245 research outputs found
Highly-Accurate Electricity Load Estimation via Knowledge Aggregation
Mid-term and long-term electric energy demand prediction is essential for the
planning and operations of the smart grid system. Mainly in countries where the
power system operates in a deregulated environment. Traditional forecasting
models fail to incorporate external knowledge while modern data-driven ignore
the interpretation of the model, and the load series can be influenced by many
complex factors making it difficult to cope with the highly unstable and
nonlinear power load series. To address the forecasting problem, we propose a
more accurate district level load prediction model Based on domain knowledge
and the idea of decomposition and ensemble. Its main idea is three-fold: a)
According to the non-stationary characteristics of load time series with
obvious cyclicality and periodicity, decompose into series with actual economic
meaning and then carry out load analysis and forecast. 2) Kernel Principal
Component Analysis(KPCA) is applied to extract the principal components of the
weather and calendar rule feature sets to realize data dimensionality
reduction. 3) Give full play to the advantages of various models based on the
domain knowledge and propose a hybrid model(XASXG) based on Autoregressive
Integrated Moving Average model(ARIMA), support vector regression(SVR) and
Extreme gradient boosting model(XGBoost). With such designs, it accurately
forecasts the electricity demand in spite of their highly unstable
characteristic. We compared our method with nine benchmark methods, including
classical statistical models as well as state-of-the-art models based on
machine learning, on the real time series of monthly electricity demand in four
Chinese cities. The empirical study shows that the proposed hybrid model is
superior to all competitors in terms of accuracy and prediction bias
States, trends, and future of aquaponics research
As an environmentally-friendly aquaculture and planting system, aquaponics has attracted attention in various fields, such as fisheries, agriculture, and ecology. The existing review qualitatively described the development and challenges of aquaponics but lacked data support. This study selected 513 related documents (2000-2019) in the Web of Science database (WOS) to mine and quantitatively analyze its text data. The keyword co-occurrence network shows that the current aquaponics research mainly focuses on the system components, wastewater treatment, nutrient management, and system production. Research areas reflect obvious regional characteristics. China, the United States and Europe are dedicated to the application of new technologies, the optimization of system production, and the exploration of multiple roles. At present, the aquaponics development is facing many pressures from management and market. Future research requires more in-depth research in the system construction, nutrient management, and microbial community structure to provide a theoretical basis. Moreover, the identity construction within the conceptual framework of green infrastructure is a research direction worth exploring to solve low social recognition for aquaponics
FreConv: Frequency Branch-and-Integration Convolutional Networks
Recent researches indicate that utilizing the frequency information of input
data can enhance the performance of networks. However, the existing popular
convolutional structure is not designed specifically for utilizing the
frequency information contained in datasets. In this paper, we propose a novel
and effective module, named FreConv (frequency branch-and-integration
convolution), to replace the vanilla convolution. FreConv adopts a dual-branch
architecture to extract and integrate high- and low-frequency information. In
the high-frequency branch, a derivative-filter-like architecture is designed to
extract the high-frequency information while a light extractor is employed in
the low-frequency branch because the low-frequency information is usually
redundant. FreConv is able to exploit the frequency information of input data
in a more reasonable way to enhance feature representation ability and reduce
the memory and computational cost significantly. Without any bells and
whistles, experimental results on various tasks demonstrate that
FreConv-equipped networks consistently outperform state-of-the-art baselines.Comment: Accepted by ICME202
PolyFormer: Referring Image Segmentation as Sequential Polygon Generation
In this work, instead of directly predicting the pixel-level segmentation
masks, the problem of referring image segmentation is formulated as sequential
polygon generation, and the predicted polygons can be later converted into
segmentation masks. This is enabled by a new sequence-to-sequence framework,
Polygon Transformer (PolyFormer), which takes a sequence of image patches and
text query tokens as input, and outputs a sequence of polygon vertices
autoregressively. For more accurate geometric localization, we propose a
regression-based decoder, which predicts the precise floating-point coordinates
directly, without any coordinate quantization error. In the experiments,
PolyFormer outperforms the prior art by a clear margin, e.g., 5.40% and 4.52%
absolute improvements on the challenging RefCOCO+ and RefCOCOg datasets. It
also shows strong generalization ability when evaluated on the referring video
segmentation task without fine-tuning, e.g., achieving competitive 61.5% J&F on
the Ref-DAVIS17 dataset
The Impact of Microstructure on Filament Growth at the Sodium Metal Anode in All‐Solid‐State Sodium Batteries
In recent years, all-solid-state batteries (ASSBs) with metal anodes have witnessed significant developments due to their high energy and powerdensity as well as their excellent safety record. While intergranular dendriticlithium growth in inorganic solid electrolytes (SEs) has been extensively studied for lithium ASSBs, comparable knowledge is missing forsodium-based ASSBs. Therefore, polycrystalline Na-′′-alumina is employedas a SE model material to investigate the microstructural influence on sodiumfilament growth during deposition of sodium metal at the anode. The research focuses on the relationship between the microstructure, in particular grainboundary (GB) type and orientation, sodium filament growth, and sodium iontransport, utilizing in situ transmission electron microscopy (TEM) measurements in combination with crystal orientation analysis. The effect ofthe anisotropic sodium ion transport at/across GBs depending on theorientation of the sodium ion transport planes and the applied electric field on the current distribution and the position of sodium filament growth is explored. The in situ TEM analysis is validated by large field of viewpost-mortem secondary ion mass spectrometer (SIMS) analysis, in which sodium filament growth within voids and along grain boundaries is observed, contributing to the sodium network formation potentially leading to failure of batteries
- …