• Journal of KIISE:Computer Systems and Theory
• /
• v.29 no.2
• /
• pp.105-115
• /
• 2002

Since NUMA architecture has to access remote memory an interconnection network determines the performance of CC-NUMA system Bus which has been used as a popular interconnection network has many limits to build a large-scale system because of the limited physical scalabilty and bandwidth Dual ring interconnection network composed of high speed point-to-point links is made up for resolving the defects of the bus for large-scale system But it also has a problem that the response latency is rapidly increased when many node are attached to snooping based CC-NUMA system with dual ring In this paper we propose a chordal ring architecture in order to overcome the problem of the dual ring on snooping based CC-NUMA system and design and efficient link controller adopted to this architecture. We also analyze the effects of chordal ring architecture on the system performance and the response latency by using probability driven simulator.

• 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.

• The Korean Journal of Archival Studies
• /
• no.6
• /
• pp.121-160
• /
• 2002

This paper reports the project of arranging archives of Administrative Headquarters of the Jogye Order. It illustrates the whole process of preliminary survey, arrangement, appraisal and description of the archives. One of the distinctive features of the project lies in its focus on practical considerations. In other words, it has avoided blindly following theoretical recommendations made by previous efforts. First step of the project has been to review the current state of the archives through preliminary survey as well as analysis of related regulations. Second step has followed to establish actual process of classifying, appraising, describing, filing and designing storage facility management as well as a computerized archival management system. In this process, every concern has been given to prevent records and archives from physical damage and to ensure their intellectual order kept so that archival information could be re-constructed and usability and efficiency of the records could be secured. Major contributions made by the project can be found in that it has reviewed the volume of administrative archives created and held by Jogye Order and improved the overall efficiency of as well as information sharing among personnel at the Headquarter. The most notable accomplishment could be, however, found in that the project has helped the personnel to rediscover their own history from their records, rather than from their memory. From the theoretical perspective of archival science, the meaning of the project can also be found in that it has provided with the starting point toward establishing organizing methodology for organizational archives including religious archives. Arranging archives of an organization requires archivists to respect theories and principles, but at the same time, adequate attention should be paid to reflect idiosyncratic characteristics of the organization. General methods applicable to a wider range of archives could be derived from the very endeavor. Though impossible in a short period of time, it could be accomplished by accumulating theoretical and practical knowledge and experience.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.280-283
• /
• 2014

Cloud Computing virtualizes logical resources like cpu, memory and disks etcs from physical machines. This virtualization technology increases computing resource utilization and supports dynamic resource allocations. Because of these benefits, global cloud computing services like Amazon AWS, Google Cloud and Apple iCloud are prevalent. With this cloud Computing services, there has been a request about cloud migration between different cloud environments. If one service which operates in a cloud computing environment wants to migrate to another cloud environment, there should have been a compitability between two different cloud environment. but even global cloud computing services do not support this compitability. In this paper, we suggest a process and technology to cloud migrations based on open sources.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.6
• /
• pp.19-28
• /
• 2021

Nowadays, interest in health care is increasing due to Coronavirus (COVID-19), and a lot of people are doing home training as there are more difficulties in using fitness centers and public facilities that are used together. In this paper, we propose a personalized exercise recommendation algorithm using personalized propensity information to provide more accurate and meaningful exercise recommendation to home training users. Thus, we classify the data according to the criteria for obesity with a k-nearest neighbor algorithm using personal information that can represent individuals, such as eating habits information and physical conditions. Furthermore, we differentiate the exercise dataset by the level of exercise activities. Based on the neighborhood information of each dataset, we provide personalized exercise recommendations to users through a dimensionality reduction algorithm (SVD) among model-based collaborative filtering methods. Therefore, we can solve the problem of data sparsity and scalability of memory-based collaborative filtering recommendation techniques and we verify the accuracy and performance of the proposed algorithms.

• Journal of Marine Bioscience and Biotechnology
• /
• v.12 no.2
• /
• pp.81-90
• /
• 2020

Sialic acid, which is contained in about 60-160 mg/kg in the edible bird's nest (EBN), is known to facilitate in the proper formation of synapses and improve memory function. The objective of this study is to extract effectively the sialic acid from edible bird's nest using affinity bead technology (ABT). After preparing the non-polar polymeric bead "KJM-278-28A" having a porous network structure, and then desorbed sialic acid was concentrated and dried. The analysis of the physicochemical properties of bead "KJM-278-28A" showed that the particle size was 400-700 ㎛, the moisture holding capacity was 67-70%, the surface area (BET) was 705-900 ㎡/g, and the average pore diameter 70-87 Å. The adsorption capacity of the bead "KJM-278-28A" for sialic acid was shown a strong physical force to bind sialic acid to the bead surface of 400 mg/L. In addition, as a result of analyzing the adsorption and desorption effects of sialic acid on water, ethanol, and 10% ethanol on the bead, it was confirmed that desorption effectively occurs from the beads when only ethanol is used. As a result of HPLC measurement of the separated sialic acid solution, a total of four sialic acid peaks of N-acetyl-neuraminic acid (Neu5Ac), α,β-anomer of Neu5Ac and N-glycoly-neuraminic acid were identified. Through these results, it was confirmed that it is possible to separate sialic acid from EBN extract with efficient and high yield when using ABT.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.7
• /
• pp.2304-2320
• /
• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• The Journal of the Korea Contents Association
• /
• v.21 no.6
• /
• pp.552-560
• /
• 2021

With the recent development of the art distribution system, interest in art investment is increasing rather than seeing art as an object of aesthetic utility. Unlike stocks and bonds, the price of artworks has a heterogeneous characteristic that is determined by reflecting both objective and subjective factors, so the uncertainty in price prediction is high. In this study, we used LSTM Recurrent Neural Network deep learning model to predict the auction winning price by inputting the artist, physical and sales charateristics of the Korean artist. According to the result, the RMSE value, which explains the difference between the predicted and actual price by model, was 0.064. Painter Lee Dae Won had the highest predictive power, and Lee Joong Seop had the lowest. The results suggest the art market becomes more active as investment goods and demand for auction winning price increases.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.6
• /
• pp.242-263
• /
• 2021

This paper presents a method to implement a low-cost driving simulator for developing autonomous driving algorithms. This is implemented by using GTA V, a physical engine-based commercial game software, containing a function to emulate output and data of various sensors for autonomous driving. For this, NF of Script Hook V is incorporated to acquire GT data by accessing internal data of the software engine, and then, various sensor data for autonomous driving are generated. We present an overall function of the developed driving simulator and perform a verification of individual functions. We explain the process of acquiring GT data via direct access to the internal memory of the game engine to build up an autonomous driving algorithm development environment. And, finally, an example applicable to artificial neural network training and performance evaluation by processing the emulated sensor output is included.