The function or performance of a network is strongly dependent on its robustness, quantifying the ability of the network to continue functioning under perturbations. While a wide variety of robustness metrics have bee...
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A constant force control method of the robot based on a force sensor is proposed for the difficult point of high difficulty and low accuracy of ceramic surface flatness inspection. This paper proposed a method that co...
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Dielectric elastomer sensor (DES) is a flexible sensor that can perform free bending deformation, thus it has broad application prospects in the fields of medical electronics, wearable devices, soft robots, etc. Previ...
Dielectric elastomer sensor (DES) is a flexible sensor that can perform free bending deformation, thus it has broad application prospects in the fields of medical electronics, wearable devices, soft robots, etc. Previous studies have mostly focused on exploring the static sensing characteristics of the DES. Considering that the DES may be used in dynamic conditions, it is very meaningful to study its dynamic sensing characteristics to broaden its application ranges. In this paper, a dynamic sensing model of the DES is established based on the gate recurrent unit (GRU) neural network. Firstly, the structure of the DES and the construction of the experimental system are introduced. In addition, the dynamic sensing characteristics of the DES are analyzed by conducting several sets of experiments, which shows that the DES has significant rate-dependent hysteresis nonlinearities, multivalued mapping and memory characteristics. After that, the dynamic sensing model of the DES is built based on the GRU neural network to describe the above dynamic sensing characteristics. Next, the dynamic displacement and force sensing models of the DES are trained according to the dynamic displacement-capacitance and dynamic force-capacitance experimental data, respectively. Finally, several experiments are performed to verify the effectiveness and generalization ability of the established dynamic sensing model.
Extraction of relations among entities from texts is critical for domain knowledge representation. In this paper, an association graph was constructed to represent the dependencies among entities and relations, upon w...
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ISBN:
(纸本)9781665492331
Extraction of relations among entities from texts is critical for domain knowledge representation. In this paper, an association graph was constructed to represent the dependencies among entities and relations, upon which a relation extraction method was proposed to predict the relations in domain texts. Experimental results on various annotated domain datasets demonstrate that the recall of our proposed method outperforms the other relation extraction models.
Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of syno...
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Grid-forming inverter is widely used in grid-connected systems of distributed generation because of its frequency and voltage support capacity and good stability in microgrid, but its large inertia will affect the dyn...
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This paper is concerned with H performance analysis of aperiodic sampled-data systems by employing the input delay ***,a novel augmented looped-functional(ALF) is presented under the double-side looped-functional te...
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This paper is concerned with H performance analysis of aperiodic sampled-data systems by employing the input delay ***,a novel augmented looped-functional(ALF) is presented under the double-side looped-functional technique,which considers coupled relationships among state vectors more fully than the current ***,by using the BesselLegendre inequality to estimate the derivative of the ALF,a stability criterion is derived in the form of linear matrix ***,a numerical example demonstrates the effectiveness and the advantages of the proposed criterion by comparing it with the existing ones.
As a representative topic in natural language processing and automated theorem proving, geometry problem solving requires an abstract problem understanding and symbolic reasoning. A major challenge here is to find a f...
As a representative topic in natural language processing and automated theorem proving, geometry problem solving requires an abstract problem understanding and symbolic reasoning. A major challenge here is to find a feasible reasoning sequence that is consistent with given axioms and the theorems already proved. Most recent methods have exploited neural network-based techniques to automatically discover eligible solving steps. Such a kind of methods, however, is greatly impacted by the expert solutions for training. To improve the accuracy, this paper proposes a new method called counterfactual evolutionary reasoning, which uses a generative adversarial network to generate initial reasoning sequences and then introduces counterfactual reasoning to explore potential solutions. By directly exploring theorem candidates rather than the neural network selection, the new method can sufficiently extend the searching space to get a more appropriate reasoning step. Through comparative experiments on the recent proposed Geometry3k, the largest geometry problem solving dataset, our method generally achieves a higher accuracy than most previous methods, bringing an overall improvement about 4.4% compared with the transformer models.
This paper proposes an output-feedback based prescribed performance consensus tracking control methodology for a class of heterogeneous multi-agent systems (HMASs) with inconsistent system structure, where the perform...
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Surface defect detection of sanitary ceramic products is an important part of the production *** deep learning method is the mainstream research direction in the field of defect *** there are many small defects on the...
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Surface defect detection of sanitary ceramic products is an important part of the production *** deep learning method is the mainstream research direction in the field of defect *** there are many small defects on the surface of sanitary ceramic products,it is an effective means to use a high-resolution camera to obtain *** detection speed of high-resolution pictures and the interference of background are the difficult problems in ***,this paper designs a two-scale detection framework to detect and locate defects of sanitary ceramics utilizing high-resolution *** two-scale detection framework speeds up the detection of high-resolution images and weakens the interference of the background by dividing the defect detection task into two parts,a workpiece region recognition task on large scale and a defect detection task on small *** experiments show that the two-scale detection method has a better detection speed and accuracy rate compared with the single-scale method,which proves the superiority of the proposed method.
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