• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.141-148
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• 2023

Accurate overlay metrology is essential to achieve high yields of semiconductor products. Overlay metrology performance is greatly affected by overlay target design and measurement method. Therefore, in order to improve the performance of the overlay target, measurement methods applicable to various targets are required. In this study, we propose a new algorithm that can measure image-based overlay. The proposed measurement algorithm can estimate the sub-pixel position by using a motion vector. The motion vector may estimate the position of the sub-pixel unit by applying a quadratic equation model through polynomial expansion using pixels in the selected region. The measurement method using the motion vector can calculate the stacking error in all directions at once, unlike the existing correlation coefficient-based measurement method that calculates the stacking error on the X-axis and the Y-axis, respectively. Therefore, more accurate overlay measurement is possible by reflecting the relationship between the X-axis and the Y-axis. However, since the amount of computation is increased compared to the existing correlation coefficient-based algorithm, more computation time may be required. The purpose of this study is not to present an algorithm improved over the existing method, but to suggest a direction for a new measurement method. Through the experimental results, it was confirmed that measurement results similar to those of the existing method could be obtained.

• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.341-352
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• 2022

The wave of a knowledge and information society led by AI, Big Data, and so on is having an all-round impact on our way of life. Therefore the Ministry of Education is in a hurry to strengthen Digital Literacy, including AI and SW Education, by improving the curriculum that can cultivate basic knowledge and capabilities to respond to changes in the future society. It can be seen that establishing a foundation for cultivating Digital Literacy through all subjects and improving basic and in-depth learning in new technology fields such as AI linked to the information curriculum is an essential part for future society. However, research on each content for cultivating Digital and AI literacy is relatively active, while research on teaching and learning strategies is insufficient. Therefore in this study, a CT-based Digital and AI teaching and learning strategy that can foster that was developed and Delphi expert verification was conducted, and the final teaching and learning strategy was completed after evaluating instructor usability and analyzing learner effectiveness.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.190-196
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• 2023

Recently, following the development of LIDAR technology which can detect distance from the object, the interest for LIDAR based 3D object detection network is getting higher. Previous networks generate inaccurate localization results due to spatial information loss during voxelization and downsampling. In this study, we propose an attention-based convergence method and a camera-LIDAR convergence system to acquire high-level features and high positional accuracy. First, by introducing the attention method into the Voxel-RCNN structure, which is a grid-based 3D object detection network, the multi-scale sparse 3D convolution feature is effectively fused to improve the performance of 3D object detection. Additionally, we propose the late-fusion mechanism for fusing outcomes in 3D object detection network and 2D object detection network to delete false positive. Comparative experiments with existing algorithms are performed using the KITTI data set, which is widely used in the field of autonomous driving. The proposed method showed performance improvement in both 2D object detection on BEV and 3D object detection. In particular, the precision was improved by about 0.54% for the car moderate class compared to Voxel-RCNN.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.161-171
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• 2023

It is largely agreed that elderly drivers (over 64 years) are more likely to cause fatal crashes than other age groups. According to national road crash statistics 2021, the number of road fatalities per 10,000 drivers over 64 years old was 1.77, while that of drivers in their 30s was 0.55. This indicates a 2.67 times higher probability of causing crashes among the former than the latter. The current study estimates how rear-end crashes may be reduced by installing Automatic Emergency Braking Systems (AEBS), particularly for elderly drivers. We analyzed data from Samsung Fire & Marine Insurance. The results show that the Odds Ratio of rear-end crash occurrence between vehicles with AEBS and without AEBS is 0.75, implying there were lesser rear-end crashes in the vehicles installed with AEBS. The Odds Ratio of male drivers was determined to be 0.78, which was lesser than the 0.81 Odds Ratio obtained for female drivers. Elderly drivers who had installed AEBS in their vehicles showed an Odds Ratio of 0.76, implying crash reduction. In particular, the Odds Ratio of male elderly drivers was found to be the lowest at 0.49. We believe incentivizing by giving discounted insurance premiums to the elderly who drive vehicles installed with AEBS will help reduce rear-end crashes.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.576-579
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• 2011

Wireless sensor network are widely all over different fields. Because of its distinguished characteristics, we must take account of the factor of energy consumed when designing routing protocol. Wireless sensor networks consist of small battery powered devices with limited energy resources. Once deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy source is not feasible. Hence, energy efficiency is a key design issue that needs to be enhanced in order to improve the life span of the network. In BCDCP, all sensors sends data from the CH (Cluster Head) and then to the BS (Base Station). BCDCP works well in a smallscale network however is not preferred in a large scale network since it uses much energy for long distance wireless communication. TBRP can be used for large scale network, but it weakness lies on the fact that the nodedry out of energy easily since it uses multi-hops transmission data to the Base Station. Here, we proposed a routing protocol. A Cluster Based Energy Efficient Tree Routing Protocol (CETRP) in Wireless Sensor Networks (WSNs) to prolong network life time through the balanced energy consumption. CETRP selects Cluster Head of cluster tree shape and uses maximum two hops data transmission to the Cluster Head in every level. We show CETRP outperforms BCDCP and TBRP with several experiments.

• Journal of the Korean Geotechnical Society
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• v.23 no.8
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• pp.17-33
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• 2007

Downhole seismic method is widely used for obtaining shear wave velocity profile of a site because it is simple and economical. Determining accurate travel time of shear wave is very important to obtain reliable result in downhole seismic method. In this paper, comparison study of various travel time determination methods was performed. Numerical study and model chamber test were performed for effective comparison study. Signal traces were acquired by performing downhole test at each numerical simulation and soil box test. Travel time data for each signal traces were determined by using six different methods and Vs profiles were evaluated. Comparing travel time data and Vs profiles with the reference value, the first arrival picking method proved to be ambiguous and unreliable. Other methods also did not always provide accurate results and the magnitude of error was dependent on the signal to noise ratio. Cross-correlation method proved to be the most adequate method for the field application and it was verified additionally with field data.

• Journal of the Korean Society of Radiology
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• v.17 no.2
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• pp.207-213
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• 2023

Computed tomography (CT) images are used as the basis for proton Bragg peak position estimation and treatment plan simulation. During the Hounsfield Unit (HU) based proton stopping power ratio (SPR) estimation, small differences in the patient's density and elemental composition lead to uncertainty in the Bragg peak positions along the path of the proton beam. In this study, we investigated the potential of dual-energy computed tomography image-based proton SPRs prediction accuracy to reduce the uncertainty of Bragg peak position prediction. Single- and dual-energy images of an electron density phantom (CIRS Model 062M electron density phantom, CIRS Inc., Norfolk, VA, USA) were acquired using a computed tomography system (Somatom Definition AS, Siemens Health Care, Forchheim, Germany) to estimate the SPRs of the proton beam. To validate the method, it was compared to the SPRs estimated from standard data provided by the National Institute of Standards and Technology (NIST). The results show that the dual-energy image-based method has the potential to improve accuracy in predicting the SPRs of proton beams, and it is expected that further improvements in predicting the position of the proton's Bragg peak will be possible if a wider variety of substitutes with different densities and elemental compositions of the human body are used to predict the SPRs.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.2
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• pp.87-96
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• 2023

Recently, research on predicting the treatment results of diseases using deep learning technology is also active in the medical community. However, small patient data and specific deep learning algorithms were selected and utilized, and research was conducted to show meaningful results under specific conditions. In this study, in order to generalize the research results, patients were further expanded and subdivided to derive the results of a study predicting mortality after lung cancer diagnosis for men and women in their 80s, 90s, and 100s. Using AutoML, which provides large-scale medical information and various deep learning algorithms from the Health Insurance Review and Assessment Service, five algorithms such as Decision Tree, Random Forest, Gradient Boosting, XGBoost, and Logistic Registration were created to predict mortality rates for 84 months after lung cancer diagnosis. As a result of the study, men in their 80s and 90s had a higher mortality prediction rate than women, and women in their 100s had a higher mortality prediction rate than men. And the factor that has the greatest influence on the mortality rate was analyzed as the treatment period.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.119-129
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• 2022

Cervical cytology has been widely used as a screening tool for cervical cancer. However, Human papillomavirus (HPV) detection and subtype testing are suggested to overcome the high false-negative rate associated with cytology. We aimed to investigate the clinical usefulness and infection rate in the HPV polymerase chain reaction (PCR) test performed in hospitals. HPV PCR data from 217 patients were analyzed. Analysis of variance revealed a significant difference in the infection rate among different age groups (P=0.015). The biopsy results showed that epithelial cell abnormalities and high HPV-positivity rate was observed in 1 (100%) subject aged <29 years, in 4 out of 5 (80%) patients in their 30s, and in 3 out of 4 (75%) patients aged ≥70 years. The prevalence of HPV infection was very high (46.1%). The highest prevalence (87.5%) was observed among patients in their <29, followed by those in their 30s (67.7%) and those in their 40s (31.9%).A high rate of epithelial cell abnormalities (≥ cervical intraepithelial neoplasia type 1, mild dysplasia) was observed in HPV-infected women aged<30 years. Therefore, extensive research and prevention activities are needed in this age group. HPV PCR testing is recommended to complement cervical cytology

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.141-158
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• 2022

Currently, there are some aptitude test systems for older drivers in Korea. However, there are no methods and systems to evaluate the real driving ability for older drivers based on filed driving test. This study was conducted to investigate the availability to use the driving course test used for driving license for identifying difference in driving ability of older and non-older drivers. For the research purpose, filed experiments were conducted using the real driving course test site and evaluation times used in the field. In particular, driving behavior data that obtained from the experiments for two driver groups, older and non-older drivers, were analyzed and compared. From several statistical analyses of driving ability and vision and cognitive ability, it was found that the currently used driving course test site and evaluation times were not appropriated to identify driving ability deficiency of older drivers. To solve the problem, this study developed five evaluation items to identify driving ability deficiency of older drivers using the currently used driving course test site. It was also found that the developed five evaluation items have statistically significant correlation with vision and cognitive ability.