• Asia pacific journal of information systems
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• v.24 no.1
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• pp.93-112
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• 2014

As personal data breach reared up as a problem domestically and globally, organizations appointing chief privacy officers (CPOs) are increasing. Related Korean laws, 'Personal Data Protection Act' and 'the Act on Promotion of Information and Communication Network Utilization and Information Protection, etc.' require personal data processing organizations to appoint CPOs. Research on the characteristics and role of CPO is called for because of the importance of CPO being emphasized. There are many researches on top management's role and their impact on organizational performance using the Upper Echelon theory. This study investigates what influence the characteristics of CPO gives on the organizational privacy performance. CPO's definition varies depending on industry, organization size, required responsibility and power. This study defines CPO as 'a person who takes responsibility for all the duties on handling the organization's privacy,' This research assumes that CPO characteristics such as role, personality and background knowledge have an influence on the organizational privacy performance. This study applies the part relevant to the upper echelon's characteristics and performance of the executives (CEOs, CIOs etc.) for CPO. First, following Mintzberg and other managerial role classification, information, strategic, and diplomacy roles are defined as the role of CPO. Second, the "Big Five" taxonomy on individual's personality was suggested in 1990. Among these five personalities, extraversion and conscientiousness are drawn as the personality characteristics of CPO. Third, advance study suggests complex knowledge of technology, law and business is necessary for CPO. Technical, legal, and business background knowledge are drawn as the background knowledge of CPO. To test this model empirically, 120 samples of data collected from CPOs of domestic organizations are used. Factor analysis is carried out and convergent validity and discriminant validity were verified using SPSS and Smart PLS, and the causal relationships between the CPO's role, personality, background knowledge and the organizational privacy performance are analyzed as well. The result of the analysis shows that CPO's diplomacy role and strategic role have significant impacts on organizational privacy performance. This reveals that CPO's active communication with other organizations is needed. Differentiated privacy policy or strategy of organizations is also important. Legal background knowledge and technical background knowledge were also found to be significant determinants to organizational privacy performance. In addition, CPOs conscientiousness has a positive impact on organizational privacy performance. The practical implication of this study is as follows: First, the research can be a yardstick for judgment when companies select CPOs and vest authority in them. Second, not only companies but also CPOs can judge what ability they should concentrate on for development of their career relevant to their job through results of this research. Cultural social value, citizen's consensus on the right to privacy, expected CPO's role will change in process of time. In future study, long-term time-series analysis based research can reveal these changes and can also offer practical implications for government and private organization's policy making on information privacy.

• Tuberculosis and Respiratory Diseases
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• v.68 no.3
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• pp.180-184
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• 2010

Slowly growing lung cancers are quite rare and the leading cause of length time bias and over-diagnosis bias in lung cancer screening. We report 6 cases of slowly growing lung cancer in a tertiary hospital between January 1999 and December 2008. The clinical characteristics of these 6 cases with slowly growing lung cancer were examined. The median age at diagnosis was 68 years (range, 49~72), and 5 patients (83%) were female. The most common histology type was adenocarcinoma (83%). After excluding two patients who showed no change in the tumor size, the median tumor doubling time was 189 months (range, 86~387). The proportion of patients with slowly growing lung cancer appears to be particularly large in women, especially among patients with adenocarcinoma. Our experience shows that slowly growly lung cancers are more heterogeneous and diverse.

• Sijohaknonchong
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• v.30
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• pp.219-240
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• 2009

Jasan An Whak did research on the essence of Sijo to show that Sijo has some features as a literature. I tried to show the formation process of Sijo theory in 1930s through Jasan's Sijo theory. As a preparatory step for it, I introduced Jasan's Sijo theory released in early 1930s and examined the characteristic aspects of it. Jasan recognized a literature as a directing post that reveals the history of our national spirit. He thought a literature as a foundation for satisfying new age. Also, he recognized the essence of a literature as a emotional expression. He emphasized that a new literature in Joseon age should have not only particularity of Joseon literature but also universality of modern literature. Jasan studied style of Sijo. Because he was at the time of modernization, he used the term, 'style', instead of 'poongkyeok', which had used before modern time. He tried to show linguistic artistry of Sijo through the series of his works about the style of Sijo. Jasan tried to find formal beauty of Sijo in the aspect of rhythm instead of rhyme. And he claimed that poetic words can be lengthened or shortened to be harmonious with the melody of Sijo. In other words, it is possible to change the words of Sijo for harmonizing with a tune. Jasan recognized that the words of Sijo have a musical function as well as a semantic function.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.485-494
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• 2008

Huge amounts of various web items such as keywords, images, and web pages are being made widely available on the Web. The popularities of such web items continuously change over time, and mining temporal patterns in popularities of web items is an important problem that is useful for several web applications. For example, the temporal patterns in popularities of search keywords help web search enterprises predict future popular keywords, enabling them to make price decisions when marketing search keywords to advertisers. However, presence of millions of web items makes it difficult to scale up previous techniques for this problem. This paper proposes an efficient method for mining temporal patterns in popularities of web items. We treat the popularities of web items as time-series, and propose gapmeasure to quantify the similarity between the popularities of two web items. To reduce the computation overhead for this measure, an efficient method using the Fast Fourier Transform (FFT) is presented. We assume that the popularities of web items are not necessarily following any probabilistic distribution or periodic. For finding clusters of web items with similar popularity trends, we propose to use a density-based clustering algorithm based on the gap measure. Our experiments using the popularity trends of search keywords obtained from the Google Trends web site illustrate the scalability and usefulness of the proposed approach in real-world applications.

• The Journal of the Acoustical Society of Korea
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• v.34 no.1
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• pp.20-35
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• 2015

Underwater acoustic channel impulse responses (CIR) are influenced by sound speed profile (SSP), and the variation of CIR has significant effects on the performance of underwater acoustic communication systems. A significant change of SSP can occur within a short period, which must be considered during the design of underwater acoustic modems. This paper statistically analyzes the effect of the variation of SSP on the long-range acoustic signal propagation in shallow-water with thermocline using numerical modeling based on the data acquired from JACE13 experiment near Jeju island. The analysis result shows that CIR changes variously according to the SSP and the depth of the transmitter and receiver. We also found that when the transmitter and receiver are deeper, the variation of sound wave propagation pattern is smaller and signal level becomes higher. All CIR obtained in this study show that a series of bottom reflections due to downward refraction and small bottom loss in the shallow water with thermocline can be very important factor for long-range signal transmission and the performance of underwater acoustic communication system in time varying ocean environment can be very sensitive to the variation of SSP even for a short period of time.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.22 no.3
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• pp.57-68
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• 2017

Continuous monitoring of spatial and temporal changes in key marine environmental parameters such as SST (sea surface temperature) near IORS (Ieodo Ocean Research Station) is demanded to investigate the ocean ecosystem, climate change, and sea-air interaction processes. In this study, we aimed to develop the system for continuously measuring SST using a TIR (thermal infrared) sensor mounted at the IORS. New SST algorithm is developed to provide SST of better quality that includes automatic atmospheric correction and emissivity calculation for different oceanic conditions. Then, the TIR-based SST products were validated against in-situ water temperature measurements during May 17-26, 2015 and July 15-18, 2015 at the IORS, yielding the accuracy of 0.72-0.85 R-square, and $0.37-0.90^{\circ}C$ RMSE. This TIR-based SST observing system can be installed easily at similar Ocean Research Stations such as Sinan Gageocho and Ongjin Socheongcho, which provide a vision to be utilized as calibration site for SST remotely sensed from satellites to be launched in future.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1199-1206
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• 2021

Accordingly, attention is also being paid to the agricultural use of remote sensing technique that non-destructively and continuously detects the growth and physiological status of crops. However, when remote sensing techniques are used for crop monitoring, it is possible to continuously monitor the abnormality of crops in real time. For this, standard growth information of crops is required and relative growth considering the cultivation environment must be identified. With the relationship between GDD (Growing Degree Days), which is the cumulative temperature related to crop growth obtained from ideal cultivation management, and the vegetation index as standard growth information, compared with the vegetation index observed with the spectralreflectance sensor(SRS_NDVI & SRS_PRI) in each rice paddy treated with standard cultivation management and non-fertilized, it was quantitatively identified as a time series. In the future, it is necessary to accumulate a database targeting various climatic conditions and varieties in the standard cultivation management area to establish a more reliable standard growth information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.301-309
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• 2021

In this study, we tried to improve the performance of the existing U-net-based deep learning rainfall prediction model, which can weaken the meaning of time series order. For this, ConvLSTM2D U-Net structure model considering temporal consistency of data was applied, and we evaluated accuracy of the ConvLSTM2D U-Net model using a RainNet model and an extrapolation-based advection model. In addition, we tried to improve the uncertainty in the model training process by performing learning not only with a single model but also with 10 ensemble models. The trained neural network rainfall prediction model was optimized to generate 10-minute advance prediction data using four consecutive data of the past 30 minutes from the present. The results of deep learning rainfall prediction models are difficult to identify schematically distinct differences, but with ConvLSTM2D U-Net, the magnitude of the prediction error is the smallest and the location of rainfall is relatively accurate. In particular, the ensemble ConvLSTM2D U-Net showed high CSI, low MAE, and a narrow error range, and predicted rainfall more accurately and stable prediction performance than other models. However, the prediction performance for a specific point was very low compared to the prediction performance for the entire area, and the deep learning rainfall prediction model also had limitations. Through this study, it was confirmed that the ConvLSTM2D U-Net neural network structure to account for the change of time could increase the prediction accuracy, but there is still a limitation of the convolution deep neural network model due to spatial smoothing in the strong rainfall region or detailed rainfall prediction.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.697-712
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• 2020

New infectious diseases have broken out repeatedly across the world over the last 20 years; COVID-19 is causing drastic changes and damage to daily lives. Furthermore, as there is no denying that new epidemics will appear in the future, there is a continuous need to develop measures aimed towards responding to economic damage. Against this backdrop, the living population is an important indicator that shows changes in citizens' life patterns. This study analyzes time-based and socio-environmental characteristics by detecting and classifying changes in everyday life caused by COVID-19 from the perspective of the floating population. k-shape Clustering is used to classify living population data of each of the 424 dong's in Seoul measured by the hour; then by applying intervention analysis and One-way ANOVA, each cluster's characteristics and aspects of change in the living population occurring in the aftermath of COVID-19 are scrutinized. In conclusion, this study confirms each cluster's obvious characteristics in changes of population flows before and after the confirmation of coronavirus patients and distinguishes groups that reacted sensitively to the intervention times on the basis of COVID-related incidents from those that did not.