• Journal of Radiation Protection and Research
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• v.35 no.2
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• pp.69-76
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• 2010

Prototype double-scattering Compton camera, which consists of three gamma-ray detectors, that is, two double-sided silicon strip detectors (DSSDs) as scatterer detectors and a NaI(Tl) scintillation detector as an absorber detector, could provide high imaging resolution with a compact system. In the present study, the energy resolution and the timing resolution of component detectors were measured, and the parameters affecting the energy resolution of the DSSD were examined in terms of equivalent noise charge (ENC). The energy resolutions of the DSSD-1 and DSSD-2 were, in average, $25.2keV{\pm}0.8keV$ FWHM and $31.8keV{\pm}4.6keV$ FWHM at the 59.5 keV peak of $^{241}Am$, respectively. The timing resolutions of the DSSD and NaI(Tl) scintillation detector were 57.25 ns FWHM and 7.98 ns FWHM, respectively. In addition, the Compton image was obtained for a point-like $^{137}Cs$ gamma source with double-scattering Compton camera. From the present experiment, the imaging resolution of 8.4 mm FWHM (angular resolution of $8.1^{\circ}$ FWHM), and the imaging sensitivity of $1.5{\times}10^{-7}$ (intrinsic efficiency of $1.9{\times}10^{-6}$) were obtained.

• Journal of Radiation Protection and Research
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• v.39 no.2
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• pp.81-88
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• 2014

Recently, a new imaging method, gamma vertex imaging (GVI), was proposed for the verification of in-vivo proton dose distribution. In GVI, the vertices of prompt gammas generated by proton induced nuclear interaction were determined by tracking the Compton-recoiled electrons. The GVI system is composed of a beryllium electron converter for converting gamma to electron, two double-sided silicon strip detectors (DSSDs) for the electron tracking, and a scintillation detector for the energy determination of the electron. In the present study, the modules of a charge sensitive preamplifier (CSP) and a shaping amplifier for the analog signal processing of DSSD were developed and the performances were evaluated by comparing the energy resolutions with those of the commercial products. Based on the results, it was confirmed that the energy resolution of the developed CSP module was a little lower than that of the CR-113 (Cremat, Inc., MA), and the resolution of the shaping amplifier was similar to that of the CR-200 (Cremat, Inc., MA). The value of $V_{rms}$ representing the magnitude of noise of the developed system was estimated as 6.48 keV and it was confirmed that the trajectory of the electron can be measured by the developed system considering the minimum energy deposition ( > ~51 keV) of Compton-recoiled electron in 145-${\mu}m$-thick DSSD.

• 대한방사선방어학회:학술대회논문집
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• 2011.11a
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• pp.240-241
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• 2011

• IE interfaces
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• v.2 no.1
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• pp.59-68
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• 1989

The determination of the optimal depreciation as constrained by the tax law is very complicated computation which is laborious and time-comsurning process. The objective of this research effort is to develop a Decision Support System for Depreciation(DSSD) that can be used by a decision maker to analyze alternative depreciation strategies and to select that strategy which will be most beneficial to the firm from a tax and net profit standpoint.

• Journal of radiological science and technology
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• v.34 no.3
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• pp.215-219
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• 2011

Monte Carlo simulation was performed to investigate optimal system of proton computed tomography and to avoid the errors by using data from X ray computed tomography in proton therapy. The informations from two DSSDs to measure position and LYSO scintillation detector to measure the residual energy of proton particle in GEANT4 were used for reconstruction computed tomography.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.418-421
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• 2021

모바일 디바이스 화면에 대하여 클릭 가능한 객체를 인식하기 위한 Object detection network architecture 를 제안한다. DSSD 를 Baseline 으로 SE block 이 추가된 Backbone network 와 SSD layer, FPN 구조를 사용한다. 기존의 1:1 비율의 네트워크의 Input resolution 을 모바일 화면과 유사한 1:2 비율로 변경하여 효율적으로 피처를 추출한다. 또한 해당 모델을 학습하기 위한 효율적인 데이터셋을 구축한다. 모바일 화면에서 클릭 가능한 객체를 기준으로 데이터를 수집하여 총 24,937 개의 Annotation data 를 Text, Image, Button, Region 등 8 개의 카테고리로 세분화하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1795-1811
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• 2019

In order to achieve rapid and accurate detection of vehicle objects in complex traffic conditions, we propose a novel vehicle detection method. Firstly, more contextual and small-object vehicle information can be obtained by our Joint Feature Network (JFN). Secondly, our Evolved Region Proposal Network (EPRN) generates initial anchor boxes by adding an improved version of the region proposal network in this network, and at the same time filters out a large number of false vehicle boxes by soft-Non Maximum Suppression (NMS). Then, our Mask Network (MaskN) generates an example that includes the vehicle occlusion, the generator and discriminator can learn from each other in order to further improve the vehicle object detection capability. Finally, these candidate vehicle detection boxes are optimized to obtain the final vehicle detection boxes by the Fine-Tuning Network(FTN). Through the evaluation experiment on the DETRAC benchmark dataset, we find that in terms of mAP, our method exceeds Faster-RCNN by 11.15%, YOLO by 11.88%, and EB by 1.64%. Besides, our algorithm also has achieved top2 comaring with MS-CNN, YOLO-v3, RefineNet, RetinaNet, Faster-rcnn, DSSD and YOLO-v2 of vehicle category in KITTI dataset.