Application Of Artificial Immune System Research Articles

Solving the Optimal Control Problem with Terminal Constraints in Modeling Chemical Processes

The paper presents a method for solving the problem of optimal control of a chemical process in the presence of terminal constraints based on the application of artificial immune systems. The formulation of the problem of optimal control of a chemical process in which the constraints on the control parameter and phase variables are given is formulated. An algorithm for solving this problem based on the penalty method and artificial immune systems is given. The penalty method allows to reduce the problem with terminal constraints to the problem without constraints by changing the optimality criterion. The new optimal control problem without terminal constraints is solved by the method of artificial immune systems. The described methodology is tested on the example of an industrially significant process of phthalic anhydride production. A mathematical description of the process, which is a system of ordinary differential equations, is given. The problem of search of optimal temperature regime with terminal constraints is formulated. As a result of calculations, the optimal temperature regime and the corresponding optimal concentrations of substances are determined. At the same time, the restrictions on the concentration value of the reaction by-product and the conversion of substances at the end of the reaction are fulfilled. It is shown that the solution of the optimal control problem with terminal constraints for the process of phthalic anhydride production satisfies the constraints of the problem and provides the highest value of the concentration of the target reaction product.

Integration of Artificial Intelligence for Real-Time Fault Detection in Semiconductor Packaging

Semiconductor manufacturing involves a complex sequence of unit processes, where even a minor error can disrupt the entire production chain. Present-day manufacturing setups rely on continuous data monitoring of equipment health, wafer measurements, and inspections to identify any abnormalities that could impact the quality and performance of the final chip. The primary aim is fault detection and classification (FDC), for which a range of techniques including statistical analysis and machine learning algorithms are commonly employed. In this study, we introduce an innovative approach utilizing an artificial immune system (AIS), inspired by biological mechanisms, for FDC in semiconductor equipment. The main culprits behind process failures are shifts caused by the aging of parts and modules over time. Our methodology integrates state variable identification (SVID) data, reflecting current equipment conditions, and optical emission spectroscopy (OES) data, capturing plasma process information under faulty scenarios induced by deliberate gas flow rate adjustments in semiconductor fabrication. Our results demonstrate a modeling prediction accuracy of 94.69% when incorporating selected SVID and OES data, and 93.68% accuracy using OES data alone. In conclusion, we suggest the potential application of AIS in semiconductor process decision-making, offering promising avenues for enhancing fault detection in semiconductor equipment.

Application of Artificial Immune Systems in Advanced Manufacturing

In recent years, the application of Advanced Manufacturing Technologies (AMT) in industrial processes represents the introduction of different Advanced Manufacturing Systems (AMS), which encourage enterprises to improve their core competitiveness and maintain sustainable development when facing the increasing demand for personalized product customization. More recently, AMT led to a new Internet revolution, mostly known as 4th Industrial Revolution. Considering the development and deployment of Artificial Intelligence to enable smart and self-behaving industrial systems, autonomic approaches allow the system to adapt itself, eliminating the need for human intervention for management. This paper presents a systematic literature review regarding Artificial Immune Systems (AIS) approaches to tackle multiple AMS problems requiring levels of autonomy. First, a systematic review of current industrial AIS applications in manufacturing environments is presented. Then, a conceptual framework is proposed to bridge the gap between research in the AIS field and the manufacturing industry while discussing key challenges and opportunities to be addressed by future research. This study aims to build a body of knowledge for researchers and manufacturers regarding AIS solutions under Advanced Manufacturing while suggesting directions for understanding the requirements for designing and managing autonomic industry applications supported by AIS. • Presentation of a systematic review of AIS methods application in AMS. • Summary of the empirical evidence of the benefits of applying AIS algorithms in AMS. • Exploration of the readiness level and autonomy of AIS applications in AMS. • Outline the key areas where future research can improve the use of AIS in AMS.

Immune systems in computer science

A simplified description of the immune system is as follows: this is an organic system intended for protecting the host organism from the threats posed to it from pathogens and toxic substances. The architecture of the immune system is such that a series of defensive layers protect the host. Once the pathogen makes it inside the host, it must contend with the innate and adaptive immune system. These two immunological sub-systems are comprised of several types of cells and molecules produced by specialized organs and processes to address the self non-self problem. Artificial Immune Systems (AIS) are computational models, based on natural immune systems. They tend to solve specific problems in computer science by resembling the natural mechanisms. These systems are more widely applied within problem domains like clustering, pattern recognition, classification, optimization, and machine learning. Modern AIS are inspired by one of three sub-fields: clonal selection, negative selection, and immune network algorithms. In computer science, the clonal selection pattern can be used for pattern matching and optimization. The negative selection algorithm was designed for change detection, novelty detection, and intrusion detection. Immune network algorithms are inspired by the immune network theory of the acquired immune system and it is an upgrade of the clonal selection theory. The objective of the immune network process is to prepare a repertoire of discrete pattern detectors for a given problem domain, where better performing solutions suppress low affinity solutions within the same network. This is an interactive process of exposing the pattern to external information to which it responds. This article explains the biological background, the mechanisms of AIS, and presents their real-world applications. It presents an overview of those important applications of AIS for solving problems from problem domains like data analysis, anomaly detection, intrusion detection, and others.

Application of AIS on Closed Loop Controller Design in Buck Converter

The present work details on design of closed loop controller in buck converter by using AIS (Artificial Immune System) algorithm. At present condition, the dynamic response by the conventional controller is not similar in various operating points. Hence, the work is carried out by keeping controller design as an optimum one and the parameters of controller are derived from AIS algorithm. The performance of the controller is tested under difference in load, reference voltage and input voltage.

Artificial Immune Systems: An Overview for Faulting Actuators

This paper reviews Artificial Immune Systems (AIS) that can be implemented to compensate for actuators that are in a faulted state or operating abnormally. Eventually, all actuators will fail or wear out, and these actuator faults must be managed if a system is to operate safely. The AIS are adaptive algorithms which are inherently well-suited to these situations by treating these faults as infections that must be combated. However, the computational intensity of these algorithms has caused them to have limited success in real-time situations. With the advent of distributed and cloud-based computing these algorithms have begun to be feasible for diagnosing faulted actuators and then generating compensating controllers in near-real-time. To encourage the application of AIS to these situations, this work presents research for the fundamental operating principles of AIS, their applications, and a brief case-study on their applicability to fault compensation by considering an overactuated rover with four independent drive wheels and independent front and rear steering.

The application of artificial immune system for parallel process of calculation and their comparison with existing methods

The present paper is devoted to the application of artificial immune system method for the solution of the symbolic regression problem. A mathematical model with the algorithm of its realization in sequential and parallel realization using some properties of the natural immune system is proposed. In the description of the proposed artificial immune system model and algorithm the terminology of the natural immune system is preserved. The concept of B-cells, which are responsible for producing antibodies, a special class of complex proteins on the surface of B-lymphocytes, capable of binding to certain types of molecules (antigens), is adopted and used. Accuracy of calculations is compared to that for other methods, i.e. methods of genetic programming. The program product involving the algorithms is developed. Computing experiments for each considered task are made.

The application of artificial immune system to solve recognition problems

The present paper is devoted to the creation of a model and algorithm of an artificial immune system (AIS). The proposed model and algorithm are designed to solve the problems of single symbols recognition, symbolic regression, managing autonomous systems (robot). The taxonomy of the proposed artificial immune system model and algorithm of research methodology is described in terms of the natural immune system. The adopted concept of B-cells are responsible for producing antibodies, a special class of complex proteins on the surface of B-lymphocytes, capable of binding to certain types of molecules (antigens) is used. The full set of antigenic receptors of all B-cells is a plurality of antibodies that can be produced by the organism. The developed program complex allows to solve all three problems: single symbols recognition, symbolic regression, managing autonomous systems (robot). Accuracy of calculations is compared to that for other methods, i. e. methods of genetic programming. Computing experiments for each considered task are made.

Towards sustainable WEEE collection and transportation methods in circular economy - Comparative study for rural and urban settlements

A circular economy approach requires methods of resource management that are sustainable and efficient. A crucial process in waste management is collection and transportation of waste. One of the most valuable streams is waste electrical and electronic equipment (WEEE). There is a variety of disposal methods available for residents and a variety of collection methods for companies.The scope of this study includes the application of artificial immune systems in planning routes of vehicles in a mobile program and in placement of containers in stationary collections of WEEE. The intent is to create a level playing field by comparing sustainability of the waste collections and transportation methods in optimal designs.The analysis includes a comparison of the three most common methods of WEEE collection including cost analysis and vehicle emission using case studies for urban and rural areas. The results show the importance of properly planned collection routes. Mobile collection is the most sustainable method of WEEE collection while drop off of waste by residents at municipal collection centers is the worst case due to high emissions and operation costs. The results and the proposed model give an opportunity to evaluate existing WEEE collection methods in urban and rural areas and propose some improvements for waste collecting companies.

Applications of artificial immune systems to computer security: A survey

For the last two decades, artificial immune systems have been studied in various fields of knowledge. They were shown to be particularly effective tools at detecting anomalous behavior in the security domain of computer systems. This article introduces the principles of artificial immune systems and surveys several works applying such systems to computer security problems. The works herein discussed are summarized and open issues are pointed out afterwards, elaborating on a novel applicability of these systems to cloud computing environments.

Application of artificial immune systems in detecting malware in a computer network

Проаналізовано поведінку шкідливих і нешкідливих програмних об'єктів з метою отриманняпротоколу їх роботи. Розглянуто варіанти розпізнавання шкідливих програм на рівні їх виявлення і блокування. Виявлено загальні для досліджуваних програм особливості поведінки. Наведено рівняння, що описують дії даного способу базуючись на операторах штучних імунних мереж. Реалізовано запропоновану структуру виявлення шкідливих програм. Запропоновано методи підвищення виявлення шкідливих програм і атак в комп'ютерній мережі

An Application of Artificial Immune System in a Wastewater Treatment Plant

Abstract: Guaranteeing the continuity and the quality of services in network plants is a key issue in the research area of asset management. Especially when the plants are located in a wide area where machines are not continuously monitored by the operators. In particular, the pervasive adoption of smart sensors could be able to develop intelligent maintenance system through an elaboration of data coming from the machines: this data could be processed by diagnostics algorithms to warn preventively the fault status of the components or machines monitored. The algorithms’ structure is contained in a multiple system of agents that have different tasks to manage both the single machine and the information exchanged within the whole system. This paper aims to present an application of Artificial Immune System defining, for each plant section, the kind of agents employed and the related sensors that must be adopted to collect the useful data. In order to provide a practical example, the structure of an Artificial Immune System has been implemented in a wastewater treatment plant where the agents are tested with noteworthy results.

A Review of Artificial Immune System Based Security Frameworks for MANET

Mobile ad hoc networks (MANETs) are collections of wireless mobile devices that form a communication network with restricted broadcast range, limited resources and without fixed infrastructure. Routing is a critical function in multi-hop MANETs. At the same time, security in MANETs—especially routing security—presents a number of new and interesting challenges. Communication is achieved by relaying data along routes that are dynamically discovered and maintained through collaboration between the nodes. Advances in the field of artificial immune systems provide an opportunity to improve MANET security and performance. Artificial immune systems mimic the functionality of the human immune system wherein there is clear distinction between self and non self and this delineation is important in a MANET where there is no centralized management. The high level of protection provided to the human body by an evolved immune system can be applied as a security feature in MANET. The current security techniques proposed for MANET have varying degrees of success due to the dynamic nature of a MANET. This paper will review different strategies for the application of artificial immune systems to MANETs.

Applications of Artificial Immune System: A Review

Applications of Artificial Immune System: A Review

An Overview of Application of Artificial Immune System in Swarm Robotic Systems

The Artificial Immune System (AIS) is a biologically inspired computation system based on specifically human immune system. AIS applications in last one decade have been developed to address the complex computational and engineering problems related to classification, optimisation and anomaly detection. Many investigations have been conducted to understand the principles of immune system to translate the knowledge into AIS applications. However, a clear understanding of principles and responses of immune system is still required for application of AIS to Swarm Robotics. This article after a review of AIS models and algorithms proposes an integration of AIS and Swarm Robotics by developing a very clear understanding of immune system structures and associated functions.

An Overview of Application of Artificial Immune System in Swarm Robotic Systems

The Artificial Immune System (AIS) is a biologically inspired computation system based on vertebrate immune system. AIS applications in last one decade have been developed to address the complex computational and engineering problems related to classification, optimization and anomaly detection. Many investigations have been conducted to understand the principles of immune system to translate the knowledge into AIS applications. However, a clear understanding of principles and responses of immune system is still required for application of AIS to Swarm Robotics. This paper after a review of AIS models and algorithms proposes an integration of AIS and Swarm Robotics by developing a very clear understanding of immune system structures and associated functions.

Reactive Power Planning for Maximum Load Margin Improvement Using Fast Artificial Immune Support Vector Machine (FAISVM)

Load margin improvement is an important issue in power system planning and operation. This paper, first, presents a newly voltage stability index called Voltage Stability Condition Indicator (VSCI) to evaluate the voltage stability state of load buses in a system. It also proposes a fast optimization algorithm for reactive power planning problem (RPP) through Fast Artificial Immune Support Vector Machine (FAISVM). FAISVM is a hybrid algorithm that incorporates the application of Artificial Immune System (AIS) and Support Vector Machine (SVM) in solving RPP problems. The newly proposed algorithm can determine the optimal tap settings of tap changing transformers, the value of reactive power injection at the reactive power sources and the injection at the reactive power generator buses. The performances of the techniques proposed were verified using the IEEE 30-bus test system and compared with another newly developed hybrid Evolutionary Support Vector Machine (ESVM). The simulation results have shown that FASIVM outperformed ESVM in terms of maximum load margin improvement and computation time significantly and also reduce active power losses.

Application of Artificial Immune System in Structural Health Monitoring

A large number of methods have been proposed in the area of structural health monitoring (SHM). However, many of them rely on the prior knowledge of structural-parameter-values or the assumption that the structural-parameter-values do not change without damage. This dependence on specific parameter values limits these methods’ applicability. This paper proposes an artificial immune system- (AIS-) based approach for the civil structural health monitoring, which does not require specific parameter values to work. A linear three-floor structure model and a number of single-damage scenarios were used to evaluate the proposed method’s performance. The high success rate showed this approach’s great potential for the SHM tasks. This approach has merits of less dependence on the structural-parameter-values and low demand on the training conditions.

Artificial Immune Systems Applied to the Analysis of Structural Integrity of a Building

This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This methodology can assist professionals in the inspection of mechanical and civil structures, to identify and characterize flaws, in order to perform preventative maintenance to ensure the integrity of the structure and decision-making. In order to evaluate the methodology was made modeling a two-story building and several situations were simulated (base-line condition and improper conditions), yielding a database of signs, which were used as input data for the negative selection algorithm. The results obtained by the present method efficiency, robustness and accuracy.

An Artificial Immune Classification Algorithm based on Particle Swarm Optimization

Biological models of the natural immune system have provided the inspiration for artificial immune system, in particular the theories of negative selection, clonal selection and immune networks. The various applications of artificial immune systems have been used for pattern recognition and classification problems, however the traditional artificial immune algorithms have two major problems: the control of the growing of the memory cells population and the major operators of cloning and mutation are taken randomly. In this paper, a new artificial immune classifier based on practical swarm optimization is proposed to solve these problems. The proposed algorithm uses practical swarm to evolve the antibody population. During the training stages, the number of memory cells for each class is predefined to avoid antibody population growing. Then the improved practical swarm optimization method is adopted to evolve antibody population. In each iteration, the antibody as a particle moves to the optimal solution with directional parameters. The new classifier is tested on benchmark datasets using different classification techniques and found to be very competitive when compared to other classifiers.