• Journal of Internet Computing and Services
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• v.9 no.2
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• pp.89-105
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• 2008

So far, most of research on automated test data generation(ATDG) deals with programs without pointers. Recently, few works hove been done on ATDG in the presence of pointers, but they ore path-oriented techniques which require the specification of on entire program path to be tested or a program to be executed. This paper presents a new technique for generating test data even without specifying a program path completely. The presented technique is a static technique which transforms the test data generation problem into a SAT(SATisfiability) problem and makes advantage of SAT solvers for ATDG. For the ends, we transform a program under test into Alloy which is the first-order relational logic and then produce test data via Alloy analyzer.

• YAKHAK HOEJI
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• v.34 no.3
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• pp.161-165
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• 1990

An automated, simple, and reliable method was developed for determining in vitro drug release rate from transdermal delivery dosage forms. The patch is held in position in the heating block by sandwiching it between the middle plate and the bottom plate of diffusion cell. The dissolution profile of the commercially available transdermal scopolamine patch was determined over a 72-h period, and the results were compared with those obtained with other methods; paddle-over-disk method, reciprocating method, and diffusion cell method. It was demonstrated that the flow-through method is equivalent in terms of release rate profile and accumulated released drug amount over the lifetime of the dosage form tested. Also this method is simple, reliable and reproducible. Therefore, this technique can be used in a quality control for assuring product uniformity.

• 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.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.335-345
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• 2018

Objectives: The retinal nerve fiber layer (RNFL) is a site of glaucomatous optic neuropathy whose early changes need to be detected because glaucoma is one of the most common causes of blindness. This paper proposes an automated RNFL detection method based on the texture feature by forming a co-occurrence matrix and a backpropagation neural network as the classifier. Methods: We propose two texture features, namely, correlation and autocorrelation based on a co-occurrence matrix. Those features are selected by using a correlation feature selection method. Then the backpropagation neural network is applied as the classifier to implement RNFL detection in a retinal fundus image. Results: We used 40 retinal fundus images as testing data and 160 sub-images (80 showing a normal RNFL and 80 showing RNFL loss) as training data to evaluate the performance of our proposed method. Overall, this work achieved an accuracy of 94.52%. Conclusions: Our results demonstrated that the proposed method achieved a high accuracy, which indicates good performance.

• Nuclear Engineering and Technology
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• v.51 no.7
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• pp.1784-1790
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• 2019

Accurate and consistent characterization of defects in steam generator tubes (SGT) in nuclear power plants is one of the key issues in the field of nondestructive testing since the large number of signals to be analyzed in a time-limited in-service inspection causes a serious problem in practice. This paper presents an effective approach to this difficult task of automated classification of motorized rotating pancake coil (MRPC) eddy current flaw acquired from tube specimens with deliberated defects using deep neural networks (DNN). This approach consists of five steps, namely, the data acquisition using the MRPC probe in the tube, the signal preprocessing to make data more suitable for training DNN, the data augmentation for boosting a training performance, the training of DNN, and finally demonstration of the trained DNN for discriminating the axial and circumferential defects. The high performance obtained in this study shows that DNN is useful for classification of defects in tubes from the MRPC eddy current signals even though the number of signals is very large.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.4
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• pp.192-199
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• 2022

With the advent of autonomous ships, it is emerging as one of the very important issues not only to operate with a minimum crew or unmanned ships, but also to secure the safety of ships to prevent marine accidents. On-site inspection of the hull is mainly performed by the inspector's visual inspection, and video information is recorded using a small camera if necessary. However, due to the shortage of inspection personnel, time and space constraints, and the pandemic situation, the necessity of introducing an automated inspection system using artificial intelligence and remote inspection is becoming more important. Furthermore, research on hardware and software that enables the automated inspection system to operate normally even under the harsh environmental conditions of a ship is absolutely necessary. For automated inspection systems, it is important to review artificial intelligence technologies and equipment that can perform a variety of hull failure detection and classification. To address this, it is important to classify the hull failure. Based on various guidelines and expert opinions, we divided them into 6 types(Crack, Corrosion, Pitting, Deformation, Indent, Others). It was decided to apply object detection technology to cracks of hull failure. After that, YOLOv5 was decided as an artificial intelligence model suitable for survey and a common hull crack dataset was trained. Based on the performance results, it aims to present the possibility of applying artificial intelligence in the field by determining and testing the equipment required for survey.

• Journal of Veterinary Science
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• v.22 no.1
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• pp.14.1-14.11
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• 2021

Background: Quantitation of urine protein is important in dogs with chronic kidney disease. Various analyzers are used to measure urine protein-to-creatinine ratios (UPCR). Objectives: This study aimed to compare the UPCR obtained by three types of analyzers (automated wet chemistry analyzer, in-house dry chemistry analyzer, and dipstick reading device) and investigate whether the differences could affect clinical decision process. Methods: Urine samples were collected from 115 dogs. UPCR values were obtained using three analyzers. Bland-Altman and Passing Bablok tests were used to analyze agreement between the UPCR values. Urine samples were classified as normal or proteinuria based on the UPCR values obtained by each analyzer and concordance in the classification evaluated with Cohen's kappa coefficient. Results: Passing and Bablok regression showed that there were proportional as well as constant difference between UPCR values obtained by a dipstick reading device and those obtained by the other analyzers. The concordance in the classification of proteinuria was very high (κ = 0.82) between the automated wet chemistry analyzer and in-house dry chemistry analyzer, while the dipstick reading device showed moderate concordance with the automated wet chemistry analyzer (κ = 0.52) and in-house dry chemistry analyzer (κ = 0.53). Conclusions: Although the urine dipstick test is simple and a widely used point-of-care test, our results indicate that UPCR values obtained by the dipstick test are not appropriate for clinical use. Inter-instrumental variability may affect clinical decision process based on UPCR values and should be emphasized in veterinary practice.

• Korean Journal of Radiology
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• v.25 no.4
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• pp.363-373
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• 2024

Objective: To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI. Materials and Methods: We included whole spine MRI scans of adult patients with bone metastasis: 662 MRI series from 302 patients (63.5 ± 11.5 years; male:female, 151:151) from three study centers obtained between January 2015 and August 2021 for training and internal testing (random split into 536 and 126 series, respectively) and 49 MRI series from 20 patients (65.9 ± 11.5 years; male:female, 11:9) from another center obtained between January 2018 and August 2020 for external testing. Three sagittal MRI sequences, including non-contrast T1-weighted image (T1), contrast-enhanced T1-weighted Dixon fat-only image (FO), and contrast-enhanced fat-suppressed T1-weighted image (CE), were used. Seven models trained using the 2D and 3D U-Nets were developed with different combinations (T1, FO, CE, T1 + FO, T1 + CE, FO + CE, and T1 + FO + CE). The segmentation performance was evaluated using Dice coefficient, pixel-wise recall, and pixel-wise precision. The detection performance was analyzed using per-lesion sensitivity and a free-response receiver operating characteristic curve. The performance of the model was compared with that of five radiologists using the external test set. Results: The 2D U-Net T1 + CE model exhibited superior segmentation performance in the external test compared to the other models, with a Dice coefficient of 0.699 and pixel-wise recall of 0.653. The T1 + CE model achieved per-lesion sensitivities of 0.828 (497/600) and 0.857 (150/175) for metastases in the internal and external tests, respectively. The radiologists demonstrated a mean per-lesion sensitivity of 0.746 and a mean per-lesion positive predictive value of 0.701 in the external test. Conclusion: The deep learning models proposed for automated segmentation and detection of bone metastases on spinal MRI demonstrated high diagnostic performance.

• Journal of the Korean Society of Propulsion Engineers
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• v.8 no.2
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• pp.46-53
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• 2004

Ultrasonic testing method has been developed to evaluate flaw of adhesive layers in blast tube for the reliability of the rocket nozzle. The ultrasonic reflection from the interface between the steel sheet and the epoxy adhesive is measured with a high-frequency Pulse-echo setup in order to identify contact debonding and missing adhesive in epoxy layer between steel and FRP layers. The steel sheet is resonated by low-frequency ultrasound, and the gap size underneath the measuring location is estimated from the resonance responses. For practical application in industry an automated testing system has been developed where the proposed approach is implemented. The performance of the proposed approach has been verified by actual measurement of gap sizes from the cross-sections of cut specimens using an optical microscope.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2003.10a
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• pp.230-237
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• 2003

Ultrasonic testing method has been developed to evaluate adhesive layers in blast tube for the reliability of the rocket. The main objective of the present work was to find debonding and missing adhesive in epoxy layer between steel and FRP layers. In this approach, the ultrasonic reflection from the interface between the steel sheet and the epoxy adhesive is measured with a high-frequency pulse-echo setup in order to identify contact debonding and missing adhesive. Then, the steel sheet is excited to resonance by low-frequency ultrasound, and the gap size underneath the measuring location is estimated from the resonance responses. For practical application in industry an automated testing system has been developed where the proposed approach is implemented. The performance of the proposed approach has been verified by actual measurement of gap sizes from the cross-sections of cut specimens using an optical microscope.