• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2167-2175
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• 2017

In recent years, an IoT is dramatically developing according to the enhancement of AI, the increase of connected devices, and the high-performance cloud systems. Huge data produced by many devices and sensors is expanding the scope of services, such as an intelligent diagnostics, a recommendation service, as well as a smart monitoring service. The studies of edge computing are limited as a role of small server system with high quality HW resources. However, there are specialized requirements in a smart factory domain needed edge computing. The edges are needed to pre-process containing tiny filtering, pre-formatting, as well as merging of group contexts and manage the regional rules. So, in this paper, we extract the features and requirements in a scope of efficiency and robustness. Our edge offers to decrease a network resource consumption and update rules and learning models. Moreover, we propose architecture of edge computing based on learning factors sharing with a cloud system in a smart factory.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.800-808
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• 2008

This paper presents a localization algorithm of the outdoor wheeled mobile robot using the sensor fusion method based on indirect Kalman filter(IKF). The wheeled mobile robot considered with in this paper is approximated to the two wheeled mobile robot. The mobile robot has the IMU and encoder sensor for inertia positioning system and GPS. Because the IMU and encoder sensor have bias errors, divergence of the estimated position from the measured data can occur when the mobile robot moves for a long time. Because of many natural and artificial conditions (i.e. atmosphere or GPS body itself), GPS has the maximum error about $10{\sim}20m$ when the mobile robot moves for a short time. Thus, the fusion algorithm of IMU, encoder sensor and GPS is needed. For the sensor fusion algorithm, we use IKF that estimates the errors of the position of the mobile robot. IKF proposed in this paper can be used other autonomous agents (i.e. UAV, UGV) because IKF in this paper use the position errors of the mobile robot. We can show the stability of the proposed sensor fusion method, using the fact that the covariance of error state of the IKF is bounded. To evaluate the performance of proposed algorithm, simulation and experimental results of IKF for the position(x-axis position, y-axis position, and yaw angle) of the outdoor wheeled mobile robot are presented.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.22-30
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• 2011

In the IT convergence environment changed with not only the quality but also the material abundance, it is the most crucial factor for the strategy of personalized recommendation services to investigate the context information. In this paper, we proposed the recommendation using the service ontology based context awareness modeling. The proposed method establishes a data acquisition model based on the OSGi framework and develops a context information model based on ontology in order to perform the device environment between different kinds of systems. In addition, the context information will be extracted and classified for implementing the recommendation system used for the context information model. This study develops the ontology based context awareness model using the context information and applies it to the recommendation of the collaborative filtering. The context awareness model reflects the information that selects services according to the context using the Naive Bayes classifier and provides it to users. To evaluate the performance of the proposed method, we conducted sample T-tests so as to verify usefulness. This evaluation found that the difference of satisfaction by service was statistically meaningful, and showed high satisfaction.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.40-43
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• 2012

3차원 영상 기술은 방송, 영화, 게임, 의료, 국방 등 다양한 기존 산업들과 융합하며 새로운 패러다임을 형성하고 있으며, 고품질 및 고해상도의 3차원 영상 획득에 대한 필요성이 강조되고 있다. 이에 따라, 최근에는 3차원 입체 영상을 제작 하는 방법 중 하나인 2D-plus-Depth 구조에 대한 연구가 활발히 진행되고 있다. 2D-plus-Depth 구조는 Charge-Coupled Device(CCD) 센서 등을 이용한 일반 카메라와 깊이 카메라를 결합한 형태로써 이 구조로부터 얻은 깊이 영상의 해상도를 상향 변환하기 위해서 Joint Bilateral Upsampling(JBU)[1], 컬러 영상의 정보를 활용한 보간법[2] 등의 방법들이 사용된다. 하지만 이 방법들은 깊이 영상을 높은 배율로 상향 변환할 경우 텍스처가 복사되거나 흐림 및 블록화 현상이 발생하는 문제점이 있다. 본 논문에서는 2D-plus-Depth 구조에서 얻은 고해상도 컬러 영상에서 보간 정보를 구하고 이 정보를 저해상도의 깊이 영상에 적용하여 상향 변환된 가이드 깊이 영상을 제작한다. 이 가이드 깊이 영상을 Bilateral Filtering[8]을 이용함으로써 고품질의 고해상도 깊이 영상을 획득한다. 실험 결과 제안하는 방법으로 해상도를 상향 변환을 할 경우에 기존의 보간법들에 비해 깊이 영상의 특성을 잘 보존함을 확인할 수 있고, 가이드 깊이 영상에 필터링을 처리한 결과가 JBU의 결과보다 향상됨을 확인할 수 있다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.163-171
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• 2012

High resolution satellite images have been used for building and road system analysis, landscape analysis, and ecological assessment for several years. However, in high resolution satellite images, shadows are necessarily cast by manmade objects such as buildings and over-pass bridges. This paper develops the shadow extraction procedures in urban area including various land-use classes, and the extracted shadow areas are evaluated by a manually digitized shadow map. For the shadow extraction, the Canny edge operator and the dilation filter are applied to make building edge buffer area. Also, the object-based segmentation was performed using Gram-Schmitt fusion image, and spectral and spatial parameters are calculated from the segmentation results. Finally, we proposed appropriate parameters and extraction rules for the shadow extraction. The accuracy of the shadow extraction results from the various assessment indices is 80% to 90%.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.901-906
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• 2007

Algorithms of path planning and obstacle avoidance are essential to autonomous mobile robots that are working in unknown environments in the real time. This paper presents a new navigation algorithm for an autonomous mobile robot with vision and IR sensors using fuzzy rules. Temporary targets are set up by distance variation method and then the algorithms of trajectory planning and obstacle avoidance are designed using fuzzy rules. In this approach, several digital image processing technique is employed to detect edge of obstacles and the distances between the mobile robot and the obstacles are measured. An autonomous mobile robot with single vision and IR sensors is built up for experiments. We also show that the autonomous mobile robot with the proposed algorithm is navigating very well in complex unknown environments.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.1
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• pp.67-75
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• 2010

This paper presents localization algorithm for mobile robot in outdoor environment. Outdoor environment includes the uncertainty on the ground. Magnetic sensor or IMU(Inertial Measurement Unit) has been used to estimate robot's heading angle. Two sensor is unavailable because mobile robot is electric car affected by magnetic field. Heading angle estimation algorithm for mobile robot is implemented using gyro sensor module consisting of 1-axis gyro sensors. Localization algorithm applied Extended Kalman filter that utilized GPS and encoder, gyro sensor module. Experiment results show that proposed localization algorithm improve considerably localization performance of mobile robots.

• Journal of Advanced Navigation Technology
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• v.17 no.6
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• pp.749-756
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• 2013

This paper presents an algorithm concerning the ground altitude measurement using a laser altimeter and an ultrasonic rangefinder for UAV(Unmanned Aerial Vehicle). A simple ground test conducted using the laser altimeter and ultrasonic rangefinder that are used for conducting the low altitude measurement of UAV and identify the characteristics of each sensor. Especially, the disadvantages of the laser altimeter were checked through the ground test. After that who those are participated in this paper planned the algorithm which is complemented by the ultrasonic rangefinder and the experiment was conducted. The laser altimeter and the ultrasonic rangefinder were fused by a loosely coupled method by Kalman filter. The paper shows that stable value of altitude complemented by the ultrasonic rangefinder that covers the laser altimeter's drawbacks can be measured through the ground test.

• Database Research
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• v.34 no.3
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• pp.99-123
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• 2018

Due to the ever expanding nature of the Web, separating more valuable information from the noisy data is getting more important. Although recommendation systems are widely used for addressing the information overloading issue, their performance does not seem meaningfully improved in currently suggested approaches. Hence, to investigate the issues, this study discusses different characteristics of popular, existing recommendation approaches, and proposes a new profiling technique that uses collaborative tags and test whether it successfully compensates the limitations of the existing approaches. In addition, the study also empirically evaluates rating/tagging patterns of users in various recommendation approaches, which include the proposed approach, to learn whether those patterns can be used as effective cues for improving the recommendations accuracy. Through the sensitivity analyses, this study also suggests the potential associated with a single recommendation system that applies multiple approaches for different users or items depending upon the types and contexts of recommendations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.1
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• pp.56-73
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• 2019

Probe-based systems have been gaining popularity in advanced traveler information systems. However, the high possibility of providing inaccurate travel-time information due to the inherent time-lag phenomenon is still an important issue to be resolved. To mitigate the time-lag problem, different prediction techniques have been applied, but the techniques are generally regarded as less effective for travel times with high variability. For this reason, current 5-min aggregated data have been commonly used for real-time travel-time provision on highways with high travel-time fluctuation. However, the 5-min aggregation interval itself can further increase the time-lags in the real-time travel-time information equivalent to 5 minutes. In this study, a new scheme that uses both individual probe and 5-min aggregated travel times is suggested to provide reliable real-time travel-time information. The scheme utilizes individual probe data under congested conditions and 5-min aggregated data under uncongested conditions, respectively. As a result of an evaluation with field data, the proposed scheme showed the best performance, with a maximum reduction in travel-time error of 18%.