The reduced-activation ferritic/martensitic(RAFM)steel CLF-1 has been designed as a candidate structural material for nuclear fusion energy *** engineering mechanical design,the effects of temperature on the strain di...
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The reduced-activation ferritic/martensitic(RAFM)steel CLF-1 has been designed as a candidate structural material for nuclear fusion energy *** engineering mechanical design,the effects of temperature on the strain distribution of CLF-1 steel during uniaxial tensile tests were explored within the temperature range from room temperature to 650°C using uniaxial tensile tests combined with in situ digital image correlation ***-concentrated regions alternately distributed±45°along the tensile direction could be attributed to the shear stress having the maximum value at±45°along the tensile direction and the coordinated deformation of the *** total strain distribution changed from a normal distribution to a lognormal distribution with increasing deformation owing to the competition between the elastic and plastic strains at all test *** localization has a strong relationship with temperature at the same engineering strain because of the temperature effects on dynamic strain aging(DSA).The stronger the DSA effect,the stronger the strain *** increasing temperature,the stronger the strain localization at the same strain,the weaker the plasticity,that is,DSA-induced embrittlement,and the slower the strength decline,that is,DSA-induced hardening.
From the perspective of state-channel interaction,standard quantum teleportation can be viewed as a communication process characterized by both input and output,functioning as a quantum depolarizing *** achieve a prec...
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From the perspective of state-channel interaction,standard quantum teleportation can be viewed as a communication process characterized by both input and output,functioning as a quantum depolarizing *** achieve a precise quantification of the quantumness introduced by this channel,we examine its uncertainties,which encompass both statedependent and state-independent ***,for qudit systems,we provide general formulas for these *** analyze the uncertainties associated with standard quantum teleportation when induced by isotropic states,Werner states,and X-states,and we elucidate the correlation between these uncertainties and the parameters of the specific mixed *** findings demonstrate the validity of quantifying these uncertainties.
This study proposes a malicious code detection model DTL-MD based on deep transfer learning, which aims to improve the detection accuracy of existing methods in complex malicious code and data scarcity. In the feature...
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In recent years, great success has been achieved in many tasks of natural language processing (NLP), e.g., named entity recognition (NER), especially in the high-resource language, i.e., English, thanks in part to the...
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Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of f...
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Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of fields. Due to the non-linear and unstable characteristics of multivariate time series, the existing methods encounter difficulties in analyzing complex high-dimensional data and capturing latent relationships between multivariates in time series, thus affecting the performance of long-term prediction. In this paper, we propose a novel time series forecasting model based on multilayer perceptron that combines spatio-temporal decomposition and doubly residual stacking, namely Spatio-Temporal Decomposition Neural Network (STDNet). We decompose the originally complex and unstable time series into two parts, temporal term and spatial term. We design temporal module based on auto-correlation mechanism to discover temporal dependencies at the sub-series level, and spatial module based on convolutional neural network and self-attention mechanism to integrate multivariate information from two dimensions, global and local, respectively. Then we integrate the results obtained from the different modules to get the final forecast. Extensive experiments on four real-world datasets show that STDNet significantly outperforms other state-of-the-art methods, which provides an effective solution for long-term time series forecasting.
The rapid development of ISAs has brought the issue of software compatibility to the forefront in the embedded *** address this challenge,one of the promising solutions is the adoption of a multiple-ISA processor that...
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The rapid development of ISAs has brought the issue of software compatibility to the forefront in the embedded *** address this challenge,one of the promising solutions is the adoption of a multiple-ISA processor that supports multiple different ***,due to constraints in cost and performance,the architecture of a multiple-ISA processor must be carefully optimized to meet the specific requirements of embedded *** exploring the RISC-V and ARM Thumb ISAs,this paper proposes RVAM16,which is an optimized multiple-ISA processor microarchitecture for embedded devices based on hardware binary translation *** results show that,when running non-native ARM Thumb programs,RVAM16 achieves a significant speedup of over 2.73×with less area and energy consumption compared to using hardware binary translation alone,reaching more than 70%of the performance of native RISC-V programs.
Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate *** inherent traits often lead to increased miss and false detection rat...
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Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate *** inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing ***,these complexities contribute to inaccuracies in target localization and hinder precise target *** paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery ***,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex *** resolve these issues,we introduce a novel ***,we propose the implementation of a lightweight multi-scale module called *** module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature *** effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing ***,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone *** allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed ***,a dynamic head attentionmechanism is *** allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different ***,the precision of object detection is significantly *** trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 a
Sharding technology achieves parallel processing of transactions by dividing the network into multiple independent parts, namely shards, significantly increasing the throughput of the blockchain system and reducing tr...
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Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone...
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Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To address the above issues, we propose the multi-label weighted broad learning system(MLW-BLS) from the perspective of label imbalance weighting and label correlation mining. Further, we propose the multi-label adaptive weighted broad learning system(MLAW-BLS) to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set. Extensive experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.
Video colorization aims to add color to grayscale or monochrome *** existing methods have achieved substantial and noteworthy results in the field of image colorization,video colorization presents more formidable obst...
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Video colorization aims to add color to grayscale or monochrome *** existing methods have achieved substantial and noteworthy results in the field of image colorization,video colorization presents more formidable obstacles due to the additional necessity for temporal ***,there is rarely a systematic review of video colorization *** this paper,we aim to review existing state-of-the-art video colorization *** addition,maintaining spatial-temporal consistency is pivotal to the process of video *** gain deeper insight into the evolution of existing methods in terms of spatial-temporal consistency,we further review video colorization methods from a novel *** colorization methods can be categorized into four main categories:optical-flow based methods,scribble-based methods,exemplar-based methods,and fully automatic ***,optical-flow based methods rely heavily on accurate optical-flow estimation,scribble-based methods require extensive user interaction and modifications,exemplar-based methods face challenges in obtaining suitable reference images,and fully automatic methods often struggle to meet specific colorization *** also discuss the existing challenges and highlight several future research opportunities worth exploring.
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