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A Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

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Computers, Materials & Continua 2024年第6期79卷 4349-4370页

作者： Jiajie Shen Yan Wang Dongxu Zhang key laboratory of symbol computation and knowledge engineering Ministry of EducationColleague of Computer Science and TechnologyJilin UniversityChangchun130012China College of Software and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun130012China

Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable *** work have achieved impressive performance in classifying steady locomotion ***,it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion *** to the similarities between the information of the transitions and their adjacent steady ***,most of these methods rely solely on data and overlook the objective laws between physical activities,resulting in lower accuracy,particularly when encountering complex locomotion modes such as *** address the existing deficiencies,we propose the locomotion rule embedding long short-term memory(LSTM)network with Attention(LREAL)for human locomotor intent classification,with a particular focus on transitions,using data from fewer sensors(two inertial measurement units and four goniometers).The LREAL network consists of two levels:One responsible for distinguishing between steady states and transitions,and the other for the accurate identification of locomotor *** classifier in these levels is composed of multiple-LSTM layers and an attention *** introduce real-world motion rules and apply constraints to the network,a prior knowledge was added to the network via a rule-modulating *** method was tested on the ENABL3S dataset,which contains continuous locomotion date for seven steady and twelve transitions *** results showed that the LREAL network could recognize locomotor intents with an average accuracy of 99.03%and 96.52%for the steady and transitions states,*** is worth noting that the LREAL network accuracy for transition-state recognition improved by 0.18%compared to other state-of-the-art network,while using data from fewer sensors.

关键词： Lower-limb prosthetics deep neural networks motion classification

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NADARAYA-WATSON ESTIMATORS FOR REFLECTED STOCHASTIC PROCESSES

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Acta Mathematica Scientia 2024年第1期44卷 143-160页

作者：韩月才张丁文 School of Mathematics Jilin UniversityChangchun130012China key laboratory of symbolic computation and knowledge engineering of Ministry of Education Jilin UniversityChangchun130012China

We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential *** estimates,based on either the continuously observed process or the discretely observed process,are *** certain conditions,we prove the strong consistency and the asymptotic normality of the two *** method is also suitable for one-sided reflected stochastic differential *** results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al.(Stat Sin,2021,31:29-51).Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.

关键词： reflected stochastic differential equation discretely observed process continuously observed process Nadaraya-Watson estimator asymptotic behavior

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A novel graph oversampling framework for node classification in class-imbalanced graphs

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Science China(Information Sciences) 2024年第6期67卷 214-229页

作者： Riting XIA Chunxu ZHANG Yan ZHANG Xueyan LIU Bo YANG key laboratory of symbolic computation and knowledge engineering of Ministry of Education Jilin University College of Artificial Intelligence Jilin University College of Computer Science and Technology Jilin University College of Communication engineering Jilin University

Graph neural network(GNN) is a promising method to analyze graphs. Most existing GNNs adopt the class-balanced assumption, which cannot deal with class-imbalanced graphs well. The oversampling technique is effective in alleviating class-imbalanced problems. However, most graph oversampling methods generate synthetic minority nodes and their edges after applying GNNs. They ignore the problem that the representations of the original and synthetic minority nodes are dominated by majority nodes caused by aggregating neighbor information through GNN before oversampling. In this paper, we propose a novel graph oversampling framework, termed distribution alignment-based oversampling for node classification in classimbalanced graphs(named Graph-DAO). Our framework generates synthetic minority nodes before GNN to avoid the dominance of majority nodes caused by message passing in GNNs. Additionally, we introduce a distribution alignment method based on the sum-product network to learn more information about minority nodes. To our best knowledge, it is the first to use the sum-product network to solve the class-imbalanced problem in node classification. A large number of experiments on four real datasets show that our method achieves the optimal results on the node classification task for class-imbalanced graphs.

关键词： graph neural network class-imbalanced graphs sum-product network oversampling node classification

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SEOE: an option graph based semantically embedding method for prenatal depression detection

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Frontiers of Computer Science 2024年第6期18卷 186911-186911页

作者： HAN Xiaosong CAO Mengchen XU Dong FENG Xiaoyue LIANG Yanchun LANG Xiaoduo GUAN Renchu College of Computer Science and Technology key laboratory of symbolic computation and knowledge engineering of Ministry of Education Jilin University Department of Electrical engineering and Computer Science and Christopher S. Bond Life Sciences Center University of Missour Department of Computer Science and Technology Zhuhai laboratory of key laboratory of symbolic computation and knowledge engineering of Ministry of Education Zhuhai College of Science and Technology Jilin Provincial Institute of Population Science and Technology

Prenatal depression, which can affect pregnant women’s physical and psychological health and cause postpartum depression, is increasing dramatically. Therefore, it is essential to detect prenatal depression early and conduct an attribution analysis. Many studies have used questionnaires to screen for prenatal depression, but the existing methods lack attributability. To diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires, we present the semantically enhanced option embedding (SEOE) model to represent questionnaire options. It can quantitatively determine the relationship and patterns between options and depression. SEOE first quantifies options and resorts them, gathering options with little difference, since Word2Vec is highly dependent on context. The resort task is transformed into an optimization problem involving the traveling salesman problem. Moreover, all questionnaire samples are used to train the options’ vector using Word2Vec. Finally, an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from depression. To verify the model, we compare it with other deep learning and traditional machine learning methods. The experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of 0.8. The most relevant factors of depression found by SEOE are also verified in the literature. In addition, our model is of low computational complexity and strong generalization, which can be widely applied to other questionnaire analyses of psychiatric disorders.

关键词： questionnaire analyses text classification graph representation learning traveling salesman problem prenatal depression

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DeciLS-PBO: an effective local search method for pseudo-Boolean optimization

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Frontiers of Computer Science 2024年第2期18卷 239-241页

作者： Luyu JIANG Dantong OUYANG Qi ZHANG Liming ZHANG College of Computer Science and Technology Jilin UniversityChangchun 130012China key laboratory of symbolic computation and knowledge engineering(Ministry of Education) Jilin UniversityChangchun 130012China

1 Introduction Local search method is a rising star for solving combinatorial optimization problems in recent years,and the state-of-the-art local search-based incomplete Maximum Satisfiability(MaxSAT)solversshowpromi... 详细信息

关键词： Max optimization local

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GENERALIZED JACOBI SPECTRAL GALERKIN METHOD FOR FRACTIONAL-ORDER VOLTERRA INTEGRO-DIFFERENTIAL EQUATIONS WITH WEAKLY SINGULAR KERNELS

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Journal of computational Mathematics 2024年第2期42卷 355-371页

作者： Yanping Chen Zhenrong Chen Yunqing Huang School of Mathematical Sciences South China Normal UniversityGuangzhou 510631China Hunan key laboratory for computation and Simulation in Science and engineering Xiangtan UniversityXiangtan 411105China

For fractional Volterra integro-differential equations(FVIDEs)with weakly singular kernels,this paper proposes a generalized Jacobi spectral Galerkin *** basis functions for the provided method are selected generalized Jacobi functions(GJFs),which can be utilized as natural basis functions of spectral methods for weakly singular FVIDEs when appropriately *** developed method's spectral rate of convergence is determined using the L^(∞)-norm and the weighted L^(2)-*** results indicate the usefulness of the proposed method.

关键词： Generalized Jacobi spectral Galerkin method Fractional-order Volterra integ-ro-differential equations Weakly singular kernels Convergence analysis

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Photoionisation detection of a single Er3+ ion with sub-100-ns time resolution

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National Science Review 2024年第4期11卷 38-45页

作者： Yangbo Zhang Wenda Fan Jiliang Yang Hao Guan Qi Zhang Xi Qin Changkui Duan Gabriele G.de Boo Brett C.Johnson Jeffrey C.Mc Callum Matthew J.Sellars Sven Rogge Chunming Yin Jiangfeng Du CAS key laboratory of Microscale Magnetic Resonance and School of Physical Sciences University of Science and Technology of China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei National laboratory University of Science and Technology of China Centre of Excellence for Quantum computation and Communication Technology School of Physics University of New South Wales Centre of Excellence for Quantum computation and Communication Technology School of Engineering RMIT University Centre of Excellence for Quantum computation and Communication Technology School of Physics University of Melbourne Centre of Excellence for Quantum computation and Communication Technology Research School of Physics and Engineering Australian National University

Efficient detection of single optical centres in solids is essential for quantum information processing, sensing and single-photon generation *** this work,we use radio-frequency（RF） reflectometry to electrically detect the photoionisation induced by a single Er3+ion in *** high bandwidth and sensitivity of the RF reflectometry provide sub-100-ns time resolution for the photoionisation *** this technique,the optically excited state lifetime of a single Er3+ion in a Si nano-transistor is measured for the first time to be 0.49±0.04μ*** results demonstrate an efficient approach for detecting a charge state change induced by Er excitation and *** approach could be used for fast readout of other single optical centres in solids and is attractive for large-scale integrated optical quantum systems thanks to the multi-channel RF reflectometry demonstrated with frequency multiplexing techniques.

关键词： single optical centres erbium ions RF reflectometry photoionisation detection

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CONVERGENCE ANALYSIS OF SOME FINITE ELEMENT PARALLEL ALGORITHMS FOR THE STATIONARY INCOMPRESSIBLE MHD EQUATIONS

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Journal of computational Mathematics 2024年第1期42卷 49-70页

作者： Xiaojing Dong Yinnian He Hunan key laboratory for computation and Simulation in Science and engineering Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationSchool of Mathematics and Computational ScienceXiangtan UniversityXiangtan 411105China Institute of Applied Physics and computational Mathematics Beijing 100088China School of Mathematics and Statistics Xi'an Jiaotong UniversityXi'an 710049China

By combination of iteration methods with the partition of unity method(PUM),some finite element parallel algorithms for the stationary incompressible magnetohydrodynamics(MHD)with different physical parameters are presented and *** algorithms are highly *** first,a global solution is obtained on a coarse grid for all approaches by one of the iteration *** parallelized residual schemes,local corrected solutions are calculated on finer meshes with overlapping *** subdomains can be achieved flexibly by a class of *** proposed algorithm is proved to be uniformly stable and ***,one numerical example is presented to confirm the theoretical findings.

关键词： Partition of unity method Local and parallel algorithm Finite element method Iteration methods Magnetohydrodynamics

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Regional 3D geological modeling along metro lines based on stacking ensemble model

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Underground Space 2024年第5期18卷 65-82页

作者： Xia Bian Zhuyi Fan Jiaxing Liu Xiaozhao Li Peng Zhao key laboratory of Ministry of Education for Geomechanics and Embankment engineering Hohai UniversityNanjing 210098China Yunlong Lake laboratory of Deep Underground Science and engineering Xuzhou 221000China State key laboratory for Geomechanics and Deep Underground engineering China University of Mining and TechnologyXuzhou 221000China

This paper presents a regional 3D geological modeling method based on the stacking ensemble technique to overcome the challenges of sparse borehole data in large-scale linear underground *** proposed method transforms the 3D geological modeling problem into a stratigraphic property classification problem within a subsurface space grid cell *** data is pre-processed and trained using stacking method with five different machine learning *** resulting modelled regional cells are then classified,forming a regional 3D grid geological model.A case study for an area of 324 km2 along Xuzhou metro lines is presented to demonstrate the effectiveness of the proposed *** study shows an overall prediction accuracy of 85.4%.However,the accuracy for key stratigraphy layers influencing the construction risk,such as karst carve strata,is only 4.3%due to the limited borehole *** address this issue,an oversampling technique based on the synthetic minority oversampling technique(SMOTE)algorithm is *** technique effectively increases the number of sparse stratigraphic samples and significantly improves the prediction accuracy for karst caves to 65.4%.Additionally,this study analyzes the impact of sampling distance on model *** is found that a lower sampling interval results in higher prediction accuracy,but also increases computational resources and time ***,in this study,an optimal sampling distance of 1 m is chosen to balance prediction accuracy and computation ***,the number of geological strata is found to have a negative effect on prediction *** mitigate this,it is recommended to merge less significant stratigraphy layers,reducing computation *** key strata layers,such as karst caves,which have a significant impact on construction risk,further onsite sampling or oversampling using the SMOTE technique is recommended.

关键词： 3D geological modeling Borehole data Stacking Machine learning

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Measuring drug similarity using drug–drug interactions

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Quantitative Biology 2024年第2期12卷 164-172页

作者： Ji Lv Guixia Liu Yuan Ju Houhou Huang Ying Sun College of Computer Science and Technology Jilin UniversityChangchunChina key laboratory of symbolic computation and knowledge engineering of Ministry of Education Jilin UniversityChangchunChina Sichuan University Library Sichuan UniversityChengduChina College of Chemistry Jilin UniversityChangchunChina Department of Respiratory Medicine The First Hospital of Jilin UniversityChangchunChina

Combination therapy is a promising approach to address the challenge of antimicrobial resistance,and computational models have been proposed for predicting drug–drug *** existing models rely on drug similarity measures based on characteristics such as chemical structure and the mechanism of *** this study,we focus on the network structure itself and propose a drug similarity measure based on drug–drug interaction *** explore the potential applications of this measure by combining it with unsupervised learning and semi-supervised learning *** unsupervised learning,drugs can be grouped based on their interactions,leading to almost monochromatic group–group *** addition,drugs within the same group tend to have similar mechanisms of action(MoA).In semi-supervised learning,the similarity measure can be utilized to construct affinity matrices,enabling the prediction of unknown drug–drug *** method exceeds existing approaches in terms of ***,our experiments demonstrate the effectiveness and practicability of the proposed similarity *** the one hand,when combined with clustering algorithms,it can be used for functional annotation of compounds with unknown *** the other hand,when combined with semi-supervised graph learning,it enables the prediction of unknown drug–drug interactions.

关键词： drug similarity drug–drug interactions drug combinations synergy effect clustering semi-supervised learning

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