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23nd Signal Processing and Communications Applications Conference (SIU)

作者： Firildak, Kazim Karakose, Mehmet Inonu Univ Bilgi Islem Daire Baskanligi Malatya Turkey Firat Univ Bilgisayar Muhendisligi Bolumu Elazig Turkey

ISBN: (纸本)9781467373869

Shadow regions are detected as objects at image segmentation, object detection and tracking applications. So, it affects negatively the accuracy and performance of algorithms. In this study, artificial immune system-based negative selection algorithm (YBSG) is proposed in order to determine shadow region. This algorithm obtains fast and effective solution to detect nonlinear change shadow region on the video scenes. This algorithm obtains us to increase in 5%-20% shadow detecting performance and 5%-10% execution time with effective method proposed to detect the shadows of different video scenes in literature.

关键词： Shadow detection Artifical immune system based shadow detection negative selection algorithm

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DGA-PSO: An improved detector generation algorithm based on particle swarm optimization in negative selection

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KNOWLEDGE-BASED SYSTEMS 2023年第1期278卷

作者： Zhang, Ge He, Junjiang Li, Wenshan Li, Tao Lan, Xiaolong Wang, Yunpeng Sichuan Univ Sch Cyber Sci & Engn 24 South Sect 1Yihuan Rd Chengdu 610065 Peoples R China Chengdu Univ Informat Technol Sch Cyber Sci & Engn 24 Sect 1Xuefu Rd Southwest Airport Econ Dev Zon Chengdu 610225 Peoples R China

The negative selection algorithm (NSA) is an essential algorithm in the artificial immune system used to achieve anomaly detection by generating detectors. The traditional NSA algorithm generates candidate detectors randomly, which leads to a partially dense and redundant distribution of detectors in the nonself areas, resulting in the presence of holes that are not covered by detectors. A detector generation algorithm based on particle swarm optimization (DGA-PSO) is proposed to overcome these defects. DGA-PSO converts the self-tolerance process into an adaptation function to guide particles to move in a specific direction by artificial settings and variants, generates efficient detectors covering the nonself space, reduces the redundancy among detectors and fills holes not covered. Thus, we successfully reduce the number of detectors while improving the detection rate of the algorithm. Through experimental validation analysis, DGA-PSO ranks first in detector training time and the detection rate on four UCI datasets compared to the classical algorithms RNSA and V-Detector and the improved algorithms BIORV-NSA, ADC-NSA and IFB-NSA.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Artificial immune system negative selection algorithm Particle swarm optimization Detector generation algorithm Anomaly detection

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Fault Diagnosis Based on the Artificial Immune algorithm and negative selection

Fault Diagnosis Based on the Artificial Immune Algorithm and...

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第17届工业工程与工程管理国际学术会议(IE&EM2010)

作者： P.Govender D.A.Kyereahene Mensah Optimization Studies Unit Dept.of Electronic EngineeringDurban University of TechnologySouth Africa

Modern manufacturing techniques depend upon systems that produce high volumes with consistent quality in order to ensure maximum *** source of reduced productivity is equipment *** minimize these production losses,we propose an intelligent system that is incorporated into the architecture of a machine for detecting the onset of equipment malfunctioning,and to generate corrective *** intelligent diagnostic system is based upon the artificial immune algorithm and the technique of negative *** proposed immune based system monitors a machine's transition states during an operating cycle and immediately detects the occurrence of an anomaly.

关键词： anomaly artificial immune system censoring and monitoring negative selection algorithm

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A POLYMORPHIC CONVEX HULL SCHEME FOR negative selection algorithmS

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2012年第5A期8卷 2953-2964页

作者： Majd, Mahshid Hamzeh, Ali Hashemi, Sattar Shiraz Univ Sch Elect & Comp Engn Dept Comp Sci & Engn & Informat Technol Shiraz Iran

Artificial Immune System (AIS) is known as the collection of all researches including computational models and knowledge discovery algorithms inspired by the natural immune system. One of the most widely used techniques in the field of artificial immune system is negative selection algorithm (NSA). The principle of this algorithm is finding a set of detectors which can discriminate the self and non-self areas. Each detector in NSA defines a subspace of problem space where no self data is located. As the obtained set of detectors paves the way of discriminating the self and non-self data, NSA finds its main application in anomaly detection problems. What significantly affects the detection performance of NSA is choosing a proper shape/representation for detectors to model the regularities of non-self area precisely in one hand, and on the other hand, number of generated detectors should be tuned in a delicate manner. Using a general and polymorphic representation scheme for covering non-self area can elegantly alleviate the above mentioned issues. This paper presents a novel representation method based on convex hulls, named CH-NSA, which is general enough to support polymorphic shapes with variable properties. Convex hull is a general form representation which supports not only regular symmetric shapes such as rectangles, spheres and ellipse but also irregular asymmetric shapes. The experimental results show that applying this new representation to well-known benchmark problems in the literature enhances the accuracy of NSA by significantly decreasing the number of needed detectors compared with the other common representation methods.

关键词： Artificial immune system negative selection algorithm Convex hull representation

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Novelty detection in a changing environment: A negative selection approach

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2010年第4期24卷 1114-1128页

作者： Surace, Cecilia Worden, Keith Politecn Torino Univ Dept Struct & Geothecn Engn Corsa Duca Abruzzi 24 I-10129 Turin Italy Univ Sheffield Dept Mech Engn Sheffield S1 3JD S Yorkshire England

In the recent past, there have been a number of engineering studies motivated by analogies with the human immune system. The immune system has provided a rich source of inspiration for pattern recognition, machine learning and data mining analyses. One of the properties of the immune system which proves particularly useful for novelty detection is that of self/non-self discrimination and this forms the basis of the negative selection algorithm which has previously been applied by other researchers to the problem of time-series novelty detection. The object of the current paper is to apply the negative selection algorithm to more general feature sets and also to consider the case of novelty detection where the normal condition set is significantly non-Gaussian or varies with operational or environmental conditions. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Novelty detection negative selection algorithm Changing operational conditions

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Multi-class iteratively refined negative selection classifier

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APPLIED SOFT COMPUTING 2008年第2期8卷 972-984页

作者： Markowska-Kaczmar, Urszula Kordas, Bartosz Wroclaw Univ Technol Inst Appl Informat PL-50370 Wroclaw Poland

In the paper a new classification method is proposed. It is based on negative selection, which was originally designed for anomaly detection and dichotomic classification. In our earlier work we described M-NSA algorithm that can be applied in multi-class classification problems. Trying to improve classification accuracy of M-NSA we propose a new version of this algorithm, called MINSA, where refinement of receptors set is applied. The accuracy of MINSA was tested in an experimental way with the use of benchmark data sets. The experiments confirmed that direction of changes introduced in MINSA improves its accuracy in comparison to M-NSA. Comparison with other methods of classification is also shown in the paper. (c) 2007 Elsevier B.V. All rights reserved.

关键词： artificial immune system negative selection algorithm classifier

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A Practical Study on Shape Space and Its Occupancy in negative selection

A Practical Study on Shape Space and Its Occupancy in Negati...

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2010 IEEE World Congress on Computational Intelligence

作者： Ma, Wanli Tran, Dat Sharma, Dharmendra Univ Canberra Fac Informat Sci & Engn Canberra ACT 2601 Australia

ISBN: (纸本)9781424481262

The success of a negative selection algorithm depends on its detectors. A shape space, conceptually, is where selves, detectors, and antigens reside. These detectors are expected to fully cover the whole shape space. The better the coverage;the better the detection rate. However, this assumption brings a major challenge to negative selection experiments - the scalability problem, where the experiments cannot process real life datasets in a timely manner. On the other hand, with any real life dataset, due to arbitrary antibody/antigen representing formats, the shape space actually cannot be fully occupied. The unoccupied locations sometimes constitute a significant, or even overwhelm, portion in a shape space. In this paper, we first briefly review the theoretic model of the shape space and then study the impact of the unoccupied locations, under the term shape space occupancy. Based on the study outcomes, we suggest the heuristics for generating detectors. We demonstrate shape space occupancy, detector generation by antigen feedback mechanism, and negative selection experiments on 4 different datasets, which cover the data presentation formats in both strings and real number valued vectors.

关键词： antigen feedback mechanism arbitrary antibody-antigen representing formats artificial immune systems biology data presentation formats detector generation negative selection algorithm scalability problem shape space shape space occupancy unoccupied locations

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Artificial Immune Systems with negative selection Applied to Health Monitoring of Aeronautical Structures

Artificial Immune Systems with Negative Selection Applied to...

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International Conference on Applied Mechanics, Fluid and Solid Mechanics (AMFSM 2013)

作者： dos Anjos Lima, Fernando Parra Chavarette, Fabio Roberto dos Santos e Souza, Adriano Frutuoso de Souza, Simone Silva Martins Lopes, Mara Lucia UNESP Univ Estadual Paulista Fac Engn Ilha Solteira FEIS Dept Mech Engn Av Brasil 56 BR-15385000 Ilha Solteira SP Brazil Fac Engn Ilha Solteira FEIS Dept Elect Engn Ilha Solteira SP Brazil Fac Engn Ilha Solteira FEIS Dept Math Ilha Solteira SP Brazil

ISBN: (纸本)9783037859674

In this paper we present a system for aircraft structural health monitoring based on artificial immune systems with negative selection. Inspired by a biological process, the principle of discrimination proper/non-proper, identifies and characterizes the signs of structural failure. The main application of this method is to assist in the inspection of aircraft structures, to detect and characterize flaws and decision making in order to avoid disasters. We proposed a model of an aluminum beam to perform the tests of the method. The results obtained by this method are excellent, showing robustness and accuracy.

关键词： Health Monitoring Aeronautical structures Artificial Immune Systems negative selection algorithm

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Prognosis and fail detection in a dynamic rotor using artificial immunological system

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ENGINEERING COMPUTATIONS 2020年第9期37卷 3127-3145页

作者： Outa, Roberto Chavarette, Fabio Roberto Mishra, Vishnu Narayan Goncalves, Aparecido C. Roefero, Luiz G. P. Moro, Thiago C. FATEC Aracatuba Fac Technol Aracatuba Dept Biofuels Aracatuba Brazil Univ Estadual Paulista Fac Engn Ilha Solteira Dept Math Ilha Solteira Brazil Indira Gandhi Natl Tribal Univ Dept Math Amarkantak India Univ Estadual Paulista Fac Engn Ilha Solteira Dept Mech Engn Ilha Solteira Brazil

Purpose In recent years, the mechanical industries began to apply many investments in research and technological development to obtain efficient methods to analyze the integrity of structures and prevent disasters and/or accidents, ensuring people's lives and preventing economic losses. Any structure, whether mechanical or aeronautical, before being put into use undergoes a structural integrity assessment and testing. In this case, non-destructive evaluations are performed, aiming to estimate the degree of safety and reliability of the structure. For this, there are techniques traditionally used such as ultrasonic inspection, X-ray, acoustic emission tests, among other techniques. The traditional techniques may even have a good instrumental apparatus and be well formulated for structural integrity assessment;however, these techniques cannot meet growing industrial needs, even more so when structures are in motion. The purpose of this paper is to demonstrate artificial immune systems (AISs), ate and strengthen the emergence of an innovative technological tool, the biological immune systems and AISs, and these are presented as computing methods in the field of structural health monitoring (SHM). Design/methodology/approach The concept of SHM is based on a fault detection mechanism used in industries, and in other applications, involving the observation of a structure or a mechanical system. This observation occurs through the dynamic response of periodic measurements, later related to the statistical analysis, determining the integrity of the system. This study aims to develop a methodology that identifies and classifies a signal in normal signals or in faults, using an algorithm based on artificial immunological systems, being the negative selection algorithm, and later, this algorithm classifies the failures in probabilities of failure and degree of fault severity. The results demonstrate that the proposed SHM is efficient and robust for prognosis and failure detect

关键词： Structural health monitoring Artificial immune systems negative selection algorithm 68W40 42C40

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A hybrid particle swarm optimization and artificial immune system algorithm for image enhancement

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SOFT COMPUTING 2015年第8期19卷 2101-2109页

作者： Mahapatra, Prasant Kumar Ganguli, Susmita Kumar, Amod CSIR Cent Sci Instruments Org V Biomed Instrumentat Div 2 Chandigarh 160030 India Tezpur Univ Dept Elect & Commun Tezpur 784028 India

Image enhancement means to improve the perception of information in images. Histogram equalization (HE) and linear contrast stretching (LCS) are the commonly used methods for image enhancement. But images obtained through these processes, generally, have excessive contrast enhancement due to which they are not suitable for use in fields where brightness is of critical importance. In this paper, a hybrid algorithm based on Particle Swarm Optimization (PSO) along with negative selection algorithm, a model of artificial immune system, is proposed for image enhancement which is achieved by enhancing the intensity of the gray levels of the images. The proposed algorithm is applied to histogram equalized images of lathe tool and MATLAB inbuilt images to verify its effectiveness. The results are compared with conventional enhancement techniques such as HE, LCS and Standard PSO algorithm based image enhancement.

关键词： Particle swarm optimization Artificial immune system negative selection algorithm Image enhancement Histogram equalization Linear contrast stretching

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