• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.261-270
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• 1999

Sintering transforms powdered ore into lumped ore so that the latter can be used in a blast furnace. The powdered or combined with coke and other materials is loaded into a container and moved along by a pallet while the ignited coke burns. The speed by which the pallet moves determines how much sintering takes place. Since the process is complicated and lacks an accurate mathematical model, human operators manually control the speed by monitoring various factors in the plant. In this paper, we propose a neural network-based pallet speed controller which copies human operator knowledge. Actual process data were collected from a sintering plant for eight months and preprocessed to remove noisy and inconsistent data. A multilayer perceptron was trained using a back-propagation learning algorithm. In on-line testing at the sinter plant, the proposed model reliably controlled pallet speed during normal operation without the help of human operators. Moreover, the quality and productivity was as good as with human operators.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.444-447
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• 2008

This study is intended to develop a improved version of Cross Impact Analysis Model based on Scaling Theory. In developing the model, we applied the scale transformation technique and regression technique to existing CIA model. Improved CIA model is composed of two sub-models: 'model for impact value measurement,' and 'model for impact value conversion'. We applied a technique which measures data by ordinal scale and then transforms them into interval scale and ratio scale data to CIA model. The accuracy of forecasting and the usability of CIA application have been improved.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.9
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• pp.417-424
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• 2002

This paper presents an industrial peak load management system for the peak demand control. Kohonen neural network and wavelet transform based techniques are adopted for industrial peak load forecasting that will be used as input data of the peak demand control. Firstly, one year of historical load data of a steel company were sorted and clustered into several groups using Kohonen neural network and then wavelet transforms are applied with Biorthogonal 1.3 mother wavelet in order to forecast the peak load of one minute ahead. In addition, for the peak demand control, composite fuzzy model is proposed and implemented in this work. The results are compared with those of conventional model, fuzzy model and composite model, respectively. The outcome of the study clearly indicates that the composite fuzzy model approach can be used as an attractive and effective means of the peak demand control.

• Journal of the Mineralogical Society of Korea
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• v.9 no.1
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• pp.35-42
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• 1996

Polycrystalline ZrH2 in tetragonal crystal system has been compressed in a modified Bassett-type diamond anvil cell up to 36.0 GPa at room temperature. X-ray diffraction data did not indicate any phase transitions at the present pressure range. The pressure dependence of the a-axis, c-axis, c/a and molar volume of ZrH2 was determined at pressures up to 36.0 GPa. Assuming the pressure derivative of the bulk modulus (K0') to be 4.11 from an ultrasonic value on Zr, bulk modulus (K0) was determined to be 160Gpa by fitting the pressure-volume data to the Birch-Murnaghan equation of state. Same sample was heated at $500^{\circ}C$ at the pressure of 9.8 GPa in a modified Sung-type diamond anvil cell. Unloaded and quenched sample revealed that the original tetragonal structure transforms into a hexagonal structured phase with a zero-pressure molar volume change of ~115.5%.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.864-867
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• 2005

In this paper we introduce a design of parallel architecture for wavelet transformation on FPGA. We implement wavelet transforms though lifting scheme and apply Daubechies4 transform equations. This technique has an advantage that we can obtain perfect reconstruction of the data. We divide our process to high pass filter and low pass filter. With this division, we can find coefficients from low and high pass filters simultaneously using parallel processing properties of FPGA to reduce processing time. From the equations, we have to design real number computation module, referred to IEEE754 standard. We choose 32 bit computation that is fine enough to reconstruct data. After that we arrange the real number module according to Daubechies4 transform though lifting scheme.

• Structural Engineering and Mechanics
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• v.67 no.3
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• pp.245-254
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• 2018

In this paper, a Cloud Model based Fruit Fly Optimization Algorithm (CMFOA) is presented for structural damage identification, which is a global optimization algorithm inspired by the foraging behavior of fruit fly swarm. It is assumed that damage only leads to the decrease in elementary stiffness. The differences on time-domain structural acceleration data are used to construct the objective function, which transforms the damaged identification problem of a structure into an optimization problem. The effectiveness, efficiency and accuracy of the CMFOA are demonstrated by two different numerical simulation structures, including a simply supported beam and a cantilevered plate. Numerical results show that the CMFOA has a better capacity for structural damage identification than the basic Fruit Fly Optimization Algorithm (FOA) and the CMFOA is not sensitive to measurement noise.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1997.06a
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• pp.25-29
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• 1997

The present block-based DCT encoder transforms images regardless of layers and then simply partitions the transformed data into a few layers, for example low and high frequency bands in JPEG. Yet, it fails to utilize the similarity of coefficients in each band. Therefore, we combine the subband coder and the block-based DCt coder in this paper. The new coding scheme enables the data to automatically be classified into several layers and increases the efficiency of transform. Various possible coding structures are investigated and the simulation results are also provided.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.155-162
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• 2003

Today's network consists of a large number of routers and servers running a variety of applications. Policy-based network provides a means by which the management process can be simplified and largely automated. In this paper we build a foundation of policy-based network modeling and simulation environment. The procedure and structure for the induction of policy rules from vulnerabilities stored in SVDB (Simulation based Vulnerability Data Base) are developed. The structure also transforms the policy rules into PCIM (Policy Core Information Model). The effect on a particular policy can be tested and analyzed through the simulation with the PCIMs and SVDB.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1307-1309
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• 2003

The EO-1 spacecraft, launched November 21, 2000 into a sun synchronous orbit behind Landsat 7, hosts advanced technology demonstration instruments, whose capabilities are currently being assessed by the user community for future missions. A significant part of the EO-1 program is to perform data comparisons between Hyperion, ALI and Landsat 7 ETM+. In this paper, a comparison of forest classification results from Hyperion, ALI, and the ETM+ of Landsat-7 are provided for Wangqing Forest Bureau, Jilin Province, Northeast China. The data have been radiometrically corrected and geometrically resampled. Feature selection and statistical transforms are used to reduce the Hyperion feature space from 86 channels to 14 features. Classes chosen for discrimination included Larch, Spruce, Oak, Birch, Popular and Mixed forest and other landuses. Classification accuracies have been obtained for each sensor. Comparison of the classification results shows : Hyperion classification results were the best, ALI's were much better than ETM+.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.88.4-88
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• 2001

In this paper, we propose a solver for differential equations, using a multi-layer neural network. The multi-layer neural network is a transformer function originally where the function is differential and the explicit representation has been developed. The learning determines the response of neural networks; however, the response is not equal to the output values. The differential relations are also the response. The differential conditions can be also set as teaching data; therefore, there is a possibility to reach a new solver for the differential equations. Since it is unknown how to define the input data for the neural network solver during long terms, we could not derive the expressions. Recently, the analogue type neural network is known and it transforms any vector to another The "any" must be...