• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.539-544
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• 2013

In this paper, we was designed three-dimensional face recognition algorithm using polynomial based RBFNNs and proposed method to calculate the recognition performance. In case of two-dimensional face recognition, the recognition performance is reduced by the external environment like facial pose and lighting. In order to compensate for these shortcomings, we perform face recognition by obtaining three-dimensional images. obtain face image using three-dimension scanner before the face recognition and obtain the front facial form using pose-compensation. And the depth value of the face is extracting using Point Signature method. The extracted data as high-dimensional data may cause problems in accompany the training and recognition. so use dimension reduction data using PCA algorithm. accompany parameter optimization using optimization algorithm for effective training. Each recognition performance confirm using PSO, DE, GA algorithm.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.134-141
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• 2003

A major research issue associated with service robots is the creation of an environment recognition system for mobile robot navigation that is robust and efficient on various environment situations. In recent years, intelligent autonomous mobile robots have received much attention as the types of service robots for serving people and industrial robots for replacing human. To help people, robots must be able to sense and recognize three dimensional space where they live or work. In this paper, we propose a three dimensional environmental modeling method based on an edge enhancement technique using a planar fitting method and a neural network technique called "Growing Neural Gas Network." Input data pre-processing provides probabilistic density to the input data of the neural network, and the neural network generates a graphical structure that reflects the topology of the input space. Using these methods, robot's surroundings are autonomously clustered into isolated objects and modeled as polygon patches with the user-selected resolution. Through a series of simulations and experiments, the proposed method is tested to recognize the environments surrounding the robot. From the experimental results, the usefulness and robustness of the proposed method are investigated and discussed in detail.in detail.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4934-4949
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• 2015

The unstable characteristic values of communication signals along with the varying SNR (Signal Noise Ratio) environment make it difficult to identify the modulations of signals. Most of relevant literature revolves around signal recognition under stable SNR, and not applicable for signal recognition at varying SNR. To solve the problem, this research developed a novel communication signal recognition algorithm based on Holder coefficient and cloud theory. In this algorithm, the two-dimensional (2D) Holder coefficient characteristics of communication signals were firstly calculated, and then according to the distribution characteristics of Holder coefficient under varying SNR environment, the digital characteristics of cloud model such as expectation, entropy, and hyper entropy are calculated to constitute the three-dimensional (3D) digital cloud characteristics of Holder coefficient value, which aims to improve the recognition rate of the communication signals. Compared with traditional algorithms, the developed algorithm can describe the signals' features more accurately under varying SNR environment. The results from the numerical simulation show that the developed 3D feature extraction algorithm based on Holder coefficient cloud features performs better anti-noise ability, and the classifier based on interval gray relation theory can achieve a recognition rate up to 84.0%, even when the SNR varies from -17dB to -12dB.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.124-132
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• 2001

Autonomous of welding process in shipyard is ultimately necessary., since welding site is spatially enclosed by floors and girders, and therefore welding operators are exposed to hostile working conditions. To solve this problem, a welding robot that can navigate autonomously within the enclosure needs to be developed. To achieve the welding ra나, the robotic welding systems needs a sensor system for the recognition of the working environments and the weld seam tracking, and a specially designed environment recognition strategy. In this paper, a three-dimensional laser vision system is developed based on the optical triangulation technology in order to provide robots with work environmental map. At the same time a strategy for environment recognition for welding mobile robot is proposed in order to recognize the work environment efficiently. The design of the sensor system, the algorithm for sensing the structured environment, and the recognition strategy and tactics for sensing the work environment are described and dis-cussed in detail.

• Journal of Bio-Environment Control
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• v.5 no.2
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• pp.167-173
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• 1996

Laser distance meter and x-y robot employed in the extraction of three dimensional image recognition of muskmelon net and recognized the characteristics of that. All data measured transmitted to the PC/AT in the computer room and programmed with Visual Basic(Microsoft). Alteration of the concentration and application time of nutrient solution modified the net height and width of hydroponically grown muskmelon. Net height and width which are the characteristics of muskmelon depended on the concentration of nutrient solution used. Decreasing with the concentration of nutrient solution lowered the occupying ratio of net and also observed the tendency of widening of muskmelon net.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.53-61
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• 2014

In this paper, we propose a method using an image received by the depth camera in order to separate the wall in a three-dimensional space indoor environment. Results of the paper may be used to provide valuable information on the three-dimensional space. For example, they may be used to recognize the indoor space, to detect adjacent objects, or to project a projector on the wall. The proposed method first detects a normal vector at each point by using the three dimensional coordinates of points. The normal vectors are then clustered into several groups according to similarity. The RANSAC algorithm is applied to separate out planes. The domain knowledge helps to determine the wall among planes in an indoor environment. This paper concludes with experimental results that show performance of the proposed method in various experimental environment.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.83-92
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• 2007

In this paper, we present a design and implementation of three dimensional pointing device using Inertial Navigation System(INS) that acquires coordinates and location information without environmental dependancy. The INS measures coordinates based on the data from gyroscope and accelerometer and corrects the measured data from accelerometer using Kalman-Filter. In order to implement the idea of three dimensional pointing device, we choose a three dimensional Space-recognition mouse and use RFIC wireless communication to send a measured data to receiver for printing out the coordinate on display equipment. Based on INS and Kalman-Filter theoretical knowledge, we design and implement a three dimensional pointing device and verified the usability as an input device that can capture a human's move. also, we describe the applicability of this device in ubiquitous computing environment.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.149-158
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• 2023

HMD, which is applied with the latest technology, minimizes motion sickness with high-resolution displays and fast motion recognition, and can accurately track location and motion. This can provide an environment where you can immerse yourself in a virtual three-dimensional space, and virtual reality contents such as disaster simulators and high-risk equipment learning spaces are developing using these characteristics. These advantages are also applicable in the field of basic science education. In particular, expanding the concepts of electric and magnetic fields in physics described by existing two-dimensional data into three-dimensional spaces and visualizing them in real time can greatly help improve learning understanding. In this paper, realistic physical education environments and contents based on three-dimensional virtual reality are developed and the developed learning contents are experienced by actual learning subjects to prove their effectiveness. A total of 46 middle school and college students were taught and experienced in real time the electric and magnetic fields expressed in three dimensions in a virtual reality environment. As a result of the survey, more than 85% of positive responses were obtained, and positive results were obtained that three-dimensional virtual space-based physical learning could be effectively applied.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.9
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• pp.776-787
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• 2002

Automation of welding process in shipyards is ultimately necessary, since the welding site is spatially enclosed by floors and girders, and therefore welding operators are exposed to hostile working conditions. To solve this problem, a welding mobile robot that can navigate autonomously within the enclosure has been developed. To achieve the welding task in the closed space, the robotic welding system needs a sensor system for the working environment recognition and the weld seam tracking, and a specially designed environment recognition strategy. In this paper, a three-dimensional laser vision system is developed based on the optical triangulation technology in order to provide robots with 3D work environmental map. Using this sensor system, a spatial filter based on neural network technology is designed for extracting the center of laser stripe, and evaluated in various situations. An environment modeling algorithm structure is proposed and tested, which is composed of the laser scanning module for 3D voxel modeling and the plane reconstruction module for mobile robot localization. Finally, an environmental recognition strategy for welding mobile robot is developed in order to recognize the work environments efficiently. The design of the sensor system, the algorithm for sensing the partially structured environment with plane segments, and the recognition strategy and tactics for sensing the work environment are described and discussed with a series of experiments in detail.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1029-1035
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• 2013

This paper proposes a real time lane detection algorithm using LRF (Laser Range Finder) for autonomous navigation of a mobile robot. There are many technologies for safety of the vehicles such as airbags, ABS, EPS etc. The real time lane detection is a fundamental requirement for an automobile system that utilizes outside information of automobiles. Representative methods of lane recognition are vision-based and LRF-based systems. By the vision-based system, recognition of environment for three dimensional space becomes excellent only in good conditions for capturing images. However there are so many unexpected barriers such as bad illumination, occlusions, and vibrations that the vision cannot be used for satisfying the fundamental requirement. In this paper, we introduce a three dimensional lane detection algorithm using LRF, which is very robust against the illumination. For the three dimensional lane detections, the laser reflection difference between the asphalt and lane according to the color and distance has been utilized with the extraction of feature points. Also a stable tracking algorithm is introduced empirically in this research. The performance of the proposed algorithm of lane detection and tracking has been verified through the real experiments.