• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.73-92
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• 2014

An Adaptive Clustering-based Collaborative Filtering Technique was proposed to solve the fundamental problems of collaborative filtering, such as cold-start problems, scalability problems and data sparsity problems. Previous collaborative filtering techniques were carried out according to the recommendations based on the predicted preference of the user to a particular item using a similar item subset and a similar user subset composed based on the preference of users to items. For this reason, if the density of the user preference matrix is low, the reliability of the recommendation system will decrease rapidly. Therefore, the difficulty of creating a similar item subset and similar user subset will be increased. In addition, as the scale of service increases, the time needed to create a similar item subset and similar user subset increases geometrically, and the response time of the recommendation system is then increased. To solve these problems, this paper suggests a collaborative filtering technique that adapts a condition actively to the model and adopts the concepts of a context-based filtering technique. This technique consists of four major methodologies. First, items are made, the users are clustered according their feature vectors, and an inter-cluster preference between each item cluster and user cluster is then assumed. According to this method, the run-time for creating a similar item subset or user subset can be economized, the reliability of a recommendation system can be made higher than that using only the user preference information for creating a similar item subset or similar user subset, and the cold start problem can be partially solved. Second, recommendations are made using the prior composed item and user clusters and inter-cluster preference between each item cluster and user cluster. In this phase, a list of items is made for users by examining the item clusters in the order of the size of the inter-cluster preference of the user cluster, in which the user belongs, and selecting and ranking the items according to the predicted or recorded user preference information. Using this method, the creation of a recommendation model phase bears the highest load of the recommendation system, and it minimizes the load of the recommendation system in run-time. Therefore, the scalability problem and large scale recommendation system can be performed with collaborative filtering, which is highly reliable. Third, the missing user preference information is predicted using the item and user clusters. Using this method, the problem caused by the low density of the user preference matrix can be mitigated. Existing studies on this used an item-based prediction or user-based prediction. In this paper, Hao Ji's idea, which uses both an item-based prediction and user-based prediction, was improved. The reliability of the recommendation service can be improved by combining the predictive values of both techniques by applying the condition of the recommendation model. By predicting the user preference based on the item or user clusters, the time required to predict the user preference can be reduced, and missing user preference in run-time can be predicted. Fourth, the item and user feature vector can be made to learn the following input of the user feedback. This phase applied normalized user feedback to the item and user feature vector. This method can mitigate the problems caused by the use of the concepts of context-based filtering, such as the item and user feature vector based on the user profile and item properties. The problems with using the item and user feature vector are due to the limitation of quantifying the qualitative features of the items and users. Therefore, the elements of the user and item feature vectors are made to match one to one, and if user feedback to a particular item is obtained, it will be applied to the feature vector using the opposite one. Verification of this method was accomplished by comparing the performance with existing hybrid filtering techniques. Two methods were used for verification: MAE(Mean Absolute Error) and response time. Using MAE, this technique was confirmed to improve the reliability of the recommendation system. Using the response time, this technique was found to be suitable for a large scaled recommendation system. This paper suggested an Adaptive Clustering-based Collaborative Filtering Technique with high reliability and low time complexity, but it had some limitations. This technique focused on reducing the time complexity. Hence, an improvement in reliability was not expected. The next topic will be to improve this technique by rule-based filtering.

• Journal of Applied Biological Chemistry
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• v.61 no.1
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• pp.83-91
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• 2018

Advances in bacterial and fungal genome mining uncover a plethora of cryptic secondary metabolite biosynthetic gene clusters. Guided by the genome information, targeted transcriptional derepression could be employed to determine the product of a cryptic gene cluster and to explore its biological role. Monascus spp. are food grade filamentous fungi popular in eastern Asia and several genome data belong to them are now available. We achieved transcription activation of a cryptic fungal polyketide synthase-nonribosomal peptide synthase gene Mpfus1 in Monascus purpureus ${\Delta}MpPKS5$ by inserting Aspergillus gpdA promoter at the upstream of Mpfus1 through double crossover gene replacement. The gene cluster with Mpfus1 show a high similarity to those for the biosynthesis of conjugated polyene derivatives with 2-pyrrolidone ring and the mycotoxin fusarin is the representative member of this group. The ${\Delta}MpPKS5$ is incapable of producing azaphilone pigment, providing an excellent background to identify chromogenic and UV-absorbing compounds. Activation of Mpfus1 resulted in a yellow hue on mycelia and its methanol extract exhibit a maximum absorption at 365 nm. HPLC analysis of the organic extracts indicated the presence of a variety of yellow compounds in the extract. This implies that the product of MpFus1 is metabolically or chemically unstable. LC-MS analysis guided us to predict the MpFus1 product and to propose that the Mpfus1-containing gene cluster encode the biosynthesis of a desmethyl analogue of fusarin. This study showcases the genome mining in Monascus and the possibility to unveil new biological activities embedded in it.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.1
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• pp.27-40
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• 2014

The functions of urban open space, which embraces community revitalization, are diverse. It is the means of public healthcare, learning centers for children, hub of arts and cultural programs, as well as promoter of urban tourism. However, in-depth discourse and research on the topic of urban open spaces has been limited so far. Hence, this study aims to investigate the motivations and satisfaction of visitation based on four representative waterfront open space in Seoul; Cheongyecheon Waterfront, Seoul Forest Park, Seonyudo Park and Banpo Hangang Park. The methods of study are literature review, observation investigation, and questionnaire survey. The findings are analyzed through the Exploratory Factor Analysis, Reliability Analysis, ANOVA Analysis and Regression Analysis by SPSS 18.0. The results of the study are as follows. First, urban waterfront open spaces in Seoul has 5 factors of visitor motivation; community amenity, nature access, cultural and educational assets, aesthetic enjoyment, and lastly means of escape. Second, factors of recognizing urban waterfront open spaces as community amenity and nature access indicate meaningful differences in visitor's perception by spatial characteristics. Third, distances between the destination and the visitor's residence influence significantly their perceived motivation. Close-range visitors perceived nature access as a principal factor, whilst medium to long-range visitors perceived visitation for aesthetic purposes more importantly. Lastly, the will to escape was shown as the influential factor in visitor satisfaction. Visiting open spaces for the enjoyment of nature and aesthetic purposes were factors that also closely relate to visitor satisfaction. In addition, it was found that there are different visitor motivations that influence visitor satisfaction in accordance with the spatial characteristics of each open space. In summary, it can be said that urban waterfront open space is a hybrid space connected to various types of urban contents beyond daily experiences. It was found that several visitor motivations including community development, design aesthetics, education and culture, entertainment, enjoyment of natural landscape, and relaxation, affect the overall satisfaction of the visiting experience. It is anticipated that the results of the study will be used by the local government in setting up strategies for the creation and management of successful urban waterfront open space, and for those involved in planning and design act as a starting point for spatial programming and amenities arrangement in accordance to the city's tourism and urban marketing approach.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.177-198
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• 2018

Recently, as economic property it has become necessary to acquire and utilize the framework for water resource measurement and performance management as the property of water resources changes to hold "public property". To date, the evaluation of water technology has been carried out by feasibility study analysis or technology assessment based on net present value (NPV) or benefit-to-cost (B/C) effect, however it is not yet systemized in terms of valuation models to objectively assess an economic value of technology-based business to receive diffusion and feedback of research outcomes. Therefore, K-water (known as a government-supported public company in Korea) company feels the necessity to establish a technology valuation framework suitable for technical characteristics of water resources fields in charge and verify an exemplified case applied to the technology. The K-water evaluation technology applied to this study, as a public interest goods, can be used as a tool to measure the value and achievement contributed to society and to manage them. Therefore, by calculating the value in which the subject technology contributed to the entire society as a public resource, we make use of it as a basis information for the advertising medium of performance on the influence effect of the benefits or the necessity of cost input, and then secure the legitimacy for large-scale R&D cost input in terms of the characteristics of public technology. Hence, K-water company, one of the public corporation in Korea which deals with public goods of 'water resources', will be able to establish a commercialization strategy for business operation and prepare for a basis for the performance calculation of input R&D cost. In this study, K-water has developed a web-based technology valuation model for public interest type water resources based on the technology evaluation system that is suitable for the characteristics of a technology in water resources fields. In particular, by utilizing the evaluation methodology of the Institute of Advanced Industrial Science and Technology (AIST) in Japan to match the expense items to the expense accounts based on the related benefit items, we proposed the so-called 'K-water's proprietary model' which involves the 'cost-benefit' approach and the FCF (Free Cash Flow), and ultimately led to build a pipeline on the K-water research performance management system and then verify the practical case of a technology related to "desalination". We analyze the embedded design logic and evaluation process of web-based valuation system that reflects characteristics of water resources technology, reference information and database(D/B)-associated logic for each model to calculate public interest-based and profit-based technology values in technology integrated management system. We review the hybrid evaluation module that reflects the quantitative index of the qualitative evaluation indices reflecting the unique characteristics of water resources and the visualized user-interface (UI) of the actual web-based evaluation, which both are appended for calculating the business value based on financial data to the existing web-based technology valuation systems in other fields. K-water's technology valuation model is evaluated by distinguishing between public-interest type and profitable-type water technology. First, evaluation modules in profit-type technology valuation model are designed based on 'profitability of technology'. For example, the technology inventory K-water holds has a number of profit-oriented technologies such as water treatment membranes. On the other hand, the public interest-type technology valuation is designed to evaluate the public-interest oriented technology such as the dam, which reflects the characteristics of public benefits and costs. In order to examine the appropriateness of the cost-benefit based public utility valuation model (i.e. K-water specific technology valuation model) presented in this study, we applied to practical cases from calculation of benefit-to-cost analysis on water resource technology with 20 years of lifetime. In future we will additionally conduct verifying the K-water public utility-based valuation model by each business model which reflects various business environmental characteristics.

• International Journal of Highway Engineering
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• v.10 no.1
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• pp.155-164
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• 2008

It is critical to develop a core ITS technology such as real-time travel time estimation in order that the efficient use of the ITS implementation can be achieved as the ITS infrastructure and relevant facilities are broadly installed in recent years. The provision of travel time information in real-time allows travellers to make informed decisions and hence not only the traveller's travel utilities but also the road utilization can be maximized. In this paper, a hybrid model is proposed to combine VDS and AVI which have different characteristics in terms of space and time dimensions. The proposed model can incorporate the immediacy of VDS data and the reality of AVI data into one single framework simultaneously. In addition, the solution algorithm is made to have no significant computational burden so that the model can be deployable in real world. A set of real field data is used to analyze the reliability and applicability of the proposed model. The analysis results suggest that the proposed model is very efficient computationally and improves the accuracy of the information provided, which demonstrates the real-time applicability of the proposed model. In particular, the data fusion methodology developed in this paper is expected to be used more widely when a new type of traffic data becomes available.

• Polymer(Korea)
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• v.32 no.4
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• pp.372-376
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• 2008

Vacuum deposited 4,4',4"-tris(N-(2-naphthyl)-N-phenylamino)-triphenylamine (2-TNATA), used as a hole injection (HIL) material in OLEDs, is placed as a thin interlayer between indium tin oxide (ITO) electrode and a hole transporting layer (HTL) in the devices. C60-doped 2-TNATA:C60 (20 wt%) film was formed via co-evaporation process and molecular ordering and topology of 2-TNATA:C60 films were investigated using XRD and AFM. The J-V, L-V and current efficiency of multi-layered devices were characterized as well. Vacuum-deposited C60 film was molecularly oriented, but neither was 2-TNATA:C60 film due to the uniform dispersion of C60 molecules in the film. By using C60-doped 2-TNATA:C60 film as a HIL, the current density and luminance of a multi-layered ITO/2-TNATA:C60/NPD/$Alq_3$/LiF/Al device were significantly increased and the current efficiency of the device was increased from 4.7 to 6.7 cd/A in the present study.

• Journal of Korea Multimedia Society
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• v.16 no.2
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• pp.190-197
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• 2013

Since domestic and foreign platform companies and mobile carriers adopt and use different kinds of smart platforms, developers should develop or convert contents according to each smart platform to provide a single smart content for customers. It takes long time and a lot of money to convert the conventional smart contents in order to serve other smart platforms. For the reason, more attention has been paid on Smart Cross Platform or Hybrid Platform, the core technologies of OSMU(One Source Multi Use) in which, once a program is coded, it can be executed in any platforms regardless of development languages. As a result, PhoneGap and HTML5 based Sencha Touch have been introduced. In this paper, we developed the smart virtual machine, which is built in smart cross platform based smart devices, unlike Android, iOS, Windows Phone devices being dependent of platforms, and helps to download and execute applications, being independent of platforms. the smart virtual machine supports C/C++, and Java language, being differentiated from JVM by sun microsystems that supports only Java language and .NET framework by microsoft that supports only C, C++ and C#. Therefore, it provides contents developers with the environment where they can get a wide range of options in choosing a language and develop smart contents.

• Journal of the Korean Ceramic Society
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• v.50 no.1
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• pp.63-69
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• 2013

Negative-temperature coefficient (NTC) thermistors based on nickel manganite spinel ($NiMn_2O_4$) are widely used for many applications, such as sensors and temperature compensators, due to their good thermistor characteristics and stabilities. However, to achieve thermistors with a high NTC B constant, which is an important figure of merit pertaining to the degree of temperature sensitivity, the activation energy should be high such that high resistivity at ambient temperatures results. To obtain a high B constant and low resistivity, Al and Si modified spinel structured $Ni_{0.6}Si_{0.2}Al_{0.6}Mn_{1.6}O_4$ hybrid thick films with the conducting metal oxide of $LaNiO_3$ were fabricated on a glass substrate by aerosol deposition at room temperature (RT). The NTC-$LaNiO_3$ hybrid thick films showed resistivity as low as < $100k{\Omega}\;cm$ at $90^{\circ}C$, which is one or two orders of magnitude lower than that of the monolithic NTC films, while retaining a high B constant of $NiMn_2O_4$ of over 5500 K when 20 wt% $LaNiO_3$ was added without a post-thermal treatment. These phenomena are explained by the percolation threshold mechanism.

• Journal of Adhesion and Interface
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• v.17 no.4
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• pp.141-148
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• 2016

In this work, mesoporous silica (SBA-15) was synthesized via self-assembly process using triblock copolymer ($PEO_{20}PPO_{70}PEO_{20}$, P123) as template and tetraethyl orthosilicate (TEOS) as silica source under acidic condition. SBA-15 have high surface area ($704m^2g^{-1}$) and uniform pore size (8.4 nm) with well-ordered hexagonal mesostructure. Spiropyran-functionalized SBA-15 (Spiropyran-SBA-15) was synthesized via post-synthesis process using 3-(triethoxysilyl)propyl isocyanate (TESPI) and 1-(2-Hydroxyethyl)-3,3-dimethy-lindolino-6'-nitrobenzopyrylo-spiran (HDINS). Spiropyran-SBA-15 was produced with hexagonal array of mesopores without damage of mesostructre. Surface area and pore size of Spiropyran-SBA-15 were $651m^2g^{-1}$ and 8.0 nm, respectively. Optochemical properties of Spiropyran-SBA-15 was studied with chemical vapors such as EtOH, THF, $CHCl_3$, Acetone and HCl. Main peaks of photofluorescence of Spiropyran-SBA-15 exhibited blue shift in the range of 603.4~592.1 nm after exposure under EtOH, THF, $CHCl_3$, and Acetone vapors. Normalized peak intensities decreased in the range of 0.8~0.3. The main peak of photofluorescence of Spiropyran-SBA-15 showed significant blue shift of 592.1 nm after exposure under HCl vapor, while normalized peak intensity decreased to 0.1.