Rough set theory places great importance on approximation accuracy,which is used to gauge how well a rough set model describes a target ***,traditional approximation accuracy has limitations since it varies with chang...
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Rough set theory places great importance on approximation accuracy,which is used to gauge how well a rough set model describes a target ***,traditional approximation accuracy has limitations since it varies with changes in the target concept and cannot evaluate the overall descriptive ability of a rough set *** overcome this,two types of average approximation accuracy that objectively assess a rough set model’s ability to approximate all information granules is *** first is the relative average approximation accuracy,which is based on all sets in the universe and has several basic *** second is the absolute average approximation accuracy,which is based on undefinable sets and has yielded significant *** also explore the relationship between these two types of average approximation ***,the average approximation accuracy has practical applications in addressing missing attribute values in incomplete information tables.
Rough set theory has a very good effect in information processing and knowledge *** an information table,the current scholars regard all objects as a universe,and then establish various rough set ***,in the analysis o...
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Rough set theory has a very good effect in information processing and knowledge *** an information table,the current scholars regard all objects as a universe,and then establish various rough set ***,in the analysis of many data problems,it is more reasonable to select parts of objects which are useful to us or can meet the actual needs as a ***,in order to make up for the deficiency of traditional models,a new model is introduced from the perspective of variable ***,some interesting properties of this model,such as approximation sets,reduct of attributes and maximum part of universe,are *** the study of this paper,it can be seen that the model developed in our paper is not only more accurate but also more effective in describing uncertain knowledge.
Dear Editor, Task allocation strategies are important in multi-robot systems and have been intensely investigated by researchers because they are critical in determining the performance of the system. In this letter, ...
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Dear Editor, Task allocation strategies are important in multi-robot systems and have been intensely investigated by researchers because they are critical in determining the performance of the system. In this letter, a novel competition-based coordination model is proposed to solve the multi-robot task allocation problem and applied to a multi-robot object tracking scenario.
Rough set (RS) theory is one of the most effective data mining tools. So far, researchers have proposed dozens or even hundreds of RS models to cope with various data challenges. However, scholars have not systematica...
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In order to efficiently identify the outliers of ship AIS trajectories under high-speed sampling conditions, this paper proposes an outlier identification model based on ship position distance distribution. Taking the...
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The advancement of image editing and compositing technologies has posed significant challenges to the authenticity of digital images. Although deep learning algorithms based on convolutional neural networks (CNNs) hav...
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Dear Editor,This letter proposes a symmetry-preserving dual-stream graph neural network(SDGNN) for precise representation learning to an undirected weighted graph(UWG). Although existing graph neural networks(GNNs) ar...
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Dear Editor,This letter proposes a symmetry-preserving dual-stream graph neural network(SDGNN) for precise representation learning to an undirected weighted graph(UWG). Although existing graph neural networks(GNNs) are influential instruments for representation learning to a UWG, they invariably adopt a unique node feature matrix for illustrating the sole node set of a UWG.
Modern advanced large language model (LLM) applications often prepend long contexts before user queries to improve model output quality. These contexts frequently repeat, either partially or fully, across multiple que...
Multi-band optical networks are a potential technology for increasing network ***,the strong interference and non-uniformity between wavelengths in multi-band optical networks have become a bottleneck restricting the ...
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Multi-band optical networks are a potential technology for increasing network ***,the strong interference and non-uniformity between wavelengths in multi-band optical networks have become a bottleneck restricting the transmission capacity of multi-band optical *** overcome these challenges,it is particularly important to implement optical power optimization targeting wavelength ***,based on the generalized Gaussian noise model,we first formulate an optimization model for the problems of routing,modulation format,wavelength,and power allocation in C+L+S multi-band optical *** objective function is to maximize the average link capacity of the network while ensuring that the Optical Signal-to-Noise(OSNR)threshold of the service request is not ***,we propose a NonLinear Interferenceaware(NLI-aware)routing,modulation format,wavelength,and power allocation ***,we conduct simulations under different test *** simulation results indicate that our algorithm can effectively reduce the blocking probability by 23.5%and improve the average link capacity by 3.78%in C+L+S multi-band optical networks.
作者:
Zhang, JialiQiao, XiaoyanSchool of Computer Science and Technology
Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China School of Mathematics and Information Science
Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China
Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetwork...
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