Using Real Valued Detectors in Ship Immune System

The paper addresses the problem of real valued detectors in ship immune system. The task of the system mentioned is to differentiate self objects, i.e. objects that are not dangerous to our ship, from other objects that can be a potential threat. To this end, mechanisms adapted from artificial immune systems are used. Since in the traditional model of artificial immune system binary strings are used to represent detectors and objects, in this paper modifications to this model are proposed. The modifications mentioned use real valued vectors instead of binary ones. To test the ship immune system equipped with real valued detectors, experiments were carried out. In the experiments, the task of the system was to differentiate self ship radio stations from non-self ones. Results of the experiments are presented at the end of the paper

Identifying clusters of anomalous payments in the salvadorian payment system

We develop an unsupervised methodology to group payments and identify possible anomalies. With our methodology, we identify clusters based on a set of network features, using transactional (unlabeled) information from a systemically important payment system of El Salvador. We first preprocess network features, such as degree and strength, through a principal components analysis we reduce the dimensionality of the newly defined data, then we place the main variables into clustering algorithms (k-means and DBSCAN) to analyze anomalous payments. We then analyze, these clusters using random forest to obtain the main network feature. Our results suggest that the proposed methodology works very well to detect anomalous payments, and it is very important to study the case of El Salvador, because of the recent restructuring of the Massive Payment System in El Salvador (promoted by the Transfer365 project), because the authorities want to increase financial inclusion. This change will make the SPM available to the public, to diversify services and incorporate more participants because, historically, it has operated with only three active participants. We expected that Transfer365 will interconnect the LBTR participants' systems with their banking core, the systems of the Ministry of Finance, and other authorized participants to channel large payment flows. Then, identifying possible anomalies through methodology will enhance risk monitoring and management by payment systems overseers

Application of the feature-detection rule to the negative selection algorithm

The Negative Selection Algorithm developed by Forrest et al. was inspired by the way in which T-cell lymphocytes mature within the thymus before being released into the blood system. The mature T-cell lymphocytes exhibit an interesting characteristic, in that they are only activated by non-self cells that invade the human body. The Negative Selection Algorithm utilises an affinity matching function to ascertain whether the affinity between a newly generated (NSA) T-cell lymphocyte and a self-cell is less than a particular threshold; that is, whether the T-cell lymphocyte is activated by the self-cell. T-cell lymphocytes not activated by self-sells become mature T-cell lymphocytes. A new affinity matching function termed the feature-detection rule is introduced in this paper. The feature-detection rule utilises the interrelationship between both adjacent and non-adjacent features of a particular problem domain to determine whether an antigen is activated by an artificial lymphocyte. The performance of the featuredetection rule is contrasted with traditional affinity matching functions, currently employed within Negative Selection Algorithms, most notably the r-chunks rule (which subsumes the r-contiguous bits rule) and the hamming distance rule. This paper shows that the feature-detection rule greatly improves the detection rates and false alarm rates exhibited by the NSA (utilising the r-chunks and hamming distance rule) in addition to refuting the way in which permutation masks are currently being applied in artificial immune systems.http://www.elsevier.com/locate/esw

Artificial Immune System for Unmanned Aerial Vehicle Abnormal Condition Detection and Identification

A detection and identification scheme for abnormal conditions was developed for an unmanned aerial vehicle (UAV) based on the artificial immune system (AIS) paradigm. This technique involves establishing a body of data to represent normal conditions referred to as “self” and differentiating these conditions from abnormal conditions, referred to as “non-self”. Data collected from simulation of the UAV attempting to autonomously fly a pre-decided trajectory were used to develop and test a scheme that was able to detect and identify aircraft sensor and actuator faults. These faults included aerodynamic control surface locks and damages and angular rate sensor biases. The method used to create the AIS is known as the partition of the universe approach. This approach differs from standard clustering approaches because the universe is divided into uniform partition clusters rather than clustering data using some clustering algorithm. It is simpler and requires less computational resources. This will be the first time that this approach has been applied for use in aerospace engineering. Data collected from nominal flights were used to define self partitions, and the non-self partitions were defined implicitly. The creation scheme is also discussed, involving all software used for simulation, as well as the process of creating the self and the logic behind the detection and identification schemes. The detection scheme was evaluated based on detection rate, detection time, and false alarms for flights under both normal and abnormal conditions. The failure identification scheme was assessed in terms of identification rate and time. Investigation of the proposed technique showed promising results for the cases explored with comparable performance with respect to clustering-based approaches and motivates further research and extension of the proposed methodology toward a more complete health management system

Survey of negative selection algorithms

对否定选择算法进行了综述,首先回顾了否定选择算法的产生与发展;接着按照不同技术标准对其进行分类,并列举否定选择算法的实际应用情况;最后讨论了该算法所存在的问题以及未来的发展方向。A review of NS was given.Firstly,the basic principle of negative selection algorithm and its history were introduced.Secondly,various negative selection algorithms were grouped into different categories by different criteria and the application of NS was described.Besides,some open problems in the development of NS algorithms were presented and analyzed.Finally,a discussion of future trends was conclued.国家自然科学基金资助项目(61272310); 福建省自然科学基金资助项目(2010J01342); 中央高校基本科研业务费基金资助项目~

Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques

This dissertation presents the design, development, and simulation testing of a monitoring and control framework for dynamic systems using artificial intelligence techniques. A comprehensive monitoring and control system capable of detecting, identifying, evaluating, and accommodating various subsystem failures and upset conditions is presented. The system is developed by synergistically merging concepts inspired from the biological immune system with evolutionary optimization algorithms and adaptive control techniques.;The proposed methodology provides the tools for addressing the complexity and multi-dimensionality of the modern power plants in a comprehensive and integrated manner that classical approaches cannot achieve. Current approaches typically address abnormal condition (AC) detection of isolated subsystems of low complexity, affected by specific AC involving few features with limited identification capability. They do not attempt AC evaluation and mostly rely on control system robustness for accommodation. Addressing the problem of power plant monitoring and control under AC at this level of completeness has not yet been attempted.;Within the proposed framework, a novel algorithm, namely the partition of the universe, was developed for building the artificial immune system self. As compared to the clustering approach, the proposed approach is less computationally intensive and facilitates the use of full-dimensional self for system AC detection, identification, and evaluation. The approach is implemented in conjunction with a modified and improved dendritic cell algorithm. It allows for identifying the failed subsystems without previous training and is extended to address the AC evaluation using a novel approach.;The adaptive control laws are designed to augment the performance and robustness of baseline control laws under normal and abnormal operating conditions. Artificial neural network-based and artificial immune system-based approaches are developed and investigated for an advanced power plant through numerical simulation.;This dissertation also presents the development of an interactive computational environment for the optimization of power plant control system using evolutionary techniques with immunity-inspired enhancements. Several algorithms mimicking mechanisms of the immune system of superior organisms, such as cloning, affinity-based selection, seeding, and vaccination are used. These algorithms are expected to enhance the computational effectiveness, improve convergence, and be more efficient in handling multiple local extrema, through an adequate balance between exploration and exploitation.;The monitoring and control framework formulated in this dissertation applies to a wide range of technical problems. The proposed methodology is demonstrated with promising results using a high validity DynsimRTM model of the acid gas removal unit that is part of the integrated gasification combined cycle power plant available at West Virginia University AVESTAR Center. The obtained results show that the proposed system is an efficient and valuable technique to be applied to a real world application. The implementation of this methodology can potentially have significant impacts on the operational safety of many complex systems

Metabolic profiling on 2D NMR TOCSY spectra using machine learning

Due to the dynamicity of biological cells, the role of metabolic profiling in discovering biological fingerprints of diseases, and their evolution, as well as the cellular pathway of different biological or chemical stimuli is most significant. Two-dimensional nuclear magnetic resonance (2D NMR) is one of the fundamental and strong analytical instruments for metabolic profiling. Though, total correlation spectroscopy (2D NMR 1H -1H TOCSY) can be used to improve spectral overlap of 1D NMR, strong peak shift, signal overlap, spectral crowding and matrix effects in complex biological mixtures are extremely challenging in 2D NMR analysis. In this work, we introduce an automated metabolic deconvolution and assignment based on the deconvolution of 2D TOCSY of real breast cancer tissue, in addition to different differentiation pathways of adipose tissue-derived human Mesenchymal Stem cells. A major alternative to the common approaches in NMR based machine learning where images of the spectra are used as an input, our metabolic assignment is based only on the vertical and horizontal frequencies of metabolites in the 1H-1H TOCSY. One- and multi-class Kernel null foley–Sammon transform, support vector machines, polynomial classifier kernel density estimation, and support vector data description classifiers were tested in semi-supervised learning and novelty detection settings. The classifiers’ performance was evaluated by comparing the conventional human-based methodology and automatic assignments under different initial training sizes settings. The results of our novel metabolic profiling methods demonstrate its suitability, robustness, and speed in automated nontargeted NMR metabolic analysis

Integrated Immunity-based Methodology for UAV Monitoring and Control

A general integrated and comprehensive health management framework based on the artificial immune system (AIS) paradigm is formulated and an automated system is developed and tested through simulation for the detection, identification, evaluation, and accommodation (DIEA) of abnormal conditions (ACs) on an unmanned aerial vehicle (UAV). The proposed methodology involves the establishment of a body of data to represent the function of the vehicle under nominal conditions, called the self, and differentiating this operation from that of the vehicle under an abnormal condition, referred to as the non-self. Data collected from simulations of the selected UAV autonomously flying a set of prescribed trajectories were used to develop and test novel schemes that are capable of addressing the AC-DIEA of sensor and actuator faults on a UAV. While the specific dynamic system used here is a UAV, the proposed framework and methodology is general enough to be adapted and applied to any complex dynamic system. The ACs considered within this effort included aerodynamic control surface locks and damage and angular rate sensor biases. The general framework for the comprehensive health management system comprises a novel complete integration of the AC-DIEA process with focus on the transition between the four different phases. The hierarchical multiself (HMS) strategy is used in conjunction with several biomimetic mechanisms to address the various steps in each phase. The partition of the universe approach is used as the basis of the AIS generation and the binary detection phase. The HMS approach is augmented by a mechanism inspired by the antigen presenting cells of the adaptive immune system for performing AC identification. The evaluation and accommodation phases are the most challenging phases of the AC-DIEA process due to the complexity and diversity of the ACs and the multidimensionality of the AIS. Therefore, the evaluation phase is divided into three separate steps: the qualitative evaluation, direct quantitative evaluation, and the indirect quantitative evaluation, where the type, severity, and effects of the AC are determined, respectively. The integration of the accommodation phase is based on a modular process, namely the strategic decision making, tactical decision marking, and execution modules. These modules are designed by the testing of several approaches for integrating the accommodation phase, which are specialized based on the type of AC being addressed. These approaches include redefining of the mission, adjustment or shifting of the control laws, or adjusting the sensor outputs. Adjustments of the mission include redefining of the trajectory to remove maneuvers which are no longer possible, while adjusting of the control laws includes modifying gains involved in determination of commanded control surface deflections. Analysis of the transition between phases includes a discussion of results for integrated example cases where the proposed AC-DIEA process is applied. The cases considered show the validity of the integrated AC-DIEA system and specific accommodation approaches by an improvement in flight performance through metrics that capture trajectory tracking errors and control activity differences between nominal, abnormal, and accommodated cases

Faculty Publications and Creative Works 2004

Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. Published by the Office of the Vice President for Research and Economic Development, it serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM

A Self-Adaptive Evolutionary Negative Selection Approach for Anomaly Detection

Forrest et al. (1994; 1997) proposed a negative selection algorithm, also termed the exhaustive detector generating algorithm, for various anomaly detection problems. The negative selection algorithm was inspired by the thymic negative selection process that is intrinsic to natural immune systems, consisting of screening and deleting self-reactive T-cells, i.e., those T-cells that recognize self-cells. The negative selection algorithm takes considerable time (exponential to the size of the self-data) and produces redundant detectors. This time/size limitation motivated the development of different approaches to generate the set of candidate detectors. A reasonable way to find suitable parameter settings is to let an evolutionary algorithm determine the settings itself by using self-adaptive techniques. The objective of the research presented in this dissertation was to analyze, explain, and demonstrate that a novel evolutionary negative selection algorithm for anomaly detection (in non-stationary environments) can generate competent non redundant detectors with better computational time performance than the NSMutation algorithm when the mutation step size of the detectors is self-adapted