• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2000년도 특별강연 및 춘계학술발표대회 개요집
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• pp.303-306
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• 2000

Quality estimation of the weld has been one of the important issues in RSW which is a main process of the sheep metal fabrication in auto-body industry, It was well known that among the various welding process variables, dynamic resistance has a close relation with nugget formation. With this variable, it is possible to estimate the weld quality in real time. In this study, a new quality estimation algorithm is developed with the primary dynamic resistance measured at welding machine timer. For this, feature recognition method of Hopfield neural network is used. Primary resistance patterns are vectorized and classified with five patterns. The network trained by these patterns recognizes the dynamic resistance pattern and estimates the weld quality Because the process variable monitored at the primary circuit is used, it is possible to apply this system to real time application without any consideration of electrode wear or shunt effect.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2016년도 제50회 동계 정기학술대회 초록집
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• pp.363.1-363.1
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• 2016

Transparent and stretchable conductors are expected to be an essential component in future stretchable optoelectronic devices. Until now, two main methods have been commonly employed to fabricate transparent and stretchable conductors by using metal nanomaterials: creating buckling configurations and creating network configurations. In this report, a novel strategy for obtaining transparent and stretchable conductors is presented, one that employs these two main approaches simultaneously. To the best of our knowledge, this proposed configuration of a buckled long nanofiber network in this study has not yet been reported. In order to provide the transparent conductors with dual mode stretchability originating from simultaneous buckled and network configurations, a buckled Au@polyvinylpyrrolidone (PVP) nanofiber network (hereafter referred to BANN for convenience) was fabricated by transferring Au-metallized electrospun PVP nanofibers onto a prestrained polydimethylsiloxane (PDMS) substrate. Our BANN shows considerably lower strain sensitivity of resistance than that of straight Au@PVP nanofiber network. Durability tests conducted by performing cyclic tensile strain reveal that the relative change in resistance of BANN (prestrain = 20%) is quite small after 1000 cycles. We also demonstrate that this BANN exhibits superior performance over widely used indium tin oxide conductors with regard to high optical transmittance and low sheet resistance.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2005년도 후기학술대회논문집
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• pp.47-48
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• 2005

This paper describes how an Artificial Neural Network(ANN) can be employed to improve a speed estimation in a vector controlled induction motor drive. The system uses the ANN to estimate changes in the motor resistance, which enable the sensorless speed control method to work more accurately. Flux Observer is used for speed estimation in this system. Obviously the accuracy of the speed control of motor is dependent upon how well the parameters of the induction machine are known. These parameters vary with the operating conditions of the motor; both stator resistance(Rs) and rotor resistance(Rr) change with temperature, while the stator leakage inductance varies with load. This paper proposes a parameter compensation technique using artificial neural network for accurate speed estimation of induction motor and simulation results confirm the validity of the proposed scheme.

• 한국정밀공학회지
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• 제12권9호
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• pp.86-94
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• 1995

Bead shape in high frequency electric resistance (HER) pipe welding gives useful information on judging current welding conditon. In most welding process, heat input is controlled by skilled operators observing color and shape of bead. In this paper, a visual monitoring system is designed to observe bead shape in HERW pipe welding process by using structured light beam and a C.I.D(Charge injection device) camera. To avoid some difficul- ties arising in extracting stable features of stripe pattern and classifying the extracted features, Kohonen neural network is used to classify such bead shapes. The experimental results show accurate classification performance of the proposed method.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.1740-1745
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• 2004

A simplified resistor network model for electrical and mass transport in anode-supported planar solid oxide fuel cell (SOFC) was constructed in order to investigate the effect of interconnect rib geometry on the cell performance. For accurate potential calculation, activation and concentration over-potentials at the electrode/electrolyte interfaces were fully considered in this calculation. When contact resistance was not considered, the optimum interconnect rib length were calculated to be $0.1{\sim}0.2$ mm for 2 mm half unit cell for given operation conditions and properties. However, with realistic contact resistance, the interconnect rib length should be increased to provide larger contact area and thus to obtain better performance.

• Asian Pacific Journal of Cancer Prevention
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• 제16권7호
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• pp.2793-2800
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• 2015

In molecular-targeted cancer therapy, acquired resistance to gemcitabine is a major clinical problem that reduces its effectiveness, resulting in recurrence and metastasis of cancers. In spite of great efforts to reveal the overall mechanism of acquired gemcitabine resistance, no definitive genetic factors have been identified that are absolutely responsible for the resistance process. Therefore, we performed a cross-platform meta-analysis of three publically available microarray datasets for cancer cell lines with acquired gemcitabine resistance, using the R-based RankProd algorithm, and were able to identify a total of 158 differentially expressed genes (DEGs; 76 up- and 82 down-regulated) that are potentially involved in acquired resistance to gemcitabine. Indeed, the top 20 up- and down-regulated DEGs are largely associated with a common process of carcinogenesis in many cells. For the top 50 up- and down-regulated DEGs, we conducted integrated analyses of a gene regulatory network, a gene co-expression network, and a protein-protein interaction network. The identified DEGs were functionally enriched via Gene Ontology hierarchy and Kyoto Encyclopedia of Genes and Genomes pathway analyses. By systemic combinational analysis of the three molecular networks, we could condense the total number of DEGs to final seven genes. Notably, GJA1, LEF1, and CCND2 were contained within the lists of the top 20 up- or down-regulated DEGs. Our study represents a comprehensive overview of the gene expression patterns associated with acquired gemcitabine resistance and theoretical support for further clinical therapeutic studies.

• 한국철도학회논문집
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• 제13권6호
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• pp.565-569
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• 2010

레일 밑에 놓이는 침목은 레일을 지지해 주는 역할을 한다. 침목의 저항은 레일의 전류 및 전압과 밀접한 관계가 있다. 침목의 저항이 낮으면 레일 사이에 침목을 통해 폐회로가 형성되어 레일의 전류 및 전압이 감소한다. 이로 인해 궤도 계전기 동작에 문제가 발생하여 열차가 항상 궤도를 점유하게 된 상태로 유지된다. 현재 도시철도 및 광역철도에서 사용하고 있는 목침목은 산화작용을 방지하기 위해 방부제를 사용한다. 방부제 성분 중에 크레오소트가 있는데 이 물질은 탄소를 기저로 하는 화학물질로 변화된다. 도체적인 성질을 가지고 있는 탄소는 목침목의 저항에 영향을 미친다. 크레오소트의 화학작용에 의해 발생한 탄소로 인해 목침목이 도체로써 작용하면 침목 간격마다 레일 사이에 폐회로가 형성되어 열차의 단락 전류 및 궤도회로 종단의 전압이 감소하여 궤도 계전기가 오동작이 발생한다. 본 논문에서는 목침목의 저항을 포함하는 궤도회로 4단자망 모델을 제시하였다. 또한 직선구간에서 목침목의 기준 저항 값을 제시하였다.

• 전기학회논문지
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• 제60권2호
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• pp.293-300
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• 2011

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance and adaptive fuzzy learning contrroller(AFLC) for speed control in IPMSM Drives. AFLC is chaged fuzzy rule base by rule base modifier for robust control of IPMSM. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator and AFLC is confirmed by comparing to conventional algorithm.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권5호
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• pp.332-337
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• 2004

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying. parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• 제어로봇시스템학회논문지
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• 제13권5호
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• pp.429-433
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• 2007

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.