• Journal of Integrative Natural Science
• /
• v.14 no.4
• /
• pp.189-196
• /
• 2021

Recently, as a countermeasure for cyber breach attacks and confidential leak incidents on PC hard disk memory storage data of the metaverse industry, it is required when reviewing and developing a remote-based regular/real-time monitoring and analysis security system. The reason for this is that more than 90% of information security leaks occur on edge-end PCs, and tangible and intangible damage, such as an average of 1.20 billion won per metaverse industrial security secret leak (the most important facts and numerical statistics related to 2018 security, 10.2018. the same time as responding to the root of the occurrence of IT WORLD on the 16th, as it becomes the target of malicious code attacks that occur in areas such as the network system web due to interworking integration when building IT infrastructure, Deep-Access-based regular/real-time remote. The concept of memory analysis and audit system is key.

• Journal of Climate Change Research
• /
• v.7 no.1
• /
• pp.19-29
• /
• 2016

In this study, the emission intensity calculation method of GHGs was developed by considering the characteristics of the models and time series. The telematics device was installed on the car (OBD-II) to collect information on the operation conditions from each sample vehicle of public authorities. Based on emission intensity of GHGs, it presented a methodology of quantitative comparison of GHGs emission by vehicles. Collected driving information of vehicle was used for operating characteristics analysis of the target vehicle, and it was confirmed different operating characteristics through comparison of the results and previous study. GHGs emission intensity were analyzed considering characteristics of vehicle type by passenger car, van, cargo, and considering characteristics of the time series by summer, winter, and intermediate. From the analysis result, it was calculated GHGs emission intensity based on mileage ($g\;CO_2\;eq./km$) and operating time ($g\;CO_2\;eq./sec$).

• Journal of the Semiconductor & Display Technology
• /
• v.16 no.3
• /
• pp.78-81
• /
• 2017

Augmented Reality(AR) is the core technology of the future knowledge service industry. It is expected to be used in various fields such as medical, education, entertainment etc. Briefly, augmented reality technology is a technique in which a mapped virtual object is augmented when a real-world object is viewed through a device after mapping a real-world object and a virtual object. In this paper, we implemented object detection and tracking system, which is a key technology of augmented reality. To speed up the object tracking, the ORB algorithm, which is a lightweight algorithm compared to the detection algorithm, is applied. In addition, KNN classifier, which is a machine learning algorithm, was applied to detect a single object by learning multiple images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.243-246
• /
• 2019

In cognitive radio networks, secondary transmitters should cease its transmission immediately under the detection of primary transmission in the spectrum they are using. Then they need to exploit another idle spectrums and resynchronize to the newly found idle spectrums. Most of related work presume the existence of separate control channel used by secondary users commonly for exchanging the information of idle spectrums. However, this presumption is not feasible in real world cognitive radio scenario. Therefore we address a proactive spectrum synchronization scheme with no need of separate control channel. Our scheme lets secondary users exchange the spectrum information periodically during normal communication process and determine the next spectrum band in advance of detecting primary transmission. We evaluate our scheme in a real world cognitive radio environments set up with USRPs.

• Advances in Computational Design
• /
• v.4 no.2
• /
• pp.141-153
• /
• 2019

This research is comprised of virtually simulating behavior while experiencing low gravity effects in advance of real world testing in low gravity aboard Zero Gravity Corporation's (Zero-G) research aircraft (727-200F). The experiment simulated a drill rig penetrating a regolith simulant. Regolith is a layer of loose, heterogeneous superficial deposits covering solid rock on surfaces of the Earth' moon, asteroids and Mars. The behavior and propagation of space debris when drilled in low gravity was tested through simulations and visualization in a leading dynamic simulation software as well as discrete element modeling software and in preparation for comparing to real world results from flying the experiment aboard Zero-G. The study of outer space regolith could lead to deeper scientific knowledge of extra-terrestrial surfaces, which could lead us to breakthroughs with respect to space mining or in-situ resource utilization (ISRU). These studies aimed to test and evaluate the drilling process in low to zero gravity environments and to determine static stress analysis on the drill when tested in low gravity environments. These tests and simulations were conducted by a team from Texas A&M University's Department of Construction Science, the United States Air Force Academy's Department of Astronautical Engineering, and Crow Industries

• The Journal of Korea Robotics Society
• /
• v.14 no.2
• /
• pp.122-130
• /
• 2019

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.

• The Journal of Health Technology Assessment
• /
• v.6 no.2
• /
• pp.81-87
• /
• 2018

With the recent change of healthcare environment including rapid technological development, evidences are more and more important and necessary to support relevant policies in health technology assessment to provide safe and effective health services, utilizing medical resources efficiently. Despite of the emphasis on the importance of real world data and real world evidence in health care research, current infrastructure supporting clinical research is considerably weak due to absence of legal and institutional basis. However, in accordance with the Article 26 of the Health and Medical Technology Promotion Act, there is a limited legal apparatus that can be used only in public data with other dataset for the purpose of healthcare technology assessment at the National Evidence-based Collaborating Agency. Although the use of linked data from various sources was often required in the field of clinical research, it was not yet working well due to insufficient environmental conditions. In order to support the decision-making of medical practice and health care policies, data-linking platform for clinical research is needed. If the legal system that can link up to the data of the private institutions without violating the significant value such as the protection of private informations is established, it will be a decisive foundation reinforcing the researches and policy making processes for the improvement of the national health care system.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.22 no.5
• /
• pp.959-974
• /
• 2012

Group signature schemes were made for serving anonymities of signers, but the group signature schemes have been seldomly adapted to the real-world applications because of their low computation and space (i.e. signature length) efficiency, complicated construction, limited user management, and so on. Kang, Hwang, etc. performed the study about the local linkability that is for helping group signature schemes to be adapted more easily to the real world. In this paper, we investigate the nature of local linkability, which did not deal with well in the previous studies, in detail and perform the formal proof for the security of special entities who hold the local linkability.

• International Journal of Computer Science & Network Security
• /
• v.21 no.1
• /
• pp.220-225
• /
• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.

• Journal of Engineering Education Research
• /
• v.24 no.3
• /
• pp.50-57
• /
• 2021

Although the mathematics education process in AI education is a very important issue, little cases are reported in developing effective methods on AI and mathematics education at the university level. The universities cover all fields of mathematics in their curriculums, but they lack in connecting and applying the math knowledge to AI in an efficient manner. Students are hardly interested in taking many math courses and it gets worse for the students in humanities, social sciences and arts. But university education is very slow in adapting to rapidly changing new technologies in the real world. AI is a technology that is changing the paradigm of the century, so every one should be familiar with this technology but it requires fundamental math knowledge. It is not fair for the students to study all math subjects and ride on the AI train. We recognize that three key elements, SW knowledge, mathematical knowledge, and domain knowledge, are required in applying AI technology to the real world problems. This study proposes a modular approach of studying mathematics knowledge while connecting the math to different domain problems using AI techniques. We also show a modular curriculum that is developed for using math for AI-driven autonomous driving.