• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3431-3437
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• 2021

In this study, one-dimensional gamma ray source positions are estimated using a plastic scintillating optical fiber, two photon counters and via data processing with a machine learning algorithm. A nonlinear regression algorithm is used to construct a machine learning model for the position estimation of radioactive sources. The position estimation results of radioactive sources using machine learning are compared with the theoretical position estimation results based on the same measured data. Various tests at the source positions are conducted to determine the improvement in the accuracy of source position estimation. In addition, an evaluation is performed to compare the change in accuracy when varying the number of training datasets. The proposed one-dimensional gamma ray source position estimation system with plastic scintillating fiber using machine learning algorithm can be used as radioactive leakage scanners at disposal sites.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.3
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• pp.108-114
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• 2008

Uniaxial tension and compression test were conducted on conventional and crosslinked ultra-high molecular polyethylene (UHMWPE) all prepared from the same lot of medical grade GUR 1050. The conventional materials were unirradiated and gamma irradiated with $25kGy{\sim}200kGy$. Gamma irradiated processing was found to significantly impact the crystallinity, and hence the mechanical behavior, of the highly crosslinked UHMWPE. The crystallinity and radiation dose were key predictors of the uniaxial yielding, hardness, plastic flow, and failure properties of conventional and highly crosslinked UHMWPE. The correlation model from experiments would be the basic information to design the liner of artificial joint.

• Journal of Radiation Protection and Research
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• v.44 no.2
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• pp.53-63
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• 2019

Background: As neutron fields are always accompanied by gamma rays, it is essential to distinguish neutrons from gamma rays in the detection of neutrons. Neutrons and gamma rays can be separated by pulse shape discrimination (PSD) methods. Recently, we performed characterization of a stilbene scintillator detector and an EJ-301 liquid scintillator detector with a high-speed digitizer DT5730 and investigated optimized PSD variables for both detectors. This study is for providing a basis for developing fast neutron/gamma-ray dual-particle imager. Materials and Methods: We conducted PSD experiments using stilbene scintillator and EJ-301 liquid scintillator and evaluated neutron and gamma ray discriminability of each PSD method with a $^{137}Cs$ gamma source and a $^{252}Cf$ neutron source. We implemented digital signal processing techniques to apply two PSD methods - the charge comparison (CC) method and the constant time discrimination (CTD) method - to distinguish neutrons from gamma rays. We tried to find optimized PSD variables giving the best discriminability in a given experimental condition. Results and Discussion: For the stilbene scintillator detector, the charge comparison method and the constant time discrimination method both delivered the PSD FOM values of 1.7. For the EJ-301 liquid scintillator detector, both PSD methods delivered the PSD FOM values of 1.79. With the same PSD variables, PSD performance was excellent in $300{\pm}100keVee$, $500{\pm}100keVee$, and $700{\pm}100keVee$ energy regions. This result shows that we can achieve an effective discrimination of neutrons from gamma rays using these scintillator detector systems. Conclusion: We applied both PSD methods to a stilbene and a liquid scintillator and optimized the PSD performance represented by FOM values. We observed a good separation performance of both scintillators combined with a high-speed digitizer and digital PSD. These results will provide reference values for the dual-particle imager we are developing, which can image both fast neutrons and gamma rays simultaneously.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.66-70
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• 2017

Radioactive materials are used in medicine, non-destructive testing, and nuclear plants. Source localization is especially important during nuclear decommissioning and decontamination because the actual location of the radioactive source within nuclear waste is often unknown. The coded-aperture imaging technique started with space exploration and moved into X-ray and gamma ray imaging, which have imaging process characteristics similar to each other. In this study, we simulated $21{\times}21$ and $37{\times}37$ coded aperture collimators based on a modified uniformly redundant array (MURA) pattern to make a gamma imaging system that can localize a gamma-ray source. We designed a $21{\times}21$ coded aperture collimator that matches our gamma imaging detector and did feasibility experiments with the coded aperture imaging system. We evaluated the performance of each collimator, from 2 mm to 10 mm thicknesses (at 2 mm intervals) using root mean square error (RMSE) and sensitivity in a simulation. In experimental results, the full width half maximum (FWHM) of the point source was $5.09^{\circ}$ at the center and $4.82^{\circ}$ at the location of the source was $9^{\circ}$. We will continue to improve the decoding algorithm and optimize the collimator for high-energy gamma rays emitted from a nuclear power plant.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4072-4079
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• 2021

This article presents a study on the state-of-the-art methods for automated radioactive material detection and identification, using gamma-ray spectra and modern machine learning methods. The recent developments inspired this in deep learning algorithms, and the proposed method provided better performance than the current state-of-the-art models. Machine learning models such as: fully connected, recurrent, convolutional, and gradient boosted decision trees, are applied under a wide variety of testing conditions, and their advantage and disadvantage are discussed. Furthermore, a hybrid model is developed by combining the fully-connected and convolutional neural network, which shows the best performance among the different machine learning models. These improvements are represented by the model's test performance metric (i.e., F1 score) of 93.33% with an improvement of 2%-12% than the state-of-the-art model at various conditions. The experimental results show that fusion of classical neural networks and modern deep learning architecture is a suitable choice for interpreting gamma spectra data where real-time and remote detection is necessary.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2002.07a
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• pp.25-37
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• 2002

The most important industrial application of gamma radiation in characterizing green compacts is the determination of the density. Examples are given where this method is applied in manufacturing technical components in powder metallurgy. The requirements imposed by modern quality management systems and operation by the workforce in industrial production are described. The accuracy of measurement achieved with this method is demonstrated and a comparison is given with other test methods to measure the density. The advantages and limitations of gamma ray densitometry are outlined. The gamma ray densitometer measures the attenuation of gamma radiation penetrating the test parts (Fig. 1). As the capability of compacts to absorb this type of radiation depends on their density, the attenuation of gamma radiation can serve as a measure of the density. The volume of the part being tested is defined by the size of the aperture screeniing out the radiation. It is a channel with the cross section of the aperture whose length is the height of the test part. The intensity of the radiation identified by the detector is the quantity used to determine the material density. Gamma ray densitometry can equally be performed on green compacts as well as on sintered components. Neither special preparation of test parts nor skilled personnel is required to perform the measurement; neither liquids nor other harmful substances are involved. When parts are exhibiting local density variations, which is normally the case in powder compaction, sectional densities can be determined in different parts of the sample without cutting it into pieces. The test is non-destructive, i.e. the parts can still be used after the measurement and do not have to be scrapped. The measurement is controlled by a special PC based software. All results are available for further processing by in-house quality documentation and supervision of measurements. Tool setting for multi-level components can be much improved by using this test method. When a densitometer is installed on the press shop floor, it can be operated by the tool setter himself. Then he can return to the press and immediately implement the corrections. Transfer of sample parts to the lab for density testing can be eliminated and results for the correction of tool settings are more readily available. This helps to reduce the time required for tool setting and clearly improves the productivity of powder presses. The range of materials where this method can be successfully applied covers almost the entire periodic system of the elements. It reaches from the light elements such as graphite via light metals (AI, Mg, Li, Ti) and their alloys, ceramics ($AI_20_3$, SiC, Si_3N_4, $Zr0_2$, ...), magnetic materials (hard and soft ferrites, AlNiCo, Nd-Fe-B, ...), metals including iron and alloy steels, Cu, Ni and Co based alloys to refractory and heavy metals (W, Mo, ...) as well as hardmetals. The gamma radiation required for the measurement is generated by radioactive sources which are produced by nuclear technology. These nuclear materials are safely encapsulated in stainless steel capsules so that no radioactive material can escape from the protective shielding container. The gamma ray densitometer is subject to the strict regulations for the use of radioactive materials. The radiation shield is so effective that there is no elevation of the natural radiation level outside the instrument. Personal dosimetry by the operating personnel is not required. Even in case of malfunction, loss of power and incorrect operation, the escape of gamma radiation from the instrument is positively prevented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.780-781
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• 2016

The stereo radiation detection system detects gamma ray source and measures the two dimensional distribution image based on the detection result. Then the system is implemented to measure the distance to the radiation source from the system in 3D space using stereo vision algorithm. In this paper, we reduced the time for a gamma-ray scan space detection through image processing algorithms. In addition, it combines radiation and visible light images. Then we conducted a study for improving the distribution of gamma-ray detection efficiency through the stereo calibration using a 3D visualization. As a result, we obtain an improved detection time by more than 30% and have acquired a visible image with a 3D monitor.

• Nuclear Engineering and Technology
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• v.52 no.8
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• pp.1771-1776
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• 2020

Adverse effects in the measured gamma spectrum caused by radioactive statistical fluctuations, gamma ray scattering, and electronic noise can be reduced by energy spectrum denoising. Wavelet threshold denoising can be used to perform multi-scale and multi-resolution analysis on noisy signals with small root mean square errors and high signal-to-noise ratios. However, in traditional wavelet threshold denoising methods, there are signal oscillations in hard threshold denoising and constant deviations in soft threshold denoising. An improved wavelet threshold calculation method and threshold processing function are proposed in this paper. The improved threshold calculation method takes into account the influence of the number of wavelet decomposition layers and reduces the deviation caused by the inaccuracy of the threshold. The improved threshold processing function can be continuously guided, which solves the discontinuity of the traditional hard threshold function, avoids the constant deviation caused by the traditional soft threshold method. The examples show that the proposed method can accurately denoise and preserves the characteristic signals well in the gamma energy spectrum.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.6
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• pp.586-591
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• 2010

VLC (Visible Light Communication) is a wireless communication method using light that is visible to the human eye. It has a major advantage that it causes no interference to RF-based devices. This makes wireless communication possible in RF hazardous areas such as nuclear facilities. In order to apply VLC communication in harsh environment of nuclear power plant, the high luminance LEDs, which are key components of the VLC communication, have been gamma-ray irradiated at the dose rate of 4kGy/h during 72 hours up to a total dose of 288 kGy. The radiation induced color-center was formed in the LED housing cap made of transparent plastic or acryl material. The beam degradations of high luminance LEDs by high dose-rate gammaray irradiation are analyzed using color CCD image processing technology.

• Journal of Mushroom
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• v.18 no.4
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• pp.393-397
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• 2020

Mycelium composites and leathers have versatile material properties based on their composition and manufacturing process. To prepare mycelium mat for the production of mushroom leather, several strains were mutated by gamma rays. Some mutant strains, including Lentinula edodes, Ganoderma lucidium, and Schizophyllum commune showed good hyphae growth rate and density on saw-dust media. Irradiation power (Gy), time, and height from the radiation source to the sample were examined. Based on the preliminary data obtained in this study, comprehensive research should be conducted to explore the optimal strains and culture conditions for mycelium-based leather production.