• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.682-684
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• 2002

최근 여러 논문에서 실 데이터 종속을 제거하기 위하여 결과 값 예상 기법을 제안하였다. 결과 값 예상 기법 중 혼합형 결과 값 예측기는 다양한 패턴을 갖는 명령어를 모두 예측함으로써 높은 예상 정확도를 얻을 수 있지만 하나의 명령어가 여러 개의 예측기 테이블에 중복 저장되어 높은 하드웨어 비용을 요구한다는 단점이 있다. 본 논문에서는 이러한 단점을 극복하기 위하여 프로파일링으로 얻어진 정적 분류 정보를 사용하여, 명령어률 예상 정확도가 높은 예측기에만 할당하여 예상 테이블 크기를 감소 시켰다. 또한 동적으로 적절한 예측기를 선택하도록 함으로써 예상 정확도를 더욱 향상 시켰다. 본 논문에서는 SPECint95 벤치마크 프로그램에 대해 SimpleScalar/PISA 3.0 툴셋을 사용하여 실험하였다. 정적-동적 분류 정보를 모두 사용하였을 경우 87.9%, VHT 크기를 4K로 축소한 경우 87.5%로 비슷한 예상정확도를 얻으면서 예상 테이블의 크기는 50%로 감소하였다. 또한 실행 패턴의 유형 비율에 따라 각 예측기의 VHT를 구성한 경우 예상 테이블 크기를 25%로 줄일 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10c
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• pp.865-867
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• 2001

데이터 종속성을 제거하기 위해서 명령어의 결과값을 예상하는 여러 결과값 예측기의 장점을 이용하여 놓은 성능을 얻을 수 있는 새로운 혼합형 예측 메커니즘을 제안한다. 제안된 혼합형 결과값 예측기는 예상 테이블을 모험적으로 갱신할 수 있기 때문에 부적절한(Stale) 데이터로 인해 잘못 예상되는 명령어의 수를 효과적으로 감소시킨다. 또한 정적 분류 정보를 사용하여 명령의 반입시 적절한 예측기에 할당함으로써 예상 정확도를 더욱 향상시키며, 하드웨어 비용을 효율적으로 감소시키도록 하였다. 5개의 SPECint 95 벤치마크 프로그램에 대해 SimpleScalar/PISA 3.0 툴셋을 사용하여 실험하였다. 16-이슈 폭에서 모험적 갱신을 사용한 평균 예상 정확도는 73%의 실험 결과가 나왔으며, 정적 분류 정보를 사용하였을 경우 예상 정확도가 88%로 증가된 결과를 얻었다.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.1
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• pp.107-115
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• 2002

We propose a new hybrid value predictor which achieves high performance by combining several predictors. Because the proposed hybrid value predictor can update the prediction table speculatively, it efficiently reduces the number of mispredicted instructions due to stale data. Also, the proposed predictor can enhance the prediction accuracy and efficiently decrease the hardware cost of predictor, because it allocates instructions into the best-suited predictor during instruction fetch stage by using the information of static classification which is obtained from the profile-based compiler implementation. For the 16-issue superscalar processors, simulation results based on the SimpleScalar/PISA tool set show that we achieve the average prediction rates of 73% by using speculative update and the average prediction rates of 88% by adding static classification for the SPECint95 benchmark programs.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.11
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• pp.592-600
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• 2001

To improve the performance of wide-issue Superscalar microprocessors, it is essential to increase the width of instruction fetch and issue rate. Data dependences are major hurdle to exploit ILP(Instruction-Level Parallelism) efficiently, so several related works have suggested that the limits imposed by data dependences can be overcome to some extent with the use of the data value prediction. But the suggested mechanisms may access the same value prediction table entry again before they have been updated with a real data value. They will cause incorrect value prediction by using stable data and incur misprediction penalty and lowering performance. In this paper, we propose a new hybrid value predictor which achieve high performance by reducing stale data. Because the proposed hybrid value predictor can update the prediction table speculatively, it efficiently reduces the number of mispredicted instruction due to stable due to stale data. For SPECint95 benchmark programs on the 16-issue superscalar processors, simulation results show that the average prediction accuracy increase from 59% for non-speculative update to 72% for speculative update.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.521-528
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• 2007

The overall objective of this research was to investigate various combination of segmentation parameters and to improve classification accuracy of object-oriented classification. This research presents a method for evaluation of segmentation parameters by calculating Moran's I and Intrasegment Variance. This research used Landsat-7/ETM image of $11{\times}14$ Km developed area in Ansung, Korea. Segmented images are generated by 75 combinations of parameter. Selecting 7 combinations of high, middle and low grade expected classification accuracy was based on calculated Moran's I and Intrasegment Variance. Selected segmentation images are classified 4 classes and analyzed classification accuracy according to method of objected-oriented classification. The research result proved that classification accuracy is related to segmentation parameters. The case of high grade of expected classification accuracy showed more than 85% overall accuracy. On the other hand, low ado showed around 50% overall accuracy.

• Journal of Internet Computing and Services
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• v.2 no.1
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• pp.23-30
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• 2001

ILP processors require an accurate branch prediction scheme to achieve higher performance. Two-Level branch predictor has been known to achieve high prediction accuracy. But, when a branch accesses a PHT entry that was, previously updated by other branch, Two-level predictor may cause interferences. Negative interferences among all interferences have a negative effect on performance, since they can cause branch mispredictions. Agree predictor achieve high prediction accuracy by converting negative interferences to positive interferences by adding bias bits to BTB, but negative interferences may occur when bias bit is set incorrectly. This paper presents a new dynamic branch predictor which reduces negative interferences. In the proposed predictor, we attach hit bits to entries in BTB to change bias bit dynamically during the execution time, h a result the proposed scheme improve the accuracy of prediction by reducing negative Interferences effectively, To illustrate the effect of the proposed scheme, we evaluate the performance of this scheme using SPEC92int benchmarks, The results show that the proposed scheme can outperform traditional branch predictors.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2018.10a
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• pp.59-59
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• 2018

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.10
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• pp.569-578
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• 2003

Data dependencies are one of major hurdles to limit ILP(Instruction Level Parallelism), so several related works have suggested that the limit imposed by data dependencies can be overcome to some extent with use of the data value prediction. Hybrid value predictor can obtain the high prediction accuracy using advantages of various predictors, but it has a defect that same instruction has overlapping entries in all predictor. In this paper, we propose a new hybrid value predictor which achieves high performance by using the information of static and dynamic classification. The proposed predictor can enhance the prediction accuracy and efficiently decrease the prediction table size of predictor, because it allocates each instruction into single best-suited predictor during the fetch stage by using the information of static classification. Also, it can enhance the prediction accuracy because it selects a best- suited prediction method for the “Unknown”pattern instructions by using the dynamic classification mechanism. Simulation results based on the SimpleScalar/PISA tool set and the SPECint95 benchmarks show the average correct prediction rate of 85.1% by using the static classification mechanism. Also, we achieve the average correction prediction rate of 87.6% by using static and dynamic classification mechanism.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.335-342
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• 2015

In this study, data linking module called GeoSWMM was developed using a typical secondary flooding model SWMM in order to improve the accuracy of the input data of SWMM and to map hourly inundation estimation areas that were not represented in the conventional inundation map. GeoSWMM is a data linking module of GIS and SWMM, which can generate a SWMM project file directly from sewer network GIS data. Utilizing the GeoSWMM the project file of SWMM model was constructed in the study area, Seocho 2-dong, Seoul. The actual flooding has occurred September 21, 2010 and the actual rainfall data were used for flood simulation. As a result, the outflow started from 2 PM due to the lack of water flow capacity of the sewage system. Based on the results, hourly inundation estimation maps were produced and compared with flood train map in 2010. The comparison showed about 66% matching in the overlap of inundation areas. By utilizing GeoSWMM that was developed in this study, it is easy to build the sewer network data for SWMM. In addition, the creation of hourly inundation estimation map using SWMM will be much help to flood disaster prevention plan.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.1062-1064
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• 2022

해외로 이동하고 있는 컨테이너 화물의 실시간 위치를 확인하고 기상정보 및 국제 이슈 등을 고려하여 도착 예상 시간을 계산해 화물의 도착 예정시간을 실시간으로 확인할 수 있는 서비스이다. 그동안 선박 추적 시스템은 해외 서비스에 의존해왔으며, 선사에서 자체적으로 제공하는 정보는 정확도가 40%에 미치는 한계가 존재했다. 이러한 문제점을 보완하여, 해당 서비스를 통해 빅데이터 기반의 분석과 향후 프로젝트 운영을 통해 축적될 시스템 상의 데이터와 현장의 데이터를 취합하여 높은 정확도를 이룰 수 있을 것으로 예상한다. 이를 통해 수출 기업들은 안전재고를 감축할 수 있게 되어 보관 관련 물류비용을 절감할 수 있게 될 것이다. 또한 보다 정확한 제조 일정을 수립할 수 있게 되어 과잉 생산을 방지할 수 있음을 기대해볼 수 있다.