• Korean Journal of Agricultural Science
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• v.48 no.2
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• pp.283-297
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• 2021

Investigation of genetic diversity and molecular characterization in high yielding rice varieties is important for their identification. The experiment was conducted during 2016 - 2017 to analyse the genetic diversity of fifteen high yielding rice varieties in Bangladesh by using random amplification of polymorphic DNA (RAPD) markers. Polymorphism was revealed in 12 RAPD primers out of 30, whereas no other reaction was detected on the remaining 18 primers. The 40 out of 45 bands (88.89%) polymorphics were produced by the primers and ranged from 50 to 100%. The maximum number of polymorphic bands was produced by the primer OPB-18 whereas the lowest number of polymorphic bands belonged to OPC-12. The genetic similarity coefficients were determined with the RAPD data, which ranged from 0.47 to 0.94. The unweighted paired group of arithmetic means (UPGMA) dendrogram presented the studied rice varieties into two major clusters. Moreover, the value of Nei's genetic diversity is 0.26 and the Shanon information index is 0.41. The study produced distinct positions, suggesting that the genotypes were different from each other. The results indicated that these markers could be efficient for comparing the genetic relationships, patterns of variation, and measurement of genetic distance among rice varieties. Considering all of these results, RAPD analysis is found to be an effective tool for estimating the genetic diversity of different rice varieties. The outcomes of this research may contribute to the germplasm data of rice accessions and a future breeding program of rice genotypes.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.7-14
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• 2018

Salient information extraction from multi-view videos is a very challenging area because of inter-view, intra-view correlations, and computational complexity. There are several techniques developed for keyframes extraction from multi-view videos with very high computational complexities. In this paper, we present a keyframes extraction approach from multi-view videos using entropy and complexity information present inside frame. In first step, we extract representative shots of the whole video from each view based on structural similarity index measurement (SSIM) difference value between frames. In second step, entropy and complexity scores for all frames of shots in different views are computed. Finally, the frames with highest entropy and complexity scores are considered as keyframes. The proposed system is subjectively evaluated on available office benchmark dataset and the results are convenient in terms of accuracy and time complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1800-1817
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• 2022

With the development of the economy and the improvement of living standards, the hot issues in the subject area have become the main research direction, and the mining of the hot issues in the subject currently has problems such as a large amount of data and a complex algorithm structure. Therefore, in response to this problem, this study proposes a method for extracting hot keywords in scientific journals based on the improved BERT model.It can also provide reference for researchers,and the research method improves the overall similarity measure of the ensemble,introducing compound keyword word density, combining word segmentation, word sense set distance, and density clustering to construct an improved BERT framework, establish a composite keyword heat analysis model based on I-BERT framework.Taking the 14420 articles published in 21 kinds of social science management periodicals collected by CNKI(China National Knowledge Infrastructure) in 2017-2019 as the experimental data, the superiority of the proposed method is verified by the data of word spacing, class spacing, extraction accuracy and recall of hot keywords. In the experimental process of this research, it can be found that the method proposed in this paper has a higher accuracy than other methods in extracting hot keywords, which can ensure the timeliness and accuracy of scientific journals in capturing hot topics in the discipline, and finally pass Use information technology to master popular key words.

• Journal of the Korean Geotechnical Society
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• v.26 no.7
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• pp.171-186
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• 2010

It is generally known that the mechanism of behavior in the flexible earth retaining system is relatively more complicated than in the rigid earth retaining system. Moreover in the case of long span strut supporting system the analysis of strut axial force change becomes more difficult when the differences of ground condition and excavation work progress on both sides of excavation section are added. When deeper excavation than the specification or installation delay of supporting system or change of ground condition happen during construction process, lots of axial force can be induced in some struts, which threaten the safety of construction. This paper introduces two examples of long span deep excavation where struts and rock bolts were used as a supporting system with flexible wall structure. The characteristics of ground deformation and strut axial force change, which were measured in the sections of two examples that are 50 meters apart in one construction site and have almost similar design and construction conditions were analysed, the similarity and difference between measurement results of two examples were compared and investigated. This article aims to improve and develop the technique of design and construction in future projects having similar ground condition and supporting method.

• The Journal of Bigdata
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• v.6 no.2
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• pp.51-60
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• 2021

The market size of wearable devices is growing rapidly every year, and manufacturers around the world are introducing products that utilize their unique characteristics to keep up with the demand. Among them, smart watches are wearable devices with a very high share in sales, and they provide a variety of services to users by using information collected in real-time. The quality of service depends on the accuracy of the data collected by the smart watch, but data measurement may not be possible depending on the situation. This paper introduces a method to restore data that a smart watch could not collect. It deals with the similarity calculation method of trajectory information measured over time for data restoration and introduces a procedure for restoring missing sections according to the similarity. To prove the performance of the proposed methodology, a comparative experiment with a machine learning algorithm was conducted. Finally, the expected effects of this study and future research directions are discussed.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.2
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• pp.58-66
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• 2023

In this study, cable tension was measured using the vibration method, and a vision-based system was applied as a sensor to measure the displacement response of a cable in a non-contact method. In the vision-based system, the camera is installed in a location that considers the target structure and the field of view of the camera. However, it can be difficult to recognize the control points required to measure the displacement response of a structure as the target structure and other structures such as vehicles may be included in the image at the intended installation location. In this study, a distorted image including a vehicle shows inaccurate results in image analysis due to the installation position of the vision-based system. Accordingly, the image including the vehicle was eliminated by calculating the similarity between the two images. To verify the validity of the method of estimating the cable tension of cable-stayed bridges using the proposed method, the vibration method was applied to cable-stayed bridges in service to measure the tension.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• Journal of Wetlands Research
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• v.24 no.1
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• pp.44-51
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• 2022

In this study, TOPSIS(Techniques for Order Performance by Similarity to Ieal Solution) was used to evaluate the installation points of water quality monitoring networks in 34 streams of the Nakdong River watershed. The Nakdong River System has been measuring water quality and flow in 195 local streams since 2011. In particular, the 34 key management points are areas with many pollutants and poor water quality, requiring continuous water quality management. For the selection of points requiring management, 10 indicators were selected for evaluation, and the selected indicators were standardized and weighted using the entropy method. As a result of weight calculation, the presence or absence of a nearby measuring network received the greatest weight, and the average water quality and presence of an industrial complex obtained the highest weight. The evaluated data are judged to be the research results necessary for the establishment of a new water quality measurement network in the Nakdong River system and continuous water quality management in tributaries.

• International Commerce and Information Review
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• v.19 no.2
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• pp.193-212
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• 2017

In order to evaluate the evaluation of bi-national products, this study was classified according to product category similarity. In addition, by comparing the types of stores, we tried to suggest alternatives that can be practically applied to companies producing composite origin products. For this purpose, this study is applied to the analysis of bi-national product by product categories, such as public luxury, public luxury, private luxury, and private necessity, based on the psychological bases of consumers. The types of stores are divided into department store/direct store, outlet/discount store to elaborate measurement. As a result of the verification, it was found that brand evaluation and store evaluation had interactive effects depending on product type and store type. Specifically, in the case of public luxury goods, outlets/discount stores in both brand evaluation and store evaluation showed positive evaluation than department store/direct store. In the case of private necessity, there was no significant difference in brand evaluation, but in store evaluation, department store/direct store was confirmed that they had a relatively positive evaluation than outlets/discount stores. As a result, it was verified that consumers' evaluations can be changed according to the type of sales store for each product.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.57-64
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• 2017

Short message service (SMS) is one of the most important communication methods for people who use mobile phones. However, illegal advertising spam messages exploit people because they can be used without the need for friend registration. Recently, spam message filtering systems that use machine learning have been developed, but they have some disadvantages such as requiring many calculations. In this paper, we implemented a spam message filtering system using the set-based POI search algorithm and sentence similarity without servers. This algorithm can judge whether the input query is a spam message or not using only letter composition without any server computing. Therefore, we can filter the spam message although the input text message has been intentionally modified. We added a specific preprocessing option which aims to enable spam filtering. Based on the experimental results, we observe that our spam message filtering system shows better performance than the original set-based POI search algorithm. We evaluate the proposed system through extensive simulation. According to the simulation results, the proposed system can filter the text message and show high accuracy performance against the text message which cannot be filtered by the 3 major telecom companies.