80 research outputs found
The operation, stability analysis and active damping of multi-port converter-based DC traction power systems
Impedance-based Stability Analysis of Metro Traction Power System Considering Regenerative Braking
A novel decomposed-ensemble time series forecasting framework: capturing underlying volatility information
Time series forecasting represents a significant and challenging task across
various fields. Recently, methods based on mode decomposition have dominated
the forecasting of complex time series because of the advantages of capturing
local characteristics and extracting intrinsic modes from data. Unfortunately,
most models fail to capture the implied volatilities that contain significant
information. To enhance the prediction of contemporary diverse and complex time
series, we propose a novel time series forecasting paradigm that integrates
decomposition with the capability to capture the underlying fluctuation
information of the series. In our methodology, we implement the Variational
Mode Decomposition algorithm to decompose the time series into K distinct
sub-modes. Following this decomposition, we apply the Generalized
Autoregressive Conditional Heteroskedasticity (GARCH) model to extract the
volatility information in these sub-modes. Subsequently, both the numerical
data and the volatility information for each sub-mode are harnessed to train a
neural network. This network is adept at predicting the information of the
sub-modes, and we aggregate the predictions of all sub-modes to generate the
final output. By integrating econometric and artificial intelligence methods,
and taking into account both the numerical and volatility information of the
time series, our proposed framework demonstrates superior performance in time
series forecasting, as evidenced by the significant decrease in MSE, RMSE, and
MAPE in our comparative experimental results
Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method
Image aesthetics assessment (IAA) is a challenging task due to its highly
subjective nature. Most of the current studies rely on large-scale datasets
(e.g., AVA and AADB) to learn a general model for all kinds of photography
images. However, little light has been shed on measuring the aesthetic quality
of artistic images, and the existing datasets only contain relatively few
artworks. Such a defect is a great obstacle to the aesthetic assessment of
artistic images. To fill the gap in the field of artistic image aesthetics
assessment (AIAA), we first introduce a large-scale AIAA dataset: Boldbrush
Artistic Image Dataset (BAID), which consists of 60,337 artistic images
covering various art forms, with more than 360,000 votes from online users. We
then propose a new method, SAAN (Style-specific Art Assessment Network), which
can effectively extract and utilize style-specific and generic aesthetic
information to evaluate artistic images. Experiments demonstrate that our
proposed approach outperforms existing IAA methods on the proposed BAID dataset
according to quantitative comparisons. We believe the proposed dataset and
method can serve as a foundation for future AIAA works and inspire more
research in this field. Dataset and code are available at:
https://github.com/Dreemurr-T/BAID.gitComment: Accepted by CVPR 202
Balanced Order Batching with Task-Oriented Graph Clustering
Balanced order batching problem (BOBP) arises from the process of warehouse
picking in Cainiao, the largest logistics platform in China. Batching orders
together in the picking process to form a single picking route, reduces travel
distance. The reason for its importance is that order picking is a labor
intensive process and, by using good batching methods, substantial savings can
be obtained. The BOBP is a NP-hard combinational optimization problem and
designing a good problem-specific heuristic under the quasi-real-time system
response requirement is non-trivial. In this paper, rather than designing
heuristics, we propose an end-to-end learning and optimization framework named
Balanced Task-orientated Graph Clustering Network (BTOGCN) to solve the BOBP by
reducing it to balanced graph clustering optimization problem. In BTOGCN, a
task-oriented estimator network is introduced to guide the type-aware
heterogeneous graph clustering networks to find a better clustering result
related to the BOBP objective. Through comprehensive experiments on
single-graph and multi-graphs, we show: 1) our balanced task-oriented graph
clustering network can directly utilize the guidance of target signal and
outperforms the two-stage deep embedding and deep clustering method; 2) our
method obtains an average 4.57m and 0.13m picking distance ("m" is the
abbreviation of the meter (the SI base unit of length)) reduction than the
expert-designed algorithm on single and multi-graph set and has a good
generalization ability to apply in practical scenario.Comment: 10 pages, 6 figure
ChatEDA: A Large Language Model Powered Autonomous Agent for EDA
The integration of a complex set of Electronic Design Automation (EDA) tools
to enhance interoperability is a critical concern for circuit designers. Recent
advancements in large language models (LLMs) have showcased their exceptional
capabilities in natural language processing and comprehension, offering a novel
approach to interfacing with EDA tools. This research paper introduces ChatEDA,
an autonomous agent for EDA empowered by a large language model, AutoMage,
complemented by EDA tools serving as executors. ChatEDA streamlines the design
flow from the Register-Transfer Level (RTL) to the Graphic Data System Version
II (GDSII) by effectively managing task planning, script generation, and task
execution. Through comprehensive experimental evaluations, ChatEDA has
demonstrated its proficiency in handling diverse requirements, and our
fine-tuned AutoMage model has exhibited superior performance compared to GPT-4
and other similar LLMs
Determinants of Residential Satisfaction During the Initial Stage of the COVID-19 Pandemic: The Case of Xiangyang City, China
Objectives: To explore the impacts of psychological character strengths, self-efficacy, and the number of confirmed COVID-19 cases on residential satisfaction at the initial stage of the COVID-19 pandemic in China.Methods: To achieve the study aim, we collected data from 281 observations from Xiangyang City, China, via an online survey. Data were analyzed using linear regression.Results: Character strengths and the number of confirmed COVID-19 cases significantly impacted residential satisfaction. While self-efficacy did not directly impact residential satisfaction, it moderated the relationship between the number of confirmed COVID-19 cases and residential satisfaction. The control variables of social trust and shared value positively impacted residential satisfaction, and their influence on residential satisfaction was higher than that of character strengths. The sociodemographic variables of marriage, age, educational attainment, and housing area per capita also impacted residential satisfaction significantly. However, strong ties and weak ties became insignificant variables due to social distancing strategies.Conclusion: The study findings offer insights for local governments to enhance residential satisfaction in the community to avoid social panic during unpredictable threats or future pandemics
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