• Journal of Institute of Control, Robotics and Systems
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• v.16 no.10
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• pp.919-926
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• 2010

This paper proposes a wearable hand rehabilitation device, DULEX, for persons with functional paralysis of upper-limbs after stoke. DULEX has three degrees of freedom for rehabilitation exercises for wrist and fingers except the thumb. The main function of DULEX is to extend the range of motions of finger and wrist being contracture. DULEX is designed by using a parallel mechanism, and its parameters such as length and location of links are determined by kinematic analysis. The motion trajectory of the designed DULEX is aligned to human hand to prevent a slip. To reduce total weight of DULEX, artificial air muscles are used for actuating each joint motion. In feedback control, each joint angle is indirectly estimated from the relations of the input air pressure and the output muscle length. Experimental results show that DULEX is feasible in hand rehabilitation for stroke survivals.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.154-157
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• 2009

High dynamic range image can describe the real world scenes that have a wide range of luminance intensity. To display high dynamic range (HDR) image into conventional displayable devices such as monitors and printers, we proposed the logarithmic based global mapping algorithm that consider the features of image using mapping parameters. Based on characteristics of image, we first modify input luminance values for reproducing perceptually tuned images and then displayable output values are obtained directly. The experimental results show that the proposed algorithm achieves good subjective results while preserving details of image, furthermore proposed algorithm has fast simple and practical structure for implementation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.302-305
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• 1996

In this paper, an optimal identification method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzz-neural networks(FNNs) and parameters of membership function are tuned using genetic algorithm(GAs). For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activated sludge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The show that the proposed method can produce the intelligence model w th higher accuracy than other works achieved previously.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.10
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• pp.139-147
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• 1996

In this study a two-stage fast pulse generaor using magnetic switches is proposed. The scheme consist of a switch, an inductor and two pairs of capacitor and saturable inductors, a linear transformer. The basic principle and the operation are described using a set of given parameters. The main issue of the magnetic pulse genration scheme is the system efficiency. This study focuses on the system efficiency improvement using magnetic switches. The voltage compression ratio, energy transfer with respect to core area are investigated. The output voltage and transferred energy as a function of input voltage are also included. Also, an analysis and experiments are performed to verify the porposed topology by implementing a 10[J] class experimental circuit. The efficiency of the transferred energy a tload side is 82%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.153-158
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• 1995

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed fuzzy GMDH modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) algorithm and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH algorithm and fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnaceare those for sewage treatment process are used for the purpose of evaluating the performance of the proposed fuzzy GMDH modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

• Computers and Concrete
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• v.10 no.6
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• pp.557-573
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• 2012

This paper discusses the combined application of two different techniques, Neural Networks (NN) and Principal Component Analysis (PCA), for improved prediction of concrete properties. The combination of these approaches allowed the development of six neural networks models for predicting slump and compressive strength of concrete with mineral additives such as blast furnace slag, fly ash and silica fume. The Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian regularization was used in all these models. They are produced to implement the complex nonlinear relationship between the inputs and the output of the network. They are also established through the incorporation of a huge experimental database on concrete organized in the form Mix-Property. Thus, the data comprising the concrete mixtures are much correlated to each others. The PCA is proposed for the compression and the elimination of the correlation between these data. After applying the PCA, the uncorrelated data were used to train the six models. The predictive results of these models were compared with the actual experimental trials. The results showed that the elimination of the correlation between the input parameters using PCA improved the predictive generalisation performance models with smaller architectures and dimensionality reduction. This study showed also that using the developed models for numerical investigations on the parameters affecting the properties of concrete is promising.

• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.60 no.1
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• pp.87-99
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• 2024

This study deals with the application of an artificial neural network (ANN) model to predict power consumption for utilizing seawater source heat pumps of recirculating aquaculture system. An integrated dynamic simulation model was constructed using the TRNSYS program to obtain input and output data for the ANN model to predict the power consumption of the recirculating aquaculture system with a heat pump system. Data obtained from the TRNSYS program were analyzed using linear regression, and converted into optimal data necessary for the ANN model through normalization. To optimize the ANN-based power consumption prediction model, the hyper parameters of ANN were determined using the Bayesian optimization. ANN simulation results showed that ANN models with optimized hyper parameters exhibited acceptably high predictive accuracy conforming to ASHRAE standards.

• ETRI Journal
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• v.29 no.2
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• pp.212-218
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• 2007

A novel low-voltage CMOS current feedback operational amplifier (CFOA) is presented. This realization nearly allows rail-to-rail input/output operations. Also, it provides high driving current capabilities. The CFOA operates at supply voltages of ${\pm}0.75V$ with a total standby current of 304 ${\mu}A$. The circuit exhibits a bandwidth better than 120 MHz and a current drive capability of ${\pm}1$ mA. An application of the CFOA to realize a new all-pass filter is given. PSpice simulation results using 0.25 ${\mu}m$ CMOS technology parameters for the proposed CFOA and its application are given.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.315-318
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• 1996

In this paper a new positioning method for cylindrical work pieces on rotating supports is studied. A work piece on a rotating axis is positioned by an impact drive mechanism (IDM) whose driving parameters are steadily updated by observing the object movement. The application of this actuator and the use of a multi-functional PC board for all necessary input and output operations such as e.g. data acquisition or wave form generation allow an alignment with a precision of less than 1.mu.m in a relatively short time and at low cost compared to conventional methods.