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 ...
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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 0.1.%compared to other state-of-the-art network,while using data from fewer sensors.
A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing *** method achieves precise...
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A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing *** method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1.norm minimization regularization techniques to control model complexity and optimize the inference process of variable *** the experiment of game network reconstruction,when the success rate of the L1.norm minimization model’s existence connection reconstruction reaches 1.0%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1.normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 1..2 ***,in the case of a signal-to-noise ratio of 1. dB,the L1.model achieves a 1.0% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent *** the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 *** mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.
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 *** ...
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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.
Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies have attempted to combine meta-learning ...
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Accurate intervertebral disc image segmentation is necessary for further treatment. However, existing methods are difficult to segment due to the intensity inhomogeneity of intervertebral disc MRI images and the simil...
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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...
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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)solversshowpromisingperformance even competitive to many complete solvers in recent MaxSAT Evaluations.
In description logic,axiom pinpointing is used to explore defects in ontologies and identify hidden justifications for a logical *** recent years,SAT-based axiom pinpointing techniques,which rely on the enumeration of...
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In description logic,axiom pinpointing is used to explore defects in ontologies and identify hidden justifications for a logical *** recent years,SAT-based axiom pinpointing techniques,which rely on the enumeration of minimal unsatisfiable subsets(MUSes)of pinpointing formulas,have gained increasing *** with traditional Tableau-based reasoning approaches,SAT-based techniques are more competitive when computing justifications for consequences in large-scale lightweight description logic *** this article,we propose a novel enumeration justification algorithm,working with a replicated *** replicated driver discovers new justifications from the explored justifications through cheap literals resolution,which avoids frequent calls of SAT ***,when the use of SAT solver is inevitable,we adjust the strategies and heuristic parameters of the built-in SAT solver of axiom pinpointing *** adjusted SAT solver is able to improve the checking efficiency of unexplored *** proposed method is implemented as a tool named *** experimental results show that RDMinA outperforms the existing axiom pinpointing tools on practical biomedical ontologies such as Gene,Galen,NCI and Snomed-CT.
Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing ***,it is essential to detect prenatal depression early and conduct an attribution...
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Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing ***,it is essential to detect prenatal depression early and conduct an attribution *** studies have used questionnaires to screen for prenatal depression,but the existing methods lack *** 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 *** can quantitatively determine the relationship and patterns between options and *** first quantifies options and resorts them,gathering options with little difference,since Word2Vec is highly dependent on *** resort task is transformed into an optimization problem involving the traveling salesman ***,all questionnaire samples are used to train the options’vector using ***,an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from *** verify the model,we compare it with other deep learning and traditional machine learning *** experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1.score of *** most relevant factors of depression found by SEOE are also verified in the *** addition,our model is of low {1.al complexity and strong generalization,which can be widely applied to other questionnaire analyses of psychiatric disorders.
Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains. However,most existing meth...
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Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains. However,most existing methods only aim at learning network dynamic behaviors generated by a specific ordinary differential equation instance, resulting in ineffectiveness for new ones, and generally require dense *** observed data, especially from network emerging dynamics, are usually difficult to obtain, which brings trouble to model learning. Therefore, learning accurate network dynamics with sparse, irregularly-sampled,partial, and noisy observations remains a fundamental challenge. We introduce a new concept of the stochastic skeleton and its neural implementation, i.e., neural ODE processes for network dynamics(NDP4ND), a new class of stochastic processes governed by stochastic data-adaptive network dynamics, to overcome the challenge and learn continuous network dynamics from scarce observations. Intensive experiments conducted on various network dynamics in ecological population evolution, phototaxis movement, brain activity, epidemic spreading, and real-world empirical systems, demonstrate that the proposed method has excellent data adaptability and computational efficiency, and can adapt to unseen network emerging dynamics, producing accurate interpolation and extrapolation with reducing the ratio of required observation data to only about 6% and improving the learning speed for new dynamics by three orders of magnitude.
Skeleton-based action recognition has long been a fundamental and intriguing problem in machine intelligence. This task is challenging due to pose occlusion and rapid motion, which typically results in incomplete or n...
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