• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.896-902
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• 2004

The purpose of the study of multi-robot system is to realize multi-robot system easy for the control of robot system in case robot is adapted in the complicated environment of task structure. The purpose of the study of multi-robot system is to realize multi-robot system easy for the control of robot system in case robot is adapted in the complicated environment of task structure. To make real time control possible by making effective use of recognized information in this dynamic environment, suitable distribution of tasks should be made in consideration of function and role of each performing robots. In this paper, IHMA (Intelligent Hybrid Modular Architecture) of Intelligent combined control architecture which utilizes the merits of deliberative and reactive controllers will be suggested and its efficiency will be evaluated through the adaptation of control architecture to representative multi-robot system.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1057-1060
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• 2004

We propose new rotation moment invariants based on multiresolution filter bank techniques. The multiresolution pyramid motivates our simple but efficient feature selection procedure based on the fuzzy C-mean clustering, combined with the Mahalanobis distance. The procedure verifies an impact of random noise as well as an interesting and less known impact of noise due to spatial transformations. The recognition accuracy of the proposed techniques has been tested with the preceding moment invariants as well as with some wavelet based schemes. The numerical experiments, with more than 30,000 images, demonstrate a tangible accuracy increase of about 3% for low noise, 8% for the average noise and 15% for high level noise.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1738-1743
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• 2004

This paper presents an automobile traction control system by using a sliding mode controller with disturbance observer for estimating the car-body speed. First, we show that the control system, which combines an automobile system and a disturbance observer, can be divided into a controllable system and an estimated one. And, we found out that the effect of the traction control and ABS depends on the air resistance of the car. Then, the sliding mode control system is designed using the obtained combined system. And finally, the stability of this control system is verified by simulation and it shows a very satisfactory results.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.835-838
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• 2003

Computer vision and natural-language dialogue play an important role in friendly human-machine interfaces for service robots. In this paper we describe an integrated face detection and face recognition system for a welfare robot, which has also been combined with the robot's speech interface. Our approach to face detection is to combine neural network (NN) and genetic algorithm (GA): ANN serves as a face filter while GA is used to search the image efficiently. When the face is detected, embedded Hidden Markov Model (EMM) is used to determine its identity. A real-time system has been created by combining the face detection and recognition techniques. When motivated by the speaker's voice commands, it takes an image from the camera, finds the face inside the image and recognizes it. Experiments on an indoor environment with complex backgrounds showed that a recognition rate of more than 88% can be achieved.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2461-2466
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• 2003

This paper presents a simple sensor fusion algorithm using neural network for navigation of mobile robots with obstacle avoidance and trap recovery. The multiple sensors input sensor data to the input layer of neural network activating the input nodes. The multiple sensors used include optical encoders, ultrasonic sensors, infrared sensors, a magnetic compass sensor, and GPS sensors. The proposed sensor fusion algorithm is combined with the VFH(Vector Field Histogram) algorithm for obstacle avoidance and AGPM(Adaptive Goal Perturbation Method) which sets adaptive virtual goals to escape trap situations. The experiment results show that the proposed low-level fusion algorithm is effective for real-time navigation of mobile robot.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.932-937
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• 2003

In this paper, we will explain about the unmanned vehicle control and modeling for combined obstacle avoidance and lane tracking. First, obstacle avoidance is considered as one of the important technologies in the unmanned vehicle. It is consisted by two parts: the first part includes the longitudinal control system for the acceleration and deceleration and the second part is the lateral control system for the steering control. Each system uses to the obstacle avoidance during the vehicle moving. Therefore, we propose the method of vehicle control, modeling and obstacle avoidance. Second, we describe a method of lane tracking by means of vision system. It is important in the unmanned vehicle and mobile robot system. In this paper, we deal with lane tracking and image processing method and it is including lane detection method, image processing algorithm and filtering method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.308-314
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• 2000

This study explores the free, out-of-plane vibrations of horizontally circular curved beams. The differential equations governing the free vibration of such beams, including the effects of warping and rotatory inertia, are derived and solved numerically. The Runge-Kutta method and the Determinant Search method combined with Regula-Falsi method are used to integrate the differential equations and to obtain the natural frequencies, respectively. The lowest three natural frequencies are calculated over a wide range of non-dimensional system parameters: the horizontal rise to span length ratio, the slenderness ratio, the stiffness parameter, and the warping parameter. It is expected that the results obtained herein can be used practically for the design of curved member systems.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.89-92
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• 2001

In this paper, we propose a new approach for tracking a moving object in moving image sequences using active contour models and optical flow. In our approach object segmentation is achieved by active contours, and object tracking is done by motion estimation based on optical flow. To get more dynamic characteristics, Lagrangian dynamics combined to the active contour models. For the optical flow computation, a method, which is based on Spatiotempo-ral Energy Models, is employed to perform robust tracking under poor environments. A prototype real tracking system has been developed and applied to a contents-based video retrieval systems.

• Structural Monitoring and Maintenance
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• v.3 no.4
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• pp.335-347
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• 2016

One of major errors in flow rate measurement for two-phase flow using an Electrical Capacitance Sensor (ECS) concerns sensor sensitivity under temperature raise. The thermal effect on electrical capacitance sensor (ECS) system for air-water two-phase flow monitoring include sensor sensitivity, capacitance measurements, capacitance change and node potential distribution is reported in this paper. The rules of 12-electrode sensor parameters such as capacitance, capacitance change, and change rate of capacitance and sensitivity map the basis of Air-water two-phase flow permittivity distribution and temperature raise are discussed by ANSYS and MATLAB, which are combined to simulate sensor characteristic. The cross-sectional void fraction as a function of temperature is determined from the scripting capabilities in ANSYS simulation. The results show that the temperature raise had a detrimental effect on the electrodes sensitivity and sensitive domain of electrodes. The FE results are in excellent agreement with an experimental result available in the literature, thus validating the accuracy and reliability of the proposed flow rate measurement system.