It is necessary to measure accurately the rolling noise generated by the friction between wheels and rails in railway transport systems. Although many systems have recently been developed to measure the surface roughness of wheels and rails, there exist large deviations in measurements between each system whose measuring mechanism is based on a single sensor. To correct the structural problems in existing systems, we developed an automatic mobile measurement platform, named the Automatic Rail Checker (ARCer), which measures the acoustic roughness of a longitudinal railhead profile maintaining a constant speed. In addition, a new chord offset synchronization algorithm has been developed. This uses three displacement sensors to improve the measuring accuracy of the acoustic roughness of a longitudinal railhead profile, thereby minimizing the limitations of mobile platform measurement systems and measurement deviation. We then verified the accuracy of the measurement system and the algorithm through field tests on rails with different surface wear conditions.

Contact and friction between wheel and rail during train operation is the main cause of the rolling noise for which railways are known. Therefore, it is necessary to accurately measure the surface roughness of wheels and rails to monitor railway noise and predict noise around tracks. Conventional systems developed to measure surface roughness have large deviations in measured values or low repeatability. The recently developed automatic mobile measurement platform known as Auto Rail Checker (ARCer) uses three displacement sensors to reduce measurement deviation and increase the accuracy of existing systems. This paper proposes enhancing the chord offset synchronization algorithm applied to the existing ARCer for high measurement precision with only two displacement sensors. As a result, when the two sensor-based measurement algorithm was applied, the spectrum level at λ = 0.314 m, the wavelength amplification associated with wheel diameter, was reduced to at least 6 dB in comparison with that of the three sensors based algorithm. We also verified the accuracy of the proposed batch algorithm through a field test on an operating rail track with a corrugated rail surface.

서 론최근Abstract Spectrum level for the acoustic roughness of wheels and rail surface should be periodically maintained under the limitation of ISO to reduce rolling noise of railway vehicles. Thus, in maintaining railway track, displacement sensor-based measuring devices are broadly used to measure the surface roughness and to perform spectral analysis. However, these measuring devices cause unexpected measuring errors since the displacement sensors are fixed at moving platforms and the main frame produces pitching motion during measurement. To increase the accuracy of the measured values, this paper has investigated the effects of design variables such as wheel base, additional wheels, and elastic deformation of wheels on the surface roughness and acoustic roughness spectrum.

In recent years, a novel skid-steered duct-cleaning mobile platform was developed to remove dust accumulated on the inner surface of an air-ventilation duct with its rolling brushes. During the cleaning process, the irregular brushing pressure acting on the upper arm makes it difficult to control the platform through the duct path. In fact, the repulsive external force due to the brushing pressure is not directly measurable or computable because of the nonlinear deformation of the brush. In addition, dynamic uncertainties in platform motion can occur during reciprocating motion of the upper arm. Therefore, a model-based trajectory-tracking controller is required to control the mobile cleaning platform by considering irregular external forces. The robustness of the developed controller based on the adaptable PD(Proportional-Derivative)-backstepping method has been proposed and evaluated through numerical analysis and experiments. For the turning motion in a narrow space, a skid-steered platform model considering wheel slippage has been also implemented. The result shows that tracking control can be successfully achieved under various conditions of frequencies in brushing-arm motion and torque limitation of the traction motors.

With the rise of misinformation epidemic, this study aims to empirically investigate the consequences of an online commenting platform's activity-capping policy on abusers' and regular users' activities. Utilizing a quasi-experimental setting, we find that restrictive policies not only curtail the activity of the abusers but also promote the activity of regular users. Results show that the policy has an asymmetric effect on abusers and regular users-while it effectively reduces the actions of the malicious users by 1.8%, it promotes the activities of the regular users by 2.2%. To better understand the behavioral change of the regular users, we draw from the rational economic perspective of voting decisions and provide initial evidence that such policy measures reinforce the subjective probability of being influential on the outcome. This study will provide valuable implications to managers and policymakers to estimate the consequences of and to combat against malicious behaviors and to promote free speech in online platforms.