• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.786-792
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• 2022

In the cloud computing environment, there has been a lot of research into the Fog/Edge Computing (FEC) paradigm for securing user proximity of application services and computation offloading to alleviate service delay difficulties. The method of predicting dynamic location change patterns of edge devices (moving objects) requesting application services is critical in this FEC environment for efficient computing resource distribution and deployment. This paper proposes an optimal moving pattern extraction algorithm in which variable weights (distance, time, congestion) are applied to selected paths in addition to a support factor threshold for frequency patterns (moving objects) of edge devices. The proposed algorithm is compared to the OPE_freq [8] algorithm, which just applies frequency, as well as the A^* and Dijkstra algorithms, and it can be shown that the execution time and number of nodes accessed are reduced, and a more accurate path is extracted through experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1410-1413
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• 2003

This paper presents the design technique for controlling the oscillation in the Disk Drive Read System via Derivative State Constrained (DSC)-Optimal $H_2$ Controller. The Optimal $H_2$, DSC-Optimal $H_2$ and Incorporating of Stability Degree Specification DSC Optimal $H_2$ are discussed. The results among these schemes are compared to verify the merit of DSC that effectively suppresses the oscillation in oscillatory system. The suggestions of how to select the weights of optimal controls are given.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2158-2165
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• 2007

In this paper, a Modified Fuzzy C-Means algorithm with Gaussian Weights(MFCM_GW) is presented for nonlinear blind channel equalization. The proposed algorithm searches the optimal channel output states of a nonlinear channel from the received symbols, based on the Bayesian likelihood fitness function and Gaussian weighted partition matrix instead of a conventional Euclidean distance measure. Next, the desired channel states of a nonlinear channel are constructed with the elements of estimated channel output states, and placed at the center of a Radial Basis Function(RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a simplex genetic algorithm(GA), a hybrid genetic algorithm(GA merged with simulated annealing(SA): GASA), and a previously developed version of MFCM. It is shown that a relatively high accuracy and fast search speed has been achieved.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1147-1151
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• 2009

In this study, we address the problem of producing probability forecasts of summer seasonal rainfall, on the basis of Hindcast experiments from a ensemble of GCMs(cwb, gcps, gdaps, metri, msc_gem, msc_gm2, msc_gm3, msc_sef and ncep). An advanced Hierarchical Bayesian weighting scheme is developed and used to combine nine GCMs seasonal hindcast ensembles. Hindcast period is 23 years from 1981 to 2003. The simplest approach for combining GCM forecasts is to weight each model equally, and this approach is referred to as pooled ensemble. This study proposes a more complex approach which weights the models spatially and seasonally based on past model performance for rainfall. The Bayesian approach to multi-model combination of GCMs determines the relative weights of each GCM with climatology as the prior. The weights are chosen to maximize the likelihood score of the posterior probabilities. The individual GCM ensembles, simple poolings of three and six models, and the optimally combined multimodel ensemble are compared.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.09a
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• pp.13-18
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• 2001

The ECMO system, including umbilical cord and membrane type oxygenator was connected with extracorporeal circulation unit, was applied to the fetus growth model of goat. The maximum survival time of goat fetus was 48 hours. Average blood rate for the extracorporeal circulation was $223{\pm}15.2 ml/min.$ The survival time of fetus was deeply related to body temperature, blood circulation and water temperature, anesthetized time, and fetus weights. Extern variables that are composed of anesthetized time, fetus weights, change of hemoglobin, circuit pressure, related to the survival time for fetus corrected the problem of previous ECMO model that is controlled by roller pump. It is directly delivered to heart on load. Applying the results from new ECMO model, further research will provide to the system of ECMO for human.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.853-858
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• 1993

The error back-propagation(BP) algorithm is widely used for finding optimum weights of multi-layer neural networks. However, the critical drawback of the BP algorithm is its slow convergence of error. The major reason for this slow convergence is the premature saturation which is a phenomenon that the error of a neural network stays almost constant for some period time during learning. An inappropriate selections of initial weights cause each neuron to be trapped in the premature saturation state, which brings in slow convergence speed of the multi-layer neural network. In this paper, to overcome the above problem, Micro-Genetic algorithms(.mu.-GAs) which can allow to find the near-optimal values, are used to select the proper weights and slopes of activation function of neurons. The effectiveness of the proposed algorithms will be demonstrated by some computer simulations of two d.o.f planar robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.83-86
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• 1995

This paper proposes an auto-tuning method of feedforward signal in boiler control of thermal power plants by using the neural network. The neural network produces an optimal feedforward signal by tuning the weights of the network. The weights are adapted effectively by using the teaching signal of PI control output. The proposed method was evaluated based on a detailed simulator which expressed non-linear characteristics of the 600 MW actual thermal power plant at load chaning operations, showed effectiveness in the learning of the weights of the neural network, and gave an accurate control performance in the temperature control of the system. Through the evaluation, the proposed method was proved to be effectively applicable to the actual thermal plants as the automatic adjustment tool.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1463-1480
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• 2014

It is well known that sparse code is effective for feature extraction of face recognition, especially sparse mode can be learned in the kernel space, and obtain better performance. Some recent algorithms made use of single kernel in the sparse mode, but this didn't make full use of the kernel information. The key issue is how to select the suitable kernel weights, and combine the selected kernels. In this paper, we propose a novel multiple kernel sparse representation based classification for face recognition (MKSRC), which performs sparse code and dictionary learning in the multiple kernel space. Initially, several possible kernels are combined and the sparse coefficient is computed, then the kernel weights can be obtained by the sparse coefficient. Finally convergence makes the kernel weights optimal. The experiments results show that our algorithm outperforms other state-of-the-art algorithms and demonstrate the promising performance of the proposed algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.163.3-163
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• 2001

This paper presents the two-step strategy method of performing optimal scheduling of paper mill processes using MINLP (Mixed-Integer Non-Linear Programming) considering the trim loss problem in sheet cutting processes. The mathematical model for a sheet cutting process in the form of MINLP is developed in this study, and minimizing total cost is performed considering the cost of raw paper roll, :hanging cutting patterns, storage of over-product and recycling/burning trim. The paper has been used to deliver and conserve information for a long time, and it is needed to have various sizes and weights ...

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1171-1173
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• 2003

There are two datasets to estimate the area-mean and time-mean precipitation rate. For one, an array of surface rain gauges represents a series of rods that have to the time axis of the volume. And another data is that of a remote sensing make periodic overpasses at a fixed interval such as radar. The problem of optimally combining data from surface rain gauge data and remote sensed data is considered. In order to combining remote sensed data with Automatic Weather Station (AWS), we use optimal weighting method, which is similar to the method of [2]. They had suggested optimal weights that minimized value of the mean square error. In this paper, optimal weight is evaluated for the cases such as Changma, summer Monsoon, Typhoon and orographic rain.