• The Journal of Engineering Geology
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• v.34 no.1
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• pp.1-12
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• 2024

South Korea's mining industry was actively developed until 1980, but subsequent declining profitability forced many mines to close. Most of the abandoned mines are susceptible to persistent subsidence because of the length of time since mining ceased. Accurate prediction of the locations and times of subsidence is difficult; therefore, this study aims to apply continuum analysis to past cases of subsidence to establish a method of predicting the location and magnitude of future subsidence. The study area is an area of ○○ mining located between the Yangsan fault zone and the Moryang fault zone, in which three subsidence events occurred between 2005 and 2009. Drilling surveys and electrical resistivity surveys were performed at subsidence sites determined the distribution of strata, and through laboratory tests obtained the physico-mechanical properties of the rock. Numerical analysis of the results found that the plastic status area includes the areas of actual subsidence and that continuum analysis can also be used to predict the location and magnitude of subsidence caused by mine goaf.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.39-58
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• 2024

Tunnel Boring Machines (TBM) use multiple disc cutters to excavate tunnels through rock. These cutters wear out due to continuous contact and friction with the rock, leading to decreased cutting efficiency and reduced excavation performance. The rock's abrasivity significantly affects cutter wear, with highly abrasive rocks causing more wear and reducing the cutter's lifespan. The Cerchar Abrasivity Index (CAI) is a key indicator for assessing rock abrasivity, essential for predicting disc cutter life and performance. This study aims to develop a new method for effectively estimating CAI using rock strength, petrological characteristics, linear regression, and machine learning. A database including CAI, uniaxial compressive strength, Brazilian tensile strength, and equivalent quartz content was created, with additional derived variables. Variables for multiple linear regression were selected considering statistical significance and multicollinearity, while machine learning model inputs were chosen based on variable importance. Among the machine learning prediction models, the Gradient Boosting model showed the highest predictive performance. Finally, the predictive performance of the multiple linear regression analysis and the Gradient Boosting model derived in this study were compared with the CAI prediction models of previous studies to validate the results of this research.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.93-105
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• 2024

Purpose: This study aims to quantitatively determine the impact on nearby risidents by selecting the amount of chemicals emitted from the workplace among the substances subject to the chemical emission plan and predicting the concentration with the atmospheric diffusion program. Method: The selection of research materials considered half-life, toxicity, and the presence or absence of available monitoring station data. The areas discharged from the materials to be studied were selected as the areas to be studied, and four areas with floating populations were selected to evaluate health risks. Result: AERMOD was executed after conducting terrain and meteorological processing to obtain predicted concentrations. The health hazard assessment results indicated that only dichloromethane exceeded the threshold for children, while tetrachloroethylene and chloroform appeared at levels that cannot be ignored for both children and adults. Conclusion: Currently, in the domestic context, health hazard assessments are conducted based on the regulations outlined in the "Environmental Health Act" where if the hazard index exceeds a certain threshold, it is considered to pose a health risk. The anticipated expansion of the list of substances subject to the chemical discharge plan to 415 types by 2030 suggests the need for efficient management within workplaces. In instances where the hazard index surpasses the threshold in health hazard assessments, it is judged that effective chemical management can be achieved by prioritizing based on considerations of background concentration and predicted concentration through atmospheric dispersion modeling.

• Tunnel and Underground Space
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• v.34 no.4
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• pp.355-373
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• 2024

The Generalized Hoek-Brown (GHB) criterion is a nonlinear failure criterion specialized for rock engineering applications and has recently seen increased usage. However, the GHB criterion expresses the relationship between minimum and maximum principal stresses at failure, and when GSI≠100, it has disadvantage of being difficult to express as an explicit relationship between the normal and shear stresses acting on the failure plane, i.e., as a Mohr failure envelope. This disadvantage makes it challenging to apply the GHB criterion in numerical analysis techniques such as limit equilibrium analysis, upper-bound limit analysis, and the critical plane approach. Consequently, recent studies have attempted to express the GHB Mohr failure envelope as an approximate analytical formula, and there is still a need for continued interest in related research. This study presents improved formulations for the approximate GHB Mohr failure envelope, offering higher accuracy in predicting shear strength compared to existing formulas. The improved formulation process employs a method to enhance the approximation accuracy of the tangential friction angle and utilizes the tangent line equation of the nonlinear GHB failure envelope to improve the accuracy of shear strength approximation. In the latter part of this paper, the advantages and limitations of the proposed approximate GHB failure envelopes in terms of shear strength prediction accuracy and calculation time are discussed.

• Korean Journal of Optics and Photonics
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• v.35 no.5
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• pp.210-217
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• 2024

We investigate the effects of mid-spatial frequency wavefront errors on the modulation transfer function (MTF) of optical imaging systems such as airborne cameras and astronomical telescopes. To reduce the prediction error of the MTF, an improved Shannon approximation is proposed. The Shannon approximation is useful for low-order wavefront errors, but it has limitations in predicting MTF with high-order wavefront errors, especially those caused by mid-spatial frequency errors from the manufacturing process of aspheric optical components. In this study, we analyze the impacts of concentric ring-shaped mid-spatial frequency wavefront errors on the MTF using MATLAB and Code V simulations and propose a method to improve the Shannon approximation, which has a new correction factor (K-factor).

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of the Korea Concrete Institute
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• v.21 no.3
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• pp.245-253
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• 2009

The importance of chloride ions in the corrosion of steel in concrete has led to the concept for chloride threshold level (CTL). The CTL can be defined as the content of chlorides at the steel depth that is necessary to sustain local passive film breakdown and hence initiate the corrosion process. Despite the importance of the CTL, due to the uncertainty determining the actual limits in various environments for chloride-induced corrosion, conservative values such as 0.4% by weight of cement or 1.2 kg in 1 $m^3$ concrete have been used in predicting the corrosion-free service life of reinforced concrete structures. The paper studies the CTL for blended cement concrete by comparing the resistance of cementitious binder to the onset of chloride-induced corrosion of steel. Mortar specimens were cast with centrally located steel rebar of 10 mm in diameter using cementitious mortars with ordinary Portland cement (OPC) and mixed mortars replaced with 30% pulverized fuel ash (PFA), 60% ground granulated blast furnace slag (GGBS) and 10% silica fume (SF), respectively, at 0.4 of a free W/B ratio. Chlorides were admixed in mixing water ranging 0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.5, 2.0, 2.5 and 3.0% by weight of binder(Based on $C1^-$). Specimens were curd 28 days at the room temperature, wrapped in polyethylene film to avoid leaching out of chloride and hydroxyl ions. Then the corrosion rate was measured using the polarization resistance method and the order of CTL for binder was determined. Thus, CTL of OPC, 60%GGBS, 30%PFA and 10%SF were determined by 1.6%, 0.45%, 0.8% and 2.15%, respectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.2
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• pp.1-16
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• 2017

This study was conducted to analyze the urban heat island(UHI) intensity of South Korea by using Moderate Resolution Imaging Spectroradiometer(MODIS) satellite imagery. For this purpose, the metropolitan area was spatially divided according to land cover classification into urban and non-urban land. From the analysis of land surface temperature(LST) in South Korea in the summer of 2009 which was calculated from MODIS satellite imagery it was determined that the highest temperature recorded nationwide was $36.0^{\circ}C$, lowest $16.2^{\circ}C$, and that the mean was $24.3^{\circ}C$, with a standard deviation of $2.4^{\circ}C$. In order to analyze UHI by cities and counties, UHI intensity was defined as the difference in average temperature between urban and non-urban land, and was calculated through RST1 and RST2. The RST1 calculation showed scattered distribution in areas of high UHI intensity, whereas the RST2 calculation showed that areas of high UHI intensity were concentrated around major cities. In order to find an effective method for analyzing UHI by cities and counties, analysis was conducted of the correlation between the urbanization ratio, number of tropical heat nights, and number of heat-wave days. Although UHI intensity derived through RST1 showed barely any correlation, that derived through RST2 showed significant correlation. The RST2 method is deemed as a more suitable analytical method for measuring the UHI of urban land in cities and counties across the country. In cities and counties with an urbanization ratio of < 20%, the rate of increase for UHI intensity in proportion to increases in urbanization ratio, was very high; whereas this rate gradually declined when the urbanization ratio was > 20%. With an increase of $1^{\circ}C$ in RST2 UHI intensity, the number of tropical heat nights and heat wave days was predicted to increase by approximately five and 0.5, respectively. These results can be used for reference when predicting the effects of increased urbanization on UHI intensity.

• Journal of Animal Environmental Science
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• v.18 no.3
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• pp.257-266
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• 2012

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid, accurate method of evaluating some chemical constituents in cereal and dired animal forages. Analysis of forage quality by NIRS usually involves dry grinding samples. Costs might be reduced if samples could be analyzed without drying or grinding. The objective of this study was to investigate effect of sample preparations on prediction ability of chemical composition and fermentation parameter for Italian ryegrass silages by NIRS. A population of 147 Italian ryegrass silages representing a wide range in chemical parameters were used in this investigation. Samples were scanned at 1nm intervals over the wavelength range 680-2500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in oven-dried grinding and fresh ungrinding condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with four spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV) and maximizing the correlation coefficient of cross validation (${R^2}_{CV}$). The results of this study show that NIRS predicted the chemical parameters with high degree of accuracy in oven-dried grinding treatment except for moisture contents. Prediction accuracy of the moisture contents was better for fresh ungrinding treatment (SECV 1.37%, $R^2$ 0.96) than for oven-dried grinding treatments (SECV 4.31%, $R^2$ 0.68). Although the statistical indexes for accuracy of the prediction were the lower in fresh ungrinding treatment, fresh treatment may be acceptable when processing is costly or when some changes in component due to the processing are expected. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation parameter of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.109-135
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• 2021

his study classified the development process of artificial intelligence (AI) speakers through analysis of the news text of artificial intelligence (AI) speakers shown in traditional news reports, and identified the characteristics of each product by period. The theoretical background used in the analysis are news frames and topic frames. As analysis methods, topic modeling and semantic network analysis using the LDA method were used. The research method was a content analysis method. From 2014 to 2019, 2710 news related to AI speakers were first collected, and secondly, topic frames were analyzed using Nodexl algorithm. The result of this study is that, first, the trend of topic frames by AI speaker provider type was different according to the characteristics of the four operators (communication service provider, online platform, OS provider, and IT device manufacturer). Specifically, online platform operators (Google, Naver, Amazon, Kakao) appeared as a frame that uses AI speakers as'search or input devices'. On the other hand, telecommunications operators (SKT, KT) showed prominent frames for IPTV, which is the parent company's flagship business, and 'auxiliary device' of the telecommunication business. Furthermore, the frame of "personalization of products and voice service" was remarkable for OS operators (MS, Apple), and the frame for IT device manufacturers (Samsung) was "Internet of Things (IoT) Integrated Intelligence System". The econd, result id that the trend of the topic frame by AI speaker development period (by year) showed a tendency to develop around AI technology in the first phase (2014-2016), and in the second phase (2017-2018), the social relationship between AI technology and users It was related to interaction, and in the third phase (2019), there was a trend of shifting from AI technology-centered to user-centered. As a result of QAP analysis, it was found that news frames by business operator and development period in AI speaker development are socially constituted by determinants of media discourse. The implication of this study was that the evolution of AI speakers was found by the characteristics of the parent company and the process of co-evolution due to interactions between users by business operator and development period. The implications of this study are that the results of this study are important indicators for predicting the future prospects of AI speakers and presenting directions accordingly.