62 research outputs found
Numerical Modeling of Partial Discharge Development Process
Partial discharge (PD), a type of low-temperature plasma, indicates a discharge event that does not bridge the electrodes of an electrical insulation system under high voltage stress. It is common in power equipment, such as transformers, cables, gas-insulated switchgears, and so on. The occurrence of PD could deteriorate the insulation performance of the equipment, but, meanwhile, it is often used to diagnose the insulation status. Therefore, it is very necessary to clarify the PD mechanism, and through modeling the PD process, a better understanding of the phenomenon could be attained. Although PD is essentially a gas discharge phenomenon, it possesses some distinctive features, for example, very narrow discharge channel, short time duration, and stochastic behavior, which determine the simulation method of PD different from that for the other types of plasmas. This chapter seeks to propose a simulation method that could reflect the physical processes of PD development after introducing some background knowledge about PD and analyzing the shortcomings of existent models
Typical Internal Defects of Gas-Insulated Switchgear and Partial Discharge Characteristics
Gas-insulated switchgear (GIS) is a common electrical equipment, which uses sulfur hexafluoride (SF6) as insulating medium instead of traditional air. It has good reliability and flexibility. However, GIS may have internal defects and partial discharge (PD) is then induced. PD will cause great harm to GIS and power system. Therefore, it is of great importance to study the intrinsic characteristics and detection of PD for online monitoring. In this chapter, typical internal defects of GIS and the PD characteristics are discussed. Several detection methods are also presented in this chapter including electromagnetic method, chemical method, and optical method
Comparative Study of Materials to SF6 Decomposition Components
In order to judge the inside insulation fault of SF6 insulated equipment, the gas-sensing properties to a series of characteristic SF6 decomposition components, SOF2, SO2F2, SO2, H2S, CF4, HF, and SF6, have been studied. In this study, a comparative study of these gas-sensing materials has been made in theoretical and experimental fields to find the optimal gas-sensing material, and put forward the effective approaches to improve the gas-sensing properties of materials
LightGrad: Lightweight Diffusion Probabilistic Model for Text-to-Speech
Recent advances in neural text-to-speech (TTS) models bring thousands of TTS
applications into daily life, where models are deployed in cloud to provide
services for customs. Among these models are diffusion probabilistic models
(DPMs), which can be stably trained and are more parameter-efficient compared
with other generative models. As transmitting data between customs and the
cloud introduces high latency and the risk of exposing private data, deploying
TTS models on edge devices is preferred. When implementing DPMs onto edge
devices, there are two practical problems. First, current DPMs are not
lightweight enough for resource-constrained devices. Second, DPMs require many
denoising steps in inference, which increases latency. In this work, we present
LightGrad, a lightweight DPM for TTS. LightGrad is equipped with a lightweight
U-Net diffusion decoder and a training-free fast sampling technique, reducing
both model parameters and inference latency. Streaming inference is also
implemented in LightGrad to reduce latency further. Compared with Grad-TTS,
LightGrad achieves 62.2% reduction in paramters, 65.7% reduction in latency,
while preserving comparable speech quality on both Chinese Mandarin and English
in 4 denoising steps.Comment: Accepted by ICASSP 202
ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMs
In this paper, we present ZeroPrompt (Figure 1-(a)) and the corresponding
Prompt-and-Refine strategy (Figure 3), two simple but effective
\textbf{training-free} methods to decrease the Token Display Time (TDT) of
streaming ASR models \textbf{without any accuracy loss}. The core idea of
ZeroPrompt is to append zeroed content to each chunk during inference, which
acts like a prompt to encourage the model to predict future tokens even before
they were spoken. We argue that streaming acoustic encoders naturally have the
modeling ability of Masked Language Models and our experiments demonstrate that
ZeroPrompt is engineering cheap and can be applied to streaming acoustic
encoders on any dataset without any accuracy loss. Specifically, compared with
our baseline models, we achieve 350 700ms reduction on First Token
Display Time (TDT-F) and 100 400ms reduction on Last Token Display Time
(TDT-L), with theoretically and experimentally equal WER on both Aishell-1 and
Librispeech datasets.Comment: accepted by interspeech 202
Fast-U2++: Fast and Accurate End-to-End Speech Recognition in Joint CTC/Attention Frames
Recently, the unified streaming and non-streaming two-pass (U2/U2++)
end-to-end model for speech recognition has shown great performance in terms of
streaming capability, accuracy and latency. In this paper, we present
fast-U2++, an enhanced version of U2++ to further reduce partial latency. The
core idea of fast-U2++ is to output partial results of the bottom layers in its
encoder with a small chunk, while using a large chunk in the top layers of its
encoder to compensate the performance degradation caused by the small chunk.
Moreover, we use knowledge distillation method to reduce the token emission
latency. We present extensive experiments on Aishell-1 dataset. Experiments and
ablation studies show that compared to U2++, fast-U2++ reduces model latency
from 320ms to 80ms, and achieves a character error rate (CER) of 5.06% with a
streaming setup.Comment: 5 pages, 3 figure
TrimTail: Low-Latency Streaming ASR with Simple but Effective Spectrogram-Level Length Penalty
In this paper, we present TrimTail, a simple but effective emission
regularization method to improve the latency of streaming ASR models. The core
idea of TrimTail is to apply length penalty (i.e., by trimming trailing frames,
see Fig. 1-(b)) directly on the spectrogram of input utterances, which does not
require any alignment. We demonstrate that TrimTail is computationally cheap
and can be applied online and optimized with any training loss or any model
architecture on any dataset without any extra effort by applying it on various
end-to-end streaming ASR networks either trained with CTC loss [1] or
Transducer loss [2]. We achieve 100 200ms latency reduction with equal
or even better accuracy on both Aishell-1 and Librispeech. Moreover, by using
TrimTail, we can achieve a 400ms algorithmic improvement of User Sensitive
Delay (USD) with an accuracy loss of less than 0.2.Comment: submitted to ICASSP 202
Cascade degradation and upcycling of polystyrene waste to high-value chemicals
Plastic waste represents one of the most urgent environmental challenges facing humankind. Upcycling has been proposed to solve the low profitability and high market sensitivity of known recycling methods. Existing upcycling methods operate under energy-intense conditions and use precious-metal catalysts, but produce low-value oligomers, monomers, and common aromatics. Herein, we report a tandem degradation-upcycling strategy to exploit high-value chemicals from polystyrene (PS) waste with high selectivity. We first degrade PS waste to aromatics using ultraviolet (UV) light and then valorize the intermediate to diphenylmethane. Low-cost AlCl3 catalyzes both the reactions of degradation and upcycling at ambient temperatures under atmospheric pressure. The degraded intermediates can advantageously serve as solvents for processing the solid plastic wastes, forming a self-sustainable circuitry. The low-value-input and high-value-output approach is thus substantially more sustainable and economically viable than conventional thermal processes, which operate at high-temperature, high-pressure conditions and use precious-metal catalysts, but produce low-value oligomers, monomers, and common aromatics. The cascade strategy is resilient to impurities from plastic waste streams and is generalizable to other high-value chemicals (e.g., benzophenone, 1,2-diphenylethane, and 4-phenyl-4-oxo butyric acid). The upcycling to diphenylmethane was tested at 1-kg laboratory scale and attested by industrial-scale techno-economic analysis, demonstrating sustainability and economic viability without government subsidies or tax credits
A prospective multicenter clinical research study validating the effectiveness and safety of a chest X-ray-based pulmonary tuberculosis screening software JF CXR-1 built on a convolutional neural network algorithm
BackgroundChest radiography (chest X-ray or CXR) plays an important role in the early detection of active pulmonary tuberculosis (TB). In areas with a high TB burden that require urgent screening, there is often a shortage of radiologists available to interpret the X-ray results. Computer-aided detection (CAD) software employed with artificial intelligence (AI) systems may have the potential to solve this problem.ObjectiveWe validated the effectiveness and safety of pulmonary tuberculosis imaging screening software that is based on a convolutional neural network algorithm.MethodsWe conducted prospective multicenter clinical research to validate the performance of pulmonary tuberculosis imaging screening software (JF CXR-1). Volunteers under the age of 15 years, both with or without suspicion of pulmonary tuberculosis, were recruited for CXR photography. The software reported a probability score of TB for each participant. The results were compared with those reported by radiologists. We measured sensitivity, specificity, consistency rate, and the area under the receiver operating characteristic curves (AUC) for the diagnosis of tuberculosis. Besides, adverse events (AE) and severe adverse events (SAE) were also evaluated.ResultsThe clinical research was conducted in six general infectious disease hospitals across China. A total of 1,165 participants were enrolled, and 1,161 were enrolled in the full analysis set (FAS). Men accounted for 60.0% (697/1,161). Compared to the results from radiologists on the board, the software showed a sensitivity of 94.2% (95% CI: 92.0–95.8%) and a specificity of 91.2% (95% CI: 88.5–93.2%). The consistency rate was 92.7% (91.1–94.1%), with a Kappa value of 0.854 (P = 0.000). The AUC was 0.98. In the safety set (SS), which consisted of 1,161 participants, 0.3% (3/1,161) had AEs that were not related to the software, and no severe AEs were observed.ConclusionThe software for tuberculosis screening based on a convolutional neural network algorithm is effective and safe. It is a potential candidate for solving tuberculosis screening problems in areas lacking radiologists with a high TB burden
A review on the effects of TiO2 surface point defects on CO2 photoreduction with H2O
Photocatalytic reduction of CO2 with water by photocatalysts such as TiO2 to produce solar fuels is an attractive approach to alleviate the environmental influences of greenhouse gases and in the meantime produce valuable carbon-neutral fuels. Among the materials properties that affect catalytic activity of CO2 photoreduction, the point defect on TiO2 is one of the most important but not frequently addressed and well understood in the literature. In this review, we have examined the major influences of TiO2 point defects on CO2 photoreduction with H2O, by changing the catalysts' gas adsorption capabilities, optical properties, and electronic structures. In addition, the performances of various defective TiO2 toward CO2 photoreduction are summarized and compared in terms of productivity, selectivity, and stability. We hope this review can contribute to understanding the mechanism of CO2 photoreduction on defective TiO2 and provide insights to the design of highly efficient defect-rich TiO2 to boost the CO2 utilization
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