• IMMUNE NETWORK
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• v.11 no.5
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• pp.245-252
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• 2011

Antimicrobial peptides/proteins are ancient and naturally-occurring antibiotics in innate immune responses in a variety of organisms. Additionally, these peptides have been recognized as important signaling molecules in regulation of both innate and adaptive immunity. During mycobacterial infection, antimicrobial peptides including cathelicidin, defensin, and hepcidin have antimicrobial activities against mycobacteria, making them promising candidates for future drug development. Additionally, antimicrobial peptides act as immunomodulators in infectious and inflammatory conditions. Multiple crucial functions of cathelicidins in antimycobacterial immune defense have been characterized not only in terms of direct killing of mycobacteria but also as innate immune regulators, i.e., in secretion of cytokines and chemokines, and mediating autophagy activation. Defensin families are also important during mycobacterial infection and contribute to antimycobacterial defense and inhibition of mycobacterial growth both in vitro and in vivo. Hepcidin, although its role in mycobacterial infection has not yet been characterized, exerts antimycobacterial effects in activated macrophages. The present review focuses on recent efforts to elucidate the roles of host defense peptides in innate immunity to mycobacteria.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.325-329
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• 2003

This paper discuss the method of the system's efficient control using a Intelligent hybrid algorithm in nonlinear dynamics systems. Existing neural network and genetic algorithm for the control of non-linear systems work well in static states. but it be not particularly good in changeable states and must re-learn for the control of the system in the changed state. This time spend a lot of time. For the solution of this problem we suggest the intelligent hybrid self-tuning controller. it includes neural network, genetic algorithm and immune system. it is based on neural network, and immune system and genetic algorithm are added against a changed factor. We will call a change factor an antigen. When an antigen broke out, immune system come into action and genetic algorithm search an antibody. So the system is controled more stably and rapidly. Moreover, The Genetic algorithm use the memory address of the immune bank as a genetic factor. So it brings an advantage which the realization of a hardware easy.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3036-3038
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• 1999

The conventional structure for an action selector of an Autonomous Mobile Robot (AMR) has been criticized for a repeated action. To overcome this problem recently many researches have been focused on the reactive planning systems such as the biological immune system. In this paper, we propose a learning aritificial immune network, the learning method is to use Genetic Algorithm (GA). The computer simulation show that the usefulness of the learning immune network.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.426-439
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• 2021

To alleviate the cold-start problem and data sparsity in web service recommendation and meet the personalized needs of users, this paper proposes a personalized web service recommendation method based on a hybrid social network and multi-objective immune optimization. The network adds the element of the service provider, which can provide more real information and help alleviate the cold-start problem. Then, according to the proposed service recommendation framework, multi-objective immune optimization is used to fuse multiple attributes and provide personalized web services for users without adjusting any weight coefficients. Experiments were conducted on real data sets, and the results show that the proposed method has high accuracy and a low recall rate, which is helpful to improving personalized recommendation.

• IMMUNE NETWORK
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• v.13 no.2
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• pp.43-54
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• 2013

Over the past 40 years, flow cytometry has emerged as a leading, application-rich technology that supports high-resolution characterization of individual cells which function in complex cellular networks such as the immune system. This brief overview highlights advances in multiparameter flow cytometric technologies and reagent applications for characterization and functional analysis of cells modulating the immune network. These advances significantly support highthroughput and high-content analyses and enable an integrated understanding of the cellular and molecular interactions that underlie complex biological systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3B
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• pp.311-317
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• 2009

The traditional network anomaly detection systems execute the threshold-based detection without considering dynamic network environments, which causes false positive and limits an effective resource utilization. To overcome the drawbacks, we present the adaptive network anomaly detection model based on artificial immune system (AIS) in centralized network. AIS is inspired from human immune system that has learning, adaptation and memory. In our proposed model, the interaction between dendritic cell and T-cell of human immune system is adopted. We design the main components, such as central node and router node, and define functions of them. The central node analyzes the anomaly information received from the related router nodes, decides response policy and sends the policy to corresponding nodes. The router node consists of detector module and responder module. The detector module perceives the anomaly depending on learning data and the responder module settles the anomaly according to the policy received from central node. Finally we evaluate the possibility of the proposed detection model through simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.532-532
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• 2000

Conventional artificial intelligence systems are not properly responding under dynamically changing environments. To overcome this problem, reactive planning systems implementing new Al principles, called behavior-based Al or emergent computation, have been proposed and confirmed their usefulness. As another alternative, biological information processing systems may provide many feasible ideas to these problems. Immune system, among these systems, plays important roles to maintain its own system against dynamically changing environments. Therefore, immune system would provide a new paradigm suitable for dynamic problem dealing with unknown environments. In this paper, a new approach to behavior-based Al by paying attention to biological immune system is investigated. The feasibility of this method is confirmed by applying to behavior control of an autonomous mobile robot in cluttered environment.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.151-151
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• 2000

The objective of this paper is to develop the new robust and adaptive control system against external environments as applying the probabilistic recognition which is one of the inherent properties of immune system, ability of learning and memorization, and regulation theory of immune network to the system under engineering point of view. In this paper, HIA(Humoral Immune Algorithm) PID controller using Neural Network Identifier was proposed to drive the autonomous guided vehicle(AGV) more effectively. To verify the performance of the proposed HIA PID controller, some experiments for the control of steering and speed of that AGV are performed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1286-1302
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• 2012

In this paper, the resource allocation problem with proportional fairness rate in cognitive OFDM-based wireless network is studied. It aims to maximize the total system throughput subject to constraints that include total transmit power for secondary users, maximum tolerable interferences of primary users, bit error rate, and proportional fairness rate among secondary users. It is a nonlinear optimization problem, for which obtaining the optimal solution is known to be NP-hard. An efficient bio-inspired suboptimal algorithm called immune clonal optimization is proposed to solve the resource allocation problem in two steps. That is, subcarriers are firstly allocated to secondary users assuming equal power assignment and then the power allocation is performed with an improved immune clonal algorithm. Suitable immune operators such as matrix encoding and adaptive mutation are designed for resource allocation problem. Simulation results show that the proposed algorithm achieves near-optimal throughput and more satisfying proportional fairness rate among secondary users with lower computational complexity.

• IMMUNE NETWORK
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• v.13 no.6
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• pp.227-234
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• 2013

The intestinal immune system has an ability to distinguish between the microbiota and pathogenic bacteria, and then activate pro-inflammatory pathways against pathogens for host defense while remaining unresponsive to the microbiota and dietary antigens. In the intestine, abnormal activation of innate immunity causes development of several inflammatory disorders such as inflammatory bowel diseases (IBD). Thus, activity of innate immunity is finely regulated in the intestine. To date, multiple innate immune cells have been shown to maintain gut homeostasis by preventing inadequate adaptive immune responses in the murine intestine. Additionally, several innate immune subsets, which promote Th1 and Th17 responses and are implicated in the pathogenesis of IBD, have recently been identified in the human intestinal mucosa. The demonstration of both murine and human intestinal innate immune subsets contributing to regulation of adaptive immunity emphasizes the conserved innate immune functions across species and might promote development of the intestinal innate immunity-based clinical therapy.