In this paper, we study the estimation and inference of change points under a functional linear regression model with changes in the slope function. We present a novel Functional Regression Binary Segmentation (FRBS) algorithm which is computationally efficient as well as achieving consistency in multiple change point detection. This algorithm utilizes the predictive power of piece-wise constant functional linear regression models in the reproducing kernel Hilbert space framework. We further propose a refinement step that improves the localization rate of the initial estimator output by FRBS, and derive asymptotic distributions of the refined estimators for two different regimes determined by the magnitude of a change. To facilitate the construction of confidence intervals for underlying change points based on the limiting distribution, we propose a consistent block-type long-run variance estimator. Our theoretical justifications for the proposed approach accommodate temporal dependence and heavy-tailedness in both the functional covariates and the measurement errors. Empirical effectiveness of our methodology is demonstrated through extensive simulation studies and an application to the Standard and Poor's 500 index dataset.
Urban digital twins are increasingly perceived as a way to pool the growing digital resources of cities for the purpose of a more sustainable and integrated urban planning. Models and simulations are central to this undertaking: They enable "what if?" scenarios, create insights and describe relationships between the vast data that is being collected. However, the process of integrating and subsequently using models in urban digital twins is an inherently complex undertaking. It raises questions about how to represent urban complexity, how to deal with uncertain assumptions and modeling paradigms, and how to capture underlying power relations. Existent approaches in the domain largely focus on monolithic and centralized solutions in the tradition of neoliberal city-making, oftentimes prohibiting pluralistic and open interoperable models. Using a participatory design for participatory systems approach together with the City of Hamburg, Germany, we find that an open Urban Model Platform can function both as a public technological backbone for modeling and simulation in urban digital twins and as a socio-technical framework for a collaborative and pluralistic representation of urban processes. Such a platform builds on open standards, allows for a decentralized integration of models, enables communication between models and supports a multi-model approach to representing urban systems.
This study experimentally investigates the aeroacoustic characteristics of tandem, co-rotating rotors operating under non-axial inflow conditions. Measurements were conducted in an anechoic wind tunnel over a range of advance ratios and tilt angles to provide a sub-scale representation of the noise generated under moderate non-axial inflows. Far-field noise was characterised using multiple in-plane and out-of-plane microphone arrays to resolve directivity, spectral content, blade passing harmonics, and broadband components. The results show that the noise from the tandem, co-rotating rotors is strongly influenced by both advance ratio and tilt angle. Operation at low tilt angles leads to significant amplification of higher blade passing harmonics and broadband noise, particularly at out-of-plane observer locations, indicating increased unsteady loading. Increasing the tilt angle substantially reduces higher harmonic and broadband noise whilst the blade passing frequency component remains comparatively insensitive. These findings highlight the sensitivity of tandem-rotor noise to operating conditions and provide insight into noise mitigation opportunities during transition flight in urban air mobility applications.
The laser diffuse ultrasonic phased array technique, which integrates a conventional piezoelectric transducer as a transmitter with a laser interferometer as a receiver and array data reconstruction technique, offers an efficient approach for imaging volumetric defects. However, time-domain amplitude decay of the acquired raw signals degrades the quality of the reconstructed array data and subsequently impairs imaging performance. In this study, an amplitude compensation method is proposed to improve data quality and enhance the resulting ultrasound images for defect detection. To further enhance inspection efficiency, square-shaped, hexagonal-shaped, and Poisson-disk reception sampling layouts are optimized to reduce the number of sampling points without compromising image quality. The proposed workflow is validated using both simulated and experimentally measured data, as evidence by more than 2 dB improvement in the signal to noise ratio of defect images and relaxation of the reception sampling pitch to one wavelength. This study provides a foundation for more robust and efficient laser-based ultrasonic imaging in real-world non-destructive evaluation applications.
