• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.10
• /
• pp.4814-4834
• /
• 2018

This paper presents an architecture for wireless sensor networks (WSNs) with blind source separation (BSS) applied to retrieve the received mixing signals of the sink nodes first. The little-to-no need of prior knowledge about the source signals of the sink nodes in the BSS method is obviously advantageous for WSNs. The optimization problem of the BSS of multiple independent source signals with complex and noncircular distributions from observed sensor nodes is considered and addressed. This paper applies Castella's reference-based scheme to Novey's negentropy-based algorithms, and then proposes a novel fast fixed-point (FastICA) algorithm, defined as the reference-signal negentropy complex FastICA (RSNT-cFastICA) for complex-valued noncircular-distribution source signals. The proposed method for the sink nodes is substantially more efficient than Novey's quasi-Newton algorithm in terms of computational speed under large numbers of samples, can effectively improve the power consumption effeciency of the sink nodes, and is significantly beneficial for WSNs and wireless communication networks (WCNs). The effectiveness and performance of the proposed method are validated and compared with three related BSS algorithms through theoretical analysis and simulations.

• Korean System Dynamics Review
• /
• v.13 no.1
• /
• pp.113-131
• /
• 2012

Applying social network analysis techniques, this study examines complex system characteristics of industry-university joint research R&D networks. In specific, it focuses on whether these R&D networks comply with the power law, whose system typically presents the-rich-get-richer and the-poor-get-poor patterns. The basic data come from 7,751 industry-university joint research projects, all of which were carried out by Daejeon, Chungbuk, and Chungnam-based universities from January 2005 to October 2008. The empirical results reveal that the R&D networks abide by the power law. That is, a handful of business units and universities command an overwhelming majority in the joint links, indicating positive feedback dominance within the system.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.26 no.4
• /
• pp.263-279
• /
• 2022

This paper presents an automatic inspection of defects in semiconductor images. We devise a statistical method to find defects on homogeneous background from the observation that it has a log-normal distribution. If computer aided design (CAD) data is available, we use it to construct a signed distance function (SDF) and change the pixel values so that the average of pixel values along the level curve of the SDF is zero, so that the image has a homogeneous background. In the absence of CAD data, we devise a hybrid method consisting of a model-based algorithm and two neural networks. The model-based algorithm uses the first right singular vector to determine whether the image has a linear or complex structure. For an image with a linear structure, we remove the structure using the rank 1 approximation so that it has a homogeneous background. An image with a complex structure is inspected by two neural networks. We provide results of numerical experiments for the proposed methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.5
• /
• pp.2469-2490
• /
• 2019

A low-energy adaptive clustering hierarchy (LEACH) protocol is a low-power adaptive cluster routing protocol which was proposed by MIT's Chandrakasan for sensor networks. In the LEACH protocol, the selection mode of cluster-head nodes is a random selection of cycles, which may result in uneven distribution of nodal energy and reduce the lifetime of the entire network. Hence, we propose a new selection method to enhance the lifetime of network, in this selection function, the energy consumed between nodes in the clusters and the power consumed by the transfer between the cluster head and the base station are considered at the same time. Meanwhile, the improved FTBA algorithm integrating the curve strategy is proposed to enhance local and global search capabilities. Then we combine the improved BA with LEACH, and use the intelligent algorithm to select the cluster head. Experiment results show that the improved BA has stronger optimization ability than other optimization algorithms, which the method we proposed (FTBA-TC-LEACH) is superior than the LEACH and LEACH with standard BA (SBA-LEACH). The FTBA-TC-LEACH can obviously reduce network energy consumption and enhance the lifetime of wireless sensor networks (WSNs).

• Journal of Advanced Navigation Technology
• /
• v.8 no.2
• /
• pp.155-162
• /
• 2004

Recently, significant interest is shown to creation rather inexpensive global systems communications on base of Low-Earth-Orbit Satellite Networks (LEOSN). One of problems of design and creation LEOSN is development of the stream control methods and estimation it's efficiency in such networks. The given problem is complicated, that the topology of the satellite networks varies in time. It essentially hinders the analytical decision of the given problem. An effective way of overcoming of these difficulties is simulation modeling. For realization of research experiments on learning the information streams routing algorithms in LEOSN a special program complex SANET was developed. In the given paper principles of development of LEOSN simulation models and architecture of the manager by the process of a simulation modeling of the unit are considered. Methods of promotion of modeling time and architecture of a simulator complex offered in the article allow to boost essentially efficiency of simulation analysis and to ensure simulation modeling of the satellite networks consisting of several hundreds space vehicles.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.24 no.10
• /
• pp.49-57
• /
• 2010

In order to provide electricity to users economically and safely, distribution automation systems (DASs) monitor and operate components of distribution systems remotely through communication networks. The fiber optic communication network has been mainly installed for the DAS of Korea Electric Power Corporation (KEPCO) because of its huge bandwidth and dielectric noise immunity. However, the fiber optic communication network has some shortcomings that its installation cost and communication fee are expensive. This paper proposes a complex network where WLANs are combined with conventional fiber optic communication networks in order to expand DAS easily and inexpensively. A fixed wireless bridge communication unit (FWB-CU) for the proposed complex network is implemented using IEEE 802.11a WLAN technology. The proposed complex network is built actually to verify its feasibility experimentally as a DAS communication network.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.71-74
• /
• 1998

In this paper, an interface method for MMS on ATM networks is proposed. It can apply to the realtime communication and the remote control in wide-area manufacturing complex and virtual factory. It is used for the downloading program, the reporting and gathering of data and the remote control for the remote client. The developed interface rule is basd on the rule for MMS on TCP/IP. The main goals of implementation are to verify whether MMS on ATM is able to meet the requirements of factory automation and manufacturing complex.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.102.1-102
• /
• 2002

In this paper, we propose Self-organizing neurofuzzy networks(SONFN) and discuss their comprehensive design methodology. The proposed SONFN is generated from the mutually combined structure of both neurofuzzy networks (NFN) and polynomial neural networks(PNN) for model identification of complex and nonlinear systems. NFN contributes to the formation of the premise part of the SONFN. The consequence part of the SONFN is designed using PNN. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. We discuss two kinds of SONFN architectures and propose a comprehensive learning algorithm. It is shown that this network...

• International Journal of Control, Automation, and Systems
• /
• v.1 no.1
• /
• pp.101-110
• /
• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• Journal of the Korean Physical Society
• /
• v.73 no.10
• /
• pp.1589-1595
• /
• 2018

Similarity index measures the topological proximity of node pairs in a complex network. Numerous similarity indices have been defined and investigated, but the dependency of structure on the performance of similarity indices has not been sufficiently investigated. In this study, we investigated the relationship between the performance of similarity indices and structural properties of a network by employing a two-state random network. A node in a two-state network has binary types that are initially given, and a connection probability is determined from the state of the node pair. The performances of similarity indices are affected by the number of links and the ratio of intra-connections to inter-connections. Similarity indices have different characteristics depending on their type. Local indices perform well in small-size networks and do not depend on whether the structure is intra-dominant or inter-dominant. In contrast, global indices perform better in large-size networks, and some such indices do not perform well in an inter-dominant structure. We also found that link prediction performance and the performance of similarity are correlated in both model networks and empirical networks. This relationship implies that link prediction performance can be used as an approximation for the performance of the similarity index when information about node type is unavailable. This relationship may help to find the appropriate index for given networks.