• Journal of Digital Convergence
• /
• v.10 no.6
• /
• pp.297-302
• /
• 2012

In this paper, we present a object detection and tracking method based on image context analysis. It is robust from the image variations such as complicated background, dynamic movement of the object. Image context analysis is carried out using the hybrid network of k-means and RBF. The proposed object detection employs context-driven adaptive Bayesian framework to relive the effect due to uneven object images. The proposed method used feature vector generator using 2D Haar wavelet transform and the Bayesian discriminant method in order to enhance the speed of learning. The system took less time to learn, and learning in a wide variety of data showed consistent results. After we developed the proposed method was applied to real-world environment. As a result, in the case of the object to detect pass outside expected area or other changes in the uncertain reaction showed that stable. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.11
• /
• pp.5512-5526
• /
• 2019

In sports video games, especially ball games, motion capture techniques are used to reproduce the ball-driven performances. The amount of motion data needed to create different situations in which athletes exchange balls is bound to increase exponentially with resolution. This paper proposes how avatars in virtual worlds can not only imitate professional athletes in ball games, but also create and edit their actions effectively. First, various ball-handling movements are recorded using motion sensors. We do not really have to control an actual ball; imitating the motions is enough. Next, motion is created by specifying what to pass the ball through, and then making motion to handle the ball in front of the motion sensor. The ball's occupant then passes the ball to the user-specified target through a motion that imitates the user's, and the process is repeated. The method proposed can be used as a convenient user interface for motion based games for players who handle balls.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2002.10a
• /
• pp.595-598
• /
• 2002

Laser scanners are getting used more and more in reverse engineering and inspection. For CNC-driven laser scanners, it is important to automate the scanning operations to improve the accuracy of capture point data and to reduce scanning time in industry. However, there are few research works on laser scan planning system. In addition, it is difficult to directly analyze multi-patched freeform models. In this paper, we propose an STL (Stereolithography) mesh based laser scan planning system for complex freeform surfaces. The scan planning system consists of three steps and it is assumed that the CAD model of the part exists. Firstly, the surface model is approximated into STL meshes. From the mesh model, normal vector of each node point is estimated. Second, scan directions and regions are determined through the region growing method. Also, scan paths are generated by calculating the minimum-bounding rectangle of points that can be scanned in each scan direction. Finally, the generated scan directions and paths are validated by checking optical constraints and the collision between the laser probe and the part to be scanned.

• Journal of Communications and Networks
• /
• v.17 no.3
• /
• pp.306-320
• /
• 2015

Due to an explosive growth in services running in different datacenters, there is need for service selection and routing to deliver user requests to the best service instance. In current solutions, it is generally the client that must first select a datacenter to forward the request to before an internal load-balancer of the selected datacenter can select the optimal instance. An optimal selection requires knowledge of both network and server characteristics, making clients less suitable to make this decision. Information-Centric Networking (ICN) research solved a similar selection problem for static data retrieval by integrating content delivery as a native network feature. We address the selection problem for services by extending the ICN-principles for services. In this paper we present Queue and Latency, a network-driven service selection algorithm which maps user demand to service instances, taking into account both network and server metrics. To reduce the size of service router forwarding tables, we present a statistical method to approximate an optimal load distribution with minimized router state required. Simulation results show that our statistical routing approach approximates the average system response time of source-based routing with minimized state in forwarding tables.

• Journal of the Ergonomics Society of Korea
• /
• v.31 no.4
• /
• pp.525-531
• /
• 2012

Objective: The aim of this study is to investigate learnability of gestures-commands pair when people use gestures to control a device. Background: In vision-based gesture recognition system, selecting gesture-command pairing is critical for its usability in learning. Subjective preference and its agreement score, used in previous study(Lim et al., 2012) was used to group four gesture-command pairings. To quantify the learnability, two learning models, average time model and marginal time model, were used. Method: Two sets of eight gestures, total sixteen gestures were listed by agreement score and preference data. Fourteen participants divided into two groups, memorized each set of gesture-command pair and performed gesture. For a given command, time to recall the paired gesture was collected. Results: The average recall time for initial trials were differed by preference and agreement score as well as the learning rate R driven by the two learning models. Conclusion: Preference rate agreement score showed influence on learning of gesture-command pairs. Application: This study could be applied to any device considered to adopt gesture interaction system for device control.

• Computers and Concrete
• /
• v.19 no.2
• /
• pp.143-152
• /
• 2017

This paper has numerically investigated the changes of loading-induced stress in concrete with the corrosion time in the sulfate-containing environment. Firstly, based on Fick's law and reaction kinetics, a diffusion-reaction equation of sulfate ion in concrete is proposed, and it is numerically solved to obtain the spatial and temporal distribution of sulfate ion concentration in concrete by the finite difference method. Secondly, by fitting the existed experimental data of concrete in sodium sulfate solutions, the chemical damage of concrete associated with sulfate ion concentration and corrosion time is quantitatively presented. Thirdly, depending on the plastic-damage mechanics, while considering the influence of sulfate attack on concrete properties, a simplified chemo-mechanical damage model, with stress-based plasticity and strain-driven damage, for concrete under axial loading and sulfate attack is determined by introducing the chemical damage degree. Finally, an axially compressed concrete prism immersed into the sodium sulfate solution is regarded as an object to investigate the time-varying stress in concrete subjected to the couplings of axial loading and sulfate attack.

• Journal of the Korea Convergence Society
• /
• v.12 no.8
• /
• pp.47-53
• /
• 2021

With increasing interest in the 4th industrial revolution, data-driven scientific methodologies have developed. However, there are limitations of data collection in the real estate field of research. In addition, as the public becomes more knowledgeable about the real estate market, the qualitative sentiment comes to play a bigger role in the real estate market. Therefore, we propose a method to collect quantitative data that reflects sentiment using text mining and k-means algorithms, rather than the existing source data, and to predict the direction of housing index through artificial neural network learning based on the collected data. Data from 2012 to 2019 is set as the training period and 2020 as the prediction period. It is expected that this study will contribute to the utilization of scientific methods such as artificial neural networks rather than the use of the classical methodology for real estate market participants in their decision making process.

• Journal of Internet of Things and Convergence
• /
• v.9 no.2
• /
• pp.61-69
• /
• 2023

In recent years, due to heightened environmental awareness, Electric Power Steering (EPS) has been increasingly adopted as the steering control unit in manufactured vehicles. This has had numerous benefits, such as improved steering power, elimination of hydraulic hose leaks and reduced fuel consumption. However, for EPS systems to respond to actions, sensors must be employed; this means that the consistency of the sensor's linear variation is integral to the stability of the steering response. To ensure quality control, a reliable method for detecting defects and assessing linearity is required to assess the sensitivity of the EPS sensor to changes in the internal design characters. This paper proposes a data-driven defect and linearity assessment monitoring system, which can be used to analyze EPS component defects and linearity based on vehicle speed interval division. The approach is validated experimentally using data collected from an EPS test jig and is further enhanced by the inclusion of a Graphical User Interface (GUI). Based on the design, the developed system effectively performs defect detection with an accuracy of 0.99 percent and obtains a linearity assessment score at varying vehicle speeds.

• The Journal of the Acoustical Society of Korea
• /
• v.43 no.2
• /
• pp.178-183
• /
• 2024

This study introduces an enhanced sound source localization technique, bolstered by a data-driven deep learning approach, to improve the precision and accuracy of direction of arrival estimation. Focused on refining Time Difference Of Arrival (TDOA) based sound source localization, the research hinges on accurately estimating TDOA from cross-correlation functions. Accurately estimating the TDOA still remains a limitation in this research field because the measured value from actual microphones are mixed with a lot of noise. Additionally, the digitization process of acoustic signals introduces quantization errors, associated with the sampling frequency of the measurement system, that limit the precision of TDOA estimation. A deep learning-based approach is designed to overcome these limitations in TDOA accuracy and precision. To validate the method, we conduct comprehensive evaluations using both two and three-microphone array configurations. Moreover, the feasibility and real-world applicability of the suggested method are further substantiated through experiments conducted in an anechoic chamber.

• Spatial Information Research
• /
• v.8 no.1
• /
• pp.15-29
• /
• 2000

In the Recent rapidly changing technology environment the computerization of administration business which is driven or will be driven to give improved information services to people by local government or central government with a huge budget. The possibility of applying GIS application component to the computerization of administration business is investigated to prevent local government from investing redundant money and to reuse the existing investment at this point of time. Land civil service application component was developed at the $\ulcorner Development of Open GIS Component S/W \lrcorner$ project which was managed by Ministry of Information and Communication . GIS application component was based on Open GIS OLE/COM specification for development of standard interface and USD(Unified System Development ) for development method and UML (Unified Modeling Language) for system design and Visual C++ for component implementation. Implemented components were Process Control, Map, Print, Statistics component and were verified by using Visual Basic and Delhi. tis study shows that the development of component is very useful at the GIS application development for local governments. But the standard of business and data and system is the essential prerequisite to maximize business application.