• Clean Technology
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• v.27 no.1
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• pp.61-68
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• 2021

Selective catalytic reduction (SCR) is widely used as a method of removing nitrogen oxide in large-capacity thermal power generation systems. Uniform mixing of the injected ammonia and the inlet flue gas is very important to the performance of the denitrification reduction process in the catalyst bed. In the present study, a computational analysis technique was applied to the ammonia injection system design process of a denitrification facility. The applied model is the denitrification facility of an 800 MW class coal-fired power plant currently in operation. The flow field to be solved ranges from the inlet of the ammonia injection system to the end of the catalyst bed. The flow was analyzed in the two-dimensional domain assuming incompressible. The steady-state turbulent flow was solved with the commercial software named ANSYS-Fluent. The nozzle arrangement gap and injection flow rate in the ammonia injection system were chosen as the design parameters. A total of four (4) cases were simulated and compared. The root mean square of the NH3/NO molar ratio at the inlet of the catalyst layer was chosen as the optimization parameter and the design of the experiment was used as the base of the optimization algorithm. The case where the nozzle pitch and flow rate were adjusted at the same time was the best in terms of flow uniformity.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.329-344
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• 2023

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3A
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• pp.341-347
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• 2008

Among coating systems used for steel bridge coatings on highway such as red lead-pigmented alkyd, chlorinated rubber, waterborne inorganic zinc, inorganic zinc/epoxy/urethane and inorganic zinc/epoxy/fluororesin, evaluation of deterioration degree and prediction of lifetime through regression analysis were carried out for coating systems widely used and grossly degraded. For evaluation of deterioration degree, 75 bridges on highway were selected, and evaluations were carried out according to point offering method regulated by Guideline of maintenance coating for steel bridges used in Korea Expressway Corporation. Lifetime prediction results showed 13.0~13.3 years for the whole nation, 11.8 years for urban and industrial region in the metropolitan area, 13.2 years for rural region except the metropolitan area, 13.5~13.7 years for chlorinated rubber coating systems, and 12.86 years for red lead-pigmented alkyd systems. For prediction of the rest life of coating, we tried to execute parallel translations of standard deterioration curve to current life and deterioration degree for both x and y axes, and it was thought that parallel translation for x axis corresponded to deterioration aspects in actual environment. Maximum and minimum equations were derived from standard deterioration equation by adding and subtracting error values deduced in regression analysis to/from each coefficient in order to establish maintenance coating criteria for overall steel bridges on highway. Whole domain was divided into 8 parts in order to predict the rest life of coating and optimum time of maintenance coating, and maintenance coating criteria for each 8 domains were presented.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1031-1040
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• 2023

Propagation characteristics in line-of-sight(LOS) paths in 3, 6, 10, and 17 GHz frequency bands were measured and analyzed in two different indoor corridors: second floors of Buildings D2 and E2. The measurement was designed to measure when the receiving antenna moved at 0.5 m intervals from 3 m to 30 m, while the transmission antenna was fixed. The analysis of the two indoor corridors was compared by applying basic transmission loss, root mean square (RMS) delay spread, and K-factor. For basic transmission loss, the loss coefficient of the floating intercept path loss model was higher in the indoor corridor of Building E2 than in that of Building D2. Similarly, the RMS delay spread in the time domain was greater in the indoor corridor of Building E2. However, the indoor corridor of Building D2 exhibited higher K-factor in the 3, 6, and 17 GHz bands with lower wave propagation in the 10 GHz band. Despite the 2 indoor corridors being identical, the propagation characteristics varied due to different internal structures and materials. The results provide measurement data for ITU-R Recommendations regarding various indoor environments.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.49-60
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• 2024

Ground-penetrating radar (GPR) enables rapid data acquisition over extensive areas, but interpreting the obtained data requires specialized knowledge. Numerous studies have utilized numerical analysis methods to examine GPR signal characteristics under various conditions. To develop more realistic numerical models, the heterogeneous nature of the ground, which causes clutter, must be considered. Clutter refers to signals reflected by objects other than the target. The Peplinski material model and fractal techniques can simulate these heterogeneous characteristics, yet there is a shortage of research on the necessary input parameters. Moreover, methods for quantitatively evaluating the similarity between field and analytical data are not well established. In this study, we calculated the autocorrelation coefficient of field data and determined the correlation length using the autocorrelation function. The correlation length represented the temporal or spatial distance over which data exhibited similarity. By comparing the correlation length of field data with that of the numerical model incorporating fractal weights, we quantitatively evaluated a numerical model for heterogeneous ground. Consequently, the results of this study demonstrated a numerical modeling technique that reflected the clutter characteristics of the field through correlation length.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of Plant Biotechnology
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• v.43 no.3
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• pp.359-366
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• 2016

Tradescantia is a perennial plant in the family of Commelinaceae. It is known to be sensitive to radiation. In this study, Tradescantia BNL 4430 was irradiated with gamma radiation at doses of 50 to 1,000 mGy in a phytotron equipped with a $^{60}Co$ radiation source at Korea Atomic Energy Research Institute, Korea. At 13 days after irradiation, we extracted RNA from irradiated floral tissues for RNA-seq. Transcriptome assembly produced a total of 77, 326 unique transcripts. In plantlets exposed to 50, 250, 500, and 1000 mGy, the numbers of up-regulated genes with more than 2-fold of expression compared that in the control were 116, 222, 246, and 308, respectively. Most of the up-regulated genes induced by 50 mGy were heat shock proteins (HSPs) such as HSP 70, indicating that protein misfolding, aggregation, and translocation might have occurred during radiation stress. Similarly, highly up-regulated transcripts of the IQ-domain 6 were induced by 250 mGy, KAR-UP oxidoreductase 1 was induced by 500 mGy, and zinc transporter 1 precursor was induced by 1000 mGy. Reverse transcriptase (RT) PCR and quantitative real time PCR (qRT-PCR) further validated the increased mRNA expression levels of selected genes, consistent with DEG analysis results. However, 2.3 to 97- fold higher expression activities were induced by different doses of radiation based on qRT-PCR results. Results on the transcriptome of Tradescantia in response to radiation might provide unique identifiers to develop in situ monitoring kit for measuring radiation exposure around radiation facilities.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.35 no.1
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• pp.10-24
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• 2017

The research takes note of the formation process and settling period of Gwandong-Palkyung(關東八景, Eight Sites of Eastern Korea), the representative palgyeong(prominent eight sites) and jipgyeong(集景, landscape collection of scenic beauty), and investigates the time of formation regarding the palkyung and jipgeyong of Gwandong's scenic beauty through the analysis and interpretation of bibliographic data, and reference data. The result of the study is as follows. As the first document that records the terminology of "Gwandong-Palkyung" is "Daphongeunggil(答洪應吉)" of Yi, Hwang(李滉), Gwandong-Palkyung is inferred to be settled within the recognition of the people even before the 16th century. The geographic analysis result including "Sinjeung Donggukyeojiseungram(新增東國輿地勝覽)", Gwandong-Palkyung expanded as Gwandong-Sipkyung in early to middle of the 16th century. The first confirmed landscape collection regarding Gwandong-Palkyung in this study is confirmed in Shin Zup(申楫)'s "Yeonggwandong-Palkyung(詠關東八景)", thus, the terminology of Gwandong-Palkyung existed before 16th century at the latest. The settlement time of current "Palkyung" collection is estimated to be early 17th century at the latest. Poetries regarding Gwandong-Palkyung, and the frequency on the appearance of Gwandong scenic beauties are analyzed as making clear of the concentrated phenomenon on the sceneries of Gwandong-Palkyung. On the other hand, the collection of Gwandong-Palkyung in the domain of arts is confirmed initially in the ${\ll}$Gwandongpalkyungdobyeong(關東八景圖屛)${\gg}$ of Heo, Pil(許泌). Gwandong-Palkyung, expressed as the actual scene landscape painting shows similar tendencies of the conditions in the jipgyeong from the poetry, but the appearance rate of the painting subject was more prominent in visual solidarity and cohesion due to the reflection of the importance on icon(圖像) of the art works produced with particular meaning in the case of fixed ideal system. From late Joseon to modern times, ${\ll}$palpokbyeongpung(八幅屛風)${\gg}$ of various forms of folk painting is a corroborative evidence notifying that the cultural phenomenon of Gwandong-Palkyung has entered the universal period of embrace. Also, the 13 scenic beauties of Gangwon-Do appearing in the games of Namseungdo and Myeongseungyuramdo include Gwandong-Palkyung, which confirms the settlement of Gwandog-Palkyung even within the culture of games in late Joseon. Such results demonstrate the existence of awareness regarding Gwandong-Palkyung from the first half of the 15th century, which is presumed to have completely settled in the 17th century through the continuous development of formative process in the 16th century. Ultimately, Gwandong-Palkyung is the concrete formation of regional scenic beauties that individually gained its reputations as scenery from the Koryo Dynasty to late 17th century. Gwandong-Palkyung of the scenic beauty of Gwandong is a unique cultural scenery of the region that have germinated and formed through the process of cutting and polishing of long time to collect the best eight of scenic beauty from the many participation of sightseeing culture.