• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.229-231
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• 2006

NLPCA(Nonlinear Principal Component Analysis) is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA operates by a feedforward neural network called AANN(Auto Associative Neural Network) which performs the identity mapping. In this work, a sensor fault detection system based on NLPCA is presented. To verify its applicability, simulation study on the data supplied from sensor network is executed.

• ETRI Journal
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• 제35권1호
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• pp.142-145
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• 2013

Network virtualization opens the door to novel infrastructure services offering connectivity and node manageability. In this letter, we focus on the cost-efficient embedding of on-demand virtual optical network requests for interconnecting geographically distributed data centers. We present a mixed integer linear programming formulation that introduces flexibility in the virtual-physical node mapping to optimize the usage of the underlying physical resources. Illustrative results show that flexibility in the node mapping can reduce the number of add-drop ports required to serve the offered demands by 40%.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1820-1823
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• 1997

For the Cartesian space position controlled robot, it is required to have the accurate mapping from the Cartesian space to the joint space in order to command the desired joint trajectories correctly. since the actual mapping from Cartesian space to joint space is obtained at the joint coordinate not at the actuator coordinate, uncertainty in Jacobian can be present. In this paper, two feasible neural network schemes are proposed to compensate for the kinematic Jacobian uncertainties. Uncertainties in Jacobian can be compensated by identifying either actuator Jacobian off-line or the inverse of that in on-line fashion. the case study of the stenciling robot is examined.

• 한국생산제조학회지
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• 제7권6호
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• pp.57-63
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• 1998

An adaptive control in the robotic GMA welding is employed to monitor information about weld characteristics and process parameters as well as to modify those parameters to hold weld quality within acceptable limits. Typical characteristics are the bead geometry, composition, microstructure, appearance, and process parameters which govern the quality of the final weld. The main objectives of this thesis are to realize the mapping characteristics of bead width through learning. After learning, the neural estimation can estimate the bead width desired form the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) are chosen from an estimation error analysis. A series of bead of bead-on-plate GMA welding experiments was carried out in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the bead width with reasonable accuracy and guarantee the uniform weld quality.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 하계종합학술대회논문집
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• pp.86-89
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• 1998

Multiple types of information in a multimedia service are delivered though multiple virtual connections on ATM network, while each virtual connection may be controlled independently. A multimedia service requires an associative relationship among multiple information streams to provide required harmonization. There may be required additional traffic descriptors to guarantee the required harmonization among multiple information streams in a multimedia service. For buffering of large bandwidth information stream(e.g., video), extremely large buffer size is necessary, but this approach should not be efficient way to compensate a severely delayed cells/blocks experienced at network. The best way to solve this problem will be minimization of relative-delayed-transfer of cells/blocks to application processes through ATM network control. To minimize a delayed transfer the mapping between relative delay parameter(i.e., associative Group QoS parameters) and per-VC traffic descriptor will be necessary. This paper is present additional functions and parameters to interpret the mapping between relative delay parameters(i.e., associative Group QoS parameters) and per-VC traffic descriptors in ATM API for multimedia application services.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2003년도 공동 춘계학술대회 논문집
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• pp.71-76
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• 2003

The purpose of this study is the development, application and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence, For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.

• ETRI Journal
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• 제39권6호
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• pp.851-858
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• 2017

Parkinson's disease is a growing and chronic movement disorder, and its diagnosis is difficult especially at the initial stages. In this paper, movement characteristics extracted by a computer using multilayer back propagation neural network mapping are converted to the symptoms of this disease. Then, modulation of three classifiers of C4.5, k-nearest neighbors, and support vector machine with majority voting are applied to support experts in diagnosing the disease. The purpose of this study is to choose appropriate characteristics and increase the accuracy of the diagnosis. Experiments were performed to demonstrate the improvement of Parkinson's disease diagnosis using this method.

• Industrial Engineering and Management Systems
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• 제8권1호
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• pp.14-21
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• 2009

We formulate a mathematical model of remanufacturing system as multi-stage reverse Logistics Network Problem (mrLNP) with minimizing the total costs for reverse logistics shipping cost and inventory holding cost at disassembly centers and processing centers over finite planning horizons. For solving this problem, in the 1st and the 2nd stages, we propose a Genetic Algorithm (GA) with priority-based encoding method combined with a new crossover operator called as Weight Mapping Crossover (WMX). A heuristic approach is applied in the 3rd stage where parts are transported from some processing centers to one manufacturer. Computer simulations show the effectiveness and efficiency of our approach. In numerical experiments, the results of the proposed method are better than pnGA (Prufer number-based GA).

• 전자공학회논문지B
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• 제33B권9호
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• pp.37-48
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• 1996

In this paper, for the single s-a-E fault detected in a triangular cellular permutation network (TCPN), we propose a method which can tolerate a fault by reconfiguring the netowrk and analyze the possibilities of the reconfiguration. The network is set up through iterative decomposition of a permutation into the right or left coset. For the s-a-E fault of a cell which is to be transpositioned for an increasing order mapping, we cna reconfigure it merely by switching te decomposition scheme from right coset to left coset or vice versa. Also for a decreasing order mapping, we make a detour around the faulty cell. Reconfiguring with the redundant connectivity of a TCPN, we could realize form 17% to 90% of the permutation for the number of inputs from 4 to 40. REconfiguration of the network by exchanging the first input with the last input and the first output with the last output resulted in more than 99% realization of the permutation. Also with the exchange of all inputs and outputs with neighboring cells, we could have 100% realization of the permutation.

• Journal of Advanced Marine Engineering and Technology
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• 제29권2호
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• pp.209-216
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• 2005

Thermo-chromic Liquid Crystal(TLC) particles were used as temperature sensor for thermal fluid flow. 1K $\times$ 1K CCD color camera and Xenon Lamp(500w) were used for the visualization of a Hele-Shaw cell The characteristic between the reflected colors from the TLC and their corresponding temperature shows strong non-linearity A neural network known as having strong mapping capability for non-linearity is adopted to quantify the temperature field using the image of the flow. Improvements of color-to-temperature mapping was attained by using the local color luminance (Y) and hue (H) information as the inputs for the constructed neural network.