Adaptive Non-Uniform Particle Swarm Application to Plasmonic Design

2012 ◽  
pp. 114-124
Author(s):  
Sameh Kessentini ◽  
Dominique Barchiesi ◽  
Thomas Grosges ◽  
Laurence Giraud-Moreau ◽  
Marc Lamy de la Chapelle
Keyword(s):  
Particle Swarm Optimization ◽  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
Particle Swarm ◽  
Great Efficiency ◽  
Swarm Optimization ◽  
Rapid Resolution ◽  
Interdisciplinary Field ◽  
Plasmonic Structures

The metaheuristic approach has become an important tool for the optimization of design in engineering. In that way, its application to the development of the plasmonic based biosensor is apparent. Plasmonics represents a rapidly expanding interdisciplinary field with numerous transducers for physical, biological and medicine applications. Specific problems are related to this domain. The plasmonic structures design depends on a large number of parameters. Second, the way of their fabrication is complex and industrial aspects are in their infancy. In this study, the authors propose a non-uniform adapted Particle Swarm Optimization (PSO) for rapid resolution of plasmonic problem. The method is tested and compared to the standard PSO, the meta-PSO (Veenhuis, 2006) and the ANUHEM (Barchiesi, 2009).These approaches are applied to the specific problem of the optimization of Surface Plasmon Resonance (SPR) Biosensors design. Results show great efficiency of the introduced method.

Adaptive Non-Uniform Particle Swarm Application to Plasmonic Design

2011 ◽  
Vol 2 (1) ◽  
pp. 18-28 ◽  
Author(s):  
Sameh Kessentini ◽  
Dominique Barchiesi ◽  
Thomas Grosges ◽  
Laurence Giraud-Moreau ◽  
Marc Lamy de la Chapelle
Keyword(s):  
Particle Swarm Optimization ◽  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
Specific Problem ◽  
Particle Swarm ◽  
Great Efficiency ◽  
Swarm Optimization ◽  
Interdisciplinary Field ◽  
Plasmonic Structures

The metaheuristic approach has become an important tool for the optimization of design in engineering. In that way, its application to the development of the plasmonic based biosensor is apparent. Plasmonics represents a rapidly expanding interdisciplinary field with numerous transducers for physical, biological and medicine applications. Specific problems are related to this domain. The plasmonic structures design depends on a large number of parameters. Second, the way of their fabrication is complex and industrial aspects are in their infancy. In this study, the authors propose a non-uniform adapted Particle Swarm Optimization (PSO) for rapid resolution of plasmonic problem. The method is tested and compared to the standard PSO, the meta-PSO (Veenhuis, 2006) and the ANUHEM (Barchiesi, 2009).These approaches are applied to the specific problem of the optimization of Surface Plasmon Resonance (SPR) Biosensors design. Results show great efficiency of the introduced method.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Comparison of sensitivity of the refractometric methods of frustrated total internal reflection and surface plasmon resonance

2020 ◽  
pp. 44-49
Author(s):  
I. N. Pavlov
Keyword(s):  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
Total Internal Reflection ◽  
Resonance Method ◽  
Internal Reflection ◽  
Optical Methods ◽  
Thin Boundary Layer ◽  
Experimental Conditions ◽  
Frustrated Total Internal Reflection

Two optical methods, namely surface plasmon resonance imaging and frustrated total internal reflection, are described in the paper in terms of comparing their sensitivity to change of refractive index of a thin boundary layer of an investigated medium. It is shown that, despite the fact that the theoretically calculated sensitivity is higher for the frustrated total internal reflection method, and the fact that usually in practice the surface plasmon resonance method, on the contrary, is considered more sensitive, under the same experimental conditions both methods show a similar result.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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