• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.2
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• pp.239-247
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• 2023

The FIDIC Red Book is an international standard contract condition in which the Employer designs and the Contractor performs the construction. The Engineer of FIDIC Red Book shall agree or determine any matter or Claim in accordance with Sub-Clause 3.7 neutrally, not as an agent of the Employer. This study aimed to derive Key Risk Sub-Clauses out of 49 Sub-Clauses that the Engineer of FIDIC Red Book recently revised in 18 years shall agree or determine according to Sub-Clause 3.7 using the Delphi method. A panel of 35 experts with more than 10 years of experience and expertise in international construction contracts was formed, and through total three Delphi surveys, errors and biases were prevented in the judgment process to improve reliability. As for the research method, 49 Sub-Clauses that engineers shall agree on or determine according to Sub-Clause 3.7 of the FIDIC Red Book were investigated through the analysis of contract conditions. In order to evaluate the probability and impact of contractual risk for each 49 Sub-Clause, the Delphi survey conducted repeatedly a closed-type survey three times on a Likert 10-point scale. The results of the first Delphi survey were delivered during the second survey, and the results of the second survey were delivered to the third survey, which was re-evaluated in the direction of increasing the consensus of experts' opinions. The reliability of the Delphi 3rd survey results was verified with the COV value of the coefficient of variation. The PI Risk Matrix was applied to the average value of risk probability and impact of each of the 49 Sub-Clauses and finally, 9 Key Risk Sub-Clauses that fell within the extreme risk range were derived.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.311-328
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• 2022

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

• Analytical Science and Technology
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• v.22 no.6
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• pp.488-497
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• 2009

In this study, analytical methodology for several organic fatty acids (OFA: propionic acid (PA), butyric acid (BA), isovaleric acid (IA), and valeric acid (VA)) designated as new offensive odorants in Korea (as of year 2010) was investigated along with some odorous VOCs (styrene, toluene, xylene, methyl ethyl ketone, methyl isobutyl ketone, butyl acetate, and isobutyl alcohol). For this purpose, working standards (WS) containing all of these 13 compounds were loaded into adsorption tube filled with Tenax TA, and analyzed by gas chromatography (GC) system thermal desorber interfaced with. The analytical sensitivities of organic fatty acids expressed in terms of detection limit (both in absolute mass (ng) and concentration (ppb)) were lower by 1.5-2 times than other compounds (PA: 0.24 ng (0.16 ppb), BA: 0.19 ng (0.11 ppb), IA: 0.15 ng (0.07 ppb), and VA: 0.28 ng (0.13 ppb)). The precision of BA, IA, and VA, if assessed in terms of relative standard error (RSE), maintained above 5%, while the precison of other compounds were below 5%. The reproducibility of analysis improved with the aid of internal standard calibration (PA: $1.1{\pm}0.4%$, BA: $10{\pm}0.46$, IA; $12{\pm}0.3%$, VA: $4{\pm}0.1%$), respectively. The results of this study showed that organic fatty acid can be analyzed using adsorption tube and thermal desorber in a more reliable way to replace alkali absorption method introduced in the odor prevention law of the Korea Ministry of Environment (KMOE).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2B
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• pp.121-135
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• 2010

This study is to assess the effect of potential future climate change on the inflow of agricultural reservoir and its impact to downstream streamflow by reservoir operation for paddy irrigation water supply using the SLURP. Before the future analysis, the SLURP model was calibrated using the 6 years daily streamflow records (1998-200398 and validated using 3 years streamflow data (2004-200698 for a 366.5 $km^2$ watershed including two agricultural reservoirs (Geumgwang8 and Gosam98located in Anseongcheon watershed. The calibration and validation results showed that the model was able to simulate the daily streamflow well considering the reservoir operation for paddy irrigation and flood discharge, with a coefficient of determination and Nash-Sutcliffe efficiency ranging from s 7 to s 9 and 0.5 to s 8 respectively. Then, the future potential climate change impact was assessed using the future wthe fu data was downscaled by nge impFactor method throuih bias-correction, the future land uses wtre predicted by modified CA-Markov technique, and the future ve potentiacovfu information was predicted and considered by the linear regression bpowten mecthly NDVI from NOAA AVHRR ima ps and mecthly mean temperature. The future (2020s, 2050s and 2e 0s) reservoir inflow, the temporal changes of reservoir storaimpand its impact to downstream streamflow watershed wtre analyzed for the A2 and B2 climate change scenarios based on a base year (2005). At an annual temporal scale, the reservoir inflow and storaimpchange oue, anagricultural reservoir wtre projected to big decrease innautumnnunder all possiblmpcombinations of conditions. The future streamflow, soossmoosture and grounwater recharge decreased slightly, whtre as the evapotransporation was projected to increase largely for all possiblmpcombinations of the conditions. At last, this study was analysed contribution of weather, vegetation and land use change to assess which factor biggest impact on agricultural reservoir and stream watershed. As a result, weather change biggest impact on agricultural reservoir inflow, storage, streamflow, evapotranspiration, soil moisture and groundwater recharge.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

• Journal of the Korean Society of Radiology
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• v.84 no.2
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• pp.427-440
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• 2023

Purpose This study aimed to investigate the correlation between the fat signal fraction (FF) of the fat-dominant bone tissue of the knee joint, measured using the MRI Dixon method (DIXON) technique, and bone mineral density (BMD). Materials and Methods Among the patients who underwent knee DIXON imaging at our institute, we retrospectively analyzed 93 patients who also underwent dual energy X-ray absorptiometry within 1 year. The FFs of the distal femur metaphyseal (Fm) and proximal tibia metaphyseal (Tm) were calculated from the DIXON images, and the correlation between FF and BMD was analyzed. Patients were grouped based on BMD of lumbar spine (L), femoral neck (FN), and common femur (FT) respectively, and the Kruskal-Wallis H test was performed for FF. Results We identified a significant negative correlation between TmFF and FN-BMD in the entire patient group (r = -0.26, p < 0.05). In female patients, TmFF showed a negative correlation with FN-BMD, FT-BMD, and L-BMD (r = -0.38, 0.28 and -0.27, p < 0.05). In male patients, FmFF was negatively correlated with only FN-BMD and FT-BMD (r = -0.58 and -0.42, p < 0.05). There was a significant difference in the TmFF between female patients grouped by BMD (p < 0.05). In male patients, there was a significant difference in FmFF (p < 0.05). Conclusion Overall, we found that FF and BMD around the knee joints showed a negative correlation. This suggests the potential of FF measurement using DIXON for BMD screening.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.3
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• pp.89-98
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• 1965

The experimental studies were intended to clarify the effects of selection, and also aimed at estimating the heritabilities, the genotypic correlations among some agronomic characters, and at calculating the selection index on some selective characters for the selection of desirable lines, under different climatic conditions. Finally practical implications of these studies, especially on the selection index, were discussed. Twenty-two varieties, determinate growing habit type, were selected at random from the 138 soybean varieties cultivated the year before, were grown in a randomized block design with three replicates at Chinju, Korea, under May and June sowing conditions. The method of estimating heritabilities for the eleven agronomic characters-flowering date, maturity date, stem length, branch numbers per plant, stem diameter, plant weight, pod numbers per plant, grain numbers per plant and 100 grain weight, shown in Table 3, was the variance components procedures in a replicated trial for the varieties. The analysis of covariance was used to obtain the genotypic correlations and phenotypic correlations among the eight characters, and the selection indexes for some agronomic characters were calculated by Robinson's method. The results are summarized as follows: Heritabilities : The experiment on the genotype-environment interaction revealed that in almost all of the characters investigated the interaction was too large to be neglected and materially affected the estimates of various genotypic parameters. The variation in heritability due to the change of environments was larger in the characters of low heritability than in those of high heritability. Heritability values of flowering date, fruiting period (days from flowering to maturity), stem length and 100 grain weight were the highest in both environments, those of yield(grain weight) and other characters were showed the lower values(Table 3). These heritability values showed a decreasing trend with the delayed sowing in the experiments. Further, all calculated heritability values were higher than anticipated. This was expected since these values, which were the broad sense heritability, contain the variance due to dominance and epistasisf in addition to the additive genetic variance. Genotypic correlations : Genotypic correlations were slightly higher than the corresponding phenotypic correlations in both environments, but the variation in values due to the change of environment appeared between grain weight and some other characters, especially an increase between grain weight and flowering date, and the total growing period(Table 6). Genotypic correlations between grain weight and other characters indicated that high seed yield was genetically correlated with late flowering, late maturity, and the other five characters namely branch numbers per plant, stem diameter, plant weight, pod numbers per plant and grain numbers per plant, but not with 100 grain weight of soybeans. Pod numbers and grain numbers per plant were more closely correlated with seed yields than with other characters. Selection index : For the comparison and the use of selection indexes in the selection, two kinds of selection indexes were calculated, the former was called selection index A and the later selection index B as shown in Table 7. Selection index A was calculated by the values of grain weight per plant as the character of yield(character Y), but the other, selection index B, was calculated by the values of pod numbers per plant, instead of grain weight per plant, as the character of yield'(character Y'). These results suggest that selection index technique is useful in soybean breeding. In reality, however, as the selection index varies with population and environment, it must be calculated in each population to which selection is applied and in each environment in which the population is located. In spite of the expected usefulness of selection index technique in soybean breeding, unsolved problems such as the expense, time and labor involved in calculating the selection index remain. For these reasons and from these experimental studies, it was recognized that in the breeding of self-fertilized soybean plants the selection for yield should be based on a more simple selection index such as selection index B of these experiments rather than on the complex selection index such as selection index A. Furthermore, it was realized that the selection index for the selection should be calculated on the basis of the data of some 3-4 agronomic characters-maturity date(X$_1$), branch numbers per plant(X$_2$), stem diameter(X$_3$) and pod numbers per plant etc. It must be noted that it should be successful in selection to select for maturity date(X$_1$) which has high heritability, and the selection index should be calculated easily on the basis of the data of branch numbers per plant(X$_2$), stem diameter(X$_3$) and pod numbers per plant, directly after the harvest before drying and threshing. These characters should be very useful agronomic characters in the selection of Korean soybeans, determinate growing habit type, as they could be measured or counted easily thus saving time and expense in the duration from harvest to drying and threshing, and are affected more in soybean yields than the other agronomic characters.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Korean Journal of Heritage: History & Science
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• v.42 no.3
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• pp.154-175
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• 2009

At the present state, studies on Gyeongbok palace are being done with history of architecture, records, and art. However, these studies have limits that they can only depend on existing buildings and record, which make it hard to research whole aspect of palaces. The foundation remains(Jeoksim) of Gyeongbok palace in the ground gives important clues that can fill the gaps of these studies. Thus I analysed jeoksim of Gyeongbok palace, assorted them by type, scale, material, and construction method. I examined jeoksim used by various types of building, and looked at changes by periods. Jeoksims are classified in 21 types. The foundation(jeoksim) varies according to types of buildings, building types and material of jeoksim also varies along the periods, and the fact proves certain peroid of time has its own jeoksim style in fashion. Jeoksims of Gyeongbok palace are divided into round-shape(I), rounded square-shape(II), rectangular-shape(III), square-shape(IV), and whole foundation of building(V) by the plane shape. They can be divided again into 21 types by construction techniques and materials used. During early Joseon(I), only three types of jeoksim; round-shape riprap jeoksim(1-1), II-1(rounded square-shape), II-2a(rounded square-shape riprap+roofingingtile brick), had been built, but as 19th century begun, all 21 types of jeoksim had built. In 19th century during Emperor Gojong, different types of jeoksim by periods were built, and especially different materials were used. During Gojong year 2(1865)~year 5(1868), in which Gyeongbok palace were rebuilt, 7 out of 10 types of jeoksim used piece of roofinging tile and brick mixture, in contrast, during Gojong year 10(1873)~13(1876), or 25(1888), 3 out of 5 types of jeoksim used sandy soil with mixture of plaster. Meanwhile palace buildings have different names by the class of owner and use such as Jeon, Dang, Hap, Gak, Jae, Heon, Nu, and Jeong, which were classified by types and buildings were built according to each level. With an analysis of jeoksim by its building types, I ascertained that jeoksim were built differently in accordance to building types(Jeon, Dang, Hap, Gak, Jae, Heon, Nu, and Jeong). By the limitation of present document, only some types of buildings such as Jeon, Dang, Gak, Bang were confirmed, as for Jeon and Gak, square-shape(IV) built with rectangular parallelepiped stone, and for Dang and Bang, rounded square-shape(IV) built with roofinginginging tile and riprap were commonly used. From the fact that other jeoksim with uncertain building names, were mostly built in early Joseon, we learn that round-shape riprap jeoksim(1-1) were commonly built. Therefore, the class of building was higher if the owner was in higher class, jeoksim is also considered to be built with the strongest and best material. And for Dang and Bang, rounded square-shape jeoksim were used, Dang has lots of II-2a (riprap + piece of roofing tile and brick rounded square-shape) type which mainly used riprap and piece of roofing tile and brick, but Bang has lots of II-2b (piece of roofing tile and brick+(riprap+piece of roofing tile and brick rounded square-shape), which paved piece of roofing tile and brick by 15~20cm above. These jeoksim by building types were confirmed to have changed its construction type by period. As for Jeon and Gak, they were built with round-shape riprap jeoksim(1-1) in early Joseon(14~15c), but in late Joseon(19c), various types of Jeoksim were built, especially square-shape(IV) were commonly built. For Dang, only changes in later Joseon were confirmed, jeoksim built in Gojong year 4(1867) mostly used mixture of riprap and piece of roofing tile and brick. In Gojong year 13(1876) or year 25(1888), unique type of plaster with sand and coal and soil layered jeoksim were built that are not found in any other building types. Through this study, I learned that various construction types of jeoksim and material were developed in later Joseon compare to early Joseon. This states that construction technique of building foundation of palace has upgraded. Above all, I learned jeoksim types are all different for various kinds of buildings. This tells us that when they constructed foundation of building, they used pre-calculated construction technique.