• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권4호
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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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• 제8권1호
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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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• 제어로봇시스템학회 2005년도 ICCAS
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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.

• 생물정신의학
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• 제29권1호
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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.

• 기술혁신학회지
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• 제11권2호
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• pp.219-240
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• 2008

본 연구는 새로운 국가자원인 생물자원에 대한 최근의 국제적인 이슈와 주요국의 정책의지 및 관리 체계 등을 살펴 본 것이다. 그렇지만 논의의 전개를 위해 생물자원과 관련된 이론적인 논의, 역사 등을 먼저 검토하였다. 생물자원과 관련된 최근의 이슈는 지적재산권, 보관과 배송의 안전과 관련된 국제규약의 강화, 생물자원의 생물학적 표준강화, 생물자원센터의 윤리문제 등으로 특징지어진다. 이러한 논의를 바탕으로 본고는 생물자원정책의 방향, 체계 및 내용을 간단히 권고하였다. 특히 범부처적 차원에서의 생물자원의 관리와 확보를 위한 종합 조정과 시스템 구축의 필요성을 강조하였다.

• 한국응용곤충학회:학술대회논문집
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• 한국응용곤충학회 2002년도 합동 춘계 학술발표회
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• pp.64-64
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• 2002

• Biomaterials and Biomechanics in Bioengineering
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• 제2권1호
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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.

• 전자공학회논문지S
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• 제35S권11호
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• pp.88-95
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• 1998

본 논문은 인공생명 기법을 이용하여 생물의 정보처리 시스템을 구현하고자 하는 것이다. 자연계의 생물은 그 자체로 훌륭한 정보처리 시스템이다. 생물체는 하나의 생식 세포로부터 발생된다. 또한 이 개체의 종은 진화의 과정을 통해 환경에 적응한다. 본 논문에서는 이와 같은 생물학적인 발생과 진화의 개념을 이용하여 신경망을 설계하는 방법을 제안한다. 생물체의 개체발생은 발생모델의 하나인 셀룰라 오토마다(CA)를 통하여 구현하였고 진화과정은 진화 알고리즘(EAs)을 사용하였다. 우리는 이와 같이 구현한 '진화하는 셀룰라 오토마타 신경망'을 줄여서 ECANS1이라 명명하였다. 셀 사이의 연결은 CA 법칙에 의하여 결정되며, 셀의 초기 패턴이 진화함으로써 유용한 신경망을 찾아낸다. 신경망의 각 셀 즉 뉴런은 생물의 발화 ${\cdot}$ 비발화의 특성을 갖는 카오스 뉴런 모델을 사용하였다. 그리고 신경마의 최종 출력값은 뉴런의 발화 빈도로서 나타내었다. 제안한 방법은 Exclusive-OR 문제 및 패리티 문제에 적용함으로써 그 유효성을 검증하였다.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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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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• 제13권5호
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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.