scholarly journals An Auto-Focus Method of Microscope for the Surface Structure of Transparent Materials under Transmission Illumination

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2487
Author(s):  
Yang Liao ◽  
Yonghua Xiong ◽  
Yunhong Yang
Keyword(s):  
Surface Structure ◽  
Search Algorithm ◽  
Classification Model ◽  
Global Maximum ◽  
Specific Situation ◽  
Focus Position ◽  
State Recognition ◽  
Deep Convolution Neural Network ◽  
Transparent Materials ◽  
Auto Focus

This paper is concerned with auto-focus of microscopes for the surface structure of transparent materials under transmission illumination, where two distinct focus states appear in the focusing process and the focus position is located between the two states with the local minimum of sharpness. Please note that most existing results are derived for one focus state with the global maximum value of sharpness, they cannot provide a feasible solution to this particular problem. In this paper, an auto-focus method is developed for such a specific situation with two focus states. Firstly, a focus state recognition model, which is essentially an image classification model based on a deep convolution neural network, is established to identify the focus states of the microscopy system. Then, an endpoint search algorithm which is an evolutionary algorithm based on differential evolution is designed to obtain the positions of the two endpoints of the region where the real focus position is located, by updating the parameters according to the focus states. At last, a region search algorithm is devised to locate the focus position. The experimental results show that our method can achieve auto-focus rapidly and accurately for such a specific situation with two focus states.

scholarly journals A Relevant Customer Identification Algorithm Based on the Internet Financial Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guo Yangyudongnanxin
Keyword(s):  
Social Relationship ◽  
Search Algorithm ◽  
Classification Model ◽  
The Internet ◽  
Identification Algorithm ◽  
Association Model ◽  
Intelligent Search ◽  
Average Sampling ◽  
Customer Classification ◽  
Customer Identification

In order to improve the intelligent search capabilities of Internet financial customers, this paper proposes a search algorithm for Internet financial data. The proposed algorithm calculates the customers corresponding to the two selected financial platforms based on the candidate customer set selected from the seed dataset and combined with the restored social relationship. Moreover, it also calculates the similarity of each field between the pairs. Furthermore, this article proposes an entity customer classification model based on logistic regression. Through the SNC model, threshold propagation, and random propagation, the model is transformed into an algorithm that identifies the associated customers, eliminates redundant customers, and realizes associated user identification. Experimental results verify that pruning increases the accuracy of identifying related customers by 8.44%. The average sampling accuracy of the entire customer association model is 79%, the lowest accuracy is 40%, and the highest is 1. From the sampling results, the overall recognition effect of the model reaches the expected goal.

scholarly journals Multilane Detection Entropy-Based Fusion Model by using Iterative Seed and Optimized Deep Convolutional Neural Network

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1967-1974
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Lane Detection ◽  
Learning Approaches ◽  
Driver Assistance ◽  
Fusion Model ◽  
Driver Assistance Systems ◽  
Proposed Model ◽  
Segmentation Approach ◽  
Deep Convolution Neural Network

In today’s world, the conditions of road is drastically improved as compared with past decade. Most of the express highways are made up of cement concrete and equipped with increased lane size. Apparently speed of the vehicle will increase. Therefore there are more chances for accidents. To avoid the accidents in recent days driver assistance systems are designed to detect the various lane. The detected information of lane path is used for controlling the vehicles and giving alerts to drivers. In this paper the entropy based fusion approach is presents for detecting multi-lanes. The Earth Worm- Crow Search Algorithm (EW-CSA) which is based on Deep Convolution Neural Network(DCNN) is utilized for consolidating the outcomes. At first, the deep learning approaches for path location is prepared using an optimization algorithm and EW-CSA, which focus on characterizing every pixel accurately and require post preparing activities to surmise path data. Correspondingly, the region based segmentation approach is utilizing for the multi-lane detection. An entropy based fusion model is used because this method preserved all the information in the image and reduces the noise effects. The performance of proposed model is analyzed in terms of accuracy, sensitivity, and specificity, providing superior results with values 0.991, 0.992, and 0.887, respectively

scholarly journals Nonlinear Inertia Classification Model and Application

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mei Wang ◽  
Pai Wang ◽  
Jzau-Sheng Lin ◽  
Xiaowei Li ◽  
Xuebin Qin
Keyword(s):  
Search Algorithm ◽  
Great Influence ◽  
Nonlinear Function ◽  
Classification Model ◽  
Support Vector ◽  
Power Cable ◽  
Optimal Position ◽  
Kernel Parameter ◽  
Svm Model

Classification model of support vector machine (SVM) overcomes the problem of a big number of samples. But the kernel parameter and the punishment factor have great influence on the quality of SVM model. Particle swarm optimization (PSO) is an evolutionary search algorithm based on the swarm intelligence, which is suitable for parameter optimization. Accordingly, a nonlinear inertia convergence classification model (NICCM) is proposed after the nonlinear inertia convergence (NICPSO) is developed in this paper. The velocity of NICPSO is firstly defined as the weighted velocity of the inertia PSO, and the inertia factor is selected to be a nonlinear function. NICPSO is used to optimize the kernel parameter and a punishment factor of SVM. Then, NICCM classifier is trained by using the optical punishment factor and the optical kernel parameter that comes from the optimal particle. Finally, NICCM is applied to the classification of the normal state and fault states of online power cable. It is experimentally proved that the iteration number for the proposed NICPSO to reach the optimal position decreases from 15 to 5 compared with PSO; the training duration is decreased by 0.0052 s and the recognition precision is increased by 4.12% compared with SVM.

scholarly journals Maximum power point tracking of partially shading PV system using cuckoo search algorithm

2019 ◽  
Vol 10 (2) ◽  
pp. 1081 ◽  
Author(s):  
Ahmed Ibrahim ◽  
Raef Aboelsaud ◽  
Sergey Obukhov
Keyword(s):  
High Efficiency ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Maximum Power ◽  
Global Maximum ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Power Point

This paper presents a cuckoo search (CS) algorithm for determining the global maximum power point (GMPP) tracking of photovoltaic (PV) under partial shading conditions (PSC). The conventional methods are fail to track the GMPP under PSC, which decrease the reliability of the power system and increase the system losses. The performance of the CS algorithm is compared with perturb and observe (P&O) algorithm for different cases of operations of PV panels under PSC. The CS algorithm used in this work to control directly the duty cycle of the DC-DC converter without proportional integral derivative (PID) controller. The proposed CS model can track the GMPP very accurate with high efficiency in less time under different conditions as well as in PSC.

THE SIMULATED ANNEALING GLOBAL SEARCH ALGORITHM APPLIED TO THE CRYSTALLOGRAPHY OF SURFACES BY LEED

1999 ◽  
Vol 06 (05) ◽  
pp. 651-661 ◽  
Author(s):  
V. B. NASCIMENTO ◽  
V. E. DE CARVALHO ◽  
C. M. C. DE CASTILHO ◽  
E. A. SOARES ◽  
C. BITTENCOURT ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Surface Structure ◽  
Structure Determination ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Global Search ◽  
Structural Parameter ◽  
Original Algorithm ◽  
R Factor ◽  
Annealing Algorithm

Surface structure determination by Low Energy Electron Diffraction (LEED) is based on a comparison between experimentally measured and theoretically calculated intensity versus energy I(V) curves for the diffracted beams. The level of agreement between these, for different structural models, is quantified using a correlation function, the so-called R factor. Minimizing this factor allows one to choose the best structure for which the theoretical simulations are computed. Surface structure determination thus requires an exhaustive search of structural parameter space in order to minimize the R factor. This minimization is usually performed by the use of directed search methods, although they have serious limitations, most notably their inability to distinguish between false and real structures corresponding to local and global R factor minima. In this work we present the implementation of a global search method based on the simulated annealing algorithm, as suggested earlier by Rous, using the Van Hove and Tong standard LEED code and the results of its application to the determination of the structure of the Ag(111) and CdTe(110) surfaces. Two different R factors, RP and R1, have been employed in the structural searches, and the statistical topographies of these two factors were studied. We have also implemented a variation of the simulated annealing algorithm (Fast Simulated Annealing) and applied it to these same two systems. Some preliminary results obtained with this algorithm were used to compare its performance with the original algorithm proposed by Rous.

Sign in / Sign up

Export Citation Format

Share Document