The emergence of power dispatching automation systems has greatly improved the efficiency of power industry operations and promoted the rapid development of the power ***,with the convergence and increase in power dat...
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The emergence of power dispatching automation systems has greatly improved the efficiency of power industry operations and promoted the rapid development of the power ***,with the convergence and increase in power data flow,the data dispatching network and the main station dispatching automation system have encountered substantial ***,themethod of online data resolution and rapid problemidentification of dispatching automation systems has been widely *** this paper,we performa comprehensive review of automated dispatching of massive dispatching data from the perspective of intelligent identification,discuss unresolved research issues and outline future directions in this *** particular,we divide intelligent identification over power big data into data acquisition and storage processes,anomaly detection and fault discrimination processes,and fault tracing for dispatching operations during communication.A detailed survey of the solutions to the challenges in intelligent identification over power big data is then ***,opportunities and future directions are outlined.
With the development of web technology, web pages become more and more complex. Using the virtual DOM method can make the web page have efficient update performance without reducing development efficiency. An essentia...
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Document key information extraction (DKIE) is a crucial topic that aims at automatically comprehending documents with complex formats and layouts (invoices, business insurance, etc.). While pre-trained approaches have...
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Document key information extraction (DKIE) is a crucial topic that aims at automatically comprehending documents with complex formats and layouts (invoices, business insurance, etc.). While pre-trained approaches have shown high performance on many DKIE tasks, they suffer from three major challenges. First of all, these approaches ignore the ambiguity resulting from similar text representations before cross-modal interaction. Secondly, they do not consider cross-modal representation alignment before cross-modal interaction. Finally, self-attention layers in cross-modal interaction incur significant computing costs, making it hard to perform joint representation learning from all negative samples. To address these issues, we propose a Dynamical Cross-Modal Alignment Interaction framework (DCMAI). To be more specific, 1) A prior knowledge-guided module is designed to adaptively mine fine-grained visual information to disambiguate similar text representations. 2) A crossover alignment loss is formulated to align cross-modal representations before cross-modal interaction. 3) A hierarchical interaction sampling scheme is introduced to obtain a small but efficient subset of cross-modal negative samples, and a contrastive loss is employed to improve joint representation learning. Comprehensive experiments show that the proposed DCMAI achieves state-of-the-art performance than competitive baselines on several public downstream benchmarks. Code will be open to the public.
Complex systems in the real world often can be modeled as network structures,and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of ...
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Complex systems in the real world often can be modeled as network structures,and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of *** community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of ***,many real-world networks consist of multiple types of nodes and edges,and there may be rich semantic information on nodes and *** methods for single-layer networks cannot effectively tackle multi-layer information,multi-relationship information,and attribute *** paper proposes a community discovery algorithm based on multi-relationship *** proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node *** node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network(GCN)to obtain the final node embedding *** strategy allows capturing of rich structural and attributes information in multi-relational *** were conducted on different datasets with baselines,and the results show that the proposed algorithm obtains significant performance improvement in community discovery,node clustering,and similarity search tasks,and compared to the baseline with the best performance,the proposed algorithm achieves an average improvement of 3.1%on Macro-F1 and 4.7%on Micro-F1,which proves the effectiveness of the proposed algorithm.
Deep convolutional neural network (CNN) has been widely used in speech recognition technology. The model based on deep CNN can effectively improve the quality of human-computer interaction. However, the existing CNN w...
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With the rapid development of big data technology, big data systems are more and more used to process SQL query jobs. However, intricate big data system parameters have big effects on the quality of service for big da...
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Computer vision and deep learning are one of the main technologies for weed intelligent recognition in farmland. However, when using the deep learning technology to identify weeds in the field broomcorn millet growing...
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Current deep forest are mostly built upon multi-grained cascade forest, i.e. a novel decision-tree ensemble, with a cascade structure that enables representation learning by forest. In this paper, we propose the Deep ...
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Real-world decision-making tasks are usually partially observable Markov decision processes (POMDPs), where the state is not fully observable. Recent progress has demonstrated that recurrent reinforcement learning (RL...
Many expensive optimization problems exist in various real -world applications. However traditional evolutionary algorithms are inadequate for solving these problems directly. Surrogateassisted evolutionary algorithm ...
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Many expensive optimization problems exist in various real -world applications. However traditional evolutionary algorithms are inadequate for solving these problems directly. Surrogateassisted evolutionary algorithm (SAEA) can effectively solve expensive optimization problems using computationally inexpensive surrogate models. However, both the Kriging and ensemble models most SAEAs adopted have limited uncertainty of prediction, especially for expensive multiobjective optimization problems (EMOPs). To enhance the optimization performance of SAEA for EMOPs, this paper proposes a new XGBoost-assisted evolutionary algorithm, calling XGBEA. Specifically, XGBoost is used as the surrogate model, and a neighborhood density selection strategy based on a mixed population and archive space (NDS-MPA) is proposed to measure the uncertainties of individuals. XGBoost helps to best fit objective functions with different fitness landscapes. NDS-MPA selects non -dominated individuals with minimal density for re-evaluation, incorporating considerations of convergence, diversity and uncertainty. Experimental results on two well -studied benchmarks demonstrated the superiority of XGBEA over seven state-of-the-art SAEAs.
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