In this paper,we consider the reducibility of three-dimensional skew symmetric *** obtain a reducibility result if the base frequency is high-dimensional weak Liouvillean and the parameter is sufficiently *** proof is...
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In this paper,we consider the reducibility of three-dimensional skew symmetric *** obtain a reducibility result if the base frequency is high-dimensional weak Liouvillean and the parameter is sufficiently *** proof is based on a modified KAM theory for 3-dimensional skew symmetric systems.
There is a growing interest in sustainable ecosystem development, which includes methods such as scientific modeling, environmental assessment, and development forecasting and planning. However, due to insufficient su...
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Permissioned blockchain is a promising methodology to build zero-trust storage foundation with trusted data storage and sharing for the zero-trust network. However, the inherent full-backup feature of the permissioned...
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The extraction of atomic-level material features from electron microscope images is crucial for studying structure-property relationships and discovering new materials. However, traditional electron microscope analyse...
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The extraction of atomic-level material features from electron microscope images is crucial for studying structure-property relationships and discovering new materials. However, traditional electron microscope analyses rely on time-consuming and complex human operations; thus, they are only applicable to images with a small number of atoms. In addition, the analysis results vary due to observers' individual deviations. Although efforts to introduce automated methods have been performed previously, many of these methods lack sufficient labeled data or require various conditions in the detection process that can only be applied to the target material. Thus, in this study, we developed AtomGAN, which is a robust, unsupervised learning method, that segments defects in classical 2D material systems and the heterostructures of MoS2/WS2automatically. To solve the data scarcity problem, the proposed model is trained on unpaired simulated data that contain point and line defects for MoS2/WS2. The proposed AtomGAN was evaluated on both simulated and real electron microscope images. The results demonstrate that the segmented point defects and line defects are presented perfectly in the resulting figures, with a measurement precision of 96.9%. In addition, the cycled structure of AtomGAN can quickly generate a large number of simulated electron microscope images.
The previous adversarial training models failed to pay attention to the influence of the changing gradient of the loss function in the current training on the model. The perturbation injected into the model is only pr...
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The existence of adsorbed water and structural water in the crystal structure of attapulgite(ATP)endows it with poor capability to store lithium ***,the chloride molten salt method was developed to function ATP materi...
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The existence of adsorbed water and structural water in the crystal structure of attapulgite(ATP)endows it with poor capability to store lithium ***,the chloride molten salt method was developed to function ATP materials based on theoretical calculations,which exhibit ground-breaking electrochemical *** the modification process,the metal ions in chloride molten salt occupy the vertices of the Mg-O octahedral structure from the liberation of structural water and hydroxyl groups in ATP,forming MaMgbAlcSixOy(M=Li,Na,or K).Using Li Cl molten salt-modified ATP(Li-ATP)as a proof-of-concept,the detailed phase transition,physicochemical properties,and lithium storage capacity were *** to the original ATP,Li-ATP achieves a nearly 7-fold increase in lithium storage capacity(498mAh/g),featuring a promising low-cost polyanionic type anode material.
Current methods for Music Emotion Recognition (MER) face challenges in effectively extracting features sensitive to emotions, especially those rich in temporal detail. Moreover, the narrow scope of music-related modal...
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Extracting useful details from images is essential for the Internet of Things ***,in real life,various external environments,such as badweather conditions,will cause the occlusion of key target information and image d...
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Extracting useful details from images is essential for the Internet of Things ***,in real life,various external environments,such as badweather conditions,will cause the occlusion of key target information and image distortion,resulting in difficulties and obstacles to the extraction of key information,affecting the judgment of the real situation in the process of the Internet of Things,and causing system decision-making errors and *** this paper,we mainly solve the problem of rain on the image occlusion,remove the rain grain in the image,and get a clear image without ***,the single image deraining algorithm is studied,and a dual-branch network structure based on the attention module and convolutional neural network(CNN)module is proposed to accomplish the task of rain *** order to complete the rain removal of a single image with high quality,we apply the spatial attention module,channel attention module and CNN module to the network structure,and build the network using the coder-decoder *** the experiment,with the structural similarity(SSIM)and the peak signal-to-noise ratio(PSNR)as evaluation indexes,the training and testing results on the rain removal dataset show that the proposed structure has a good effect on the single image deraining task.
Iris biometrics allow contactless authentication, which makes it widely deployed human recognition mechanisms since the couple of years. Susceptibility of iris identification systems remains a challenging task due to ...
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Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud *** a reasonable resource allocation solution is the key to adequately utilize th...
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Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud *** a reasonable resource allocation solution is the key to adequately utilize the hybrid ***,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other *** on the above problems,a many-objective hybrid cloud task scheduling optimization model(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion ***,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model *** algorithm uses opposition-based learning to generate initial populations for faster ***,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search *** comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and model optimization effects.
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