• Smart Structures and Systems
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• v.8 no.1
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• pp.69-92
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• 2011

This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.4
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• pp.22-27
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• 2000

In this paper, new neural network called dynamic multidimensional wavelet neural network (DMWNN) is proposed. The resulting network from wavelet theory provides a unique and efficient representation of the given function. Also the proposed DMWNN have ability to store information for later use. Therefore it can represent dynamic mapping and decreases the dimension of the inputs needed for network. This feature of DMWNN can compensate for the weakness of diagonal recurrent neural network(DRNN) and feedforward wavelet neural network(FWNN). The efficacy of this type of network is demonstrated through experimental results.

• Journal of the Korea Society for Simulation
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• v.27 no.4
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• pp.27-45
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• 2018

The terrestrial communication network and the underwater acoustic communication network have very different communication characteristics each other in operational environments, communication media, propagation delay, frequency bandwidth, transmission speed, bit error rate, and so on. These different characteristics cause some different address schemes and different maximum transmission units and, as a result, these differences must form certainly obstacles to the intercommunication between a terrestrial communication network and an underwater acoustic communication network. In this paper, we presents a method to use the virtual addresses to resolve the interconnection problem caused by different address schemes between a terrestrial communication network and an underwater acoustic communication network, and, through a mathematical modeling, we analyze the performance on the message transceiving delay time in the underwater environment.

• Journal of the Korean Society for information Management
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• v.39 no.2
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• pp.233-254
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• 2022

Link reduction algorithms such as pathfinder network are the widely used methods to overcome problems with the visualization of weighted networks for knowledge domain analysis. This study proposed NetRSQ, an indicator to measure the goodness of fit of a link reduction algorithm for the network visualization. NetRSQ is developed to calculate the fitness of a network based on the rank correlation between the path length and the degree of association between entities. The validity of NetRSQ was investigated with data from previous research which qualitatively evaluated several network generation algorithms. As the primary test result, the higher degree of NetRSQ appeared in the network with better intellectual structures in the quality evaluation of networks built by various methods. The performance of 4 link reduction algorithms was tested in 40 datasets from various domains and compared with NetRSQ. The test shows that there is no specific link reduction algorithm that performs better over others in all cases. Therefore, the NetRSQ can be a useful tool as a basis of reliability to select the most fitting algorithm for the network visualization of intellectual structures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1110-1115
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• 2014

The matrix-star and the transposition graphs are considered as star graph variants that have various merits in graph theory such as node symmetry, fault tolerance, recursive scalability, etc. This paper describes an one-to-one mapping algorithm from a matrix-star graph to a transposition graph using adjacent properties in graph theory. The result show that a matrix-star graph $MS_{2,n}$ can be embedded in a transposition graph $T_{2n}$ with dilation n or less and average dilation 2 or less.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.677-684
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• 2009

With various sensor network and ubiquitous technologies, we can extend interaction area from a virtual domain to physical space domain. This spatial interaction is differ in that traditional interaction is mainly processed by direct interaction with the computer machine which is a target machine or provides interaction tools and the spatial interaction is performed indirectly between users with smart interaction tools and many distributed components of space. So, this interaction gives methods to users to control whole manageable space components by registering and recognizing objects. Finally, this paper provides an effective spatial interaction method with template-based task mapping algorithm which is sorted by historical interaction data for support of users' intended task. And then, we analyze how much the system performance would be improved with the task mapping algorithm and conclude with an introduction of a GUI method to visualize results of spatial interaction.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2923-2930
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• 1997

This paper describes two algorithms for technology mapping of multiple output functions into interesting and popular FPGAs (Field Programmable Gate Arrays) that lise look-up table memories. For improvement of technology mapping for FPGA, we use the functional decomposition method for multiple output functions. Two algorithms are proposed. The one is the Roth-Karp algorithm extended for multiple output functions. The other is the novel and efficient algorithm which looks for common decomposition functions through the decomposition procedure. The cost function is used to minimize the number of CLBs and nets and to improve performance of the network. Finally we compare our new algorithm with previous logic design technique. Experimental results show significant reduction in the number of CLBs and nets.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.257-264
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• 2019

Recently, ITU-T recommends that FlexE, which allows the flexible configuration of Ethernet signals, be received by OTN for transparent transmission through OTN. To compensate for the difference in bit rate that can occur when mapping FlexE signals to OTN payload, an idle codeword is removed or inserted. However, the detailed functional blocks required to implement this method are not yet available. In this paper, based on the recent ITU-T requirements, a detailed functional block for OTN mapping of FlexE signals is proposed based on 400G class. In addition, based on the detailed functional blocks, mathematical analysis was performed to obtain the characteristics of removing and inserting idle code words, and the simulation results are shown.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.3
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• pp.239-248
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• 2020

Exact rock classification helps suitable support patterns to be installed. Face mapping is usually conducted to classify the rock mass using RMR (Rock Mass Ration) or Q values. There have been several attempts to predict the grade of rock mass using mechanical data of jumbo drills or probe drills and photographs of excavation surfaces by using deep learning. However, they took long time, or had a limitation that it is impossible to grasp the rock grade in ahead of the tunnel surface. In this study, a method to predict the Q value ahead of excavation surface is developed using recurrent neural network (RNN) technique and it is compared with the Q values from face mapping for verification. Among Q values from over 4,600 tunnel faces, 70% of data was used for learning, and the rests were used for verification. Repeated learnings were performed in different number of learning and number of previous excavation surfaces utilized for learning. The coincidence between the predicted and actual Q values was compared with the root mean square error (RMSE). RMSE value from 600 times repeated learning with 2 prior excavation faces gives a lowest values. The results from this study can vary with the input data sets, the results can help to understand how the past ground conditions affect the future ground conditions and to predict the Q value ahead of the tunnel excavation face.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.238-243
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• 2007

The TAM(Topographic Attentive Mapping) network neural network model is an especially effective one for pattern analysis. It is composed of of Input layer, category layer, and output layer. Fuzzy rule, lot input and output data are acquired from it. The TAM network with three pruning rules for reducing links and nodes at the layer is called fuzzy TAM network. In this paper, we apply fuzzy TAM network to pattern analysis of leadership type for organizational leader and show its usefulness. Here, criteria of input layer and target value of output layer are the value and leadership related personality type variables of the Egogram and Enneagram, respectively.