• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.279-284
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• 2007

Nowadays, due to development of automatic control devices and various sensors, one operator can freely handle several remote plants and processes. Automatic diagnosis and warning systems have been adopted in various fields, in order to prepare an operator's absence for patrolling plants. In this paper, a Bayesian networks based on-line diagnosis system is proposed for a wastewater treatment process. Especially, the suggested system is included learning structure, which can continuosly update conditional probabilities in the networks. To evaluate performance of proposed model, we made a lab-scale five-stage step-feed enhanced biological phosphorous removal process plant and applied on-line diagnosis system to this plant in the summer.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.920-924
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• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• Korean Journal of Biological Psychiatry
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• v.29 no.1
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• pp.15-21
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• 2022

Objectives Maltreatment experiences can alter brain function related to emotion regulation, such as cognitive reappraisal. While dysregulation of emotion is an important risk factor to mental health problems in maltreated people, studies reported alterations in brain networks related to cognitive reappraisal are still lacking. Methods Twenty-seven healthy subjects were recruited in this study. The maltreatment experiences and positive reappraisal abilities were measured using the Childhood Trauma Questionnaire-Short Form and the Cognitive Emotion Regulation Questionnaire, respectively. Twelve subjects reported one or more moderate maltreatment experiences. Subjects were re-exposed to pictures after the cognitive reappraisal task using the International Affective Picture System during fMRI scan. Results The maltreatment group reported more negative feelings on negative pictures which tried cognitive reappraisal than the no-maltreatment group (p < 0.05). Activities in the right superior marginal gyrus and right middle temporal gyrus were higher in the maltreatment group (uncorrected p < 0.001, cluster size > 20). Conclusions We found that paradoxical activities in semantic networks were shown in the victims of maltreatment. Further study might be needed to clarify these aberrant functions in semantic networks related to maltreatment experiences.

• Journal of Korea Technology Innovation Society
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• v.11 no.2
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• pp.219-240
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• 2008

This paper aims to suggest a policy for biological resource based on a comprehensive understanding on biological resources. Biological resources are different from traditionally recognized viable organisms (Biodiversity) in ecosystems. Biological resources are culturable and replicable resources of living organisms such as tissues, cells and genes. Moreover, biological resources include human-derived biological materials. Biological resources is not simply a matter of science and technology. Biological resources should be dealt with as national resources. There are many international issues regarding biological resources, such as intellectual property rights (IPRs), safety on handling and distribution, material transfer agreements (MTAs) for mutual benefits and biological standards. Ethical debates are also being raised because biological resources are related with human-derived biological materials. Every nation has tendency to adopt its government policies to strengthen its sovereignty on biological resources and international cooperation. In addition, international linkages are essential for providing enhanced worldwide accessibility to biological resources. Japan has shown several international initiatives in the field of biological resources. Korea has just begun to design appropriate policies for the use and R&D of biological resources. Therefore, this paper suggests the following needs: 1) policy at the national level beyond the interests of researchers, 2) inter-ministerial coordination across government ministries, 3) expansion of scope and size of each BRC (Biological Resource Centers), and 4) building networks and systems such as national information center, representative centers by field, and each BRC.

• Proceedings of the Korean Society of Applied Entomolohy Conference
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• 2002.05a
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• pp.64-64
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• 2002

• Biomaterials and Biomechanics in Bioengineering
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• v.2 no.1
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• pp.33-45
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• 2015

To engineer tissue-like structures, cells are required to organize themselves into three-dimensional networks that mimic the native tissue micro-architecture. Here, we present agarose-based multi-well platform incorporated with electrical stimulation to build skeletal muscle-like tissues in a facile and highly reproducible fashion. Electrical stimulation of C2C12 cells encapsulated in collagen/matrigel hydrogels facilitated the formation 3D muscle tissues. Consequently, we confirmed the transcriptional upregulations of myogenic related genes in the electrical stimulation group compared to non-stimulated control group in our multi-well 3D culture platform. Given the robust fabrication, engineered muscle tissues in multi-well platform may find their use in high-throughput biological studies drug screenings.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.11
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• pp.88-95
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• 1998

This paper is the result of constructing information processing system such as living creatures' brain based on artificial life techniques. The living things are best information processing system in themselves. One individual is developed from a generative cell. And a species of this individual has adapted itself to the environment through evolution. In this paper, we propose a new method of designing neural networks using biological inspired developmental and evolutionary concept. Ontogeny of organism is embodied in cellular automata(CA) and phylogeny of species is realized by evolutionary algorithms(EAs). We call 'Evolving Cellular Automata Neural Systems' as ECANSI. The connection among cells is determined by the rule of cellular automata. In order to obtain the best neural networks in given environment, we evolve the arragemetn of initial cells. The cell, that is a neuron of neural networks, is modeled on chaotic neuron with firing or rest state like biological neuron. A final output of network is measured by frequency of firing state. The effectiveness of the proposed scheme is verified by applying it to Exclusive-OR and parity problem.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.202-205
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• 2005

Cell phenotypes are determined by the concerted activity of thousands of genes and their products. This activity is coordinated by a complex network that regulates the expression of genes. Understanding this organization is crucial to elucidate cellular activities, and many researches have tried to construct gene regulatory networks from mRNA expression data which are nowadays the most available and have a lot of information for cellular processes. Several computational tools, such as Boolean network, Qualitative network, Bayesian network, and so on, have been applied to infer these networks. Among them, Bayesian networks that we chose as the inference tool have been often used in this field recently due to their well-established theoretical foundation and statistical robustness. However, the relative insufficiency of experiments with respect to the number of genes leads to many false positive inferences. To alleviate this problem, we had developed the algorithm of MONET(MOdularized NETwork learning), which is a new method for inferring modularized gene networks by utilizing two complementary sources of information: biological annotations and gene expression. Afterward, we have packaged and improved MONET by combining dispersed functional blocks, extending species which can be inputted in this system, reducing the time complexities by improving algorithms, and simplifying input/output formats and parameters so that it can be utilized in actual fields. In this paper, we present the architecture of MONET system that we have improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2732-2753
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• 2019

With the application of wireless sensor networks in the fields of ecological observation, defense military, architecture and urban management etc., the security problem is becoming more and more serious. Characteristics and constraint conditions of wireless sensor networks such as computing power, storage space and battery have brought huge challenges to protection research. Inspired by the danger theory in biological immune system, this paper proposes an intrusion detection model for wireless sensor networks. The model abstracts expressions of antigens and antibodies in wireless sensor networks, defines meanings and functions of danger signals and danger areas, and expounds the process of intrusion detection based on the danger theory. The model realizes the distributed deployment, and there is no need to arrange an instance at each sensor node. In addition, sensor nodes trigger danger signals according to their own environmental information, and do not need to communicate with other nodes, which saves resources. When danger is perceived, the model acquires the global knowledge through node cooperation, and can perform more accurate real-time intrusion detection. In this paper, the performance of the model is analyzed including complexity and efficiency, and experimental results show that the model has good detection performance and reduces energy consumption.