketplace has an influenced on the manner in which organizational buyers and sellers interact. As a result, it is important to develop an understanding of the behaviors of firms that use these electronic marketplaces. The purpose of this study is to develop a comprehensive model for trust and commitment of B2B e-Marketplace and empirically to examine their structural relationships. Drawing from trust and commitment theory in the interorganizational relationship and B2B electronic commerce context, this study identifies network externality, interactivity, justice, quality of information sharing, institutional assurance as the determinants of trust and commitment of e-Marketplace. The proposed model hypothesized that (1) trust is a function of network externality, interactivity, justice, quality of information sharing, institutional assurance, (2) attitudinal and behavioral commitment is a function of trust, (3) behavioral commitment is a function of attitudinal commitment. The proposed model is tested using organizational-level survey data from 187 buying organizations that conduct business in MRO e-Marketplaces. The data were tested by reliability test, correlation analysis, exploratory factor analysis, confirmatory factor analysis and covariance structure analysis. The results indicate that (1) trust is influenced by network externality, interactivity, justice, institutional assurance, (2) attitudinal commitment and behavioral commitment is influenced by trust (3) behavioral commitment is influenced by attitudinal commitment. Also, the empirical results confirmed that trust play a strong, central role in determinging e-Marketplace commitment. The key theoretical contribution of this research is that it begins to extend interorganizational information system literature in areas such as B2B Internet e-Marketplace. Managerially, this study contributes tn the understanding of the role of B2B e-Markeplace providers in Internet situation. And Limitations of this study and guidelines for future researches are also discussed.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.13 no.4
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• pp.179-183
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• 1980

The efficiency of energy transfer by a population of the farmed pacific oyster, Crassostrea gigas was studied during culture period of 10 months July 1979-April 1980, in Geoje-Hansan Bay near Chungmu City. Energy use by the farmed oyster population was calculated from estimates of half-a-month unit age specific natural mortality rate and data on growth, gonad output, shell organic matter production and respiration. Total mortality during the culture period was estimated approximate $36\%$ from data on survivor individual number per cluster. Growth may be dual consisted of a curved line during the first half culture period (July-November) and a linear line in the later half period (December-April). The first half growth was approximated by the von Bertalanffy growth model; shell height, $SH=6.33\;(1-e^{0.2421(t+0.54)})$, where t is age in half-a-month unit. In the later half growth period shell height was related to t by SH=4.44+0.14t. Dry meat weight (DW) was related to shell height by log $DW=-2.2907+2.589{\cdot}log\;SH,\;(2, and/or log $DW=-5.8153+7.208{\cdot}log\;SH,\;(5. Size specific gonad output (G) as calculated by condition index of before and after the spawning season, was related to shell height by $G=0.0145+(3.95\times10^{-3}{\times}SH^{2.9861})$. Shell organic matter production (SO) was related to shell height by log $SO=-3.1884+2.527{\cdot}1og\;SH$. Size and temperature specific respiration rate (R) as determined in biotron system with controlled temperature, was related to dry meat weight and temperature (T) by log $R=(0.386T-0.5381)+(0.6409-0.0083T){\cdot}log\;DW$. The energy used in metabolism was calculated from size, temperature specific respiration and data on body composition. The calorie contents of oyster meat were estimated by bomb calorimetry based on nitrogen correction. The assimilation efficiency of the oyster estimated directly by a insoluble crude silicate method gave $55.5\%$. From the information presently available by other workers, the assimilation efficiency ranges between $40\%\;and\;70\%$. Twenty seven point four percent of the filtered food material expressed by energy value for oyster population was estimated to have been rejected as pseudofaeces : $17.2\%$ was passed as faeces; $35.04\%$ was respired and lost as heat; $0.38\%$ was bounded up in shell organics; $2.74\%$ was released as gonad output, $2.06\%$ was fell as meat reducing by mortality. The remaining $15.28\%$ was used as meat production. The net efficiency of energy transfer from assimilation to meat production (yield/assimilation) of a farm population of the oyster was estimated to be $28\%$ during culture period July 1979-April 1980. The gross efficiency of energy transfer from ingestion to meat production (yield/food filtered) is probably between $11\%\;and\;20\%$.

• Korean Journal of Cognitive Science
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• v.27 no.1
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• pp.1-25
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• 2016

The present study investigated the effect size of attention and consciousness on change detection. We confirmed the effect size of consciousness by comparing the condition which combined attention and consciousness and the condition of attention without consciousness. Then, we confirmed the effect size of attention by comparing the condition of attention without consciousness and the control condition which excluded attention and consciousness. For this purpose, change detection task and continuous flash suppression (CFS) were used. CFS renders a highly visible image invisible. In CFS, one eye is presented with a static stimulus, while the other eye is presented with a series of rapidly changing stimuli, such as mondrian patterns. The result is that the static stimulus becomes suppressed from conscious awareness by the stimuli presented in the other eye. We used a customized device with smartphone and google cardboard instead of stereoscope to trigger CFS. In Experiment 1-1, we reenacted some study to validate our experimental setup. Our experimental setup produced the duration of stimulus suppression that were similar to those of preceding research. In Experiment 1-2, we reenacted a study for attention without consciousness using an customized device. The results showed that attention without consciousness more strongly work as a cue. We think that it is reasonable to use CFS treatment employing smartphone and google cardboard for a follow-up study. In Experiment 2, when performing the change detection task, we measured the effect size of consciousness and attention by manipulating the consciousness level of cue. We used the method in which everything but the variable of interest kept being fixed. That way, the difference this independent variable makes to the action of the entire system can be isolated. We found that there was significant difference of correct response rate on change detection performance among different consciousness level of cue. In this study, we investigated that not only the role of attention and consciousness were different also we were able to estimated the effect size.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.5
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• pp.293-299
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• 2007

Intelligent pipeline inspection gauges (PIGs) are inspection vehicles that move along within a (gas or oil) pipeline and acquire signals (also called sensor data) from their surrounding rings of sensors. By analyzing the signals captured in intelligent PIGs, we can detect pipeline defects, such as holes and curvatures and other potential causes of gas explosions. There are two major data access patterns apparent when an analyzer accesses the pipeline signal data. The first is a sequential pattern where an analyst reads the sensor data one time only in a sequential fashion. The second is the repetitive pattern where an analyzer repeatedly reads the signal data within a fixed range; this is the dominant pattern in analyzing the signal data. The existing PIG software reads signal data directly from the server at every user#s request, requiring network transfer and disk access cost. It works well only for the sequential pattern, but not for the more dominant repetitive pattern. This problem becomes very serious in a client/server environment where several analysts analyze the signal data concurrently. To tackle this problem, we devise a fast in-memory cache manager, called T-Cache, by considering pipeline sensor data as multiple time-series data and by efficiently caching the time-series data at T-Cache. To the best of the authors# knowledge, this is the first research on caching pipeline signals on the client-side. We propose a new concept of the signal cache line as a caching unit, which is a set of time-series signal data for a fixed distance. We also provide the various data structures including smart cursors and algorithms used in T-Cache. Experimental results show that T-Cache performs much better for the repetitive pattern in terms of disk I/Os and the elapsed time. Even with the sequential pattern, T-Cache shows almost the same performance as a system that does not use any caching, indicating the caching overhead in T-Cache is negligible.

• Microbiology and Biotechnology Letters
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• v.40 no.4
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• pp.414-418
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• 2012

The quantity of research on microbial fuel cells has been rapidly increasing. Microbial fuel cells are unique in their ability to utilize microorganisms and to generate electricity from sewage, pig excrement, and other wastewaters which include organic matter. This system can directly produce electrical energy without an inefficient energy conversion step. However, with MFCs maximum power production is limited by several factors such as activation losses, ohmic losses, and mass transfer losses in cathodes. Therefore, electron acceptors such as nitrate and ferric ion in the cathodes were utilized to improve the cathode reaction rate because the cathode reaction is very important for electricity production. When 100 mM nitrate as an electron acceptor was fed into cathodes, the current in single-cathode and dual-cathode MFCs was noted as $3.24{\pm}0.06$ mA and $4.41{\pm}0.08$ mA, respectively. These values were similar to when air-saturated water was fed into the cathodes. One hundred mM nitrate as an electron acceptor in the cathode compartments did not affect an increase in current generation. However, when ferric ion was used as an electron acceptor the current increased by $6.90{\pm}0.36$ mA and $6.67{\pm}0.33$ mA, in the single-cathode and dual-cathode microbial fuel cells, respectively. These values, in single-cathode and dual-cathode microbial fuel cells, represent an increase of 67.1% and 17.6%, respectively. Furthermore, when supplied with ferric ion without air, the current was higher than that of only air-saturated water. In this study, we attempted to reveal an inexpensive and readily available electron acceptor which can replace platinum in cathodes to improve current generation by increasing the cathode reaction rate.

• Journal of the Korean Professional Engineers Association
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• v.28 no.4
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• pp.54-75
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• 1995

This synthesis has been prepared from a review of literature on Truck Escape Ramps technology and a survey of current practice by state department of transportation. Their locations have been determined usually from a combination of accident experience and en-gineering judgement, but new tools are emerging that can identify needs and sites without waiting for catastrophic accidents to happen. The Grade Severity Rating Systems holds promise in this regard. Design Procedures for truck excape ramps continue to evolve. Gravel arrester beds are clearly the preferred choice across the country Rounded aggregate, uniformly graded in the approximate size range of 13 to 18mm. Tech-nical publications typically have dassified TER types as paved gravity, sandpile, and ar-rester bed ramps. The design speed for vehicle entry into the ramp in critical to the deter-mination of ramp length. An escape ramp should be designed for a minimum entry speed of 130km/hr, a 145km/hr design being preferred. The ramps should be straight and their angle to the roadway align-ment should be as possible. The grade of truck escape ramps show the adjustment of ramp design to local topography, such as the tradeoff of ramp length against earthwork requirements. A width of 9 to 12m would more safety acommodate two or more outof con-trol vehicles. Reguarding comments on the most effective material, most respondents cited their own specification or referred to single graded, rounded pea gravel. The consensus essentially Is that single graded, well -rounded gravel is the most desirable material for use in arrester beds. The arrester beds should be constructed with a minimum aggregate depth of 30cm. Successful ramps have used depths between 30 and 90cm.

• Proceedings of the Korean DIstribution Association Conference
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• 2003.02a
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• pp.123-166
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• 2003

Marketing channel is recognized as one of the society systems which have the character of functional organization. These organizations are related to each other for specialized and cooperative work. Channel members in distribution channel are striving to accomplish exchange through reciprocal action. Thus channel members exercise their power to take better position in exchange. There will be struggling between members about satisfaction and conflict during this power exercise. Now a days, buyers use more harsh power as large retail firms are increasing. This phenomenon is occurring in the distribution channel. However, there will be different phenomenon in case of agricultural products. Not like industrial product suppliers, agricultural product suppliers have various supply channels and many agricultural products are seasonal. It has also unstable amount supplies. There should be differentiated marketing in agricultural products. Relatively weaker powered suppliers have to strengthen comparative factors and also have to be technically specialized through assessed experience in order to establish strong product sales chain. Making a brand of agricultural product would be also a good idea to increase the product comparability. Channel members need to be recognized their specialized functions in order to make balanced distribution channel. There have to be conversion of concept of relation between suppliers and buyers from subordinate relationship to cooperative relationship.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.25-41
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• 2011

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved them more powerful than traditional artificial neural networks (ANNs) (Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al., 2005; Kim, 2003).The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is so cost-sensitive particularly in financial classification problems such as the credit ratings that if the credit ratings are misclassified, a terrible economic loss for investors or financial decision makers may happen. Therefore, it is necessary to convert the outputs of the classifier into wellcalibrated posterior probabilities-based multiclass credit ratings according to the bankruptcy probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create the probabilities (Platt, 1999; Drish, 2001). This paper applied AdaBoost algorithm-based support vector machines (SVMs) into a bankruptcy prediction as a binary classification problem for the IT companies in Korea and then performed the multi-class credit ratings of the companies by making a normal distribution shape of posterior bankruptcy probabilities from the loss functions extracted from the SVMs. Our proposed approach also showed that their methods can minimize the misclassification problems by adjusting the credit grade interval ranges on condition that each credit grade for credit loan borrowers has its own credit risk, i.e. bankruptcy probability.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.21-44
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• 2018

In recent years, the rapid development of internet technology and the popularization of smart devices have resulted in massive amounts of text data. Those text data were produced and distributed through various media platforms such as World Wide Web, Internet news feeds, microblog, and social media. However, this enormous amount of easily obtained information is lack of organization. Therefore, this problem has raised the interest of many researchers in order to manage this huge amount of information. Further, this problem also required professionals that are capable of classifying relevant information and hence text classification is introduced. Text classification is a challenging task in modern data analysis, which it needs to assign a text document into one or more predefined categories or classes. In text classification field, there are different kinds of techniques available such as K-Nearest Neighbor, Naïve Bayes Algorithm, Support Vector Machine, Decision Tree, and Artificial Neural Network. However, while dealing with huge amount of text data, model performance and accuracy becomes a challenge. According to the type of words used in the corpus and type of features created for classification, the performance of a text classification model can be varied. Most of the attempts are been made based on proposing a new algorithm or modifying an existing algorithm. This kind of research can be said already reached their certain limitations for further improvements. In this study, aside from proposing a new algorithm or modifying the algorithm, we focus on searching a way to modify the use of data. It is widely known that classifier performance is influenced by the quality of training data upon which this classifier is built. The real world datasets in most of the time contain noise, or in other words noisy data, these can actually affect the decision made by the classifiers built from these data. In this study, we consider that the data from different domains, which is heterogeneous data might have the characteristics of noise which can be utilized in the classification process. In order to build the classifier, machine learning algorithm is performed based on the assumption that the characteristics of training data and target data are the same or very similar to each other. However, in the case of unstructured data such as text, the features are determined according to the vocabularies included in the document. If the viewpoints of the learning data and target data are different, the features may be appearing different between these two data. In this study, we attempt to improve the classification accuracy by strengthening the robustness of the document classifier through artificially injecting the noise into the process of constructing the document classifier. With data coming from various kind of sources, these data are likely formatted differently. These cause difficulties for traditional machine learning algorithms because they are not developed to recognize different type of data representation at one time and to put them together in same generalization. Therefore, in order to utilize heterogeneous data in the learning process of document classifier, we apply semi-supervised learning in our study. However, unlabeled data might have the possibility to degrade the performance of the document classifier. Therefore, we further proposed a method called Rule Selection-Based Ensemble Semi-Supervised Learning Algorithm (RSESLA) to select only the documents that contributing to the accuracy improvement of the classifier. RSESLA creates multiple views by manipulating the features using different types of classification models and different types of heterogeneous data. The most confident classification rules will be selected and applied for the final decision making. In this paper, three different types of real-world data sources were used, which are news, twitter and blogs.