168 research outputs found
Recent developments in comprehensive analytical instruments for the culture heritage objects-A review
This paper introduces the necessity and significance of the investigation of
cultural heritage objects. The multi-technique method is useful for the study
of cultural heritage objects, but a comprehensive analytical instrument is a
better choice since it can guarantee that different types of information are
always obtained from the same analytical point on the surface of cultural
heritage objects, which may be crucial for some situations. Thus, the X-ray
fluorescence (XRF)/X-ray diffraction (XRD) and X-ray fluorescence (XRF)/Raman
spectroscopy (RS) comprehensive analytical instruments are more and more widely
used to study cultural heritage objects. The two types of comprehensive
analytical instruments are discussed in detail and the XRF/XRD instruments are
further classified into different types on the basis of structure, type and
number of detectors. A new comprehensive analytical instrument prototype that
can perform XRF, XRD and RS measurements simultaneously has been successfully
developed by our team and the preliminary application has shown the analysis
performance and application potential. This overview contributes to better
understand the research progress and development tendency of comprehensive
analytical instruments for the study of cultural heritage objects. The new
comprehensive instruments will make researchers obtain more valuable
information on cultural heritage objects and further promote the study on
cultural heritage objects
SELM: Speech Enhancement Using Discrete Tokens and Language Models
Language models (LMs) have shown superior performances in various speech
generation tasks recently, demonstrating their powerful ability for semantic
context modeling. Given the intrinsic similarity between speech generation and
speech enhancement, harnessing semantic information holds potential advantages
for speech enhancement tasks. In light of this, we propose SELM, a novel
paradigm for speech enhancement, which integrates discrete tokens and leverages
language models. SELM comprises three stages: encoding, modeling, and decoding.
We transform continuous waveform signals into discrete tokens using pre-trained
self-supervised learning (SSL) models and a k-means tokenizer. Language models
then capture comprehensive contextual information within these tokens. Finally,
a detokenizer and HiFi-GAN restore them into enhanced speech. Experimental
results demonstrate that SELM achieves comparable performance in objective
metrics alongside superior results in subjective perception. Our demos are
available https://honee-w.github.io/SELM/.Comment: Accepted by ICASSP 202
Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches
Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting.
Document type: Articl
Soil moisture and electrical conductivity relationships under typical Loess Plateau land covers
Vegetation changes that are driven by soil conservation measures significantly affect subsurface water flow patterns and soil water status. Much research on water consumption and sustainability of newly introduced vegetation types at the plot scale has been done in the Loess Plateau of China (LPC), typically using local scale measurements of soil water content (SWC). However, information collected at the plot scale cannot readily be up-scaled. Geophysical methods such as electromagnetic induction (EMI) offer large spatial coverage and therefore could bridge between the scales. A non-invasive, multi-coil, frequency domain, EMI instrument was used to measure the apparent soil electrical conductivity (σ_a) from six effective depths under four typical land-covers; shrub, pasture, natural fallow and crop, in the north of the LPC. Concurrently, SWC was monitored to a depth of 4 m depth using an array of 44 neutron probes distributed along the plots. The measurements of σ_a for six effective depths and the integrated SWC over these depths, show consistent behavior. High variability of σ_a under shrub cover, in particular, is consistent with long term variability of SWC, highlighting the potential unsustainability of this land cover. Linear relationships between SWC and σ_a were established using cumulative sensitivity forward models. The conductivity-SWC model parameters show clear variation with depth, despite lack of appreciable textural variation. This is likely related to the combined effect of elevated pore water conductivity as was illustrated by the simulations obtained with water flow and solute transport models. The results of the study highlight the potential for the implementation of the EMI method for investigations of water distribution in the vadose zone of the LPC, and in particular for qualitative mapping of the vulnerability to excessive vegetation demands, and hence unsustainable land cover
Demand side management of plug-in electric vehicles and coordinated unit commitment: A novel parallel competitive swarm optimization method
Decreasing initial costs, the increased availability of charging infrastructure and favorable policy measures have resulted in the recent surge in plug-in electric vehicle (PEV) ownerships. PEV adoption increases electricity consumption from the grid that could either exacerbate electricity supply shortages or smooth demand curves. The optimal coordination and commitment of power generation units while ensuring wider access of PEVs to the grid are, therefore, important to reduce the cost and environmental pollution from thermal power generation systems, and to transition to a smarter grid. However, flexible demand side management (DSM) considering the stochastic charging behavior of PEVs adds new challenges to the complex power system optimization, and makes existing mathematical approaches ineffective. In this research, a novel parallel competitive swarm optimization algorithm is developed for solving large-scale unit commitment (UC) problems with mixed integer variables and multiple constraints typically found in PEV integrated grids. The parallel optimization framework combines binary and real-valued competitive swarm optimizers for solving the UC problem and demand side management of PEVs simultaneously. Numerical case studies have been conducted with multiple scales of unit numbers and various demand side management strategies of plug-in electric vehicles. The results show superior performance of proposed parallel competitive swarm optimization based method in successfully solving the proposed complex optimization problem. The flexible demand side management strategies of plug-in electric vehicles have shown large potentials in bringing considerable economic benefit
Electric vehicle charging load forecasting: A comparative study of deep learning approaches
Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model
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