• Journal of KIISE:Databases
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• v.29 no.4
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• pp.321-333
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• 2002

As web-based services are becoming more and more popular, concurrent updates of spatial data should be possible in the web-based environments in order to use the various services. Web-based GIS applications are characterized by large quantity of data providing and these data should be continuously updated according to various user's requirements. Faced with such an enormous data providing system, it is inefficient for a server to do all of the works of updating spatial data requested by clients. Besides, the HTTP protocol used in the web environment is established under the assumption of 'Connectionless'and 'Stateless'. Lots of problems may occur if the scheme of transaction processing based on the LAN environment is directly applied to the web environment. Especially for long transactions of updating spatial data, it is very difficult to control the concurrency among clients and to keep the consistency of the server data. This paper proposes a solution of keeping consistency during updating directly spatial data in the client-side by resolving the Dormancy Region Lock problem caused by the 'Connectionless'and 'Stateless'feature of the HTTP protocol. The RX(Region-eXclusive) lock and the periodically sending of ALIVE_CLIENTi messages can solve this problem. The protocol designed here is verified as effective enough through implementing in the main memory spatial database system, called CyberMap.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.429-440
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• 2021

With the recent development of deep convolutional neural network learning, deep learning techniques applied to single image super-resolution are showing good results. One of the existing deep learning-based super-resolution techniques is RDN(Residual Dense Network), in which the initial feature information is transmitted to the last layer using residual dense blocks, and subsequent layers are restored using input information of previous layers. However, if all hierarchical features are connected and learned and a large number of residual dense blocks are stacked, despite good performance, a large number of parameters and huge computational load are needed, so it takes a lot of time to learn a network and a slow processing speed, and it is not applicable to a mobile system. In this paper, we use the residual dense structure, which is a continuous memory structure that reuses previous information, and the residual dense channel attention block using the channel attention method that determines the importance according to the feature map of the image. We propose a method that can increase the depth to obtain a large receptive field and maintain a concise model at the same time. As a result of the experiment, the proposed network obtained PSNR as low as 0.205dB on average at 4× magnification compared to RDN, but about 1.8 times faster processing speed, about 10 times less number of parameters and about 1.74 times less computation.

• Industry Promotion Research
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• v.6 no.2
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• pp.55-62
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• 2021

The number of single-parent families in South Korea increased since 2000, related to a sharp rise in the divorce rate of 50s and an increase in male mortality rates among those aged 40s-50s. Middle-aged single mothers experience a critical period realizing self-actualization needs, while being in the middle adulthood from the lifespan developmental perspective. In this respect, it is significant to study self-actualization of middle-aged single mothers through guided imagery and music (GIM) in order to provide them with psychological support. This study was conducted from September 2018 to June 2020, and the GIM sessions were conducted at least 10 times. Four participants were selected among the middle-aged single mothers. The imagery experiences of participants in the GIM sessions were classified into four sub-elements: physicalness, emotion, memory, and sense. Within those sub-elements, eight semantic units were categorized into 46 elements. Finally, 152 semantic units were derived. Moreover, the self-actualization which participants experienced through GIM presented three archetypal images: shadow, persona, and the self. In the GIM sessions, experiences of putting their negative emotions associated with family into words and changing passive self-imagery into active one enabled participants to bring the shadow into their consciousness, there by recognizing their positive and bright internal self. Furthermore, participants could map that their current status as people marginalized by siblings and parents, enraged and holding double standards for others, was suppressed by their 'good daughter' and 'religious' personas. This realization lead them to realize and restore their persona. The use of GIM in the study allowed participants to elicit re-experiences of the negative events, while experiencing various imagery and music. This process helped participants achieve self-actualization.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.171-173
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• 2022

The 4th industrial revolution technology is developing people's lives more efficiently. GIS provided on the Internet services such as traffic information and time information makes people getting more quickly to destination. National geographic information service(NGIS) and each local government are making basic data to investigate SOC accessibility for analyzing optimal point. To construct the shortest distance, the accessibility from the starting point to the arrival point is analyzed. Applying road network map, the starting point and the ending point, the shortest distance, the optimal accessibility is calculated by using Dijkstra algorithm. The analysis information from multiple starting points to multiple destinations was required more than 3 steps of manual analysis to decide the position for the optimal point, within about 0.1% error. It took more time to process the many-to-many (M×N) calculation, requiring at least 32G memory specification of the computer. If an optimal proximity analysis service is provided at a desired location more versatile, it is possible to efficiently analyze locations that are vulnerable to business start-up and living facilities access, and facility selection for the public.

• Korean Journal of Radiology
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• v.22 no.5
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• pp.770-781
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• 2021

Objective: Chemical exchange-dependent saturation transfer (CEST) MRI is sensitive for detecting solid-like proteins and may detect changes in the levels of mobile proteins and peptides in tissues. The objective of this study was to evaluate the characteristics of chemical exchange proton pools using the CEST MRI technique in patients with dementia. Materials and Methods: Our institutional review board approved this cross-sectional prospective study and informed consent was obtained from all participants. This study included 41 subjects (19 with dementia and 22 without dementia). Complete CEST data of the brain were obtained using a three-dimensional gradient and spin-echo sequence to map CEST indices, such as amide, amine, hydroxyl, and magnetization transfer ratio asymmetry (MTR_asym) values, using six-pool Lorentzian fitting. Statistical analyses of CEST indices were performed to evaluate group comparisons, their correlations with gray matter volume (GMV) and Mini-Mental State Examination (MMSE) scores, and receiver operating characteristic (ROC) curves. Results: Amine signals (0.029 for non-dementia, 0.046 for dementia, p = 0.011 at hippocampus) and MTR_asym values at 3 ppm (0.748 for non-dementia, 1.138 for dementia, p = 0.022 at hippocampus), and 3.5 ppm (0.463 for non-dementia, 0.875 for dementia, p = 0.029 at hippocampus) were significantly higher in the dementia group than in the non-dementia group. Most CEST indices were not significantly correlated with GMV; however, except amide, most indices were significantly correlated with the MMSE scores. The classification power of most CEST indices was lower than that of GMV but adding one of the CEST indices in GMV improved the classification between the subject groups. The largest improvement was seen in the MTR_asym values at 2 ppm in the anterior cingulate (area under the ROC curve = 0.981), with a sensitivity of 100 and a specificity of 90.91. Conclusion: CEST MRI potentially allows noninvasive image alterations in the Alzheimer's disease brain without injecting isotopes for monitoring different disease states and may provide a new imaging biomarker in the future.

• Journal of The Korean Association For Science Education
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• v.26 no.4
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• pp.475-491
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• 2006

In physics education, the research on students' conceptions has developed in the discussion on the nature and the difficulty of conceptual change. Recently, mental models have been a theoretical background in concrete arguments on "how students' conceptions are constructed or created." Mental models that integrate information in the presented problem and individual knowledge in their long-term memory have important information about not only expressed ideas but also in the thinking process behind the expressed ideas. The purpose of this study is to investigate the forming process and the characteristics of high school student's mental models about circular motion, and how they were changed by instruction. We used the think-aloud method based on the instrument for identifying student's mental models about circular motion, pretest of physics concept, mind map and interview for investigating student's characteristics. The results of the study showed that instructions based on the mental model theory facilitated scientific expressed model, but several factors that affected forming mental models like epistemological belief didn't change scientifically after 3 lessons.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Spatial Information Research
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• v.20 no.5
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• pp.99-109
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• 2012

Recently, due to the growth of social media and spread of smart-phone, the amount of data has considerably increased by full use of SNS (Social Network Service). According to it, the Big Data concept is come up and many researchers are seeking solutions to make the best use of big data. To maximize the creative value of the big data held by many companies, it is required to combine them with existing data. The physical and theoretical storage structures of data sources are so different that a system which can integrate and manage them is needed. In order to process big data, MapReduce is developed as a system which has advantages over processing data fast by distributed processing. However, it is difficult to construct and store a system for all key words. Due to the process of storage and search, it is to some extent difficult to do real-time processing. And it makes extra expenses to process complex event without structure of processing different data. In order to solve this problem, the existing Complex Event Processing System is supposed to be used. When it comes to complex event processing system, it gets data from different sources and combines them with each other to make it possible to do complex event processing that is useful for real-time processing specially in stream data. Nevertheless, unstructured data based on text of SNS and internet articles is managed as text type and there is a need to compare strings every time the query processing should be done. And it results in poor performance. Therefore, we try to make it possible to manage unstructured data and do query process fast in complex event processing system. And we extend the data complex function for giving theoretical schema of string. It is completed by changing the string key word into integer type with filtering which uses keyword set. In addition, by using the Complex Event Processing System and processing stream data at real-time of in-memory, we try to reduce the time of reading the query processing after it is stored in the disk.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• The Korean Journal of Archival Studies
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• no.34
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• pp.221-250
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• 2012

This paper introduces the characteristics of the records and archives management of Korea from Joseon dynasty to now. This paper also explains historical background of making the records and archives management in Joseon dynasty. This paper introduces the process of establishment of modern records management system by adopting records management system and public administration of USA after liberation in 1945. The Joseon bureaucrats established systematic methodologies for managing and arranging the records. Jeseon dynasty managed its records systematically since it was a bureaucratic regime. It is also noticeable that the famous Joseonwangjosilrok(Annals of Joseon dynasty) came out of the power struggles for the control of the national affairs between the king and the nobility during the time of establishment of the dynasty. Another noticeable feature of the records tradition in Joseon dynasty was that the nobility recorded their experience and allowed future generations use and refer their experiences and examples when they performed similar business. The records of Joseon period are the historical records which recorded contemporary incidents and the compilers expected the future historians evaluate the incidents they recorded. In 1894, the reformation policy of Gaboh governments changed society into modernity. The policy of Gaboh governments prescribed archive management process through 'Regulation(命令頒布式)'. They revised the form of official documents entirely. They changed a name of an era from Chinese to unique style of Korean, and changed original Chinese into Korean or Korean-Chinese together. Also, instead of a blank sheet of paper they used printed paper to print the name of each office. Korea was liberated from Japanese Imperialism in 1945 and the government of Republic of Korea was established in 1948. In 1950s Republic of Korea used the records management system of the Government-General of Joseon without any alteration. In the late of 1950's Republic of Korea constructed the new records management system by adopting records management system and public administration of USA. However, understanding of records management was scarce, so records and archives management was not accomplished. Consequently, many important records like presidential archives were deserted or destroyed. A period that made the biggest difference on National Records Management System was from 1999 when was enacted. Especially, it was the period of President Roh's five-year tenure called Participation Government (2003-2008). The first distinctive characteristic of Participation Government's records management is that it implemented governance actively. Another remarkable feature is a nomination of records management specialists at public institutions. The Participation Government also legislated (completely revised) . It led to a beginning of developing records management in Republic of Korea.