Deep learning-based person re-identification (re-id) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely conside...
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This paper presents a moving window-recursive sparse principal component analysis(M WRSPCA)-based online fault monitoring method, aim at providing effective fau lt monitoring solution for the large-scale complex indus...
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This paper presents a moving window-recursive sparse principal component analysis(M WRSPCA)-based online fault monitoring method, aim at providing effective fau lt monitoring solution for the large-scale complex industrial processes with time varying and dynamic changing *** establishes a sparse principal component model with a sliding window block matrix to monitor the industrial data sample set online and re-adds the normal industrial sample data to the training set during the fault monitoring process, so that the process monitoring model can be adaptive to the time varying *** the window is sliding, the covariance information of window data block matrix is updated recursively, and the sparse loading matrix is also updated recursively by using the modified algorithm of the rank 1 matrix, so that the computational complexity of the adaptive process monitoring model is greatly reduced and the model real-time monitoring capability is *** proposed method is experimentally verified in the Tennessee-Eastman(TE) *** with the traditional fault monitoring methods, the proposed method can effectively improve the accuracy of fault detection and adapt to fault monitoring of long process and complex industrial processes.
In recent years, classification and rating of social emergency event have attracted more and more attentions in emergency management. However, most of the current studies adopt the rule-based methods to identify the e...
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Achieving simultaneous polarization and wavefront control,especially circular polarization with the auxiliary degree of freedom of light and spin angular momentum,is of fundamental importance in many optical *** are t...
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Achieving simultaneous polarization and wavefront control,especially circular polarization with the auxiliary degree of freedom of light and spin angular momentum,is of fundamental importance in many optical *** are typically undesirable in highly integrated photonic circuits and ***,we propose an interference-assisted metasurface-multiplexer(meta-plexer)that counterintuitively exploits constructive and destructive interferences between hybrid meta-atoms and realizes independent spin-selective wavefront *** kaleidoscopic meta-plexers are experimentally demonstrated via two types of single-layer spinwavefront multiplexers that are composed of spatially rotated anisotropic *** type generates a spinselective Bessel-beam wavefront for spin-down light and a low scattering cross-section for stealth for spin-up *** other type demonstrates versatile control of the vortex wavefront,which is also characterized by the orbital angular momentum of light,with frequency-switchable numbers of beams under linearly polarized wave *** findings offer a distinct interference-assisted concept for realizing advanced multifunctional photonics with arbitrary and independent spin-wavefront features.A variety of applications can be readily anticipated in optical diodes,isolators,and spin-Hall meta-devices without cascading bulky optical elements.
In the multimedia network environment, it is necessary to effectively filter negative information in the multimedia network and enhance the ability to mine and identify valid data. This paper presents a new algorithm ...
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To better retain useful weak low-frequency magnetotelluric(MT)signals with strong interference during MT data processing,we propose a SVM-CEEMDWT based MT data signal-noise separation method,which extracts the weak MT...
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To better retain useful weak low-frequency magnetotelluric(MT)signals with strong interference during MT data processing,we propose a SVM-CEEMDWT based MT data signal-noise separation method,which extracts the weak MT signal affected by strong ***,the approximate entropy,fuzzy entropy,sample entropy,and Lempel-Ziv(LZ)complexity are extracted from the magnetotelluric ***,four robust parameters are used as the inputs to the support vector machine(SVM)to train the sample library and build a model based on the different complexity of *** on this model,we can only consider time series with strong interference when using the complementary ensemble empirical mode decomposition(CEEMD)and wavelet threshold(WT)for noise *** results suggest that the SVM based on the robust parameters can distinguish the time periods with strong interference well before noise *** with the CEEMD WT,the proposed SVM-CEEMDWT method retains more low-frequency low-variability information,and the apparent resistivity curve is smoother and more ***,the results better reflect the deep electrical structure in the field.
Multimodal data fusion has a long research history since audio-visual speech recognition, which is inspired by the McGurk effect. Because of the limited model capacity of the traditional methods, multimodal data fusio...
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ISBN:
(数字)9781665403924
ISBN:
(纸本)9781665403931
Multimodal data fusion has a long research history since audio-visual speech recognition, which is inspired by the McGurk effect. Because of the limited model capacity of the traditional methods, multimodal data fusion researches are not so popular for a period. Recently, the advances of deep learning techniques open up new opportunities for the multimodal data fusion field. However, there is still a great gap in multimodal data processing ability between artificial intelligence and human beings. Many problems in multimodal data processing are still necessary to be researched. In this work, we propose to gain an insight into the information fusion level and apply different information fusion strategy to different situations. We analyze the different situations of the multimodal data fusion process and divide them into two categories, including consistent information fusion and contradictory information fusion. We demonstrate some toy examples of the different cases of the multimodal data fusion process.
Learning early warning is of great significance for coping with students' learning risks. The existing research fails in modeling the fluctuation of students' learning states and providing the multi-level earl...
Learning early warning is of great significance for coping with students' learning risks. The existing research fails in modeling the fluctuation of students' learning states and providing the multi-level early warning for students at different levels. To address them, a new approach of learning early warning is proposed to predict at-risk students in e-learning environment by combining cognitive diagnosis with learning behaviors analysis. In this approach, the students' learning process is modeled from four dimensions, i.e., learning quality, learning engagement, latent learning state, and historical learning performance. The convolutional neural network and long short-term memory network are used to explore the students' latent learning features. Then, the Adaboost algorithm is applied to predict students' learning performance. Based on the predicted performance, the evaluation rules are designed to provide multi-level learning early warning for students. Finally, the experiments demonstrate that the proposed method could predict at-risk students efficiently and accurately.
Evolutionary algorithms have shown their promise in addressing multiobjective problems (MOPs). However, the Pareto dominance used in multiobjective optimization loses its effectiveness when addressing many-objective p...
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Existing methods for modeling recommendation systems based on knowledge graphs include embedding-based, pathbased, and propagation-based methods. The embedding-based approach is flexible but more suitable for intra-gr...
Existing methods for modeling recommendation systems based on knowledge graphs include embedding-based, pathbased, and propagation-based methods. The embedding-based approach is flexible but more suitable for intra-graph applications, the path-based approach can model complex relationships but has a high computational cost, and the propagation-based approach considers global information but may introduce noise. This study proposed a simple and efficient model, called SEKGAT, which comprehensive the ideology of path-based and propagation approach to personalized recommendation by aggregating the user preferences through graph attention mechanism and fusing multiple feature representations on the knowledge graph into item features through pooling aggregators. Experimental results for the CTR prediction and Top-K recommendation tasks on three datasets of real-world scenarios show that our model approach is competitive.
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