• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.451-454
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• 2011

In this paper, a wideband clock generator using novel Automatic frequency calibration(AFC) scheme is proposed. Wideband clock generator using AFC has the advantage of small VCO gain and wide frequency band. The conventional AFC compares whether the feedback frequency is faster or slower then the reference frequency. However, the proposed AFC can detect frequency difference between reference frequency with feedback frequency. So it can be reduced an operation time than conventional methods AFC. Conventional AFC goes to the initial code if the frequency step changed. This AFC, on the other hand, can a prior state code so it can approach a fast operation. In simulation results, the proposed clock generator is designed for DisplayPort using the CMOS ring-VCO. The VCO tuning range is 350MHz, and a VCO frequency is 270MHz. The lock time of clock generator is less then 3us at input reference frequency, 67.5MHz. The phase noise is -109dBC/Hz at 1MHz offset from the center frequency. and power consumption is 10.1mW at 1.8V supply and layout area is $0.384mm^2$.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2849-2852
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• 1999

This paper describes the fuzzy PD controller using simplified indirect inference method. First, the fuzzy PD controller is derived from the conventional continuous time linear PD controller. Then the fuzzification, control-rule base, and defuzzification using SIIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional PD controller. which has the same linear structure. but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability. particularly when the process to be controlled is nonlinear. As the SIIM is applied, the fuzzy Inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the Proposed method has the capability of the high speed inference and extending the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control Performance of the one Proposed by D. Misir et at. Final)y. we simulated the contour control of the X-Y tables with direct control strategies using the proposed fuzzy PD controller.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2898-2901
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• 1999

This paper describes the design of nonlinear fuzzy I+PD controller using simplified indirect inference method. First, the fuzzy I+PD controller is derived from the conventional continuous time linear I+PD controller. Then the fuzzification, control-rule base, and defuzzification using SIIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional I+PD controller. which has the same linear structure. but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability. Particularly when the process to be controlled is nonlinear When the SIIM is applied, the fuzzy inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control performance of the one Proposed by D. Misir et at.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2839-2842
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• 1999

This paper describes the design of fuzzy PID controller using simplified indirect inference method. First, the fuzzy PID controller is derived from the conventional continuous time linear PID controller. Then the fuzzification, control-rule base, and defuzzification using SIIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional PID controller, which has the same linear structure. but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability, particularly when the process to be controlled is nonlinear. When the SIIM is applied, the fuzzy inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control performance of the one proposed by D. Misir et al.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1377-1379
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• 1996

In this paper, each parameter was identified by the gradient descent method to overcome difficulty deciding fuzzy rules of FLC for the unknown process and the type of membership Junctions. Usually PID or optimal control theories have been mostly usee in control field so far. However, optimal control requires much time for calculation because of adaptation for disturbance and nonlinearity. And intricate technique such as MRAS which can be realized only by an expert are limited to be used in the systems requiring rapid and precise response because of comparatively longer calculating time and complicateness. Gradient descent method is a method to find Z minimizing a function about a certain vector Z. And required output of FLC is gained using gradient approaching method in order to adapt control rule parameters of FLC. Simulation proved validation of this algorithm.

• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.3
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• pp.223-230
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• 1999

A conventional sliding mode control approach is often impractical or difficult when it is applied to high order process b because the number of tuning parameters in the sliding mode controller increases with the order of the plant. C Camacho(l996) proposed a design method of a fixed structure sliding mode controller based on a first order plus dead t time approximation to the higher-order process. But, his method has such problems as chattering, over‘shoot, and c command following due to the Taylor the approximation en‘ors for the time delay term of the first order model. In this p paper, a new design technique for a sliding mode controller based on the modified Taylor approximation considered a w weight is developed to improve the Camacho's problems.

• Smart Structures and Systems
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• v.13 no.2
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• pp.319-341
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• 2014

A family of smart tuned mass dampers (STMDs) with variable frequency and damping properties is analyzed under harmonic excitations and ground motions. Two types of STMDs are studied: one is realized by a semi-active independently variable stiffness (SAIVS) device and the other is realized by a pendulum with an adjustable length. Based on the feedback signal, the angle of the SAIVS device or the length of the pendulum is adjusted by using a servomotor such that the frequency of the STMD matches the dominant excitation frequency in real-time. Closed-form solutions are derived for the two types of STMDs under harmonic excitations and ground motions. Results indicate that a small damping ratio (zero damping is the best theoretically) and an appropriate mass ratio can produce significant reduction when compared to the case with no tuned mass damper. Experiments are conducted to verify the theoretical result of the smart pendulum TMD (SPTMD). Frequency tuning of the SPTMD is implemented through tracking and analyzing the signal of the excitation using a short time Fourier transformation (STFT) based control algorithm. It is found that the theoretical model can predict the structural responses well. Both the SAIVS STMD and the SPTMD can significantly attenuate the structural responses and outperform the conventional passive TMDs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1829-1846
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• 2021

Transient faults occur in computation units of a processor, which can cause control flow errors (CFEs) and compromise system reliability. The software-based methods perform illegal control flow detection by inserting redundant instructions and monitoring signature. However, the existing methods not only have drawbacks in terms of performance overhead, but also lack of configurability. We propose a configurable approach CCFCA for detecting CFEs. The configurability of CCFCA is implemented by analyzing the criticality of each region and tuning the detecting granularity. For critical regions, program blocks are divided according to space-time overhead and reliability constraints, so that protection intensity can be configured flexibly. For other regions, signature detection algorithms are only used in the first basic block and last basic block. This helps to improve the fault-tolerant efficiency of the CCFCA. At the same time, CCFCA also has the function of solving confusion and instruction self-detection. Our experimental results show that CCFCA incurs only 10.61% performance overhead on average for several C benchmark program and the average undetected error rate is only 9.29%. CCFCA has high error coverage and low overhead compared with similar algorithms. This helps to meet different cost requirements and reliability requirements.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.755-778
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• 2024

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.