• Nuclear Engineering and Technology
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• v.42 no.6
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• pp.704-711
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• 2010

Modal testing requires measuring the vibration of many points, for which an accelerometer, a gab sensor and laser vibrometer are generally used. Conventional modal testing requires mounting of these sensors to all measurement points in order to acquire the signals. However, this can be disadvantageous because it requires considerable measurement time and effort when there are many measurement points. In this paper, we propose a method for modal testing using a camera image. A camera can measure the vibration of many points at the same time. However, this task requires that the measurement points be classified frame by frame. While it is possible to classify the measurement points one by one, this also requires much time. Therefore, we try to classify multiple points using pattern recognition. The feasibility of the proposed method is verified by a beam experiment. The experimental results demonstrate that we can obtain good results.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.3122-3125
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• 2021

Electromagnetic vortex flowmeter is a new type of instrument for detecting leakage of steam generator, and the signal processing method based on the envelope to calculate energy ratio can effectively detect bubbles in sodium flow. The signal processing method is not affected by changes in the amplitude of the sensor output signal, which is caused by changes in magnetic field strength and other factors. However, the detection sensitivity of the electromagnetic vortex flowmeter is reduced. To this end, a signal processing method based on inverse Fourier transform to calculate energy ratio is proposed. According to the difference between the frequency band of the bubble noise signal and the flow signal, only the amplitude in the frequency band of the flow signal is retained in the frequency domain, and then the flow signal is obtained by the inverse Fourier transform method, thereby calculating the energy ratio. Using this method to process the experimental data, the results show that it can detect 0.1 g/s leak rate of water in the steam generator, and its performance is significantly better than that of the signal processing method based on the envelope to calculate energy ratio.

• Journal of Sensor Science and Technology
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• v.30 no.5
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• pp.326-330
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• 2021

Reactions between gold (Au) surface plasmon resonance (SPR) chips and bovine serum albumin (BSA) dissolved in solutions of different pH were investigated. The charge on the BSA depends on the pH of the solution in which it is dissolved. Thus, dissolving BSA in different pH solutions resulted in different charges of BSA. Among the BSA dissolved in solutions with pH 4.01, 7.4, and 10.01, the SPR response was the highest for BSA dissolved in the solution of pH 4.01. To eliminate the response variation owing to the difference in the refractive indices of the solutions, phosphate buffered saline (PBS) was injected into the system after the reaction of BSA with the Au SPR chip had happened. In this case too, the BSA dissolved in the solution with pH 4.01 exhibited the highest response. This may be attributed to the non-uniform distribution of ionic patches on the BSA, which can induce electrostatic attraction to the surface even though BSA has a positive charge at pH 4.01, and the absolute values of the net charge of BSA at pH 4.01 and 7.4 were very close.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.860-865
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• 2021

A data-based model, such as an AAKR model is widely used for monitoring the drifts of sensors in nuclear power plants. However, since a training dataset and a test dataset for a data-based model cannot be constructed with the data from all the possible states, the model uncertainty cannot be good enough to represent the uncertainty of estimations. In fact, the errors of estimation grow much bigger if the incoming data come from inexperienced states. To overcome this limitation of the model uncertainty, a new measure of uncertainty for a data-based model is developed and the predicted uncertainty is introduced. The predicted uncertainty is defined in every estimation according to the incoming data. In this paper, the AAKR model is used as a data-based model. The predicted uncertainty is similar in magnitude to the model uncertainty when the estimation is made for the incoming data from the experienced states but it goes bigger otherwise. The characteristics of the predicted model uncertainty are studied and the usefulness is demonstrated with the pressure signals measured in the flow-loop system. It is expected that the predicted uncertainty can quite reduce the false alarm by using the variable threshold instead of the fixed threshold.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1760-1768
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• 2022

When gamma-camera sensor modules, which are key components of radiation imagers, are derived from the coupling between scintillators and photosensors, the light collection efficiency is an important factor in determining the effectiveness with which the instrument can identify nuclides via their derived gamma-ray spectra. If the pixel area of the scintillator is larger than the pixel area of the photosensor, light loss and cross-talk between pixels of the photosensor can result in information loss, thereby degrading the precision of the energy estimate and the accuracy of the position-of-interaction determination derived from each active pixel in a coded-aperture based gamma camera. Here we present two methods to overcome the information loss associated with the loss of photons created by scintillation pixels that are coupled to an associated silicon photomultiplier pixel. Specifically, we detail the use of either: (1) light guides, or (2) scintillation pixel areas that match the area of the SiPM pixel. Compared with scintillator/SiPM couplings that have slightly mismatched intercept areas, the experimental results show that both methods substantially improve both the energy and spatial resolution by increasing light collection efficiency, but in terms of the image sensitivity and image quality, only slight improvements are accrued.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.2871-2876
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• 2022

The sensitivity of polymer-based capacitive relative humidity (RH) sensors after irradiation with neutrons, electrons and protons was measured. Degradation consists of the decreasing of the upper RH limit that can be measured. At the same time, low RH-level sensitivity is almost stable. After 30 krad of absorption dose, RH cut off is equal to 85% of max value, after 60 krad-40%. Degradation reduces after annealing which indicates high radiation sensitivity of the internal circuit in comparison to RH-sensing polymer film.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3970-3990
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• 2021

In view of the problems of insufficient data collection and processing capability of multi-source heterogeneous equipment, and low visibility of equipment status at the ship block construction site. A data collection method for ship block construction equipment based on wireless sensor network (WSN) technology and a data processing method based on edge computing were proposed. Based on the Browser/Server (B/S) architecture and the OneNET platform, an online monitoring system for ship block construction equipment was designed and developed, which realized the visual online monitoring and management of the ship block construction equipment status. Not only that, the feasibility and reliability of the monitoring system were verified by using the intelligent tire frame system as the application object. The research of this project can lay the foundation for the ship block construction equipment management and the ship block intelligent construction, and ultimately improve the quality and efficiency of ship block construction.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.1024-1029
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• 2022

Composite scintillation screens on a base of Gd_1.2Y_1.8Ga_2.5Al_2.5O₁₂:Ce (GYAGG) scintillator have been evaluated for neutron detection. Besides the powdered scintillator, the composite includes ⁶LiF particles; both are merged with a binder and deposited onto the light-reflecting aluminum substrate. Results obtained demonstrates that screens are suitable for use with a silicon photomultiplier readout to create a prospective solution for a compact and low-cost thermal neutron sensor. Composite GYAGG/⁶LiF scintillation screen shows a pretty matched sensitivity and γ-background rejection with a widely used ZnS/⁶LiF screens however, possesses forty times faster response.

• 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.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.3985-3995
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• 2022

The emergence of new nanoscale technologies has imposed significant challenges to designing reliable electronic systems in radiation environments. A few types of radiation like Total Ionizing Dose (TID) can cause permanent damages on such nanoscale electronic devices, and current state-of-the-art technologies to tackle TID make use of expensive radiation-hardened devices. This paper focuses on a novel and different approach: using machine learning algorithms on consumer electronic level Field Programmable Gate Arrays (FPGAs) to tackle TID effects and monitor them to replace before they stop working. This condition has a research challenge to anticipate when the board results in a total failure due to TID effects. We observed internal measurements of FPGA boards under gamma radiation and used three different anomaly detection machine learning (ML) algorithms to detect anomalies in the sensor measurements in a gamma-radiated environment. The statistical results show a highly significant relationship between the gamma radiation exposure levels and the board measurements. Moreover, our anomaly detection results have shown that a One-Class SVM with Radial Basis Function Kernel has an average recall score of 0.95. Also, all anomalies can be detected before the boards are entirely inoperative, i.e. voltages drop to zero and confirmed with a sanity check.