• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.151-154
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• 2001

In this Paper, a new harmonic imaging technique is proposed and evaluated experimentally. In the proposed method, a weighted chin signal with a hanning window is transmitted. The RF samples obtained on each array element are individually compressed by correlating with the reference signal defined as the 2nd harmonic (2f0) component of a transmitted chirp signal generated in a square-law system. The proposed method uses the compressed 2f0 component to form an image, for which the crosscorrelation term with f0 component should be suppressed below at least -60dB. After experiment, the 6dB pulse width and peak sidelobe level of the compressed 2f0 component were 0.7us and -60dB, respectively. This result shows that the proposed method can successfully eliminate the f0 component with a single transmit-receive event and therefore is more efficient than the conventional pulse inversion (PI) method in terms of frame rate. We also observed that the 2nd harmonic compont starts to decrease for source pressure higher than 210kPa in water, which implies that SNR of the 2nd harmonic imaging using short pulses cnanot be incresed beyond a certain limit.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1363-1369
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• 2018

Breast cancer is one of the most common cancers in women worldwide. In Korea, breast cancer is most common cancer in women followed by thyroid cancer. The purpose of this study is to evaluate the possibility of using deep - run model for segmentation of breast masses and to identify the best deep-run model for breast mass segmentation. In this study, data of patients with breast masses were collected at Asan Medical Center. We used 596 images of mammography and 596 images of gold standard. In the area of interest of the medical image, it was cut into a rectangular shape with a margin of about 10% up and down, and then converted into an 8-bit image by adjusting the window width and level. Also, the size of the image was resampled to $150{\times}150$. In Deconvolution net, the average accuracy is 91.78%. In U-net, the average accuracy is 90.09%. Deconvolution net showed slightly better performance than U-net in this study, so it is expected that deconvolution net will be better for breast mass segmentation. However, because of few cases, there are a few images that are not accurately segmented. Therefore, more research is needed with various training data.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.

• Journal of the Korean Wood Science and Technology
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• v.48 no.5
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• pp.676-684
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• 2020

This study was carried out in order to evaluate the bending creep deflection of glulams and bolted glulams beam-to-beam connection with steel-gusset plates and bolts under changing relative humidity. The two types of glulam beams (130 mm in width, 175 mm in thickness, and 3000 mm in length) used in this study were made from domestic larch and composed of seven layers. The gussets were made of 8-mm-thick steel plates. Creep testing was conducted under constant loads in an uncontrolled environment. The test was carried out in a room that was well ventilated through a window. The creep test specimens were loaded for 33,000 hours. A bending creep test for the glulams was conducted through four-point loading. The applied stresses were 20% and 30% of the MOR in the static bending test for the glulam and bolted glulam, respectively. After 33,000 hours, the creep deflection of the glulam at a 20% stress level increased by 39% to 99%, while the creep deflection of the glulam at a 30% stress level increased by 27% to 67%, as compared with instantaneous elastic deflection. The relative creep increased during autumn and winter, and recovered during spring and summer. The relative creep of the bolted glulams was changed abruptly by loading up to 5,000 hours, but stabilized after 5,000 hours, and then gradually increased until 33,000 hours. The relative creep of the bolted glulam increased 2.11 times on average after 33,000 hours.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.75-88
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• 2008

This paper presents a spatial-level nonparametric multi-focus image fusion technique based on kernel estimates of input image blocks' underlying class-conditional probability density functions. Image fusion is approached as a classification task whose posterior class probabilities, P($wi{\mid}Bikl$), are calculated with likelihood density functions that are estimated from the training patterns. For each of the C input images Ii, the proposed method defines i classes wi and forms the fused image Z(k,l) from a decision map represented by a set of $P{\times}Q$ blocks Bikl whose features maximize the discriminant function based on the Bayesian decision principle. Performance of the proposed technique is evaluated in terms of RMSE and Mutual Information (MI) as the output quality measures. The width of the kernel functions, ${\sigma}$, were made to vary, and different kernels and block sizes were applied in performance evaluation. The proposed scheme is tested with C=2 and C=3 input images and results exhibited good performance.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.196.2-196.2
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• 2013

본 연구에서는 분자선 에피택시 (MBE)법으로 성장된 InAs submonolayer quantum dot (SML-QD)을 태양전지에 응용하여 광학 및 전기적 특성을 평가하였다. 본 연구에서 사용된 양자점 태양전지(quantum dot solar cell, QDSC)의 구조는 n+-GaAs 기판 위에 n+-GaAs buffer와 n-GaAs base layer를 차례로 성장 한 후, 활성영역에 InAs/InGaAs SML-QD와 n-GaAs spacer layer를 8주기 형성하였다. 그 위에 p+-GaAs emitter, p+-AlGaAs window layer를 성장하고 ohmic contact을 위하여 p+-GaAs 를 성장하였다. SML-QD 구조의 두께는 0.3 ML 이며, 이때 SML-QD의 적층수를 4 stacks 으로 고정하였다. SML-QD 와의 비교를 위하여 2.0 ML크기의 InAs자발 형성 양자점 태양전지(SK-QDSC)과 GaAs 단일 접합 태양전지 (reference-SC)를 동일한 성장조건에서 제작하였다. PL 측정 결과, 300 K에서 SML-QD의 발광 피크는 SK-QD 보다 고에너지에서 나타나는데(1.349 eV), 이것은 SML-QD가 SK-QD보다 작은 크기를 가지기 때문으로 사료된다. SML-QD는 single peak를 보이는 반면, SK-QD는 dual peaks (1.112 / 1.056 eV)을 확인하였다. SML-QD의 반치폭(full width at half maximum, FWHM)이 SK-QD에 비하여 작은 것으로 보아 SML-QD가 SK-QD보다 양자점 크기 분포의 균일도가 높은 것으로 해석된다. Illumination I-V 측정 결과, SML-QDSC의 개방 전압(VOC) 과 단락전류밀도(JSC)는 SK-QDSC의 값과 비교해 보면, 각각 47 mV와 0.88 mA/cm2만큼 증가하였다. 이는 SK-QD보다 상대적으로 작은 크기를 가진 SML-QD로 인해 VOC가 증가되었으며, SML-QD가 SK-QD 보다 태양광을 흡수할 수 있는 영역이 비교적 적지만, QD내에 존재하는 energy level에서 탈출 할 수 있는 확률이 더 높음으로써 JSC가 증가한 것으로 분석 된다.

• The Korean Journal of Nuclear Medicine Technology
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• v.12 no.3
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• pp.208-213
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• 2008

Purpose: Generally a whole body bone scan has been known as one of the most frequently executed exams in the nuclear medicine fields. Asan medical center, usually use various gamma camera systems - manufactured by PHILIPS (PRECEDENCE, BRIGHTVIEW), SIEMENS (ECAM, ECAM signature, ECAM plus, SYMBIA T2), GE (INFINIA) - to execute whole body scan. But, as we know, each camera's sensitivity is not same so it is hard to consistent diagnosis of patients. So our purpose is when we execute whole body bone scans, we exclude uncontrollable factors and try to correct controllable factors such as inherent sensitivity of gamma camera. In this study, we're going to measure each gamma camera's sensitivity and study about reasonable correction factors of whole body bone scan to follow up patient's condition using different gamma cameras. Materials and Methods: We used the $^{99m}Tc$ flood phantom, it recommend by IAEA recommendation based on general counts rate of a whole body scan and measured counts rates by the use of various gamma cameras - PRECEDENCE, BRIGHTVIEW, ECAM, ECAM signature, ECAM plus, IFINIA - in Asan medical center nuclear medicine department. For measuring sensitivity, all gamma camera equipped LEHR collimator (Low Energy High Resolution multi parallel Collimator) and the $^{99m}Tc$ gamma spectrum was adjusted around 15% window level, the photo peak was set to 140-kev and acquirded for 60 sec and 120 sec in all gamma cameras. In order to verify whether can apply calculated correction factors to whole body bone scan or not, we actually conducted the whole body bone scan to 27 patients and we compared it analyzed that results. Results: After experimenting using $^{99m}Tc$ flood phantom, sensitivity of ECAM plus was highest and other sensitivity order of all gamma camera is ECAM signature, SYMBIA T2, ECAM, BRIGHTVIEW, IFINIA, PRECEDENCE. And yield sensitivity correction factor show each gamma camera's relative sensitivity ratio by yielded based on ECAM's sensitivity. (ECAM plus 1.07, ECAM signature 1.05, SYMBIA T2 1.03, ECAM 1.00, BRIGHTVIEW 0.90, INFINIA 0.83, PRECEDENCE 0.72) When analyzing the correction factor yielded by $^{99m}Tc$ experiment and another correction factor yielded by whole body bone scan, it shows statistically insignificant value (p<0.05) in whole body bone scan diagnosis. Conclusion: In diagnosing the bone metastasis of patients undergoing cancer, whole body bone scan has been conducted as follow up tests due to its good points (high sensitivity, non invasive, easily conducted). But as a follow up study, it's hard to perform whole body bone scan continuously using same gamma camera. If we use same gamma camera to patients, we have to consider effectiveness of equipment's change by time elapsed. So we expect that applying sensitivity correction factor to patients who tested whole body bone scan regularly will add consistence in diagnosis of patients.