• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권9호
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• pp.3243-3257
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• 2021

Deep neural networks provide excellent performance in pattern recognition, audio classification, and image recognition. It is important that they accurately recognize input data, particularly when they are used in autonomous vehicles or for medical services. In this study, we propose a data correction method for increasing the accuracy of an unknown classifier by modifying the input data without changing the classifier. This method modifies the input data slightly so that the unknown classifier will correctly recognize the input data. It is an ensemble method that has the characteristic of transferability to an unknown classifier by generating corrected data that are correctly recognized by several classifiers that are known in advance. We tested our method using MNIST and CIFAR-10 as experimental data. The experimental results exhibit that the accuracy of the unknown classifier is a 100% correct recognition rate owing to the data correction generated by the proposed method, which minimizes data distortion to maintain the data's recognizability by humans.

• 융복합기술연구소 논문집
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• 제9권1호
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• pp.31-36
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• 2019

After 30 years of KKP model analysis and extended 30 years of accuracy analysis, the unique correlation and various problems between measured horizontal surface insolation and measured weather data are found in this paper. The KKP model's 10yrs daily total horizontal surface insolation forecasting was averaged about 97.7% on average, and the forecasting accuracy at peak times per day was about 92.1%, which is highly applicable regardless of location and weather conditions nationwide. The daily total solar radiation forecasting accuracy of the modified KKP cloud model was 98.9%, similar to the KKP model, and 93.0% of the forecasting accuracy at the peak time per day. And the results of evaluating the accuracy of calculation for 30 years of KKP model were cloud model 107.6% and cloud model 95.1%. During the accuracy analysis evaluation, this study found that inaccuracies in measurement data of cloud cover should be clearly assessed by the Meteorological Administration.

• 대한공간정보학회지
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• 제10권4호
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• pp.51-60
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• 2002

GIS의 사용이 확대되면서 사용자들은 데이터의 품질과 원자료에 대한 관심이 매우 증대되었다. 정확도는 전체 품질 중의 한 요소이며 또한 위치정확도는 정확도의 일부분이다. 정확도 측면에서 본다면 수작업 종래의 측량기술, GPS 또는 리모트센싱 등 무엇이든 간에 위치와 속성 정보에 대한 동시 수집방법은 위치와 속성이 정확하게 서로 관련되어 있는 것을 확인하는 가장 실질적인 방법이다. 본 연구는 도로기반시설물의 품질을 확보하고 지속적으로 유지관리하기 위해 고려할 사항으로서 위치정확도를 사용자측면과 공급자측면에서 분석하였다. 도로기반시설물의 위치정확도는 도로기반시설물 데이터베이스를 구축하는 과정에서 발생되는 기대 정확도, 실제 업무에 활용하는 과정에서 발생되는 사용자 요구정확도로 크게 나누어 분석할 수 있으며, 계획기관에서 데이터베이스 구축방법을 결정할 때에는 비용대비 기대효과 측면에서의 정확도를 고려하여야 한다.

• 한국군사과학기술학회지
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• 제11권6호
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• pp.63-73
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• 2008

The helicopter position, heading data and the direction finding data of ESM are essentially required to compensate the parallax and analyze the direction finding accuracy of heliborne ESM in flight test phase. In the case of the long test range compared with small platform like as LYNX helicopter and Jisim Island test site, the parallax compensation for direction finding accuracy calculation and GPS position error can be neglected. In this paper, the direction finding accuracy on the basis of helicopter propeller was calculated by coordinate changing between helicopter and transmitting antenna from WGS84 coordinate to navigation coordinate using helicopter position and direction finding data.

• 대한공간정보학회지
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• 제14권4호통권38호
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• pp.19-26
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• 2006

본 연구에서는 LiDAR 자료로부터의 지면정보 추출기법들에 대한 정확도를 평가하였다. 특히, 포인트 형태의 벡터자료인 LiDAR 원시자료를 직접 활용하는 기법과 정규격자형식의 DSM 형식으로 변형하여 활용하는 기법의 정확도를 비교하였다. 정규격자형식의 자료를 이용하는 방법으로는 경계추출 및 필터링 기법을 이용하는 방법, 평균필터링에 의하여 생성된 추세면을 이용하는 방법을 적용하였으며, 벡터구조의 원시LiDAR 자료를 직접 활용하는 기법으로써 Local Maxima 및 엔트로피를 이용하는 방법을 적용하였다. 또한, 수작업을 통하여 제작된 DEM 및 수치지도의 축척별 오차허용범위를 이용하여 정확도 평가를 수행하였으며, 경계검출 및 필터링, 추세면, Local Maxima, 엔트로피를 이용한 각 기법의 DEM의 평균 오차는 0.27m, 2.43m, 0.13m, 0.10m로써 엔트로피를 이용한 방법이 가장 높은 정확도를 나타내었다. 또한, 벡터형식의 LiDAR원시자료를 직접 이용하는 방법이 격자형식으로 변환하는 방법에 비하여 상대적으로 높은 정확도를 나타내었다.

• 대한공간정보학회지
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• 제19권4호
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• pp.109-118
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• 2011

본 연구는 지면 데이터�V을 이용하여 다중선형 회귀분석에 의한 평면방정식을 도출하여 전역필터링 한 것을 기준으로 전체 데이터�V을 이용하여 도출된 평면방정식으로 전역필터링 한 것과 가상격자별로 평면방정식을 도출하여 지역필터링을 수행한 결과를 분석하여 정확도를 평가하였다. 그 결과 지면 데이터�V을 이용한 전역필터링의 평균정확도를 기준으로 전체 데이터�V을 이용한 전역필터링의 정확도는 약 2~3%정도 떨어지고, 가상격자를 이용한 지역필터링의 정확도는 약 2~4% 떨어지는 것으로 나타났다. 특히 가상격자가 3~4cm일 때 기준자료와 약 2%의 정확도의 차이가 나타낸 것으로 보아 가상격자 사이즈를 라이다 스캔간격의 3~4배 크기로 지정하여 필터링 하는 것이 바람직 할 것으로 판단된다. 따라서 필터링의 적용방법에 따라 평균정확도가 차이가 발생하였으며, 향후 보다 다양한 실제지형을 선정하여 필터링의 정확도에 대한 연구가 필요할 것으로 생각된다.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제19권1호
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• pp.7-21
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• 2012

The fuzzy time series is introduced by Song and Chissom([8]) to construct a pattern for time series with vague or linguistic value. Many methods using the interval and fuzzy logical relationship related with historical data have been suggested to enhance the forecasting accuracy. But they do not fully reflect the fluctuation of historical data. Therefore, we propose the interval rearranged method to reflect the fluctuation of historical data and to improve the forecasting accuracy of fuzzy time series. Using the well-known enrollment, the proposed method is discussed and the forecasting accuracy is evaluated. Empirical studies show that the proposed method in forecasting accuracy is superior to existing methods and it fully reflects the fluctuation of historical data.

• 음성과학
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• 제12권4호
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• pp.31-42
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• 2005

Recently, Gaussian Mixture Model (GMM), a special form of CHMM, has been applied to speaker identification and it has proved that performance of GMM is better than CHMM. Therefore, in this paper the speaker models based on GMM and a new GMM using the modified EM algorithm are introduced and evaluated for text-independent speaker identification. Various experiments were performed to evaluate identification performance of two algorithms. As a result of the experiments, the GMM speaker model attained 94.6% identification accuracy using 40 seconds of training data and 32 mixtures and 97.8% accuracy using 80 seconds of training data and 64 mixtures. On the other hand, the new GMM speaker model achieved 95.0% identification accuracy using 40 seconds of training data and 32 mixtures and 98.2% accuracy using 80 seconds of training data and 64 mixtures. It shows that the new GMM speaker identification performance is better than the GMM speaker identification performance.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.23-30
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• 2021

Sentiment Analysis has become very important field of research because posting of reviews is becoming a trend. Supervised, unsupervised and semi supervised machine learning methods done lot of work to mine this data. Feature engineering is complex and technical part of machine learning. Deep learning is a new trend, where this laborious work can be done automatically. Many researchers have done many works on Deep learning Convolutional Neural Network (CNN) and Long Shor Term Memory (LSTM) Neural Network. These requires high processing speed and memory. Here author suggested two models simple & bidirectional deep leaning, which can work on text data with normal processing speed. At end both models are compared and found bidirectional model is best, because simple model achieve 50% accuracy and bidirectional deep learning model achieve 99% accuracy on trained data while 78% accuracy on test data. But this is based on 10-epochs and 40-batch size. This accuracy can also be increased by making different attempts on epochs and batch size.

• 한국산업융합학회 논문집
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• 제26권6_3호
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• pp.1233-1241
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• 2023

This paper investigates the efficient application of drone LiDAR technology for acquiring precise point cloud data in construction and civil engineering. A structured workflow encompassing data acquisition, processing, and accuracy verification is introduced. Practical testing on a construction site affirms that drone LiDAR surveying yields accurate and reliable data across various applications. With a focus on accuracy and verification, the results contribute to the progression of surveying methodologies in construction and civil engineering. The findings provide valuable insights into the dynamic technological landscape of these fields, establishing a foundation for more effective and precise surveying techniques. This study underscores the transformative potential of drone LiDAR technology in shaping the future of construction and civil engineering survey practices.