• 전기학회논문지
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• 제66권6호
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• pp.955-961
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• 2017

In this paper, we propose a parallel approximate string matching algorithm with k-mismatches for multiple fixed-length patterns (PMASM) in DNA sequences. PMASM is developed from parallel single pattern approximate string matching algorithms to effectively calculate the Hamming distances for multiple patterns with a fixed-length. In the preprocessing phase of PMASM, all target patterns are binary encoded and stored into a look-up memory. With each input character from the input string, the Hamming distances between a substring and all patterns can be updated at the same time based on the binary encoding information in the look-up memory. Moreover, PMASM adopts graphics processing units (GPUs) to process the data computations in parallel. This paper presents three kinds of PMASM implementation methods in GPUs: thread PMASM, block-thread PMASM, and shared-mem PMASM methods. The shared-mem PMASM method gives an example to effectively make use of the GPU parallel capacity. Moreover, it also exploits special features of the CUDA (Compute Unified Device Architecture) memory structure to optimize the performance. In the experiments with DNA sequences, the proposed PMASM on GPU is 385, 77, and 64 times faster than the traditional naive algorithm, the shift-add algorithm and the single thread PMASM implementation on CPU. With the same NVIDIA GPU model, the performance of the proposed approach is enhanced up to 44% and 21%, compared with the naive, and the shift-add algorithms.

• 정보보호학회논문지
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• 제28권1호
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• pp.85-93
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• 2018

생체 정보를 이용한 사용자 인증은 차세대 인증 방법으로서 기존의 인증 시스템에서 급진적으로 사용되고 있는 인증 방법이다. 대부분의 생체 인증 시스템은 수집된 생체 정보가 가지는 노이즈로 인한 문제, 데이터의 품질에 대한 문제, 인식률의 한계 등 많은 문제점들을 가지고 있다. 이를 해결하기 위한 방법으로 본 논문에서는 비선형적인 실제 데이터를 정확하게 처리하기 위해 비선형기법인 Dual QML을 사용하고, 또한 정확한 영역을 추출하여 인증의 정확도를 증가시키는 전처리 과정을 추가로 제안하여 정확도 증가뿐만 아니라 성능을 향상시키는 방법을 제안하고자 한다. 앞서 발표된 Dual QML은 생체 정보로 얼굴, 장문, 귀를 사용하였다. 본 논문은 앞선 Dual QML 실험에 사용하지 않은 홍채를 생체 정보로 사용하여 홍채 인식을 위한 방법으로도 Dual QML이 우수하다는 것을 보이고자 한다. 마지막으로 실험을 통해 이에 대한 실증을 보이고자 한다.

• 정보과학회 논문지
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• 제43권7호
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• pp.744-750
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• 2016

오늘날, 클라우드 인프라 서비스는 일정한 성능을 보장해주면서도 사용한 만큼 비용을 지불하는 과금체계를 통해 많은 인터넷 서비스들의 운영비용을 개선시켜 주고 있다. 특히 오브젝트 스토리지는 필요한 만큼 파일을 저장할 수 있고, 언제 어디서나 접근할 수 있다는 점에서 각광받고 있다. 이에 따라, 최근에는 오브젝트 스토리지를 HTTP 기반의 RESTful API 방식이 아닌 POSIX 기반 파일 액세스가 가능한 클라우드 기반 파일 시스템 연구들이 등장하고 있다. 하지만 이러한 파일 시스템들은 오브젝트 스토리지에 저장되는 모든 파일을 동일한 크기의 단위 오브젝트로 분할하기 때문에 데이터 접근 시 비효율적인 입출력이 발생할 여지가 있다. 따라서 본 연구에서는 파일 및 워크로드의 특성을 구별하여 오브젝트 스토리지에 저장되는 단위 오브젝트의 적합한 크기를 추정하고, 각 파일의 접근 성능을 향상시킬 수 있다는 점을 검증한다. 제안하는 기법은 S3QL에 비해 평균 18.6% 빨라진 것으로 나타났다.

• 한국정보과학회논문지:데이타베이스
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• 제37권3호
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• pp.171-175
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• 2010

본 논문에서는 RFID 전자태그에 부착된 메모리의 정보를 접근할 때 발생하는 태그 연산 실행의 불완전성에 따른 태그 데이터의 불일치 문제를 분석하고, 이를 해결하기 위한 프로토콜을 제안한다. 수동형 RFID 태그는 통신의 불확실성과 단절성으로 인해 태그 메모리 접근연산의 완전한 실행을 보장하지 못하므로, 불완전하게 실행된 연산으로 인해 태그 데이터의 비일관성을 초래하는 문제가 발생한다. 본 논문에서는 태그 접근의 일관성을 유지하면서 불완전 연산의 실행을 완료시키기 위한 동시성 제어 프로토콜을 제안한다. 이 프로토콜은 불완전 실행된 연산의 대상태그를 연속질의로 정의하고 태그의 인식을 모니터링 함으로써 다른 연산들에 의한 불확실 데이타의 접근을 차단하고, 재수행을 통해 불완전하게 실행된 연산의 수행을 완료시킨다. 또한, 증명을 통해 제안한 프로토콜의 정확성, 일관성을 입증하였으며, 실험을 통해 본 프로토콜이 기존의 일관성 유지기법보다 좋은 성능을 나타냄을 보였다.

• 한국IT서비스학회지
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• 제11권4호
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• pp.233-250
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• 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
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• 제18권4호
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• pp.199-202
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• 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.

• 상하수도학회지
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• 제25권1호
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• pp.15-21
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• 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.

• 동의신경정신과학회지
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• 제16권1호
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• pp.119-128
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• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 추계학술대회 논문집
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• pp.754-758
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• 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
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• 제9권4호
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• pp.23-40
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• 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.