• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• The Journal of Korean Society for Radiation Therapy
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• v.16 no.2
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• pp.69-79
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• 2004

In the process of photon treatments, linear accelerators with energies higher than 10 MV produce neutrons through the (${\gamma}$, n) interactions with the composite materials of the linac head md these materials further produce the induced radiations. We investigate the possible risks from these induced radiations especially in the wedge filters to the radiation workers. Wedge filters are used to modify the isodose profiles in the radiation treatment using the linear accelerator and always be handled by the radiation workers. For the background radiation, we measured the radiation in both the waiting room and the outside of the building for two hospitals, S and H. The results of S hospital were $0.11\;{\mu}Sv/hr$ and $0.10\;{\mu}Sv/hr$ for waiting room and outside respectively, and in the case of H hospital, they were $0.12\;{\mu}Sv/hr$ and $0.11\;{\mu}Sv/hr$. Using a survey meter, we measured the radiation from wedge filters inserted in 10 MV and 15 MV Siemens linear accelerators. The time series measurements were done in ${\sim}1$ minutes after exposure of 5 Gy of monitor units for the field size of $25{\times}25cm^2$. The starting value of 10 MV machine was about $3.26\;{\mu}Sv/hr$, which was three times higher than that of 10 MV. The measured radiation was from $^{28}Al$ and $^{53}Fe$ with a half life of 3.5 min. If the treatment patients are $20{\sim}50$ per day and the number of process of wedge filter change per patient is one or two, the annual dose equivalent is $0.08{\sim}0.4\;mSv$ for 10 MV, and $0.27{\sim}1.36\;mSv$ for 15 MV, which are in the range of dose equivalent limits of radiation workers.

• 한국사회정책
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• v.25 no.2
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• pp.429-446
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• 2018

The $4^{th}$ Industrial Revolution, symbolizing the explosive innovation of digital technologies, is expected to have a great impact on labor relations and produce a lot of contested issues. The labor-management issues, created by the $4^{th}$ Industrial Revolution, are as follows: (1) employment restructuring, job re-allocation, and skill-reformation, driven by the technological displacement, resetting of worker-machine relationship, and negotiation on labor intensity and autonomy, (2) the legislation of institutional protection for the digital dependent self-employed, derived from the proliferation of platform-mediated labor, and the statutory recognition of their 'workerness', (3) unemployment safety net, income guarantee, and skill formation assistance for precarious workeforce, (4) the protection of worker privacy from workplace surveillance, (5) protecting labor rights of the digital dependent self-employed and prcarious workers and guaranteeing their unionization and collective bargaining. In comparing how labor unions in Western countries have responded to the $4^{th}$ Industrial Revolution, German unions have showed a strategic approach of policy formation toward digital technological innovations by effectively building and utilizing diverse channel of social dialogue and collective bargaining, while those in the US and UK have adopted the traditional approach of organizing and protesting in attempting to protect the interest of platform-mediated workers (i.e. Uber drivers). In light of the best practice demonstrated by German unions, it is necessary to build the process of productive policy consultation among three parties- the government, employers, and labor unions - at multi levels (i.e. workplace, sectoral and national levels), in order to prevent the destructive damage as well as labor-management confrotation, caused by digital technological innovations. In such policy consultation procesess, moreover, the inclusive and integrated approach is required to tackle with diverse problems, derived from the $4^{th}$ Industrial Revolution, in a holistic manner.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.53-62
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• 2023

This paper has suggested the necessity of introducing training contents using XR(Extended reality) technology as a way to lower the high rate of nursing accidents among unskilled technical personnel in domestic shipbuilding industry, focusing on cable pulling using winch. The occurrence rate of nursing accidents in the domestic shipbuilding industry was almost double(197.4%) (2017~2020) when compared with other manufacturing industries. In particular, it is worth noting that more than 31.8% of nursing accidents in the shipbuilding industry occurred among workers whose job experience is no more than 6 months. Most of new workers are seen to have hard time due to several factors such as lack of work information, inexperience, and unfamiliarity with the working environments. This indicates that it is essential to incorporate more effective training method that could help new workers become familiar with technical skills as well as working environments in a short period of time. Currently, education/training at the domestic shipyard is biased toward technical skills such as welding, painting, machine installation, and electrical installation. Contrary, even more important training required to get new workers used to the working environment has remained at a superficial level such as explaining ship building processes using 2D drawings. This may be the reason why it is inevitable to repeat similar training at OJT (On-the-Job Training) even at the leading domestic companies. Domestic shipbuilding industries have been attracting a lot of new workers thanks to recent economic recovery, which is very likely to increase the occurrence of disasters. In this paper, the introduction of training using XR technology was proposed, and as a specific example, the process of pulling cables using winches on ships was implemented as XR-based training content by using Unity. Using the developed content, it demonstrated that new workers can experience the actual work process in advance through simulation in a virtual space, thereby becoming more effective training content that can help new workers become familiar with the work environment.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.2
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• pp.78-95
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• 2024

For the establishment and comparison of environmental plans across various domains, considering climate change and urban issues, it is crucial to build spatial data at the regional scale classified with consistent criteria. This study mapping the Local Climate Zone (LCZ) of Changwon City, where active climate and environmental research is being conducted, using the protocol suggested by the World Urban Database and Access Portal Tools (WUDAPT). Additionally, to address the fragmentation issue where some grids are classified with different climate characteristics despite being in regions with homogeneous climate traits, a filtering technique was applied, and the LCZ classification characteristics were compared according to the filtering radius. Using satellite images, ground reference data, and the supervised classification machine learning technique Random Forest, classification maps without filtering and with filtering radii of 1, 2, and 3 were produced, and their accuracies were compared. Furthermore, to compare the LCZ classification characteristics according to building types in urban areas, an urban form index used in GIS-based classification methodology was created and compared with the ranges suggested in previous studies. As a result, the overall accuracy was highest when the filtering radius was 1. When comparing the urban form index, the differences between LCZ types were minimal, and most satisfied the ranges of previous studies. However, the study identified a limitation in reflecting the height information of buildings, and it is believed that adding data to complement this would yield results with higher accuracy. The findings of this study can be used as reference material for creating fundamental spatial data for environmental research related to urban climates in South Korea.

• Korean Journal of Heritage: History & Science
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• v.57 no.3
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• pp.24-35
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• 2024

Recently, research using deep learning technologies such as artificial intelligence, convolutional neural networks, etc. has been actively conducted in various fields including healthcare, manufacturing, autonomous driving, and security, and is having a significant influence on society. In line with this trend, the present study attempted to apply deep learning to the classification of archaeological artifacts, specifically ancient Korean roof-end tiles. Using 100 images of roof-end tiles from each of the Goguryeo, Baekje, and Silla dynasties, for a total of 300 base images, a dataset was formed and expanded to 1,200 images using data augmentation techniques. After building a model using transfer learning from the pre-trained EfficientNetB0 model and conducting five-fold cross-validation, an average training accuracy of 98.06% and validation accuracy of 97.08% were achieved. Furthermore, when model performance was evaluated with a test dataset of 240 images, it could classify the roof-end tile images from the three dynasties with a minimum accuracy of 91%. In particular, with a learning rate of 0.0001, the model exhibited the highest performance, with accuracy of 92.92%, precision of 92.96%, recall of 92.92%, and F1 score of 92.93%. This optimal result was obtained by preventing overfitting and underfitting issues using various learning rate settings and finding the optimal hyperparameters. The study's findings confirm the potential for applying deep learning technologies to the classification of Korean archaeological materials, which is significant. Additionally, it was confirmed that the existing ImageNet dataset and parameters could be positively applied to the analysis of archaeological data. This approach could lead to the creation of various models for future archaeological database accumulation, the use of artifacts in museums, and classification and organization of artifacts.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.