• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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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.

• Journal of Institute of Convergence Technology
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• v.9 no.1
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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.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.4 s.22
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• pp.51-60
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• 2002

As the use of GIS becomes more widespread, the quality and source of data is becoming more of a concern among users. But accuracy is a component of quality, and the positional accuracy is a component of total accuracy. If only we consider the positional accuracy, simultaneously collecting technology of location and attribute information, whether it be manually, using conventional surveying method, GPS, or remote sensing, is a practical way of insuring that location and attribute information are correctly correlated. This study analyse the positional accuracy from a view-point of user and supplier, which is the considerations that can ensure quality level and continuously maintain the road and underground utility information. The positional accuracy of road and underground utility information are considered as two categories - expected accuracy of data collection procedure, required accuracy of data usage process. And the project manager must consider the cost/benefit view of data generation in order to determine the surveying method.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.6
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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.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.4 s.38
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• pp.19-26
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• 2006

This study assessed the accuracies of the ground information extracting methods from the LiDAR data. Especially, it compared two kinds of method, one of them is using directly the raw LiDAR data which is point type vector data and the other is using changed data to DSM type as the normal grid type. The methods using Local Maxima and Entropy methods are applied as a former case, and for the other case, this study applies the method using edge detection with filtering and the generated reference surface by the mean filtering. Then, the accuracy assessment are performed with these results, DEM constructed manually and the error permitted limit in scale of digital map. As a results, each DEM mean errors of methods using edge detection with filtering, reference surface, Local Maxima and Entropy are 0.27m, 2.43m, 0.13m and 0.10m respectively. Hence, the method using entropy presented the highest accuracy. And an accuracy from a method directly using the raw LiDAR data has higher accuracy than the method using changed data to DSM type relatively.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.109-118
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• 2011

In this research estimated accuracies that were results in all the area of filtering of the plane equation that was used by whole data set, and regional of filtering that was driven by the plane equation for each vertual Grid. All of this estimates were based by all the area of filtering that deduced the plane equation by multiple linear regression analysis that was used by ground data set. Therefore, accuracy of all the area of filtering that used whole data set has been dropped about 2~3% when average of accuracy of all the area of filtering was based on ground data set while accuracy of Regional of filtering dropped 2~4% when based on virtual Grid. Moreover, as virtual Grid which was set 3~4 cm was difference about 2% of accuracy from standard data. Thus, it leads conclusion of set 3~4 times bigger size in virtual Grid filtering over LiDAR scan gap will be more appropriated. Hence, the result of this research allow us to conclude that there was difference in average accuracy has been noticed when we applied each different approaches, I strongly suggest that it need to research more about real topography for further filtering accuracy.

• The Pure and Applied Mathematics
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• v.19 no.1
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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.

• Speech Sciences
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• v.12 no.4
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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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• v.21 no.11
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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.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_3
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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.