A multiple-pattern complex event matching model based on merge sharing for massive event streams

2021 ◽  
pp. 2150025
Author(s):  
Jianhua wang ◽  
Junhe Liu ◽  
Feng Lin ◽  
Jing Zhao ◽  
Yongbing Long ◽  
...  
Keyword(s):  
Pattern Matching ◽  
Experimental Results ◽  
Model Construction ◽  
Matching Model ◽  
Model Based ◽  
Single Pattern ◽  
Complex Events ◽  
Matching Models

Quickly matching the related primitive events for multiple complex events from the massive event streams usually faces with a great challenge due to the single-pattern characteristics of the existing complex event matching models. Aiming to solve the problem, a multiple-pattern complex event matching model based on merge sharing is proposed in this paper. The achievement of the paper lies in the fact that a multiple-pattern complex event matching model based on merge sharing is presented to successfully realize the quick matching of related primitive events for multiple complex events from the massive event streams. Specifically, in our scheme, we successfully use merge sharing technology to merge all the same prefixes, suffixes or subpatterns existing in single-pattern matching models into shared ones and to construct a multiple-pattern complex event matching model. As a result, our proposed matching model in this paper can effectively solve the above problem. The experimental results show that our proposed matching model in this paper outperforms the existing single-pattern matching models in model construction and related events matching for massive event streams.

Forecasting Short-Term Oil Price with a Generalised Pattern Matching Model Based on Empirical Genetic Algorithm

2018 ◽  
Vol 55 (4) ◽  
pp. 1151-1169 ◽  
Author(s):  
Lu-Tao Zhao ◽  
Guan-Rong Zeng ◽  
Ling-Yun He ◽  
Ya Meng
Keyword(s):  
Genetic Algorithm ◽  
Pattern Matching ◽  
Oil Price ◽  
Short Term ◽  
Matching Model ◽  
Model Based

scholarly journals Generalized analytical model based on harmonic coupling for hybrid plasmonic modes: comparison with numerical and experimental results

2015 ◽  
Vol 23 (21) ◽  
pp. 27376 ◽  
Author(s):  
Mitradeep Sarkar ◽  
Jean-François Bryche ◽  
Julien Moreau ◽  
Mondher Besbes ◽  
Grégory Barbillon ◽  
...  
Keyword(s):  
Analytical Model ◽  
Experimental Results ◽  
Model Based

scholarly journals Research of Personalized Recommendation Technology Based on Knowledge Graphs

2021 ◽  
Vol 11 (15) ◽  
pp. 7104
Author(s):  
Xu Yang ◽  
Ziyi Huan ◽  
Yisong Zhai ◽  
Ting Lin
Keyword(s):  
Neural Network ◽  
Hot Spot ◽  
Experimental Results ◽  
Graph Representation ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Learning Technology ◽  
Data Set ◽  
Model Based ◽  
Knowledge Graphs

Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs(KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.

An Analysis of Conventional and Self-Aligned four Terminal Resistor Structures for The Determination of The Contact Resistivity from End Resistance Measurements

1986 ◽  
Vol 71 ◽  
Author(s):  
I. Suni ◽  
M. Finetti ◽  
K. Grahn
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Computer Model ◽  
Contact Resistivity ◽  
Experimental Results ◽  
The Finite Element Method ◽  
Model Based ◽  
A Value ◽  
And Diffusion

AbstractA computer model based on the finite element method has been applied to evaluate the effect of the parasitic area between contact and diffusion edges on end resistance measurements in four terminal Kelvin resistor structures. The model is then applied to Al/Ti/n+ Si contacts and a value of contact resistivity of Qc = 1.8×10−7.Ωcm2 is derived. For comparison, the use of a self-aligned structure to avoid parasitic effects is presented and the first experimental results obtained on Al/Ti/n+Si and Al/CoSi2/n+Si contacts are shown and discussed.

scholarly journals Person Reidentification Model Based on Multiattention Modules and Multiscale Residuals

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yongyi Li ◽  
Shiqi Wang ◽  
Shuang Dong ◽  
Xueling Lv ◽  
Changzhi Lv ◽  
...  
Keyword(s):  
Local Features ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Original Network ◽  
Fine Grained ◽  
Backbone Network ◽  
Model Based ◽  
Local Branch ◽  
Feature Expression ◽  
Global And Local

At present, person reidentification based on attention mechanism has attracted many scholars’ interests. Although attention module can improve the representation ability and reidentification accuracy of Re-ID model to a certain extent, it depends on the coupling of attention module and original network. In this paper, a person reidentification model that combines multiple attentions and multiscale residuals is proposed. The model introduces combined attention fusion module and multiscale residual fusion module in the backbone network ResNet 50 to enhance the feature flow between residual blocks and better fuse multiscale features. Furthermore, a global branch and a local branch are designed and applied to enhance the channel aggregation and position perception ability of the network by utilizing the dual ensemble attention module, as along as the fine-grained feature expression is obtained by using multiproportion block and reorganization. Thus, the global and local features are enhanced. The experimental results on Market-1501 dataset and DukeMTMC-reID dataset show that the indexes of the presented model, especially Rank-1 accuracy, reach 96.20% and 89.59%, respectively, which can be considered as a progress in Re-ID.

The Onset of Whirl Instability in Journal Bearings of Various Bore Shapes and Groove Sizes

1983 ◽  
Vol 105 (3) ◽  
pp. 342-352 ◽  
Author(s):  
M. Akko¨k ◽  
C. M. McC. Ettles
Keyword(s):  
Beneficial Effect ◽  
Load Capacity ◽  
Journal Bearings ◽  
Experimental Results ◽  
General Validity ◽  
Model Based ◽  
Linearized Model ◽  
Varying Viscosity

Experimental results are given for load capacity and whirl onset in journal bearings of circular, elliptical and offset halves bore shape. The general validity of the linearized model for predicting whirl is confirmed experimentally. Deviations between experimental results and the model, based on an isoviscous film, are attributed to the varying viscosity that occurs in practice, and to unavoidable excitation that gives rise to premature whirl. It is shown that increasing groove size has a destabilizing effect that can more than cancel the beneficial effect of preloading. This result is particularly relevant to the design of journal bearings in turbomachinery.

Color-matching model of digital rotor spinning viscose mélange yarn based on the Kubelka–Munk theory

2021 ◽  
pp. 004051752110408
Author(s):  
Ruihua Yang ◽  
Chuang He ◽  
Bo Pan ◽  
Zhuo Wang
Keyword(s):  
Least Squares ◽  
Color Difference ◽  
Test Sample ◽  
Experimental Results ◽  
Color Matching ◽  
Matching Model ◽  
K Value ◽  
Rotor Spinning ◽  
Viscose Fibers ◽  
Relative Value

The color-matching model is conducive to expanding the scope of application of colorful fabrics and can speed up the achievement of intelligent production. To solve the problem in which the existing color-matching system of intelligent colored spun yarn cannot be applied to the digital rotor-spinning products of dope dyed viscose fiber, 66 types of mélange yarn were spun with a digital rotor-spinning frame using red, yellow, and blue dope dyed viscose fibers at a ratio gradient of 10%. Furthermore, the knitted fabric samples were produced using a circular machine. Meanwhile, a Datacolor 650 spectrophotometer was used for color testing, and the experimental results were recorded. Based on the color-matching model of the Kubelka–Munk theory, a color-matching model is built based on the experimental results. In addition, the accuracy of the model was analyzed and verified using the least-squares and relative value methods. The results show that, compared with the relative value method, the color-matching model constructed using the absorption coefficient K value and scattering coefficient S value calculated based on the least-squares approach is more accurate. The error between the predicted ratio of the test sample and the actual ratio was only 0.0979, the average color difference was only 0.465, and there were no visible differences between the predicted color of the sample and the actual color.

scholarly journals A steering model for articulated tracked vehicle considering soil deformation on track–soil interaction

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880270 ◽  
Author(s):  
Yu Yao ◽  
Kai Cheng ◽  
Bangcheng Zhang ◽  
Jinhua Lin ◽  
Dawei Jiang ◽  
...  
Keyword(s):  
Experimental Results ◽  
Simulation Studies ◽  
Soil Deformation ◽  
Tracked Vehicle ◽  
Tracked Vehicles ◽  
Model Based ◽  
Theoretical Results ◽  
Strong Agreement

With the advantage of steering performance, articulated tracked vehicles have excellent mobility in off-road application. However, in current models for steering performance, soil deformation on the interaction between track and soil cannot always be taken into account. Therefore, steering performance cannot always be calculated accurately. In order to solve the problem, it is essential to propose a steering model which can take the effect of soil deformation on track–soil interaction into consideration. In this article, a steering model of articulated tracked vehicle is proposed on track–soil interaction. Moreover, in order to improve steering performance, a track–soil sub-model is developed that can consider soil deformation on track–soil interaction. Using this steering model based on track–soil sub-model, steering performance can be calculated more accurately. Simulation studies and experimental results are in strong agreement with the theoretical results in this article. The results show that equipped with the track–soil sub-model, the proposed steering model can be used to accurately predict steering performance. The steering model of articulated tracked vehicle proposed in this article can provide a basis for other similar vehicles.

scholarly journals Exploiting the Relationship between Pruning Ratio and Compression Effect for Neural Network Model Based on TensorFlow

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Bo Liu ◽  
Qilin Wu ◽  
Yiwen Zhang ◽  
Qian Cao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Accuracy ◽  
Computing Time ◽  
Experimental Results ◽  
Data Set ◽  
Compression Effect ◽  
Model Based ◽  
The Relationship

Pruning is a method of compressing the size of a neural network model, which affects the accuracy and computing time when the model makes a prediction. In this paper, the hypothesis that the pruning proportion is positively correlated with the compression scale of the model but not with the prediction accuracy and calculation time is put forward. For testing the hypothesis, a group of experiments are designed, and MNIST is used as the data set to train a neural network model based on TensorFlow. Based on this model, pruning experiments are carried out to investigate the relationship between pruning proportion and compression effect. For comparison, six different pruning proportions are set, and the experimental results confirm the above hypothesis.

Sign in / Sign up

Export Citation Format

Share Document