Research

14 pages, 3294 KiB

Open AccessArticle

Research on Detection of Rice Pests and Diseases Based on Improved yolov5 Algorithm

by Hua Yang, Dang Lin, Gexiang Zhang, Haifeng Zhang, Junxiong Wang and Shuxiang Zhang

Appl. Sci. 2023, 13(18), 10188; https://doi.org/10.3390/app131810188 - 11 Sep 2023

Cited by 2 | Viewed by 1040

Rice pests and diseases have a significant impact on the quality and yield of rice, and even have a certain impact on and cause a loss in the national agricultural industry and economy. The timely and accurate detection of pests and diseases is [...] Read more.

Rice pests and diseases have a significant impact on the quality and yield of rice, and even have a certain impact on and cause a loss in the national agricultural industry and economy. The timely and accurate detection of pests and diseases is the basic premise of formulating effective rice pest control and prevention programs. However, the complexity and diversity of pests and diseases and the high similarity between some pests and diseases make the detection and classification task of pests and diseases extremely difficult without detection tools. The existing target detection algorithms can barely complete the task of detecting pests and diseases, but the detection effect is not ideal. In the actual situation of rice disease and insect pest detection, the detection algorithm is required to have fast speed, high accuracy, and good performance for small target detection, and so this paper improved the popular yolov5 algorithm to achieve an ideal detection performance suitable for rice disease and insect pest detection. This paper briefly introduces the current status and influence of rice pests and diseases and several target detection algorithms based on deep learning. Based on the yolov5 algorithm, the RepVGG network structure is introduced, 3*3 convolution is combined with ReLU, a training time model with multi-branch topology is adopted, and the inference time is reduced through layer merging. To improve algorithm detection speed, the SK attention mechanism is introduced to improve the receptive field of the convolution kernel to obtain more information and improve accuracy. In addition, Adaptive NMS is replaced by Adaptive NMS, the dynamic suppression strategy is adopted, and scores for learning density are set, which greatly improves the problems of missing detection and the false detection of small targets. Finally, the improved algorithm model is combined with membrane calculation to further improve the accuracy and speed of the algorithm. According to the experimental results, the accuracy of the improved algorithm is increased by about 2.7 percentage points, and the mAP is increased by 4.3 percentage points, up to 94.4%. The speed is improved by about 2.8 percentage points, and indicators such as recall rate and AP are improved. Full article

(This article belongs to the Special Issue Membrane Computing and Its Applications)

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18 pages, 2188 KiB

Open AccessArticle

Conversion between Number Systems in Membrane Computing

by Hai Nan, Jiqiao Jiang, Jie Zhang, Ran Liu and Aijuan Wang

Appl. Sci. 2023, 13(17), 9945; https://doi.org/10.3390/app13179945 - 02 Sep 2023

Viewed by 1103

Abstract

The number system is the representation method of numbers, and number system conversion is the most basic function of a computing system. The decimal system is the most commonly used number system; however, it may not necessarily be the most suitable number system [...] Read more.

The number system is the representation method of numbers, and number system conversion is the most basic function of a computing system. The decimal system is the most commonly used number system; however, it may not necessarily be the most suitable number system in a computing system. This paper investigates the conversion methods between different number systems and designs corresponding cells, such as P systems, to implement them. The P systems we designed include Π_{10_2} and Π_{2_10} to implement conversion between decimal and binary, Π_{10_m} and Π_{m_10} to implement conversion between decimal and m-base. The operation process of each P system was explained through examples, and their feasibility and effectiveness were verified through simulation software, UPSimulator. Full article

(This article belongs to the Special Issue Membrane Computing and Its Applications)

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20 pages, 3134 KiB

Open AccessArticle

P System Design for Integer Factorization

by Hai Nan, Zhijian Xue, Chaoyue Li, Mingqiang Zhou and Xiaoyang Liu

Appl. Sci. 2023, 13(15), 8910; https://doi.org/10.3390/app13158910 - 02 Aug 2023

Viewed by 784

Abstract

Membrane computing is a natural computing branch inspired by the structure of biological cells. The mathematical abstract model of a membrane computing system is called a P System, which is one of the main topics in membrane computing research for the design and [...] Read more.

Membrane computing is a natural computing branch inspired by the structure of biological cells. The mathematical abstract model of a membrane computing system is called a P System, which is one of the main topics in membrane computing research for the design and verification of a P System. Integer factorization is still a world-class problem and a very important research direction. If a fast method can be found to solve the integer factorization problem, several important cryptographic systems including the RSA public key algorithm will be broken. The aim of this paper is to design a P System capable of implementing integer decomposition, taking advantage of the characteristics of parallelism of P Systems. We construct a process with a main goal to study the modal exponential function f(x) = a^x mod N and explore the possible periodic behavior for different values of a. We attempt to compute nontrivial prime factors by the period found and constrain the operation of the P System in polynomial time. Full article

(This article belongs to the Special Issue Membrane Computing and Its Applications)

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45 pages, 6695 KiB

Open AccessArticle

P System with Fractional Reduction

by Hai Nan, Yumeng Kong, Jie Zhan, Mingqiang Zhou and Ling Bai

Appl. Sci. 2023, 13(14), 8514; https://doi.org/10.3390/app13148514 - 23 Jul 2023

Viewed by 687

Abstract

Membrane computing is a branch of natural computing, which is a new computational model abstracted from the study of the function and structure of living biological cells. The study of numerical computation based on membrane computation has received increasing attention in recent years, [...] Read more.

Membrane computing is a branch of natural computing, which is a new computational model abstracted from the study of the function and structure of living biological cells. The study of numerical computation based on membrane computation has received increasing attention in recent years, where maximum parallelism in the execution of evolutionary rules plays an important role in improving the efficiency of numerical computation. Numbers in numerical computation are usually represented as decimals or fractions, and this paper investigates the fundamental problem in fraction representation and operations—fraction simplification. By improving the parallelization of two traditional fractional reduction algorithms, we design the corresponding fractional reduction class cells P System

Π_{1}

and P System

Π_{2}

. Combining these two P Systems, this paper designs P System

Π_{3}

. The feasibility and effectiveness of the P System designed in this paper are verified experimentally with the simulation software UPSimulator, and the characteristics and application scenarios of the three P Systems are analyzed. Full article

(This article belongs to the Special Issue Membrane Computing and Its Applications)

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33 pages, 1614 KiB

Open AccessArticle

Performing Arithmetic Operations with Locally Homogeneous Spiking Neural P Systems

by Xu Zhang, Zongrong Hu, Jingyi Li and Ran Liu

Appl. Sci. 2023, 13(14), 8460; https://doi.org/10.3390/app13148460 - 21 Jul 2023

Viewed by 630

Abstract

The parallelism of rule execution in membrane computing provides support for improving computational efficiency. Membrane computing models have been applied in many fields. In arithmetic operations, designing basic arithmetic operation spiking neural P systems using fewer neurons and rule types has been an [...] Read more.

The parallelism of rule execution in membrane computing provides support for improving computational efficiency. Membrane computing models have been applied in many fields. In arithmetic operations, designing basic arithmetic operation spiking neural P systems using fewer neurons and rule types has been an important field of membrane computing application research in recent years. We discuss the application of locally homogeneous spiking neural P systems in arithmetic operations. The purpose is to design a spiking neural P system with fewer neurons and rule types to perform arithmetic operations. We designed the addition and subtraction of a locally homogeneous spiking neural P system without weight and delay. They include two input neurons to achieve any two binary number subtraction, one input neuron to achieve any two binary number addition and subtraction, and one input neuron to achieve any n binary number addition and subtraction. This is an attempt to apply the locally homogeneous spiking neural P system in arithmetic operations. Compared with the current excellent spiking neural P system performing arithmetic operations, our designed locally homogeneous spiking neural P system is more concise. The system we designed reduces the number of neurons required for n number addition operations by k − 6 and reduces the number of rule types by 5k − 14. Full article

(This article belongs to the Special Issue Membrane Computing and Its Applications)

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30 pages, 4525 KiB

Open AccessArticle

Q-MeaMetaVC: An MVC Solver of a Large-Scale Graph Based on Membrane Evolutionary Algorithms

by Chunmei Liao, Ping Guo, Jiaqi Gu and Qiuju Deng

Appl. Sci. 2023, 13(14), 8021; https://doi.org/10.3390/app13148021 - 09 Jul 2023

Viewed by 748

Abstract

In recent years, the rapid development of the internet and the advancement of information technology have produced a large amount of large-scale data, some of which are presented in the form of large-scale graphs, such as social networks and sensor networks. Minimum vertex [...] Read more.

In recent years, the rapid development of the internet and the advancement of information technology have produced a large amount of large-scale data, some of which are presented in the form of large-scale graphs, such as social networks and sensor networks. Minimum vertex cover (MVC) is an important problem in large-scale graph research. This paper proposes a solver Q-MeaMetaVC based on the MVC framework PEAF and the membrane evolution algorithm framework MEAF. First, the graph is reduced and divided into two types of connected components (bipartite graph and non-bipartite graph) to reduce the scale of the problem. Second, different membrane structures are designed for different types of connected components to better represent the connected component features and facilitate solutions. Third, a membrane evolution algorithm (MEA), which includes fusion, division, cytolysis, and selection operators, is designed to solve the connected components. Then, Q-MeaMetaVC is compared with the best MVC solver in recent years on the test set, and good experimental results that are obtained verify the feasibility and effectiveness of Q-MeaMetaVC in solving the MVC of large-scale graphs. Full article

(This article belongs to the Special Issue Membrane Computing and Its Applications)

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25 pages, 8862 KiB

Open AccessArticle

An Extended Membrane System with Monodirectional Tissue-like P Systems and Enhanced Particle Swarm Optimization for Data Clustering

by Lin Wang, Xiyu Liu, Jianhua Qu, Yuzhen Zhao, Liang Gao and Qianqian Ren

Appl. Sci. 2023, 13(13), 7755; https://doi.org/10.3390/app13137755 - 30 Jun 2023

Viewed by 661

Abstract

In order to establish a highly efficient P system for resolving clustering problems and overcome the computation incompleteness and implementation difficulty of P systems, an attractive clustering membrane system, integrated with enhanced particle swarm optimization (PSO) based on environmental factors and crossover operators [...] Read more.

In order to establish a highly efficient P system for resolving clustering problems and overcome the computation incompleteness and implementation difficulty of P systems, an attractive clustering membrane system, integrated with enhanced particle swarm optimization (PSO) based on environmental factors and crossover operators and a distributed parallel computing model of monodirectional tissue-like P systems (MTP), is constructed and proposed, which is simply named ECPSO-MTP. In the proposed ECPSO-MTP, two kinds of evolution rules for objects are defined and introduced to rewrite and modify the velocity of objects in different elementary membranes. The velocity updating model uses environmental factors based on partitioning information and randomly replaces global best to improve the clustering performance of ECPSO-MTP. The crossover operator for the position of objects is based on given objects and other objects with crossover probability and is accomplished through the hybridization of the global best of elementary membranes to reject randomness. The membrane structure of ECPSO-MTP is abstracted as a network structure, and the information exchange and resource sharing between different elementary membranes are accomplished by evolutional symport rules with promoters for objects of MTP, including forward and backward communication rules. The evolution and communication mechanisms in ECPSO-MTP are executed repeatedly through iteration. At last, comparison experiments, which are conducted on eight benchmark clustering datasets from artificial datasets and the UCI Machine Learning Repository and eight image segmentation datasets from BSDS500, demonstrate the effectiveness of the proposed ECPSO-MTP. Full article

(This article belongs to the Special Issue Membrane Computing and Its Applications)

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19 pages, 7883 KiB

Open AccessArticle

Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue—Like P System

by Fuhua Ge and Xiyu Liu

Appl. Sci. 2023, 13(4), 2293; https://doi.org/10.3390/app13042293 - 10 Feb 2023

Cited by 1 | Viewed by 1224

Abstract

Density Peaks Clustering (DPC) has recently received much attention in many fields by reason of its simplicity and efficiency. Nevertheless, empirical studies have shown that DPC has some shortfalls: (i) similarity measurement based on Euclidean distance is prone to misclassification. When dealing with [...] Read more.

Density Peaks Clustering (DPC) has recently received much attention in many fields by reason of its simplicity and efficiency. Nevertheless, empirical studies have shown that DPC has some shortfalls: (i) similarity measurement based on Euclidean distance is prone to misclassification. When dealing with clusters of non-uniform density, it is very difficult to identify true clustering centers in the decision graph; (ii) the clustering centers need to be manually selected; (iii) the chain reaction; an incorrectly assigned point will affect the clustering outcome. To settle the above limitations, we propose an improved density peaks clustering algorithm based on a divergence distance and tissue—like P system (TP-DSDPC in short). In the proposed algorithm, a novel distance measure is introduced to accurately estimate the local density and relative distance of each point. Then, clustering centers are automatically selected by the score value. A tissue—like P system carries out the entire algorithm process. In terms of the three evaluation metrics, the improved algorithm outperforms the other comparison algorithms using multiple synthetic and real-world datasets. Full article

(This article belongs to the Special Issue Membrane Computing and Its Applications)

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