• Journal of KIISE:Computing Practices and Letters
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• v.6 no.3
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• pp.319-328
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• 2000

The Message-Passing Interface(MPI) is a standard interface for parallel programming environment, based on that application programs run on the processors of a parallel computer. Processor nodes execute processes consisting the program by passing messages to one another. During executing, however, if a fault occurs on a processor node or a process, this will result an inconsistent state, and consequently, the whole program will have to be stopped. To solve this problem, in this paper, we propose a fault-tolerant message passing interface(FT-MPI) by adding a fault manager module to MPI. The proposed FT-MPI does not need any hardware support, and each application program based on MPI can run on the FT-MPI without any modification. The proposed fault tolerance scheme uses the so-called hot-spare process duplication method, and verified by simulations that application programs run despite of any fault with less than 5% overhead on execution time.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.12
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• pp.541-552
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• 2013

Recently software security of the smart phone is an important issue in the field of information science and technology due to fast propagation of smart technology in our life. The smart phone as the security critical systems which are utilizing in terminal systems of the banking, ubiquitous home management, airline passengers screening, and so on are related to the risk of costs, risk of loss, risk of availability, and risk by usage. For the security issues, software hazard analysis of smart phone is the key approaching method by use of observed failures. In this paper, we propose an efficient integrative framework for software security analysis of the smart phone using Fault Tree Analysis (FTA) and Failure Mode Effect Analysis (FMEA) to gain a convergence security and reliability analysis technique on hand handle devices. And we discuss about that if a failure mode effect analysis performs simpler, not only for improving security but also reducing failure effects on this smart device, the proposed integrative framework is a key solution.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.69-75
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• 2021

In a contact-free society, online services are becoming more important than classic offline services. At the same time, the role of a contact center, which executes customer relation management (CRM), is increasingly essential. For supporting the CRM tasks and their effectiveness, techniques of process automation need to be applied. Quality assurance (QA) is one of the time and resource consuming, and typical processes that are suitable for automation. In this paper, a method of automatic quality evaluation for voice based consultations is proposed. Firstly, the speech in consultations is transformed into a text by speech recognition. Then quantitative evaluation based on the QA metrics, including checking the elements in opening and closing mention, the existence of asking the mandatory information, the attitude of listening and speaking, is executed. 92.7% of the automated evaluations are the same to the result done by human experts. It was found that the non matching cases of the automated evaluations were mainly caused from the mistranslated Speech-to-Text (STT) result. With the confidence of STT result, this proposed method can be employed for enhancing the efficiency of QA process in contact centers.

• Journal of the Korean Geotechnical Society
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• v.38 no.5
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• pp.19-33
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• 2022

Newmark's sliding block analysis is the most commonly used method for predicting earthquake-induced permanent displacement of embankment slopes. Additionally, it yields the amount of slip circle sliding using the limit equilibrium theory. Thus, permanent displacement does not occur until the seismic load exceeds the yield acceleration, which induces sliding of the slip circle. The evolution of Newmark's sliding block analysis has been made by introducing the numerical seismic response analysis results since it was introduced. This study compares seismic performance evaluation results for the example enclosure dam section with the analysis methods. As a result, earthquake-induced permanent displacement using Newmark's sliding block analysis did not occur for the enclosure dam, indicating a high safety factor. However, nonlinear response history analysis gave reasonable results.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.497-510
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• 2022

Agricultural reservoirs are essential structures for water supplies during dry period in the Korean peninsula, where water resources are temporally unequally distributed. For efficient water management, systematic and effective monitoring of medium-small reservoirs is required. Synthetic Aperture Radar (SAR) provides a way for continuous monitoring of those, with its capability of all-weather observation. This study aims to evaluate the applicability of SAR in monitoring medium-small reservoirs using Sentinel-1 (10 m resolution) and Capella X-SAR (1 m resolution), at Chari (CR), Galjeon (GJ), Dwitgol (DG) reservoirs located in Ulsan, Korea. Water detected results applying Z fuzzy function-based threshold (Z-thresh) and Chan-vese (CV), an object detection-based segmentation algorithm, are quantitatively evaluated using UAV-detected water boundary (UWB). Accuracy metrics from Z-thresh were 0.87, 0.89, 0.77 (at CR, GJ, DG, respectively) using Sentinel-1 and 0.78, 0.72, 0.81 using Capella, and improvements were observed when CV was applied (Sentinel-1: 0.94, 0.89, 0.84, Capella: 0.92, 0.89, 0.93). Boundaries of the waterbody detected from Capella agreed relatively well with UWB; however, false- and un-detections occurred from speckle noises, due to its high resolution. When masked with optical sensor-based supplementary images, improvements up to 13% were observed. More effective water resource management is expected to be possible with continuous monitoring of available water quantity, when more accurate and precise SAR-based water detection technique is developed.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• Journal of Life Science
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• v.23 no.9
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• pp.1133-1139
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• 2013

During the collection of boar semen, bacterial contamination usually occurs. The contamination has deleterious effects both on semen quality and on sow fertility. The majority of contaminants are gram-negative bacteria, especially Serratia marcescens. In this study, we developed a PCR assay for the identification of S. marcescens targeting the luxS gene (GenBank no. EF164926). S. marcescens yielded a specific 306 bp PCR product. However, no amplification was observed in the other strains tested. The detection limit of PCR was $50pg/{\mu}l$ of template DNA of S. marcescens. The antimicrobial susceptibility patterns of S. marcescens isolated from boar semen were tested using the disk diffusion method. Gentamicin, ceftiofur, florfenicol, and neomycin showed high sensitivity in this test. The minimum inhibitory concentration (MIC) was also determined by the broth microdilution method. The $MIC_{90}$ values of ceftiofur, enrofloxacin, gentamicin, and neomycin were 8, 8, 8, and $16{\mu}g/ml$, respectively. These results indicate that PCR amplification of the luxS gene is a reliable and effective method for the identification of S. marcescens and that ceftiofur, enrofloxacin, gentamicin, and neomycin are effective semen extenders for controlling S. marcescens.

• Journal of Korean Society of Transportation
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• v.36 no.1
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• pp.23-37
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• 2018

In recent years, it is very difficult to construct and expand new infrastructures in a city center because of long-term low growth and lack of space due to urban overcrowding. So, there is a need to study a variety of Retrofitting techniques and urban applications that can lead to sustainable development while efficiently utilizing existing facilities. 'Retrofit' means a sustainable urban retrofitting as a directed alteration of the structures, formations and systems of existing facilities to improve energy, water and waste efficiencies. In this study, we applied a hierarchical network design technique that can reflect the structural hierarchy of a city to study how to retrofit public transportation routes in Seoul. The hierarchical network design means dividing the hierarchy according to the functions of hubs and connecting different hierarchies to form a hierarchical network. As a result of comparing the application results of various retrofitting scenarios of public transport, the differences of daily PKT and PHT by about 2.6~3.2% less than before the improvement address that the convenience of passengers is increased. Therefore, it is expected that if the route planning is established according to the proposed method, it will increase the number of passengers and the operational efficiency by the improved convenience of public transit passengers.

• Journal of the Korea Society of Computer and Information
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• v.19 no.4
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• pp.1-8
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• 2014

As cores in multi-core processors are integrated in a single chip, power density increased considerably, resulting in high temperature. For this reason, many research groups have focused on the techniques to solve thermal problems. In general, the approaches using mechanical cooling system or DTM(Dynamic Thermal Management) have been used to reduce the temperature in the microprocessors. However, existing approaches cannot solve thermal problems due to high cost and performance degradation. However, floorplan scheme does not require extra cooling cost and performance degradation. In this paper, we propose the diverse floorplan schemes in order to alleviate the thermal problem caused by the hottest unit in multi-core processors. Simulation results show that the peak temperature can be reduced efficiently when the hottest unit is located near to L2 cache. Compared to baseline floorplan, the peak temperature of core-central and core-edge are decreased by $8.04^{\circ}C$, $8.05^{\circ}C$ on average, respectively.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.795-835
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• 2019

Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field.