• Earthquakes and Structures
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• 제3권1호
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• pp.17-35
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• 2012

Simplified equations for fundamental period of vibration of skeletal structures provided by most seismic design provisions suffer from the absence of any associated confidence levels and of any reference to their empirical basis. Therefore, such equations may typically give a sector of designers the false impression of yielding a fairly accurate value of the period of vibration. This paper, although not addressing simplified codes equations, introduces a set of mathematical equations utilizing the theory of error propagation and First-Order Second-Moment (FOSM) techniques to determine bounds on the relative error in theoretically calculated fundamental period of vibration of skeletal structures. In a complementary step, and for verification purposes, Monte Carlo simulation technique has been also applied. The latter, despite involving larger computational effort, is expected to provide more precise estimates than FOSM methods. Studies of parametric uncertainties applied to reinforced concrete frame bents - potentially idealized as SDOF systems - are conducted demonstrating the effect of randomness and uncertainty of various relevant properties, shaping both mass and stiffness, on the variance (i.e. relative error) in the estimated period of vibration. Correlation between mass and stiffness parameters - regarded as random variables - is also thoroughly discussed. According to achieved results, a relative error in the period of vibration in the order of 19% for new designs/constructions and of about 25% for existing structures for assessment purposes - and even climbing up to about 36% in some special applications and/or circumstances - is acknowledged when adopting estimates gathered from the literature for relative errors in the relevant random input variables.

• 제어로봇시스템학회논문지
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• 제18권4호
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• pp.384-390
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• 2012

In this paper, we analyzed the navigation error of TERCOM (TErrain COntour Matching), which is TRN (Terrain Referenced Navigation) batch processing, caused by scale factor error of radar altimeter and proved the possibility of false position fix when we use the TERCOM's feature matching algorithm. Based on these, we proposed the new TRN batch processing algorithm using the slope measurements of terrain. The proposed technique measures on periodic changes in the slope of the terrain elevation profile, and these measurements are used in the feature matching algorithm. By using the slope of terrain data, the impact of scale factor errors can be compensated. By simulation, we verified improved outcome using this approach compared to the result using the conventional method.

• IEIE Transactions on Smart Processing and Computing
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• 제6권4호
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• pp.237-245
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• 2017

This paper presents a fire detection algorithm using color and motion models from video sequences. The proposed method detects change in color and motion of overall regions for detecting fire, and thus, it can be implemented in both fixed and pan/tilt/zoom (PTZ) cameras. The proposed algorithm consists of three parts. The first part exploits color models of flames and smoke. The candidate regions in the video frames are extracted with the hue-saturation-value (HSV) color model. The second part models the motion information of flames and smoke. Optical flow in the fire candidate region is estimated, and the spatial-temporal distribution of optical flow vectors is analyzed. The final part accumulates the probability of fire in successive video frames, which reduces false-positive errors when fire-like color objects appear. Experimental results from 100 fire videos are shown, where various types of smoke and flames appear in indoor and outdoor environments. According to the experiments and the comparison, the proposed fire detection algorithm works well in various situations, and outperforms the conventional algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3095-3111
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• 2018

Spectrum sensing (SS) is one of the fundamental tasks for cognitive radio. In SS, decisions can be made via comparing the test statistics with a threshold. Conventional adaptive algorithms for SS usually adjust their thresholds according to the radio environment. This paper concentrates on the issue of adaptive SS whose threshold is adjusted based on the Markovian behavior of primary user (PU). Moreover, Bayesian cost is adopted as the performance metric to achieve a trade-off between false alarm and missed detection probabilities. Two novel adaptive algorithms, including Markov Bayesian energy detection (MBED) algorithm and IMBED (improved MBED) algorithm, are proposed. Both algorithms model the behavior of PU as a two-state Markov process, with which their thresholds are adaptively adjusted according to the detection results at previous slots. Compared with the existing Bayesian energy detection (BED) algorithm, MBED algorithm can achieve lower Bayesian cost, especially in high signal-to-noise ratio (SNR) regime. Furthermore, it has the advantage of low computational complexity. IMBED algorithm is proposed to alleviate the side effects of detection errors at previous slots. It can reduce Bayesian cost more significantly and in a wider SNR region. Simulation results are provided to illustrate the effectiveness and efficiencies of both algorithms.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권7호
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• pp.468-474
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• 2005

Brain-machine interface (BMI) based on neuronal spike trains is regarded as one of the most promising means to restore basic body functions of severely paralyzed patients. The spike train decoding algorithm, which extracts underlying information of neuronal signals, is essential for the BMI. Previous studies report that a linear filter is effective for this purpose and there is no noteworthy gain from the use of nonlinear mapping algorithms, in spite of the fact that neuronal encoding process is obviously nonlinear. We designed several decoding algorithms based on the linear filter, and two nonlinear mapping algorithms using multilayer perceptron (MLP) and support vector machine regression (SVR), and show that the nonlinear algorithms are superior in general. The MLP often showed unsatisfactory performance especially when it is carelessly trained. The nonlinear SVR showed the highest performance. This may be due to the superiority of the SVR in training and generalization. The advantage of using nonlinear algorithms were more profound for the cases when there are false-positive/negative errors in spike trains.

• 정보처리학회논문지B
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• 제12B권4호
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• pp.403-412
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• 2005

본 논문에서는 비디오 데이터로부터 장면 사이의 경계를 검출하고, 이들을 그 종류별로 분류하는 장면전환 검출 방법을 제안한다 제안한 장면전환 검출 방법은 급진적인 장면전환인 컷(cut)과 점진적인 장면전환인 페이드(fade) 및 디졸브(dissolve)를 검출한다. 본 논문에서는 영상 내에 포함된 카메라의 동작 정보를 이용하여 영상을 보정하고, 보정된 영상으로부터 특징을 추출하여 장면전환을 검출한다. 따라서 카메라의 동작으로 인해 기인하는 여러 가지 오 검출을 방지한다. 또한, 영상을 보정하는 과정에서 지역적인 이동 물체의 동작을 제거하므로 이동 물체의 동작으로 인해 기인하는 장면전환의 오 검출도 방지한다. 실험에서는 다양한 비디오 데이터를 입력 받아 기존의 장면전환 검출 방법들과 제안한 방법의 성능을 비교 분석함으로써 제안한 방법의 우수함을 보인다.

• Nuclear Engineering and Technology
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• 제53권3호
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• pp.860-865
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• 2021

A data-based model, such as an AAKR model is widely used for monitoring the drifts of sensors in nuclear power plants. However, since a training dataset and a test dataset for a data-based model cannot be constructed with the data from all the possible states, the model uncertainty cannot be good enough to represent the uncertainty of estimations. In fact, the errors of estimation grow much bigger if the incoming data come from inexperienced states. To overcome this limitation of the model uncertainty, a new measure of uncertainty for a data-based model is developed and the predicted uncertainty is introduced. The predicted uncertainty is defined in every estimation according to the incoming data. In this paper, the AAKR model is used as a data-based model. The predicted uncertainty is similar in magnitude to the model uncertainty when the estimation is made for the incoming data from the experienced states but it goes bigger otherwise. The characteristics of the predicted model uncertainty are studied and the usefulness is demonstrated with the pressure signals measured in the flow-loop system. It is expected that the predicted uncertainty can quite reduce the false alarm by using the variable threshold instead of the fixed threshold.

• Genomics & Informatics
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• 제20권1호
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• pp.9.1-9.6
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• 2022

Mendelian randomization (MR) uses genetic variation as a natural experiment to investigate the causal effects of modifiable risk factors (exposures) on outcomes. Two-sample Mendelian randomization (2SMR) is widely used to measure causal effects between exposures and outcomes via genome-wide association studies. 2SMR can increase statistical power by utilizing summary statistics from large consortia such as the UK Biobank. However, the first-order term approximation of standard error is commonly used when applying 2SMR. This approximation can underestimate the variance of causal effects in MR, which can lead to an increased false-positive rate. An alternative is to use the second-order approximation of the standard error, which can considerably correct for the deviation of the first-order approximation. In this study, we simulated MR to show the degree to which the first-order approximation underestimates the variance. We show that depending on the specific situation, the first-order approximation can underestimate the variance almost by half when compared to the true variance, whereas the second-order approximation is robust and accurate.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.348-358
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• 2022

Software fault prediction is a method to compute fault in the software sections using software properties which helps to evaluate the quality of software in terms of cost and effort. Recently, several software fault detection techniques have been proposed to classifying faulty or non-faulty. However, for such a person, and most studies have shown the power of predictive errors in their own databases, the performance of the software is not consistent. In this paper, we propose a hybrid soft computing technique for SFP based on optimal feature extraction and classification (HST-SFP). First, we introduce the bat induced butterfly optimization (BBO) algorithm for optimal feature selection among multiple features which compute the most optimal features and remove unnecessary features. Second, we develop a layered recurrent neural network (L-RNN) based classifier for predict the software faults based on their features which enhance the detection accuracy. Finally, the proposed HST-SFP technique has the more effectiveness in some sophisticated technical terms that outperform databases of probability of detection, accuracy, probability of false alarms, precision, ROC, F measure and AUC.

• Journal of Chest Surgery
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• 제32권8호
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• pp.693-701
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• 1999

최근 혈관 질환의 진단을 위한 비침습적 영상이 발달하면서, 기존의 도자술은 중재적 치료에 국한되는 실 정이다. 그러나 관상동맥이나 우회로는 작은 직경, 심박동 움직임 등으로 도자술이 아직도 진단에 필수적이 며, 비침 응\ulcorner영상 진단의 마지막 도전 영역이다. 전자선 단층 촬영기는 높은 시간 해상능으로 심장 영상을 얻을 수 있다. 전자선 단층 촬영을 이용하여 모관상 동맥 협착이나 관상동맥 우회로 이식술 후 개존성의 평 가가 시도되고 있으며, 이중 관상동맥 우회로술 평가의 정확도는 매우 높아서 임상 적용이 가능하다. 저자와 다른 연구자의 경험에 의하면 복재 정맥은 넓은 직경, 비교적 짧고 직선적인 경로, 심박동에 덜 영 향 받음으로써 EBT조영술의 정확도가 높았다. 전향적 민감도와 특이도가 각각 92%, 97%를 보였다. 그러나 위양성과 위음성을 보인 두 예는 후향적으로 분석 할 때 경험 부족에 의한 초기의 판독 오류로 사료되어 복 재 정맥의 경우 후향적으로는 100%의 정확도를 보였다. 반면 내유동맥 이식혈관은 작은 내경과 주변의 수술 클립에 의한 인공산물로 개존성을 확인하기가 대체로 어려웠고, 역동적 검사를 병행하여 우회로내 혈류를 확인하는 것이 필요하다. 내유동맥의 경우 상대적으로 정확도가 낮아 민감도, 특이도가 각각 100%, 80%를 보였으며, 위양성을 보인 2예는 후향적으로도 개존을 확인할 수 없었다. 전자선 단층 촬영 혈관 조영술은 관상 동맥 우 막\ulcorner이식술 후의 우회 혈관 개존성의 평가, 특히 복재 정맥 우회로의 경우 매우 정확도가 높은 비침습적 검사로써, 임상 적용이 기대된다. 앞으로 촬영 기기와 영상 재구성 software의 발달로 정확도를 더욱 높일 수 있는 잠재성이 있다.