• Journal of KIISE
• /
• v.43 no.7
• /
• pp.744-750
• /
• 2016

Recent cloud infrastructures provide competitive performances and operation costs for many internet services through pay-per-use model. Particularly, object storages are highlighted, as they have unlimited file holding capacity and allow users to access the stored files anytime and anywhere. Several lines of research are based on cloud-backed file systems, which support traditional POSIX interface rather than RESTful APIs via HTTP. However, these existing file systems handle all files with uniform size backing objects. Consequently, the accesses to cloud object storages are likely to be inefficient. In our research, files are profiled according to characteristics, and appropriate backing unit sizes are determined. We experimentally verify that different backing unit sizes for the object storage improve the performance of cloud-backed file systems. In our comparative experiments with S3QL, our prototype cloud-backed file system shows faster performance by 18.6% on average.

• Journal of KIISE:Databases
• /
• v.37 no.3
• /
• pp.171-175
• /
• 2010

This paper analyzes the tag data inconsistency problem caused by incomplete execution of the tag access operation to the RFID tag's memory and proposes a protocol to control consistent tag data accesses with finalizing the incomplete operation. Passive RFID tag cannot guarantee complete execution of the tag access operations because of uncertainty and unexpected disconnection of RF communications. This leads to the tag data inconsistency problem. To handle this, we propose a concurrency control protocol which defines incomplete tag operations as continuous queries and monitors the tags're-observation continuously. The protocol finalizes the incomplete operation when the tag is re-observed while it blocks inconsistent data accesses from other operations. We justify the proposed protocol by analyzing the completeness and consistency. The experiments show that the protocol shows better performance than the traditional lock-based concurrency control protocol.

• Journal of Information Technology Services
• /
• v.11 no.4
• /
• pp.233-250
• /
• 2012

In this paper, credit card delinquency means the possibility of occurring bad debt within the certain near future from the normal accounts that have no debt and the problem is to predict, on the monthly basis, the occurrence of delinquency 3 months in advance. This prediction is typical binary classification problem but suffers from the issue of data imbalance that means the instances of target class is very few. For the effective prediction of bad debt occurrence, Support Vector Machine (SVM) with kernel trick is adopted using credit card usage and payment patterns as its inputs. SVM is widely accepted in the data mining society because of its prediction accuracy and no fear of overfitting. However, it is known that SVM has the limitation in its ability to processing the large-scale data. To resolve the difficulties in applying SVM to bad debt occurrence prediction, two stage clustering is suggested as an effective data reduction method and ensembles of SVM models are also adopted to mitigate the difficulty due to data imbalance intrinsic to the target problem of this paper. In the experiments with the real world data from one of the major domestic credit card companies, the suggested approach reveals the superior prediction accuracy to the traditional data mining approaches that use neural networks, decision trees or logistics regressions. SVM ensemble model learned from T2 training set shows the best prediction results among the alternatives considered and it is noteworthy that the performance of neural networks with T2 is better than that of SVM with T1. These results prove that the suggested approach is very effective for both SVM training and the classification problem of data imbalance.

• Transactions on Electrical and Electronic Materials
• /
• v.18 no.4
• /
• pp.199-202
• /
• 2017

Silicon carbide (SiC) is being spotlighted as a next-generation power semiconductor material owing to the characteristic limitations of the existing silicon materials. SiC has a wider band gap, higher breakdown voltage, higher thermal conductivity, and higher saturation electron mobility than those of Si. When using this material to implement Schottky barrier diode (SBD) devices, SBD-state operation loss and switching loss can be greatly reduced as compared to that of traditional Si. However, actual SiC SBDs exhibit a lower dielectric breakdown voltage than the theoretical breakdown voltage that causes the electric field concentration, a phenomenon that occurs on the edge of the contact surface as in conventional power semiconductor devices. Therefore in order to obtain a high breakdown voltage, it is necessary to distribute the electric field concentration using the edge termination structure. In this paper, we designed an edge termination structure using a field plate structure through oxide etch angle control, and optimized the structure to obtain a high breakdown voltage. We designed the edge termination structure for a 650 V breakdown voltage using Sentaurus Workbench provided by IDEC. We conducted field plate experiments. under the following conditions: $15^{\circ}$, $30^{\circ}$, $45^{\circ}$, $60^{\circ}$, and $75^{\circ}$. The experimental results indicated that the oxide etch angle was $45^{\circ}$ when the breakdown voltage characteristics of the SiC SBD were optimized and a breakdown voltage of 681 V was obtained.

• Journal of Korean Society of Water and Wastewater
• /
• v.25 no.1
• /
• pp.15-21
• /
• 2011

Arsenic is one of the most abundant contaminant found in waste mine tailings, because of it's carcinogenic property, the countries like United states of America and Europe have made stringent regulations which govern the concentration of arsenic in drinking water. The current study focuses on different treatment methods for removal of arsenic from waste water. Treatment method the high strength arsenic waste water is treated with Fe(III)-ettringite by co-precipitation method. Number of experiments were carried out to decide the optimal dosage of Fe(III)-ettringite to treat arsenic waste water. The Fe(III)-ettringite was synthesized by taking appropriate equivalent ratios of calcium oxide and ferric chloride in proportion to the arsenic. The best removal efficiencies of 94% were observed at a As/(Ca: Fe) ratio of 1:3. The maximum removal of arsenic was observed in pH range of 12. But as the pH increases the arsenic removal efficiency decreases as portlandite is formed in the pH above 12. The analysis of surface of precipitate conform the needle like structure of ettringite. This treatment technique has promising features such as, the chemicals required in the treatment as well as the sludge generated can be reduced. The operating pH range is in alkaline region which is advantageous over traditional treatment process which has lower pH. Also the co-precipitation not only helps in removal of arsenic but also heavy metals.

• Journal of Oriental Neuropsychiatry
• /
• v.16 no.1
• /
• pp.119-128
• /
• 2005

Objective : The purpose of this study was to estimate the effects of Ramulus et Uncus Uncariae DM fraction on memory enhancing in rats Methods : We oral administered Ramulus et Uncus Uncariae DM fraction to rats then executed passive avoidance test and observed figure of pyramidal neuron on CA1 Results : Findings from our experiments have shown that REUD(>1mg/100g/ml) was effective in memorial improvement. and oral administration of REUD(100mg/100g/ml) for 2 weeks was found to induced the figure of pyramidal neuron on CA1 in rat hippocampus injured by scopolamine. Conclusions : As the result of this study, Decrease of memory induced by injection of scopolamine into rat was also attenuted by REUD, based on passive avoidance test, and REUD was found to reduce the activity of AChE and induced about the CA1 in rat hippocampus. Base on these findings, REUD may be beneficial for the treatment of AD.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2005.10a
• /
• pp.754-758
• /
• 2005

Optimization of process variables such as arc current, welding voltage and welding speed in terms of the weld characteristics desired is the key step in achieving high quality and improving performance characteristics without increasing the cost. Consequently, incorrect settings of those process variables give rise to deviations in the welding characteristics from the desired bead geometry. Therefore, trainee welders are referred to the tabulated information relating different metal types and thickness as to recommend the desired values of process variables. Basically, the bead geometry plays an important role in determining the mechanical properties of the weld. So that it is very important to select the process variables for obtaining optimal bead geometry. However, it is difficult for the traditional identification methods to provide an accurate model because the optimized welding process is non-linear and time-dependent. In this paper, the possibilities of the Infra-red sensor in sensing and control of the bead geometry in the automated welding process are presented. Infra-red sensor is a well-known method to deal with the problems with a high degree of fuzziness so that the sensor is employed to build the relationship between process variables and the quality characteristic the proposed above respectively. Based on several neural networks, the mathematical models are derived from extensive experiments with different welding parameters and complex geometrical features. The developed system enables to select the optimal welding parameters and control the desired weld dimensions during arc welding process.

• Asia pacific journal of information systems
• /
• v.9 no.4
• /
• pp.23-40
• /
• 1999

This paper proposes a hybrid intelligent system that predicts the failure of firms based on the past financial performance data, combining neural network and rough set approach, We can get reduced information table, which implies that the number of evaluation criteria such as financial ratios and qualitative variables and objects (i.e., firms) is reduced with no information loss through rough set approach. And then, this reduced information is used to develop classification rules and train neural network to infer appropriate parameters. Through the reduction of information table, it is expected that the performance of the neural network improve. The rules developed by rough sets show the best prediction accuracy if a case does match any of the rules. The rationale of our hybrid system is using rules developed by rough sets for an object that matches any of the rules and neural network for one that does not match any of them. The effectiveness of our methodology was verified by experiments comparing traditional discriminant analysis and neural network approach with our hybrid approach. For the experiment, the financial data of 2,400 Korean firms during the period 1994-1996 were selected, and for the validation, k-fold validation was used.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.3
• /
• pp.155-166
• /
• 2017

As the utilization of aerial images increases, a variety of software using unmanned aerial photogrammetric procedures as well as traditional aerial photogrammetric procedures are being provided. Previously, software that used the unmanned aerial photogrammetric procedure was used for images captured in small areas. Recently, however, software that uses unmanned aerial photogrammetric procedures for large-scale images taken by using aerial photogrammetric cameras has appeared. Therefore, this study generated ortho-images using aerial photogrammetry and unmanned aerial photogrammetry for large aerial images, and compared the features of both procedures through qualitative and quantitative comparisons. Experiments in the study area show that using the 3D mesh effectively removes the relief displacement of the building rather than using the digital surface model to generate ortho-images.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.14 no.3
• /
• pp.152-159
• /
• 2010

Recently, the public interest in eco-friendly material and structure has been increasing and many Hwang-toh researches are being actively performed. Hwang-toh is one of the traditional environment friendly construction materials used as a construction and plastering material. Hwang-toh has many advantages as construction material due to its high heat storage capacity, auto-purification, antibiotic ability, and infrared ray emission characteristics. But, currently it has not been developed into construction material and used in modern construction due to its low strength and dry shrinkage cracking prone characteristics. According to the recent researches and study results, Hwang-toh can be used as a natural pozzolanic material like fly-ash or pozzolan. In this study, mechanical properties and structural flexure behavior experiments of slag, recycled PET fiber, and Hwang-toh added concrete are carried out. The test results showed that drying shrinkage of concrete mixed with Hwang-toh has lower compressive strength and elastic modulus than those of control cement concrete specimen, but it has the similar flexural behavior in reinforced concrete beams.