• Asian Pacific Journal of Cancer Prevention
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• v.16 no.3
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• pp.1303-1306
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• 2015

Studies of epidemiological and prognostic factors are very important for oncology practice. There is a rapidly increasing amount of research and resultant knowledge in the scientific literature. This means that health professionals have major challenges in accessing relevant information and they increasingly require best available evidence to make their clinical decisions. Meta-analyses of prognostic and other epidemiological factors are very practical statistical approaches to define clinically important parameters. However, they also feature many obstacles in terms of data collection, standardization of results from multiple centers, bias, and commentary for intepretation. In this paper, the obstacles of meta-analysis are briefly reviewed, and potential problems with this statistical method are discussed.

• Journal of Biosystems Engineering
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• v.25 no.2
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• pp.141-150
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• 2000

This study was carried out to de develop a control strategy of a fruit harvesting redundant robot. The method of generating a safe trajectory, which avoids collisions with obstracles such as branches or immature fruits, in the 3D(3-dimension) space using artificial potential field technique and virtual plane concept was proposed. Also, the method of setting reference velocity vectors to follow the trajectory and to avoid obstacles in the 3D space was proposed. Developed methods were verified with computer simulations and with actual robot tests. Fro the actual robot tests, a machine vision system was used for detecting fruits and obstacles, Results showed that developed control method could reduce the occurrences of the robot manipulator located in the possible collision distance. with 10 virtual obstacles generated randomly in the 3 D space, maximum rates of the occurrences of the robot manipulator located in the possible collision distance, 0.03 m, from the obstacles were 8 % with 5 degree of freedom (DOF), 8 % with 6-DOF, and 4% with 7-DOF, respectively.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.490-496
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• 1995

A four-legged mobile robot can move on the plain terrain with mobility and stability, but if there exist any obstacles on the terrain of the motion direction, it takes extra times for a mobile robot to cross those obstacles and the stability should be considered during motion. The main objevtive is the study of a quadruped which can cross obstacles with better mobility, stability and fuel economy than any other wheeled or tracked vehicles. Vertical step, isolated wall and ditch are the basic obstacles and by understanding those three cases perfectly, a quadruped can move on any mixed rough terrain as 4-legged animal moves. Each leg of a determine the crossing capability in a static analysis. A quadruped can be simplified with links and joints. By applying the research method, a quadruped can determine the control procedures as soon as it receives the terrain informations from scanner and finally can be moved as animals move with mobility and stability.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.7
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• pp.36-48
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• 2010

This paper is concerned with stereo vision-based approach to detect obstacles and to generate the path of destination from the start. The hexapod robot in the experiment is cable of walking by legs and driving by wheels simultaneously. The hexapod robot operates under the driving mode normally, and it changes driving mode to walking mode to overcome obstacles using its legs. Disparity map, which is the correlation between two images taken by stereo camera, is employed for calculation of the distance between the robot and obstacles. When the obstacles information is extracted from the disparity map, the potential field algorithm is applied to create the obstacle-avoidance path. Simulator, based on OpenGL, is developed to generate the graphical path, and the experimental results are shown for the verification of the proposed algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.2
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• pp.64-70
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• 2002

This paper proposes a new construction method of neural networks. The construction method consists of two fundmental ideas, which are a parallel selection-style evaluation and rules evolution. A new collision avoidance algorithm using genetic and neural network is proposed to avoid moving obstacles such as mobile robots. The input parameters of this algorithm is position of moving obstacles and target. Output is a regenerated direction of mobile robot. This algorithm is very simple and so, it is available to application of real time process. The pattern of collision avoidance is learned through test execution.

• The Journal of Korea Robotics Society
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• v.2 no.3
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• pp.212-220
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• 2007

This paper proposes a method of avoiding obstacles and tracking a moving object continuously and simultaneously by using new concepts of virtual tow point and fuzzy danger factor for differential wheeled mobile robots. Since differential wheeled mobile robot has smaller degree of freedom to control and are non-holonomic systems, there exist multiple solutions (trajectories) to control and reach a target position. The paper proposes 'fuzzy danger factor' for obstacles avoidance, 'virtual tow point' to solve non-holonomic object tracking control problem for unique solution and three kinds of fuzzy logic controller. The fuzzy logic controller is policy decision controller with fuzzy danger factor to decide which controller's result is more valuable when the mobile robot is tracking a moving object with obstacles to be avoided.

• The Journal of Korea Robotics Society
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• v.13 no.2
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• pp.121-128
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• 2018

Obstacle avoidance is one of the most important parts of autonomous mobile robot. In this study, we proposed safe and efficient local path planning of robot for obstacle avoidance. The proposed method detects and tracks obstacles using the 3D depth information of an RGB-D sensor for path prediction. Based on the tracked information of obstacles, the paths of the obstacles are predicted with probability circle-based spatial search (PCSS) method and Gaussian modeling is performed to reduce uncertainty and to create the cost function of caution. The possibility of collision with the robot is considered through the predicted path of the obstacles, and a local path is generated. This enables safe and efficient navigation of the robot. The results in various experiments show that the proposed method enables robots to navigate safely and effectively.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.321-326
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• 2013

GPS reception rate in the room has less features. To overcome these shortcomings, the AP positioning using RSS technology research and development is being done. If we use positioning technology and signal strength in order to detect a obstacles, it has the advantage of no-cost in terms of utilization and efficiency when we do this applied service. In this paper, We are presented method to determine the obstacles using RSS(Received Signal Strength).

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.970-975
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• 2002

We suggests an effective method to construct time-varying C-obstacles in the 3-dimensional configuration space (C-space) using free arc. The concept of free arc was defined mathematically and the procedure to find free arc in the case off-dimensional C-space was derived in [1]. We showed that time-varying C-obstacles can be constructed efficiently using this concept, and presented simulation results for two SCARA robot manipulators to verify the efficacy of the proposed approach. In this paper, extensions of this approach to the 3-dimensional C-space is introduced since nearly all industrial manipulators are reasonably treated ill the too or three dimensional C-space f3r collision avoidance problem The free arc concept is summarized briefly and the method to find lice arc in the 3-dimensional f-space is explained. To show that this approach enables us to solve a practical collision avoidance problem simulation results f3r two PUMA robot manipulators are presented.

• Wind and Structures
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• v.15 no.1
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• pp.43-64
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• 2012

Many potential small wind turbine locations are near obstacles such as buildings and shelterbelts, which can have a significant, detrimental effect on the local wind climate. A neural network-based model has been developed which predicts mean wind speed and turbulence intensity at points in an obstacle's region of influence, relative to unsheltered conditions. The neural network was trained using measurements collected in the wakes of 18 scale building models exposed to a simulated rural atmospheric boundary layer in a wind tunnel. The model obstacles covered a range of heights, widths, depths, and roof pitches typical of rural buildings. A field experiment was conducted using three unique full scale obstacles to validate model predictions and wind tunnel measurements. The accuracy of the neural network model varies with the quantity predicted and position in the obstacle wake. In general, predictions of mean velocity deficit in the far wake region are most accurate. The overall estimated mean uncertainties associated with model predictions of normalized mean wind speed and turbulence intensity are 4.9% and 12.8%, respectively.