Search results

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

What are the mechanisms through which Chinese municipal leaders overcome implementation breakdown? This study, through process tracing, archival work and semi-structured interviews, examines the implementation of three sub-municipal-level railway projects involving the same principals and agents over the same period of time.

The analysis was guided by the hypothesis that political coordination and the exercise of political and Party leadership played an indispensable role in the two cases of successful policy implementation, and its absence accounts for the case of implementation breakdown.

The principal finding is that an informal “strategic group” was created to “herd” cadres to overcome the problem of implementation. Herding here refers to the idea that Party leadership, through the use of moral persuasion, encourages cadres moving towards a desired common goal and direction.

This study is limited in the number of secondary resources (government documents and government and media releases) available to the field interviewees, which the author heavily relied on to complete the study.

Building on the conceptual work of “strategic groups” by Thomas Heberer, Anna Ahlers, and Gunter Schubert, this study makes an empirical contribution by tracing the process through which an informal strategic group exercises its power to overcome implementation breakdown.