• Journal of Magnetics
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• v.18 no.3
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• pp.348-351
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• 2013

Inconel 600 alloy, Ni base alloy, is widely used for steam generator tubings where sensitization occurs at grain boundaries and sensitization will induce tubing failures. This alloy has usually paramagnetic property, however, it transforms into ferromagnetic property along grain boundaries when sensitization occurs: this means NDE using magnetism for sensitization is possible. Therefore, in this study, Inconel 600 alloys were heat treated at 873 K from 0 to 400 hours so as to generate sensitization and their magnetic properties were investigated in detail. The saturation and the residual magnetization increase with increasing heat treatment time and take a maximum. On the other hand, the coercive force decreases with the increase in time of heat treatment. We confirmed that characteristics at only grain boundaries change into ferromagnetic phase by a MFM observation. As a trial for industrial application, heat treated Inconel 600 alloy was scanned by a magnetic field sensor, and the variations in magnetization were obtained nondestructively. The results indicate a feasibility of magnetic NDE for sensitization of Inconel 600 alloy.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.2_1
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• pp.169-176
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• 2022

Prognostics and health management (PHM) is recently converging throughout the industry, one of the trending issue is to detect abnormal conditions at decanter centrifuge during water treatment facilities. Wastewater treatment operation produces corrosive gas which results failures on attached sensors. This scenario causes frequent sensor replacement and requires highly qualified manager's visual inspection while replacing important parts such as bearings and screws. In this paper, we propose anomaly detection by measuring the vibration of the decanter centrifuge based on the video camera images. Measuring the vibration of the screw decanter by applying the optical flow technique, the amount of movement change of the corresponding pixel is measured and fed into the LST M model. As a result, it is possible to detect the normal/warning/dangerous state based on LSTM classification. In the future work, we aim to gather more abnormal data in order to increase the further accuracy so that it can be utilized in the field of industry.

• Journal of Auto-vehicle Safety Association
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• v.14 no.3
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• pp.71-76
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• 2022

In autonomous driving cars, 3D pose estimation can be one of the effective methods to enhance safety control for OOP (Out of Position) passengers. There have been many studies on human pose estimation using a camera. Previous methods, however, have limitations in automotive applications. Due to unexplainable failures, CNN methods are unreliable, and other methods perform poorly. This paper proposes robust real-time multi-human 3D pose estimation architecture in vehicle using monocular RGB camera. Using particle filter, our approach integrates CNN 2D/3D pose measurements with available information in vehicle. Computer simulations were performed to confirm the accuracy and robustness of the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.2
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• pp.147-152
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• 2016

This paper proposes a new TPC (Transmission Priority Change) algorithm which is used to diagnose failures of a CAN (Controller Area Network) based network system for the oil tank monitoring. The TPC algorithm is aimed to increase the total amount of data transmission and to minimize the latency for an urgent message by changing transmission priority. The urgency of the data transmission has been determined by the conditions of sensors. There are multiple sensors inside of the oil tank, such as temperature, valve, pressure and level sensors. When the sensors operate normally, the sensory data can be collected through the CAN network by the monitoring system. However when there is a dangerous situation or failure situation happened at a sensor, the data need to be handled quickly by the monitoring system, which is implemented by using the TPC algorithm. The effectiveness of the TPC algorithm has been verified by the real experiments. In addition, this paper introduces a method that people can figure out the condition of oil tanks and also can perform the fault diagnosis in real-time by using transmitted packet data. By applying this TPC algorithm to various industries, the convenience and reliability of multiple sensors network system can be improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3066-3079
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• 2016

Recent years have witnessed a dramatic increase in topology research of wireless sensor networks (WSNs) where both energy consumption and survivability need careful consideration. To balance energy consumption and ensure survivability against both random failures and deliberate attacks, we resort to complex network theory and propose an energy-aware preferential attachment (EPA) model to generate a robust topology for WSNs. In the proposed model, by taking the transmission range and energy consumption of the sensor nodes into account, we combine the characters of Erdős -Rényi (ER) model and Barabasi-Albert (BA) model in this new model and introduce tunable coefficients for balancing connectivity, energy consumption, and survivability. The correctness of our theoretic analysis is verified by simulation results. We find that the topology of WSNs built by EPA model is asymptotically power-law and can have different characters in connectivity, energy consumption, and survivability by using different coefficients. This model can significantly improve energy efficiency as well as enhance network survivability by changing coefficients according to the requirement of the real environment where WSNs deployed and therefore lead to a crucial improvement of network performance.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.269-276
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• 2016

Public Safty-LTE(Long Term Evolution) is being deployed in the direction of reducing cost by using both of municipal network and commercial network. However, LTE Network is difficult to ensure the survivability during the information communication infrastructure failures. In addition, it is vulnerable in communication coverage of inside buildings and underground. In this study, we propose to implement effectively the network survivability technique through the convergence to the proven technology. As the advent of the IoT Age, smart sensors which are embedded in the environment and the things will be able to provide a useful infrastructure for ensuring the network survivability. Based on the feature of the smart sensor, we designed the sink node architecture to guarantee the network survivability in disaster situation through the convergence of the small cell technology and extension of wireless network coverage technology. The computing power inherent in the environment is a valuable resource that can be utilized in the disaster situation.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.245-252
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• 2020

Unexpected equipment defects can cause a huge economic losses in the society at large. Although condition monitoring can provide solutions, the signal processing algorithms must be developed to predict mechanical failures using data acquired from various sensors attached to the equipment. The signal processing algorithms used in a condition monitoring requires high computing efficiency and resolution. To improve condition monitoring on a wireless sensor network(WSN), data visualization can maximize the expressions of the data characteristics. Thus, this paper proposes the extraction of visual feature from temperature data over time using condition monitoring based on a WSN to identify environmental conditions of equipment in a large-scale infrastructure. Our results show that time-frequency analysis can visually track temperature changes over time and extract the characteristics of temperature data changes.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1081-1085
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• 2010

In the wireless sensor networks, power consumption and interference among the nodes can be reduced by using the transmission power control. Because link quality is changed by spatial and temporal effect, link failures are frequently occurred. In order to adapt to link quality variation, existing transmission power control schemes broadcast beacon messages periodically to neighbor nodes and control the transmission power dynamically. However, it can effect on the time and energy overhead according to period of transmission power control. In this paper, the dynamic method of transmission power control by the link quality variation and variable period are proposed. When a link quality is unstable, the control duty cycle is reduced and the link quality is agilely maintained. In contrast, when link quality is stable, the control period is increased and control overhead is decreased.

• The Journal of Engineering Geology
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• v.25 no.3
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• pp.331-337
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• 2015

A new technique is presented to detect unstable slope behavior, based on Hotelling's T² analysis of pore pressure and water content obtained during flume tests using granitic and gneissic weathered soils. Three sets of pore pressure-water content values were simultaneously obtained during each test, and T² statistics at the 90.0% and 95.0% confidence levels were calculated based on the correlations between values. The results show that unsuccessful detection of some local failures of the flume slope depended on the sensor position. In the case of global slope failures, anomalous behavior was detected between several hundred and several thousand seconds before the event as T² statistics exceeded the confidence interval 90%. Hotelling's T² analysis provides a single control criterion because it enables correlations between diverse measured values within the same slope; the criterion also includes stepwise criteria for a forecasting and warning system based on confidence levels.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.1-11
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• 2021

Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.