Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Models

We propose a general approach to evaluating the performance of robust estimators based on adversarial losses under misspecified models. We first show that adversarial risk is equivalent to the risk induced by a distributional adversarial attack under certain smoothness conditions. This ensures that the adversarial training procedure is well-defined. To evaluate the generalization performance of the adversarial estimator, we study the adversarial excess risk. Our proposed analysis method includes investigations on both generalization error and approximation error. We then establish non-asymptotic upper bounds for the adversarial excess risk associated with Lipschitz loss functions. In addition, we apply our general results to adversarial training for classification and regression problems. For the quadratic loss in nonparametric regression, we show that the adversarial excess risk bound can be improved over those for a general loss.Comment: 27 pages, 3 table

Documentation and Control of Flow Separation on a Low Pressure Turbine Linear Cascade of Pak-B Blades Using Plasma Actuators

This work involved the documentation and control of flow separation that occurs over low pressure turbine (LPT) blades at low Reynolds numbers. A specially constructed linear cascade was utilized to study the flow field over a generic LPT cascade consisting of Pratt & Whitney "Pak-B" shaped blades. Flow visualization, surface pressure measurements, LDV measurements, and hot-wire anemometry were conducted to examine the flow fields with and without separation control. Experimental conditions were chosen to give a range of chord Reynolds numbers (based on axial chord and inlet velocity) from 10,000 to 100,000, and a range of freestream turbulence intensities from u'/U(infinity) = 0.08 to 2.85 percent. The blade pressure distributions were measured and used to identify the region of separation that depends on Reynolds number and the turbulence intensity. Separation control was performed using dielectric barrier discharge (DBD) plasma actuators. Both steady and unsteady actuation were implemented and found to work well. The comparison between the steady and unsteady actuators showed that the unsteady actuators worked better than the steady ones. For the steady actuators, it was found that the separated region is significantly reduced. For the unsteady actuators, where the signal was pulsed, the separation was eliminated. The total pressure losses (a low Reynolds number) was reduced by approximately a factor of two. It was also found that lowest plasma duty cycle (10 percent in this work) was as effective as the highest plasma duty cycle (50 percent in this work). The mechanisms of the steady and unsteady plasma actuators were studied. It was suggested by the experimental results that the mechanism for the steady actuators is turbulence tripping, while the mechanism for the unsteady actuators is to generate a train of spanwise structures that promote mixing

Split Time Series into Patches: Rethinking Long-term Series Forecasting with Dateformer

Time is one of the most significant characteristics of time-series, yet has received insufficient attention. Prior time-series forecasting research has mainly focused on mapping a past subseries (lookback window) to a future series (forecast window), and time of series often just play an auxiliary role even completely ignored in most cases. Due to the point-wise processing within these windows, extrapolating series to longer-term future is tough in the pattern. To overcome this barrier, we propose a brand-new time-series forecasting framework named Dateformer who turns attention to modeling time instead of following the above practice. Specifically, time-series are first split into patches by day to supervise the learning of dynamic date-representations with Date Encoder Representations from Transformers (DERT). These representations are then fed into a simple decoder to produce a coarser (or global) prediction, and used to help the model seek valuable information from the lookback window to learn a refined (or local) prediction. Dateformer obtains the final result by summing the above two parts. Our empirical studies on seven benchmarks show that the time-modeling method is more efficient for long-term series forecasting compared with sequence modeling methods. Dateformer yields state-of-the-art accuracy with a 40% remarkable relative improvement, and broadens the maximum credible forecasting range to a half-yearly level

AI-based technology to prognose and diagnose complex crack characteristics of railway concrete sleepers

Railway concrete sleepers are key safety-critical components in ballasted railway tracks. Due to frequent high-intensity impact loadings from train-track interaction over irregularities together with hostile environmental conditions, complicated characteristics of various crack patterns can incur on railway concrete sleepers, which will decrease their durability and service life overtime. Early warning of those cracks can help railway engineers to plan and schedule for renewal and maintenance timely and effectively. This study thus explores the artificial intelligence application of YOLOv5OBB (YOLOv5 with Oriented Bounding Box output) in the identification and classification of cracks in railway sleepers into three distinct types: longitudinal, transverse, and inclined, based on their specific crack angles, which have not been investigated in the past. The identification of crack angles is the novelty of this study. Recognising the various types of cracks is critical, given their varying causes and degrees of severity. Current corrective maintenance methods pose considerable safety risks to workers and exhibit low efficiency, underscoring the need for a more autonomous and efficient solution. This study marks a significant stride towards revolutionising railway maintenance, evidenced by an impressive mAP (Mean Average Precision) of 0.72 for crack detection and a 92% accuracy rate for angle detection. These promising results substantiate our study's potential to pioneer advancements in railway infrastructure maintenance