• Smart Structures and Systems
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• v.17 no.4
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• pp.647-667
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• 2016

In this paper, dynamic displacement is estimated with high accuracy by blending high-sampling rate acceleration data with low-sampling rate displacement measurement using a two-stage Kalman estimator. In Stage 1, the two-stage Kalman estimator first approximates dynamic displacement. Then, the estimator in Stage 2 estimates a bias with high accuracy and refines the displacement estimate from Stage 1. In the previous Kalman filter based displacement techniques, the estimation accuracy can deteriorate due to (1) the discontinuities produced when the estimate is adjusted by displacement measurement and (2) slow convergence at the beginning of estimation. To resolve these drawbacks, the previous techniques adopt smoothing techniques, which involve additional future measurements in the estimation. However, the smoothing techniques require more computational time and resources and hamper real-time estimation. The proposed technique addresses the drawbacks of the previous techniques without smoothing. The performance of the proposed technique is verified under various dynamic loading, sampling rate and noise level conditions via a series of numerical simulations and experiments. Its performance is also compared with those of the existing Kalman filter based techniques.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.69-78
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• 2021

Venturi meter is frequently used in feed water flow control system in a nuclear power plant. Its accurate measurement plays a vital role in the safe operation of the plant. This paper firstly investigates the influence of the length of each section of pipeline, the throat inner diameter of Venturi and the flow characteristics in a single-phase flow on the accuracy of Venturi measurement by numerical calculation. Then the flow and the accuracy are discussed in a multi-phase flow. Numerical results show that the geometrical parameters and the characteristics of complex turbulent flow in the single-phase flow have little impact on the accuracy of Venturi flow rate measurement. In the multi-phase flow, the calculated flow rate of Venturi deviated from the actual flow rate and this deviation value is closely related to the amount of steam in the pipeline and increases sharply with the increase of the amount of steam. The over-reading of Venturi flow rate is present.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.41-48
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• 2019

In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.40-44
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• 2023

The defect detection based on deep learning requires minimal loss and high accuracy to pinpoint product defects. In this paper, we confirm the loss rate of deep learning training based on disc-shaped artificial scaffold images. It is intended to compare the performance of Cross-Entropy functions used in object detection algorithms. The model was constructed using normal, defective artificial scaffold images and category cross entropy and sparse category cross entropy. The data was repeatedly learned five times using each loss function. The average loss rate, average accuracy, final loss rate, and final accuracy according to the loss function were confirmed.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.209-210
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• 2023

In this study, the accuracy of the assembly rate of fine aggregate and the cleavage rate of coarse aggregate was analyzed using the constructed learning data. As a result, it was possible to predict the distribution of assembly rate for fine aggregate through a simple sample collection image, showing an accuracy of 96%. The classification of the aggregates could be confirmed by analyzing the fracture shape of the gravel, showing an accuracy of 97%.

• Journal of radiological science and technology
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• v.44 no.6
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• pp.635-643
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• 2021

The results of empirical researches on the diagnosis of lung cancer are insufficient, so it is limited to objectively judge the clinical possibility and utilization according to the accuracy of diagnosis. Thus, this study retrospectively analyzed the lung cancer diagnostic performance of PET-MRI (Positron Emission Tomography-Magnetic Resonance Imaging) by using the decision matrix. This study selected and experimented total 165 patients who received both hematological CEA (Carcinoembryonic Antigen) test and hybrid PET-MRI (¹⁸F-FDG, 5.18 MBq/kg / Body TIM coil. VIVE-Dixon). After setting up the result of CEA (positive:>4 ㎍/ℓ. negative:<2.5㎍/ℓ) as golden data, the lung cancer was found in the image of PET-MRI, and then the SUV_max (positive:>4, negative:<1.5) was measured, and then evaluated the correlation and significance of results of relative diagnostic performance of PET-MRI compared to CEA through the statistical verification (t-test, P>0.05). Through this, the PET-MRI was analyzed as 96.29% of sensitivity, 95.23% of specificity, 3.70% of false negative rate, 4.76% of false positive rate, and 95.75% of accuracy. The false negative rate was 1.06% lower than the false positive rate. The PET-MRI that significant accuracy of diagnosis through high sensitivity and specificity, and low false negative rate and false positive rate of lung cancer, could acquire the fusion image of specialized soft tissue by combining the radio-pharmaceuticals with various sequences, so its clinical value and usefulness are regarded as latently sufficient.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.195-198
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• 2003

The accuracy of automatic speech recognition system depends on the presence of background noise and speaker variability such as sex, intonation of speech, and speaking rate. Specially, the speaking rate of both inter-speaker and intra-speaker is a serious cause of mis-recognition. In this paper, we propose the compensation method of the speaking rate by the ratio of each vowel's length in a phrase. First the number of feature vectors in a phrase is estimated by the information of speaking rate. Second, the estimated number of feature vectors is assigned to each syllable of the phrase according to the ratio of its vowel length. Finally, the process of feature vector extraction is operated by the number that assigned to each syllable in the phrase. As a result the accuracy of automatic speech recognition was improved using the proposed compensation method of the speaking rate.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.5
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• pp.646-652
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• 2013

PRA(Position Reporting Accuracy) for EO/IR(Electro-Optic/Infrared) airborne camera is an important factor in geo-pointing accuracy. Generally, rate table is used to measure PRA of gimbal actuated camera like EO/IR. However, it is not always possible to fix an EUT(Equipment for Under Test) to rate table due to capacity limit of the table on the size and weight of the object(EUT). Our EO/IR is too big and heavy to emplace on it. Therefore, we propose a new verification method of PRA for airborne camera and assess the validity of our proposition. In this method we use collimator, angle measuring instrument, 6 dof motion simulator, optical surface plate, leveling laser, inclinometer and poster(for alignment).

• International Journal of Highway Engineering
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• v.14 no.5
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• pp.179-188
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• 2012

PURPOSES: The aim of this study is to analyze overloading control effectiveness of enforcing overweighted vehicles using HS-WIM (High-Speed Weigh-in-Motion) at main lane of expressway. METHODS: To analyze the weight distribution statistically, HS-WIM system should has an appropriate weighing accuracy. Thus, the weighing accuracy of the two HS-WIM systems was estimated by applying European specifications and ASTM (American Standards for Testing and Materials) for WIM in this study. Based on the results of accuracy test, overweight enforcement system has been operated at main lanes of two expressway routes in order to provide weight informations of overweighted vehicle in real time for enforcement squad. To evaluate the overloading control effectiveness with enforcement, traffic volume and axle loads of trucks for two months at the right after beginning of the enforcement were compared with data set for same periods before the enforcement. RESULTS: As the results of weighing accuracy test, both WIM systems were accepted to the most precise type that can be useful to applicate not only statistical purpose but enforcing on overweight vehicles directly. After the enforcement, the rate of overweighted trucks that weighed over enforcement limits had been decreased by 27% compared with the rate before the enforcement. Especially, the rate of overweighted trucks that weighed over 48 tons had been decreased by 91%. On the other hand, in counterpoint to decrease of the overweighted vehicle, the rate of trucks that weighed under enforcement limits had been increased by 7%. CONCLUSIONS: From the results, it is quite clear that overloading has been controlled since the beginning of the enforcement.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.123-128
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• 2023

This paper presents the results of an analysis of the discrimination accuracy of a bridge load carrying capacity estimation model based on data from inspection reports. The load carrying rate estimation model was derived using statistical methods through the collection of 2,161 inspection reports. By entering the bridge specifications and maintenance information, you can check the estimated load carrying capacity of the bridge. In order to verify the discrimination accuracy of the estimated load carrying rate model, the estimated load carrying rate was compared with the load carrying rate in the inspection and diagnosis report for 164 public bridges for which data was available. Although there are differences depending on the bridge type, the results were obtained with an accuracy of over 80% in determining the estimated load carrying capacity.