• Journal of Information Science Theory and Practice
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• v.11 no.1
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• pp.14-30
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• 2023

The purpose of this study is to compare research trends in the information and communication technology (ICT) field between North and South Korea and analyze the differences by using data mining. Frequency analysis, clustering, and network analysis were performed using keywords from seven South Korean and two North Korean ICT academic journals published for five years (2015-2019). In the case of South Korea (S. Korea), the frequency of research on image processing and wireless communication was high at 16.7% and 16.3%, respectively. North Korea (N. Korea) had a high frequency of research, in the order of 18.2% for image processing, 16.9% for computer/Internet applications/security, and 16.4% for industrial technology. N. Korea's natural language processing (NLP) sector was 11.9%, far higher than S. Korea's 0.7 percent. Student education is a unique subject that is not clustered in S. Korea. In order to promote exchanges between the two Koreas in the ICT field, the following specific policies are proposed. Joint research will be easily possible in the image processing sector, with the highest research rate in both Koreas. Technical cooperation of medical images is required. If S. Korea's high-quality image source is provided free of charge to N. Korea, research materials can be enriched. In the field of NLP, it calls for proposing exchanges such as holding a Korean language information conference, developing a Korean computer operating system. The field of student education encourages support for remote education contents and management know-how, as well as joint research on student remote evaluation.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.65-72
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• 2010

Nowadays, most hospitals have been used to create MRI or CT and managed them. Doctors depend on fast access to images such as magnetic resonance imaging (MRIs), computerized tomography (CT) scans, and X-rays for accurate diagnoses. Those image data are related privacy of a patient. Therefore, it should be protected from hackers and managed perfectly. In this paper, we propose a data hiding method into MRI or CT related a condition and intervention of a patient, and it is suggested that how to authenticate patient information from an image. In this way, we create hash code using HMAC with patient information, and hash code and patient information is hided into an image. After then, doctor will check authentication using HMAC. In addition, we use a reversible data hiding DE(Difference Expansion) algorithm to hide patient information. This technique is possible to reconstruct the original image with stego image. Therefore, doctor can easily be possible to check condition of a patient. As a consequence of an experiment with MRI image, data hiding, extraction and reconstruct is shown compact performance.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.73-82
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• 2009

Due to the many advantages including low price, low power consumption, and miniaturization, the CMOS camera has been utilized in many applications, including mobile phones, the automotive industry, medical sciences and sensoring, robotic controls, and research in the security field. In particular, the 360 degree omni-directional camera when utilized in multi-camera applications has displayed issues of software nature, interface communication management, delays, and a complicated image display control. Other issues include energy management problems, and miniaturization of a multi-camera in the hardware field. Traditional CMOS camera systems are comprised of an embedded system that consists of a high-performance MCU enabling a camera to send and receive images and a multi-layer system similar to an individual control system that consists of the camera's high performance Micro Controller Unit. We proposed the SL-AVS (Small Size/Low power Around-View System) to be able to control a camera while collecting image data using a high speed synchronization technique on the foundation of a single layer low performance MCU. It is an initial model of the omni-directional camera that takes images from a 360 view drawing from several CMOS camera utilizing a 110 degree view. We then connected a single MCU with four low-power CMOS cameras and implemented controls that include synchronization, controlling, and transmit/receive functions of individual camera compared with the traditional system. The synchronization of the respective cameras were controlled and then memorized by handling each interrupt through the MCU. We were able to improve the efficiency of data transmission that minimizes re-synchronization amongst a target, the CMOS camera, and the MCU. Further, depending on the choice of users, respective or groups of images divided into 4 domains were then provided with a target. We finally analyzed and compared the performance of the developed camera system including the synchronization and time of data transfer and image data loss, etc.

• Journal of the Korean Society of Radiology
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• v.6 no.2
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• pp.143-149
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• 2012

Today each hospital is trend that change rapidly by up to date, digitization and introducing newest medical treatment equipment. So, we introduce new CR system and supplement film system's shortcoming and PACS, EMR, RTP system's network that is using in hospital harmoniously and accomplish quality improvement of medical treatment and service elevation about business efficiency enlargement and patient Accordingly, we wish to introduce our case that integrate reflex that happen with radiation oncology here upon to PACS using CR system and estimate the availability. We measured that is Gantry, Collimator Star Shot, Light vs. Radiation, HDR QA(Dwell position accuracy) with Medical LINAC(MEVATRON-MX) Then, PACS was implemented on the digital images on the monitor that can be confirmed through the QA. Also, for cooperation with OCS system that is using from present source and impose code that need in treatment in each treatment, did so that Order that connect to network, input to CR may appear, did so that can solve support data mistake (active Pinacle's case supports DICOM3 file from present source but PACS does not support DICOM3 files.) of Pinacle and PACS that is Planning System and look at Planning premier in PACS. All image and data constructed integration to PACS as can refer and conduct premier in Hospital anywhere using CR system. Use Dosimetry IP in Filmless environment and QA's trial such as Light/Radition field size correspondence, gantry rotation axis' accuracy, collimator rotation axis' accuracy, brachy therapy's Dwell position check is available. Business efficiency by decrease and so on of unnecessary human strength consumption was augmented accordingly with session shortening as that integrate premier that is neted with radiation oncology using CR system to PACS. and for the future patient information security is essential.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.67-76
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• 2023

Although deep learning models are making innovative achievements in the field of computer vision, the problem of vulnerability to adversarial examples continues to be raised. Adversarial examples are attack methods that inject fine noise into images to induce misclassification, which can pose a serious threat to the application of deep learning models in the real world. In this paper, we propose a model that detects adversarial examples using differences in predictive values between edge-learned classification models and underlying classification models. The simple process of extracting the edges of the objects and reflecting them in learning can increase the robustness of the classification model, and economical and efficient detection is possible by detecting adversarial examples through differences in predictions between models. In our experiments, the general model showed accuracy of {49.9%, 29.84%, 18.46%, 4.95%, 3.36%} for adversarial examples (eps={0.02, 0.05, 0.1, 0.2, 0.3}), whereas the Canny edge model showed accuracy of {82.58%, 65.96%, 46.71%, 24.94%, 13.41%} and other edge models showed a similar level of accuracy also, indicating that the edge model was more robust against adversarial examples. In addition, adversarial example detection using differences in predictions between models revealed detection rates of {85.47%, 84.64%, 91.44%, 95.47%, and 87.61%} for each epsilon-specific adversarial example. It is expected that this study will contribute to improving the reliability of deep learning models in related research and application industries such as medical, autonomous driving, security, and national defense.