• 한국정보처리학회논문지
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• 제3권4호
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• pp.791-802
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• 1996

본 논문에서는 대규모 다중처리기 시스템에서 다중쓰레드를 지원하는 기법에 관하여 다룬다. 분산공유메로리에서의 주소번역표 관리, 블록 일관성 유지 방법, 그리고 블록 대치 정책에 대하여 쓰레드 프로그래밍 환경에 적합한 새로운 기법을 제안한다. 이 기법은 분산공유메모리에서 일반적으로 발생하는 문제점들인 거짓 공유, 불필요한 중복, 블록 바운싱, 그리고 주소 엘리어싱 등을 효율적으로 해결한다. 그리고 응용프 로그램의 투명성을 제공하고, 시스템의 확장과 구현 용이하도록 해주며, 다중쓰레드 환경을 사용자에서 제공한다.

• 대한전자공학회논문지SP
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• 제42권1호
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• pp.101-108
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• 2005

적응 CFAR(Constant False Alarm Rate) 알고리즘은 클러터 배경 환경에서 일정한 오경보 율을 유지하면서 탐지확률을 높이기 위해 사용된다. 특히 공간 상관관계, 크기 편차가 큰 비 균일한 클러터 환경에서 탐지성능을 향상시키기 위해서는 공간변화에 적응적인 필터링 기법이 요구된다. 본 논문에서는 클러터 배경추정을 위해 이차원적으로 영역을 구분하여 대표 추정 값을 구하고, 보간(interpolation) 필터를 이용하여 최종 추정 값을 결정하는 이차원 블록 보간(Two-dimensional Block Interpolation : TBI) 적응 CFAR 알고리즘을 제안한다. 제안한 방법은 부분영역의 히스토그램 분포 중앙값을 영역 추정 값으로 선택함으로 불규칙 간섭신호 제거에 효과적이며, 블록 노드 추정 값을 이용하여 각 셀에 대한 최종 추정 값을 얻는 방식을 취함으로 인해 거리 셀 수가 많고, 고도 빔 수가 많은 시스템에서 클러터 필터링에 필요한 메모리 공간을 줄이는데 이점이 있다. 컴퓨터 모의실험을 통해 기존의 트랜스버설(transversal) 방식, 회귀(recursive)방식의 적응 CFAR 알고리즘과 탐지성능, 필요메모리 측면에서 성능을 비교하여 제안한 방법의 우수성을 확인한다.

• 한국공간정보시스템학회:학술대회논문집
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• 한국공간정보시스템학회 2004년도 국내 LBS 기술개발 및 표준화 동향세미나
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• pp.69-74
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• 2004

Selectivity estimation for spatial query is very important process in finding the most efficient execution plan. Many works have been performed to estimate accurately selectivity. Although they deal with some problems such as false-count, multi-count, they require a large amount of memory to retain accurate selectivity, so they can not get good results in little memory environments such as mobile-based small database. In order to solve this problem, we propose a new technique called MW histogram which is able to compress summary data and get reasonable results. It also has a flexible structure to react dynamic update. The experimental results showed that the MW histogram has lower relative error than MinSkew histogram and gets a good selectivity in little memory.

• 대한약침학회지
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• 제4권1호
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• pp.49-53
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• 2001

This experiment has investigated the influence of Yamen (Du. 15) point injection on learning and memory dysfunction caused by cerebral ischemia and reprofusion in bilateral cervical general artery combined with bleeding on mouse tail to mimic vascular dementia in human beings. By dividing 40 mice into 4 groups (group1false operation group, group2model group, group3point injection with Cerebrolysin group4point injection with saline.) According to random dividing principles, we observed the influence of Yamen(Du. 15) point injection on the time of swimming the whole course used by model mice which had received treatment for different days in different groups, and the influence of those mice on wrong times they entered blind end. The result showed that point injection with Cerebrolysin and saline could improve learning and memory dysfunction of the mice caused by cerebral ischemia.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.766-768
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• 2003

Selectivity estimation for spatial query is curial in Spatial Database Management Systems(SDBMS). Many works have been performed to estimate accurate selectivity. Although they deal with some problems such as false-count, multi-count arising from properties of spatial dataset, they can not get such effects in little memory space.* Therefore, we need to compress spatial dataset into little memory. In this paper, we propose a new technique called MW Histogram which is able to compress summary data and get reasonable results. Our method is based on two techniques:(a)MinSkew partitioning algorithm which deal with skewed spatial datasets. efficiently (b) Wavelet transformation which compression effect is proven. We evaluate our method via real datasets. The experimental result shows that the MW Histogram has the ability of providing estimates with low relative error and retaining the similar estimates even if memory space is small.

• 유통과학연구
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• 제14권1호
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• pp.93-102
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• 2016

Purpose - The purpose of paper is studying the static and dynamic side for long-term memory storage properties, and increase the explanatory power regarding the long-term memory process by looking at the long-term storage attributes, Korea Composite Stock Price Index. The reason for the use of GPH statistic is to derive the modified statistic Korea's stock market, and to research a process of long-term memory. Research design, data, and methodology - Level shifts were subjected to be an empirical analysis by applying the GPH method. It has been modified by taking into account the daily log return of the Korea Composite Stock Price Index a. The Data, used for the stock market to analyze whether deciding the action by the long-term memory process, yield daily stock price index of the Korea Composite Stock Price Index and the rate of return a log. The studies were proceeded with long-term memory and long-term semiparametric method in deriving the long-term memory estimators. Chapter 2 examines the leading research, and Chapter 3 describes the long-term memory processes and estimation methods. GPH statistics induced modifications of statistics and discussed Whittle statistic. Chapter 4 used Korea Composite Stock Price Index to estimate the long-term memory process parameters. Chapter 6 presents the conclusions and implications. Results - If the price of the time series is generated by the abnormal process, it may be located in long-term memory by a time series. However, test results by price fixed GPH method is not followed by long-term memory process or fractional differential process. In the case of the time-series level shift, the present test method for a long-term memory processes has a considerable amount of bias, and there exists a structural change in the stock distribution market. This structural change has implications in level shift. Stratum level shift assays are not considered as shifted strata. They exist distinctly in the stock secondary market as bias, and are presented in the test statistic of non-long-term memory process. It also generates an error as a long-term memory that could lead to false results. Conclusions - Changes in long-term memory characteristics associated with level shift present the following two suggestions. One, if any impact outside is flowed for a long period of time, we can know that the long-term memory processes have characteristic of the average return gradually. When the investor makes an investment, the same reasoning applies to him in the light of the characteristics of the long-term memory. It is suggested that when investors make decisions on investment, it is necessary to consider the characters of the long-term storage in reference with causing investors to increase the uncertainty and potential. The other one is the thing which must be considered variously according to time-series. The research for price-earnings ratio and investment risk should be composed of the long-term memory characters, and it would have more predictability.

• 인터넷정보학회논문지
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• 제19권3호
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• pp.15-23
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• 2018

코드 재사용 공격은 프로그램 메모리상에 존재하는 실행 가능한 코드 조각을 조합하고, 이를 연속적으로 실행함으로써 스택에 직접 코드를 주입하지 않고도 임의의 코드를 실행시킬 수 있는 공격 기법이다. 코드 재사용 공격의 대표적인 종류로는 ROP(Return-Oriented Programming) 공격이 있으며, ROP 공격에 대응하기 위한 여러 방어기법들이 제시되어왔다. 그러나 기존의 방법들은 특정 규칙을 기반으로 공격을 탐지하는 Rule-base 방식을 사용하기 때문에 사전에 정의한 규칙에 해당되지 않는 ROP 공격은 탐지할 수 없다는 한계점이 존재한다. 본 논문에서는 RNN(Recurrent Neural Network)을 사용하여 ROP 공격 코드에 사용되는 명령어 패턴을 학습하고, 이를 통해 ROP 공격을 탐지하는 방법을 소개한다. 또한 정상 코드와 ROP 공격 코드 판별에 대한 False Positive Ratio, False Negative Ratio, Accuracy를 측정함으로써 제안한 방법이 효과적으로 ROP 공격을 탐지함을 보인다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2000년도 봄 학술발표논문집 Vol.27 No.1 (A)
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• pp.624-626
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• 2000

통신 오버헤드 및 거짓 공유(false sharing)등의 문제를 해결하기 위하여 소프트웨어 분산공유메모리 시스템을 위한 다양한 메모리 모델등이 제안되었다. HLRC(Home based Lazy Release)[1]는 Keleher에 의해 제안된 LRC[2] 모델에 home 개념을 도입한 모델로서 최근의 소프트웨어 분산공유 메모리 시스템에서 널리 채용되고 있다. 본 논문에서는 HLRC 모델을 기반으로 한 메모리 일관성 프로토콜의 설계, 구현, 그리고 성능 측정 결과에 관하여 기술한다.

• 전기학회논문지
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• 제58권1호
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• pp.210-215
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• 2009

In this study event-related optical signals were extracted from the prefrontal cortexes using functional near infrared spectroscopy while subjects were carrying out 2-back working memory tasks. Four events such as start, yes, no, and error were considered based on the onsets of the stimulus, positive true responses, positive false responses, and negative responses in the 2-back working memory task, respectively. The optical signals recorded were analyzed by peri-event histograms and power spectrum distributions. The results showed specific characteristics of the event-related optical neuronal signals and an opened possibility of an application to control a non-invasive brain-computer interface system or an object of a virtual reality.

• Structural Monitoring and Maintenance
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• 제5권1호
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• pp.51-65
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• 2018

A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.