• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Korean Chemical Engineering Research
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• v.59 no.4
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• pp.574-583
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• 2021

Waste air containing ethanol (100 ppmv) and hydrogen sulfide (10 ppmv) was continuously treated by waste air-treating system composed of two annular photocatalytic reactors (effective volume: 1.5 L) packed with porous SiO₂ media carrying TiO₂-anatase photocatalyst, one of which was alternately operated for 32 d/run while the other was regenerated by 100 ℃ hot air with 15 W UV(-A)-light on. As its elimination-behavior of ethanol, the removal efficiencies of ethanol at 1^st, 2^nd and 3^rd operation of the photocatalytic reactor system(A), turned out to be ca. 60, 55 and 54%, respectively, at their steady state condition. Unlike the elimination-behavior of ethanol, its hydrogen sulfide-elimination behavior showed repeated decrease of hydrogen sulfide removal efficiency by its resultant arrival at a lower level of steady state condition. Nevertheless, the removal efficiencies of hydrogen sulfide at 1^st, 2^nd and 3^rd operation of the photocatalytic reactor system, turned out to be ca. 80, 75 and 73%, respectively, at their final steady state condition, higher by ca. 20, 20 and 19% than those of ethanol, respectively. Therefore, assuming that adsorption on porous SiO₂-photocatalyst carrier was regarded to belong to a reversible deactivation and that decreased % of removal efficiency due to the reversible deactivation of photocatalyst including the adsorption was independent of the number of its use upon regeneration, the increments of the decreased % of removal efficiency of ethanol and hydrogen sulfide, due to an irreversible deactivation of photocatalyst, for the 3^rd use of regenerated photocatalyst, compared with the 2^nd use of regenerated photocatalyst, were ca. 1 and 2%, respectively, which was insignificant or the less than those of ca. 5 and 5%, respectively, for the 2^nd use of regenerated photocatalyst compared with the 1^st use of virgin photocatalyst. This trend of the photocatalytic reactor system was observed to be similar to that of the other alternately-operating photocatalytic reactor system.

• Journal of the Korea Organic Resources Recycling Association
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• v.30 no.3
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• pp.13-22
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• 2022

In order to improve water quality of the water system contaminated with dyes, biochars prepared using discarded waste resources were applied in this study. Biochars with a large specific surface area were manufactured using street tree pruning products or waste wood, and were applied to remove an organic dye in synthetic water. Biochars were made by pyrolysis of typical street tree porch products (Platanas, Ginkgo, Aak) and waste wood under air-controlled conditions. Methylene blue (MB), which is widely used in phosphofibers, paper, leather, and cotton media, was selected in this study. The adsorption capacity of Platanas for MB was the highest and the q_max value obtained using the Langmuir model equation was 78.47 mg/g. In addition, the adsorption energy (E) (kJ/mol) of MB using the Dubinin-Radushkevich (D-R) model equation was 4.891 kJ/mol which was less than 8 kJ/mol (a criteria distinguishing physical adsorption from chemical adsorption). This result suggests a physical adsorption with weak interactions such as van der Waals force between the biochar and MB. In addition, the physical adsorption may resulted from that Platanas-based biohar has the largest specific surface area and pore volume. The ∆G value obtained through the adsorption experiment according to temperature variation was -3.67 to -7.68, which also suggests a physical adsorption. Considering these adsorption results, the adsorption of MB onto Platanas-based biochar seems to occur through physical adsorption. Overall, it was possible to suggest that adsorption capacity of the biochr prepared from this study was equal to or greater than that of commercial activated carbon reported in other studies.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.