The soot formation model based on inverse ethylene diffusion flames was performed to study the sensitivity of the soot formation process to the prediction *** effects of efficiency parameters such as soot inception,su...
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The soot formation model based on inverse ethylene diffusion flames was performed to study the sensitivity of the soot formation process to the prediction *** effects of efficiency parameters such as soot inception,surface growth and coagulation on the simulation results were studied by using the adjustable efficiency *** addition,the reversible soot model and conjugate heat transfer(CHT)model were also introduced to explore their *** indicated that,among adjustable efficiency parameters,the nucleation efficiency had the greatest influence on the predicted soot and PAHs distributions,while the Habstraction-C2H2-addition(HACA)process and PAH adsorption surface growth efficiencies impacted *** adjustable efficiency parameters had a significant effect on the concentration of soot gaseous precursors and soot particles,but their effects on temperature,gas phase molecules,and intermediate species were not *** the nucleation efficiency increased from 2×10^(-6)to 1×10^(-4),the predicted value of the integrated soot was increased by nearly 50%,and the maximum primary particle number density and the number of aggregates were increased by an order of *** maximum concentration of BAPYR was ***,the peak temperature along the axial direction increased by only 3.5 *** the reversible soot model,the approximation results of the adjustable efficiency parameters could be modified,which showed the feasibility of the *** use of the CHT model promoted pyrolysis of the fuel below the outlet of the fuel tube,with high-temperature zones,soot zones,and PAHs zones moving towards higher flame ***,when using the reversible model and the CHT model,the maximum soot volume fraction decreased by 39%compared with the basic efficiency parameters,while the concentration of BAPYR increased by 162%,and the concentrations of gas phase species were decreased.
This paper will improve the fundamental ant colony optimization algorithm in the context of mobile robot path planning in response to its flaws, which include easy descent into a local optimum, a large number of infle...
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To address the issues of unknown target size,blurred edges,background interference and low contrast in infrared small target detection,this paper proposes a method based on density peaks searching and weighted multi-f...
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To address the issues of unknown target size,blurred edges,background interference and low contrast in infrared small target detection,this paper proposes a method based on density peaks searching and weighted multi-feature local ***,an improved high-boost filter is used for preprocessing to eliminate background clutter and high-brightness interference,thereby increasing the probability of capturing real targets in the density peak ***,a triple-layer window is used to extract features from the area surrounding candidate targets,addressing the uncertainty of small target *** calculating multi-feature local differences between the triple-layer windows,the problems of blurred target edges and low contrast are *** balance the contribution of different features,intra-class distance is used to calculate weights,achieving weighted fusion of multi-feature local differences to obtain the weighted multi-feature local differences of candidate *** real targets are then extracted using the interquartile *** on datasets such as SIRST and IRSTD-IK show that the proposed method is suitable for various complex types and demonstrates good robustness and detection performance.
Graph structure expression plays a vital role in distinguishing various graphs. In this work, we propose a structure-sensitive graph dictionary embedding (SS-GDE) framework to transform input graphs into the embedding...
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Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both enti...
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Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.
Code review is a critical process in software development, contributing to the overall quality of the product by identifying errors early. A key aspect of this process is the selection of appropriate reviewers to scru...
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Code review is a critical process in software development, contributing to the overall quality of the product by identifying errors early. A key aspect of this process is the selection of appropriate reviewers to scrutinize changes made to source code. However, in large-scale open-source projects, selecting the most suitable reviewers for a specific change can be a challenging task. To address this, we introduce the Code Context Based Reviewer Recommendation (CCB-RR), a model that leverages information from changesets to recommend the most suitable reviewers. The model takes into consideration the paths of modified files and the context derived from the changesets, including their titles and descriptions. Additionally, CCB-RR employs KeyBERT to extract the most relevant keywords and compare the semantic similarity across changesets. The model integrates the paths of modified files, keyword information, and the context of code changes to form a comprehensive picture of the changeset. We conducted extensive experiments on four open-source projects, demonstrating the effectiveness of CCB-RR. The model achieved a Top-1 accuracy of 60%, 55%, 51%, and 45% on the Android, OpenStack, QT, and LibreOffice projects respectively. For Mean Reciprocal Rank (MRR), CCB achieved 71%, 62%, 52%, and 68% on the same projects respectively, thereby highlighting its potential for practical application in code reviewer recommendation.
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.
Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multip...
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The lightest uranium isotope^(214)U has been produced at the Heavy lon Research Facility in Lanzhou,*** a-decay reduced width($2)of^(214)U has been found to be significantly larger than those of other nuclei by a fact...
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The lightest uranium isotope^(214)U has been produced at the Heavy lon Research Facility in Lanzhou,*** a-decay reduced width($2)of^(214)U has been found to be significantly larger than those of other nuclei by a factor of ***,the extraction ofσ^(2)depends on the penetration probability(P)through the barrier,and P is related to the theoretical method of obtaining it and the form of theα-core *** aim of this study is to investigate whether the selections of theα-core potential and the method of calculating P can affect the above *** different phenomenological a-core potentials and two microscopic double-folding potentials,together with the Wentzel-Kramers-Brillouin(WKB)approximation and transfer matrix(TM)approach,are used to obtain *** value of P obtained using the WKB approximation is about 20%-40%smaller than the one obtained using the TM ***,the deduced s2 is *** choice of a-core potential can significantly affect the value ofσ^(2).With the spherical form for theα-core potentials,theσ^(2)of^(214)U obtained with both the WKB and TM approaches are about twice as large as those of the surrounding *** the deformed double-folding potential,the ratio between 32 of^(214)U and that of the surrounding nuclei is observed to be slightly belowσ^(2).The effects of nuclear deformation and theα-core potential should be considered when studying theα-decay reduced width in the NpNn systematics.
For a sub-connected hybrid multiple-input multiple-output(MIMO) receiver with K subarrays and N antennas, there exists a challenging problem of how to rapidly remove phase ambiguity in only single time-slot. A directi...
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For a sub-connected hybrid multiple-input multiple-output(MIMO) receiver with K subarrays and N antennas, there exists a challenging problem of how to rapidly remove phase ambiguity in only single time-slot. A direction of arrival(DOA) estimator of maximizing received power(Max-RP) is proposed to find the maximum value of K-subarray output powers, where each subarray is in charge of one sector, and the center angle of the sector corresponding to the maximum output is the estimated true DOA. To make an enhancement on precision, Max-RP plus quadratic interpolation(Max-RP-QI) method is designed. In the proposed Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three DOA values corresponding to the largest three receive powers of *** achieve the Cramer Rao lower bound, a Root-MUSIC plus Max-RP-QI scheme is developed. Simulation results show that the proposed three methods eliminate the phase ambiguity during one time-slot and also show low computational complexities. The proposed Root-MUSIC plus Max-RP-QI scheme can reach the Cramer Rao lower bound,and the proposed Max-RP and Max-RP-QI are still some performance losses 2–4 d B compared to the Cramer Rao lower bound.
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