• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.11
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• pp.394-403
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• 1983

This paper discusses a model reference adaptive control for a multi-input multi-output continuos system in matrix fraction description. The controller is of Monopoli-Narendra type with a time-varying gain matrix in the parameter adaptation law. The transfer matrix of the given plant with an adjustable controller is made to approach to that of the reference model asymptotically. It is shown that, under some plausible assumptions such as on the knowlidge of an interactor matrix, the algorithm for a single-input single-output system can be appropriately extended to a multi-input multi-output system. The convergence of an adaptation law is estavlished with some stability theory and stability of the overall system is asserted by an analytical investigation.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.246-246
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• 2023

Multi-purpose dams are operated accounting for both physical and socioeconomic factors. This study aims to evaluate the utility of a deep learning algorithm-based model for three multi-purpose dam operation (Seomjin River dam, Juam dam, and Juam Control dam) in Seomjin River. In this study, the Gated Recurrent Unit (GRU) algorithm is applied to predict hourly water level of the dam reservoirs over 2002-2021. The hyper-parameters are optimized by the Bayesian optimization algorithm to enhance the prediction skill of the GRU model. The GRU models are set by the following cases: single dam input - single dam output (S-S), multi-dam input - single dam output (M-S), and multi-dam input - multi-dam output (M-M). Results show that the S-S cases with the local dam information have the highest accuracy above 0.8 of NSE. Results from the M-S and M-M model cases confirm that upstream dam information can bring important information for downstream dam operation prediction. The S-S models are simulated with altered outflows (-40% to +40%) to generate the simulated water level of the dam reservoir as alternative dam operational scenarios. The alternative S-S model simulations show physically inconsistent results, indicating that our deep learning algorithm-based model is not explainable for multi-purpose dam operation patterns. To better understand this limitation, we further analyze the relationship between observed water level and outflow of each dam. Results show that complexity in outflow-water level relationship causes the limited predictability of the GRU algorithm-based model. This study highlights the importance of socioeconomic factors from hidden multi-purpose dam operation processes on not only physical processes-based modeling but also aritificial intelligence modeling.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1091-1097
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• 1991

A logical and systematic procedure to derive a mathematical model for magnetically levitation(maglev) systems with a combined lift and guidance is developed by using and graph. First, bond graph is constructed for the energy-feeding system with magnetic leakage flux. And, the overall maglev system in which lift and guidance dynamics are coupled is modeled by using the concept of multi-port field in bond notations. Finally, the LQG/LTR control systems are designed for single-input single-output and for multi-input multi-output maglev systems. In this paper, it has been shown that the bond graph is an excellent method for modeling multi-energy domain systems such as maglev systems and the multivariable control system is required to improve the performance of the maglev system with a combined lift and guidance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.11A
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• pp.892-902
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• 2011

The opportunistic spatial orthogonalization (OSO) scheme, proposed by Cong Shen and Michael P. Fitz, allows the existence of secondary users during the period in which the primary user is occupying all licensed bands. This paper introduces an active secondary user selection algorithm which mitigates the interference from the primary user transmitter to the secondary user receiver based on single-input multi-output system without altering a primary user's transmission strategy. A proposed algorithm guarantees the minimum average throughput of the primary user and overcomes the average sum throughput of a conventional OSO. We have numerically analyzed the average throughput under various constraints.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.11
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• pp.2298-2304
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• 2015

In this paper, we provide a new approach which optimizes the vertical tilting angle of the base station for multi-cell multiple-input single-output (MISO) downlink active antenna systems (AAS). Instead of the conventional optimal algorithm which requires an exhaustive search, we propose simple and near optimal algorithms. First, we represent a large system approximation based vertical beamforming algorithm which is applied to the average sum rate by using the random matrix theory. Next, we suggest a signal-to-leakage-and-noise ratio (SLNR) based vertical beamforming algorithm which simplifies the optimization problem considerably. In the simulation results, we demonstrate that the performance of the proposed algorithms is near close to the exhaustive search algorithm with substantially reduced complexity.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.111-117
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• 2011

In this paper, the BER(Bit Error Rate) performance of a DVB-T2(Second Generation Digital Terrestrial Television Broadcasting System) in MISO(Multiple Input Single Output) transmission mode is evaluated by the computer simulation. In the DVB-T2 receiver, an IDD(Iterative Demapping and Decoder) technique is employed that exchanges extrinsic information between the demapper and the LDPC decoder. Simulation results show that the IDD-based DVB-T2 receiver in MISO transmission mode provides 2dB gain at BER of $10^{-4}$ but suffer from the frequency offsets between transmit antennas.

• Journal of Internet of Things and Convergence
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• v.9 no.2
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• pp.71-76
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• 2023

In this paper, we propose a smoothed received signal strength indicator (RSSI)-based distance estimation using deep neural network (DNN) for accurate distance estimation in an environment where a single receiver is used. The proposed scheme performs a data preprocessing consisting of data splitting, missing value imputation, and smoothing steps to improve distance estimation accuracy, thereby deriving the smoothed RSSI values. The derived smoothed RSSI values are used as input data of the Multi-Input Single-Output (MISO) DNN model, and are finally returned as an estimated distance in the output layer through input layer and hidden layer. To verify the superiority of the proposed scheme, we compared the performance of the proposed scheme with that of the linear regression-based distance estimation scheme. As a result, the proposed scheme showed 29.09% higher distance estimation accuracy than the linear regression-based distance estimation scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.9
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• pp.822-830
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• 2000

A conventional advanced control algorithm is proposed in this paper for improved temperature regulation of a TV-glass melting furnace. The TV-Glass melting furnace is a typical MIMO(Multi-Input Multi Output) system which is subject to various thermal disturbances. Because of its complexity, a detailed mathematical model of the furnace is hard to establish. To design a temperature control control system of the furnace, major input-output variables are selected first, and simple FOPDT(First Order Plus Dead Time) models are established based on the physical meaning and experimental process data. Based on the FOPDT models, a multi-loop control system composed of cascade and single loops are designed for effective control of the MIMO system. Practical implementation on the 150 ton/day furnace using the DCS(Distributed Control System) showed that the proposed control technique performs better than manual control.

• International Journal of Automotive Technology
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• v.8 no.6
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• pp.771-780
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• 2007

In recent studies, various types of multi mode electric variable transmissions of hybrid electric vehicles have been proposed. Multi mode electric variable transmission consists of two or more different types of planetary gear hybrid powertrain system(PGHP), which can change its power flow type by means of clutches for improving transmission efficiencies. Generally, the power flows can be classified into three different types such as input split, output split and compound split. In this study, we analyzed power transmission characteristics of the possible six input split systems, and found the suitable system for single or multi mode hybrid powertrain. The input split system used in PRIUS is identified as a best system for single mode, and moreover we identified some suitable systems for dual mode.

• Journal of Korea Multimedia Society
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• v.7 no.6
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• pp.781-790
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• 2004

This paper presents a fourth-order cumulants based iterative algorithm for blind channel equalization. It is robust with respect to the existence of heavy Gaussian noise in a channel and does not require the minimum phase characteristic of the channel. In this approach, the transmitted signals at the receiver are over-sampled to ensure the channel described by a full-column rank matrix. It changes a single-input/single-output (SISO) finite-impulse response (FIR) channel to a single-input/multi-output (SIMO) channel. Based on the properties of the fourth-order cumulants of the over-sampled channel outputs, the iterative algorithm is derived to estimate the deconvolution matrix which makes the overall transfer matrix transparent, i.e., it can be reduced to the identity matrix by simple reordering and scaling. Both a closed-form and a stochastic version of the proposed algorithm are tested with three-ray multi-path channels in simulation studies, and their performances are compared with a method based on conventional second-order cumulants. Relatively good results are achieved, even when the transmitted symbols are significantly corrupted with Gaussian noise.