Entity recognition is an essential component of knowledge representation and knowledge extraction research. To enhance military situation awareness through the construction of a knowledge graph, this paper presents a ...
Entity recognition is an essential component of knowledge representation and knowledge extraction research. To enhance military situation awareness through the construction of a knowledge graph, this paper presents a novel method, BERTATT_POSBiLSTMLSTMCRF, which is based on the traditional entity recognition model BERT_BiLSTM_CRF. The local location information and the impact of the entity's position in the sentence on the entity recognition task are both fully considered by introducing the attention mechanism. Additionally, an LSTM layer is added after the BiLSTM layer to deal with long-distance label dependencies while improving the model's ability to recognize long entities. Comparative experiments demonstrate that the improved model proposed in this paper is effective in entity recognition with Wikipedia data.
The non-uniform distribution of smoke and laser spot seriously limits the imaging ability of single-photon lidar through smoke. To this end, based on the collision theory between photons and smoke particles, this pape...
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Aiming at the task of detecting ships on the sea surface in remote sensing images, there are a lot of disturbances such as variable object sizes, cloud occlusion, complex image backgrounds, and different ship orientat...
Aiming at the task of detecting ships on the sea surface in remote sensing images, there are a lot of disturbances such as variable object sizes, cloud occlusion, complex image backgrounds, and different ship orientations, etc. In this paper, we propose a ship rotating object detection network based on the improved YOLOv5, which is constructed through the strategies of introducing Swin Transformer as a feature extraction network to enhance the feature extraction performance of the network, introducing a rotating detection head to realize the detection of the rotation angle, and modifying the network loss function to accelerate the convergence of the network. The network finally achieves a 71.7% map on ShipRSImageNet validation set, which is an improvement of 2.4% compared with the original network model. The network proposed in this paper solves the problem that the YOLOv5 algorithm is unable to detect rotating objects, and the network based on the self-attention mechanism is used to further enhance the ability to detect small objects. Finally, a ship object detector that can be used in real remote sensing satellite images is obtained.
The inverse heat transfer method is an effective inversion algorithm for solving the heat transfer at non-measurable places of the study object. However, the current inverse heat transfer inversion method has the prob...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
The inverse heat transfer method is an effective inversion algorithm for solving the heat transfer at non-measurable places of the study object. However, the current inverse heat transfer inversion method has the problems of low accuracy and difficult selection of temperature measurement point locations. To solve this problem, this paper proposes an optimization algorithm for measurement point locations based on improved fuzzy c-means clustering(IFCM). The method first generates the initial clustering center through a specific density function and introduces the Mahalanobis distance to solve the problem that the traditional fuzzy c-means clustering results depend on the initial clustering center and the results are unstable. Then, an optimized deployment algorithm based on IFCM temperature measurement points is proposed and compared with other mean value clustering algorithms. Finally, the optimal temperature measurement position on the crystal surface is selected by the IFCM temperature measurement point optimization deployment algorithm on the CZ(Czochraski)silicon single crystal growth equipment, and the inversion results of the randomly selected temperature measurement point scheme and the optimized temperature measurement point scheme are compared by the real-time inversion algorithm based on the dynamic matrix control multi-boundary heat flux, and the results of the optimized temperature measurement point scheme are obtained to be better than that of the randomly selected temperature measurement point scheme.
Oriented towards the requirements for reliable positioning and navigation of aircraft under the condition of rejection of navigation satellite, we proposed cross-domain guide positioning methods based on multi-layer n...
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Aiming at the optimal motion planning problem of the 6-DOF manipulator in Cartesian space, a motion planning method based on the hybrid bat algorithm (CPTDBA) and 4-3-4 piecewise polynomial interpolation is proposed t...
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Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault *** this paper,we first introduce RCMDE into the fie...
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Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault *** this paper,we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise,and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor(KNN),termed *** results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise,and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy(MPE)and KNN,multi-scale weighted-permutation entropy(MW-PE)and KNN,and multi-scale dispersion entropy(MDE)and KNN,termed MPE-KNN,MW-PE-KNN,and *** is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective,and can obtain a very high recognition rate.
Low-light enhancement task is an essential component of computer low-level visual tasks, which involves processing images captured under dim lighting conditions to make them appear as if they were taken under normal i...
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This paper addresses the stabilization of linear systems with multiple time-varying input delays. In scenarios where neither the exact delays information nor their bound is known, we propose a class of linear time-var...
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*** Artificial intelligence(AI)is a rapidly growing field of technol-ogy,which“will enliven inert objects,much as electricity did more than a century *** that we formerly electrified will now cognitize”[1].AI advanc...
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*** Artificial intelligence(AI)is a rapidly growing field of technol-ogy,which“will enliven inert objects,much as electricity did more than a century *** that we formerly electrified will now cognitize”[1].AI advances are constantly pushing the frontier of what machines can *** attention is being placed on AI research,as well as its development and deployment by commer-cial investors,defense strategists,and policy makers[2].
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