Model‐based towed array processor: Experimental results

2000 ◽  
Vol 107 (5) ◽  
pp. 2890-2890
Author(s):  
Edmund J. Sullivan ◽  
Dieter Brecht ◽  
Leif Persson
Keyword(s):  
Experimental Results ◽  
Array Processor ◽  
Model Based ◽  
Towed Array

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.

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.

Model-Based Multi-Objective Reinforcement Learning by a Reward Occurrence Probability Vector

2020 ◽  
pp. 269-295
Author(s):  
Tomohiro Yamaguchi ◽  
Shota Nagahama ◽  
Yoshihiro Ichikawa ◽  
Yoshimichi Honma ◽  
Keiki Takadama
Keyword(s):  
Reinforcement Learning ◽  
Experimental Results ◽  
Previous Model ◽  
Pareto Optimal ◽  
Occurrence Probability ◽  
Contrast Model ◽  
Multi Objective ◽  
Model Based ◽  
Model Free ◽  
Optimal Policies

This chapter describes solving multi-objective reinforcement learning (MORL) problems where there are multiple conflicting objectives with unknown weights. Previous model-free MORL methods take large number of calculations to collect a Pareto optimal set for each V/Q-value vector. In contrast, model-based MORL can reduce such a calculation cost than model-free MORLs. However, previous model-based MORL method is for only deterministic environments. To solve them, this chapter proposes a novel model-based MORL method by a reward occurrence probability (ROP) vector with unknown weights. The experimental results are reported under the stochastic learning environments with up to 10 states, 3 actions, and 3 reward rules. The experimental results show that the proposed method collects all Pareto optimal policies, and it took about 214 seconds (10 states, 3 actions, 3 rewards) for total learning time. In future research directions, the ways to speed up methods and how to use non-optimal policies are discussed.

Speech Enhancement Using Heterogeneous Information

2018 ◽  
Vol 10 (3) ◽  
pp. 46-59
Author(s):  
Yan Xiong ◽  
Fang Xu ◽  
Qiang Chen ◽  
Jun Zhang
Keyword(s):  
Speech Enhancement ◽  
Experimental Results ◽  
Noisy Environments ◽  
Heterogeneous Information ◽  
New Model ◽  
Model Based ◽  
Single Stream

This article describes how to use heterogeneous information in speech enhancement. In most of the current speech enhancement systems, clean speeches are recovered only from the signals collected by acoustic microphones, which will be greatly affected by the acoustic noises. However, heterogeneous information from different kinds of sensors, which is usually called the “multi-stream,” are seldom used in speech enhancement because the speech waveforms cannot be recovered from the signals provided by many kinds of sensors. In this article, the authors propose a new model-based multi-stream speech enhancement framework that can make use of the heterogeneous information provided by the signals from different kinds of sensors even when some of them are not directly related to the speech waveform. Then a new speech enhancement scheme using the acoustic and throat microphone recordings is also proposed based on the new speech enhancement framework. Experimental results show that the proposed scheme outperforms several single-stream speech enhancement methods in different noisy environments.

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