• 기계와재료
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• v.6 no.3 s.21
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• pp.142-151
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• 1994

• 기계와재료
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• v.9 no.2 s.32
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• pp.168-175
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• 1997

• Smart Structures and Systems
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• v.23 no.6
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• pp.579-587
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• 2019

In this paper, pounding tuned mass dampers (PTMDs) were designed to mitigate the multi-mode vortex-induced vibration (VIV) of stay cable utilizing the viscous-elastic material's energy-dissipated ability. The PTMD device consists of a cantilever metal rod beam, a metal mass block and a specially designed damping element covered with viscous-elastic material layer. Wind-tunnel experiment on VIV of stay cable model was set up to validate the effectiveness of the PTMD on multi-mode VIV mitigation of stay cable. By analyzing and comparing testing results of all testing cases, it could be verified that the PTMD with viscous-elastic pounding boundary can obviously mitigate the VIV amplitude of the stay cable. Moreover, the installed location and the design parameters of the PTMD device based on the controlled modes of the primary stay cable, would have a certain extent suppression on the other modal vibration of the stay cable, which means that the designed PTMDs are effective among a large band of frequency for the multi-mode VIV control of the stay cable.

• Proceedings of the Materials Research Society of Korea Conference
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• 1998.11a
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• pp.93-93
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• 1998

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.560-567
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• 1998

This paper concerned about a imputed color printed material image in camera to decrease noise and distortion by processing median filtering with input image to identical condition. Also this paper proposed the way of compares a normal printed material with an abnormal printed material color tone with trained a learning of the error back-propagation to block classification by extracting five place from identical block(3${\times}$3) of color printed material R, G, B value. As a representative algorithm of multi-layer perceptron the error Back-propagation technique used to solve complex problems. However, the Error Back-propagation is algorithm which basically used a gradient descent method which can be converged to local minimum and the Back Propagation train include problems, and that may converge in a local minimum rather than get a global minimum. The network structure appropriate for a given problem. In this paper, a good result is obtained by improve initial condition and adjust th number of hidden layer to solve the problem of real time process, learning and train.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.135-144
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• 2003

In this paper, the implementation techniques of intelligent nuclear material surveillance system based on the SOM(Self Organized Mapping) was described. Unattended continuous surveillance systems for nuclear facility result in large amounts of data, which require much time and effort to inspect. Therefore, it is necessary to develop system that automatically pinpoints and diagnoses the anomalies from data. In this regards, this paper presents a novel concept of a continuous surveillance system that integrates visual image and radiation data by the use of neural networks based on self-organized feature mapping

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.11 s.170
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• pp.1904-1911
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• 1999

The most effective material in the shape memory alloy(SMA) is the TiNi alloy, because its shape recovery characteristics are very excellent. We molded the composite material with shape memory function. The fiber of it is $Ti_{50}-Ni_{50}$ shape memory alloy and matrix of it is epoxy resin(Araldite B41, Hardner HT903. Ciba Geigy), its adhesive and optical sensitivity are very excellent. It was assured that the composite material could be used as model material of photoelastic experiment for intelligent materials or structures. In this research, the composite material with shape memory function is used as model material of photoelastic experiment. Photoelastic experimental hybrid method is developed in this research, it is assured that it is useful on the obtaining stress intensity factor and the separation of stress components from only isochromatic data. The measuring method of stress intensity factor of intelligent material by photoelastic experiment is introduced. In the mode I state, we can know that stress intensity factors are decreased more than 50% of stress intensity factor of room temperature when temperature of fiber is greater than 4$0^{\circ}C$, prestrain greater than 5% and fiber volume ratio greater than 0.42% and that stress intensity factors are decreased by 100% when fiber volume ratio is greater than 0.84%, prestrain greater than 5% and temperature greater than 60 $^{\circ}C$.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.79-90
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• 1995

The material handling equipment selection, that is a key task in the material handling system design, is a complex, difficult task, and requires a massive technical knowledge and systematic analysis. It is invaluable to justify the selected equipment model by the performance evaluation before its actual implementation. This paper presents an intelligent knowledge-based expert system called "IMESE" created by authors, for the selection and evaluation of material handling equipment model suitable for movement and storage of materials in a manufacturing facility. The IMESE is consisted of four modules: a knowledge base to select an appropriate equipment type, a multiple criteria decision making procedure to choose the most favorable commercial model of the selected equipment type, a database to store the list of commercial models of equipment types with their specifications, and simulators to evaluate the performance of the equipment model. The whole process of IMESE is executed under VP-Expert expert system environment.vironment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.15-24
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• 1995

This paper describes a system that can be used to recognize an unknown material regardless of the fuzzy neural network(FNN). There are some problems to realize the recognition system using temperature response. It requires too many memories to store the vast temperature response data and it has to be filtered to remove noise which occurs in experiment. And the temperature response is influenced by the change of ambient temperature. So, this paper proposes a practical method using curve fitting to remove above problems of memories and noise. and FNN is proposed to overcome the problem caused by the change of ambient temperature. Using the FNN which is learned by temperature responses on fixed ambient. Temperatures and known thermal conductivity, the thermal conductivity of the material can be inferred on various ambient temperatures. So the material can be recognized by the thermal conductivity.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.27 no.4
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• pp.226-231
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• 2014

Thin thermistor films of solutions with nickel and manganese oxides were prepared by metal-organic decomposition (MOD). The structural properties of the thin films were investigated as a function of annealing temperature. Field emission scanning electron microscope (FE-SEM) results indicated that the thin films had a thin thickness, smooth and dense surface. The crystallization temperature of $414.9^{\circ}C$ was confirmed from thermogavimetric-differential thermal analysis (TG-DTA) curve. A single phase of cubic spinel structure was obtained for the thin film annealed from $700^{\circ}C$ to $800^{\circ}C$, which was confirmed from the X-ray diffraction (XRD). From the selected area electron diffraction (SAED) in high resolution transmission electron microscope (HRTEM), the nano grains (2~3 nm) of spinel phase with (311) and (222) planes were detected for the thin film annealed at $500^{\circ}C$, which could be applicable to read-out integrated circuit (ROIC) substrate of the uncooled microbolometer with low processing temperature.