• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.6 no.1
• /
• pp.179-213
• /
• 1984

The history of recording and conserving for maintenance the human's memory from ancient times to modern's has brought about a lot of changing process of the recorded information media with developing in culture, and each society has made important recorded materials in his library as essentially the social apparatus. But most of them that were damaged by factors of the natural disaster, humidity, temperature, light, fungi and insects in the library, and were resulted in deterioration and losing the numerous materials. For removing the inevitable phases repeated, there are studying for counterplan of the fundamental environment factors about preservation, restoration and chemical research of materials in advanced countries, but I get a few researches about protecting the cultural properties. Therefore I research the survey of the actual conditions on 72 university libraries centering around them, and then I have researched the collection rate 81 percent, 58 university libraries. (local : 35, in seoul : 23) As the result of this research, I propose the model of the environment factors of conserving the library materials. 1) To apply the equipment of processing the radiant energy to the new construction and buildings. 2) To remove factors that occur fungi and insects by facilities being controlled relative humility, temperature, and to equip the ventilation arrangement in the library. 3) To shelve all acquired and bound materials after proceeding the vacuum fumigator. 4) Those who want to enter into the library stack were surely taken in sterilzing their hands and shoes, and must put on the gown. 5) To use the vacuum dusting thing (machine) for removing the dust without spreading out the floor of the library at any time. 6) To set up the gas automatic fire extinguisher worked by smoke sensor. 7) To assist the research into the preservation, natures and environment of recorded materials, and to supply financial funds for librarian. 8) To hold regularly the workshop be able to educate the methods of preservation materials by the constant system. (Library Association) 9) To add to responsibilities on certification of preservation materials for librarian. 10) To hold the constant committee system in each library. 11) To keep up with the ideal environment (humidity, temperature, light, ventilation, etc.) of preservation materials in the arrangement room, and to put on the gown.

• Journal of KIISE:Computer Systems and Theory
• /
• v.31 no.8
• /
• pp.421-431
• /
• 2004

This paper presents adaptive execution techniques that determine whether parallelized loops are executed in parallel or sequentially in order to maximize performance. The adaptation and performance estimation algorithms are implemented in a compiler preprocessor. The preprocessor inserts code that automatically determines at compile-time or at run-time the way the parallelized loops are executed. Using a set of standard numerical applications written in Fortran77 and running them with our techniques on a distributed shared memory multiprocessor machine (SGI Origin2000), we obtain the performance of our techniques, on average, 26%, 20%, 16%, and 10% faster than the original parallel program on 32, 16, 8, and 4 processors, respectively. One of the applications runs even more than twice faster than its original parallel version on 32 processors.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38A no.9
• /
• pp.773-781
• /
• 2013

The Hough transform for line detection is widely used in many machine vision applications due to its robustness against data loss and distortion. However, it is not appropriate for real-time embedded vision systems, because it has inefficient computation structure and demands a large number of memory accesses. Thus, this paper proposes an improved voting scheme of the Hough transform, and then applies this scheme to a Hough transform hardware architecture so that it can provide real-time performance with less hardware resource. The proposed voting scheme reduces computation overhead of the voting procedure using correlation between adjacent pixels, and improves computational efficiency by increasing reusability of vote values. The proposed hardware architecture, which adopts this improved scheme, maximizes its throughput by computing and storing vote values for many adjacent pixels in parallel. This parallelization for throughput improvement is accomplished with little hardware overhead compared with sequential computation.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.11
• /
• pp.521-526
• /
• 2016

As on-line informal text data have massive in its volume and have unstructured characteristics in nature, there are limitations in applying traditional relational data model technologies for data storage and data analysis jobs. Moreover, using dynamically generating massive social data, social user's real-time reaction analysis tasks is hard to accomplish. In the paper, to capture easily the semantics of massive and informal on-line documents with unsupervised learning mechanism, we design and implement automatic topic extraction systems according to the mass of the words that consists a document. The input data set to the proposed system are generated first, using N-gram algorithm to build multiple words to capture the meaning of the sentences precisely, and Hadoop and Spark (In-memory distributed computing framework) are adopted to run topic model. In the experiment phases, TB level input data are processed for data preprocessing and proposed topic extraction steps are applied. We conclude that the proposed system shows good performance in extracting meaningful topics in time as the intermediate results come from main memories directly instead of an HDD reading.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2006.06a
• /
• pp.242-242
• /
• 2006

Increasingly complex tasks are performed by computers or cellular phone, requiring more and more memory capacity as well as faster and faster processing speeds. This leads to a constant need to develop more highly integrated circuit systems. Therefore, there have been numerous studies by many engineers investigating circuit patterning. In particular, PCB including module/package substrates such as FCB (Flip Chip Board) has been developed toward being low profile, low power and multi-functionalized due to the demands on miniaturization, increasing functional density of the boards and higher performances of the electric devices. Imprint lithography have received significant attention due to an alternative technology for photolithography on such devices. The imprint technique. is one of promising candidates, especially due to the fact that the expected resolution limits are far beyond the requirements of the PCB industry in the near future. For applying imprint lithography to FCB, it is very important to control thermal properties and mechanical properties of dielectric materials. These properties are very dependent on epoxy resin, curing agent, accelerator, filler and curing degree(%) of dielectric materials. In this work, the epoxy composites filled with silica fillers and cured with various accelerators having various curing degree(%) were prepared. The characterization of the thermal and mechanical properties wasperformed by thermal mechanical analysis (TMA), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), rheometer, an universal test machine (UTM).

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.10
• /
• pp.487-492
• /
• 2017

Clustering is a technique which is used to measure similarities between data in big data analysis and data mining field. Among various clustering methods, k-Modes algorithm is representatively used for categorical data. To increase the performance of iterative-centric tasks such as k-Modes, a distributed and concurrent framework Spark has been received great attention recently because it overcomes the limitation of Hadoop. Spark provides an environment that can process large amount of data in main memory using the concept of abstract objects called RDD. Spark provides Mllib, a dedicated library for machine learning, but Mllib only includes k-means that can process only continuous data, so there is a limitation that categorical data processing is impossible. In this paper, we design RDD for k-Modes algorithm for categorical data clustering in spark environment and implement an algorithm that can operate effectively. Experiments show that the proposed algorithm increases linearly in the spark environment.

• KIPS Transactions on Computer and Communication Systems
• /
• v.3 no.11
• /
• pp.409-418
• /
• 2014

The amount of data located in storage servers has dramatically increased with the growth in cloud and social networking services. Storage systems with very large capacities may suffer from poor reliability and long latency, problems which can be addressed by the use of a hybrid disk, in which mechanical and flash memory storage are combined. The Linux-based SSD(solid-state disk) uses a caching technique based on the DM-cache utility. We assess the limitations of DM-cache by evaluating its performance in diverse environments, and identify problems with the caching policy that it operates in response to various commands. This policy is effective in reducing latency when Linux is running in native mode; but when Linux is installed as a guest operating systems on a virtual machine, the overhead incurred by caching actually reduces performance.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.61 no.6
• /
• pp.123-132
• /
• 2019

This study is to estimate the spatial soil moisture using Terra MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data and machine learning technique. Using the 3 years (2015~2017) data of MODIS 16 days composite NDVI (Normalized Difference Vegetation Index) and daily Land Surface Temperature (LST), ground measured precipitation and sunshine hour of KMA (Korea Meteorological Administration), the RDA (Rural Development Administration) 10 cm~30 cm average TDR (Time Domain Reflectometry) measured soil moisture at 78 locations was tested. For daily analysis, the missing values of MODIS LST by clouds were interpolated by conditional merging method using KMA surface temperature observation data, and the 16 days NDVI was linearly interpolated to 1 day interval. By applying the RNN-LSTM (Recurrent Neural Network-Long Short Term Memory) artificial neural network model, 70% of the total period was trained and the rest 30% period was verified. The results showed that the coefficient of determination ($R^2$), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency were 0.78, 2.76%, and 0.75 respectively. In average, the clay soil moisture was estimated well comparing with the other soil types of silt, loam, and sand. This is because the clay has the intrinsic physical property for having narrow range of soil moisture variation between field capacity and wilting point.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.15 no.1
• /
• pp.86-98
• /
• 2019

Recently, the trend of building container-based PaaS (Platform-as-a-Service) is expanding. Container-based platform technology has been a core technology for realizing a PaaS. Containers have lower operating overhead than virtual machines, so hundreds or thousands of containers can be run on a single physical machine. However, recording and monitoring the storage logs for a large number of containers running in cloud computing environment occurs significant overhead. This work has identified two problems that occur when detecting a file system change event of a container running in a cloud computing environment. This work also proposes a system for container file system event detection in the environment by solving the problem. In the performance evaluation, this work performed three experiments on the performance of the proposed system. It has been experimentally proved that the proposed monitoring system has only a very small effect on the CPU, memory read and write, and disk read and write speeds of the container.

• Genomics & Informatics
• /
• v.19 no.1
• /
• pp.11.1-11.8
• /
• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.