• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.2
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• pp.287-304
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• 2020

There have been a number of legal & policy studies on the affecting factors of big data utilization, but empirical research on the composition factors of personal information regulation or data combination, which acts as a constraint, has been hardly done due to the lack of relevant statistics. Therefore, this study empirically explores the priority of personal information regulation factors and data combination factors that influence big data utilization through Delphi Analysis. As a result of Delphi analysis, personal information regulation factors include in order of the introduction of pseudonymous information, evidence clarity of personal information de-identification, clarity of data combination regulation, clarity of personal information definition, ease of personal information consent, integration of personal information supervisory authority, consistency among personal information protection acts, adequacy punishment intensity in case of violation of law, and proper penalty level when comparing EU GDPR. Next, data combination factors were examined in order of de-identification of data combination, standardization of combined data, responsibility of data combination, type of data combination institute, data combination experience, and technical value of data combination. These findings provide implications for which policy tasks should be prioritized when designing personal information regulations and data combination policies to utilize big data.

• Informatization Policy
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• v.27 no.3
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• pp.82-111
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• 2020

Recently, increasing use of big data have caused regulation factors of personal information and combination of pseudonymized information to emerge as key policy measures. Therefore, this study empirically analyzed the mediating effect and moderating effect of pseudonymized information combination as the third variable in the relationship between regulation factors of personal information and big data utilization. The analysis showed the following results: First, among personal information regulation factors, definition regulation, consent regulation, supervisory authority regulation, and punishment intensity regulation showed a positive(+) relationship with the big data utilization, while among pseudonymized information combination factors, non-identification of combination, standardization of combined pseudonymized information, and responsibility of combination were also found to be in a positive relationship with the use of big data. Second, among the factors of pseudonymized information combination, non-identification of combination, standardization of combined pseudonymized information, and responsibility of combination showed a positive(+) mediating effect in relation to regulation factors of personal information and big data utilization. Third, in the relationship between personal information regulation factors and big data utilization, the moderating effect hypothesis that each combination institution type of pseudonymized information (free-type, intermediary-type, and designated-type) would play a different role as a moderator was rejected. Based on the results of the empirical research, policy alternatives of 'Good Regulation' were proposed, which would maintain balance between protection of personal information and big data utilization.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.309-320
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• 2007

The data imbalance problem which can be uncounted in data mining classification problems typically means that there are more or less instances in a class than those in other classes. It causes low prediction accuracy of the minority class because classifiers tend to assign instances to major classes and ignore the minor class to reduce overall misclassification rate. In order to solve the data imbalance problem, there has been proposed a number of techniques based on resampling with replacement, adjusting decision thresholds, and adjusting the cost of the different classes. In this paper, we study the feasibility of the combination usage of the techniques previously proposed to deal with the data imbalance problem, and suggest a combination method using genetic algorithm to find the optimal combination ratio of the techniques. To improve the prediction accuracy of a minority class, we determine the combination ratio based on the F-value of the minority class as the fitness function of genetic algorithm. To compare the performance with those of single techniques and the matrix-style combination of random percentage, we performed experiments using four public datasets which has been generally used to compare the performance of methods for the data imbalance problem. From the results of experiments, we can find the usefulness of the proposed method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.1
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• pp.99-116
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• 2023

The importance of data in the era of the 4th industrial revolution, a hyper-connected society based on information technology such as data and AI, is increasing, and the government is actively enacting and revising laws to revitalize the data economy. It is necessary to prevent and improve problems that may set an obstacle to the revitalization of the data industry or setting the wrong direction, such as possibility of conflict between the regulatory law(Personal Information Protection Act) and the Data Activation Act, differences in position by type of specialized agencies, performance scope of Data Specialist Organization and Expert Data Combination Agency, etc. In regard, I would like to analyze the role, current situation, and use cases of Expert Data Combination Agency, listen to field opinions, and derive and introduce measures to expand the role of Expert Data Combination Agency and improve them to vitalize the data economy

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.567-576
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• 2013

The determination of the geoid in South Korea is a national imperative for the modernization of height datums, specifically the orthometric height and the dynamic height, that are used to monitor hydrological systems and environments with accuracy and easy revision, if necessary. The geometric heights above a reference ellipsoid, routinely obtained by GPS, lead immediately to vertical control with respect to the geoid for hydrological purposes if the geoid height above the ellipsoid is known accurately. The geoid height is determined from gravimetric data, traditionally ground data, but in recent times also from airborne data. This paper illustrates the basic concepts for combining these two types of data and gives a preliminary performance assessment of either set or their combination for the determination of the geoid in South Korea. It is shown that the most critical aspect of the combination is the gravitational effect of the topographic masses above the geoid, which, if not properly taken into account, introduces a significant bias of about 8 mgal in the gravity anomalies, and which can lead to geoid height bias errors of up to 10 cm. It is further confirmed and concluded that achieving better than 5 cm precision in geoid heights from gravimetry remains a challenge that can be surmounted only with the proper combination of terrestrial and airborne data, thus realizing higher data resolution over most of South Korea than currently available solely from the airborne data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.426-428
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• 2003

Optical and microwave remote sensing data have been widely used in land cover and land use classification. Thanks to the spectral absorption characteristics of ground object in visible and near infrared region, optical data enables to extract different land cover types according to their material composition like water body, vegetation cover or bare land. On the other hand, microwave sensor receives backscatter radiance which contains information on surface roughness, object density and their 3-D structure that are very important complementary information to interpret land use and land cover. Separate use of these data have brought many successful results in practice. However, the accuracy of the land use / land cover established by this methodology still has some problems. One of the way to improve accuracy of the land use / land cover classification is just combination of both optical and microwave data in analysis. In this paper for the research, the author used LANDSAT TM scene 127/45 acquired on October 21, 1992, JERS-1 SAR scene 119/265 acquired on October 27, 1992 and aerial photographs taken on October 21, 1992. The study area has been selected in Hanoi City and surrounding area, Vietnam. This is a flat agricultural area with various land use types as water rice, secondary crops like maize, cassava, vegetables cultivation as cucumber, tomato etc. mixed with human settlement and some manufacture facilities as brick and ceramic factories. The use of only optical or microwave data could result in misclassification among some land use features as settlement and vegetables cultivation using frame stages. By combination of multitemporal JERS-1 SAR and TM data these errors have been eliminated so that accuracy of the final land use / land cover map has been improved. The paper describes a methodology for data combination and presents results achieved by the proposed approach.

• Journal of the Korean earth science society
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• v.39 no.1
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• pp.1-22
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• 2018

Eight different data sets are examined in order to gain insight into the surface heat flux traits of the East Asian marginal seas. In the case of solar radiation of the East Sea (Japan Sea), Coordinated Ocean-ice Reference Experiments ver. 2 (CORE2) and the Objectively Analyzed Air-Sea Fluxes (OAFlux) are similar to the observed data at meteorological stations. A combination is sought by averaging these as well as the Climate Forecast System Reanalysis (CFSR) and the National Centers for Environmental Prediction (NCEP)-1 data to acquire more accurate surface heat flux for the East Asian marginal seas. According to the Combination Data, the annual averages of net heat flux of the East Sea, Yellow Sea, and East China Sea are -61.84, -22.42, and $-97.54Wm^{-2}$, respectively. The Kuroshio area to the south of Japan and the southern East Sea were found to have the largest upward annual mean net heat flux during winter, at -460- -300 and at $-370--300Wm^{-2}$, respectively. The long-term fluctuation (1984-2004) of the net heat flux shows a trend of increasing transport of heat from the ocean into the atmosphere throughout the study area.

• Journal of Intelligence and Information Systems
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• v.14 no.3
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• pp.133-154
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• 2008

The data imbalance problem which can be uncounted in data mining classification problems typically means that there are more or less instances in a class than those in other classes. In order to solve the data imbalance problem, there has been proposed a number of techniques based on re-sampling with replacement, adjusting decision thresholds, and adjusting the cost of the different classes. In this paper, we study the feasibility of the combination usage of the techniques previously proposed to deal with the data imbalance problem, and suggest a combination method using genetic algorithm to find the optimal combination ratio of the techniques. To improve the prediction accuracy of a minority class, we determine the combination ratio based on the F-value of the minority class as the fitness function of genetic algorithm. To compare the performance with those of single techniques and the matrix-style combination of random percentage, we performed experiments using four public datasets which has been generally used to compare the performance of methods for the data imbalance problem. From the results of experiments, we can find the usefulness of the proposed method.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.285-295
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• 2024

Driven by the vague assessment big data, a product service system (PSS) evaluation method is developed based on a hybrid model of multi-weight combination and improved TOPSIS by relative entropy. The index values of PSS alternatives are solved by the integration of the stakeholders' vague assessment comments presented in the form of trapezoidal fuzzy numbers. Multi-weight combination method is proposed for index weight solving of PSS evaluation decision-making. An improved TOPSIS by relative entropy (RE) is presented to overcome the shortcomings of traditional TOPSIS and related modified TOPSIS and then PSS alternatives are evaluated. A PSS evaluation case in a printer company is given to test and verify the proposed model. The RE closeness of seven PSS alternatives are 0.3940, 0.5147, 0.7913, 0.3719, 0.2403, 0.4959, and 0.6332 and the one with the highest RE closeness is selected as the best alternative. The results of comparison examples show that the presented model can compensate for the shortcomings of existing traditional methods.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1393-1395
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• 2003

In this research, we presented a data integration, which integrates ultra high resolution images and complementary data for 3D building reconstruction. In our method, as the ultra high resolution image, Three Line Sensor (TLS) images are used in combination with 2D digital maps, DSMs and both of them. Reconstructed 3D buildings, correctness rate and the accuracy of results were presented. As a result, optimized combination scheme of data sets , sensors and methods was proposed.