The swing motion bogie system for a freight car is more effective regarding the vibration damping effect than other freight car bogie systems while operating, and it is a bogie system that can travel up to 120 km/h despite being a freight car. Imported in 2006 in Korea and operated for more than 10 years in the domestic railway environment, the performance and maintenance efficiency have been proven compared to the existing welding bogies. As a result, the domestic demand will continue increasing in the future, but it is now dependent on overseas imports. In the long term, it is expected to cause problems such as loss of foreign currency and delay in procurement during maintenance. For this reason, development of the localization of the swing motion bogie system is underway, and it requires accurate performance analysis and validation of operating behavior characteristics because the bogie system is one of the main devices of the railway vehicles. Thus, in this study, we could confirm the suitability of the swing motion bogie system in the domestic operating environment based on the analysis of the operating behavior characteristics, the validation at the laboratory environment, and the operating test on the track.
This paper proposes a practical method, for evaluating 3-D positioning of outdoor mobile robots using the Unscented Kalman Filter (UKF). The UKF method does not require the linearization process unlike conventional EKF localization, so it can minimize effects of errors caused by linearization of non-linear models for position estimation. Also, this method does not require Jacobian calculations difficult to calculate in the actual implementation. The 3-D position of the robot is predicted using an encoder and tilt sensor, and the optimal position is estimated by fusing these predicted positions with the GPS and digital compass information. Experimental results revealed the proposed method is stable for localization of the 3D position regardless of initial error size, and observation period.
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In this paper, a prosthetic robot hand was designed and fabricated and experimental evaluation of the realization of basic gripping motions was performed. As a first step, a robot finger was designed with same structural configuration of the human hand and the movement of the finger was evaluated via kinematic analysis. Electromyogram (EMG) signals for hand motions were measured using commercial wearable EMG sensors and classification of hand motions was achieved by applying the artificial neural network (ANN) algorithm. After training and testing for three kinds of gripping motions via ANN, it was observed that high classification accuracy can be obtained. A prototype of the proposed robot hand is manufactured through 3D printing and servomotors are included for position control of fingers. It was demonstrated that effective realization of gripping motions of the proposed prosthetic robot hand can be achieved by using EMG measurement and machine learning-based classification under a real-time environment.
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It has been an on-going issue to develop a high voltage motor with high capacity and reliability. In this study, we investigated the effective coil insulator materials in terms of thermal conductivity. To quantify the contribution of the coil insulator material, two different motors with and without the cooling structure were numerically studied. Based on the measured thermal conductivity of six different coil insulators, we have achieved the effectiveness of thermal conductivity. Consequently, the high voltage motor can be developed with the proposed effectiveness of thermal conductivity regarding coil insulator materials. Our study of fundamental material characteristics will be beneficial in enhancing thermal management technology of a high voltage motor.
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The purpose of this study was to develop an efficient mold heating technology by an embedded heating unit. To localize the heating effect in the mold core and prevent heat transfer to surrounding mold plates, the core module with embedded heating unit was assembled to a mold plate in a detachable manner. The detachable core module was then separated from the mold plate when the mold was opened, and thus could be rapidly heated by the embedded heater. The heated core contacted with the mold plate when the mold was closed, and could be cooled by heat conduction to the mold plate of which thermal inertia was much larger than that of the core module. To verify thermal efficiency of the proposed structure, heat transfer simulation was performed with an experimental validation. Mold filling simulation was also performed to investigate the effect of mold heating on improving flow characteristics through a thin and narrow channel. Injection molding experiments were also conducted by adopting the proposed embedded heating module.
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Hands perform various functions. There are many inconveniences in life without the use of hands. People without the use of hands wear prostheses. Recently, there have been many developments and studies about robotic prosthetic hands performing hand functions. Grasping motions of robotic prosthetic hands are integral in performing various functions. Grasping motions of robotic prosthetic hands are required recognition of grasping targets. A path toward using images to recognize grasping targets exists. In this study, object recognition in images for grasping motions are performed by using object detection based on deep-learning. A suitable model for the grasping motion was examined through three object detection models. Also, we present a method for selecting a grasping target when several objects are recognized. Additionally, it will be used for grasping control of robotic prosthetic hands in the future and possibly enable automatic control robotic prosthetic hands.
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