• Proceedings of the KIPE Conference
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• 2016.11a
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• pp.75-76
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• 2016

The relationship of widely used Open circuit Voltage (OCV) versus State of Charge (SOC) is critical for any reliable SOC estimation technique. However, the hysteresis existing in all type of battery which has been come to the market leads this relationship to a complicated one, especially in Lithium Iron Phosphate (LiFePO4) battery. An accurate model for hysteresis phenomenon is essential for a reliable SOC identification. This paper aims to investigate and propose a method for hysteresis modeling. The SOC estimation is done by using Extended Kalman Filter (EKF), the parameter of the battery is modeled by Auto Regressive Exogenous (ARX) and estimated by using Recursive Least Square (RLS) filter to tract each element of the parameter of the model.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.25-35
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• 2024

In the present modern day, as the complexity and size of SoC(System on Chip) increase, the importance of design verification are increasing, Therefore it takes a lot of time to verify the design. There is an emerging need to manage the verification environment faster and more efficiently by reusing the existing verification environment. UVM-based verification is a standardized and highly reliable verification method widely adopted and used in the semiconductor industry. This paper presents a UVM-based verification for the 4 tap equalizer module with a systolic array structure. Through the constraints randomization, it was confirmed that various test scenarios stimulus were generated. In addition, by verifying a simulation comparing the actual DUT outputs with the MATLAB reference outputs, the reuse and efficiency of the UVM test bench could be confirmed.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.129-143
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• 2021

This paper proposes actuator fault detection and adaptive fault-tolerant control algorithms using performance index and human-like learning for longitudinal autonomous vehicles. Conventional longitudinal controller for autonomous driving consists of supervisory, upper level and lower level controllers. In this paper, feedback control law and PID control algorithm have been used for upper level and lower level controllers, respectively. For actuator fault-tolerant control, adaptive rule has been designed using the gradient descent method with estimated coefficients. In order to adjust the control parameter used for determination of adaptation gain, human-like learning algorithm has been designed based on perceptron learning method using control errors and control parameter. It is designed that the learning algorithm determines current control parameter by saving it in memory and updating based on the cost function-based gradient descent method. Based on the updated control parameter, the longitudinal acceleration has been computed adaptively using feedback law for actuator fault-tolerant control. The finite window-based performance index has been designed for detection and evaluation of actuator performance degradation using control error.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2949-2952
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• 2000

A new neural networks and learning algorithm are proposed in order to measure nonlinear heights of complexed road environments in realtime without pre-information. This new neural networks is Error Self Recurrent Neural Networks(ESRN), The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by back-propagation and each weights are updated by RLS(Recursive Least Square). Consequently. this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. We can estimate nonlinear models in realtime by ESRN and learning algorithm and control nonlinear models. To show the performance of this one. we control 7 degree of freedom full car model with several control method. From this simulation. this estimation and controller were proved to be effective to the measurements of nonlinear road environment systems.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.99-108
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• 1998

In this paper, we proposed a multi-layer neural network for direct control method of nonlinear system. The proposed control method uses neural network as the controller to learn inverse model of plant. The neural network used consists of two parts; one part is for identification of linear part, and the other is for identification of nonlinear part of inverse system. The neural network has to be learned the liner part with RLS algorithm and the nonlinear part with error of plant. From the simulation and experiment of tracking control to use one link manipulator as plant, we proved usefulness of the proposed control method to comparing to conventional direct neural network control method. By comparing the two methods, from simulation and experiment, we were convinced that the proposed control method is more simple and accuracy than the conventional method. Moreover, number of weight and bias to be controller parameter are small, and it has smaller steady state error than conventional method.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.3
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• pp.1-7
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• 1999

An important task for any software project manager is to be able to predict and control project size. Unfortunately, there is comparatively little work that deals with the problem of building prediction methods for software size in fourth-generation languages and database projects. In this paper, we propose a new estimation method for estimating for software size based on minimum relative error(MRE) criterion. The characteristic of the proposed method is insensitive to the extreme values of the observed measures which can be obtained early in the development life cycle. In order to verify the performance of the proposed estimation method for software size in terms of both quality of fit and predictive quality, the experiments has been conducted for the dataset Ⅰ and Ⅱ, respectively. For the data set Ⅰ and Ⅱ, our proposed prediction method was shown to be superior to the traditional method LS and RLS in terms of both the quality of fit and predictive quality when applied to data obtained from actual software development projects.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.05a
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• pp.97-105
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• 1999

An important task for any software project manager is to be able to predict and control project size. Unfortunately, there is comparatively little work that deals with the problem of building estimation methods for software size in fourth-generation languages systems. In this paper, we propose a new estimation method for estimating for software size based on minimum relative error(MRE) criterion. The characteristic of the proposed method is insensitive to the extreme values of the observed measures which can be obtained early in the development life cycle. In order to verify the performance of the proposed estimation method for software size in terms of both quality of fit and predictive quality, the experiments has been conducted for the dataset I and II, respectively. For the data set I and II, our proposed estimation method was shown to be superior to the traditional method LS and RLS in terms of both the quality of fit and predictive quality when applied to data obtained from actual software development projects.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.11a
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• pp.573-577
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• 2002

A new estimated neural networks are proposed in order to measure nonlinear road environments in realtime. This new neural networks is Error Estimated Neural Networks. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we control 7 degree simulation, this controller and driver were proved to be effective to drive a car in the environments of nonlinear road systems.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.109-114
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• 2003

Wireless communication systems are inevitable for efficient underwater activities. Because of the poor propagation characteristics of light and electromagnetic waves, acoustic waves are generally used for the underwater wireless communication. Although there are many kinds of information type, visual images take an essential role especially for search and identification activities. For this reason, we developed an acoustic-based underwater image transmission system under a dual use technology project supported by MOCIE (Ministry of Commerce, Industry and Energy). For the application to complicated and time-varying underwater environments all-digital transmitter and receiver systems are investigated. Array acoustic transducers are used at the receiver, which have the center frequency of 32kHz and the bandwidth of 4kHz. To improve transmission speed and quality, various algorithms and systems are used. The system design techniques will be discussed in detail including image compression/ decompression system, adaptive beam- forming, fast RLS adaptive equalizer, ${\partial}/4$ QPSK (Quadrilateral Phase Shift Keying) modulator/demodulator, and convolution coding/ Viterbi. Decoding.

• Journal of Satellite, Information and Communications
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• v.6 no.2
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• pp.117-124
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• 2011

In this paper, we present more effective throughput enhancement technique to improve the data rate and reliability by using the multiple antenna technique. The conventional spatial diversity scheme is limited in according with the interference from each antenna channel status, and the orthogonality of spreading codes and subcarriers are destroyed due to the frequency selectivity. Proposed system is considered MC-CDMA system with 4 transmit antennas and 1 receive antenna. Proposed system based on SVD with the MS-RLS MMSE subcarrier combining method in order to achieve better performance with low computational complexity. Via computer simulation, we confirm that the proposed system is able to improve the BER performance by suppressing the interference of other antenna signals.