• Advances in materials Research
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• v.3 no.2
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• pp.117-128
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• 2014

Processable conducting polyaniline derivative with free amine functional groups was successfully synthesized from the monomer o-phenylenediamine in aqueous hydrochloric acid medium using ammonium persulfate as an oxidative initiator. The synthesized poly(o-phenylenediamine) (PoPD) in critical condition was found to be completely soluble in common organic solvents like dimethyl sulfoxide, N,N-dimethyl formamide etc. From the intrinsic viscosity measurement, the optimum condition for the polymerization was established. The polymer was characterized by ultraviolet visible spectroscopy, Fourier transform infrared spectroscopy, proton magnetic resonance spectroscopy ($^1HNMR$) and thermogravimetric (TGA) analyses. The weight average molecular weights of the synthesized polymers were determined by the dynamic light scattering (DLS) method. From the spectroscopic analysis the structure was found to resemble that of polyaniline derivative with free amine functional groups attached to ortho/meta position in the phenyl ring. However, very little ladder unit was also present with in the polymer chain. The moderate thermal stability of the synthesized polymer could be found from the TGA analysis. The average DC conductivity of $2.8{\times}10^{-4}S/cm$ was observed for the synthesized polymer pellet after doping with hydrochloric acid.

• Magazine of the Korean Society of Agricultural Engineers
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• v.42 no.1
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• pp.99-104
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• 2000

This study is performed to evaluate the physical and mechanical properties of pine needle ash (PNA) concrete. Materials used for this experiment are PNA, normal portland cement, natural fine and coarse aggregate. Test results show that the unit weights of PNA concrete are decreased 1 % ∼3% and the highest strength is achieved by 5% PNA filled PNA concrete. Compresive strength increased by 5% , tensile strength by 20% and bending strength by 15% as compared with those of the normla cement concrete , respectively. The highest ultrasonic pulse velicity and dynamic mudulus of elasticity are acheved by 5% PNA filled PNA concrete, which are similar to those of the normal cement concrete.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.920-924
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• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.8
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• pp.1294-1302
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• 1990

In this paper, optical implementation of single layer perceptron to discriminate the even and odd numbers using commericla LCTV spatial light modulator is described. In order to overcome the low dynamic range of gray levels of LCTV, nonlinear quantized perceptron model is introduced, which is analyzed to have faster convergent time with small gray levels through the computer simulation. And the analog weights containing positive and negative values of single layer perceptron is represented by using the polarization-based encoding method. Finally, optical implementation of the nonlinear quantized perceptron learning model based on polarization property of the commercial LCTV is proposed and some experimental results are given.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.114-116
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• 1993

A novel Neural networks controller for Buck type DC-DC converter is presented and compared with the operation of sliding node coupled several control strategies for the converter. The connection weights of neural networks are trained by error back propagation algorithm. The behavior of the control system that arises fred the use of those methods is analyzed from the viewpoint of dynamic and steady state errors and simulation results are presented.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.5
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• pp.1168-1175
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• 1995

The application of cubic spline is presented for basic curve (DRD motion) of cam motion. The purpose of this paper is to achieve better dynamic characteristics than general cam curves. A cubic spline is a piecewise function that is continuous in displacement, velocity and acceleration. The best cam curve is obtained by changing the weights of the object function. So the method can be used to any machine system case by case. For the proposed object function, the result has improved all characteristics such as velocity, acceleration and jerk compared with that of the modified sine curve.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.816-821
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• 2003

Researchers have utilized artificial evolution techniques and learning techniques for studying the interactions between learning and evolution. Adaptation in dynamic environments gains a significant advantage by combining evolution and learning. We propose an on-line, realtime evolutionary learning mechanism to determine the structure and the synaptic weights of a neural network controller for mobile robot navigations. We support our method, based on (1+1) evolutionary strategy which produces changes during the lifetime of an individual to increase the adaptability of the individual itself, with a set of experiments on evolutionary neural controller for physical robots behaviors. We investigate the effects of learning in evolutionary process by comparing the performance of the proposed realtime evolutionary learning method with that of evolutionary method only. Also, we investigate an interactive evolutionary algorithm to overcome the difficulties in evaluating complicated tasks.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.318-324
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• 2015

This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.32 no.3
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• pp.1-9
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• 2000

The MDDA (Modified Dynamic Drainage Analyzer) was developed to evaluate the drainage characteristics on paper machine. The initial forming vacuum velocity was decreased with the increase of stock consistency and there were no significant effects of the applied vac-uum. On the other hand the initial forming drainage velocity was rapidly decreased with the increase of stock consistency above the 0.35 bar of applied vacuum. The final drainage time and wet web permeability showed similar trends under 0.075% consistency but increased rapidly at the higher consistencies. SFR(Specific Filtration Resistance) and drainage obtained for different vacuum level applied and deposited weights measured by using MDDA showed the linear relationship.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.461-464
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• 1998

Lateral Information Propagation Neural Networks (LIPN) is proposed for on-line interpolation. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D LIPN hardware has been implemented with general purpose analog ICs to test the interpolation capability of the proposed neural networks. Experiments with static and dynamic signals have been done upon the LIPN hardware.