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Damage detection in composite beams using negative selection algorithm method

Materials Today: Proceedings 2023年

作者： Kosanam Ashwini Sasmita Sahu Hemalata Jena Rita Kumari Sahu KIIT University Bhubaneswar 751024 India

Most of the structural members act under periodic loading conditions which lead to cracks or damages. The initiation of cracks may change the physical properties of a structure mostly the flexibility and stiffness changes. The dynamic changes will directly change the natural frequencies and mode shapes. This study was an adaptive evolutionary algorithm to identify the damage location based on the immune system of the vertebrates. The first three natural frequencies from FEA and experimental techniques act as an input to the negative selection algorithm. The proposed method results are compared with the results of experimental analysis and FEA. The percentage error in both cases was found to be 3 % which is measurable.

关键词： Damage Vibration Artificial immune system negative selection algorithm

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Intrusion Detection systems using Real-Valued negative selection algorithm with Optimized Detectors 5

Intrusion Detection systems using Real-Valued Negative Selec...

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5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)

作者： Selahshoor, Fatemeh Jazayeriy, Hamid Omranpour, Hesam Babol Noshirvani Univ Technol Elect & Comp Engn Dept Babol Iran

ISBN: (纸本)9781728153506

Intrusion detection system (IDS) is one of the key security mechanisms to preserve integrity against attacks in networks Immunity-base intrusion detection is a well-known approach to develop IDS in WSN. The negative selection algorithm (NSA) is one of the immunity-base methods for classification of network attacks. The selection of the proper radius for self-samples is a challenging problem in NSA. These radiuses are used for training and constructing detectors. The incorrect selection of a radius can cause the problem of boundaries invasion and the overlap between the samples, which increase the false detection rate. To solve this problem, self-samples must be optimized before being used in the detection phase. In this paper, we propose a variable radius for self-sampling based on affinity density to reduce the false detection. The NSL-KDD data set is used to evaluate the proposed method. The results indicate that the proposed method can reduce the false detection rate and along with increasing true positive rate.

关键词： Intrusion Detection System negative selection algorithm Artficial Immune System Network

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Augmented negative selection algorithm with Complete Random Subspace Technique for Anomaly Detection 2

Augmented Negative Selection Algorithm with Complete Random ...

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2nd IEEE International Conference on Information and Computer Technologies (ICICT)

作者： Wang, Yi Li, Tao Zhu, Fangdong Sichuan Univ Coll Comp Sci Chengdu Sichuan Peoples R China Sichuan Univ Coll Cybersecur Chengdu Sichuan Peoples R China

ISBN: (纸本)9781728133232

negative selection algorithm is an important algorithm in the artificial immune system, inspired by the biological immune system. Traditional negative selection algorithms lack adaptive learning ability in high-dimensional space due to data sparsity and meaningless distance measurement. To solve these problems, an improved negative selection algorithm called negative selection algorithm with Complete Random Subspace Technique (RS-NSA), is proposed in this paper. It adopts a bootstrap method to reduce the rate of misclassification resulting from the anomalies covered by the regions of normal samples. By using the complete random subspace technology, it reduces dimensionality to alleviate the curse of dimensionality. In addition, the ensemble learning technique is introduced to improve accuracy, in which component classifiers can be replaced by any negative selection algorithm. Empirical evaluation on UCI datasets reveals that, compared with V-detector, our proposed method can not only achieve a higher detection rate and a lower false alarm rate, but also shorten the training time.

关键词： negative selection algorithm artificial immune system anomaly detection ensemble learning complete random subspace

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A new fault diagnosis approach for induction motor using negative selection algorithm and its real-time implementation on FPGA

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第1期34卷 689-701页

作者： Aydin, Ilhan Karakose, Mehmet Karakose, Ebru Akin, Erhan Firat Univ Dept Comp Engn Elazig Turkey Firat Univ Civil Aviat Coll Elazig Turkey

Condition monitoring of induction motors has become an important issue of researchers in recent years. The detection of broken rotor bar faults is one of the most difficult problems and many methods have been proposed for accurate detection of these faults. In recent years, some studies have been proposed to improve the diagnostic performance by combining different signal processing techniques. However, the proposed methods require high computational complexity. The contribution of this study is threefold. The first one is a new feature extraction method to distinguish different motor conditions by analyzing one phase of induction motor steady-state current. The phase space of the feature signal is constructed by using determined time delay and embedding dimension. The second contribution is to optimize the detectors of the negative selection algorithm by clonal selection. The proposed clonal selection algorithm minimizes the overlap between the detectors and maximizes the coverage of the anomalous data. Because the feature extraction method and test stage of the negative selection algorithm have low computational complexity, the last contribution is Field-Programmable-Gate-Array (FPGA) implementation for online detection of rotor related faults. The obtained results indicate that the proposed methodology demonstrates a high performance for diagnosis of rotor faults in induction motors.

关键词： negative selection algorithm clonal selection FPGA induction motors fault detection

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Deterministically generated negative selection algorithm for damage detection in civil engineering systems

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ENGINEERING STRUCTURES 2019年 197卷 109444-000页

作者： Barontini, Alberto Perera, Ricardo Masciotta, Maria Giovanna Amado-Mendes, Paulo Ramos, Luis Lourenco, Paulo Univ Minho Dept Civil Engn ISISE Guimaraes Portugal Tech Univ Madrid Dept Mech Engn Madrid Spain Univ Coimbra Dept Civil Engn ISISE Coimbra Portugal

In the framework of Structural Health Monitoring, vibration-based methods are commonly used to assess the condition of a structural system, being the dynamic properties sensitive to damage-induced changes. Within this context, negative selection, a bin-inspired classification algorithm, can be exploited to distinguish anomalous from normal behaviours by comparing the monitored system features with a set of detectors appropriately trained to spot any possible anomaly inside the unitary feature space. Such method results particularly convenient due to its easy implementation, low computational cost and capability to carry out the classification based on a training set of data belonging only to a healthy-state condition. This circumstance is extremely common in real civil engineering applications where no knowledge might exist about different structural conditions over time. In this paper, a negative-selection algorithm with a non-random strategy for detector generation is developed and tested on a numerical case study, namely a model simulating the 1-40 Bridge over the Rio Grande in Albuquerque, New Mexico (USA). The work carried out proves that the algorithm is suitable for the purpose of damage detection (a binary classification problem) and, by introducing the anomaly score as a qualitative measure of the level of damage, provides a sound analysis of the method multiclass classification skills, aiming at the quantification of the damage.

关键词： Structural health monitoring Anomaly detection Damage identification Classification problems negative selection algorithm

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dnyNSA: A Novel Real-Value Based negative selection algorithm 8

dnyNSA: A Novel Real-Value Based Negative Selection Algorith...

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8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Zhang Fan Yang Jin Li Tao Zhu Fangdong Sichuan Univ Coll Comp Sci Chengdu Sichuan Peoples R China Sichuan Univ Coll Cybersecur Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538692769

Real-value based negative selection algorithm (NSA) is an important detector generation algorithm of Artificial Immune System. Traditional real-value based negative selection algorithm generates immune detectors randomly and does not consider the sample distribution, therefore too many candidate detectors overlap in the feature space resulting in excessive redundancy. This leads to a low efficiency of detector generation and decreases the performance of NSA. To aim at this problem, the paper proposes a novel real-value negative selection algorithm based on Delaunay triangulation (dnyNSA). Firstly, dnyNSA uses Delaunay triangulation method to analyze the topological structure of training dataset and produces a set of simplicial cells (triangle in 2-dimensional space and tetrahedron in 3-dimensional space, etc.). Then, based on the simplicial cell circum-circle(-sphere) property, dnyNSA generates detectors in more reasonable position and size. Therefore, it only needs a small number of detectors to cover the same feature space, avoiding the generation of redundant detectors in traditional random mode, which greatly improves the efficiency of NSA. Theoretical analysis showed the time complexity of dnyNSA is in logarithmic level. The experimental results showed that, compared with the RNSA and V-Detector, dnyNSA can raise the detector generation efficiency over ten times while maintain similar detection performance.

关键词： negative selection algorithm Artificial immune system Delaunay triangulation Detector generation

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DENSA: An effective negative selection algorithm with flexible boundaries for self-space and dynamic number of detectors

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 62卷 359-372页

作者： Fouladvand, Sajjad Osareh, Alireza Shadgar, Bita Pavone, Mario Sharafi, Siyamack Acad Ctr Educ Culture & Res Lorestan Iran Langara Coll Dept Comp Sci & Informat Syst Vancouver BC Canada Simon Fraser Univ Sch Comp Sci Burnaby BC Canada Univ Catania Dept Math & Comp Sci Catania Italy

The negative selection algorithm is an anomaly detection technique inspired by the self-nonself discrimination behavior observed in the Biological Immune System. The most controversial question of these algorithms is their poor performance on real world applications. To overcome such limitation this research work focuses on generating more efficient detectors through a more flexible boundary for self patterns. Rather than applying conventional affinity measures, the detectors are generated benefiting from a Gaussian Mixture Model (GMM) fitted on normal space. From the GMM capabilities the algorithm is able to dynamically determine efficient subsets of detectors. In order to evaluate the efficiency and robustness of the proposed algorithm, different data sets have been examined as benchmark, including 2D synthesis data sets. Furthermore, for evaluating the capability and effectiveness of the proposed algorithm on real-world problems, it has been performed and tested for detecting anomalies in archaeological sites located in Lorestan, Iran. The experimental results prove how the proposed approach helps the negative selection algorithm to improve its detection capability, because the detectors can be efficiently distributed into the non-self space. It is important to note how this research work presents also a first analysis of the anomaly detection capabilities in the field of archaeology, introducing a novel application method, which can be efficiently used by the archaeologists for interpreting their growing amount of data and draw valuable conclusions about the historical past. Finally, in order to analyse the convergence and the running time of the proposed algorithm, a study has been conducted using the classical Time-To-Target plots, which present a standard graphical methodology for data analysis based on the comparisons between the empirical and theoretical distributions. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Anomaly detection Gaussian Mixture Model Artificial Immune System negative selection algorithm DENSA

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Parameter analysis of negative selection algorithm

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INFORMATION SCIENCES 2017年 420卷 218-234页

作者： Wen, Chen Tao, Li Sichuan Univ Coll Cybersecur Chengdu 610065 Sichuan Peoples R China

The performance of negative selection algorithm (NSA) is affected by several parameters such as its self radius and expected coverage. Traditionally, most of the parameters are selected based on experience, resulting in varying performances of NSAs. In the present study, the NSA parameters were analyzed based on a new set of evaluation criteria. The criteria were used to calculate the self radius by an iterative algorithm using some non self samples as reference points for the nonself boundary. The difficult problem of estimating the overlap volume between immune hyperspheres was solved by the Monte Carlo method. It was found that the error of the estimated nonself coverage was the primary cause of the poor performance of some existing artificial immune systems. A confidence estimation method was thus developed for improving the estimation precision. Experiments were performed in which both fixed- and variable-radius detectors were generated using different parameter value combinations. The results revealed that a significantly higher NSA performance could be achieved by the proposed parameter calculation method. Statistical analysis of the experimental results further confirmed the effectiveness and practicability of the proposed method. (C) 2017 Elsevier Inc. All rights reserved.

关键词： negative selection algorithm Parameter estimation Artificial immune system Detector Coverage

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Outlier detection of GPS monitoring data using relational analysis and negative selection algorithm

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SMART STRUCTURES AND SYSTEMS 2017年第2期20卷 219-229页

作者： Yi, Ting-Hua Ye, X. W. Li, Hong-Nan Guo, Qing Dalian Univ Technol Sch Civil Engn Dalian 116023 Peoples R China Zhejiang Univ Dept Civil Engn Hangzhou 310058 Zhejiang Peoples R China

Outlier detection is an imperative task to identify the occurrence of abnormal events before the structures are suffered from sudden failure during their service lives. This paper proposes a two-phase method for the outlier detection of Global Positioning System (GPS) monitoring data. Prompt judgment of the occurrence of abnormal data is firstly carried out by use of the relational analysis as the relationship among the data obtained from the adjacent locations following a certain rule. Then, a negative selection algorithm (NSA) is adopted for further accurate localization of the abnormal data. To reduce the computation cost in the NSA, an improved scheme by integrating the adjustable radius into the training stage is designed and implemented. Numerical simulations and experimental verifications demonstrate that the proposed method is encouraging compared with the original method in the aspects of efficiency and reliability. This method is only based on the monitoring data without the requirement of the engineer expertise on the structural operational characteristics, which can be easily embedded in a software system for the continuous and reliable monitoring of civil infrastructure.

关键词： structural health monitoring global positioning system outlier detection grey relational analysis negative selection algorithm

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An immunological approach based on the negative selection algorithm for real noise classification in speech signals

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2017年 72卷 125-133页

作者： Enside de Abreu, Caio Cesar Queiroz Duarte, Marco Aparecido Villarreal, Francisco Univ Estado Mato Grosso Dept Comp UNEMAT Santa Rita St 128 Alto Araguaia Brazil Sao Paulo State Univ UNESP Dept Elect Engn Brasil Ave 56 Ilha Solteira Brazil Univ Estadual Mato Grosso do Sul UEMS Dept Math Rodovia MS 306 KM 06 Cassilandia Brazil Sao Paulo State Univ UNESP Dept Math Brasil Ave 56 Ilha Solteira Brazil

This paper presents a new approach to detect and classify background noise in speech sentences based on the negative selection algorithm and dual-tree complex wavelet transform. The energy of the complex wavelet coefficients across five wavelet scales are used as input features. Afterward, the proposed algorithm identifies whether the speech sentence is, or is not, corrupted by noise. In the affirmative case, the system returns the type of the background noise amongst the real noise types considered. Comparisons with classical supervised learning methods are carried out. Simulation results show that the artificial immune system proposed overcomes classical classifiers in accuracy and capacity of generalization. Future applications of this tool will help in the development of new speech enhancement or automatic speech recognition systems based on noise classification. (C) 2016 Elsevier GmbH. All rights reserved.

关键词： Noise classification Speech enhancement Artificial immune systems negative selection algorithm Dual-tree complex wavelet transform

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