• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.11
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• pp.1143-1149
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• 2008

In a nuclear power plant, loose part monitoring and its diagnostic technique is one of the major issues for ensuring the structural integrity of the reactor system. Typically, accelerometers are mounted on the surface of a reactor vessel to localize impact location cavsed by the impact of metallic substances on the reactor system. However, in some cases, the number of the accelerometers is not enough to estimate the impact location precisely. In such a case, one of alternative plan is to utilize another type sensors that can measure the vibration of the reactor structure even though the measuring frequency ranges are different from each others. The AE sensors installed on the reactor structure can be utilized as additional sensors for loose part monitoring. In this paper, we proposed a new method to estimate impact location by using both accelerometer signal and AE signal, simultaneously. The feasibility of the proposed method is verified by an experiment. The experimental results demonstrate that we can enhance the reliability and precision of the loose part monitoring.

• Steel and Composite Structures
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• v.29 no.6
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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• pp.74-84
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• 2022

As the semiconductor industry develops, the difficulty of newly required process technology becomes difficult, and the importance of production yield and product reliability increases. As an effort to minimize yield loss in the manufacturing process, interests in the process defect process for facility diagnosis and defect identification are continuously increasing. This research observed the plasma condition changes in the multi oxide/nitride layer deposition (MOLD) process, which is one of the 3D-NAND manufacturing processes through optical emission spectroscopy (OES) and monitored the result of whether the change in plasma characteristics generated in repeated deposition of oxide film and nitride film could directly affect the film. Based on these results, it was confirmed that if a change over a certain period occurs, a change in the plasma characteristics was detected. The change may affect the quality of oxide film, such as the film thickness as well as the interfacial surface roughness when the oxide and nitride thin film deposited by plasma enhenced chemical vapor deposition (PECVD) method.

• Journal of Biomedical Engineering Research
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• v.43 no.6
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• pp.417-423
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• 2022

The present study proposed a new interface system for capsule endoscopy by using head mounted display (HMD) device, which can control the orientation of the capsule endoscope with electromagnetic actuator (EMA) system. The orientation information of the HMD user was detected by the gyroscope sensor built into the device and then calculated to as an angle increment using Unity Engine compiler. The measured angle changes from the HMD were converted to the current values of the corresponding coils to be changed in the EMA system. Two experiments were designed to measure the accuracy and the intuitiveness of the HMD interface system. In the angle accuracy measurement, the capsule endoscope driven by HMD interface system showed the averaged errors of 0.68 degrees horizontally and 1.001 degrees vertically for given test angles. In the intuitiveness measurement, HMD interface system showed 1.33 times faster manipulation speed rather than the joystick interface system. In this respect, the HMD interface system for capsule endoscopy was expected to improve the overall diagnostic environment while maintaining comfort of patients and clinicians.

• Journal of Sensor Science and Technology
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• v.32 no.5
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• pp.275-279
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• 2023

Throughout the 20th century, the transition of pig farms from extensive to intensive commercial operations amplified the risk of disease transmission, particularly involving African swine fever (ASF). Real-time temperature monitoring systems have emerged as essential tools for early ASF diagnosis. In this paper, we introduce new real-time temperature monitoring ear tags (RTMEs) modeled after existing ear tag designs. Our crafted Pig-Temp platforms have three primary advantages. First, they can be effortlessly attached to pig ears, ensuring superior compatibility. Second, they enable real-time temperature detection, and the data can be displayed on a personal computer or smartphone application. Furthermore, they demonstrate excellent measurement accuracy, ranging from 98.9% to 99.8% at temperatures between 2.2 and 360℃. A linear regression approach enables fever symptoms associated with ASF to be identified within 3 min using RTMEs. The communication range extends to approximately 12 m (452 m²), enabling measurements from an estimated 75 to 2,260 pigs per gateway. These newly developed Pig-Temp platforms offer singifcant enhancement of early ASF detection.

• Journal of Oral Medicine and Pain
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• v.34 no.3
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• pp.317-324
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• 2009

This study aimed to assess stiffness and elasticity of the masticatory muscle in the patients with the masticatory muscle pain using a tactile sensor and to investigate whether the masticatory muscle pain affects the facial expression muscles. From those who visited Department of Oral Medicine in Dankook University Dental Hospital, 27 patients presenting with unilateral muscle pain and tenderness in the masseter muscle (Ms) were selected (mean age: $36.4{\pm}13.8$ years). Exclusion criterion was those who also had temporomandibular joint (TMJ) disorders or any neurological pain. Muscle stiffness and elasticity for the muscles of mastication and facial expression was investigated with the tactile sensor (Venustron, Axiom Co., JAPAN) and the muscles measured were the Ms, anterior temporal muscle (Ta), frontalis (Fr), inferior orbicularis oculi (Ooci), zygomaticus major (Zm), superior and inferior orbicularis oris (Oors, Oori) and mentalis (Mn). t-tests was used to compare side difference in muscle stiffness and elasticity. Side differences were also compared between diagnostic groups (local muscle soreness (LMS) vs myofascial pain syndrome (MPS) and between acute (< 6M) and chronic ($\geq$ 6M) groups. This study showed that Ms and Zm at affected side exhibited significantly increased stiffness and decreased elasticity as compared to the unaffected side.(p<0.05) There was no significant difference between local muscle soreness and myofascial pain syndrome groups and between acute and chronic groups. The results of this study suggests that masticatory muscle pain in Ms can affect muscle stiffness and elasticity not only for Ms but also for Zm, the facial expression muscle.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.27 no.4
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• pp.196-205
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• 2017

In recent years, the service design in the medical sector evolves through practical service research and development that can visualize both intangible and intangible service elements in an integrative way and derive innovative solutions to help customers feel the service more important value. With the improvement of personal income, interest in medical welfare and well-being is increasing day by day, and the focus of the medical sector shifts from the concept of treatment of diseases and illness to preventive medicine. In response to this trend, research and development of home health care system, which greatly reduces the time and space constraint of health checkup and health care by combining ubiquitous concept with medical welfare, are being actively conducted, and the needs for improving products and medical environment based on user-centered medical service and user needs in accordance with the Health Care 3.0 Era, it becomes necessary to develop on-site medical diagnostic products that reflect user-centered needs and needs. This study is intended to research and develop a product that sufficiently reflects the needs of users by applying suitable materials and shape for on-site diagnostic product in researching and developing Wireless X-ray Detector.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.5
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• pp.345-352
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• 2016

As aircrafts are being operated at high altitude, wing structures experience various fatigue loadings under cryogenic environments. As a result, fatigue damage such as a crack could be develop that could eventually lead to a catastrophic failure. For this reason, fatigue damage monitoring is an important process to ensure efficient maintenance and safety of structures. To implement damage detection in real-world flight environments, a special cooling chamber was built. Inside the chamber, the temperature was maintained at the cryogenic temperature, and harmonic fatigue loading was given to a wing structure. In this study, piezoelectric active-sensing based guided waves were used to detect the fatigue damage. In particular, a beamforming technique was applied to efficiently measure the scattering wave caused by the fatigue damage. The system was used for detection, growth monitoring, and localization of a fatigue crack. In addition, a sensor diagnostic process was also applied to ensure the proper operation of piezoelectric sensors. Several experiments were implemented and the results of the experiments demonstrated that this process could efficiently detect damage in such an extreme environment.

• Journal of Sensor Science and Technology
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• v.4 no.3
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• pp.51-59
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• 1995

Stress-balanced flat 150 nm-$Si_{3}N_{4}$/300 nm-$SiO_{2}$/150 nm-$Si_{3}N_{4}$ dielectric membrane on silicon substrate has been fabricated. Analyses of stress-deflection and stress-temperature, and visual inspection for the strain diagnostic test patterns were performed in order to characterize stress properties of the membrane. The $SiO_{2}$ layers sandwiched between two $Si_{3}N_{4}$ layers were deposited by three different techniques(PECVD, LPCVD, and APCVD) for the purpose of investigating the dependence of stress on the deposition methods. Some extent of tensile stress in the membrane was always observed regardless of the deposition methods, however it could be balanced against silicon substrate by post-wet oxidation in $1,150^{\circ}C$. Stress-temperature characteristics of the membranes showed that APCVD-LTO was better as mid-$SiO_{2}$ layer than PECVD - or LPCVD - $SiO_{2}$ when there was no oxidation process.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.242-250
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• 2019

In this paper, we propose a method to predict the failure of industrial robot using Seq2Seq (Sequence to Sequence) model, which is a model for transforming time series data among Artificial Neural Network models. The proposed method uses the data of the joint current and angular value, which can be measured by the robot itself, without additional sensor for fault diagnosis. After preprocessing the measured data for the model to learn, the Seq2Seq model was trained to convert the current to angle. Abnormal degree for fault diagnosis uses RMSE (Root Mean Squared Error) during unit time between predicted angle and actual angle. The performance evaluation of the proposed method was performed using the test data measured under different conditions of normal and defective condition of the robot. When the Abnormal degree exceed the threshold, it was classified as a fault, and the accuracy of the fault diagnosis was 96.67% from the experiment. The proposed method has the merit that it can perform fault prediction without additional sensor, and it has been confirmed from the experiment that high diagnostic performance and efficiency are available without requiring deep expert knowledge of the robot.