• Transactions on Electrical and Electronic Materials
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• v.17 no.2
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• pp.57-68
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• 2016

In this study, we present the modern hybrid system based power generation for electric vehicle applications. We describe the hybrid structure of modified current source based DC - DC converters used to extract the maximum power from Photovoltaic (PV) and Fuel Cell system. Due to reduced dc-link capacitor requirement and higher reliability, the current source inverters (CSI) better compared to the voltage source based inverter. The novel control strategy includes Distributed Maximum Power Point Tracking (DMPPT) for photovoltaic (PV) and fuel cell power generation system. The proposed DC - DC converters have been analyzed in both buck and boost mode of operation under duty cycle 0.5>d, 0.5<d<1 and 0.5<d for capable electric vehicle applications. The proposed topology benefits include one common DC-AC inverter that interposes the generated power to supply the charge for the sharing of load in a system of hybrid supply with photovoltaic panels and fuel cell PEM. An improved control of Direct Torque and Flux Control (DTFC) based induction motor fed by current source converters for electric vehicle.In order to achieve better performance in terms of speed, power and miles per gallon for the expert, to accepting high regenerative braking current as well as persistent high dynamics driving performance is required. A simulation model for the hybrid power generation system based electric vehicle has been developed by using MATLAB/Simulink. The Direct Torque and Flux Control (DTFC) is planned using Xilinx ISE software tool in addition to a Modelsim 6.3 software tool that is used for simulation purposes. The FPGA based pulse generation is used to control the induction motor for electric vehicle applications. FPGA has been implemented, in order to verify the minimal error between the simulation results of MATLAB/Simulink and experimental results.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1857-1862
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• 2016

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.53-62
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• 2011

In this paper, high-tech IT Convergence, to the development of complex technology, special technology, video object recognition technology was considered only as a smart - phone technology with the development of personal portable terminal has been developed crossroads. Technology-based detection of 3D face recognition technology that recognizes objects detected through the intelligent video recognition technology has been evolving technologies based on image recognition, face detection technology with through the development speed is booming. In this paper, based on human face recognition technology to detect the object recognition image processing technology is applied through the face recognition technology applied to the IP camera is the party of the mouth, and allowed the ability to identify and apply the human face recognition, measurement techniques applied research is suggested. Study plan: 1) face model based face tracking technology was developed and applied 2) algorithm developed by PC-based measurement of human perception through the CPU load in the face value of their basic parameters can be tracked, and 3) bilateral distance and the angle of gaze can be tracked in real time, proved effective.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.3
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• pp.568-576
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• 2003

This paper proposes a moving object tracking algorithm by using hierarchical search method in dynamic scenes. Proposed algorithm is based on two main steps: generation step of initial model from different pictures, and tracking step of moving object under the time-yawing scenes. With a series of this procedure, tracking process is not only stable under far distance circumstance with respect to the previous frame but also reliable under shape variation from the 3-dimensional(3D) motion and camera sway, and consequently, by correcting position of moving object, tracking time is relatively reduced. Partial Hausdorff distance is also utilized as an estimation function to determine the similarity between model and moving object. In order to testify the performance of proposed method, the extraction and tracking performance have tested using some kinds of moving car in dynamic scenes. Experimental results showed that the proposed algorithm provides higher performance. Namely, matching order is 28.21 times on average, and considering the processing time per frame, it is 53.21ms/frame. Computation result between the tracking position and that of currently real with respect to the root-mean-square(rms) is 1.148. In the occasion of different vehicle in terms of size, color and shape, tracking performance is 98.66%. In such case as background-dependence due to the analogy to road is 95.33%, and total average is 97%.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.9
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• pp.794-806
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• 2017

It is essential to protect the national space assets and space environment safely as a space development country from the continuously increasing space debris. And Active Debris Removal(ADR) is the most active way to solve this problem. In this paper, we studied the Artificial Neural Network(ANN) for a stable recognition model of vision-based space debris tracking system. We obtained the simulated image of the space environment by the KARICAT which is the ground-based space debris clearing satellite testbed developed by the Korea Aerospace Research Institute, and created the vector which encodes structure and color-based features of each object after image segmentation by depth discontinuity. The Feature Vector consists of 3D surface area, principle vector of point cloud, 2D shape and color information. We designed artificial neural network model based on the separated Feature Vector. In order to improve the performance of the artificial neural network, the model is divided according to the categories of the input feature vectors, and the ensemble technique is applied to each model. As a result, we confirmed the performance improvement of recognition model by ensemble technique.

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.417-424
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• 2012

In this paper, we propose an automatic system that recognizes dynamic human gestures activity, including Arabic numbers from 0 to 9. We assume the gesture trajectory is almost in a plane that called principal gesture plane, then the Least Squares Method is used to estimate the plane and project the 3-D trajectory model onto the principal. An improved FNN model combined with HMM is proposed for dynamic gesture recognition, which combines ability of HMM model for temporal data modeling with that of fuzzy neural network. The proposed algorithm shows that satisfactory performance and high recognition rate.

• Wind and Structures
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• v.4 no.1
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• pp.31-44
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• 2001

Mean concentrations of ammonia gas released as a tracer from an isolated low-rise building have been measured and predicted. Predictions were calculated using computational fluid dynamics (CFD) and two dispersion models: a diffusion model and a Lagrangian particle tracking technique. Explicit account was taken of the natural variation of wind direction by a technique based on the weighted summation of individual steady state wind direction results according to the probability density function of the wind direction. The results indicated that at distances >3 building heights downstream the weighted predictions from either model are satisfactory but that in the near wake the diffusion model is less successful. Weighted solutions give significantly improved predictions over unweighted results. Lack of plume spread is identified as the main cause of inaccuracies in predictions and this is linked to inadequate resolution of flow features and mixing in the CFD model. Further work on non-steady state simulation of wake flows for dispersion studies is recommended.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.159-170
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• 2009

It is very important to extract the expression data and capture a face image from a video for online-based 3D face animation. In recently, there are many researches on vision-based approach that captures the expression of an actor in a video and applies them to 3D face model. In this paper, we propose an automatic data extraction system, which extracts and traces a face and expression data from realtime video inputs. The procedures of our system consist of three steps: face detection, face feature extraction, and face tracing. In face detection, we detect skin pixels using YCbCr skin color model and verifies the face area using Haar-based classifier. We use the brightness and color information for extracting the eyes and lips data related facial expression. We extract 10 feature points from eyes and lips area considering FAP defined in MPEG-4. Then, we trace the displacement of the extracted features from continuous frames using color probabilistic distribution model. The experiments showed that our system could trace the expression data to about 8fps.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.597-606
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• 2010

This paper describes a real-time camera tracking framework specifically designed to track a monocular camera in an AR workspace. Typically, the Kalman filter is often employed for the camera tracking. In general, however, tracking performances of conventional methods are seriously affected by unpredictable situations such as ambiguity in feature detection, occlusion of features and rapid camera shake. In this paper, a recursive Bayesian sampling framework which is also known as the particle filter is adopted for the camera pose estimation. In our system, the camera state is estimated on the basis of the Gaussian distribution without employing additional uncertainty model and sample weight computation. In addition, the camera state is directly computed based on new sample particles which are distributed according to the true posterior of system state. In order to verify the proposed system, we conduct several experiments for unstable situations in the desktop AR environments.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1179-1184
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• 2007

An unmaned surveillance robot consists of rifle, laser receiver, thermal imager, color CCD camera, and laser illuminator. A human guard can be replaced with such a robot to take care dangerous surveillance tasks. Currently most of surveillance robots are mounded at a fixed post to take care of surveillance tasks. In order to watch blind areas, it is necessary to modify such a surveillance robot to become a mobile robot. In this paper, simulation based design procedure of mobile surveillance robot has been introduced. 3D CAD geometry model has been produced using Pro-Engineer. Required pen and tilt motor capacities have been analyzed using ADAMS inverse dynamics analysis. A target tracking and stabilization control algorithm of the mobile surveillance robot has been developed in order to stabilize the system from the motion of the vehicle which experiences the rough terrain. ADAMS-Matlab co-simulation has been also carried out to validate the proposed target tracking and stabilization algorithm.