• 전기학회논문지
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• 제66권1호
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• pp.137-143
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• 2017

In this paper, we investigate static control of Boolean networks described in the framework of semi-tensor product (STP) operation. The control objective is to determine control input nodes and their logical values so as to stabilize the considered Boolean network to a desired fixed point or cycle. Using topology of Boolean networks such as incidence matrix and hub nodes, a set of appropriate control input nodes is selected, and based on STP operations, we assign constant control inputs so that the controlled network can converge to a prescribed fixed point or cycle. To validate applicability of the proposed scheme, we conduct a numerical study on the problem of determining control input nodes for a Boolean network representing hierarchical differentiation of myeloid progenitors.

• 대한전자공학회논문지
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• 제13권3호
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• pp.1-6
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• 1976

이 논문에서는 Boolean difference를 이용하여 combinational 및 sequential 논리회로에서 발생하는 기능적인 고장에 대한 test pattern을 얻는 방법을 연구하였다. 이 방법은 test pattern을 얻고자 하는 회로의 Boolean 함수의 Boolean difference를 계산하므로써 체계적으로 test pattern을 얻는 절차를 보여주고 있다. 컴퓨터에 의한 실험결과에 의하며 이 방법은 combinational 회로 및 asynchronous sequential 회로에 적합하며, clock이 있는 flip flop을 적당히 모형화함으로서 이 방법을 synchronous sequential회로에도 적용할 수 있음이 입증되었다. In this paper, a method of test pattern generation for the functional failure in both combinational and sequentlal logic networks by using exterded Boole an difference is proposed. The proposed technique provides a systematic approach for the test pattern generation procedure by computing Boolean difference of the Boolean function that represents the Logic network for which the test patterns are to be generated. The computer experimental results show that the proposed method is suitable for both combinational and asynchronous sequential logic networks. Suitable models of clocked flip flops may make it possible for one to extend this method to synchronous sequential logic networks.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.339-343
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• 2005

Boolean networks(BN) construction is one of the commonly used methods for building gene networks from time series microarray data. However, BN has two major drawbacks. First, it requires heavy computing times. Second, the binary transformation of the microarray data may cause a loss of information. This paper propose two methods using liner regression to construct gene regulatory networks. The first proposed method uses regression based BN variable selection method, which reduces the computing time significantly in the BN construction. The second method is the regression based network method that can flexibly incorporate the interaction of the genes using continuous gene expression data. We construct the network structure from the simulated data to compare the computing times between Boolean networks and the proposed method. The regression based network method is evaluated using a microarray data of cell cycle in Caulobacter crescentus.

• 한국정보과학회논문지:시스템및이론
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• 제37권3호
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• pp.138-146
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• 2010

생체네트워크는 여러 종류의 환경 변화에 매우 강건하다고 알려져 있지만 그 메커니즘은 아직 밝혀지지 않고 있다. 본 논문에서는 랜덤 네트워크에 비해 생체네트워크에 되먹임 루프가 매우 많이 존재한다는 구조적 특징을 발견하고 그것이 네트워크의 강건성에 어떤 영향을 미치는지를 살펴보았다. 이를 위해 불리언 네트워크 모델을 이용하여 네트워크 강건성을 적절하게 측정하는 방법을 정의하고 많은 불리언 네트워크에 대해서 시뮬레이션하였다. 그 결과, 불리언 네트워크에서 되먹임 루프의 개수가 증가하면 고정점 끌개의 개수는 거의 변화가 없지만 유한순환 끌개의 개수는 크게 줄어든다는 사실을 밝혔다. 또한, 되먹임 루프의 개수가 증가함에 따라 고정점 끌개로 수렴하는 거대한 끌개 영역이 생성됨을 보였다. 이러한 사실들은 매우 많은 수의 되먹임 루프가 네트워크의 강건성을 높이는 데 중요한 요인임을 설명한다.

• 대한전자공학회논문지
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• 제11권4호
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• pp.12-18
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• 1974

본 논문은 출력분준가 있는 조합논리회로의 최소 고장검출실험의 생성에 관한 것이다. 조합논리회로의 출력분준선에 있어서의 신청반전의 우기성을 고려함으로서 출력분준가 있는 회로에 대한 특성그래프와 그 부분그래프를 작성하여 필요한 테스트수의 하한과 그 최소실험을 구하였다. 출력분준선의 고장검출 가능가부를 판정하는데 Boolean Difference를 이용하였다.

• 대한전자공학회논문지
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• 제27권3호
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• pp.137-144
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• 1990

본 논문은 단층 perceptron과 새로 개발한 비대칭 궤환형 신경회로망 모델을 CMOS VLSI로 구현 하는 방법에 관한 연구로써, boolean 식과 산술 연산을 수행할 수 있는 50여개의 칩을 이중 금속 2마이크로메터 설계 규칙에 의해 설계하였으며 제작중에 있다. 이들 칩은 문자 인식, 디지털 처리 및 신경회로망 컴퓨터에 기본 칩으로 사용할 수 있도록 개발되었다.

• Nuclear Engineering and Technology
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• 제50권4호
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• pp.599-605
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• 2018

Fault diagnosis depends critically on the selection of sensors monitoring crucial process variables. Boolean network (BN) is composed of nodes and directed edges, where the node state is quantized to the Boolean values of True or False and is determined by the logical functions of the network parameters and the states of other nodes with edges directed to this node. Since BN can describe the fault propagation in a sensor network, it can be applied to propose sensor selection strategy for fault diagnosis. In this article, a sufficient condition for parameter identifiability of BN is first proposed, based on which the sufficient condition for fault identifiability of a sensor network is given. Then, the fault identifiability condition induces a sensor selection strategy for sensor selection. Finally, the theoretical result is applied to the fault diagnosis-oriented sensor selection for a nuclear heating reactor plant, and both the numerical computation and simulation results verify the feasibility of the newly built BN-based sensor selection strategy.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2014년도 추계학술발표대회
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• pp.849-852
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• 2014

Reverse engineering of gene regulatory network is a challenging task in computational biology. To detect a regulatory relationship among genes from time series data is called reverse engineering. Reverse engineering helps to discover the architecture of the underlying gene regulatory network. Besides, it insights into the disease process, biological process and drug discovery. There are many statistical approaches available for reverse engineering of gene regulatory network. In our paper, we propose pairwise mutual information for the reverse engineering of a gene regulatory network from time series data. Firstly, we create random boolean networks by the well-known $Erd{\ddot{o}}s-R{\acute{e}}nyi$ model. Secondly, we generate artificial time series data from that network. Then, we calculate pairwise mutual information for predicting the network. We implement of our system on java platform. To visualize the random boolean network graphically we use cytoscape plugins 2.8.0.

• 한국신뢰성학회지:신뢰성응용연구
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• 제6권4호
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• pp.297-306
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• 2006

In this paper, We presented the algorithm for estimating a reliability between nodes in wireless distributed sensor networks (DSN). To estimate the reliability between nodes, we first modeled DSN as probability graph. Links of the graph are always reliable and the probability of node failure is independent. After all possible simple path which can be established between two nodes are examined, we perform sharp operation to remove repetition event between two nodes. Using probability for each variable of the minimized Boolean equation, we present the reliability formula.

• 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.