• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.2 s.314
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• pp.24-31
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• 2007

This paper presents self tuning PI(STPI) controller of IPMSM drive using neural network. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, STPI controller proposes a new method based neural network. STPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.4
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• pp.33-39
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• 2014

In this paper, a new on-chip aging sensing circuit based on flip-flops is proposed to detect a circuit failure of MOSFET digital circuits casued by aging phenomenon such as HCI and BTI. The proposed circuit uses timing windows to warn against a guardband violation of sequential circuits, and generates three warning bits right before circuit failures occur. The generated bits can apply to an adaptive self-tuning method for reliable system design as control signals. The aging sensor circuit has been implemented using 0.11um CMOS technology and evaluated by $4{\times}4$ multiplier with power gating structure.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.1
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• pp.131-148
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• 2008

Today, it is not difficult to use information technology (IT), especially, Internet based ones. Many people can not only access IT without learning how to use it but also find and develop new techniques and usages which couldn't be expected by system engineers or designers. This is owing to social interactions among users as well as advancement of IT. Theoretically, such social interactions in using IT can be well explained by adaptive structuration theory (AST) which has been considered as one of trying to capture the change of using IT due to social interactions between users and system. This study is to analyze the relationship between social interactions and motivation in using IT which can determine attitude and intention of using IT. For this purpose we provide a research model, in which two AST related variables, faithfulness of appropriation and consensus on appropriation, are independent variables and three beliefs for using IT, usefulness, ease of use and playfulness, are dependent ones. Additionally, for reflection of changing uses, usefulness is formed as second order factor by two first order factors-usefulness of self-expression and communication. An empirical test of our model for blog users which is analyzed by Partial Least Square method shows supporting most of hypotheses except one, consensus-ease of use.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.4
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• pp.67-75
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• 1998

In general, Evoluationary Algorithm(EAs) are refered to as methods of population-based optimization. And EAs are considered as very efficient methods of optimal sytem design because they can provice much opportunity for obtaining the global optimal solution. This paper presents a co-evolution scheme of artifical neural networks, which has two different, still cooperatively working, populations, called as a host popuation and a parasite population, respectively. Using the conventional generatic algorithm the host population is evolved in the given environment, and the parastie population composed of schemata is evolved to find useful schema for the host population. the structure of artificial neural network is a diagonal recurrent neural netork which has self-feedback loops only in its hidden nodes. To find optimal neural networks we should take into account the structure of the neural network as well as the adaptive parameters, weight of neurons. So we use the genetic algorithm that searches the structure of the neural network by the co-evolution mechanism, and for the weights learning we adopted the evolutionary stategies. As a results of co-evolution we will find the optimal structure of the neural network in a short time with a small population. The validity and effectiveness of the proposed method are inspected by applying it to the stabilization and position control of the invered-pendulum system. And we will show that the result of co-evolution is better than that of the conventioal genetic algorithm.

• Structural Engineering and Mechanics
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• v.73 no.1
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• pp.65-73
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• 2020

The present work describes a new time marching procedure for structural dynamics analyses. In this novel technique, time integration parameters are automatically evaluated according to the properties of the model. Such parameters are locally defined, allowing the user to input a numerical dissipation property for each element, which defines the amount of numerical dissipation to be introduced. Since the integration parameters are locally defined as a function of the structural element itself, the time marching technique adapts according to the model, providing enhanced accuracy. The new methodology is based on displacement-velocity relations and no computation of accelerations is required. Furthermore, the method is second order accurate, it has guaranteed stability, it is truly self-starting and it allows highly controllable algorithm dissipation in the higher modes. Numerical results are presented and compared to those provided by the Newmark and the Bathe methods, illustrating the good performance of the new time marching procedure.

• Journal of Power Electronics
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• v.16 no.4
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• pp.1513-1525
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• 2016

Rapid and accurate phase synchronization is critical for the reliable control of grid-tied inverters. However, the commonly used software phase-locked loop methods do not always satisfy the need for high-speed and accurate phase synchronization under severe grid imbalance conditions. To address this problem, this study develops a novel open-loop phase locking scheme based on a synchronous reference frame. The proposed scheme is characterized by remarkable response speed, high accuracy, and easy implementation. It comprises three functional cascaded blocks: fast orthogonal signal generation block, fast fundamental-frequency positive sequence component construction block, and fast phase calculation block. The developed virtual orthogonal signal generation method in the first block, which is characterized by noise immunity and high accuracy, can effectively avoid approximation errors and noise amplification in a wide range of sampling frequencies. In the second block, which is the foundation for achieving fast phase synchronization within 3 ms, the fundamental-frequency positive sequence components of unsymmetrical grid voltages can be achieved with the developed orthogonal signal construction strategy and the symmetrical component method. The real-time grid phase can be consequently obtained in the third block, which is free from self-tuning closed-loop control and thus improves the dynamic performance of the proposed scheme. The proposed scheme is adaptive to severe unsymmetrical grid voltages with sudden changes in magnitude, phase, and/or frequency. Moreover, this scheme is able to eliminate phase errors induced by harmonics and random noise. The validity and utility of the proposed scheme are verified by the experimental results.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.1_2
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• pp.1-14
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• 2006

Sort-last parallel rendering using a cluster of GPUs has been widely used as an efficient method for visualizing large- scale volume datasets. The performance of this method is constrained by load balancing when data parallelism is included. In previous works static partitioning could lead to self-balance when only task level parallelism is included. In this paper, we present a load balancing scheme that adapts to the characteristic of volume dataset when data parallelism is also employed. We effectively combine the hierarchical data structures (octree and BSP tree) in order to skip empty regions and distribute workload to corresponding rendering nodes. Moreover, we also exploit a 3D clustering method to determine visibility order and save the AGP bandwidths on each rendering node. Experimental results show that our scheme can achieve significant performance gains compared with traditional static load distribution schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1330-1339
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• 2022

3D object detection generally aims to detect relatively large data such as automobiles, buses, persons, furniture, etc, so it is vulnerable to small object detection. In addition, in an environment with limited resources such as embedded devices, it is difficult to apply the model because of the huge amount of computation. In this paper, the accuracy of small object detection was improved by focusing on local features using only one layer, and the inference speed was improved through the proposed knowledge distillation method from large pre-trained network to small network and adaptive quantization method according to the parameter size. The proposed model was evaluated using SUN RGB-D Val and self-made apple tree data set. Finally, it achieved the accuracy performance of 62.04% at mAP@0.25 and 47.1% at mAP@0.5, and the inference speed was 120.5 scenes per sec, showing a fast real-time processing speed.

• Sleep Medicine and Psychophysiology
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• v.21 no.2
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• pp.74-79
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• 2014

Objectives: Shift work is a stressful situation. It is important to know the factors associated with the ability to adapt to a shift work schedule. The aim of the present study was to investigate the association between sleep, as well as personality variables, and the resilience of shift work nurses. Method: Self-report questionnaires were administered to 95 nurses who worked in one national university hospital. Connor-Davidson resilience scale, hospital anxiety and depression scale, morningness-eveningness scale, Pittsburgh sleep quality index, other sleep-related questionnaires, and Korean defense style questionnaires were used. Results: Age, shift work duration, off-day oversleep, depression, anxiety, adaptive defense style, and self-suppressive defense style were significantly associated with resilience (p < 0.05). Multiple regression analysis showed that age (${\beta}=0.34$, p < 0.05), depression (${\beta}=-0.25$, p < 0.05), adaptive defense style (${\beta}=0.45$, p < 0.001), and self-suppressive defense style (${\beta}=-0.19$, p < 0.05) significantly predicted the resilience of shift work nurses. Concerning individual defense mechanisms, resignation (${\beta}=-0.20$, p < 0.05), sublimation (${\beta}=0.19$, p < 0.05), omnipotence (${\beta}=0.19$, p < 0.05), and humor (${\beta}=0.20$, p < 0.05) significantly predicted the resiliency. Conclusion: The findings indicate that a specific defense style and other mechanisms were associated with the resilience of shift work nurses. A future prospective study with more participants could further clarify the relationship between sleep-related variables, as well as personality factors, and resilience of shift work nurses.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.3-8
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• 2016

We propose a modified form of the decision-directed least mean square (DD LMS) algorithm that is widely used in the optimization of self-adaptive equalizers, and show the modified version greatly improves the initial convergence properties of the conventional algorithm. Existing DD LMS regards the difference between a equalizer output and a quantization value for it as an error, and achieves an optimization of the equalizer based on minimizing the mean squared error cost function for the equalizer coefficients. This error generating method is useful for binary signal or a single-level signals, however, in the case of multi-level signals, it is not effective in the initialization of the equalizer. The modified DD LMS solves this problem by modifying the error generation. We verified the usefulness and performance of the modified DD LMS through experiments with multi-level signals under distortions due to intersymbol interference and additive noise.