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A Dual Discriminator Method for Generalized Zero-Shot Learning

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Computers, Materials & Continua 2024年第4期79卷 1599-1612页

作者： Tianshu Wei Jinjie Huang School of Computer science and technology Harbin University of Science and TechnologyHarbin150006China School of Automation Harbin University of Science and TechnologyHarbin150006China

Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen *** the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.

关键词： Generalized zero-shot learning modality consistent discriminator domain shift problem feature fusion

Enhanced Differentiable Architecture Search Based on Asymptotic Regularization

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Computers, Materials & Continua 2024年第2期78卷 1547-1568页

作者： Cong Jin Jinjie Huang Yuanjian Chen Yuqing Gong School of Computer science and technology Harbin University of Science and TechnologyHarbin150006China School of Automation Harbin University of Science and TechnologyHarbin150006China

In differentiable search architecture search methods,a more efficient search space design can significantly improve the performance of the searched architecture,thus requiring people to carefully define the search space with different complexity according to various *** rationalizing the search strategies to explore the well-defined search space will further improve the speed and efficiency of architecture *** this in mind,we propose a faster and more efficient differentiable architecture search method,***,we introduce a more efficient search space enriched by the introduction of two redefined convolution ***,we utilize a more efficient architectural parameter regularization method,mitigating the overfitting problem during the search process and reducing the error brought about by gradient ***,we introduce a natural exponential cosine annealing method to make the learning rate of the neural network training process more suitable for the search ***,group convolution and data augmentation are employed to reduce the computational ***,through extensive experiments on several public datasets,we demonstrate that our method can more swiftly search for better-performing neural network architectures in a more efficient search space,thus validating the effectiveness of our approach.

关键词： Differentiable architecture search allegro search space asymptotic regularization natural exponential cosine annealing

Time-Aware LSTM Neural Networks for Dynamic Personalized Recommendation on Business Intelligence

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Tsinghua science and technology 2024年第1期29卷 185-196页

作者： Xuan Yang James A.Esquivel Graduate School Angeles University FoundationAngeles City 2009Philippines Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 262700China

Personalized recommendation plays a critical role in providing decision-making support for product and service analysis in the field of business ***,deep neural network-based sequential recommendation models gained considerable ***,existing approaches pay litle attention to users'dynamically evolving interests,which are influenced by product attributes,especially product *** overcome these challenges,we propose a dynamic personalized recommendation model:***,we first embed product information and attribute information into a unified data ***,we exploit long short-term memory(LsTM)networks to characterize sequential behavior over multiple time periods and seize evolving interests by hierarchical LSTM ***,similarity values between users are measured through pairwise interest features,and personalized recommendation lists are generated.A series of experiments reveal the superiority of the proposed method compared withotheradvanced methods.

关键词： personalized recommendations evolving interests embedding LsTM networks

Privacy-Preserving Consensus-Based Distributed Economic Dispatch of Smart Grids via State Decomposition

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1250-1261页

作者： Wei Chen Guo-Ping Liu The authors are with the Center for Control science and technology Southern University of Science and TechnologyShenzhen 518055China

This paper studies the privacy-preserving distributed economic dispatch(DED)problem of smart *** autonomous consensus-based algorithm is developed via local data exchange with neighboring nodes,which covers both the islanded mode and the grid-connected mode of smart *** prevent power-sensitive information from being disclosed,a privacy-preserving mechanism is integrated into the proposed DED algorithm by randomly decomposing the state into two parts,where only partial data is *** objective is to develop a privacy-preserving DED algorithm to achieve optimal power dispatch with the lowest generation cost under physical constraints while preventing sensitive information from being *** this end,a comprehensive analysis framework is established to ensure that the proposed algorithm can converge to the optimal solution of the concerned optimization problem by means of the consensus theory and the eigenvalue perturbation *** particular,the proposed autonomous algorithm can achieve a smooth transition between the islanded mode and the grid-connected ***,rigorous analysis is given to show privacy-preserving performance against internal and external ***,case studies illustrate the feasibility and validity of the developed algorithm.

关键词： preserving autonomous mode

A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

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Defence technology（防务技术） 2024年第3期33卷 463-474页

作者： Yili Wang Changsheng Li Xiaofeng Wang Nanjing University of science and technology Nanjing 210094JiangsuChina State Key Laboratory of Explosion science and technology Beijing Institute of TechnologyBeijing 100081China

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of *** address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent *** leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration *** analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,*** model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target *** maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed ***,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

关键词： Penetration fuze Temporal convolutional network(TCN) Long short-term memory(LSTM) Layer counting Multi-source fusion

Adaptive sampling for corrugated plate digitization using a laser displacement sensor

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science China(Technological sciences) 2024年第5期67卷 1510-1523页

作者： WU ChengXing QI Qi CHEN BaiJin YANG JiXiang DING Han State Key Laboratory of Intelligent Manufacturing Equipment and technology School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China State Key Laboratory of Intelligent Manufacturing Equipment and technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China

The surface quality of a corrugated plate directly determines the heat transfer property of the thermal power mechanical *** detection methods are impractical for real-world production,being slow and *** contrast,the point laser displacement sensor,employing the optical triangle method,emerges as a promising device for assessing parts with variable curvature and highly reflective *** its benefits,high-density sampling by an innate frequency introduces challenges such as data redundancy and a poor signal-to-noise ratio,potentially affecting the efficiency and precision of subsequent data *** address these challenges,adjustable frequency data sampling has been developed for this sensor,allowing adaptive sampling for corrugated plate *** process begins with surface digitization to extract discrete points,which are transformed into intersection curves using the B-spline fitting ***,dominant points are identified,considering multigeometric constraints for curvature and arch ***,the sampling signal is adjusted based on the distribution information of dominant *** results indicate that the proposed method effectively minimizes redundant sampling without compromising the accurate capture of essential geometric features.

关键词： corrugated plate laser displacement sensor adaptive sampling dominant point curvature arch height

Traveling wave solutions of the nonlinear Gilson-Pickering equation in crystal lattice theory

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Journal of Ocean Engineering and science 2024年第1期9卷 40-49页

作者： A.T.Nguyen O.Nikan Z.Avazzadeh Division of Applied Mathematics Science and Technology Advanced InstituteVan Lang UniversityHo Chi Minh CityVietnam Faculty of technology Van Lang UniversityHo Chi Minh CityVietnam School of Mathematics Iran University of Science and TechnologyNarmakTehranIran Department of Applied Mathematics Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China

This paper focuses on obtaining the traveling wave solutions of the nonlinear Gilson-Pickering equa-tion(GPE),which describes the prorogation of waves in crystal lattice theory and plasma *** solution of the GPE is approximated via the finite difference technique and the localized meshless radial basis function generated finite *** association of the technique results in a meshless approach that does not require linearizing the nonlinear *** the first step,the PDE is converted to a system of nonlinear ODEs with the help of the radial *** the second step,a high-order ODE solver is adopted to discretize the nonlinear ODE *** global collocation techniques pose a considerable computationl burden due to the calculation of the dense algebraic *** proposed method approx-imates differential operators over the local support domain,leading to sparse differentiation matrices and decreasing the computational *** results and comparisons are provided to confirm the efficiency and accuracy of the method.

关键词： Nonlinear Gilson–Pickering equation Soliton wave solutions Meshless technique RBF LRBF-FD Optimal shape parameter

Double quantum images encryption scheme based on chaotic system

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Chinese Physics B 2024年第4期33卷 305-320页

作者：蒋社想李杨石锦张茹 School of Computer science and Engineering Anhui University of Science and TechnologyHuainan 232001China Anhui Key Laboratory of Mine Intelligent Equipment and technology Anhui University of Science&TechnologyHuainan 232001China

This paper explores a double quantum images representation(DNEQR)model that allows for simultaneous storage of two digital images in a quantum superposition ***,a new type of two-dimensional hyperchaotic system based on sine and logistic maps is investigated,offering a wider parameter space and better chaotic behavior compared to the sine and logistic *** on the DNEQR model and the hyperchaotic system,a double quantum images encryption algorithm is ***,two classical plaintext images are transformed into quantum states using the DNEQR ***,the proposed hyperchaotic system is employed to iteratively generate pseudo-random *** chaotic sequences are utilized to perform pixel value and position operations on the quantum image,resulting in changes to both pixel values and ***,the ciphertext image can be obtained by qubit-level diffusion using two XOR operations between the position-permutated image and the pseudo-random *** corresponding quantum circuits are also *** results demonstrate that the proposed scheme ensures the security of the images during transmission,improves the encryption efficiency,and enhances anti-interference and anti-attack capabilities.

关键词： double quantum images encryption chaotic system pixel scrambling XOR operation

Unsupervised Multi-Expert Learning Model for Underwater Image Enhancement

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 708-722页

作者： Hongmin Liu Qi Zhang Yufan Hu Hui Zeng Bin Fan the School of Intelligence science and technology and the Institute of Artificial Intelligence University of Science and Technology BeijingBeijing 100083China the School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China IEEE

Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded *** methods feed the whole image directly into the model for ***,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the *** address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color ***,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater *** on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater ***,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,*** addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced *** experiments on public datasets demonstrate that our method achieves more pleasing results in vision *** metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.

关键词： Multi-expert learning underwater image enhancement unsupervised learning