• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.5
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• pp.589-595
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• 2010

This paper presents a reliability-based shape optimization (RBSO) using the evolutionary structural optimization (ESO). An actual design involves uncertain conditions such as material property, operational load, poisson's ratio and dimensional variation. The deterministic optimization (DO) is obtained without considering of uncertainties related to the uncertainty parameters. However, the RBSO can consider the uncertainty variables because it has the probabilistic constraints. In order to determine whether the probabilistic constraint is satisfied or not, simulation techniques and approximation methods are developed. In this paper, the reliability-based shape design optimization method is proposed by utilization the reliability index approach (RIA), performance measure approach (PMA), single-loop single-vector (SLSV), adaptive-loop (ADL) are adopted to evaluate the probabilistic constraint. In order to apply the ESO method to the RBSO, a sensitivity number is defined as the change of strain energy in the displacement constraint. Numerical examples are presented to compare the DO with the RBSO. The results of design example show that the RBSO model is more reliable than deterministic optimization.

• Journal of Mechanical Science and Technology
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• v.19 no.6
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• pp.1290-1303
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• 2005

In this research, a remote control system has been developed and implemented, which combines autonomous obstacle avoidance in real-time with force-reflective tele-operation. A tele-operated mobile robot is controlled by a local two-degrees-of-freedom force-reflective joystick that a human operator holds while he is monitoring the screen. In the system, the force-reflective joystick transforms the relation between a mobile robot and the environment to the operator as a virtual force which is generated in the form of a new collision vector and reflected to the operator. This reflected force makes the tele-operation of a mobile robot safe from collision in an uncertain and obstacle-cluttered remote environment. A mobile robot controlled by a local operator usually takes pictures of remote environments and sends the images back to the operator over the Internet. Because of limitations of communication bandwidth and the narrow view-angles of the camera, the operator cannot observe shadow regions and curved spaces frequently. To overcome this problem, a new form of virtual force is generated along the collision vector according to both distance and approaching velocity between an obstacle and the mobile robot, which is obtained from ultrasonic sensors. This virtual force is transferred back to the two-degrees-of-freedom master joystick over the Internet to enable a human operator to feel the geometrical relation between the mobile robot and the obstacle. It is demonstrated by experiments that this haptic reflection improves the performance of a tele-operated mobile robot significantly.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.142-148
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• 2003

Reinforcement learning is considered as an important tool for robotic learning in unknown/uncertain environments. In this paper, we propose an evaluation function expressed in a vector form to realize multi-dimensional reinforcement learning. The novel feature of the proposed method is that learning one behavior induces parallel learning of other behaviors though the objectives of each behavior are different. In brief, all behaviors watch other behaviors from a critical point of view. Therefore, in the proposed method, there is cross-criticism and parallel learning that make the multi-dimensional learning process more efficient. By ap-plying the proposed learning method, we carried out multi-dimensional evaluation (reward) and multi-dimensional learning simultaneously in one trial. A special neural network (Q-net), in which the weights and the output are represented by vectors, is proposed to realize a critic net-work for Q-learning. The proposed learning method is applied for behavior planning of mobile robots.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.4
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• pp.469-475
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• 2010

The objective of this study is to integrate reliability analysis into shape optimization problem using the evolutionary structural optimization (ESO) in the application example. Reliability-based shape optimization is formulated as volume minimization problem with probabilistic stress constraint under minimization max. von Mises stress and allow stress. Young's modulus, external load and thickness are considered as uncertain variables. In order to compute reliability index, four methods, i.e., reliability index approach (RIA), performance measure approach (PMA), single-loop singlevector (SLSV) and adaptive-loop (ADL), are used. Reliability-based shape optimization design process is conducted to obtain optimal shape satisfying max. von Mises stress and reliability index constraints with the above four methods, and then each result is compared with respect to numerical stability and computing time.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.8
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• pp.585-591
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• 2003

A virtual force is generated and fed back to the operator to make the teleoperation more reliable, which reflects the relationship between a slave robot and an uncertain remote environment as a form of an impedance. In general, for the teleoperation, the teleoperated mobile robot takes pictures of the remote environment and sends the visual information back to the operator over the Internet. Because of the limitations of communication bandwidth and narrow view-angles of camera, it is not possible to watch certain regions, for examples, the shadow and curved areas. To overcome this problem, a virtual force is generated according to both the distance between the obstacle and the robot and the approaching velocity of the obstacle w.r.t the collision vector based on the ultrasonic sensor data. This virtual force is transferred back to the master (two degrees of freedom joystick) over the Internet to enable a human operator to estimate the position of obstacle at the remote site. By holding this master, in spite of limited visual information, the operator can feel the spatial sense against the remote environment. It is demonstrated by experiments that this collision vector based haptic reflection improves the performance of teleoperated mobile robot significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1496-1521
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• 2012

Uncertainty is ubiquitous in target tracking wireless sensor networks due to environmental noise, randomness of target mobility and other factors. Sensing results are always unreliable. This paper considers unreliability as it occurs in wireless sensor networks and its impact on target-tracking accuracy. Firstly, we map intersection pairwise sensors' uncertain boundaries, which divides the monitor area into faces. Each face has a unique signature vector. For each target localization, a sampling vector is built after multiple grouping samplings determine whether the RSS (Received Signal Strength) for a pairwise nodes' is ordinal or flipped. A Fault-Tolerant Target-Tracking (FTTT) strategy is proposed, which transforms the tracking problem into a vector matching process that increases the tracking flexibility and accuracy while reducing the influence of in-the-filed factors. In addition, a heuristic matching algorithm is introduced to reduce the computational complexity. The fault tolerance of FTTT is also discussed. An extension of FTTT is then proposed by quantifying the pairwise uncertainty to further enhance robustness. Results show FTTT is more flexible, more robust and more accurate than parallel approaches.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.4
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• pp.701-715
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• 2018

A reliability based design approach of the tunnel reinforcement with umbrella arch method was considered to better represent the uncertainties of the weak rock properties around the tunnel. For this, a machine learning approach called an Adaptive Support Vector Machine (ASVM) together with the limit equilibrium method were introduced to minimize the iteration numbers during the classification training of the tunnel stability. The proposed method was compared with the results of typical Monte Carlo simulations. It was concluded that the ASVM was very efficient and accurate to calculate the probability of failure having auxiliary umbrella arches and uncertain material properties of the tunnel. Future work will be concentrated on the refinement of the fast adaptation of the SVM classification so that the minimum number of numerical analyses can be used where the limit solution is not available.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.2
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• pp.247-254
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• 2010

In this paper, a new approach has been studied correcting the geometric distortion of SPOT 4 imagery. Two new equations were induced by the relationship between satellite and the Earth in the space. line-of-sight (LOS) vector adjustment model for SPOT 4 imagery was implemented in this study. This model is to adjust LOS vector under the assumption that the orbital information of satellite provided by receiving station is uncertain and this uncertainty makes a constant error over the image. This model is verified using SPOT 4 satellite image with high look angle and thirty five ground points, which include 10 GCPs(Ground Control Points) and 25 check points, measured by the GPS. In total thirty five points, the geometry of satellite image calculated by given satellite information(such as satellite position, velocity, attitude and look angles, etc) from SPOT 4 satellite image was distorted with a constant error. Through out the study, it was confirmed that the LOS vector adjustment model was able to be applied to SPOT4 satellite image. Using this model, RMSEs (Root Mean Square Errors) of twenty five check points taken by increasing the number of GCPs from two to ten were less than one pixel. As a result, LOS vector adjustment model could efficiently correct the geometry of SPOT4 images with only two GCPs. This method also is expected to get good results for the different satellite images that are similar to the geometry of SPOT images.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.7
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• pp.597-603
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• 2004

In retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster a number of image data adequately. Moreover, current retrieval methods using similarities are uncertain of retrieval accuracy and take much retrieving time. In this paper, a suggested image retrieval system combines Fuzzy ART neural network algorithm to reinforce defects and to support them efficiently. This image retrieval system takes color and texture as specific feature required in retrieval system and normalizes each of them. We adapt Fuzzy ART algorithm as neural network which receive normalized input-vector and propose improved Fuzzy ART algorithm. The result of implementation with 200 image data shows approximately retrieval ratio of 83%.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.4
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• pp.385-390
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• 2009

This paper presents a novel active noise control (ANC) system using least mean square (LMS) algorithm and neural network approach for decreasing acoustic noise signals inside high-speed trains. We construct a LMS framework as a nominal ANC system and additionally design an artificial single-layered perceptron model as an auxiliary ANC which is aimed to reduce real-time residuary noise due to its nonstationary and uncertain nature. Parameter vector of the hybrid ANC is determined through online estimation to realize an adaptive ANC configuration by means of the steepest descent algorithm. We achieve simulation experiment to demonstrate the proposed ANC system employing realistic acoustic noise signals measured in Korea Train eXpress (KTX).