• Journal of the Optical Society of Korea
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• v.17 no.5
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• pp.384-391
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• 2013

We propose a split-encryption scheme on converting original images to multiple ciphertexts. This conversion introduces one random phase-only function (POF) to influence phase distribution of the preliminary ciphertexts. In the encryption process, the original image is mathematically split into two POFs. Then, they are modulated on a spatial light modulator one after another. And subsequently two final ciphertexts are generated by utilizing two-step phase-shifting interferometry. In the decryption process, a high-quality reconstructed image with relative error $RE=7.6061{\times}10^{-31}$ can be achieved only when the summation of the two ciphertexts is Fresnel-transformed to the reconstructed plane. During the verification process, any silhouette information was invisible in the two reconstructed images from different single ciphertexts. Both of the two single REs are more than 0.6, which is better than in previous research. Moreover, this proposed scheme works well with gray images.

• Journal of Biomedical Engineering Research
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• v.15 no.2
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• pp.183-188
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• 1994

In clinical orthopaedics, bone resoption in the cortex is often seen post operatively on X-rays or bone densitometry after total hip replacement (THR) in the form of cortical osteoporosis or atropy. Stress shielding of bone occurs, when a load, normally carried by the bone alone, is shared with an implant as a result, the bone stresses are abnormal and with remodelling analysis this may cause extensive proximal bone resoption, possibly weakening the bone bed to the point of failure. The author made finite element models of the cemented and non-cemented type implanted femoral stem with bone resorption of the proximal medial femur and studied the feed back effect of the various degree of bone resoption to THR system by parametric analysis on the stress of the femoral stem and interface. The results of the present finite element analysis implied that the extent of proximal bone resorption has the effect of more increasing stress on the distal stem tip, cement mantle and interface in both type of femoral stem and this high distal stress possibly can cause the mechanical failure of loosening or failure after THR.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1430-1432
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• 2003

Agricultural areas in the north region of Bangladesh suffer from water shortages during the dry season as well as dry spells in the monsoon period. The Teesta Barrage was constructed in 1990 to provide supplemental irrigation water during the monsoon period. After completion of the project high yielding variety of crops were introduced more in the project area. Due to this reason unforeseen needs of irrigation water during the dry season has emerged. This study reviews the current irrigation status and related constraints to a full development of the project and provides suggestions for future improvement of the project. Also suggested is to apply remote sensing technique for the management of the system as a whole. Use of remote sensing technique for the management of irrigation water resources is a new approach in Bangladesh. Application of such a powerful tool will provide better management options for large-scale irrigation projects in the country.

• Nuclear Engineering and Technology
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• v.50 no.2
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• pp.280-291
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• 2018

In the framework of OECD/NEA Working Group on Fuel Safety, a RIA fuel-rod-code Benchmark Phase I was organized in 2010-2013. It consisted of four experiments on highly irradiated fuel rodlets tested under different experimental conditions. This benchmark revealed the need to better understand the basic models incorporated in each code for realistic simulation of the complicated integral RIA tests with high burnup fuel rods. A second phase of the benchmark (Phase II) was thus launched early in 2014, which has been organized in two complementary activities: (1) comparison of the results of different simulations on simplified cases in order to provide additional bases for understanding the differences in modelling of the concerned phenomena; (2) assessment of the uncertainty of the results. The present paper provides a summary and conclusions of the second activity of the Benchmark Phase II, which is based on the input uncertainty propagation methodology. The main conclusion is that uncertainties cannot fully explain the difference between the code predictions. Finally, based on the RIA benchmark Phase-I and Phase-II conclusions, some recommendations are made.

• Journal of the Korean Wood Science and Technology
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• v.49 no.5
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• pp.491-503
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• 2021

Wood identification is regularly performed by observing the wood anatomy, such as colour, texture, fibre direction, and other characteristics. The manual process, however, could be time consuming, especially when identification work is required at high quantity. Considering this condition, a convolutional neural networks (CNN)-based program is applied to improve the image classification results. The research focuses on the algorithm accuracy and efficiency in dealing with the dataset limitations. For this, it is proposed to do the sample selection process or only take a small portion of the existing image. Still, it can be expected to represent the overall picture to maintain and improve the generalisation capabilities of the CNN method in the classification stages. The experiments yielded an incredible F1 score average up to 93.4% for medium sample area sizes (200 × 200 pixels) on each CNN architecture (VGG16, ResNet50, MobileNet, DenseNet121, and Xception based). Whereas DenseNet121-based architecture was found to be the best architecture in maintaining the generalisation of its model for each sample area size (100, 200, and 300 pixels). The experimental results showed that the proposed algorithm can be an accurate and reliable solution.

• Smart Structures and Systems
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• v.29 no.1
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• pp.63-75
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• 2022

The raw data collected by structural health monitoring (SHM) systems may suffer multiple patterns of anomalies, which pose a significant barrier for an automatic and accurate structural condition assessment. Therefore, the detection and classification of these anomalies is an essential pre-processing step for SHM systems. However, the heterogeneous data patterns, scarce anomalous samples and severe class imbalance make data anomaly detection difficult. In this regard, this study proposes a convolutional neural network-based data anomaly detection method. The time and frequency domains data are transferred as images and used as the input of the neural network for training. ResNet18 is adopted as the feature extractor to avoid training with massive labelled data. In addition, the focal loss function is adopted to soften the class imbalance-induced classification bias. The effectiveness of the proposed method is validated using acceleration data collected in a long-span cable-stayed bridge. The proposed approach detects and classifies data anomalies with high accuracy.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.109-117
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• 2023

Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denim fabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extraction architecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the large dataset ImageNet and uses its portability to train the defect detection classifier and the defect recognition classifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layer were retrained and adjusted from of these two training models on the high-definition fabric defect dataset. The last step is merging these two models to get the defect detection algorithm based on cascading architecture. Then there are two comparative experiments between this improved defect detection algorithm and other feature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show that the defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increased by 1-3 percentage points.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.3
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• pp.234-245
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• 2023

The Arena Fragmentation Test(AFT) is designed to analyze warhead performance by measuring fragmentation data. In order to evaluate the results of the AFT, a set of AFT images are captured by high-speed cameras. To detect objects in the AFT image set, ResNet-50 based Faster R-CNN is used as a detection model. However, because of the low resolution of the AFT image set, a detection model has shown low performance. To enhance the performance of the detection model, Super-resolution(SR) methods are used to increase the AFT image set resolution. To this end, The Bicubic method and three SR models: ZSSR, EDSR, and SwinIR are used. The use of SR images results in an increase in the performance of the detection model. While the increase in the number of pixels representing a fragment flame in the AFT images improves the Recall performance of the detection model, the number of pixels representing noise also increases, leading to a slight decreases in Precision performance. Consequently, the F1 score is increased by up to 9 %, demonstrating the effectiveness of SR in enhancing the performance of the detection model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.655-657
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• 2021

It is challenging to find breast ultrasound image training dataset to develop an accurate machine learning model due to various regulations, personal information issues, and expensiveness of acquiring the images. However, studies targeting transfer learning for ultrasound breast cancer images classification have not been able to achieve high performance compared to radiologists. Here, we propose an improved transfer learning model for ultrasound breast cancer classification using publicly available dataset. We argue that with a proper combination of ImageNet pre-trained model and optimizer, a better performing model for ultrasound breast cancer image classification can be achieved. The proposed model provided a preliminary test accuracy of 99.5%. With more experiments involving various hyperparameters, the model is expected to achieve higher performance when subjected to new instances.

• Journal of the Korean Ceramic Society
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• v.39 no.3
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• pp.245-251
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• 2002

We prepared γ-alumina beads using the amorphous alumina, obtained by fast calcination of gibbsite, and its were immersed in aqueous solution of the mixture of 21.87% nitric acid and 28.57% acetic acid. The beads thus were hydrothermaly treated at 200$^{\circ}$C for 3h, and were investigated changes of crystal, pore characteristics, $N_2$ adsorption and desorption isotherms, mechanical strengths and thermal resistance. Acicular platelet crystals of 0.1∼0.3${\mu}$m were transformed into acicular boehmite crystals of 1∼2${\mu}$m having the same crystal structure. Through this changes, we found that reversible phase transformation due to hydrothermal reaction took placed between boehmite and ${\gamma}$-alumina. In comparison to the ${\gamma}$-alumina bead before hydrothermal treatment, $N_2$ adsorption capacity was increased from 450㎖/g to 670㎖/g, and pore volume between 100${\AA}$ and 1000${\AA}$ was increased form 0.15㎖/g to 0.77㎖g, and mechanical strength was increased form 1.4MPa to 2.2MPa. Also, it showed the remarkable thermal resistance which sustained ${\theta}$-alumina crystal structure and pores between 100${\AA}$ and 1000${\AA}$ at 1000$^{\circ}$C in 40vol% steam.