scholarly journals A Complex Chained P System Based on Evolutionary Mechanism for Image Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-19 ◽  
Author(s):  
Xiyu Liu ◽  
Lin Wang ◽  
Jianhua Qu ◽  
Ning Wang
Keyword(s):  
Image Segmentation ◽  
Real Life ◽  
Membrane System ◽  
Global Search ◽  
P System ◽  
Evolutionary Mechanism ◽  
Modified Particle Swarm Optimization ◽  
Computing Model ◽  
Distributed Parallel Computing ◽  
Clustering Problems

A new clustering membrane system using a complex chained P system (CCP) based on evolutionary mechanism is designed, developed, implemented, and tested. The purpose of CCP is to solve clustering problems. In CCP, two kinds of evolution rules in different chained membranes are used to enhance the global search ability. The first kind of evolution rules using traditional and modified particle swarm optimization (PSO) clustering techniques are used to evolve the objects. Another based on differential evolution (DE) is introduced to further improve the global search ability. The communication rules are adopted to accelerate the convergence and avoid prematurity. Under the control of evolution-communication mechanism, the CCP can effectively search for the optimal partitioning and improve the clustering performance with the help of the distributed parallel computing model. This proposed CCP is compared with four existing PSO clustering approaches on eight real-life datasets to verify the validity. The computational results on tested images also clearly show the effectiveness of CCP in solving image segmentation problems.

scholarly journals An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Lin Wang ◽  
Xiyu Liu ◽  
Minghe Sun ◽  
Jianhua Qu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Membrane System ◽  
P System ◽  
Swarm Optimization ◽  
Local Optima ◽  
Distributed Parallel Computing ◽  
Active Membranes ◽  
Accelerate Convergence ◽  
Clustering Problems

An extended clustering membrane system using a cell-like P system with active membranes based on particle swarm optimization (PSO), named PSO-CP, is designed, developed, implemented, and tested. The purpose of PSO-CP is to solve clustering problems. In PSO-CP, evolution rules based on the standard PSO mechanism are used to evolve the objects and communication rules are adopted to accelerate convergence and avoid prematurity. Subsystems of membranes are generated and dissolved by the membrane creation and dissolution rules, and a modified PSO mechanism is developed to help the objects escape from local optima. Under the control of the evolution-communication mechanism, the extended membrane system can effectively search for the optimal partitioning and improve the clustering performance with the help of the distributed parallel computing model. This extended clustering membrane system is compared with five existing PSO clustering approaches using ten benchmark clustering problems, and the computational results demonstrate the effectiveness of PSO-CP.

scholarly journals Dynamic Threshold Neural P Systems with Multiple Channels and Inhibitory Rules

Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1281
Author(s):  
Xiu Yin ◽  
Xiyu Liu
Keyword(s):  
Neural Networks ◽  
Parallel Computing ◽  
Ion Channels ◽  
Inhibitory Synapses ◽  
Dynamic Threshold ◽  
P System ◽  
Excitatory Synapses ◽  
Multiple Channels ◽  
Computing Model ◽  
Distributed Parallel Computing

In biological neural networks, neurons transmit chemical signals through synapses, and there are multiple ion channels during transmission. Moreover, synapses are divided into inhibitory synapses and excitatory synapses. The firing mechanism of previous spiking neural P (SNP) systems and their variants is basically the same as excitatory synapses, but the function of inhibitory synapses is rarely reflected in these systems. In order to more fully simulate the characteristics of neurons communicating through synapses, this paper proposes a dynamic threshold neural P system with inhibitory rules and multiple channels (DTNP-MCIR systems). DTNP-MCIR systems represent a distributed parallel computing model. We prove that DTNP-MCIR systems are Turing universal as number generating/accepting devices. In addition, we design a small universal DTNP-MCIR system with 73 neurons as function computing devices.

Multilevel Image Segmentation Using Modified Particle Swarm Optimization

2017 ◽  
pp. 106-142 ◽  
Author(s):  
Sourav De ◽  
Firoj Haque
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Optimization Technique ◽  
Real Life ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Gray Scale ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Segmented Images

Particle Swarm Optimization (PSO) is a well-known swarm optimization technique. PSO is very efficient to optimize the image segmentation problem. PSO algorithm have some drawbacks as the possible solutions may follow the global best solution at one stage. As a result, the probable solutions may bound within that locally optimized solutions. The proposed chapter tries to get over the drawback of the PSO algorithm and proposes a Modified Particle Swarm Optimization (MfPSO) algorithm to segment the multilevel images. The proposed method is compared with the original PSO algorithm and the renowned k-means algorithm. Comparison of the above mentioned existing methods with the proposed method are applied on three real life multilevel gray scale images. For this purpose, three standard objective functions are applied to evaluate the quality of the segmented images. The comparison shows that the proposed MfPSO algorithm is done better than the PSO algorithm and the k-means algorithm to segment the real life multilevel gray scale images.

scholarly journals A Novel Clustering Algorithm Inspired by Membrane Computing

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hong Peng ◽  
Xiaohui Luo ◽  
Zhisheng Gao ◽  
Jun Wang ◽  
Zheng Pei
Keyword(s):  
Parallel Computing ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Life ◽  
P System ◽  
Data Sets ◽  
Evolutionary Clustering ◽  
Distributed Parallel Computing ◽  
Real Life Data ◽  
Neighborhood Topology

P systems are a class of distributed parallel computing models; this paper presents a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. The objects of the cells express the candidate centers of clusters and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the coevolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal partitioning with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive tok-means algorithm and several evolutionary clustering algorithms recently reported in the literature.

scholarly journals Novel Numerical Spiking Neural P Systems with a Variable Consumption Strategy

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 549
Author(s):  
Xiu Yin ◽  
Xiyu Liu ◽  
Minghe Sun ◽  
Qianqian Ren
Keyword(s):  
Production Functions ◽  
P Systems ◽  
P System ◽  
Computing Device ◽  
Spiking Neural P Systems ◽  
Distributed Parallel Computing ◽  
Production Values ◽  
Novel Variant ◽  
Turing Universality ◽  
Variable Consumption

A novel variant of NSN P systems, called numerical spiking neural P systems with a variable consumption strategy (NSNVC P systems), is proposed. Like the spiking rules consuming spikes in spiking neural P systems, NSNVC P systems introduce a variable consumption strategy by modifying the form of the production functions used in NSN P systems. Similar to the delay feature of the spiking rules, NSNVC P systems introduce a postponement feature into the production functions. The execution of the production functions in NSNVC P systems is controlled by two, i.e., polarization and threshold, conditions. Multiple synaptic channels are used to transmit the charges and the production values in NSNVC P systems. The proposed NSNVC P systems are a type of distributed parallel computing models with a directed graphical structure. The Turing universality of the proposed NSNVC P systems is proved as number generating/accepting devices. Detailed descriptions are provided for NSNVC P systems as number generating/accepting devices. In addition, a universal NSNVC P system with 66 neurons is constructed as a function computing device.

scholarly journals Vision and Deep Learning-Based Algorithms to Detect and Quantify Cracks on Concrete Surfaces from UAV Videos

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6299 ◽  
Author(s):  
Sutanu Bhowmick ◽  
Satish Nagarajaiah ◽  
Ashok Veeraraghavan
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Network Architecture ◽  
Structural Integrity ◽  
Real Life ◽  
Concrete Surface ◽  
Video Camera ◽  
Bending Test ◽  
Morphological Operations ◽  
Civil Infrastructures

Immediate assessment of structural integrity of important civil infrastructures, like bridges, hospitals, or dams, is of utmost importance after natural disasters. Currently, inspection is performed manually by engineers who look for local damages and their extent on significant locations of the structure to understand its implication on its global stability. However, the whole process is time-consuming and prone to human errors. Due to their size and extent, some regions of civil structures are hard to gain access for manual inspection. In such situations, a vision-based system of Unmanned Aerial Vehicles (UAVs) programmed with Artificial Intelligence algorithms may be an effective alternative to carry out a health assessment of civil infrastructures in a timely manner. This paper proposes a framework of achieving the above-mentioned goal using computer vision and deep learning algorithms for detection of cracks on the concrete surface from its image by carrying out image segmentation of pixels, i.e., classification of pixels in an image of the concrete surface and whether it belongs to cracks or not. The image segmentation or dense pixel level classification is carried out using a deep neural network architecture named U-Net. Further, morphological operations on the segmented images result in dense measurements of crack geometry, like length, width, area, and crack orientation for individual cracks present in the image. The efficacy and robustness of the proposed method as a viable real-life application was validated by carrying out a laboratory experiment of a four-point bending test on an 8-foot-long concrete beam of which the video is recorded using a camera mounted on a UAV-based, as well as a still ground-based, video camera. Detection, quantification, and localization of damage on a civil infrastructure using the proposed framework can directly be used in the prognosis of the structure’s ability to withstand service loads.

A Dynamic Genetic Algorithm for Clustering Problems

2013 ◽  
Vol 411-414 ◽  
pp. 1884-1893
Author(s):  
Yong Chun Cao ◽  
Ya Bin Shao ◽  
Shuang Liang Tian ◽  
Zheng Qi Cai
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Life ◽  
Search Space ◽  
Adaptive Mutation ◽  
Data Sets ◽  
Data Set ◽  
Local Optima ◽  
Clustering Problems

Due to many of the clustering algorithms based on GAs suffer from degeneracy and are easy to fall in local optima, a novel dynamic genetic algorithm for clustering problems (DGA) is proposed. The algorithm adopted the variable length coding to represent individuals and processed the parallel crossover operation in the subpopulation with individuals of the same length, which allows the DGA algorithm clustering to explore the search space more effectively and can automatically obtain the proper number of clusters and the proper partition from a given data set; the algorithm used the dynamic crossover probability and adaptive mutation probability, which prevented the dynamic clustering algorithm from getting stuck at a local optimal solution. The clustering results in the experiments on three artificial data sets and two real-life data sets show that the DGA algorithm derives better performance and higher accuracy on clustering problems.

A Novel Membrane Clustering Algorithm Based on Tissue-like P System

2016 ◽  
Vol 2 (2) ◽  
pp. 409
Author(s):  
Yan Huaning ◽  
Xiang Laisheng ◽  
Liu Xiyu ◽  
Xue Jie
Keyword(s):  
Clustering Algorithm ◽  
Membrane Computing ◽  
P System ◽  
Data Sets ◽  
Turing Machines ◽  
Computing Systems ◽  
Swarm Optimization ◽  
Computing Model ◽  
Clustering Quality ◽  
Data Points

<span lang="EN-US">Clustering is a process of partitioning data points into different clusters due to their similarity, as a powerful technique of data mining, clustering is widely used in many fields. Membrane computing is a computing model abstracting from the biological area, </span><span lang="EN-US">these computing systems are proved to be so powerful that they are equivalent with Turing machines. In this paper, a modified inversion particle swarm optimization was proposed, this method and the mutational mechanism of genetics algorithm were used to combine with the tissue-like P system, through these evolutionary algorithms and the P system, the idea of a novel membrane clustering algorithm could come true. Experiments were tested on six data sets, by comparing the clustering quality with the GA-K-means, PSO-K-means and K-means proved the superiority of our method.</span>

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