A recognition method of vibration parameter image based on improved immune negative selection algorithm for rotating machinery

To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.

A self region based real-valued negative selection algorithm

Point-wise negative selection algorithms,which generate their detector sets based on point of self data,have lower training efficiency and detection rate.To solve this problem,a self region based real-valued negative selection algorithm is presented.In this new approach,the continuous self region is defined by the collection of self data,the partial training takes place at the training stage according to both the radius of self region and the cosine distance between gravity of the self region and detector candidate,and variable detectors in the self region are deployed.The algorithm is tested using the triangle shape of self region in the 2-D complement space and KDD CUP 1999 data set.Results show that,more information can be provided when the training self points are used together as a whole,and compared with the point-wise negative selection algorithm,the new approach can improve the training efficiency of system and the detection rate significantly.

A negative selection algorithm with neighborhood representation

This paper proposes a negative selection with neighborhood representation named as neighborhood negative selection algorithm.This algorithm employs a new representation method which uses the fully adjacent but mutually disjoint neighborhoods to present the self samples and detectors.After normalizing the normal samples into neighborhood shape space,the algorithm uses a special matching rule similar as Hamming distance to train mature detectors at the training stage and detect anomaly at the detection stage.The neighborhood negative selection algorithm is tested using KDD CUP 1999 dataset.Experimental results show that the algorithm can prevent the negative effect of the dimension of shape space,and provide a more accuracy and stable detection performance.

Fault Detection Using Negative Selection and Genetic Algorithms

In this paper,negative selection and genetic algorithms are combined and an improved bi-objective optimization scheme is presented to achieve optimized negative selection algorithm detectors.The main aim of the optimal detector generation technique is maximal nonself space coverage with reduced number of diversified detectors.Conventionally,researchers opted clonal selection based optimization methods to achieve the maximal nonself coverage milestone;however,detectors cloning process results in generation of redundant similar detectors and inefficient detector distribution in nonself space.In approach proposed in the present paper,the maximal nonself space coverage is associated with bi-objective optimization criteria including minimization of the detector overlap and maximization of the diversity factor of the detectors.In the proposed methodology,a novel diversity factorbased approach is presented to obtain diversified detector distribution in the nonself space.The concept of diversified detector distribution is studied for detector coverage with 2-dimensional pentagram and spiral self-patterns.Furthermore,the feasibility of the developed fault detection methodology is tested the fault detection of induction motor inner race and outer race bearings.

A Cuckoo Search Detector Generation-based Negative Selection Algorithm

The negative selection algorithm(NSA)is an adaptive technique inspired by how the biological immune system discriminates the self from nonself.It asserts itself as one of the most important algorithms of the artificial immune system.A key element of the NSA is its great dependency on the random detectors in monitoring for any abnormalities.However,these detectors have limited performance.Redundant detectors are generated,leading to difficulties for detectors to effectively occupy the non-self space.To alleviate this problem,we propose the nature-inspired metaheuristic cuckoo search(CS),a stochastic global search algorithm,which improves the random generation of detectors in the NSA.Inbuilt characteristics such as mutation,crossover,and selection operators make the CS attain global convergence.With the use of Lévy flight and a distance measure,efficient detectors are produced.Experimental results show that integrating CS into the negative selection algorithm elevated the detection performance of the NSA,with an average increase of 3.52%detection rate on the tested datasets.The proposed method shows superiority over other models,and detection rates of 98%and 99.29%on Fisher’s IRIS and Breast Cancer datasets,respectively.Thus,the generation of highest detection rates and lowest false alarm rates can be achieved.

Research on a randomized real-valued negative selection algorithm

A real-valued negative selection algorithm with good mathematical foundation is presented to solve some of the drawbacks of previous approach. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance.

Sampled Value Attack Detection for Busbar Differential Protection Based on a Negative Selection Immune System

Considering a variety of sampled value(SV)attacks on busbar differential protection(BDP)which poses challenges to conventional learning algorithms,an algorithm to detect SV attacks based on the immune system of negative selection is developed in this paper.The healthy SV data of BDP are defined as self-data composed of spheres of the same size,whereas the SV attack data,i.e.,the nonself data,are preserved in the nonself space covered by spherical detectors of different sizes.To avoid the confusion between busbar faults and SV attacks,a self-shape optimization algorithm is introduced,and the improved self-data are verified through a power-frequency fault-component-based differential protection criterion to avoid false negatives.Based on the difficulty of boundary coverage in traditional negative selection algorithms,a self-data-driven detector generation algorithm is proposed to enhance the detector coverage.A testbed of differential protection for a 110 kV double busbar system is then established.Typical SV attacks of BDP such as amplitude and current phase tampering,fault replays,and the disconnection of the secondary circuits of current transformers are considered,and the delays of differential relay operation caused by detection algorithms are investigated.

Negative Selection of Written Language Using Character Multiset Statistics

We study the combination of symbol frequence analysis and negative selection for anomaly detection of discrete sequences where conventional negative selection algorithms are not practical due to data sparsity.Theoretical analysis on ergodic Markov chains is used to outline the properties of the presented anomaly detection algorithm and to predict the probability of successful detection.Simulations are used to evaluate the detection sensitivity and the resolution of the analysis on both generated artificial data and real-world language data including the English Wikipedia.Simulation results on large reference corpora are used to study the effects of the assumptions made in the theoretical model in comparison to real-world data.

Clone Selection Algorithm with Niching Strategy for Computer Immune System

A clone selection algorithm for computer immune system is presented. Clone selection principles in biological immune system are applied to the domain of computer virus detection. Based on the negative selection algorithm proposed by Stephanie Forrest, combining mutation operator in genetic algorithms and niching strategy in biology is adopted, the number of detectors is decreased effectively and the ability on self-nonself discrimination is improved. Simulation experiment shows that the algorithm is simple, practical and is adapted to the discrimination for long files.

Multi-agent decision support system for missile defense based on improved PSO algorithm

Ballistic missile defense system (BMDS) is important for its special role in ensuring national security and maintaining strategic balance. Research on modeling and simulation of the BMDS beforehand is essential as developing a real one requires lots of manpower and resources. BMDS is a typical complex system for its nonlinear, adaptive and uncertainty characteristics. The agent-based modeling method is well suited for the complex system whose overall behaviors are determined by interactions among individual elements. A multi-agent decision support system (DSS), which includes missile agent, radar agent and command center agent, is established based on the studies of structure and function of BMDS. Considering the constraints brought by radar, intercept missile, offensive missile and commander, the objective function of DSS is established. In order to dynamically generate the optimal interception plan, the variable neighborhood negative selection particle swarm optimization (VNNSPSO) algorithm is proposed to support the decision making of DSS. The proposed algorithm is compared with the standard PSO, constriction factor PSO (CFPSO), inertia weight linear decrease PSO (LDPSO), variable neighborhood PSO (VNPSO) algorithm from the aspects of convergence rate, iteration number, average fitness value and standard deviation. The simulation results verify the efficiency of the proposed algorithm. The multi-agent DSS is developed through the Repast simulation platform and the constructed DSS can generate intercept plans automatically and support three-dimensional dynamic display of missile defense process.

Smartphone Malware Detection Model Based on Artificial Immune System

In order to solve the problem that me traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model （SP-MDM） based on artificial immune system, in which static malware analysis and dynamic malware analysis techniques are combined, and antigens are generated by encoding the characteristics extracted from the malware. Based on negative selection algorithm, the mature detectors are generated. By introducing clonal selection algorithm, the detectors with higher affinity are selected to undergo a proliferation and somatic hyper-mutation process, so that more excellent detector offspring can be generated. Experimental result shows that the detection model has a higher detection rate for unknown smartphone malware, and better detection performance can be achieved by increasing the clone generation.

A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm

Negative selection algorithm(NSA)is one of the classic artificial immune algorithm widely used in anomaly detection.However,there are still unsolved shortcomings of NSA that limit its further applications.For example,the nonselfdetector generation efficiency is low;a large number of nonselfdetector is needed for precise detection;low detection rate with various application data sets.Aiming at those problems,a novel radius adaptive based on center-optimized hybrid detector generation algorithm(RACO-HDG)is put forward.To our best knowledge,radius adaptive based on center optimization is first time analyzed and proposed as an efficient mechanism to improve both detector generation and detection rate without significant computation complexity.RACO-HDG works efficiently in three phases.At first,a small number of self-detectors are generated,different from typical NSAs with a large number of self-sample are generated.Nonself-detectors will be generated from those initial small number of self-detectors to make hybrid detection of self-detectors and nonself-detectors possible.Secondly,without any prior knowledge of the data sets or manual setting,the nonself-detector radius threshold is self-adaptive by optimizing the nonself-detector center and the generation mechanism.In this way,the number of abnormal detectors is decreased sharply,while the coverage area of the nonself-detector is increased otherwise,leading to higher detection performances of RACOHDG.Finally,hybrid detection algorithm is proposed with both self-detectors and nonself-detectors work together to increase detection rate as expected.Abundant simulations and application results show that the proposed RACO-HDG has higher detection rate,lower false alarm rate and higher detection efficiency compared with other excellent algorithms.

Immune Based Intrusion Detector Generating Algorithm

Immune-based intrusion detection approaches are studied. The methods ofconstructing self set and generating mature detectors are researched and improved. A binary encodingbased self set construction method is applied. First, the traditional mature detector generatingalgorithm is improved to generate mature detectors and detect intrusions faster. Then, a novelmature detector generating algorithm is proposed based on the negative selection mechanism.According to the algorithm, less mature detectors are needed to detect the abnormal activities inthe network. Therefore, the speed of generating mature detecotrs and intrusion detection isimproved. By comparing with those based on existing algorithms, the intrusion detection system basedon the algorithm has higher speed and accuracy.

An immunity-based technique to detect network intrusions

This paper briefly reviews other people’s works on negative selection algorithm and their shortcomings. With a view to the real problem to be solved, authors bring forward two assumptions, based on which a new immune algorithm, multi-level negative selection algorithm, is developed. In essence, compared with Forrest’s negative selection algorithm, it enhances detector generation efficiency. This algorithm integrates clonal selection process into negative selection process for the first time. After careful analyses, this algorithm was applied to network intrusion detection and achieved good results.

Immunity Based Worm Detection System

Current worm detection methods are unable to detect multi-vector polymorphic worms effectively. Based on negative selection mechanism of the immune system, a local network worm detection system that detects worms was proposed. Normal network service requests were represented by self-strings, and the detection system used self-strings to monitor the network for anomaly. According to the properties of worm propagation, a control center correlated the anomalies detected in the form of binary trees to ensure the accuracy of worm detection. Experiments show the system to be effective in detecting the traditional as well as multi-vector polymorphic worms.

How does male-male competition generate negative frequency-dependent selection and disruptive selection during speciation？

Natural selection has been shown to drive population differentiation and speciation. The role of sexual selection in this process is controversial; however, most of the work has centered on mate choice while the role of male-male competition in speciation is relatively understudied. Here, we outline how male-male competition can be a source of diversifying selection on male competitive phenotypes, and how this can contribute to the evolution of reproductive isolation. We highlight how negative frequency-dependent selection （advantage of rare phenotype arising from stronger male-male competition between similar male phenotypes compared with dissimilar male pheno- types） and disruptive selection （advantage of extreme phenotypes） drives the evolution of diversity in competitive traits such as weapon size, nuptial coloration, or aggressiveness. We underscore that male-male competition interacts with other life-history functions and that variable male com- petitive phenotypes may represent alternative adaptive options. In addition to competition for mates, aggressive interference competition for ecological resources can exert selection on compet- itor signals. We call for a better integration of male-male competition with ecological interference competition since both can influence the process of speciation via comparable but distinct mecha- nisms. Altogether, we present a more comprehensive framework for studying the role of male-male competition in speciation, and emphasize the need for better integration of insights gained from other fields studying the evolutionary, behavioral, and physiological consequences of agonistic interactions.

Dynamic detection for computer virus based on immune system

Inspired by biological immune system, a new dynamic detection model for computer virus based on immune system is proposed. The quantitative description of the model is given. The problem of dynamic description for self and nonself in a computer virus immune system is solved, which reduces the size of self set. The new concept of dynamic tolerance, as well as the new mechanisms of gene evolution and gene coding for immature detectors is presented, improving the generating efficiency of mature detectors, reducing the false-negative and false-positive rates. Therefore, the difficult problem, in which the detector training cost is exponentially related to the size of self-set in a traditional computer immune system, is thus overcome. The theory analysis and experimental results show that the proposed model has better time efficiency and detecting ability than the classic model ARTIS.

Conserved Cytochrome P450 Evolved in Seed Plants Regulates Flower Maturation

Global inspection of plant genomes identifies genes maintained in low copies across taxa and under strong purifying selection, which are likely to have essential functions. Based on this rationale, we investigated the function of the low-duplicated CYP715 cytochrome P450 gene family that appeared early in seed plants and evolved under strong negative selection. Arabidopsis CYP715A 1 showed a restricted tissue-specific expres- sion in the tapetum of flower buds and in the anther filaments upon anthesis, cyp715a1 insertion lines showed a strong defect in petal development, and transient alteration of pollen intine deposition. Comparative expres- sion analysis revealed the downregulated expression of genes involved in pollen development, cell wall biogenesis, hormone homeostasis, and floral sesquiterpene biosynthesis, especially TPS21 and several key genes regulating floral development such as MYB21, MYB24, and MYC2. Accordingly, floral sesquiterpene emission was suppressed in the cyp715a1 mutants. Flower hormone profiling, in addition, indicated a modi- fication of gibberellin homeostasis and a strong disturbance of the turnover of jasmonic acid derivatives. Petal growth was partially restored by the active gibberellin GA3 or the functional analog of jasmonoyl-isoleucine, coronatine. CYP715 appears to function as a key regulator of flower maturation, synchronizing petal expan- sion and volatile emission. It is thus expected to be an important determinant of flower-insect interaction.

Medullary thymic epithelial cells,the indispensable player in central tolerance

Crosstalk between thymocytes and thymic epithelial cells is critical for T cell development and the establishment of central tolerance.Medullary thymic epithelial cells(mTECs) play important roles in the late stage of T cell development,especially negative selection and Treg generation.The function of mTECs is highly dependent on their characteristic features such as ectopic expression of peripheral tissue restricted antigens(TRAs) and their master regulator-autoimmune regulator(Aire),expression of various chemokines and cytokines.In this review,we summarize the current understanding of cellular and molecular mechanisms of mTEC development and its functions in T cell development and the establishment of central tolerance.The open questions in this field are also discussed.Understanding the function and underlying mechanisms of mTECs will contribute to the better control of autoimmune diseases and the improvement of immune reconstitution during aging or after infection,chemotherapy or radiotherapy.