The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos ***, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of t...
详细信息
The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos ***, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.
With the development of deep learning and computer vision, face detection has achieved rapid progress owing. Face detection has several application domains, including identity authentication, security protection, medi...
详细信息
Data analysis tasks aim to provide insightful analysis for given data by incorporating background knowledge of the represented phenomenon, which in turn supports decision-making. While existing large language models(L...
详细信息
Data analysis tasks aim to provide insightful analysis for given data by incorporating background knowledge of the represented phenomenon, which in turn supports decision-making. While existing large language models(LLMs) can describe data trends, they still lag behind human data analysts in terms of integrating external knowledge and in-depth data analysis. Therefore, we propose a multi-agent data analysis framework based on LLMs. The framework decomposes the data analysis task into subtasks by employing three different agents. By empowering agents with the ability to utilize data search tools, the framework enables them to search for arbitrary relevant knowledge during the analysis process, leading to more insightful analysis. Moreover, to enhance the quality of the analysis results, we propose a multi-stage iterative optimization method that iteratively performs data analysis to form more in-depth conclusions. To validate the performance of our framework, we apply it to three real-world problems in the research development of higher education in China data. Experimental results demonstrate that our approach can achieve more insightful data analysis results compared to directly using LLMs alone.
INTRODUCTION Store signboards provide important information in street view images,andcharacter recognition in natural scenes is an important research direction in computer *** view store signboard character recognitio...
INTRODUCTION Store signboards provide important information in street view images,andcharacter recognition in natural scenes is an important research direction in computer *** view store signboard character recognition technology,acombination of the two,
Recently, Mix-style data augmentation methods (e.g., Mixup and CutMix) have shown promising performance in various visual tasks. However, these methods are primarily designed for single-label images, ignoring the cons...
详细信息
A good quality(5 at.%Yb:GdScO_(3))single crystal of F30 mm37 mm was grown successfully by the Czochralski *** structure is studied by the x-ray diffraction(XRD),and its atomic coordinates are obtained by Rietveld ***...
详细信息
A good quality(5 at.%Yb:GdScO_(3))single crystal of F30 mm37 mm was grown successfully by the Czochralski *** structure is studied by the x-ray diffraction(XRD),and its atomic coordinates are obtained by Rietveld *** crystal field energy level splitting of Yb^(3+)in GdScO_(3) is determined by employing the absorption and photoluminescence spectra at 8 *** ^(2)F_(7/2)(4)is far from the ground state ^(2)F_(7/2)(1)by 710 cm^(-1) among the crystal field energy levels split from ^(2)F_(7/2),so it is more easier to realize the laser operation of ^(2)F_(5/2)(1)^(2)F_(7/2)(4)with wavelength 1060 *** spin–orbit coupling parameters and intrinsic crystal field parameters(CFPs).The intrinsic crystal field parameters¯B k(k=2,4,6)of the crystal were fitted by the superposition *** CFPs evaluated with¯Bk and coordination factor are taken as the initial parameters to fit the crystal field energy levels of the crystal,and the crystal field parameters B_(q)^(k) are obtained finally with the root-mean-square deviation 9 *** is suggested that the ligand point charge,covalency and overlap interaction are slightly weaker than charge interpenetration and coulomb exchange interaction for Yb^(3+)in GdScO_(3).The obtained Hamiltonian parameters can be used to calculate crystal field energy levels and wave functions of Yb:GdScO_(3) to analyze the mechanism of the luminescence or laser.
Combinatorial Optimization (CO), as a crucial domain within optimization, has been spanned across a broad spectrum of fields, imparting significant practical relevance to the study of combinatorial optimization proble...
详细信息
Risk prediction is an important task to ensuring the driving safety of railway trams. Although data-driven intelligent methods are proved to be effective for driving risk prediction, accuracy is still a top concern fo...
详细信息
Risk prediction is an important task to ensuring the driving safety of railway trams. Although data-driven intelligent methods are proved to be effective for driving risk prediction, accuracy is still a top concern for the challenges of data quality which mainly represent as the unbalanced datasets. This study focuses on applying feature extraction and data augmentation methods to achieve effective risk prediction for railway trams, and proposes an approach based on a self-adaptive K-means clustering algorithm and the least squares deep convolution generative adversarial network(LS-DCGAN). The data preprocessing methods are proposed, which include the K-means algorithm to cluster the locations of trams and the extreme gradient boosting recursive feature elimination based feature selection algorithm to retain the key features. The LS-DCGAN model is designed for sparse sample expansion, aiming to address the sample category distribution imbalance problem. The experiments implemented with the public and real datasets show that the proposed approach can reach a high accuracy of 90.69%,which can greatly enhances the tram driving safety.
The detection of road defects is crucial for ensuring vehicular safety and facilitating the prompt repair of roadway imperfections. Existing YOLOv8-based models face the following issues: extraction capabilities and i...
详细信息
Learning diverse detailed feature is crucial for fine-grained visual categorization (FGVC). However, most of existing methods for FGVC use the standard convolution for feature extraction which leads to the loss of man...
详细信息
暂无评论