• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4B
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• pp.357-363
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• 2009

When yields the mean velocity of the closed conduit which is used generally, it is available to use Darcy Weisbach Friction Loss Head equation. But, it is inconvenient very because Friction Loss coefficient f is the function of Reynolds Number and Relative roughness (${\varepsilon}$/d). So, it is demanded more convenient equation to estimate. In order to prove the reliability and an accuracy of Chiu's velocity equation from the research which sees hereupon, proved agreement very well about measured velocity measurement data by using Laser velocimeter which is a non-insertion velocity measuring equipment from the closed conduit (Laser Doppler Velocimeter: LDV) and an insertion velocity measuring equipment and the Pitot tube which is a supersonic flow meter (Transit-Time Flowmeters). By proving theoretical linear-relation between maximum velocity and mean velocity in laboratory flume without increase and decrease of discharge, the equilibrium state of velocity in the closed conduit which reachs to equilibrium state corresponding to entropy parameter M value has a trend maintaining consistently this state. If entropy M value which is representing one section is determinated, mean velocity can be gotten only by measuring the velocity in the point appearing the maximum velocity. So, it has been proved to estimate simply discharge and it indicates that this method can be a theoretical way, which is the most important in the future, when designing, managing and operating the closed conduit.

• The Journal of the Korea Contents Association
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• v.5 no.4
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• pp.85-93
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• 2005

In this paper, we propose recognition system which tracks the unmarked hand of a person performing sign language in complex background. First of all, we measure entropy for the difference image between continuous frames. Using a color information that is similar to a skin color in candidate region which has high value, we extract hand region only from background image. On the extracted hand region, we detect a contour and recognize sign language by applying improved centroidal profile method. In the experimental results for 6 kinds of sing language movement, unlike existing methods, we can stably recognize sign language in complex background and illumination changes without marker. Also, it shows the recognition rate with more than 95% for person and $90\sim100%$ for each movement at 15 frames/second.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.473-478
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• 2003

In this paper, we propose the gesture recognition system using a motion information from extracted hand region in complex background image. First of all, we measure entropy for the difference image between continuous frames. Using a color information that is similar to a skin color in candidate region which has high value, we extract hand region only from background image. On the extracted hand region, we detect a contour using the chain code and recognize hand gesture by applying improved centroidal profile method. In the experimental results for 6 kinds of hand gesture, unlike existing methods, we can stably recognize hand gesture in complex background and illumination changes without marker. Also, it shows the recognition rate with more than 95% for person and 90∼100% for each gesture at 15 frames/second.

• Journal of applied mathematics & informatics
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• v.26 no.3_4
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• pp.661-672
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• 2008

In this paper an algorithm, based on extracting a certain target of an image, is proposed that is capable of performing bilevel thresholding of image with multimodal distribution. Each pixel in the image has a membership value which is used to denote the characteristic relationship between the pixel and its belonging region (i.e. the object or background). Using the membership values of image set, a new measurement, which simultaneously measures the measure of fuzziness and the conditional entropy of the image, is calculated. Then, thresholds are found by optimally minimizing calculated measurement. In addition, a fuzzy range is defined to improve the threshold values. The experimental results demonstrate that the proposed approach can select the thresholds automatically and effectively extract the meaningful target from the input image. The resulting image can preserve the object region we target very well.

• Journal of Powder Materials
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• v.26 no.4
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• pp.319-326
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• 2019

Additive manufacturing (AM) is a highly innovative method for joining dissimilar materials for industrial applications. In the present work, AM of STS630 and Ti-6Al-4V powder alloys on medium entropy alloys (MEAs) NiCrCo and NiCrCoMn is studied. The STS630 and Ti64 powders are deposited on the MEAs. Joint delamination and cracks are observed after the deposition of Ti64 on the MEAs, whereas the deposition of STS630 on the MEAs is successful, without any cracks and joint delamination. The microstructure around the fusion zone interface is characterized by scanning electron microscopy and X-ray diffraction. Intermetallic compounds are formed at the interfacial regions of MEA-Ti64 samples. In addition, Vicker's hardness value increased dramatically at the joint interface between MEAs and Ti-6Al-4V compared to that between MEAs and STS630. This result is attributed to the brittle nature of the joint, which can lead to a decrease in the joint strength.

• Smart Structures and Systems
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• v.16 no.2
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• pp.243-262
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• 2015

Optimal sensor placement (OSP) is a critical issue in construction and implementation of a sophisticated structural health monitoring (SHM) system. The uncertainties in the identified structural parameters based on the measured data may dramatically reduce the reliability of the condition evaluation results. In this paper, the information entropy, which provides an uncertainty metric for the identified structural parameters, is adopted as the performance measure for a sensor configuration, and the OSP problem is formulated as the multi-objective optimization problem of extracting the Pareto optimal sensor configurations that simultaneously minimize the appropriately defined information entropy indices. The nondirective movement glowworm swarm optimization (NMGSO) algorithm (based on the basic glowworm swarm optimization (GSO) algorithm) is proposed for identifying the effective Pareto optimal sensor configurations. The one-dimensional binary coding system is introduced to code the glowworms instead of the real vector coding method. The Hamming distance is employed to describe the divergence of different glowworms. The luciferin level of the glowworm is defined as a function of the rank value (RV) and the crowding distance (CD), which are deduced by non-dominated sorting. In addition, nondirective movement is developed to relocate the glowworms. A numerical simulation of a long-span suspension bridge is performed to demonstrate the effectiveness of the NMGSO algorithm. The results indicate that the NMGSO algorithm is capable of capturing the Pareto optimal sensor configurations with high accuracy and efficiency.

• Journal of Korea Water Resources Association
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• v.48 no.8
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• pp.647-657
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• 2015

This study assessed the water use vulnerability for 12 basins of South Korea. The annual runoff of 12 basins are derived using a Soil and Water Assessment Tool (SWAT) and the calculated runoff per unit area and population are compared with each basin. The 18 indicators are selected in order to assess the vulnerability. Those are classified by aspects of demand, loss and supply of water use. Their weighting values used Entropy method to determine objective weights. To quantitatively assess the water use vulnerability, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based on multi-criteria decision making are applied. The results show that the water availability vulnerability of Hyeongsan River has the highest value followed by Sapgyo River; Dongjin River; Seomjin River; Anseong River; Mangyung River; Nakdong River; Tamjin River; Youngsan River, Geum River; Taehwa River; and Han River. The result of this study has a capability to provide references for the index deveopment of climate change vulnerability assessment.

• Journal of Yeungnam Medical Science
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• v.39 no.1
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• pp.24-30
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• 2022

Background: Texture analysis has been used as a method for quantifying image properties based on textural features. The aim of the present study was to evaluate the usefulness of magnetic resonance imaging (MRI) texture analysis for the evaluation of viable ovarian tissue on the perfusion map of ovarian endometriosis. Methods: To generate a normalized perfusion map, subtracted T1-weighted imaging (T1WI), T1WI and contrast-enhanced T1W1 with sequences were performed using the same parameters in 25 patients with surgically confirmed ovarian endometriosis. Integrated density is defined as the sum of the values of the pixels in the image or selection. We investigated the parameters for texture analysis in ovarian endometriosis, including angular second moment (ASM), contrast, correlation, inverse difference moment (IDM), and entropy, which is equivalent to the product of area and mean gray value. Results: The perfusion ratio and integrated density of normal ovary were 0.52±0.05 and 238.72±136.21, respectively. Compared with the normal ovary, the affected ovary showed significant differences in total size (p<0.001), fractional area ratio (p<0.001), and perfusion ratio (p=0.010) but no significant differences in perfused tissue area (p=0.158) and integrated density (p=0.112). In comparison of parameters for texture analysis between the ovary with endometriosis and the contralateral normal ovary, ASM (p=0.004), contrast (p=0.002), IDM (p<0.001), and entropy (p=0.028) showed significant differences. A linear regression analysis revealed that fractional area had significant correlations with ASM (r²=0.211), IDM (r²=0.332), and entropy (r²=0.289). Conclusion: MRI texture analysis could be useful for the evaluation of viable ovarian tissues in patients with ovarian endometriosis.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.12-21
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• 2012

IEEE 802.11 MAC protocol standard, DCF(CSMA/CA), is originally designed to ensure the fair channel access between mobile nodes sharing the local wireless channel. It has been, however, revealed that some misbehavior nodes transmit more data than other nodes through artificial means in hot spot area spreaded rapidly. The misbehavior nodes may modify the internal process of their MAC protocol or interrupt the MAC procedure of normal nodes to achieve more data transmission. This problem has been referred to as a selfish node problem and almost literatures has proposed methods of analyzing the MAC procedures of all mobile nodes to detect the selfish nodes. However, these kinds of protocol analysis methods is not effective at detecting all kinds of selfish nodes enough. This paper address this problem of detecting selfish node using Tsallis-Entropy which is a kind of statistical method. Tsallis-Entropy is a criteria which can show how much is the density or deviation of a probability distribution. The proposed algorithm which operates at a AP node of wireless LAN extracts the probability distribution of data interval time for each node, then compares the one with a threshold value to detect the selfish nodes. To evaluate the performance of proposed algorithm, simulation experiments are performed in various wireless LAN environments (congestion level, how selfish node behaviors, threshold level) using ns2. The simulation results show that the proposed algorithm achieves higher successful detection rate.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.29-37
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• 2017

The range of problems that can be handled by the activation of big data and the development of hardware has been rapidly expanded and machine learning such as deep learning has become a very versatile technology. In this paper, mnist data set is used as experimental data, and the Cross Entropy function is used as a loss model for evaluating the efficiency of machine learning, and the value of the loss function in the steepest descent method is We applied the Gradient Descent Optimize algorithm to minimize and updated weight and bias via backpropagation. In this way we analyze optimal reliability value corresponding to the number of exercises and optimal reliability value without overfitting. And comparing the overfitting time according to the number of data changes based on the number of training times, when the training frequency was 1110 times, we obtained the result of 92%, which is the optimal reliability value without overfitting.