• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.2
• /
• pp.187-192
• /
• 2008

The Intelligent Space(ISpace) provides challenging research fields for surveillance, human-computer interfacing, networked camera conferencing, industrial monitoring or service and training applications. ISpace is the space where many intelligent devices, such as computers and sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human Jollowing by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd. This paper describes appearance based unknown object tracking with the distributed vision system in intelligent space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.743-744
• /
• 2016

Due to the many amenities of a modern power with solar power and green energy alternative to injury by cutting down on the number of solar energy and pollution and environmental factors. There is still much research and trying to incorporate a variety of applications. In this paper, create a daytime energy by fusing solar panels and billboards, and at night has the advantage that you can save more energy used to sign by the sign serves also and at the same time energy acts like a tent when the day by using the power usage is expected to be higher.

• Journal of the Korean Society of Costume
• /
• v.65 no.5
• /
• pp.1-13
• /
• 2015

The purpose of this study is to make a contribution to men's fashion by analyzing changes related to excessive and decorative expressions of latest men's wear design in the viewpoint of Maximalism, and using the results to develop a new fashion style. Results of the study are as follows: The characteristics of Maximalism in men's wear collection in the last 5 years can be classified as 'Fusion Maximalism, 'Decorative Maximalism', 'Exaggerative Maximalism', 'Humorous Maximalism', and 'Duplicative Maximalism'. The characteristics of each type of Maximalism can be summarized as follows: First, Fusion Maximalism creates new men's fashion style by fusing designs from different cultures in clothing items as well as dismantling gender boundaries in clothing design, and by having designs which transcends time and space. Second, Decorative Maximalism expresses ambiguous decadence by implementing feminine decoration that applies handicraft technique, as well as exaggeratedly using details and trimmings of contrasting images, and applying women's clothing items. Third, Exaggerative Maximalism represents extreme avant-garde that changes the idea of fashion design by changing the styling or the exaggerated silhouette. Fourth, Humorous Maximalism expresses not only the creation of new men's fashion look, but also expresses humanism with exaggerative enjoyment and pleasant ideas. Fifth, Duplicative Maximalism expresses creative design by implementing vivid images of different period and culture, and reproducing successful images of the past into modern fashion.

• Computers and Concrete
• /
• v.24 no.6
• /
• pp.541-553
• /
• 2019

Alkali-silica reaction (ASR) in concrete can induce degradation in its mechanical properties, leading to compromised serviceability and even loss in load capacity of concrete structures. Compared to other properties, ASR often affects the modulus of elasticity more significantly. Several empirical models have thus been established to estimate elastic modulus reduction based on the ASR expansion only for condition assessment and capacity evaluation of the distressed structures. However, it has been observed from experimental studies in the literature that for any given level of ASR expansion, there are significant variations on the measured modulus of elasticity. In fact, many other factors, such as cement content, reactive aggregate type, exposure condition, additional alkali and concrete strength, have been commonly known in contribution to changes of concrete elastic modulus due to ASR. In this study, an artificial intelligent model using artificial neural network (ANN) is proposed for the first time to provide an innovative approach for evaluation of the elastic modulus of ASR-affected concrete, which is able to take into account contribution of several influence factors. By intelligently fusing multiple information, the proposed ANN model can provide an accurate estimation of the modulus of elasticity, which shows a significant improvement from empirical based models used in current practice. The results also indicate that expansion due to ASR is not the only factor contributing to the stiffness change, and various factors have to be included during the evaluation.

• Journal of the Korea Convergence Society
• /
• v.11 no.5
• /
• pp.9-17
• /
• 2020

Human activity recognition(HAR) using smartphones is a hot research topic in computational intelligence. Smartphones are equipped with a variety of sensors. Fusing the data of these sensors could enable applications to recognize a large number of activities. However, these devices have fewer resources because of the limited number of sensors available, and feature selection and classification methods are required to achieve optimal performance and efficient feature extraction. This paper proposes a smartphone-based HAR scheme according to these requirements. The proposed method in this paper extracts time-domain features from acceleration sensors, gyro sensors, and barometer sensors, and recognizes activities with high accuracy by applying KDA and SVM. This approach selects the most relevant feature of each sensor for each activity. Our comparison results shows that the proposed system outperforms previous smartphone-based HAR systems.

• Korean Journal of Remote Sensing
• /
• v.22 no.1
• /
• pp.25-40
• /
• 2006

Synthetic aperture radar (SAR) from satellites provides the opportunity to regularly incorporate microwave information into forest classification. Radar backscatter can improve classification accuracy, and SAR interferometry could provide improved thematic information through the use of coherence. This research examined the potential of using multi-temporal JERS-l SAR (L band) backscatter information and interferometry in distinguishing forest classes of mountainous areas in the Northeastern U.S. for future forest mapping and monitoring. Raw image data from a pair of images were processed to produce coherence and backscatter data. To improve the geometric characteristics of both the coherence and the backscatter images, this study used the interferometric techniques. It was necessary to radiometrically correct radar backscatter to account for the effect of topography. This study developed a simplified method of radiometric correction for SAR imagery over the hilly terrain, and compared the forest-type discriminatory powers of the radar backscatter, the multi-temporal backscatter, the coherence, and the backscatter combined with the coherence. Statistical analysis showed that the method of radiometric correction has a substantial potential in separating forest types, and the coherence produced from an interferometric pair of images also showed a potential for distinguishing forest classes even though heavily forested conditions and long time separation of the images had limitations in the ability to get a high quality coherence. The method of combining the backscatter images from two different dates and the coherence in a multivariate approach in identifying forest types showed some potential. However, multi-temporal analysis of the backscatter was inconclusive because leaves were not the primary scatterers of a forest canopy at the L-band wavelengths. Further research in forest classification is suggested using diverse band width SAR imagery and fusing with other imagery source.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.21 no.6
• /
• pp.807-816
• /
• 2018

Threat hunting is defined as a process of proactively and iteratively searching through networks to detect and isolate advanced threats that evade existing security solutions. The main concept of threat hunting is to find out weak points and remedy them before actual cyber threat has occurred. And HMM(Hunting Maturity Matrix) is suggested to evolve hunting processes with five levels, therefore, CSOC(Cyber Security Operations Center) can refer HMM how to make them safer from complicated and organized cyber attacks. We are developing a system for cyber situation awareness system with pro-active threat hunting process called unMazeTM. With this unMaze, it can be upgraded CSOC's HMM level from initial level to basic level. CSOC with unMaze do threat hunting process not only detecting existing cyber equipment post-actively, but also proactively detecting cyber threat by fusing and analyzing cyber asset data and threat intelligence.

• Journal of Urban Science
• /
• v.7 no.2
• /
• pp.53-60
• /
• 2018

Digital Twin is a technology that creates a photocopy of real-world objects on a computer and analyzes the past and present operational status by fusing the structure, context, and operation of various physical systems with property information, and predicts the future society's countermeasures. In particular, 3D rendering technology (UAS, LiDAR, GNSS, etc.) is a core technology in digital twin. so, the research and application are actively performed in the industry in recent years. However, UAS (Unmanned Aerial System) and LiDAR (Light Detection And Ranging) have to be solved by compensating blind spot which is not reconstructed according to the object shape. In addition, the terrestrial LiDAR can acquire the point cloud of the object more precisely and quickly at a short distance, but a blind spot is generated at the upper part of the object, thereby imposing restrictions on the forward digital twin modeling. The UAS is capable of modeling a specific range of objects with high accuracy by using high resolution images at low altitudes, and has the advantage of generating a high density point group based on SfM (Structure-from-Motion) image analysis technology. However, It is relatively far from the target LiDAR than the terrestrial LiDAR, and it takes time to analyze the image. In particular, it is necessary to reduce the accuracy of the side part and compensate the blind spot. By re-optimizing it after fusion with UAS and Terrestrial LiDAR, the residual error of each modeling method was compensated and the mutual correction result was obtained. The accuracy of fusion-based 3D model is less than 1cm and it is expected to be useful for digital twin construction.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.2
• /
• pp.193-200
• /
• 2019

The 2015 revised curriculum aims to cultivate a 'creative fusion talent' capable of creating new knowledge and fusing various knowledge to create new value. Therefore, it is strengthening reading education to raise humanistic imagination and software education to promote scientific creativity. In addition, we have created [Safe Living] textbooks based on experiential activities as a way to strengthen safety education that is becoming a social issue. And we use it to conduct safety education at creative activity time. Novel Engineering believes that it can develop thinking skills in the process of reading books and finding and solving problems in life in them. Therefore, in this study, we will develop software education programs for safety education that are applied with Novel Engineering and apply them to actual classes to verify the educational effectiveness of students' creative problem solving skills and safety education.

• Journal of Internet Computing and Services
• /
• v.22 no.3
• /
• pp.17-26
• /
• 2021

Recently, COVID-19 has spread and time to stay at home has been increasing in accordance with quarantine guidelines of the government such as recommendations to refrain from going out. As a result, the number of single-person households staying at home is also increasingsingle-person households are less likely to be notified to the outside world in times of emergency than multi-person households. This study collects various situations occurring in the home with lidar, image, and voice sensors and analyzes the data according to the sensors through their respective algorithms. Using this method, we analyzed abnormal patterns such as emergency situations and conducted research to detect abnormal signs in humans. Artificial intelligence algorithms that detect abnormalities in people by each sensor were studied and the accuracy of anomaly detection was measured according to the sensor. Furthermore, this work proposes a fusion method that complements the pros and cons between sensors by experimenting with the detectability of sensors for various situations.