• Korean Journal of Computational Design and Engineering
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• v.7 no.3
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• pp.170-176
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• 2002

To exchange CAD data between heterogeneous CAD systems, we generally use a neutral format especially STEP, which is the international standard (ISO-10303) for product model data exchange. AP214 (Application Protocol) for the automotive industry not only takes into account geometry and organizational data, but also provides a classification mechanism for product modeling. When reading a STEP file during a design process that is exported from other CAD systems, it is a burden to a designer to go through the tedious process of removing duplicate or non-manifold entities, adjusting parts, and rearranging text. We analyze the structure of AP214 and develop a healing tool to solve the following problem. Without the assembly information in the Master workspace of CATIA, or to read a STEP file from Pro/Engineer, a designer should do a repetitive process of disintegrating an assembly into parts one by one. We have developed a post-processing tool for STEP AP214 that separates out a part from an assembly model and adjusts superfluous or useless entities using the ACIS kernel.

• Journal for History of Mathematics
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• v.27 no.3
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• pp.211-231
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• 2014

Although Monty Hall dilemma is used in many areas including philosophy, economics, and psychology, it is used in the current mathematics textbooks only as a material for reading or one of probability questions. The present study tries to explore students' understanding of Monty Hall dilemma through a class case. In this study, a group of high-school students participated in group activities, in which they read an argument about Monty Hall dilemma, and tried to resolve it through small-group and whole-class discussions, and then studied the conditional probability. The analysis supports the studies in psychology that intuitive understandings on probability do not change easily, and that counter-intuitivity in Monty Hall dilemma induces confusion and offers a basis for discussions among students. Similar results are anticipated when other dilemmas on probability are used.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.290-297
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• 2022

During surgery, Surgical instruments are often left behind due to accidents. Most of these are surgical gauze, so radioactive non-permeable gauze (X-ray gauze) is used for preventing of accidents which gauze is left in the body. This gauze is divided into wire and pad type. If it is confirmed that the gauze remains in the body, gauze must be detected by radiologist's reading by imaging using a mobile X-ray device. But most of operating rooms are not equipped with a mobile X-ray device, but equipped C-Arm equipment, which is of poorer quality than mobile X-ray equipment and furthermore it takes time to read them. In this study, Use C-Arm equipment to acquire gauze image for detection and Build dataset using artificial intelligence and select a detection model to Assist with the relatively low image quality and the reading of radiology specialists. mAP@50 and detection time are used as indicators for performance evaluation. The result is that two-class gauze detection dataset is more accurate and YOLOv5 model mAP@50 is 93.4% and detection time is 11.7 ms.

• Journal of Advanced Navigation Technology
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• v.11 no.4
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• pp.423-429
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• 2007

Since the wireless smart card has played a main role in the identification security application for the building access; this research has its purpose to improve the performance of the smart card system and aims to offer more convenient to user. The contactless cards do not require insertion into a card reader and can work up to centimeters away from the reading device. To be able to cope with this performance the controlling of power consumption through the adaptive modulation and error control is needed. This paper addresses a forward error control (FEC) scheme with the adaptive Reed-Solomon code rate and an M-ary frequency shift keying (M-FSK) modulation scheme with the varying symbol size M over the link. The result of comparing energy efficiencies of adaptive error correction and adaptive modulation to other various static schemes shows to save over 50% of the energy consumption.

• Journal of Digital Contents Society
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• v.18 no.2
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• pp.257-266
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• 2017

Recently, a variety of books have been published in various fields, and the range of selection for both online and offline bookstores have been widened. However, there is a vinyl packaging phenomenon to prevent damage being made to the books in bookstores. This makes it difficult to have a look through on the contents of the book, affecting consumers' purchasing decisions. These factors causes the annual average reading volume to decrease. Therefore, in this paper, we would like to propose a "bookmark" application to provide consumers with book information service using AR and Marker technology, tackling the decrease in sales caused by the problem.

• Electronics and Telecommunications Trends
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• v.35 no.6
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• pp.37-47
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• 2020

Today, the volume of international trade by airplanes and ships is rapidly increasing, and the volume of trade over land is expected to increase as inter-Korean relations change. In customs processes, humans inspect using the naked eye; therefore, computer vision technology can be used to assist customs inspectors responsible for X-ray security screening. In particular, because of recent advances in deep learning technology, algorithms for image understanding and object detection performance are improving, and studies on their application to X-ray screening have been published. This manuscript describes trends in artificial intelligence X-ray image-reading technology to detect prohibited items. X-ray inspection AI technology is similar to the Sphinx, which was the guardian of the pyramids in ancient Egyptian mythology.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.77-81
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• 2006

An attempt has been made to measure existing steel stress using magnetoelasticity. A device has been developed and used for the measurement of magnetism in response to the deformation of a steel bar. The proposed technique can be used for the assessment of existing reinforced concrete structures by the measurements of steel stress embedded inside concrete. A traditional technique requires to break the existing steel bar to measure existing strain. However, the proposed technique is developed to measure the stress without damaging the steel bar. A successful application of magnetoelasticity depends on the establishment of relationship between elastic and magnetic response due to loading. To investigate the correlation between the two, steel bars are loaded in tension under uniaxial loading while the magnetic reading is recorded. Based on the test results, equations are suggested to predict stress for steel bars with different diameters.

• Journal of Sensor Science and Technology
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• v.30 no.1
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• pp.15-19
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• 2021

Image processing is one of the major techniques that are used for computer vision. Nowadays, researchers are using machine learning and deep learning for the aforementioned task. In recent years, digit recognition tasks, i.e., automatic meter recognition approach using electric or water meters, have been studied several times. However, two major issues arise when we talk about previous studies: first, the use of the deep learning technique, which includes a large number of parameters that increase the computational cost and consume more power; and second, recent studies are limited to the detection of digits and not storing or providing detected digits to a database or mobile applications. This paper proposes a system that can detect the digital number of meter readings using a lightweight deep neural network (DNN) for low power consumption and send those digits to an Android mobile application in real-time to store them and make life easy. The proposed lightweight DNN is computationally inexpensive and exhibits accuracy similar to those of conventional DNNs.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.4
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• pp.9-16
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• 2020

Pronunciation recognition systems that use only video information and ignore voice information can be applied to various customized services. In this paper, we develop a system that applies a Bayesian classifier to distinguish Korean vowels via lip shapes in images. We extract feature vectors from the lip shapes of facial images and apply them to the designed machine learning model. Our experiments show that the system's recognition rate is 94% for the pronunciation of 'A', and the system's average recognition rate is approximately 84%, which is higher than that of the CNN tested for comparison. Our results show that our Bayesian classification method with feature values from lip region landmarks is efficient on a small training set. Therefore, it can be used for application development on limited hardware such as mobile devices.

• Journal of the Semiconductor & Display Technology
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• v.23 no.3
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• pp.140-147
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• 2024

This paper explores the recent advancements in speech recognition technology, focusing on the integration of artificial intelligence to improve recognition accuracy in challenging environments, such as noisy or low-quality audio conditions. Traditional speech recognition methods often suffer from performance degradation in noisy settings. However, the application of deep neural networks (DNN) has led to significant improvements, enabling more robust and reliable recognition in various industries, including banking, automotive, healthcare, and manufacturing. A key area of advancement is the use of Silent Speech Interfaces (SSI), which allow communication through non-speech signals, such as visual cues or other auxiliary signals like ultrasound and electromyography, making them particularly useful for individuals with speech impairments. The paper further discusses the development of multi-modal speech recognition, combining both audio and visual inputs, which enhances recognition accuracy in noisy environments. Recent research into lip-reading technology and the use of deep learning architectures, such as CNN and RNN, has significantly improved speech recognition by extracting meaningful features from video signals, even in difficult lighting conditions. Additionally, the paper covers the use of self-supervised learning techniques, like AV-HuBERT, which leverage large-scale, unlabeled audiovisual datasets to improve performance. The future of speech recognition technology is likely to see further integration of AI-driven methods, making it more applicable across diverse industries and for individuals with communication challenges. The conclusion emphasizes the need for further research, especially in languages with complex morphological structures, such as Korean