This article represents a microchip balun for UHF mobile RFID system at 910 MHz. This balun is designed by transforming coupled transmission lines into lumped-element equivalent circuit, and thus monolithic elements that art fabricated by IPD technology replace it for entirely miniaturized size. The corresponding balun performs under 3 dB return loss, with nearly 176 (176.42) degree of phase difference characteristic; therefore, this meets the UHF mobile RFID reader system consideration. (C) 2008 Wiley Periodicals, Inc.

This paper describes the recognition method of moving objects in mobile robot with an omnidirectional camera. The moving object is detected using the specific pattern of an optical flow in omnidirectional image. This paper consists of two parts. In the first part, the pattern of an optical flow is investigated in omnidirectional image. The optical flow in omnidirectional image is influenced on the geometry characteristic of an omnidirectional camera. The pattern of an optical flow is theoretically and experimentally investigated. In the second part, the detection of moving objects is presented from the estimated optical flow. The moving object is extracted through the relative evaluation of optical flows which is derived from the pattern of optical flow. In particular, Focus-Of-Expansion (FOE) and Focus-Of-Contraction (FOC) vectors are defined from the estimated optical flow. They are used as reference vectors for the relative evaluation of optical flows. The proposed algorithm is performed in four motions of a mobile robot such as straight forward, left turn, right turn and rotation. Experimental results using real movie show the effectiveness of the proposed method.

A transmitter (TX) leakage cancellation scheme using a micro controller (MCU) is introduced. This letter highlights an adaptive anti-leakage signal generation via an MCU, which plays the key role in reducing the TX leakage signal level. High transmitter-to-receiver (TX-to-RX) isolation is verified with measurement results.

Simple and new analysis method of phase noise requirements on a local oscillator (LO) for a RFID system is proposed. In general case, phase noise requirements are derived mathematically considering a reciprocal mixing with an interference signal. However, in RFID systems using the same oscillator for the transmitted signal and the LO, range correlation phenomenon effects the phase noise requirements. Thus we tried to derive new phase noise equation considering the range correlation effect on the residual phase noise in RFID system. Simulation results for UHF RFID system show that range correlation effect is a crucial factor in RFID system design.

This paper describes the omnidirectional vision-based ego-pose estimation method of an in-pipe mobile robot. An in-pipe mobile robot has been developed for inspecting the inner surface of various pipeline configurations, such as the straight pipeline, the elbow and the multiple-branch. Because the proposed in-pipe mobile robot has four individual drive wheels, it has the ability of flexible motions in various pipelines. The ego-pose estimation is indispensable for the autonomous navigation of the proposed in-pipe robot. An omnidirectional camera and four laser modules mounted on the mobile robot are used for ego-pose estimation. An omnidirectional camera is also used for investigating the inner surface of the pipeline. The pose of the in-pipe mobile robot is estimated from the relationship equation between the pose of a robot and the pixel coordinates of four intersection points where light rays that emerge from four laser modules intersect the inside of the pipeline. This relationship equation is derived from the geometry analysis of an omnidirectional camera and four laser modules. In experiments, the performance of the proposed method is evaluated by comparing the result of our algorithm with the measurement value of a specifically designed sensor, which is a kind of a gyroscope.

This paper presents a new moving obstacle detection method using an optical flow in mobile robot with an omnidirectional camera. Because an omnidirectional camera consists of a nonlinear mirror and CCD camera, the optical flow pattern in omnidirectional image is different from the pattern in perspective camera. The geometry characteristic of an omnidirectional camera has influence on the optical flow in omnidirectional image. When a mobile robot with an omnidirectional camera moves, the optical flow is not only theoretically calculated in omnidirectional image, but also investigated in omnidirectional and panoramic images. In this paper, the panoramic image is generalized from an omnidirectional image using the geometry of an omnidirectional camera. In particular, Focus of expansion (FOE) and focus of contraction (FOC) vectors are defined from the estimated optical flow in omnidirectional and panoramic images. FOE and FOC vectors are used as reference vectors for the relative evaluation of optical flow. The moving obstacle is turned out through the relative evaluation of optical flows. The proposed algorithm is tested in four motions of a mobile robot including straight forward, left turn, right turn and rotation. The effectiveness of the proposed method is shown by the experimental results.

A prototype of autonomous mobile robot with two vision sensors for automatic welding of steel plates was constructed. The robot can move straight, steer and turn around the robot center by controlling the driving speed of the two wheels respectively. At the tip of the movable arm, two CCD cameras are fixed. A local camera observes the welding line near the welding torch and another wide camera observes relatively wide area in front of the welding part. The robot controls the traveling speed in accordance with the shape of the welding line. In the case of straight welding line, the speed of the robot is accelerated and the welding efficiency is improved. However, if the robot finds a corner of welding line, the speed is decelerated in order to realize the precise seam tracking and stable welding. Therefore, the robot can realize precise and high speed seam-tracking by controlling the travel speed. The effectiveness of the control system is confirmed by welding experiments.

This paper presents a new fuzzy inference system for modeling of nonlinear dynamic systems based on input and output data with measurement noise. The proposed fuzzy system has a number of fuzzy rules and parameter values of membership functions which are automatically generated using the extended relevance vector machine (RVM). The RVM has a probabilistic Bayesian learning framework and has good generalization capability. The RVM consists of the sum of product of weight and kernel function which projects input space into high dimensional feature space. The structure of proposed fuzzy system is same as that of the Takagi-Sugeno fuzzy model. However, in the proposed method, the number of fuzzy rules can be reduced under the process of optimizing a marginal likelihood by adjusting parameter values of kernel functions using the gradient ascent method. After a fuzzy system is determined, coefficients in consequent part are found by the least square method. Examples illustrate effectiveness of the proposed new fuzzy inference system.

Hybrid welding combined YAG laser welding process and TIG arc welding process was developed for thin plate welding. It was applied to welding of 2mm thickness SUS304 stainless steel plate and AZ31B magnesium alloy plate. In consequence, sound penetration bead of stainless steel was obtained by hybrid welding process. In case of magnesium alloy hybrid welding, improvement of heat efficiency was remarkable. Moreover, tensile test results of hybrid weld joint shows sufficient joint strength of magnesium alloy.

This paper proposes a new approach to fuzzy inference system for modeling nonlinear systems based on measured input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the extended kernel method. The extended kernel method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. Especially, the process of linear transformation is needed in order to solve difficulty determining the type of kernel function which presents the nonlinear mapping in according to nonlinear system. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated results of the proposed technique are illustrated by examples involving benchmark nonlinear systems.

In this paper, we present a new Support Vector Fuzzy Inference System (SVFIS) for nonlinear system modeling. The proposed SVFIS is constructed using the support vector machine which does not have a bias term. The number of fuzzy rules is reduced by adjusting the parameter values of membership functions using the gradient descent method. Once a structure is selected, the parameter values in the consequent part of the Tagaki-Sugeno (TS) fuzzy model are determined by the least square method. The simulation result illustrates the effectiveness of the proposed SVFIS.

A horizontal type furnace of a pickling alloy plant in steel works is used for annealing treatment for strips, and has 25 units of hearth rolls. The hearth rolls are in a poor condition due to the high ambient temperature of around 750~950℃ in the furnace. In this paper, the design of a hearth roll in the condition is optimized by thermal elastic analysis using MSC/MARC.