• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.4
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• pp.551-557
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• 2017

Commanders want to receive quickly scientific and analytic results about the battlefield situation. Unfortunately, decision support system of Army Tactical Command Information System(ATCIS) is restricted to message procedures and searching function based on manual work. In this paper, we propose applying Data Mining to ATCIS for supporting rapid decision making based on the scientific and analytic method. The purpose of this proposal is to efficiently execute the tactical planning and employment of the subordinate units in order to achieve the mission.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.101-108
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• 2013

In this paper, we propose a method in which the negative acknowledge (NACK) message is used as command for cooperation and spectrum sharing. This allows for an automatic request for cooperation and sharing when the direct link of the primary user is in outage, and also allows for saving the number of control messages in cooperation-spectrum sharing based paradigm. In the sharing phase, the selected relay shares a power fraction of $1-{\alpha}$ for secondary transmitted signal while the remaining of ${\alpha}$ is for primary retransmitted signal. In the case of no relay collected, primary transmitter uses NACK as a command to retransmit the signal with fully power fraction (${\alpha}=1$). Both systems are assumed to employ BPSK signals. In this scheme, we propose the joint optimal decoding in the secondary user. The frame error rate (FER) performance at both systems is then analyzed. The theoretical and simulation results validate the analysis and confirm the efficiency of the protocol.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.129-137
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• 2013

As the usage of computer based systems continues to increase in our normal life, there are constant efforts to enhance the accessibility of information for handicapped people. For this, it is essential to develop new interface ways for physical disabled peoples by means of human-computer interface (HCI) or human-machine interface (HMI). In this paper, we developed HMI using electromyogram (EMG) and electrooculogram (EOG) for people with physical disabilities. Developed system is composed of two modules, hardware module for signal sensing and software module for feature extraction and pattern classification. To maximize ease of use, only two skin contact electrodes are attached on both ends of brow, and EOG and EMG are measured simultaneously through these two electrodes. From measured signal, nine kinds of command patterns are extracted and defined using signal processing and pattern classification method. Through Java based real-time monitoring program, developed system showed 92.52% of command recognition rate. In addition, to show the capability of the developed system on real applications, five different types of commands are used to control ER1 robot. The results show that developed system can be applied to disabled person with quadriplegia as a novel interface way.

• Journal of Power Electronics
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• v.18 no.3
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• pp.714-722
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• 2018

This paper presents a wide-range speed control scheme of brushless DC (BLDC) motors based on a hall sensor with separated low- and normal-speed controllers. However, the use of the hall sensor signal is insufficient to detect motor speed in the low-speed region because of low sensor resolution and time delay. In the proposed method, a micro-stepping current control method according to the torque angle variation is presented. In this mode, the motor current frequency and rotating angle are determined by the reference speed without the actual speed fed by the hall sensor. The detected torque angle is used to adjust the current value in a limited band to control the current value in accordance with the load. The torque angle is detected exactly at the changing point of the hall sensor signal. The rotor can follow the rotating flux with the variable torque angle. In a normal speed range, the conventional vector control scheme is used to control the motor current with a PI speed controller using the hall sensor. The torque characteristics are analyzed on the basis of the back EMF and current shape. To adopt the vector control scheme, the continuous rotor position is estimated by the measured speed and hall sensor position. At the mode changing point between low and normal speed range, the proper initial current command and reference rotor position are calculated. The calculated current command can reduce the torque ripple during transient mode. The proposed method is simple but effective in extending the speed control range of a conventional BLDC motor with hall sensor without the need for a high-resolution encoder. The effectiveness of the proposed method is verified by various experiments on a practical BLDC motor.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.685-692
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• 2024

In this paper, we propose a new CNN model to improve the recognition performance of command voice signals. This method obtains a spectrogram image after performing a short-time Fourier transform (STFT) of the input signal and improves command recognition performance through supervised learning using a CNN model. After Fourier transforming the input signal for each short-time section, a spectrogram image is obtained and multi-classification learning is performed using a CNN deep learning model. This effectively classifies commands by converting the time domain voice signal to the frequency domain to express the characteristics well and performing deep learning training using the spectrogram image for the conversion parameters. To verify the performance of the speech recognition system proposed in this study, a simulation program using Tensorflow and Keras libraries was created and a simulation experiment was performed. As a result of the experiment, it was confirmed that an accuracy of 92.5% could be obtained using the proposed deep learning algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.797-802
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• 2008

High Speed Rail Train Control System consists of CTC, IXL and ATC. IXL and ATC perform train control and command via interchanging relevant information between a signal room and CTC. However, it is proved that IXL and ATC are attributed to train delay error since those systems are highly sensitive to trackside conditions. Especially, network error on IXL blocks transmitting signal information to adjacent signal room so that its effects give rise to system overall problems. In order to figure out the measures for which minimizing the occurrence rate of train delay error due to HSR TCS, This paper is performed analysis on communication network structure, the length of SSI network roof and SSI-TFM distance by examining and analyzing the error cases related to IXL in a network aspect.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.4
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• pp.66-72
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• 2021

Since the behavior of an autonomous underwater vehicle (AUV) is influenced by disturbances and moments that are not accurately known, the depth control law of AUVs must have the ability to track the input signal and to reject disturbances simultaneously. Here, we proposed robust tracking control for controlling the depth of an AUV. An augmented closed-loop system is represented by an error dynamic equation, and we can easily show the asymptotic stability of the overall system by using a Lyapunov function. The robust tracking controller is consisted of the internal model of the command signal and a state feedback controller, and it has the ability to track the input signal and reject disturbances. The closed-loop control system is robust to parameter uncertainties. Simulation results showed the control performance of the robust tracking controller to be better than that of a P + PD controller.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.1
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• pp.45-52
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• 2017

EEG signals that are the same as those that have the same disabled people. So, the EEG signals are becoming the next generation. In this paper, we propose an internet of things system that controls the indoor environment using EEG signal. The proposed system consists EEG measurement device, EEG simulation software and indoor environment control device. We use data as EEG signal data on emotional imagination condition in a comfortable state and logical imagination condition in concentrated state. The noise of measured signal is removed by the ICA algorithm and beta waves are extracted from it. then, it goes through learning and test process using SVM. The subjects were trained to improve the EEG signal accuracy through the EEG simulation software and the average accuracy were 87.69%. The EEG signal from the EEG measurement device is transmitted to the EEG simulation software through the serial communication. then the control command is generated by classifying emotional imagination condition and logical imagination condition. The generated control command is transmitted to the indoor environment control device through the Zigbee communication. In case of the emotional imagination condition, the soft lighting and classical music are outputted. In the logical imagination condition, the learning white noise and bright lighting are outputted. The proposed system can be applied to software and device control based BCI.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.105-108
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• 1989

This paper proposes a performance improvement to a control system with state feedback control loops and feedforward control loops proposed in a previous paper. The technique allows to solve the regulator problem and at the same time to make the system follow command signal. The scheme is implemented in a 16 bit microcomputer using a floating coprocessor. Tests on a DC machine have been conducted, comparisons with the previous results are made.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.673-677
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• 1986

This paper deals with the state observer-controller which observes unmeasurable state variables of the system and then uses the estimated values as feedback signals. The linearized model is deduced from the nonlinear electrohydraulic servo system. The 4th order analog linear observer-controller and the 2nd order digital one are modelled and implemented using OP amplifiers and IBM PC/XT, respectively. The two observer are experimentally used in the control of an electrohydraulic system. The results are satisfactory in estimation performance and in tracking performance to command signal.