• Fire Science and Engineering
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• v.24 no.1
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• pp.111-115
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• 2010

For the development of fire detector equipments that was fused independent type of fire detector and a wireless communication function, this paper adopted a signal processing method of an independent formation fire sensor by a ZigBee communication method, and it was designed that the combinations of fire detector and wireless communications network were enabled. When a fire occurred, output voltage of a fire detector was 0.4 V, the movement pulse width was $600{\mu}s$, and that was confirmed by an experiment. When it has lookout state, LED worked by 30 s cycle and working current is 0.12 mA. When a fire occurred, LED worked by 0.5 s cycle and working current was 7 mA, and through this experiment of power source, Chamber tester, smoke tester,IRED, LED lighting, the propriety of the method that we showed in this paper confirmed.

• Journal of IKEEE
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• v.20 no.2
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• pp.123-127
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• 2016

In this study, gamma radiation detectors are created by integrating the following combinations of different YSO scintillators and PMT(photomultiplier-tubes) respectively: $3mm{\times}3mm{\times}2mm$, $3mm(Dia){\times}15mm$, $3mm(Dia){\times}20mm$, $10mm(Dia){\times}20mm$. In addition, the scintillator with a 10mm diameter was integrated with a light guide with a 2mm thickness, 10mm entry and 3mm exit, using LightTools. The constructed detector used the standard gamma ray sources $^{137}Cs$(662keV) to analyze the spectral characteristics of gamma rays. The results indicate that at 662keV, the energy resolutions were 14.46%, 21.10%, and 10.71% for the first three combinations respectively. The best results were recorded for the $10mm(Dia){\times}20mm$ detector with light guide, which had an energy resolution of 7.48%.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.8
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• pp.24-33
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• 2002

For mixed signal circuits that integrate both analog and digital blocks onto the same chip, testing the mixed circuits has become the bottleneck. Since most of mixed signal circuits are functionally tested, mixed signal testing needs expensive automatic test equipments for test input generation and response acquisition. In this paper, a new efficient BIST is developed which can be used for mixed signal circuits. In the new BIST, only faults on embedded resistances, capacitances and its combinations are considered. To guarantee the quality of chips, the new BIST performs both voltage testing and phase testing. Using these two testing modes, all the faults are detected. In order to support this technique, the voltage detector and the phase detector are developed. Experimental results prove the efficiency of the new BIST.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.617-626
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• 2022

To effectively monitor the variety of distributions of neutron flux, fuel power or temperatures in the reactor core, usually the ex-core and in-core neutron detectors are employed. The thermocouples for temperature measurement are installed in the coolant inlet or outlet of the respective fuel assemblies. It is necessary to reconstruct the measurement information of the whole reactor position. However, the reading of different types of detector in the core reflects different aspects of the 3D power distribution. The feasibility of reconstruction the core three-dimension power distribution by using different combinations of in-core, ex-core and thermocouples detectors is analyzed in this paper to synthesize the useful information of various detectors. A comparison of multilayer perceptron (MLP) network and radial basis function (RBF) network is performed. RBF results are more extreme precision but also more sensitivity to detector failure and uncertainty, compare to MLP networks. This is because that localized neural network could offer conservative regression in RBF. Adding random disturbance in training dataset is helpful to reduce the influence of detector failure and uncertainty. Some convolution neural networks seem to be helpful to get more accurate results by use more spatial layout information, though relative researches are still under way.

• Economic and Environmental Geology
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• v.51 no.1
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• pp.39-48
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• 2018

We carried out the standoff compensation data from 2007 to 2011 into four different density borehole models to find out the most effectiveness of standoff compensation charts. First, we investigated the irregular collapse characteristics of gamma ray and cut the non-ideal gamma response to improve the effectiveness of the standoff compensation error data. Effectiveness of detector combinations, density of borehole, and spine and ribs techniques from the modified standoff compensation data was analyzed. As the result of comparison, LSD-MSD combination has been suitable for standoff compensation more than LSD-SSD combination and it is possible to do standoff compensation for soil or weathered zone under groundwater level without fatal errors. Even though error scales of density transformed spine and ribs techniques were generally large compared to the conventional standoff compensation, standoff compensation for soil and weathered zone under groundwater level were sufficiently effective.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.11C
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• pp.932-939
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• 2008

Recently lattice-reduction (LR) has been used in signal detection for multiple-input multiple-output (MIMO) systems. The conventional LR aided detection schemes are combinations of LR and signal detection methods such as zero-forcing (ZF) and minimum mean square error (MMSE) detection. In this paper, we propose the Lattice-Reduction-aided scheme based on extended noise variance matrix to search good candidate symbol set in quantization step. Then this scheme estimates transmitted symbol with Semidefinite Relaxation by candidate symbol set. Simulation results in a random MIMO system show that the proposed scheme exhibits improved performance and a slight increase in complexity.

• Journal of the Korean Society of Radiology
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• v.13 no.1
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• pp.15-20
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• 2019

We developed a novel depth-of-interaction positron emission tomography (PET) detector using wavelength shifting (WLS) fibers and optimized it. The optimization module was designed by using two scintillators and three WLS fibers and attaching a sensor to the end of the WLS fiber. Optimum combinations were obtained through the light collection efficiency and the light collection ratio between sensors depending on coupling materials and reflectors of scintillators, WLS fibers and sensors. The highest light collection efficiency and the light collection ratio between sensors were obtained in the combination of the epoxy (coupling materials)-diffuse reflector (scintillators)-specular reflector (WLS fibers).

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.63-69
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• 2017

In order to observe the noise property of the flat-panel digital radiography detector, measuring the normalized noise power spectrum (NNPS) from acquired x-ray images is conducted. However, the conventional NNPS measurement has an unstable property depending on the acquired image. Averaging the sample periodograms of the input image is usually performed to estimate the NNPS values and increasing the number of samples can provide a reliable NNPS measurement. In this paper, for a finite number of images, two measurement methods, which are based on averaging spectra, such as the image periodogram, are proposed and their performances are analyzed. Using x-ray images acquired from two types of radiography detectors, the two spectrum averaging methods are compared and it is shown that averaging spectra based on the maximal number of combinations of the image pairs provides the best performance in measuring NNPS.

• Journal of Radiation Protection and Research
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• v.46 no.4
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• pp.204-212
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• 2021

Background: Identification of radioisotopes for plastic scintillation detectors is challenging because their spectra have poor energy resolutions and lack photo peaks. To overcome this weakness, many researchers have conducted radioisotope identification studies using machine learning algorithms; however, the effect of data normalization on radioisotope identification has not been addressed yet. Furthermore, studies on machine learning-based radioisotope identifiers for plastic scintillation detectors are limited. Materials and Methods: In this study, machine learning-based radioisotope identifiers were implemented, and their performances according to data normalization methods were compared. Eight classes of radioisotopes consisting of combinations of ²²Na, ⁶⁰Co, and ¹³⁷Cs, and the background, were defined. The training set was generated by the random sampling technique based on probabilistic density functions acquired by experiments and simulations, and test set was acquired by experiments. Support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN) were implemented as radioisotope identifiers with six data normalization methods, and trained using the generated training set. Results and Discussion: The implemented identifiers were evaluated by test sets acquired by experiments with and without gain shifts to confirm the robustness of the identifiers against the gain shift effect. Among the three machine learning-based radioisotope identifiers, prediction accuracy followed the order SVM > ANN > CNN, while the training time followed the order SVM > ANN > CNN. Conclusion: The prediction accuracy for the combined test sets was highest with the SVM. The CNN exhibited a minimum variation in prediction accuracy for each class, even though it had the lowest prediction accuracy for the combined test sets among three identifiers. The SVM exhibited the highest prediction accuracy for the combined test sets, and its training time was the shortest among three identifiers.

• Ocean Systems Engineering
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• v.6 no.3
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• pp.217-231
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• 2016

This paper presents a new approach for underwater image analysis using the bi-dimensional empirical mode decomposition (BEMD) technique and the phase congruency information. The BEMD algorithm, fully unsupervised, it is mainly applied to texture extraction and image filtering, which are widely recognized as a difficult and challenging machine vision problem. The phase information is the very stability feature of image. Recent developments in analysis methods on the phase congruency information have received large attention by the image researchers. In this paper, the proposed method is called the EP model that inherits the advantages of the first two algorithms, so this model is suitable for processing underwater image. Moreover, the receiver operating characteristic (ROC) curve is presented in this paper to solve the problem that the threshold is greatly affected by personal experience when underwater image edge detection is performed using the EP model. The EP images are computed using combinations of the Canny detector parameters, and the binaryzation image results are generated accordingly. The ideal EP edge feature extractive maps are estimated using correspondence threshold which is optimized by ROC analysis. The experimental results show that the proposed algorithm is able to avoid the operation error caused by manual setting of the detection threshold, and to adaptively set the image feature detection threshold. The proposed method has been proved to be accuracy and effectiveness by the underwater image processing examples.