• Nuclear Engineering and Technology
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• v.48 no.1
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• pp.228-235
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• 2016

Flaws at dissimilar metal welds (DMWs), such as reactor coolant systems components, Control Rod Drive Mechanism (CRDM), Bottom Mounted Instrumentation (BMI) etc., in nuclear power plants have been found. Notably, primary water stress corrosion cracking (PWSCC) in the DMWs could cause significant reliability problems at nuclear power plants. Therefore, phased array ultrasound is widely used for inspecting surface break cracks and stress corrosion cracks in DMWs. However, inspection of DMWs using phased array ultrasound has a relatively low probability of detection of cracks, because the crystalline structure of welds causes distortion and splitting of the ultrasonic beams which propagates anisotropic medium. Therefore, advanced evaluation techniques of phased array ultrasound are needed for improvement in the probability of detection of flaws in DMWs. Thus, in this study, an investigation of focusing and steering phased array ultrasound in DMWs was carried out using a time reversal technique, and an adaptive focusing technique based on finite element method (FEM) simulation. Also, evaluation of focusing performance of three different focusing techniques was performed by comparing amplitude of phased array ultrasonic signals scattered from the targeted flaw with three different time delays.

• The Korean Journal of Nuclear Medicine
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• v.25 no.2
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• pp.165-176
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• 1991

The purpose of this study was to assess the ability of quantitative Tl-201 tomography to identify and localize coronary artery disease (CAD). The study population consisted of 41 patients (31 males, 10 females; mean age $55{\pm}7$ yr) including 14 with prior myocardial infarction who underwent both exercise Tl-201 myocardium SPECT and coronary angiography for the evaluation of chest pain. From the short axis and vertical long axis tomograms, stress extent polar maps were generated by Cedars-Sinai Medical Center program, and the % stress defect extent (SDE) was quantified for each coronary artery territory. For the purpose of this study, the coronary circulation was divided into 6 arterial segments, and the "myocardial ischemic score" (MIS) was calculated from the coronary angiogram. Sensitivity for the detection of CAD ($\geq50%$ coronary stenosis by angiography) by angiography) by stress extent polar map was 95% in single vessel disease, and 100% in double and triple vessel deseases. Overall sensitivity was 97%. Sensitivity and specificity for the detection of individual diseased vessels were, respectively, 87% and 90% for the left anterior descending artery (LAD), 36% and 93% for the left circumflex artery (LCX), and 71% and 70% for the right coronary artery (RCA). Concordance for the detection of individual diseased vessels between the coronary angiography and stress polar map was fair for the LAD (kappa=0.70), and RCA (kappa=0.41) lesions, whereas it was poor for the LCX lesions (kappa : 0.32). There were siginificant correlations between the MIS and SDE in LAD (rs=0.56, p=0.0027), and RCA territory (rs=0.60, p=0.0094). No significant correlation was found in LCX territory. When total vascular territories were combined, there was a significant correlation between the MIS and SDE (rs=0.42, p=0.0116). In conclusion, the quantitative analysis of Tl-201 tomograms appears to be accurate for determining the presence and location of CAD.

• Smart Structures and Systems
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• v.16 no.6
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• pp.1107-1132
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• 2015

A damage detection algorithm based on neuro fuzzy hybrid system is presented in this study for location and severity predictions of cracks in beam-like structures. A combination of eigenfrequencies and rotation deviation curves are utilized as input to the soft computing technique. Both single and multiple damage cases are considered. Theoretical expressions leading to modal properties of damaged beam elements are provided. The beam formulation is based on Euler-Bernoulli theory. The cracked section of beam is simulated employing discrete spring model whose compliance is computed from stress intensity factors of fracture mechanics. A hybrid neuro fuzzy technique is utilized to solve the inverse problem of crack identification. Two different neuro fuzzy systems including grid partitioning (GP) and subtractive clustering (SC) are investigated for the highlighted problem. Several error metrics are utilized for evaluating the accuracy of the hybrid algorithms. The study is the first in terms of 1) using the two models of neuro fuzzy systems in crack detection and 2) considering multiple damages in beam elements employing the fused neuro fuzzy procedures. At the end of the study, the developed hybrid models are tested by utilizing the noise-contaminated data. Considering the robustness of the models, they can be employed as damage identification algorithms in health monitoring of beam-like structures.

• Composites Research
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• v.27 no.4
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• pp.152-157
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• 2014

Non-destructive testing methods of composite materials are very important for improving material reliability and safety. AE measurement is based on the detection of microscopic surface movements from stress waves in a material during the fracture process. The examination of AE is a useful tool for the sensitive detection and location of active damage in polymer and composite materials. FBG (Fiber Bragg Grating) sensors have attracted much interest owing to the important advantages of optical fiber sensing. Compared to conventional electronic sensors, fiber-optical sensors are known for their high resolution and high accuracy. Furthermore, they offer important advantages such as immunity to electromagnetic interference, and electrically passive operation. In this paper, the crack detection capability of AE (Acoustic Emission) measurement was compared with that of an FBG sensor under tensile testing and buckling test of composite materials. The AE signals of the PVDF sensor were measured and an AE signal analyzer, which had a low pass filter and a resonance filter, was designed and fabricated. Also, the wavelength variation of the FBG sensor was measured and its strain was calculated. Calculated strains were compared with those determined by finite element analysis.

• Journal of Korea Game Society
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• v.16 no.1
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• pp.83-92
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• 2016

An online game is a huge distributed system comprised of servers and untrusted clients. In such circumstances, cheaters may employ abnormal behaviors through client modification or network packet tampering. Client-side detection methods have the merit of distributing the burden to clients but can easily be breached. In the other hand, server-side detection methods are trustworthy but consume tremendous amount of resources. Therefore, this paper proposes a security reinforcement method which involves both the client and the server. This method is expected to provide meaningful security fortification while minimizing server-side stress.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.1
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• pp.19-24
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• 2023

In traditional fish farming way, the workers have to observe all of the pools every time and every day to feed at the right timing. This method causes tremendous stress on workers and wastes time. To solve this problem, we implemented an automatic detection system for feeding time using deep neural network. The detection system consists of two steps: classification of the presence or absence of feed and checking DO (Dissolved Oxygen) of the pool. For the classification, the pretrained ResNet18 model and transfer learning with custom dataset are used. DO is obtained from the DO sensor in the pool through HTTP in real time. For better accuracy, the next step, checking DO proceeds when the result of the classification is absence of feed several times in a row. DO is checked if it is higher than a DO reference value that is set by the workers. These actions are performed automatically in the UI programs developed with LabVIEW.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.1-18
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• 2017

Recently, identification of the extremely stressed condition of children is an essential skill for real-time recognition of a dangerous situation because incidents of children have been dramatically increased. In this paper, therefore, we present a model based on machine learning techniques for stress status identification of a child by using bio-signals such as voice and heart rate that are major factors for presenting a child's emotion. In addition, a smart band for collecting such bio-signals and a mobile application for monitoring child's stress status are also suggested. Specifically, the proposed method utilizes stress patterns of children that are obtained in advance for the purpose of training stress status identification model. Then, the model is used to predict the current stress status for a child and is designed based on conventional machine learning algorithms. The experiment results conducted by using a real-world dataset showed that the possibility of automated detection of a child's stress status with a satisfactory level of accuracy. Furthermore, the research results are expected to be used for preventing child's dangerous situations.

• Journal of Ocean Engineering and Technology
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• v.35 no.5
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• pp.327-335
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• 2021

Microcracks can rapidly grow and develop in high-strength steels used in offshore structures. It is important to render these microcracks harmless to ensure the safety and reliability of offshore structures. Here, the dependence of the aspect ratio (As) of the maximum depth of harmless crack (a_hlm) was evaluated under three different conditions considering the threshold stress intensity factor (Δk_th) and residual stress of offshore structural steel F690. The threshold stress intensity factor and fatigue limit of fatigue crack propagation, dependent on crack dimensions, were evaluated using Ando's equation, which considers the plastic behavior of fatigue and the stress ratio. a_hlm by peening was analyzed using the relationship between Δk_th obtained by Ando's equation and Δk_th obtained by the sum of applied stress and residual stress. The plate specimen had a width 2W = 12 mm and thickness t = 20 mm, and four value of As were considered: 1.0, 0.6, 0.3, and 0.1. The a_hlm was larger as the compressive residual stress distribution increased. Additionally, an increase in the values of As and Δk_th(l) led to a larger a_hlm. With a safety factor (N) of 2.0, the long-term safety and reliability of structures constructed using F690 can be secured with needle peening. It is necessary to apply a more sensitive non-destructive inspection technique as a non-destructive inspection method for crack detection could not be used to observe fatigue cracks that reduced the fatigue limit of smooth specimens by 50% in the three types of residual stresses considered. The usefulness of non-destructive inspection and non-damaging techniques was reviewed based on the relationship between a_hlm, a_NDI (minimum crack depth detectable in non-destructive inspection), a_{cr N} (crack depth that reduces the fatigue limit to 1/N), and As.

• Corrosion Science and Technology
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• v.22 no.1
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• pp.44-54
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• 2023

The rupture disk corrosion test (RDCT) method was recently developed to evaluate stress corrosion cracking (SCC) and was found to have great potential for the real-time detection of SCC initiation in a high temperature and pressure environment, simulating the primary water coolant of pressurized water reactors. However, it is difficult to directly measure the stress applied to a disk specimen, which is an essential factor in SCC initiation. In this work, finite element analysis (FEA) was performed using ABAQUS^TM to calculate the stress and deformation of a disk specimen. To determine the best mesh design for a thin disk specimen, hexahedron, hex-dominated, and tetrahedron models were used in FEA. All models revealed similar dome-shaped deformation behavior of the disk specimen. However, there was a considerable difference in stress distribution in the disk specimens. In the hex-dominated model, the applied stress was calculated to be the maximum at the dome center, whereas the stress was calculated to be the maximum at the dome edge in the hexahedron and tetrahedron models. From a comparison of the FEA results with deformation behavior and SCC location on the disk specimen after RDCT, the most proper FE model was found to be the tetrahedron model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.481-484
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• 2011

Recent Automotive technology is driven from mechanical device to the electronic components which improve the vehicle's safety and convenience. The future competitiveness of the car will come from safety issues and energy efficiency, convenience and the application of the technologies. In this study, various techniques for driver drowsiness detection are introduced and compared with each others. The advantages and disadvantages of commercially available technologies and developed technologies are compared. To enhance the detection resolution, multiple sensing technologies are introduced in this paper. The feasibility of two drowsiness detection methods, that is, existing camera image recognition method and bio signal analysis method, are tested. The direct drowsiness detection by the camera image of eyes and driver's vital signs detected indirectly are combined and analyzed by the developed noble algorithm for stress, fatigue, drowsiness detection with a more accurate high-drowsiness detection.