• Korean Management Science Review
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• v.32 no.4
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• pp.175-192
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• 2015

Due to the emergence of open innovation driven by development of network service technologies and convergence in ICT service industry, It is necessary for ICT service firms to examine their capabilities for open innovation. The purpose of this paper is to empirically examine determinants affecting open innovation in Korean ICT service industry. In order to analyze, this paper uses logistic and multiple regression models based on survey data of Korean ICT service firms. Estimation results show that external network for collaboration is positive on the technological innovation activity regardless of the innovation type. Specifically, user networks are significant in all types of technology innovation, revealing that it is important to innovation activities of the ICT service firms.

• Structural Engineering and Mechanics
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• v.28 no.2
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• pp.153-166
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• 2008

This paper suggests the use of wavelet multiresolution analysis (WMRA) and neural network for generation of artificial earthquake accelerograms from target spectrum. This procedure uses the learning capabilities of radial basis function (RBF) neural network to expand the knowledge of the inverse mapping from response spectrum to earthquake accelerogram. In the first step, WMRA is used to decompose earthquake accelerograms to several levels that each level covers a special range of frequencies, and then for every level a RBF neural network is trained to learn to relate the response spectrum to wavelet coefficients. Finally the generated accelerogram using inverse discrete wavelet transform is obtained. An example is presented to demonstrate the effectiveness of the method.

• Smart Structures and Systems
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• v.15 no.4
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• pp.1103-1120
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• 2015

A self-sensing magnetorheological (MR) damper with embedded piezoelectric force sensor has recently been devised to facilitate real-time close-looped control of structural vibration in a simple and reliable manner. The development and characterization of the self-sensing MR damper are presented based on experimental work, which demonstrates its reliable force sensing and controllable damping capabilities. With the use of experimental data acquired under harmonic loading, a nonparametric dynamic model is formulated to portray the nonlinear behaviors of the self-sensing MR damper based on NARX modeling and neural network techniques. The Bayesian regularization is adopted in the network training procedure to eschew overfitting problem and enhance generalization. Verification results indicate that the developed NARX network model accurately describes the forward dynamics of the self-sensing MR damper and has superior prediction performance and generalization capability over a Bouc-Wen parametric model.

• Journal of IKEEE
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• v.2 no.1 s.2
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• pp.134-138
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• 1998

Because of the statistical fluctuations and the high 'time-variability' nature of the traffic, managing the resources of the network require highly dynamic techniques with minimal Intervention and reaction times, and adaptive and learning capabilities. The neural networks normalizes the ATM cell arrival rate and queue length and has the adaptive learning algorithm, and experimentally investigated the method to prevent the congestion generated in ATM networks.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.436-440
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• 1992

As an approach to design the intelligent controller, this paper proposes a new FNN(Fuzzy Neural Network) control method using the hybrid combination of fuzzy logic control and neural network. The proposed FNN controller has two important capabilities, namely, adaptation and learning. These functions are performed by the following process. Firstly, identification of the parameters and estimation of the states for the unknown plant are achieved by the MNN(Model Neural Network) which is continuously trained on-line. And secondly, the learning is performed by FNN controller. The error back propagation algorithm is adopted as a learning technique. The effectiveness of the proposed method will be demonstrated by computer simulation of a two d.o.f. robot manipulator.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.113-118
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• 1999

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important n the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.143-146
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• 1990

The human brain has the most powerful capabilities in thinking, interpreting, remembering, and problem-solving. Artificial neural network is appeared by scientists who have tried to simulate such a human brain. The artificial neural network has the capability of learning, massive parallelism capability and robustness for disturbance which are necessary for power system application. In this paper, We reviewed the typical topologies and learning algorithms of artifical neural networks which can be used for pattern classification. And we surveyed for the applications of artifical neural network to the power system.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.483-485
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• 1998

Neural Network has advantages of learning and normalizing capabilities. Fuzzy controller is based on a fuzzy logic that is so effective to represent uncertain phenomena of real world and make its approximation. In this paper, Fuzzy Neural Network controller which equipped with adaptive control algorithm is described. Proposed Fuzzy Neural Network Controller applied to a ball on beam system which have nonlinear characteristics shows a good performance.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.821-822
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• 2006

Ubiquitous healthcare monitoring and measuring system based on wireless sensor network was implemented and tested. The system can measure the ECG and body temperature of patients or elderly persons and transfer the data wirelessly in ad-hoc network to remote base-station connected to doctor's PDA/PC or hospital server, using wireless sensor motes. The data obtained can be analyzed by doctors and care providers to monitor a health status of patient in real time environment. To prove the capabilities of the wireless sensor network platform for ubiquitous healthcare applications, the performance of our monitoring and measuring system was tested with positive results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.126-131
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• 2008

Wireless Sensor Network(WSNs) consists of small sensor nodes with sensing, computation, and wireless communication capabilities. It has new information collection scheme and monitoring solution for a variety of applications. Faults occurring to sensor nodes are common due to the limited resources and the harsh environment where the sensor nodes are deployed. In order to ensure the network quality of service it is necessary for the WSN to be able to detect the faulty sensors and take necessary actions for the reconstruction of the lost sensor data caused by fault as earlier as possible. In this paper, we propose an recursive PCA-based fault detection and lost data reconstruction algorithm for sensor networks. Also, the performance of proposed scheme was verified with simulation studies.