• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.12
• /
• pp.6455-6460
• /
• 2013

Learner modeling forms the foundations for intelligent tutoring systems that provide adaptive and active learning guidance for learning and education quality enhancement. The aim of this study was to develop learner modeling technologies to form the foundation of intelligent tutoring systems. Specific research tasks include learner modeling building techniques, diverse learner state diagnosis methods and educational data mining.

• Journal of KIISE:Software and Applications
• /
• v.29 no.3
• /
• pp.180-191
• /
• 2002

We present a new text localization method in images using a multi-layer perceptron(MLP) and a multiple continuously adaptive mean shift (MultiCAMShift) algorithm. An automatically constructed MLP-based texture classifier generates a text probability image for various types of images without an explicit feature extraction. The MultiCAMShift algorithm, which operates on the text probability Image produced by an MLP, can place bounding boxes efficiently without analyzing the texture properties of an entire image.

• Journal of information and communication convergence engineering
• /
• v.6 no.2
• /
• pp.228-232
• /
• 2008

In recent years, wireless ad hoc networks have become increasingly popular in both military and civilian applications due to their capability of building networks without the need for a pre-existing infrastructure. Recently, IEEE 802.11 Task Group e has been working on a new mechanism, the Enhanced Distributed Coordination Function (EDCF), to enhance the performance of 802.11 DCF. However, EDCF only reduces the internal collisions within a station, and external collisions between stations remain high in ad-hoc networks. In this paper, we propose to adopt an adaptive backoff window control technique, based on a dynamic value of the initial value of the range in which the backoff is chosen, so the backoff timer is randomly chosen in the range (InitRng, CW-1). We use ns-2 simulation to evaluate the throughput of our scheme. Results show that the throughput is improved for our scheme compared to the original DCF due to the reduced the number of collisions.

• Proceedings of the KIPE Conference
• /
• 2004.07b
• /
• pp.944-948
• /
• 2004

This paper presents a simple torque estimation method and switching angle control of Switched Reluctance Motor (SRM) using Neural Network (NN). SRM has gaining much interest as industrial applications due to the simple structure and high efficiency. Adaptive switching angle control is essential for the optimal driving of SRM because of the driving characteristic varies with the load and speed. The proper switching angle which can increase the efficiency was investigated in this paper. NN was adapted to regulate the switching angle and nonlinear inductance modelling. Experimental result shows the validity of the switching angle controller.

• Proceedings of the KIPE Conference
• /
• 2004.07b
• /
• pp.738-741
• /
• 2004

The overall performance of AC servo system is greatly affected by the uncertainties of unpredictable mechanical parameter variations and external load disturbances. Therefore, to compensate this problem, it is necessary to know different parameters and load disturbances subjected to position/speed control. This paper proposes an online identification method of mechanical parameters/load disturbances for AC servo system using Support Vector Regression (SVR). The proposed methodology advocates analytic parameter regression directly from the training data, rather than adaptive controller and observer approaches commonly used in motion control applications. The experimental results demonstrate that the proposed SVR algorithm is appropriate for control of unknown servo systems even with large measurement noise.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.586-589
• /
• 2005

Recently, the applications of unmanned system are steadily increasing. Unmanned automatic system is suitable for routine mission such as reconnaissance, environment monitoring, resource conservation and investigation. Especially, for the ocean environmental probe mission, many ocean engineers had scoped with the routine and even risky works. The unmanned surface vessel designed for sea probes can replace the periodic and routine missions such as water sampling, temperature and salinity measuring, etc. In this paper, an unmanned surface vessel was designed for ocean environmental probe missions. A classical and an adaptive fuzzy control system were designed and tested for the unmanned surface vessel. The design methodologies and performance of the surface vessel and fuzzy control algorithm were illustrated and verified with this unmanned vessel system designed for sea probes.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.7 no.5
• /
• pp.267-275
• /
• 2012

In this paper, we present the model-based autonomic computing framework for a cyber-physical system which provides a self-management and a self-adaptation characteristics. A development process using this framework consists of two phases: a design phase in which a developer models faults, normal status constrains, and goals of the CPS, and an operational phase in which an autonomic computing engine operates monitor-analysis-plan-execute(MAPE) cycle for managed resources of the CPS. We design a hierachical architecture for autonomic computing engines and adopt the Model Reference Adaptive Control(MRAC) as a basic feedback loop model to separate goals and resource management. According to the GroundVehicle example, we demonstrate the effectiveness of the framework.

• The Bulletin of The Korean Astronomical Society
• /
• v.39 no.2
• /
• pp.65.2-65.2
• /
• 2014

We present a new hydrodynamic simulation code based on the Voronoi tessellation for estimating the density precisely. The code employs both of Lagrangian and Eulerian description by adopting the movable mesh scheme, which is superior to the conventional SPH (smoothed particle hydrodynamics) and AMR (adaptive mesh refinement) schemes. The code first generates unstructured meshes by the Voronoi tessellation at every time step, and then solves the Riemann problem for all surfaces of each Voronoi cell so as to update the hydrodynamic states as well as to move current meshes. Besides, the IEM (incremental expanding method) is devised to compute the Voronoi tessellation to desired degree of speed, thereby the CPU time is turned out to be just proportional to the number of particles, i.e., O(N). We discuss the applications of our code in the context of cosmological simulations as well as numerical experiments for galaxy formation.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.7
• /
• pp.641-648
• /
• 2001

A face detection in color images is important for many multimedia applications. It is first step for face recognition and can be used for classifying specific shorts. This paper describes a new method to detect faces in color images based on the skin color and hair color. This paper presents a fuzzy-based method for classifying skin color region in a complex background under varying illumination. The Fuzzy rule bases of the fuzzy system are generated using training method like a genetic algorithm(GA). We find the skin color region and hair color region using the fuzzy system and apply the convex-hull to each region and find the face from their intersection relationship. To validity the effectiveness of the proposed method, we make experiment with various cases.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.96.2-96
• /
• 2002

In this paper, a new method for the mixed image separation is presented using the independent component analysis, the innovation process, and the expectation-maximization. In general, the independent component analysis (ICA) is one of the widely used statistical signal processing scheme that represents the information from observations as a set of random variables in the form of linear combinations of another statistically independent component variables. In various useful applications, ICA provides a more meaningful representation of the data than the principal component analysis through the transformation of the data to be quasi-orthogonal to each other, which can be utilized in linear p...