• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2799-2805
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• 2024

Energy-resolved neutron imaging is an effective way to investigate the internal structure and residual stress of materials. Different sample sizes have varying requirements for the detector's imaging field of view (FOV) and spatial resolution. Therefore, a dual-mode energy-resolved neutron imaging detector was developed, which mainly consisted of a neutron scintillator screen, a mirror, imaging lenses, and a time-stamping optical fast camera. This detector could operate in a large FOV mode or a high spatial resolution mode. To evaluate the performance of the detector, the neutron wavelength spectra and the multiple spatial resolution tests were conducted at CSNS. The results demonstrated that the detector accurately measured the neutron wavelength spectra selected by a bandwidth chopper. The best spatial resolution was about 20 ㎛ in high spatial resolution mode after event reconstruction, and a FOV of 45.0 mm × 45.0 mm was obtained in large FOV mode. The feasibility was validated to change the spatial resolution and FOV by replacing the scintillator screen and adjusting the lens magnification.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.1942-1946
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• 2021

Gadolinium oxysulfide (GOS) is regarded as a novel scintillator for the realization of ultra-high spatial resolution in neutron imaging. Monte Carlo simulations of GOS scintillator show that the capability of its spatial resolution is towards the micron level. Through the time-of-flight method, the light output of a GOS scintillator was measured to be 217 photons per captured neutron, ~100 times lower than that of a ZnS/LiF:Ag scintillator. A detector prototype has been developed to evaluate the imaging solution with the GOS scintillator by neutron beam tests. The measured spatial resolution is ~36 ㎛ (28 line pairs/mm) at the modulation transfer function (MTF) of 10%, mainly limited by the low experimental collimation ratio of the beamline. The weak light output of the GOS scintillator requires an enormous increase in the neutron flux to reduce the exposure time for practical applications.

• Bulletin of the Korean Chemical Society
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• v.35 no.8
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• pp.2400-2402
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• 2014

Cu (II) detection is of great importance owing to its significant function in various biological processes. In this report, we developed a novel coumarin-based chemosensor bearing the salicylaldimine unit (2) for $Cu^{2+}$ selective detection. The results from fluorescence spectra demonstrated that the sensor could selectively recognize $Cu^{2+}$ over other metal cations and the detection limit is as low as $0.2{\mu}M$. Moreover, the confocal fluorescence imaging in HepG2 cells illustrated its potential for biological applications.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.410-413
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• 2008

Existing Outlier detections have been widely studied in geosensor networks. Recently, machine learning and data mining have been applied the outlier detection method to build a model that distinguishes outliers based on anchored criterion. However, it is difficult for the existing methods to detect outliers against incoming time-variant data, because outlier detection needs to monitor incoming data and classify irregular attacks. Therefore, in order to solve the problem, we propose a time-variant outlier detection using 2-dimensional grid method based on unanchored criterion. In the paper, outliers using geosensor data was performed to classify efficiently. The proposed method can be utilized applications such as network intrusion detection, stock market analysis, and error data detection in bank account.

• Computers and Concrete
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• v.32 no.6
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• pp.615-623
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• 2023

Crack detection is an essential method to ensure the safety of dam concrete structures. Low-quality crack images of dam concrete structures limit the application of neural network methods in crack detection. This research proposes a modified attentional mechanism model to reduce the disturbance caused by uneven light, shadow, and water spots in crack images. Also, the focal loss function solves the small ratio of crack information. The dataset collects from the network, laboratory and actual inspection dataset of dam concrete structures. This research proposes a novel method for crack detection of dam concrete structures based on the U-Net neural network, namely AF-UNet. A mutual comparison of OTSU, Canny, region growing, DeepLab V3+, SegFormer, U-Net, and AF-UNet (proposed) verified the detection accuracy. A binocular camera detects cracks in the experimental scene. The smallest measurement width of the system is 0.27 mm. The potential goal is to achieve real-time detection and localization of cracks in dam concrete structures.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1036-1038
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• 1998

The while, PD source detection in the oil TR was the level of the planar source detection. and it is to respected scientific research. The planar source detection technique had limits which have difficulties finding out the point of deterioration generation. In this study, our purpose is a development of PD source detection technique with Three-Dimensional using a principle and a technique of the planar source location.

• Journal of Veterinary Science
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• v.23 no.5
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• pp.32.1-32.11
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• 2022

Background: Classical swine fever (CSF) is a severe infectious disease of pigs that causes significant economic losses to the swine industry. Objectives: This study developed a solid-phase blocking enzyme-linked immunosorbent assay (spbELISA) method for the specific detection of antibodies against the CSF virus (CSFV) in porcine serum samples. Methods: A spbELISA method was developed based on the recombinant E2 expressed in Escherichia coli. The specificity of this established spbELISA method was evaluated using reference serum samples positive for antibodies against other common infectious diseases. The stability and sensitivity were evaluated using an accelerated thermostability test. Results: The spbELISA successfully detected the antibody levels in swine vaccinated with the C-strain of CSFV. In addition, the detection ability of spbELISA for CSFV antibodies was compared with that of other commercial ELISA kits and validated using an indirect immunofluorescence assay. The results suggested that the spbELISA provides an alternative, stable, and rapid serological detection method suitable for the large-scale screening of CSFV serum antibodies. Conclusions: The spbELISA has practical applications in assessing the vaccination status of large pig herds.

• Food Science and Biotechnology
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• v.18 no.4
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• pp.823-832
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• 2009

In recent years, special concerns have been raised about the safety assessment of foods and food ingredients derived from genetically modified organisms (GMOs). A growing number of countries establish regulations and laws for GMOs in order to allow consumers an informed choice. In this case, a lot of methods have been developed for the detection of GMOs. However, the reproducibility among methods and laboratories is still a problem. Consequently, it is still in great demand for more effective methods. In comparison with the gel electrophoresis, the capillary electrophoresis (CE) technology has some unique advantages, such as high resolution efficiency and less time consumption. Therefore, some CE-based methods have been developed for the detection of GMOs in recent years. All kinds of CE detection methods, such as ultraviolet (UV), laser induced fluorescence (LIF), and chemiluminescence (CL) detection, have been used for GMOs detection. Microchip capillary electrophoresis (MCE) methods have also been used for GMOs detection and they have shown some unique advantages.

• ETRI Journal
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• v.37 no.2
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• pp.212-221
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• 2015

As the amount of re-sequencing genome data grows, minimizing the execution time of an analysis is required. For this purpose, recent computing systems have been adopting both high-performance coprocessors and host processors. However, there are few applications that efficiently utilize these heterogeneous computing resources. This problem equally refers to the work of single nucleotide polymorphism (SNP) detection, which is one of the bottlenecks in genome data processing. In this paper, we propose a method for speeding up an SNP detection by enhancing the utilization of heterogeneous computing resources often used in recent high-performance computing systems. Through the measurement of workload in the detection procedure, we divide the SNP detection into several task groups suitable for each computing resource. These task groups are scheduled using a window overlapping method. As a result, we improved upon the speedup achieved by previous open source applications by a magnitude of 10.

• ETRI Journal
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• v.39 no.1
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• pp.124-134
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• 2017

Data races are one of the most difficult types of bugs in concurrent multithreaded systems. It requires significant time and cost to accurately detect bugs in complex large-scale programs. Although many race detection techniques have been proposed by various researchers, none of them are effective in all aspects. In this paper, we compare the performance of five recent dynamic race detection techniques: FastTrack, Acculock, Multilock-HB, SimpleLock+, and causally precedes (CP) detection. We experimentally demonstrate the strengths and weaknesses of these dynamic race detection techniques in terms of their detection capability, running time, and runtime overhead using 20 benchmark programs with different characteristics. The comparison results show that the detection capability of CP detection does not differ from that of FastTrack, and that SimpleLock+ generates the lowest overhead among the hybrid detection techniques (Acculock, SimpleLock+, and Multilock-HB) for all benchmark programs. SimpleLock+ is 1.2 times slower than FastTrack on average, but misses one true data race reported from Mutilock-HB on the large-scale benchmark programs.