A hybrid array minimizing the effects of the random weight vector errors in the LMS array and the Applebaum array

1990 ◽  
Vol 38 (5) ◽  
pp. 711-721 ◽  
Author(s):  
S.D. Lin ◽  
M. Barkat
Keyword(s):  
Weight Vector ◽  
Random Weight

scholarly journals Bounding basic characteristics of spatial epidemics with a new percolation model

2011 ◽  
Vol 43 (2) ◽  
pp. 335-347 ◽  
Author(s):  
Ronald Meester ◽  
Pieter Trapman
Keyword(s):  
Finite Graph ◽  
Weight Vector ◽  
Percolation Model ◽  
Locally Finite ◽  
Locally Finite Graph ◽  
Percolation Probability ◽  
Special Cases ◽  
Random Weight ◽  
Dependent Percolation ◽  
Basic Characteristics

We introduce a new 1-dependent percolation model to describe and analyze the spread of an epidemic on a general directed and locally finite graph. We assign a two-dimensional random weight vector to each vertex of the graph in such a way that the weights of different vertices are independent and identically distributed, but the two entries of the vector assigned to a vertex need not be independent. The probability for an edge to be open depends on the weights of its end vertices, but, conditionally on the weights, the states of the edges are independent of each other. In an epidemiological setting, the vertices of a graph represent the individuals in a (social) network and the edges represent the connections in the network. The weights assigned to an individual denote its (random) infectivity and susceptibility, respectively. We show that one can bound the percolation probability and the expected size of the cluster of vertices that can be reached by an open path starting at a given vertex from above by the corresponding quantities for independent bond percolation with a certain density; this generalizes a result of Kuulasmaa (1982). Many models in the literature are special cases of our general model.

scholarly journals Bounding basic characteristics of spatial epidemics with a new percolation model

2011 ◽  
Vol 43 (02) ◽  
pp. 335-347 ◽  
Author(s):  
Ronald Meester ◽  
Pieter Trapman
Keyword(s):  
Finite Graph ◽  
Weight Vector ◽  
Percolation Model ◽  
Locally Finite ◽  
Locally Finite Graph ◽  
Percolation Probability ◽  
Special Cases ◽  
Random Weight ◽  
Dependent Percolation ◽  
Basic Characteristics

We introduce a new 1-dependent percolation model to describe and analyze the spread of an epidemic on a general directed and locally finite graph. We assign a two-dimensional random weight vector to each vertex of the graph in such a way that the weights of different vertices are independent and identically distributed, but the two entries of the vector assigned to a vertex need not be independent. The probability for an edge to be open depends on the weights of its end vertices, but, conditionally on the weights, the states of the edges are independent of each other. In an epidemiological setting, the vertices of a graph represent the individuals in a (social) network and the edges represent the connections in the network. The weights assigned to an individual denote its (random) infectivity and susceptibility, respectively. We show that one can bound the percolation probability and the expected size of the cluster of vertices that can be reached by an open path starting at a given vertex from above by the corresponding quantities for independent bond percolation with a certain density; this generalizes a result of Kuulasmaa (1982). Many models in the literature are special cases of our general model.

scholarly journals Deep ConvNet: Non-Random Weight Initialization for Repeatable Determinism, Examined with FSGM

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4772
Author(s):  
Richard N. M. Rudd-Orthner ◽  
Lyudmila Mihaylova
Keyword(s):  
Cross Validation ◽  
Color Image ◽  
Validation Dataset ◽  
Number Sequence ◽  
Random Weight ◽  
Fast Gradient ◽  
Weight Initialization ◽  
Fitting In ◽  
Sequence Substitution ◽  
Sign Method

A repeatable and deterministic non-random weight initialization method in convolutional layers of neural networks examined with the Fast Gradient Sign Method (FSGM). Using the FSGM approach as a technique to measure the initialization effect with controlled distortions in transferred learning, varying the dataset numerical similarity. The focus is on convolutional layers with induced earlier learning through the use of striped forms for image classification. Which provided a higher performing accuracy in the first epoch, with improvements of between 3–5% in a well known benchmark model, and also ~10% in a color image dataset (MTARSI2), using a dissimilar model architecture. The proposed method is robust to limit optimization approaches like Glorot/Xavier and He initialization. Arguably the approach is within a new category of weight initialization methods, as a number sequence substitution of random numbers, without a tether to the dataset. When examined under the FGSM approach with transferred learning, the proposed method when used with higher distortions (numerically dissimilar datasets), is less compromised against the original cross-validation dataset, at ~31% accuracy instead of ~9%. This is an indication of higher retention of the original fitting in transferred learning.

A complementary webometric ranking system based on the website quality and traffic measures: A study focusing on top-ranked world universities

2021 ◽  
pp. 1-18
Author(s):  
Sajjad Farashi ◽  
Saeed Bashirian
Keyword(s):  
Weight Vector ◽  
Quality Factors ◽  
Ranking System ◽  
Web Based ◽  
Website Quality ◽  
Scientific Advancement ◽  
Standard Models ◽  
Algorithm Framework ◽  
Scientific Results ◽  
Optimal Weight Vector

Ranking of universities regarding their web-based activities plays a pivotal role in promoting scientific advancement since it motivates the open access accessibility to scientific results. In this study, a new ranking system based on the website quality factors and traffic evaluation was proposed. Since top-ranked universities are usually considered as the standard models for lower ranked ones, the focus of this study was on top-ranked universities. The proposed ranking was compared with well-known Webometrics ranking system. The website traffic and quality assessment were acquired for websites of top-ranked world universities and the correlation between these indices and the Webometrics ranking was evaluated. The summation of the weighted value of obtained measures according to an optimal weight vector obtained by a genetic algorithm framework was used for ranking purposes. The results showed that the website total traffic size was correlated with Webometrics rank (R≈-0.6, p< 0.01). Also, using the weighted value of website quality and traffic measures, the proposed ranking system could predict Webometrics ranking by the accuracy of up to 69%. Even though the method was proposed for universities, it could be applied for ranking other types of centers or companies, provided that the suitable cost function for the genetics algorithm framework was defined.

scholarly journals Study on coloring method of airport flight-gate allocation problem

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hongyan Li ◽  
Xianfeng Ding ◽  
Jiang Lin ◽  
Jingyu Zhou
Keyword(s):  
Graph Coloring ◽  
Programming Model ◽  
Weight Vector ◽  
Multi Objective ◽  
Average Value ◽  
Two Factors ◽  
Minimum Number ◽  
First Come First Serve ◽  
Airport Terminals ◽  
Single Objective

Abstract With the development of economy, more and more people travel by plane. Many airports have added satellite halls to relieve the pressure of insufficient boarding gates in airport terminals. However, the addition of satellite halls will have a certain impact on connecting flights of transit passengers and increase the difficulty of reasonable allocation of flight and gate in airports. Based on the requirements and data of question F of the 2018 postgraduate mathematical contest in modeling, this paper studies the flight-gate allocation of additional satellite halls at airports. Firstly, match the seven types of flights with the ten types of gates. Secondly, considering the number of gates used and the least number of flights not allocated to the gate, and adding the two factors of the overall tension of passengers and the minimum number of passengers who failed to transfer, the multi-objective 0–1 programming model was established. Determine the weight vector $w=(0.112,0.097,0.496,0.395)$ w = ( 0.112 , 0.097 , 0.496 , 0.395 ) of objective function by entropy value method based on personal preference, then the multi-objective 0–1 programming model is transformed into single-objective 0–1 programming model. Finally, a graph coloring algorithm based on parameter adjustment is used to solve the transformed model. The concept of time slice was used to determine the set of time conflicts of flight slots, and the vertex sequences were colored by applying the principle of “first come first serve”. Applying the model and algorithm proposed in this paper, it can be obtained that the average value of the overall tension degree of passengers minimized in question F is 35.179%, the number of flights successfully allocated to the gate maximized is 262, and the number of gates used is minimized to be 60. The corresponding flight-gate difficulty allocation weight is $\alpha =0.32$ α = 0.32 and $\beta =0.40$ β = 0.40 , and the proportion of flights successfully assigned to the gate is 86.469%. The number of passengers who failed to transfer was 642, with a failure rate of 23.337%.

scholarly journals Noisy Optimization of Dispatching Policy for the Cranes at the Storage Yard in an Automated Container Terminal

2021 ◽  
Vol 11 (15) ◽  
pp. 6922
Author(s):  
Jeongmin Kim ◽  
Ellen J. Hong ◽  
Youngjee Yang ◽  
Kwang Ryel Ryu
Keyword(s):  
Container Terminal ◽  
Scoring Function ◽  
Weight Vector ◽  
Container Terminals ◽  
Exploration And Exploitation ◽  
Noisy Optimization ◽  
Automated Container Terminal ◽  
Simulation Based ◽  
The Cost ◽  
Operation Schedule

In this paper, we claim that the operation schedule of automated stacking cranes (ASC) in the storage yard of automated container terminals can be built effectively and efficiently by using a crane dispatching policy, and propose a noisy optimization algorithm named N-RTS that can derive such a policy efficiently. To select a job for an ASC, our dispatching policy uses a multi-criteria scoring function to calculate the score of each candidate job using a weighted summation of the evaluations in those criteria. As the calculated score depends on the respective weights of these criteria, and thus a different weight vector gives rise to a different best candidate, a weight vector can be deemed as a policy. A good weight vector, or policy, can be found by a simulation-based search where a candidate policy is evaluated through a computationally expensive simulation of applying the policy to some operation scenarios. We may simplify the simulation to save time but at the cost of sacrificing the evaluation accuracy. N-RTS copes with this dilemma by maintaining a good balance between exploration and exploitation. Experimental results show that the policy derived by N-RTS outperforms other ASC scheduling methods. We also conducted additional experiments using some benchmark functions to validate the performance of N-RTS.

An Evolutionary-based Random Weight Networks with Taguchi Method for Arabic Web Pages Classification

2021 ◽  
Author(s):  
Arwa Shawabkeh ◽  
Hossam Faris ◽  
Ibrahim Aljarah ◽  
Bilal Abu-Salih ◽  
Dabiah Alboaneen ◽  
...  
Keyword(s):  
Taguchi Method ◽  
Web Pages ◽  
Random Weight

Convolutional neural network for 2D adaptive beamforming of phased array antennas with robustness to array imperfections

2021 ◽  
pp. 1-7
Author(s):  
Tarek Sallam ◽  
Ahmed M. Attiya
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Phased Array ◽  
Weight Vector ◽  
Adaptive Beamforming ◽  
Phased Array Antenna ◽  
Phased Array Antennas ◽  
Array Antennas ◽  
Optimum Weight ◽  
High Computational Complexity

Abstract Achieving robust and fast two-dimensional adaptive beamforming of phased array antennas is a challenging problem due to its high-computational complexity. To address this problem, a deep-learning-based beamforming method is presented in this paper. In particular, the optimum weight vector is computed by modeling the problem as a convolutional neural network (CNN), which is trained with I/O pairs obtained from the optimum Wiener solution. In order to exhibit the robustness of the new technique, it is applied on an 8 × 8 phased array antenna and compared with a shallow (non-deep) neural network namely, radial basis function neural network. The results reveal that the CNN leads to nearly optimal Wiener weights even in the presence of array imperfections.

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