• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.5
• /
• pp.621-625
• /
• 2010

This paper presents a hand motion tracking method for hand gesture interface using a camera in mobile devices such as a smart phone and PDA. When a camera moves according to the hand gesture of the user, global optical flows are generated. Therefore, robust hand movement estimation is possible by considering dominant optical flow based on histogram analysis of the motion direction. A continuous hand gesture is segmented into unit gestures by motion state estimation using motion phase, which is determined by velocity and acceleration of the estimated hand motion. Feature vectors are extracted during movement states and hand gestures are recognized at the end state of each gesture. Support vector machine (SVM), k-nearest neighborhood classifier, and normal Bayes classifier are used for classification. SVM shows 82% recognition rate for 14 hand gestures.

• Korean Journal of Plant Taxonomy
• /
• v.42 no.1
• /
• pp.13-23
• /
• 2012

The trnL-F region islocated in the large single-copy region of the chloroplast genome. It consists of the trnL gene, the trnL intron, and the trnL-F IGS. Molecular evolution and phylogenetic relationships in Korean Thalictrum L. were investigated using data from the cpDNA trnL-F region. Bayesian and parsimony analyses of the data set with the gap characteristics recovered well-resolved trees that are topologically similar, with clades supported by some indels evolution. Indel events of cpDNA trnL-F in Korean Thalictrum were interpreted as phylogenetically informative characteristics. Sect. Physocarpum (excluding T. osmorhizoides) was an early-diverging group with in the genus and the remaining section formed strongly supported clades. Korean Thalictrum has various evolutionary patterns, such as the spatial distribution of the nucleotide diversity and transversion-type base substitutions in the trnL-F region.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.4
• /
• pp.381-390
• /
• 2010

This paper describes a procedure of the map-based localization for mobile robots by using a sensor fusion technique in structured environments. A combination of various sensors with different characteristics and limited sensibility has advantages in view of complementariness and cooperation to obtain better information on the environment. In this paper, for robust self-localization of a mobile robot with a monocular camera and a laser structured light sensor, environment information acquired from two sensors is combined and fused by a Bayesian sensor fusion technique based on the probabilistic reliability function of each sensor predefined through experiments. For the self-localization using the monocular vision, the robot utilizes image features consisting of vertical edge lines from input camera images, and they are used as natural landmark points in self-localization process. However, in case of using the laser structured light sensor, it utilizes geometrical features composed of corners and planes as natural landmark shapes during this process, which are extracted from range data at a constant height from the navigation floor. Although only each feature group of them is sometimes useful to localize mobile robots, all features from the two sensors are simultaneously used and fused in term of information for reliable localization under various environment conditions. To verify the advantage of using multi-sensor fusion, a series of experiments are performed, and experimental results are discussed in detail.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.4B
• /
• pp.354-361
• /
• 2011

Increasing interest and technological advances in smart home has led to active research on context-awareness service and prediction algorithms such as Bayesian Networks, Tree-Dimensional Structures and Genetic prediction algorithms. Context-awareness service presents that providing automatic customized service regarding individual user's pattern surely helps users improve the quality of life. However, it is difficult to implement context-awareness service because the problems are that handling coincidence with context information and exceptional cases have to consider. To overcome this problem, we proposes an Intelligent Sequential Matching Algorithm(ISMA), models context-awareness service using Timed Petri-net(TPN) which is petri-net to have time factor. The example scenario illustrates the effectiveness of the Timed Petri-net model and our proposed algorithm improves average 4~6% than traditional in the accuracy and reliability of prediction.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.49 no.6
• /
• pp.521-528
• /
• 2021

In order to allocate an appropriate interceptor weapon to an air track for which the threat assessment has been completed, it is necessary to evaluate the suitability of engagement in consideration of the expected point of engagement. In this thesis, a method of calculating the kill probability is proposed according to the position in the engagement space using Bayesian theorem with multivariate attribute information such as relative distance, approach azimuth angle, and altitude of the air track when passing through the engagement space. As a result of the calculation, it was confirmed that the distribution form of the kill probability value for each point in the engagement space follows a multivariate normal distribution based on the optimal predicted intercepting point. It is expected to be applicable to the engagement suitability evaluation of the engagement space.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.4
• /
• pp.253-258
• /
• 2016

This paper proposes a method that utilizes optimization approach for localization of an indoor mobile robot. Bayesian filters which have been widely used for localization of a mobile robot use many control parameters to take the uncertainties in measurement and environment into account. The estimation performance depends on the selection of these parameter values. Also, the performance of the Bayesian filters deteriorate as the non-linearity of the motion and measurement increases. On the other hand, the optimization approach uses fewer control parameters and is less influenced by the non-linearity than the Bayesian methods. This paper compares the localization performance of the proposed method with the performance of the extended Kalman filter to verify the feasibility of the proposed method. Measurements of ranges from beacons of ultrasonic satellite to the robot are used for localization. Mahalanobis distance is used for detection and rejection of outlier in the measurements. The optimization method sets performance index as a function of the measured range values, and finds the optimized estimation of the location through iteration. The method can improve the localization performance and reduce the computation time in corporation with Bayesian filter which provides proper initial location for the iteration.

• Journal of Broadcast Engineering
• /
• v.9 no.4 s.25
• /
• pp.305-321
• /
• 2004

With the advent of the digital broadcasting, the audiences can access a large number of TV programs and their information through the multiple channels on various media devices. The access to a large number of TV programs can support a user for many chances with which he/she can sort and select the best one of them. However, the information overload on the user inevitably requires much effort with a lot of patience for finding his/her favorite programs. Therefore, it is useful to provide the persona1ized broadcasting service which assists the user to automatically find his/her favorite programs. As the growing requirements of the TV personalization, we introduce our automatic user preference learning algorithm which 1) analyzes a user's usage history on TV program contents: 2) extracts the user's watching pattern depending on a specific time and day and shows our automatic TV program recommendation system using MPEG-7 MDS (Multimedia Description Scheme: ISO/IEC 15938-5) and 3) automatically calculates the user's preference. For our experimental results, we have used TV audiences' watching history with the ages, genders and viewing times obtained from AC Nielson Korea. From our experimental results, we observed that our proposed algorithm of the automatic user preference learning algorithm based on the Bayesian network can effectively learn the user's preferences accordingly during the course of TV watching periods.