Microstructural investigation of creep behaviour in Grade 92 power plant steels

The aim of this research project was to obtain a deep and quantified understanding of the effects of chemical composition and heat treatment on the microstructures of Grade 92 parent metals, and in turn, their corresponding creep behaviour. Comprehensive microstructural characterisation on the virgin and creep-tested samples was carried out in this study to give valid and reliable data in order to correlate the microstructural changes with creep behaviour, which can provide steel manufacturers with useful information to optimise the manufacturing route for fabrication.Predicted long term creep behaviour based on the numerical extrapolations of short term data has been shown to be overestimated due to series of microstructural degradations. In particular, creep cavitation, which is equally important compared to other microstructural degradation mechanisms during creep exposure, was investigated by using a wide range of complementary microscopy techniques including field emission gun scanning electron microscopy (FEG-SEM), transmission electron microscopy (TEM) on focused ion beam (FIB) prepared thin foil lift-out samples with energy dispersive X-ray spectroscopy (EDS), dual beam serial sectioning with post image 3D reconstruction on the cavities and particles, and combined in-lens SEM examination and EDS analysis. BN, MnS and Al2O3 inclusion particles were observed to be the preferential cavity nucleation sites. Within these three types of inclusion particles, BN was the most frequent particle observed to be associated with cavities. [Continues.]</div

Time allocation optimization and trajectory design in UAV-assisted energy and spectrum harvesting network

The scarcity of energy resources and spectrum resources has become an urgent problem with the exponential increase of communication devices. Meanwhile, unmanned aerial vehicle (UAV) is widely used to help communication network recently due to its maneuverability and flexibility. In this paper, we consider a UAV-assisted energy and spectrum harvesting (ESH) network to better solve the spectrum and energy scarcity problem, where nearby secondary users (SUs) harvest energy from the base station (BS) and perform data transmission to the BS, while remote SUs harvest energy from both BS and UAV but only transmit data to UAV to reduce the influence of near-far problem. We propose an unaligned time allocation scheme (UTAS) in which the uplink phase and downlink phase of nearby SUs and remote SUs are unaligned to achieve more flexible time schedule, including schemes (a) and (b) in remote SUs due to the half-duplex of energy harvesting circuit. In addition, maximum throughput optimization problems are formulated for nearby SUs and remote SUs respectively to find the optimal time allocation. The optimization problem can be divided into three cases according to the relationship between practical data volume and theoretical throughput to avoid the waste of time resource. The expressions of optimal energy harvesting time and data transmission time of each node are derived. Lastly, a successive convex approximation based iterative algorithm (SCAIA) is designed to get the optimal UAV trajectory in broadcast mode. Simulation results show that the proposed UTAS can achieve better performance than traditional time allocation schemes

Indentation Plastometry of Welds

This investigation concerns the application of the profilometry‐based indentation plastometry (PIP) methodology to obtain stress–strain relationships for material in the vicinity of fusion welds. These are produced by The Welding Institute (TWI), using submerged arc welding to join pairs of thick steel plates. The width of the welds varies from about 5 mm at the bottom to about 40–50 mm at the top. For one weld, the properties of parent and weld metal are similar, while for the other, the weld metal is significantly harder than the parent. Both weldments are shown to be approximately isotropic in terms of mechanical response, while there is a small degree of anisotropy in the parent metal (with the through‐thickness direction being slightly softer than the in‐plane directions). The PIP procedure has a high sensitivity for detecting such anisotropy. It is also shown that there is excellent agreement between stress–strain curves obtained using PIP and via conventional uniaxial testing (tensile and compressive). Finally, the PIP methodology is used to explore properties in the transition regime between weld and parent, with a lateral resolution of the order of 1–2 mm. This reveals variations on a scale that would be very difficult to examine using conventional testing

Load carrying capability of regional electricity-heat energy systems:Definitions, characteristics, and optimal value evaluation

Evaluating the load carrying capability of regional electricity-heat energy systems is of great significance to its planning and construction. Existing methods evaluate energy supply capability without considering load characteristics between various users. Besides, the impact of integrated demand response is not fully considered. To address these problems, this paper builds a load carrying capability interval model, which uses reliability as a security constraint and considers integrated demand response. An evaluation method for the optimal load carrying capability considering uncertainties of load growth is proposed. First, this paper defines energy supply capability, available capacity, and load carrying capability. Interval models are built to achieve the visualization display of these indices. Their characteristics are studied and the impact factors of interval boundary are analyzed. Secondly, a two-layer optimization model for the evaluation of optimal load carrying capability is constructed, considering the uncertainties of load growth. The upper-layer model aims at optimizing the sum of load carrying capability benefit, integrated demand response cost, and load curtailment penalty. The lower-layer model maximizes energy supply capability. Thereafter, the lower-layer model is linearized based on piecewise linearization and the least square method. The computation efficiency is greatly enhanced. In the case study, a real regional electricity-heat energy system is used to validate the proposed model and method.</p

Rep2wav: Noise Robust text-to-speech Using self-supervised representations

Benefiting from the development of deep learning, text-to-speech (TTS) techniques using clean speech have achieved significant performance improvements. The data collected from real scenes often contains noise and generally needs to be denoised by speech enhancement models. Noise-robust TTS models are often trained using the enhanced speech, which thus suffer from speech distortion and background noise that affect the quality of the synthesized speech. Meanwhile, it was shown that self-supervised pre-trained models exhibit excellent noise robustness on many speech tasks, implying that the learned representation has a better tolerance for noise perturbations. In this work, we therefore explore pre-trained models to improve the noise robustness of TTS models. Based on HiFi-GAN, we first propose a representation-to-waveform vocoder, which aims to learn to map the representation of pre-trained models to the waveform. We then propose a text-to-representation FastSpeech2 model, which aims to learn to map text to pre-trained model representations. Experimental results on the LJSpeech and LibriTTS datasets show that our method outperforms those using speech enhancement methods in both subjective and objective metrics. Audio samples are available at: https://zqs01.github.io/rep2wav.Comment: 5 pages,2 figure

Self-supervised Learning for Electroencephalogram: A Systematic Survey

Electroencephalogram (EEG) is a non-invasive technique to record bioelectrical signals. Integrating supervised deep learning techniques with EEG signals has recently facilitated automatic analysis across diverse EEG-based tasks. However, the label issues of EEG signals have constrained the development of EEG-based deep models. Obtaining EEG annotations is difficult that requires domain experts to guide collection and labeling, and the variability of EEG signals among different subjects causes significant label shifts. To solve the above challenges, self-supervised learning (SSL) has been proposed to extract representations from unlabeled samples through well-designed pretext tasks. This paper concentrates on integrating SSL frameworks with temporal EEG signals to achieve efficient representation and proposes a systematic review of the SSL for EEG signals. In this paper, 1) we introduce the concept and theory of self-supervised learning and typical SSL frameworks. 2) We provide a comprehensive review of SSL for EEG analysis, including taxonomy, methodology, and technique details of the existing EEG-based SSL frameworks, and discuss the difference between these methods. 3) We investigate the adaptation of the SSL approach to various downstream tasks, including the task description and related benchmark datasets. 4) Finally, we discuss the potential directions for future SSL-EEG research.Comment: 35 pages, 12 figure

Indentation Plastometry of Welds

This investigation concerns the application of the profilometry-based indentation plastometry (PIP) methodology to obtain stress–strain relationships for material in the vicinity of fusion welds. These are produced by The Welding Institute (TWI), using submerged arc welding to join pairs of thick steel plates. The width of the welds varies from about 5 mm at the bottom to about 40–50 mm at the top. For one weld, the properties of parent and weld metal are similar, while for the other, the weld metal is significantly harder than the parent. Both weldments are shown to be approximately isotropic in terms of mechanical response, while there is a small degree of anisotropy in the parent metal (with the through-thickness direction being slightly softer than the in-plane directions). The PIP procedure has a high sensitivity for detecting such anisotropy. It is also shown that there is excellent agreement between stress–strain curves obtained using PIP and via conventional uniaxial testing (tensile and compressive). Finally, the PIP methodology is used to explore properties in the transition regime between weld and parent, with a lateral resolution of the order of 1–2 mm. This reveals variations on a scale that would be very difficult to examine using conventional testing