• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.9
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• pp.2456-2469
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• 1996

A digital satellite communication channel has a nonlinearity with memory due to saturation characeristis of the high poer amplifier in the satellite and transmitter/receiver linear filter used in the overall system. In this paper, we propose a complex radial basis function network(CRBFN) based adaptive equalizer for compensation of nonlinearities in digital satellite communication channels. The proposed CRBFN untilizes a complex-valued hybrid learning algorithm of k-means clustering and LMS(least mean sequare) algorithm that is an extension of Moody Darken's algorithm for real-valued data. We evaluate performance of CRBFN in terms of symbol error rates and mean squared errors nder various noise conditions for 4-PSK(phase shift keying) digital modulation schemes and compare with those of comples pth order inverse adaptive Volterra filter. The computer simulation results show that the proposed CRBFN ehibits good equalization, low computational complexity and fast learning capabilities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.1030-1050
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• 2021

The edges with "bridge characteristic" play the role of connecting the communication between regions in power optical cable network. To solve the problem of mining edges with "bridge characteristic" in provincial power optical cable network, the complex power optical cable network model is constructed. Firstly, to measure the generated potential energy of all nodes in n-level neighborhood local structure for one edge, the n-level neighborhood local structure topological potential is designed. And the minimum connected chain attenuation is designed to measure the attenuation degree caused by substituted edges. On the basis of that, the minimum connected chain attenuation topological potential based measurement is designed. By using the designed measurement, a bridge-edges mining algorithm is proposed to mine edges with "bridge characteristic". The experiments are conducted on the physical topology of the power optical cable network in Jilin Province. Compared with that of other three typical methods, the network efficiency and connectivity of the proposed method are decreased by 3.58% and 28.79% on average respectively. And the proposed method can not only mine optical cable connection with typical "bridge characteristic" but also can mine optical cables without obvious characteristics of city or voltage, but it have "bridge characteristic" in the topology structure.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.443-456
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• 2022

An efficient and reasonable resource allocation strategy can greatly improve the service quality of Internet of Vehicles (IoV). However, most of the current allocation methods have overestimation problem, and it is difficult to provide high-performance IoV network services. To solve this problem, this paper proposes a network resource allocation strategy based on deep learning network model DDQN. Firstly, the method implements the refined modeling of IoV model, including communication model, user layer computing model, edge layer offloading model, mobile model, etc., similar to the actual complex IoV application scenario. Then, the DDQN network model is used to calculate and solve the mathematical model of resource allocation. By decoupling the selection of target Q value action and the calculation of target Q value, the phenomenon of overestimation is avoided. It can provide higher-quality network services and ensure superior computing and processing performance in actual complex scenarios. Finally, simulation results show that the proposed method can maintain the network delay within 65 ms and show excellent network performance in high concurrency and complex scenes with task data volume of 500 kbits.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.195-204
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• 2003

Network optimization is being increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. Networks provide a useful way to modeling real world problems and are extensively used in practice. Many real world applications impose on more complex issues, such as, complex structure, complex constraints, and multiple objects to be handled simultaneously and make the problem intractable to the traditional approaches. Recent advances in evolutionary computation have made it possible to solve such practical network optimization problems. The invited talk introduces a thorough treatment of evolutionary approaches, i.e., hybrid genetic algorithms approach to network optimization problems, such as, fixed charge transportation problem, minimum cost and maximum flow problem, minimum spanning tree problem, multiple project scheduling problems, scheduling problem in FMS.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.790-795
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• 1994

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item, component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introducd to Hierarchical Artificial Neural Network(HANN) for this purpose. HANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification,forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trainined by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing HANN with multitasking and message transfer between processes in SUN workstation. We tested HANN in reactor system.

• Journal of computational fluids engineering
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• v.14 no.4
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• pp.86-92
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• 2009

Nowadays, the development of microfluidic chip [i.e. biochip, micro-total analysis system ($\mu$-TAS) and LOC (lab-on-a-chip)] becomes more active, and the microchannels to deliver fluid by pressure or electroosmotic forces tend to be more complex like electronic circuits or networks. For a simple network of channels, we may calculate the pressure and the flow rate easily by using suitable formula. However, for complex network it is not handy to obtain such information with that simple way. For this reason, Graphic User Interface (GUI) program which can rapidly give required information should be necessary for microchip designers. In this paper, we present a GUI program developed in our laboratory and the simple theoretical formula used in the program. We applied our program to simple case and could get results compared well with other numerical results. Further, we applied our program to several complex cases and obtained reasonable results.

• Journal of the Economic Geographical Society of Korea
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• v.20 no.4
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• pp.522-535
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• 2017

It is necessary to develop a new location model to support the application of smart technology and confluence in the 4th industrial revolution era. In this study, we propose 'network-type industrial complex' as a method to link several industrial integrated spaces which are dispersed in an area. As a result of conducting a survey of companies in order to develop a new location model, about 89% of companies recognized the necessity of network-type industrial complex. As a condition for activation of the industrial complex, 'complementary function formation', and 'convenience of nodular transportation' were selected. It is expected that it will be possible to supply low-cost, high-efficiency industrial complexes through opening and linking with urban space and infrastructure sharing.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1715-1723
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• 2018

This work investigates the performance of fully complex- valued radial basis function network(FC-RBF) and complex extreme learning machine (CELM) based neural approaches for classification of power quality disturbances. This work engages the use of S-Transform to extract the features relating to single and combined power quality disturbances. The performance of the classifiers are compared with their real valued counterparts namely extreme learning machine(ELM) and support vector machine(SVM) in terms of convergence and classification ability. The results signify the suitability of complex valued classifiers for power quality disturbance classification.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.5
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• pp.526-538
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• 2007

Since air quality monitoring data sets are important base for developing of air quality management strategies including policy making and policy performance assessment, the environmental protection authorities need to organize and operate monitoring network properly. Air quality monitoring network of Busan, consisting of 18 stations, was allocated under unscientific and irrational principles. Thus the current state of air quality monitoring networks was reassessed the effect and appropriateness of monitoring objectives such as population protection and sources surveillance. In the process of the reassessment, a GIS-based decision support system was constructed and used to simulate air quality over complex terrain and to conduct optimization analysis for air quality monitoring network with multi-objective. The maximization of protection capability for population appears to be the most effective and principal objective among various objectives. The relocation of current monitoring stations through optimization analysis of multi-objective appears to be better than the network building for maximization of population protection capability. The decision support system developed in this study on the basis of GIS-based database appear to be useful for the environmental protection authorities to plan and manage air quality monitoring network over complex terrain.

• Korea-Australia Rheology Journal
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• v.14 no.2
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• pp.49-55
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• 2002

To understand the large amplitude oscillatory shear (LAOS) behavior of complex fluids, we have investigated the flow behavior of a network model in the LAOS environment. We applied the LAOS flow to the model proposed by Vaccaro and Marrucci (2000), which was originally developed to describe the system of associating telechelic polymers. The model was found to predict at least three different types of LAOS behavior; strain thinning (G' and G" decreasing), strong strain overshoot (G' and G" increasing followed by decreasing), and weak strain overshoot (G' decreasing, G" increasing followed by decreasing). The overshoot behavior in the strain sweep test, which il often observed in some complex fluid systems with little explanation, could be explained in terms of the model parameters, or in terms of the overall balance between the creation and loss rates of the network junctions, which are continually created and destroyed due to thermal and flow energy. This model does not predict strain hardening behavior because of the finitely extensible nonlinear elastic (FENE) type nonlinear effect of loss rate. However, the model predicts the LAOS behavior of most of the complex fluids observed in the experiments.he experiments.