• Tunnel and Underground Space
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• v.34 no.1
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• pp.28-53
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• 2024

In general, high-level radioactive waste (HLW) generated as a result of nuclear power generation should be disposed within the country. Determination of the disposal site and host rock for HLW deep geological repository is an important issue not only scientifically but also politically, economically, and socially. Considered host rock types worldwide for geological disposal include crystalline rocks, sedimentary rocks, volcanic rocks, and salt dome. However, South Korea consists of various rock types except salt dome. This paper not only analyzed the geological and rock mechanical characteristics on a nationwide scale with the preliminary results on various rock type studies for the disposal host rock, but also reviewed the characteristics and possibility of various rock types as a host rock through deep drilling surveys. Based on the nationwide screening for host rock types resulted from literature review, rock distributions, and detailed case studies, Jurassic granites and Cretaceous sedimentary rocks (Jinju and Jindong formations) were derived as a possible candidate host rock types for the geological disposal. However, since the analyzed data for candidate rock types from this study is not enough, it is suggested that the disposal rock type should be carefully determined from additional and detailed analysis on disposal depth, regional characteristics, multidisciplinary investigations, etc.

• Journal of Radiation Industry
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• v.17 no.2
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• pp.161-172
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• 2023

Coal-fired power plants handle large quantities of coal, one of the most prominent NORM, and the coal ash produced after the coal is burned can be tens of times more radioactive than the coal. Workers in these industries may be exposed to internal exposure by inhalation of particles while handling NORM. This study evaluated the size, concentration, particle shape and density, and radioactivity concentrations of airborne suspended particles in the main processes of a coal-fired power plant. Finally, the internal radiation dose to workers from particle inhalation was evaluated. For this purpose, airborne particles were collected by size using a multi-stage particle collector to determine the size, shape, and concentration of particles. Samples of coal and coal ash were collected to measure the density and radioactivity of particles. The dose conversion factor and annual radionuclide inhalation amount were derived based on the characteristics of the particles. Finally, the internal radiation dose due to particle inhalation was evaluated. Overall, the internal radiation dose to workers in the main processes of coalfired power plants A and B ranged from 1.47×10^-5~1.12×10^-3 mSv y^-1. Due to the effect of dust generated during loading operations, the internal radiation dose of fly ash loading processes in both coal-fired power plants A and B was higher than that of other processes. In the case of workers in the coal storage yard at power plants A and B, the characteristic values such as particle size, airborne concentration, and working time were the same, but due to the difference in radioactivity concentration and density depending on the origin of the coal, the internal radiation dose by origin was different, and the highest was found when inhaling coal imported from Australia among the five origins. In addition, the main nuclide contributing the most to the internal radiation dose from the main processes in the coal-fired power plants was thorium due to differences in dose conversion factors. However, considering the external radiation dose of workers in coal-fired power plants presented in overseas research cases, the annual effective dose of workers in the main processes of power plants A and B does not exceed 1mSv y^-1, which is the dose limit for the general public notified by the Nuclear Safety Act. The results of this study can be utilized to identify the internal exposure levels of workers in domestic coal-fired power plants and will contribute to the establishment of a data base for a differential safety management system for NORM-handling industries in the future.

• Journal of the Korean Society of Radiology
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• v.85 no.4
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• pp.714-726
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• 2024

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%-80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors' experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.

• Korean Journal of Agricultural Science
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• v.51 no.2
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• pp.187-192
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• 2024

This research aimed to investigate the relationship between meat quality and muscle-to-fat ratio in specific cuts of pork (pork belly and Boston butt) utilizing magnetic resonance imaging (MRI). Twenty-eight pigs were selected, and 24 hours post-slaughter, pork belly and Boston butt samples were individually extracted from the left half carcass for MRI assessment. The MRI scans were reviewed using the Picture Archiving and Communications System. Muscle and fat volumes in the pork belly and Boston butt from the cross-sectional images captured by MRI were estimated using Vitrea workstation version 7. Subsequently, these data were processed using Vitrea post-processing software to automatically determine the volumes, measured in milliliters (mL). Additionally, a three-dimensional reconstruction of the organ being studied was generated. The relationship between regions (pork belly and Boston butt) was assessed using Pearson's correlation coefficient, and statistical analysis was conducted using Graph Pad Prism 8. The muscle-to-fat ratio determined by MRI for pork belly was 1 : 0.64, whereas for Boston butt it was 1 : 0.35. Results of comparing the muscle-fat ratio, the correlation coefficient between pork belly and Boston butt was found to be 0.6127 (R² = 0.3754, p < 0.001) based on MRI analysis. As a result of measuring the muscle-to-fat ratio using MRI as a non-destructive approach, there was a positive correlation between the muscle-to-fat ratios of pork belly and Boston butt.

• Journal of the Korean Society of Radiology
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• v.18 no.4
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• pp.383-389
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• 2024

In this study, we aimed to analyze the probability of secondary cancer occurring in the abdomen, a normal organ, due to photoneutron exposure during intensity-modulated radiotherapy for prostate cancer. The design of the radiation treatment plan for prostate cancer was established as a daily prescription dose of 220 cGy, a total of 35 treatments, and 7700 cGy. The experimental equipment was a True Beam STx (Varian, USA) linear accelerator from Varian. The energy used in the experiment was 15 MV, and the treatment plan was designed so that the photoneutron dose would be generated within the planning target volume (PTV). The radiation treatment plan was an Eclipse System (Varian Ver. 10.0, USA), and the number of irradiation portals was set to 5 to 9. The irradiation angle was designed so that 95% of the prescription dose area was set to 0 to 320°, and the number of beamlets per irradiation portal was set to 100. The optically stimulated luminescence dosimeter used in this study to measure the dose of photoneutrons was designed to measure photoneutron doses by coating ⁶LiCO₃ on a device containing aluminum oxide components. It was studied that there is a minimum of 7.07 to 11 cases per 1,000 people with secondary cancer due to the photoneutron dose to the abdomen during intensity-modulated radiotherapy. In this study, we studied the risk of secondary radiation dose that may occur during intensity-modulated radiotherapy, and we expect that this will be used as meaningful data related to the probabilistic effects of radiation in the future.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1295-1295
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• 2024

As extreme weather events such as heavy snowfall and typhoon become more frequent, climate change significantly impacts across various worldwide industries. With demands for dealing with this phenomenon, continuous achievements in safety diagnosis have been announced for large structures. Conversely, in agricultural infrastructures having lower risk to human life, there is lack of established safety diagnosis methods. However, considering expansion of high-value smart farm, the importance of plastic greenhouse cannot be overlooked. Therefore, this study aimed to develop on-site diagnosis technique for structural safety of steel structure greenhouse. To build an analysis model, we generated point cloud data of on-site greenhouse using a camera with LiDAR sensor. Subsequently, we extracted points corresponding to pipes using a pre-trained semantic segmentation model, achieving a pipe segmentation accuracy of 78.1%. These points were then converted into 3D frame model, with a location coordinate error of 5.4 cm for nine reference points, as measured by an on-site survey. In FEM structural analysis, nonlinearity of pipe connection was reflected. The loads were determined based on expected wind speed and snow depth in Korea. The structural safety of on-site model was diagnosed more vulnerable with 10.3% higher maximum axial stress, compared with standard model. Through this research, we expect the quantitative safety diagnosis of predicting greenhouse collapse risk. In addition, this technique will enable localized reinforcement strategies within the structure.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.5
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• pp.533-549
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• 2024

Soil conditioning is a critical process when tunneling with an earth pressure balance (EPB) shield tunnel boring machine (TBM) to enhance performance. To determine the optimal additive injection conditions, it is important to understand the rheological properties of conditioned soil, which is typically assessed using a rheometer. However, a rheometer cannot simulate the actual process of muck discharge in a TBM. Therefore, in this study, a scaled-down model of an 8-meter-class EPB shield TBM chamber and screw conveyor, reduced by a factor of 1:20, was fabricated and its applicability was evaluated through laboratory experiments. A lab-scale model experiment was conducted on artificial sandy soil using foam and polymer as additives. The experimental results confirmed that screw torque was consistent with trends observed in previous laboratory pressurized vane shear test data, establishing a positive proportional relationship between screw torque and yield stress. The muck discharge efficiency according to foam injection ratio (FIR) showed similar values overall, but decreased slightly at 60% of FIR and when the polymer was added. In addition, the pressure distribution generated along the chamber and screw conveyor was assessed in a manner similar to the actual EPB TBM. This study demonstrates that the lab-scale screw conveyor model can be used to evaluate the shear properties and muck discharge efficiency.

• Asia pacific journal of information systems
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• v.21 no.2
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• pp.89-116
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• 2011

In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.197-218
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• 2019

In addition to economic growth and national income increase, China is also experiencing rapid growth in consumption of cosmetics. About 67% of the total trade volume of Chinese cosmetics is made by e-commerce and especially K-Beauty products, which are Korean cosmetics are very popular. According to previous studies, 80% of consumer goods such as cosmetics are affected by the word of mouth information, searching the product information before purchase. Mostly, consumers acquire information related to cosmetics through comments made by other consumers on SNS such as SINA Weibo and Wechat, and recently they also use information about beauty related video channels. Most of the previous online word-of-mouth researches were mainly focused on media itself such as Facebook, Twitter, and blogs. However, the informational characteristics and the expression forms are also diverse. Typical types are text, picture, and video. This study focused on these types. We analyze the unstructured data of SINA Weibo, the SNS representative platform of China, and Meipai, the video platform, and analyze the impact of K-Beauty brand sales by dividing online word-of-mouth information with quantity and direction information. We analyzed about 330,000 data from Meipai, and 110,000 data from SINA Weibo and analyzed the basic properties of cosmetics. As a result of analysis, the amount of online word-of-mouth information has a positive effect on the sales of cosmetics irrespective of the type of media. However, the online videos showed higher impacts than the pictures and texts. Therefore, it is more effective for companies to carry out advertising and promotional activities in parallel with the existing SNS as well as video related information. It is understood that it is important to generate the frequency of exposure irrespective of media type. The positiveness of the video media was significant but the positiveness of the picture and text media was not significant. Due to the nature of information types, the amount of information in video media is more than that in text-oriented media, and video-related channels are emerging all over the world. In particular, China has made a number of video platforms in recent years and has enjoyed popularity among teenagers and thirties. As a result, existing SNS users are being dispersed to video media. We also analyzed the effect of online type of information on the online cosmetics sales by dividing the product type of cosmetics into basic cosmetics and color cosmetics. As a result, basic cosmetics had a positive effect on the sales according to the number of online videos and it was affected by the negative information of the videos. In the case of basic cosmetics, effects or characteristics do not appear immediately like color cosmetics, so information such as changes after use is often transmitted over a period of time. Therefore, it is important for companies to move more quickly to issues generated from video media. Color cosmetics are largely influenced by negative oral statements and sensitive to picture and text-oriented media. Information such as picture and text has the advantage and disadvantage that the process of making it can be made easier than video. Therefore, complaints and opinions are generally expressed in SNS quickly and immediately. Finally, we analyzed how product diversity affects sales according to online word of mouth information type. As a result of the analysis, it can be confirmed that when a variety of products are introduced in a video channel, they have a positive effect on online cosmetics sales. The significance of this study in the theoretical aspect is that, as in the previous studies, online sales have basically proved that K-Beauty cosmetics are also influenced by word-of-mouth. However this study focused on media types and both media have a positive impact on sales, as in previous studies, but it has been proven that video is more informative and influencing than text, depending on media abundance. In addition, according to the existing research on information direction, it is said that the negative influence has more influence, but in the basic study, the correlation is not significant, but the effect of negation in the case of color cosmetics is large. In the case of temporal fashion products such as color cosmetics, fast oral effect is influenced. In practical terms, it is expected that it will be helpful to use advertising strategies on the sales and advertising strategy of K-Beauty cosmetics in China by distinguishing basic and color cosmetics. In addition, it can be said that it recognized the importance of a video advertising strategy such as YouTube and one-person media. The results of this study can be used as basic data for analyzing the big data in understanding the Chinese cosmetics market and establishing appropriate strategies and marketing utilization of related companies.