scholarly journals Sulphur-Bridged BAl5S5+ with 17 Counting Electrons: A Regular Planar Pentacoordinate Boron System

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5205
Author(s):  
Yuhan Ye ◽  
Yiqiao Wang ◽  
Min Zhang ◽  
Yun Geng ◽  
Zhongmin Su
Keyword(s):  
Particle Swarm Optimization ◽  
Chemical Bonding ◽  
Theoretical Basis ◽  
Optimization Method ◽  
Energy Minimum ◽  
Swarm Optimization ◽  
Bonding Analysis ◽  
Global Energy Minimum ◽  
Star Configuration ◽  
Pointed Star

At present, most of the reported planar pentacoordinate clusters are similar to the isoelectronic substitution of CAl5+, with 18 counting electrons. Meanwhile, the regular planar pentacoordinate boron systems are rarely reported. Hereby, a sulphur-bridged BAl5S5+ system with a five-pointed star configuration and 17 counting electrons is identified at the global energy minimum through the particle-swarm optimization method, based on the previous recognition on bridged sulphur as the peripheral tactics to the stable planar tetracoordinate carbon and boron. Its outstanding stability has been demonstrated by thermodynamic analysis at 900 K, electronic properties and chemical bonding analysis. This study provides adequately theoretical basis and referable data for its experimental capture and testing.

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

A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization

Sensor Review ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 304-311 ◽  
Author(s):  
Pengfei Jia ◽  
Fengchun Tian ◽  
Shu Fan ◽  
Qinghua He ◽  
Jingwei Feng ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Wound Infection ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Sensor Array ◽  
Particle Swarm ◽  
Optimization Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Content Type

Purpose – The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach – An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings – The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection. Research limitations/implications – To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose. Practical implications – In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring. Originality/value – The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

Sign in / Sign up

Export Citation Format

Share Document