After emergencies occur, decision-makers can reference historical cases with similar causes to take similar emergency response measures. However, information about emergencies is usually recorded and stored in textual...
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After emergencies occur, decision-makers can reference historical cases with similar causes to take similar emergency response measures. However, information about emergencies is usually recorded and stored in textual form, and it is difficult for decision-makers to obtain effective information from large amounts of textual data and make decisions that balance various factors. To address these issues, this paper presents an emergency assisted decision-making model based on a knowledge graph and many-objective optimization. First, we preprocess the information by extracting entities to construct a knowledge graph of emergencies to make the event information more structured and easier to use. A knowledge graph is also used to narrow the range of matching historical events. Second, we construct a many-objective model with four objectives: similarity, diversity, processing time, and resource cost. Finally, combining the model characteristics, we design genetic operations for duplicate location matching and substitution removal strategies to obtain the nonrepetitive VaEA algorithm. This algorithm is used to optimize the model and generate a list of reference cases in the reduced matching range to provide rescue strategy suggestions for the current situation. The experimental results show that the algorithm outperforms the comparison algorithms under all four evaluation metrics. This indicates that the method in this paper can match the higher-quality historical emergency solutions applicable to the current decision-making situation in the case base and provide support for decision-makers to respond reasonably in emergencies.
This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection *** Strengths,Weaknesses,Opportunities,Threats(SWOT)ana...
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This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection *** Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data *** model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT *** model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample *** data is used to retain more semantic information to generate *** model was applied to species in Southern California,USA,citing SWOT analysis data to train the *** show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development *** model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data *** study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.
Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework f...
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Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.
The adjoint reversal of long evolutionary calculations (e.g. loops), where each iteration depends on the output of the previous iteration, is a common occurrence in computational engineering (e.g. computational fluid ...
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Lattice Boltzmann method (LBM) has become a powerful method in computational fluid dynamics and has drawn more and more attention in high-performance computing due to its particulate nature and local dynamics, especia...
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Information Extraction (IE) plays a crucial role in Natural Language Processing (NLP) by extracting structured information from unstructured text, thereby facilitating seamless integration with various real-world appl...
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Mapping of data between nonmatching meshes is a key ingredient of multiphysics simulations. Black-box data mapping, which only operates on clouds of mesh vertices without connectivity, enables modular software environ...
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Mapping of data between nonmatching meshes is a key ingredient of multiphysics simulations. Black-box data mapping, which only operates on clouds of mesh vertices without connectivity, enables modular software environments. In this paper, we develop such a black-box approach that is capable of handling very large data sets on parallel systems. More precisely, we implement partition-of-unity radial-basis-function interpolation into the coupling library preCICE. The method tackles the data mapping problem by decomposing it into smaller, independent subproblems, which makes it well-suited for parallel computing. To this end, we develop a tailor-made clustering algorithm and study numerical details to ensure robustness and accuracy. We, moreover, deduce user-friendly mapping parameters for which we determine robust default values. Tests on real-world geometries show that the method is scalable and orders of magnitude more efficient than previous data mapping in preCICE. Consequently, the implementation greatly extends the applicability of preCICE, benefiting the library's large user community.
This research focuses on the predictive analysis of ChiNext and blue-chip stocks in the Chinese stock market, employing the random forest model to conduct in-depth research on the stock trends of these two sectors. In...
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Benefiting from Pre-trained Language Model (PLM), Event Argument Extraction (EAE) methods have achieved SOTA performance in general scenarios of Event Extraction (EE). However, with increasing concerns and regulations...
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Byzantine-robust distributed learning (BRDL), in which computing devices are likely to behave abnormally due to accidental failures or malicious attacks, has recently become a hot research topic. However, even in the ...
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