• Journal of Energy Engineering
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• v.25 no.4
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• pp.103-109
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• 2016

Polyethylene pipes has useful benefits which are anti-corrosive and flexible material, so it is used to gas pipes but also class 3 water pipes of nuclear power plant, process pipes of petrochemical plant and chemical plant. So the usage of polyethylene pipes is widely increased. But it has been limited for the usage of polyethylene, because it can not be directly detected to fusion joints by using non destructive evaluation. Polyethylene pipes are connected by two methods, one is butt fusion and the other is electrofusion. Butt fusion is widely used to connecting the pipes. It is proposed to method for determining the reliability of joints in this study that is detection of the melt flow zone at fusion joints. In this study, middle density polyethylene is used, outside diameter of the test specimen is 225mm and thickness is 20.5mm. Speed of ultrasonic of this test specimen is 2,200m/s. Test specimens were fabricated by varying the heating time which means from 0% to 130% applying time through heating plate to polyethylene for detecting melt flow zone. Also 4 additional test specimens were made, one was made that not scrapping attached surface of pipes but applying 100% of the proper heating time and the others were made to include of soil, gravel and vinly tape paper at fusion joints, that were also applied 100% of proper heating time. Ultrasonic testing to measure the melt flow zone of 20 test specimens was conducted by using 3.5MHz and 5.0MHz ultrasonic probes and melt flow zone measuring was conducted to three times at different point to one specimen. To differentiate the melt flow zone signal, post image processing was equally conducted to all test results and image levels, contrast, sharpen, threshold were adopted to all teat results and the test results were displayed gray scale. From the results, for the shorter heating times the reflection area of multiple echo have been increased, so the data was obtained from the position where it can be eliminated as much as possible. At 80% of proper heating time(168 sec.), the signal of melt flow zone was obtained clearly, so measuring could be conducted. From 7% of proper heating time(15 sec.) to shorter heating times. we could not obtain the signal because test specimen was not fused. From the result, we can verify that measuring of melt flow zone by using phased array ultrasonic imaging method is possible. And we can verify to complete and incomplete butt fusion by measuring the melt flow zone.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Journal of Chest Surgery
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• v.35 no.7
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• pp.501-510
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• 2002

Background: The various pathogeneses of acute respiratory distress syndrome have been suggested but not established yet. In the present study, the role of group II phospholipase $A_2$($PLA_2$) in the pathogenesis of gut ischemia-reperfusion(I/R) induced acute lung injury (ALI), especially in the pulmonary oxidative stress with infiltration of neutrophils was investigated. Material and Method: To induce ALI, reperfusion of mesentery was done for 120 min after clamping of superior mesenteric artery for 60 min in Sprague-Dawley rats that weighed about 300g. To exmaine the role of group II $PLA_2$ in ALI, especially endothelial injury associated with the action of neutrophils, lung myeloperoxidase activity, lung leak index, bronchoalveolar lavage fluid protein were measured, and pulmonary $PLA_2$ activity changes in gut I/R were also measured. The role of group II $PLA_2$in the neutrophilic generation of free radicals was assessed by inhibiting group II $PLA_2$ with rutin, manoalide and scalaradial. Furthermore, to verify the oxidative stress in the lung, histologic and free radical detecting cytochemical electron microscopy were done. Result: After reperfusion, ALI was developed with accumulation of neutrophils in the lung, which was confirmed by the increase of myeloperoxidase activity, lung leak index and bronchoalveolar lavage protein (p<0.001). The pulmonary and intestinal group II $PLA_2$ activities significantly increased after gut I/R which were reversed by rutin(p<0.001). In vitro, cytochrome-c reduction assay denoted the inhibitory effects of rutin, scalaradial and manoalide on the production of free radicals from isolated human neutrophils. Histologically, neutrophilic accumulation and pericapillary edema in the lung after gut I/R was detected by light microscopy which was suppressed by rutin. In $CeCl_3$ cytochemical electron microscopy, the increased production of hydrogen peroxide in the lung after gut I/R was confirmed and also the production of hydrogen peroxide was decreased by rutin. Conclusion: On the basis of these experimental results, the inhibition of group II $PLA_2$ seemed to mitigate gut I/R-induced ALI by suppressing the production of free radicals from the infiltrated neutrophils. Collectively, group II $PLA_2$ seems to play a crucial role in gut I/R-induced ALI by neutrophilic oxidative stress.

• Investigative Magnetic Resonance Imaging
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• v.4 no.2
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• pp.85-93
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• 2000

Purpose: To evaluate the availability of magnetic resonance (MRI voiding cystography for the diagnosis of vesicoureteral reflux (VUR) and to compare the sensitivity of MR voiding cystography (MRVC) with that of radiographic voiding cystourethrography (VCUG) in the detection of VUR. Material and Methods : MRVC was performed upon 20 children referred for investigation of VUR. Either coronal T1-weighted spin-echo or spoiled gradient-echo images were obtained before and after transurethral administration of a mixture of normal saline and gadopentetate dimeglumine, and immediately after voiding. The findings of MRVC were compared with those of VCUG performed within 6 months of MRVC. Results 1 VUR was detected in 23 ureterorenal units f16 VUR's by both methods, five VUR's by VCUG, and two VUR's by MRVC). The sensitivity of VCUG and MRVC in detecting VUR was 91.3% (21/23) and 78.3% (18/23), respectively. MRVC detected renal scarring in 15 out of 17 kidneys with scintigraphically detected renal scarring. Conclusion : Although MRVC is slightly less sensitive than VCUG in the detection of VUR, it can be used for the diagnosis of VUR and renal scarring simultaneouslyl and thus will reduce the radiation hazard.

• Journal of Chest Surgery
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• v.36 no.5
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• pp.348-355
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• 2003

Background: The prognostic significance of lymph node micrometastasis in non-small cell lung cancer remains controversial. We therefore investigated the clinicopathologic factors related to lymph node micrometastsis and evaluated the clinical relevance of micrometastasis with regard to recurrence. Material and Method: Five hundred six lymph nodes were obtained from 41 patients with stage 1 non-small ceil lung cancer who underwent curative resection between 1994 and 1998. Immunohistochemical staining using anti-cytokeratin Ab was used to detect micrometastasis in these lymph nodes. Result: Micrometastatic tumor cells were identified in pN0 lymph nodes in 14 (34.1%) of 41 patients. The presence of lymph node micrometastasis was not related to any clinicopathoiogic factor (p) 0.05). The recurrence rate was higher in patients with micrometastasis (57.1%) than in those without (37.0%), but the difference was not significant (p=0.22). Patients with micrometastasis had a lower 5-year recurrence-free survival rate (48.2%) than those without micrometastasis (64.1%), with a borderline significance (p=0.11), The S-year recurrence-free survival rate (25.0%) in the patients with 2 or more micrometastatic lymph nodes was significantly lower than that in the patients with no or single micrometastasis (p=0.02). In multivariate analysis, multiple lymph node micromestasis us was a significant independent predictor of recurrence (p=0.028, Risk ratio=3.568). Conclusion: Immunehistochemical anti-cytokeratin staining was a rapid, sensitive, and easy way of detecting lymph node micrometastasis. The presence of lymph node micrometastasis was not significantly associated with the recurrence, but had a tendency toward a poor prognosis in stage 1 non-small cell lung cancer. Especially, the presence of multiple micrometastatic lymph nodes was a significant and independent predictor of recurrence.

• Journal of radiological science and technology
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• v.29 no.1
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• pp.1-6
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• 2006

Purpose: Renal stones are common and typically arise within the collecting system. The renal sinus are contains the collection system, the renal vessels, lymphatcs, fat, and fibrous tissue. Because of the compression of all the large echoes in signal processing, the echo from the renal stone generally cannot be distinguished from large echoes emanating from normal structures of the renal sinus. Use of ultrasonography has been difficult for detecting small renal stone without posterior shadowing and chemical composition of stone. The aim of study was measuring for posterior acoustic shadowing to a stone for various scan parameter and it examines a help in renal stone diagnosis. Material & Methods: The stone was place on sponge examined in a water bath with a 3.5MHz or 7.5MHz transducer(LOGIQ 400, USA). First, tested a variety of gain. Second, tested a variety of dynamic range. Third, tested a variety of focal zone. Fourth, measuring of the echo level for low and high frequency for depth. Results: 1) Average echo level was 98 for low total gain(10 dB) and was 142 for high total gain(40 dB). Posterior acoustic shadowing of renal stone was clear for low gain. 2) Average echo level was 129 for low dynamic range(42 dB) and was 101 for high dynamic range(72 dB). Posterior acoustic shadowing of renal stone was clear for high dynamic range. 3) When stone is in focal zone of transducer, definite posterior acoustic shadow is identified. 4) Stone was clear appeared for high frequency(7.5 MHz) than low frequency(3.5 MHz) and it is not distorted. Conclusion: The demonstration of an posterior acoustic shadow of renal stone dependents on several technical factors such as gain, dynamic range, focus, and frequency. This various factors are a help in renal stone diagnosis.