• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.195-200
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• 1998

Since the neural network was introduced, significant progress has been made on data handling and learning algorithms. Currently, the most popular learning algorithm in neural network training is feed forward error back-propagation (FFEBP) algorithm. Aside from the success of the FFEBP algorithm, polynomial neural networks (PNN) learning has been proposed as a new learning method. The PNN learning is a self-organizing process designed to determine an appropriate set of Ivakhnenko polynomials that allow the activation of many neurons to achieve a desired state of activation that mimics a given set of sampled patterns. These neurons are interconnected in such a way that the knowledge is stored in Ivakhnenko coefficients. In this paper, the PNN model has been developed using the plasma enhanced chemical vapor deposition (PECVD) experimental data. To characterize the PECVD process using PNN, SiO$_2$films deposited under varying conditions were analyzed using fractional factorial experimental design with three center points. Parameters varied in these experiments included substrate temperature, pressure, RF power, silane flow rate and nitrous oxide flow rate. Approximately five microns of SiO$_2$were deposited on (100) silicon wafers in a Plasma-Therm 700 series PECVD system at 13.56 MHz.

• Steel and Composite Structures
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• v.18 no.3
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• pp.547-563
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• 2015

Deflection in a beam of a composite frame is a serviceability design criterion. This paper presents a methodology for rapid prediction of long-term mid-span deflections of beams in composite frames subjected to service load. Neural networks have been developed to predict the inelastic mid-span deflections in beams of frames (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage in concrete) from the elastic moments and elastic mid-span deflections (neglecting cracking, and time effects). These models can be used for frames with any number of bays and stories. The training, validating, and testing data sets for the neural networks are generated using a hybrid analytical-numerical procedure of analysis. Multilayered feed-forward networks have been developed using sigmoid function as an activation function and the back propagation-learning algorithm for training. The proposed neural networks are validated for an example frame of different number of spans and stories and the errors are shown to be small. Sensitivity studies are carried out using the developed neural networks. These studies show the influence of variations of input parameters on the output parameter. The neural networks can be used in every day design as they enable rapid prediction of inelastic mid-span deflections with reasonable accuracy for practical purposes and require computational effort which is a fraction of that required for the available methods.

• Korean Journal of Materials Research
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• v.24 no.7
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• pp.381-387
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• 2014

The ${\beta}$-transus temperature in titanium alloys plays an important role in the design of thermo-mechanical treatments. It primarily depends on the chemical composition of the alloy and the relationship between them is non-linear and complex. Considering these relationships is difficult using mathematical equations. A feed-forward neural-network model with a back-propagation algorithm was developed to simulate the relationship between the ${\beta}$-transus temperature of titanium alloys, and the alloying elements. The input parameters to the model consisted of the nine alloying elements (i.e., Al, Cr, Fe, Mo, Sn, Si, V, Zr, and O), whereas the model output is the ${\beta}$-transus temperature. The model developed was then used to predict the ${\beta}$-transus temperature for different elemental combinations. Sensitivity analysis was performed on a trained neural-network model to study the effect of alloying elements on the ${\beta}$-transus temperature, keeping other elements constant. Very good performance of the model was achieved with previously unseen experimental data. Some explanation of the predicted results from the metallurgical point of view is given. The graphical-user-interface developed for the model should be very useful to researchers and in industry for designing the thermo-mechanical treatment of titanium alloys.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.472-479
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• 1999

In this paper, we propose a land cover pattern classifier for remote sensing image by using neuro-fuzzy algorithm. The proposed pattem classifier has a 3-layer feed-forward architecture that is derived from generic fuzzy perceptrons, and the weights are con~posed of h u y sets. We also implement a neuro-fuzzy pattern classification system in the Visual C++ environment. To measure the performance of this, we compare it with the conventional neural networks with back-propagation learning and the Maximum-likelihood algorithms. We classified the remote sensing image into the eight classes covered the majority of land cover feature, selected the same training sites. Experimental results show that the proposed classifier performs well especially in the mixed composition area having many classes rather than the conventional systems.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.1 s.116
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• pp.90-95
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• 2007

Chaotic OOK(On-Off Keying) modulation method can be used in LDR(Low Data Rate) UWB systems. The chaotic generator in one of the most important circuit in this system. The traditional chaotic generator circuits using analog feed back technique have low yield characteristic due to the process variation. A novel quasi-chaotic signal generator using digital PN-sequence in proposed in this paper and verified in 0.18um CMOS teleology.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.210-213
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• 1999

In this paper, we propose a new neural network for the control of a robot manipulator The proposed neural network structure is that all of network outputs feed bark into hidden units and output units from feedback units The feedback units are only to memorize the previous activations of the hidden units and output units and can be considered to function as one-step time delays. The proposed neural network works standard back-propagation Loaming algorithm. The simulation and experiment results showed the effectiveness of using the modified neural network structure in the control of the robot manipulator.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1818-1823
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• 2015

In this paper, we analyzed the optimal configuration of protection wire that have been installed in the electric railway power supply system. Protection wires are to suppress the ground potential rise when the short circuit fault between contact wire-rail(C-F), and protect the electronics equipments(signalling and communication) that are facility the wayside. The role of protection wires must be feed back quickly the fault current to the substation when a short circuit fault occurs. In this paper, we proposed that only one line to install the protection wire. Comparing how to newly proposed and existing system, most of the performance is similar. The reason is that most of the current flowing in the protection wire near the location where the fault occurred. There is no problem even if in one line for human safe and the low impedance of the return circuit in dimension to ensure the safety of the facility during the fault. To ensure safety during an fault occurs, it is sufficient even by one line. But, In the protection wire of facilities planning it is necessary to design taking into account the potential utility.

• Journal of the Korean Society of Combustion
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• v.9 no.3
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• pp.27-35
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• 2004

Diode laser absorption sensors are advantageous because they may provide fast, sensitive, absolute, and selective measurements of species concentration. These systems are very attractive for practical applications owing to its compactness, resonable cost, robustness, and ease of use. In addition, diode lasers are fiber-optic compatible and thus enable simultaneous measurements of multiple species along a line-of-sight. Recent advances of room-temperature, near-IR and visible diode laser sources for telecommunication, optical data storage applications make it possible to be applied for combustion diagnostics based on diode laser absorption spectroscopy. Therefore, combined with fiber-optics and high sensitive detection strategies, compact and portable sensor systems are now appearing for variety of applications. The objectives of this research are to develope a new gas sensing system and to verify feasibility of this system. Wavelength and power characteristics as a function of injection current and temperature are experimentally found out. Direct absorption spectroscopy has been demonstrated in these experiments and has a bright prospect to this diode laser system.

• Journal of Microbiology and Biotechnology
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• v.22 no.7
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• pp.939-946
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• 2012

Antibiotic production with Streptomyces sindenensis MTCC 8122 was optimized under submerged fermentation conditions by artificial neural network (ANN) coupled with genetic algorithm (GA) and Nelder-Mead downhill simplex (NMDS). Feed forward back-propagation ANN was trained to establish the mathematical relationship among the medium components and length of incubation period for achieving maximum antibiotic yield. The optimization strategy involved growing the culture with varying concentrations of various medium components for different incubation periods. Under non-optimized condition, antibiotic production was found to be $95{\mu}g/ml$, which nearly doubled ($176{\mu}g/ml$) with the ANN-GA optimization. ANN-NMDS optimization was found to be more efficacious, and maximum antibiotic production ($197{\mu}g/ml$) was obtained by cultivating the cells with (g/l) fructose 2.7602, $MgSO_4$ 1.2369, $(NH_4)_2PO_4$ 0.2742, DL-threonine 3.069%, and soyabean meal 1.952%, for 9.8531 days of incubation, which was roughly 12% higher than the yield obtained by ANN coupled with GA under the same conditions.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.6
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• pp.556-561
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• 2009

The UAV(Unmanned Aerial Vehicle) that is remotely operating in various and long flight environments must have a very reliable propulsion system. Precise fault diagnosis of the turbo shaft engine for the Smart UAV that has the vertical take-off, landing and forward flight behaviors can promote reliability and availability. This work proposes a new diagnostic method that can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. The proposed diagnostic method can detect not only single fault but also multiple faults.