• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1695-1700
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• 2005

In this paper, we propose an optimal thresholding method for the voxel coloring in the reconstruction of a 3D shape. Our purposed method is a new approach to resolve the trade-off error of the threshold value on determining the photo-consistency in the conventional method. Optimal thresholding value is decided to compare the surface voxel of photo-consistency with inside voxel on the optic ray of the center camera. As iterating the process of the voxels, the threshold value is approached to the optimal value for the individual surface voxel. And also, graph cut method is reduced to the surface noise on eliminating neighboring voxel. To verify the proposed algorithm, we simulated in the virtual and real environment. It is advantaged to speed up and accuracy of a 3D face reconstruction by applying the methods of optimal threshold and graph cut as compare with conventional algorithms.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.8
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• pp.761-767
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• 2011

In this paper, to improve performance of spectrum sensing, we propose the method which can find optimal threshold based on power of PU(Primary User) signal. To find optimal threshold value, we will use mathematical method, and find threshold which can has lowest error probability. Each SU(Secondary User) use this threshold and All Su makes local decision. All Su Send local decision to FC(Fusion Center). In this paper we consider K-out-of-n rule to combining local decision. To make global decision value, FC find optimal n. In the FC. FC received local decision which has lowest error probability and using optimal n and these vaule. FC make global decision value. In this paper, to analysis performance proposed scheme, we simulate proposed scheme using matlab and compare with traditional OR Rule. As a result of simulation, we can know that preposed scheme can get a better performance than traditional OR rule.

• Korean Journal of Radiology
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• v.21 no.6
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• pp.756-763
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• 2020

Objective: To evaluate the effects of attenuation threshold on CT pulmonary vascular volume ratios in children and young adults with congenital heart disease, and to suggest an optimal attenuation threshold. Materials and Methods: CT percentages of right pulmonary vascular volume were compared and correlated with percentages calculated from nuclear medicine right lung perfusion in 52 patients with congenital heart disease. The selected patients had undergone electrocardiography-synchronized cardiothoracic CT and lung perfusion scintigraphy within a 1-year interval, but not interim surgical or transcatheter intervention. The percentages of CT right pulmonary vascular volumes were calculated with fixed (80-600 Hounsfield units [HU]) and adaptive thresholds (average pulmonary artery enhancement [PA_avg] divided by 2.50, 2.00, 1.75, 1.63, 1.50, and 1.25). The optimal threshold exhibited the smallest mean difference, the lowest p-value in statistically significant paired comparisons, and the highest Pearson correlation coefficient. Results: The PA_avg value was 529.5 ± 164.8 HU (range, 250.1-956.6 HU). Results showed that fixed thresholds in the range of 320-400 HU, and adaptive thresholds of PA_avg/1.75-1.50 were optimal for quantifying CT pulmonary vascular volume ratios. The optimal thresholds demonstrated a small mean difference of ≤ 5%, no significant difference (> 0.2 for fixed thresholds, and > 0.5 for adaptive thresholds), and a high correlation coefficient (0.93 for fixed thresholds, and 0.91 for adaptive thresholds). Conclusion: The optimal fixed and adaptive thresholds for quantifying CT pulmonary vascular volume ratios appeared equally useful. However, when considering a wide range of PAavg, application of optimal adaptive thresholds may be more suitable than fixed thresholds in actual clinical practice.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.100-104
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• 2013

In this paper, we present an optimal threshold setting method for flame detection of infrared thermal image. Conventional infrared flame detection methods used fixed intensity threshold to segment candidate flame regions and further processing is performed to decide correct flame detection. So flame region segmentation step using the threshold is important processing for fire detection algorithm. The threshold should be change in input image depends on camera types and operation conditions. We have analyzed the conventional thresholds composed of fixed-intensity, average, standard deviation, maximum value. Finally, we extracted that the optimal threshold value is more than summation of average and standard deviation, and less than maximum value. it will be enhance flame detection rate than conventional fixed-threshold method.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.77-91
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• 2022

The optimal threshold estimation is considered in order to discriminate the mixture distribution in the fields of Biostatistics and credit evaluation. There exists well-known various accuracy measures that examine the discriminant power. Recently, Matthews correlation coefficient and the F1 statistic were studied to estimate optimal thresholds. In this study, we explore whether these accuracy measures are appropriate for the optimal threshold to discriminate the mixture distribution. It is found that some accuracy measures that depend on the sample size are not appropriate when two sample sizes are much different. Moreover, an alternative method for finding the optimal threshold is proposed using the correlation coefficient that defines the ratio of the confusion matrix, and the usefulness and utility of this method are also discusses.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1420-1428
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• 2017

It is difficult to guarantee the reliability of the algorithm due to the difference of the sampling frequency among the various ECG databases used for the R wave detection in case of applying to different environments. In this study, we propose an optimal threshold setting method for R wave detection according to the sampling frequency of ECG signals. For this purpose, preprocessing process was performed using moving average and the squaring function based the derivative. The optimal value for the peak threshold was then detected according to the sampling frequency by changing the threshold value according to the variation of the signal and the previously detected peak value. The performance of R wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. When the optimal values of the differential section, window size, and threshold coefficient for the MIT-BIH sampling frequency of 360 Hz were 7, 8, and 6.6, respectively, the R wave detection rate was 99.758%.

• Management Science and Financial Engineering
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• v.22 no.1
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• pp.13-20
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• 2016

We investigate the optimal consumption and investment problem when a working debtor has an option to file for bankruptcy. By applying the duality approach, the closed-form solutions are obtained for the case of CRRA utility function. The optimal bankruptcy time is determined by the first hitting time when the financial wealth hits the wealth threshold derived from the optimal stopping time problem. Moreover, the numerical results show that the investment increases as the wealth approaches the threshold and the value gain from the bankruptcy option is vanished as wealth increases.

• IE interfaces
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• v.16 no.2
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• pp.195-200
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• 2003

In this paper, the selective use of RTS and CTS frames is considered to analyze the capacity of IEEE 802.11 WLAN (Wireless Local Area Network). The RTS and CTS frames are used to transmit the data frames longer than dot11RTSThreshold according to IEEE 802.11 specification. The analysis of the optimal RTS-CTS threshold is derived to maximize the capacity of IEEE 802.11 WLAN. And, numerical examples are also presented for IEEE 802.11 a and b WLANs.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.113-122
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• 2015

Feature subset selection is as a pre-processing step in learning algorithms. In this paper, we propose an efficient algorithm, ModifiedFAST, for feature subset selection. This algorithm is suitable for text datasets, and uses the concept of information gain to remove irrelevant and redundant features. A new optimal value of the threshold for symmetric uncertainty, used to identify relevant features, is found. The thresholds used by previous feature selection algorithms such as FAST, Relief, and CFS were not optimal. It has been proven that the threshold value greatly affects the percentage of selected features and the classification accuracy. A new performance unified metric that combines accuracy and the number of features selected has been proposed and applied in the proposed algorithm. It was experimentally shown that the percentage of selected features obtained by the proposed algorithm was lower than that obtained using existing algorithms in most of the datasets. The effectiveness of our algorithm on the optimal threshold was statistically validated with other algorithms.

• The Journal of Information Systems
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• v.26 no.3
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• pp.63-90
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• 2017

Purpose This study proposes a novel system trading model using case-based reasoning (CBR) based on absolute similarity threshold. The proposed model is designed to optimize the absolute similarity threshold, feature selection, and instance selection of CBR by using genetic algorithm (GA). With these mechanisms, it enables us to yield higher returns from stock market trading. Design/Methodology/Approach The proposed CBR model uses the absolute similarity threshold varying from 0 to 1, which serves as a criterion for selecting appropriate neighbors in the nearest neighbor (NN) algorithm. Since it determines the nearest neighbors on an absolute basis, it fails to select the appropriate neighbors from time to time. In system trading, it is interpreted as the signal of 'hold'. That is, the system trading model proposed in this study makes trading decisions such as 'buy' or 'sell' only if the model produces a clear signal for stock market prediction. Also, in order to improve the prediction accuracy and the rate of return, the proposed model adopts optimal feature selection and instance selection, which are known to be very effective in enhancing the performance of CBR. To validate the usefulness of the proposed model, we applied it to the index trading of KOSPI200 from 2009 to 2016. Findings Experimental results showed that the proposed model with optimal feature or instance selection could yield higher returns compared to the benchmark as well as the various comparison models (including logistic regression, multiple discriminant analysis, artificial neural network, support vector machine, and traditional CBR). In particular, the proposed model with optimal instance selection showed the best rate of return among all the models. This implies that the application of CBR with the absolute similarity threshold as well as the optimal instance selection may be effective in system trading from the perspective of returns.