• Steel and Composite Structures
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• v.28 no.5
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• pp.517-525
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• 2018

The specimens made by Z2CND18.12N austenitic stainless steel were conducted on a 100 kN closed loop servo hydraulic tension-compression testing machine with a digital controller. Uniaxial tension and uniaxial ratcheting effect tests were carried out at $25^{\circ}C$. Moreover, Uniaxial tension tests were conducted at $150^{\circ}C$, $250^{\circ}C$ and $350^{\circ}C$. Based on these experimental data, the prediction models of stress-strain curve and the relationship of ratcheting strain and number of cycles were established by the algorithm principle of BP neural network. The results indicated that the predicted results of neural network model were in well agreement with experimental data. It was found that the BP neural network model had high validity and accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.7
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• pp.149-159
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• 1998

The importance of soundness and safety evaluation in weld zone using by the ultrasonic wave has been recently increased rapidly because of the collapses of huge structures and safety accidents. Especially, the ultrasonic method that has been often used for a major non-destructive testing(NDT) technique in many engineering fields plays an important role as a volume test method. Hence, the defecting any defects of weld Bone in austenitic stainless steel type 304 using by ultrasonic wave and neural network is explored in this paper. In order to detect defects, a distance amplitude curve on standard scan sensitivity and preliminary scan sensitivity represented of the relation between ultrasonic probe, instrument, and materials was drawn based on a quantitative standard. Also, a total of 93％ of defect types by testing 30 defect patterns after organizing neural network system, which is learned with an accuracy of 99％, based on ultrasonic evaluation is distinguished in order to classify defects such as holes or notches in experimental results. Thus, the proposed ultrasonic wave and neural network is useful for defect detection and Ultrasonic Non-Destructive Evaluation(UNDE) of weld zone in austenitic stainless steel 304.

• Corrosion Science and Technology
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• v.20 no.2
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• pp.62-68
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• 2021

Artificial neural network techniques show an excellent ability to predict the data (output) for various complex characteristics (input). It is primarily specialized to solve nonlinear relationship problems. This study is an experimental investigation that applies artificial neural network techniques and an experimental design to predict the cyclic polarization curves of the super-austenitic stainless steel AL-6XN alloy with sensitization. A cyclic polarization test was conducted in a 3.5% NaCl solution based on an experimental design matrix with various factors (degree of sensitization, temperature, pH) and their levels, and a total of 36 cyclic polarization data were acquired. The 36 cyclic polarization patterns were used as training data for the artificial neural network model. As a result, the supervised learning algorithms with back-propagation showed high learning and prediction performances. The model showed an excellent training performance (R²=0.998) and a considerable prediction performance (R²=0.812) for the conditions that were not included in the training data.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.98-106
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• 2022

A condition of the retina known as hypertensive retinopathy (HR) is connected to high blood pressure. The severity and persistence of hypertension are directly correlated with the incidence of HR. To avoid blindness, it is essential to recognize and assess HR as soon as possible. Few computer-aided systems are currently available that can diagnose HR issues. On the other hand, those systems focused on gathering characteristics from a variety of retinopathy-related HR lesions and categorizing them using conventional machine-learning algorithms. Consequently, for limited applications, significant and complicated image processing methods are necessary. As seen in recent similar systems, the preciseness of classification is likewise lacking. To address these issues, a new CAD HR-diagnosis system employing the advanced Deep Dense CNN Learning (DD-CNN) technology is being developed to early identify HR. The HR-diagnosis system utilized a convolutional neural network that was previously trained as a feature extractor. The statistical investigation of more than 1400 retinography images is undertaken to assess the accuracy of the implemented system using several performance metrics such as specificity (SP), sensitivity (SE), area under the receiver operating curve (AUC), and accuracy (ACC). On average, we achieved a SE of 97%, ACC of 98%, SP of 99%, and AUC of 0.98. These results indicate that the proposed DD-CNN classifier is used to diagnose hypertensive retinopathy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2101-2123
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• 2023

Recent studies have shown that the neural network-based binary code similarity detection technology performs well in vulnerability mining, plagiarism detection, and malicious code analysis. However, existing cross-architecture methods still suffer from insufficient feature characterization and low discrimination accuracy. To address these issues, this paper proposes a cross-architecture binary function similarity detection method based on composite feature model (SDCFM). Firstly, the binary function is converted into vector representation according to the proposed composite feature model, which is composed of instruction statistical features, control flow graph structural features, and application program interface calling behavioral features. Then, the composite features are embedded by the proposed hierarchical embedding network based on a graph neural network. In which, the block-level features and the function-level features are processed separately and finally fused into the embedding. In addition, to make the trained model more accurate and stable, our method utilizes the embeddings of predecessor nodes to modify the node embedding in the iterative updating process of the graph neural network. To assess the effectiveness of composite feature model, we contrast SDCFM with the state of art method on benchmark datasets. The experimental results show that SDCFM has good performance both on the area under the curve in the binary function similarity detection task and the vulnerable candidate function ranking in vulnerability search task.

• Proceedings of the Korea Association of Crystal Growth Conference
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• 1997.06a
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• pp.41-44
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• 1997

Dispersion and flocculation behaviors of clay-ash powders in a slurry state were examined, and we have systematically investigated the effects of colloid/interfaces variables on slip properties and the rheological behavior of ash-clay slurry. Ash-clay slurries have been characterized on the basis of the time dependent rheology which was done out by the gel-curve test. Gel-curve for the coagulated slip shows interesting rheological behavior which was caused by the formation of the new network structure and the readsoption of the polysilicon hydroxo species on the particle surfaces.

• Journal of the Korean Society of Industry Convergence
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• v.8 no.4
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• pp.213-220
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• 2005

The Youngwal 1, Youngwal 2 and Youngchun gaging stations are observed flood flow and low flow during Mar. 2004~Oct. 2004. They are observed water stages and flow velocities for flood and low flow. The observed data are used to derived rating curve and equations. The HEC-RAS model is applied for hydraulic modeling in gauging stations. The model is designed to perform one-dimensional hydraulic calculations for an river improvement plan in a full network of natural and constructed channels, and is comprised of a graphical user interface(GUI), separate hydraulic analysis components, data storage and management capabilities, graphics and reporting facilities.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.6
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• pp.47-52
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• 2012

DAS(Distribution Automation System) is equipped with several software applications such as service restoration, loss minimization, and protective relay coordination. The software applications of DAS are very sensitive to the amount of section load being carried by a particular section of distribution lines. Moreover, each software application requires a different parameter of the section load according to its purpose. Therefore, This paper proposes a new section load management method using real-time measurement data of the distribution lines. In order to provide accurate data to DAS applications, this method considers section loads in terms of the relationship of power versus time. In order to establish that the proposed method is feasible, a performance-testing simulator was developed, and case studies were conducted for a modified real distribution network.

• 한국신재생에너지학회:학술대회논문집
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• 2007.11a
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• pp.372-375
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• 2007

750kW gearless type wind turbine, named U50, is developed by UNISON in Korea. The newly developed wind turbine should be evaluated the power curve and the estimated annual energy production by following international standard to verify the power performance characteristics. This paper shows the test and evaluation procedure according to IEC 61400-12-1 which specifies a procedure of measuring the power performance characteristics of a single wind turbine and applies to the testing of wind turbines of all types and sized connected to the electrical power network. And this paper also shows the power performance characteristics for U50 wind turbine which is determined in accordance with IEC regulation.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.39 no.6
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• pp.19-19
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• 2002

In this paper the authors explore the effect of bandwidth sharing to the utility of the customer for the best-effort Internet services and draw a basis for the pricing principle in Internet Protocol networks. Especially, we investigate the behavior of a customer′s utility in case an arbitrary amount of additional bandwidth is allowed to each customer for the elastic traffic, which is the typical example of the non-real time Internet data traffic. After drawing the utility curve, which will be proved to follow the concave curve, we will apply it to the pricing of Internet traffic. Finally, via numerical experiments, we will illustrate the validity and implication of the proposed method.