scholarly journals Deployment Optimization Method of Multistatic Radar for Constructing Circular Barrier Coverage

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6573
Author(s):  
Hai-Peng Li ◽  
Da-Zheng Feng ◽  
Shao-Feng Chen ◽  
Ya-Peng Zhou
Keyword(s):  
Optimization Method ◽  
Barrier Coverage ◽  
Circular Area ◽  
Inner Model ◽  
Proposed Model ◽  
Deployment Optimization ◽  
Optimal Deployment ◽  
Modified Formula ◽  
Multistatic Radar ◽  
Deployment Cost

To construct circular barrier coverage (CBC) with multistatic radars, a deployment optimization method based on equipartition strategy is proposed in this paper. In the method, the whole circular area is divided into several sub-circles with equal width, and each sub-circle is blanketed by a sub-CBC that is built based on the multistatic radar deployment patterns. To determine the optimal deployment patterns for each sub-CBC, the optimization conditions are firstly studied. Then, to optimize the deployment of the whole circular area, a model based on minimum deployment cost is proposed, and the proposed model is divided into two sub-models to solve the optimization issue. In the inner model, it is assumed that the width of a sub-circle is given. Based on the optimization conditions of the deployment pattern, integer linear programming (ILP) and exhaustive method (EM) are jointly adopted to determine the types and numbers of deployment patterns. Moreover, a modified formula is introduced to calculate the maximum valid number of receivers in a pattern, thus narrowing the search scope of the EM. In the outer model, the width of a sub-circle is assumed to be a variable, and the EM is adopted to determine the minimum total deployment cost and the optimal deployment patterns on each sub-circle. Moreover, the improved formula is exploited to determine the range of width for a sub-circle barrier and reduce the search scope of the EM. Finally, simulations are conducted in different conditions to verify the effectiveness of the proposed method. The simulation results indicate that the proposed method can spend less deployment cost and deploy fewer transmitters than the state-of-the-artwork.

A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition for corporate bankruptcy prediction

2020 ◽  
pp. 1-17
Author(s):  
Dongqi Yang ◽  
Wenyu Zhang ◽  
Xin Wu ◽  
Jose H. Ablanedo-Rosas ◽  
Lingxiao Yang ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Rapid Development ◽  
Optimization Method ◽  
Selection Method ◽  
Bankruptcy Prediction ◽  
Ensemble Model ◽  
Composition Method ◽  
Classifier Selection ◽  
Multi Stage ◽  
Proposed Model

With the rapid development of commercial credit mechanisms, credit funds have become fundamental in promoting the development of manufacturing corporations. However, large-scale, imbalanced credit application information poses a challenge to accurate bankruptcy predictions. A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition is proposed herein by combining the fuzzy clustering-based classifier selection method, the random subspace (RS)-based classifier composition method, and the genetic algorithm (GA)-based classifier compositional optimization method to achieve accuracy in predicting bankruptcy among corporates. To overcome the inherent inflexibility of traditional hard clustering methods, a new fuzzy clustering-based classifier selection method is proposed based on the mini-batch k-means algorithm to obtain the best performing base classifiers for generating classifier compositions. The RS-based classifier composition method was applied to enhance the robustness of candidate classifier compositions by randomly selecting several subspaces in the original feature space. The GA-based classifier compositional optimization method was applied to optimize the parameters of the promising classifier composition through the iterative mechanism of the GA. Finally, six datasets collected from the real world were tested with four evaluation indicators to assess the performance of the proposed model. The experimental results showed that the proposed model outperformed the benchmark models with higher predictive accuracy and efficiency.

scholarly journals Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System

2021 ◽  
Vol 13 (4) ◽  
pp. 707
Author(s):  
Yu’e Shao ◽  
Hui Ma ◽  
Shenghua Zhou ◽  
Xue Wang ◽  
Michail Antoniou ◽  
...  
Keyword(s):  
Optimization Methods ◽  
Optimization Method ◽  
Satellite System ◽  
Radar System ◽  
Target Localization ◽  
Computational Load ◽  
Matrix Fusion ◽  
Global Navigation Satellite ◽  
Multistatic Radar ◽  
Combined Algorithm

To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source network is that it brings a huge amount of information and leads to considerable computational load. Aiming at the problem, this paper introduces the idea of selecting external illuminators in the multistatic passive radar system. Its essence is to optimize the configuration of multistatic T/R pairs. Based on this, this paper respectively proposes two multi-source optimization algorithms from the perspective of resolution unit and resolution capability, the Covariance Matrix Fusion Method and Convex Hull Optimization Method, and then uses a Global Navigation Satellite System (GNSS) as an external illuminator to verify the algorithms. The experimental results show that the two optimization methods significantly improve the accuracy of multistatic positioning, and obtain a more reasonable use of system resources. To evaluate the algorithm performance under large number of transmitting/receiving stations, further simulation was conducted, in which a combination of the two algorithms were applied and the combined algorithm has shown its effectiveness in minimize the computational load and retain the target localization precision at the same time.

scholarly journals Optimization of Consignment-Store-Based Supply Chain with Black Hole Algorithm

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Ágota Bányai ◽  
Tamás Bányai ◽  
Béla Illés
Keyword(s):  
Black Hole ◽  
Supply Chain ◽  
Optimization Problems ◽  
Optimization Method ◽  
Chain Model ◽  
Supply Chain Optimization ◽  
Proposed Model ◽  
Supply Chain Model ◽  
Supply Model ◽  
Black Hole Algorithm

The globalization of economy and market led to increased networking in the field of manufacturing and services. These manufacturing and service processes including supply chain became more and more complex. The supply chain includes in many cases consignment stores. The design and operation of these complex supply chain processes can be described as NP-hard optimization problems. These problems can be solved using sophisticated models and methods based on metaheuristic algorithms. This research proposes an integrated supply model based on consignment stores. After a careful literature review, this paper introduces a mathematical model to formulate the problem of consignment-store-based supply chain optimization. The integrated model includes facility location and assignment problems to be solved. Next, an enhanced black hole algorithm dealing with multiobjective supply chain model is presented. The sensitivity analysis of the heuristic black hole optimization method is also described to check the efficiency of new operators to increase the convergence of the algorithm. Numerical results with different datasets demonstrate how the proposed model supports the efficiency, flexibility, and reliability of the consignment-store-based supply chain.

PMP-Based Optimization Method for Determining Airline Fleet Composition

2014 ◽  
Vol 505-506 ◽  
pp. 645-649
Author(s):  
Yu Wang
Keyword(s):  
Network Effects ◽  
Optimization Method ◽  
Numerical Example ◽  
Fleet Planning ◽  
Decision Variables ◽  
Proposed Model ◽  
Fleet Composition ◽  
Aircraft Type ◽  
Airline Fleet ◽  
The Impact

Traditional methods for determining airline fleet composition could not reflect the impact of network effects on fleet composition. To solve this problem for airlines operating in the mode of Hub & Spoke network, the passenger mix problem was incorporated into the model of determining airline fleet composition. The purchasing number of aircrafts in each fleet type, the frequencies of each aircraft type flying on legs and the spilling number of passengers from each itinerary were treated as decision variables. The limitations including maximum flying frequencies on each leg, available flying time each fleet type can provide and maximum passengers spilled from each flight leg were considered as constraints. A model to minimize the fleet planning cost was constructed. The numerical example shows that the fleet planning cost derived from this proposed model is 46266381.64 Yuan and reduces by 3914969.70 Yuan compared to the result from the traditional leg-based model. In hence, this proposed model is effective and feasible.

scholarly journals An Optimization Method for an Integrated Energy System Scheduling Process Based on NSGA-II Improved by Tent Mapping Chaotic Algorithms

Processes ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 426 ◽  
Author(s):  
Shengran Chen ◽  
Shengyan Wang
Keyword(s):  
Economic Dispatch ◽  
Energy System ◽  
Optimization Method ◽  
Nsga Ii ◽  
Tent Map ◽  
Chaos Optimization ◽  
Basic Model ◽  
Proposed Model ◽  
Integrated Energy System ◽  
Supply Equipment

The integrated energy system is a vital part of distributed energy industries. In addition to this, the optimal economic dispatch model, which takes into account the complementary coordination of multienergy, is an important research topic. Considering the constraints of power balance, energy supply equipment, and energy storage equipment, a basic model of optimal economic dispatch of an integrated energy system is established. On this basis, a multiobjective function solving algorithm of NSGA-II, based on tent map chaos optimization, is proposed. The proposed model and algorithm are applied. The simulation results show that the optimal economic scheduling model of the integrated energy system established in this paper can provide a more economic system operation scheme and reduce the operation cost and risks associated with an integrated energy system. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) multiobjective function solving algorithm, based on tent map chaos optimization, has better performance and efficiency.

scholarly journals Delay Bound Optimization in NoC Using a Discrete Firefly Algorithm

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1507
Author(s):  
Gaoming Du ◽  
Chao Tian ◽  
Zhenmin Li ◽  
Duoli Zhang ◽  
Chuan Zhang ◽  
...  
Keyword(s):  
Firefly Algorithm ◽  
Optimization Method ◽  
Video Object ◽  
Worst Case ◽  
Delay Bound ◽  
Proposed Model ◽  
Discrete Firefly Algorithm ◽  
Firefly Optimization ◽  
On Chip ◽  
Bound Optimization

The delay bound in system on chips (SoC) represents the worst-case traverse time of on-chip communication. In network on chip (NoC)-based SoC, optimizing the delay bound is challenging due to two aspects: (1) the delay bound is hard to obtain by traditional methods such as simulation; (2) the delay bound changes with the different application mappings. In this paper, we propose a delay bound optimization method using discrete firefly optimization algorithms (DBFA). First, we present a formal analytical delay bound model based on network calculus for both unipath and multipath routing in NoCs. We then set every flow in the application as the target flow and calculate the delay bound using the proposed model. Finally, we adopt firefly algorithm (FA) as the optimization method for minimizing the delay bound. We used industry patterns (video object plane decoder (VOPD), multiwindow display (MWD), etc.) to verify the effectiveness of delay bound optimization method. Experiments show that the proposed method is both effective and reliable, with a maximum optimization of 42.86%.

A robust tolerance optimization method based on fuzzy quality loss

2009 ◽  
Vol 223 (11) ◽  
pp. 2647-2653 ◽  
Author(s):  
Y Cao ◽  
J Mao ◽  
H Ching ◽  
J Yang
Keyword(s):  
Loss Function ◽  
Optimization Method ◽  
Quality Level ◽  
Tolerance Design ◽  
Quality Loss ◽  
Quality Loss Function ◽  
Proposed Model ◽  
Quality Grade ◽  
Practical Engineering ◽  
Manufacturing Time

Using the quality loss function developed by Taguchi, the manufacturing time and cost of a product can be reduced to improve the factory's competitiveness. However, the fuzziness in quality loss has not been considered in the Taguchi method. This article presents a fuzzy quality loss function model. First, fuzzy logic is used to describe the semantic of the quality, and the quality level is divided into several grades. Then the fuzzy quality loss function is developed utilizing the loss in monetary terms, which indicates the quality loss of each quality level and the normalized expected probability to each quality grade. Moreover, a new optimization model for tolerance design under fuzzy quality loss function is established. An example is used to illustrate the validity of the proposed model. The result shows that the proposed method is more flexible and can achieve the balance of quality and cost in tolerance design. It can be easily used in accordance with practical engineering applications.

Interval Reactive Power Optimization for Distribution System with DGs

2014 ◽  
Vol 1008-1009 ◽  
pp. 421-425
Author(s):  
Yong Jin Chen ◽  
Jie He Su ◽  
Yong Jun Zhang ◽  
Ying Qi Yi
Keyword(s):  
Interval Arithmetic ◽  
Distribution System ◽  
Power Flow ◽  
Reactive Power ◽  
Power Optimization ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Reactive Power Optimization ◽  
Load Fluctuation ◽  
Proposed Model

A reactive power optimization method based on interval arithmetic is presented to solve the uncertainty of the output of distributed generation (DG) and the effects of load fluctuation. The concept of interval number and interval arithmetic is introduced to model the interval power flow of distribution system, which is iterated by using the Krawczyk-Moore operator. The objective function is to minimize the interval midpoint value of system’s power loss, with taking the interval voltage constraints into consideration for the interval reactive power optimization model. A modified IEEE 14-bus system is used to validate the proposed model and its Particle Swarm Optimization (PSO) algorithm. The simulation results show that the proposed method is effective.

scholarly journals Facial Gender Analysis using Gabor-DWT Feature Extraction Method

2019 ◽  
Vol 9 (2) ◽  
pp. 4904-4907
Keyword(s):  
Gabor Filter ◽  
Real Life ◽  
Optimization Method ◽  
Gender Analysis ◽  
Illumination Variation ◽  
Feature Extraction Method ◽  
Proposed Model ◽  
The Face ◽  
Age Variation ◽  
And Gender

Facial Gender Analysis has application of specific gender entry detection, human machine interface for digital marketing, real time targeted advertisement and gender demographic analysis. The facial gender can be predicted by classification of the texture and unique edges pattern. Gabor filter can extract the edge- texture patterns on the face but has problem of high dimensionality with redundancy. For accuracy enhancement, the dimension and redundancy is needed to reduce by proposed technique as maxDWT feature optimization method. The proposed model is evaluated on real life challenging dataset of face as illumination variation, POSE, face profile, age variation and obstruction on face as hat, birthmark, moles, speckles, beard, etc. Results shows that proposed technique far better than existing state of art methods of gender prediction

scholarly journals Production inventory model with disruption considering shortage and time proportional demand

2018 ◽  
Vol 28 (1) ◽  
pp. 123-139 ◽  
Author(s):  
U.K. Khedlekar ◽  
A. Namdeo ◽  
A. Nigwal
Keyword(s):  
Simulation Study ◽  
Optimization Method ◽  
Political Issue ◽  
Optimal Production ◽  
Economic Production Quantity ◽  
Economic Production ◽  
Proposed Model ◽  
Production Inventory ◽  
Before And After ◽  
Production Quantity

The disruption in a production system occurs due to labor problem, machines breakdown, strikes, political issue, and weather disturbance, etc. This leads to delay in the supply of the products, resulting customer to approach other dealers for the products. This paper is an attempt to develop an economic production quantity model using optimization method for deteriorating items with production disruption. We obtained optimal production time before and after the system gets disrupted. We have also devised the model for optimizing the shortage of the products. This research is useful to determine the time for start and stop of the production when system gets disrupted. The optimal production and inventory plan are provided, so that the manufacturer can reduce the loss occurred due to disruption. Finally a graph based simulation study has been given to illustrate the proposed model.

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