• Transactions of the Korean Society of Mechanical Engineers
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• v.11 no.3
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• pp.355-361
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• 1987

Creep and fatigue tests were carried out on crack growth properties of small surface cracks in 304 stainless steel at 538.deg.C, 593.deg. C and 650.deg. C in air, by using small plate specimens with a small artificial pit. All the data of the crack growth rate per hour obtained in the present tests were correlated with the maximum stress intensity factor, so that the applicability of linear fracture mechanics to the crack growth of surface cracks at elevated temperature was investigated. In the creep test, relatiion of .sigma.$\^$n/.t$\_$f/=C is obtained between failure time and nominal stress at each temperature level, where n has the value of 11-14 depending on the temperature level. In the creep and fatigue crack growth properties of surface cracks at the elevated temperatures, the maximum stress intensity factor, $_{4}$$\_$max/, is some extent applicable parameter to describe the surface crack growth rate under the present experimental conditions. The crack growth rate per hour increases when the holding time decreases, and creep crack growth rate per hour becomes the lowest limit of crack growth rate per hour in this tests.

• Journal of KIISE:Software and Applications
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• v.27 no.2
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• pp.120-133
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• 2000

Major obstruction of using formal methods for hybrid real-time systems in industry is the difficulty that engineers have in understanding and applying the quantitative methods in an abstract requirements phase. While formal methods technology in safety-critical systems can help increase confidence of software, difficulty and complexity in using them can cause another hazard. In order to overcome this obstruction, we propose a framework for qualitative requirements engineering of the hybrid real-time systems. It consists of a qualitative method for requirements specification, called QFM (Qualitative Formal Method), and a safety analysis method for the requirements based on a causality information, called CRSA (Causal Requirements Safety Analysis). QFM emphasizes the idea of a causal and qualitative reasoning in formal methods to reduce the cognitive burden of designers when specifying and validating the software requirements of hybrid safety systems. CRSA can evaluate the logical contribution of the software elements to the physical hazard of systems by utilizing the causality information that is kept during specification by QFM. Using the Shutdown System 2 of Wolsong nuclear power plants as a realistic example, we demonstrate the effectiveness of our approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.11B
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• pp.655-662
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• 2007

Monitoring systems used at present to operate HVAC(Heating, Ventilation and Air Conditioning) optimally do not have a function that enables to detect faults properly when there are faults of such as operating plants or performance falling, so they are unable to manage faults rapidly and operate optimally. In this paper, we have developed a classified rule-based fault detection system which can be inclusively used in HVAC system of a building by installation of sensor which is composed of HVAC system and required low costs compare to the model based fault detection system which can be used only in a special building or system. In order to experiment this algorithm, it was applied to HVAC system which is installed inside EC(Environment Chamber), verified its own practical effect, and confirmed its own applicability to the related field in the future.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.217-223
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• 2019

A study in predicting defects of spot welding in real time in automotive field is essential for cost reduction and high quality production. Welding quality is determined by shear strength and the size of the nugget, and results depend on different independent variables. In order to develop the real-time prediction system, multiple regression analyses were conducted and the two dependent variables were obtained with sufficient statistical results with three independent variables, however, the quality prediction by the regression formula could not ensure accuracy. In this study, a multi-layer neural network circuit was constructed. The neural network by 10 dynamic resistance variables was constructed with three hidden layers to obtain execution functions and weighting matrix. In this case, the neural network was established with three independent variables based on regression analysis, as there could be difficulties in real-time control due to too many input variables. As a result, all test data were divided into poor, partial, and modalities. Therefore, a real-time welding quality determination system by three independent variables obtained by multiple regression analysis was completed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.199-206
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• 2022

A malfunction or breakdown of a manufacturing facility leads to product defects and the suspension of production lines, resulting in huge financial losses for manufacturers. Due to the spread of smart factory services, a large amount of data is being collected in factories, and AI-based research is being conducted to predict and diagnose manufacturing facility breakdowns or manufacturing site efficiency. However, because of the characteristics of manufacturing data, such as a severe class imbalance about abnormalities and ambiguous label information that distinguishes abnormalities, developing classification or anomaly detection models is highly difficult. In this paper, we present an deep learning algorithm for anomaly detection of a manufacturing facility using reconstruction loss of CNN-based model and ananlyze its performance. The algorithm detects anomalies by relying solely on normal data from the facility's manufacturing data in the exclusion of abnormal data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.213-215
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• 2021

With the development of IoT technology, various technologies such as artificial intelligence and automation are being grafted into industrial sites, and accordingly, the importance of data processing is increasing. In particular, a system based on a digital image may cause a malfunction due to noise in the image due to a sensor defect or a communication environment problem. Therefore, research on image processing has been continued as a pre-processing process, and an effective noise reduction technique is required depending on the type of noise and the characteristics of the image. In this paper, we propose a modified spatial weight filter to protect edge components in the impulse noise reduction process. The proposed algorithm divides the filtering mask into four regions and calculates the standard deviation of each region. The final output was filtered by applying a spatial weight to the region with the lowest standard deviation value. Simulation was conducted to evaluate the performance of the proposed algorithm, and it showed superior impulse noise reduction performance compared to the existing method.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.69-78
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• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.17-23
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• 2024

Losses in domestic water supply due to leaks are very large, such as fractures and defects in pipelines. Therefore, preventive measures to prevent water leakage are necessary. We propose the development of a leakage detection sensor utilizing vibration sensors and present an optimal leakage detection algorithm leveraging artificial intelligence. Vibrational sound data acquired from water pipelines undergo a preprocessing stage using FFT (Fast Fourier Transform), followed by leakage classification using an optimized tree-based boosting algorithm. Applying this method to approximately 260,000 experimental data points from various real-world scenarios resulted in a 97% accuracy, a 4% improvement over existing SVM(Support Vector Machine) methods. The processing speed also increased approximately 80 times, confirming its suitability for edge device applications.

• Journal of Chest Surgery
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• v.43 no.1
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• pp.1-10
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• 2010

Background: Bioprosthetic materials have been made using glutaraldehyde fixation of porcine or bovine pericardium during cardiovascular surgery. But these bioprostheses have the problems of calcification and mechanical failure. We determined changes in tensile strength and elasticity of pericardium after glutaraldehyde, solvent, decellularization and detoxification. Material and Method: Tissues were allocated to four groups: glutaraldehyde with and without solvent, decellularization, and detoxification. We studied tensile strength and strain on tissues. We measured the tensile strength of fresh pericardium stretched in six directions (with 5 mm width), and % strain, which we calculated from the breaking point when we pulled the pericardium in two directions. Result: Tensile strength was reduced when we used the usual concentrated glutaraldehyde fixation (n=83, $MPa=11.47{\pm}5.40$, p=0.006), but there was no change when we used solvent. Elasticity was increased after glutaraldehyde fixation (n=83, strain $(%)=24.55{\pm}9.81$, p=0.00), but there was no change after solvent. After decellularization of pericardium, the tensile strength was generally reduced. The decrease in tensile strength after concentrated glutaraldehyde fixation for a long time was significantly greater less than after concentrated solvent (p=0.01, p=0.00). After detoxification, the differences in strength and strain were not significant. Conclusion: After glutaraldehyde treatment of pericardium there is no loss in tensile strength (even though we did the glutaraldehyde, solvent and detoxification treatments LOGIC IS UNCLEAR). Also, these treatments had a tendency to increase elasticity. Although post-treatment decellularization led to a significant loss in strength, this effect could be attenuated using a low concentration of solvent or hypertonic solution.

• Journal of the Korean Wood Science and Technology
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• v.2 no.2
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• pp.22-24
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• 1974

1. Object and importance of the research. The exports of plywood are increasing annually and it has ranked first in the world market because of the high quality product developed and manufactured using modern techniques. However, it is known that the exports of the other wood products, except plywood, is inactive because of their low quality. Accordingly, to increase the exports of various wood products investigations were carried out on kiln drying techniques to improve the quality of the wood. 2. The details and scope of the research Wet wood should be kiln dried before use to prevent various drying defects such as distortion, shrinkage etc, which would develop after processing, and also wet wood is not suitable for cutting, gluing and finishing. Therefore, the kiln drying properties of lumber from such species as Persimmon, Oak, Ramin and Meranti which are used in large quantity for manufacturing exporting wood products have been studied. Also the real state of kiln drying industry in Korea was investigated. 3. Results and proposal for practical use of the research 3. 1 Results of the research 3.1.1 The end checks and the time for drying from intial moisture content of about 40 percent to 5 percent moisture content in ovendry were investigated as Table 1. 3.1.2 The kiln dried results, for 30mm stock, which are presented by using kiln schedule Table 2 are as Table 3. 3.1.3 The kiln schedule for Persimmon which has a normal drying properties is given in Table 4. However, the persimmon which has easy checking properties should be air dried under a relative humidity of above 85% until reaching about 25 percent moisture content. 3.1.4 The kiln schedules for ramin, meranti and oak are given respectively as follows. Ramin kiln schedule ............ Table 5 and Table 6 Meranti kiln schedule ............ Table 7 Oak kiln schedule ............ Table 8 3.2 Proposal for practical use of the research Firms using the above species should be informed the results of the research so they can be used to preventing drying defects and shortening drying time.