• Journal of Power Electronics
• /
• v.18 no.1
• /
• pp.129-136
• /
• 2018

An accurate state-of-charge (SOC) estimation ensures the reliable and efficient operation of a lithium-ion battery management system. On the basis of a combined electrochemical model, this study adopts the forgetting factor least squares algorithm to identify battery parameters and eliminate the influence of test conditions. Then, it implements online SOC estimation with high accuracy and low run time by utilizing the low computational complexity of the unscented Kalman filter (UKF) and the rapid convergence of a particle filter (PF). The PF algorithm is adopted to decrease convergence time when the initial error is large; otherwise, the UKF algorithm is used to approximate the actual SOC with low computational complexity. The effect of the number of sampling particles in the PF is also evaluated. Finally, experimental results are used to verify the superiority of the combined method over other individual algorithms.

• Journal of IKEEE
• /
• v.25 no.3
• /
• pp.412-419
• /
• 2021

In this paper, we propose a performance improvement of the LwF method using efficient path selection in Continuous Learning Environment. We compare performance and structure with conventional LwF. For comparison, we experiment with performance using MNIST, EMNIST, Fashion MNIST, and CIFAR10 data with different complexity configurations. Experiments show up to 20% improvement in accuracy for each task, which mitigating the Catastrophic Forgetting phenomenon in Continuous Learning environments.

• Journal of Drive and Control
• /
• v.14 no.2
• /
• pp.16-22
• /
• 2017

This paper presents the algorithm of an adaptive model-free-control-based steering control for multi-axle all-terrain cranes for which the recursive least squares with forgetting are applied. To optimally control the actual system in the real world, the linear or nonlinear mathematical model of the system should be given for the determination of the optimal control inputs; however, it is difficult to derive the mathematical model due to the actual system's complexity and nonlinearity. To address this problem, the proposed adaptive model-free controller is used to control the steering angle of a multi-axle crane. The proposed model-free control algorithm uses only the input and output signals of the system to determine the optimal inputs. The recursive least-squares algorithm identifies first-order systems. The uncertainty between the identified system and the actual system was estimated based on the disturbance observer. The proposed control algorithm was used for the steering control of a multi-axle crane, where only the steering input and the desired yaw rate were employed, to track the reference path. The controller and performance evaluations were constructed and conducted in the Matlab/Simulink environment. The evaluation results show that the proposed adaptive model-free-control-based steering-control algorithm produces a sound path-tracking performance.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.3
• /
• pp.347-354
• /
• 2022

To reduce forgetfulness in continuous learning, in this paper, we propose an improved LwF model based on the knowledge transfer method, and we show its effectiveness by experiment. In LwF, if the domain of the learned data is different or the complexity of the data is different, the previously learned results are inaccurate due to forgetting. In particular, when learning continues from complex data to simple data, the phenomenon tends to get worse. In this paper, to ensure that the previous learning results are sufficiently transferred to the LwF model, we apply the knowledge transfer method to LwF, and propose an algorithm for efficient use. As a result, the forgetting phenomenon was reduced by an average of 8% compared to the existing LwF results, and it was effective even when the learning task became long. In particular, when complex data was first learned, the efficiency was improved more than 30% compared to LwF.

• Journal of Korea Game Society
• /
• v.15 no.3
• /
• pp.151-160
• /
• 2015

This paper presents English vocabulary memorization system using forgetting curve to automatically adjust the vocabulary difficulty to match learner's level. Our system will decide the appropriate repetition cycle, depending on the number of memorizing words through the forgetting curve, then requires an iterative learning. No matter what learners know or do not know, words are reviewed. To save time by reviewing some words which have the highest probability that learners forget. And it provides vocabulary based on learner level, which makes learner maintain their interest and achievement. A general system provides vocabularies which difficulty matches with evaluated ones, or randomly provides some vocabularies without consideration of users' level. But we apply the "Glicko" system which is being used in the online chess game ranking system to adjust the vocabulary's difficulty. We utilize the system used in the one-by-one player system to our vocabulary-human system. As a result, learners's level and the vocabularies's difficulty is measured in the review process. Moreover it maximizes the performance of English vocabulary memorization by applying feedbacks from practice testing and distributed learning.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.12
• /
• pp.2246-2253
• /
• 2011

This paper presents the error correction method of magnetic position sensor using recursive least square method (RLSM) with forgetting factor. Magnetic position sensor is proposed for linear position detection of the linear motor which has tooth shape stator, consists of permanent magnet, iron core and linear hall sensor, and generates sine and cosine waveforms according to the movement of the mover of the linear motor. From the output of magnetic position sensor, the position of the linear motor can be detected using arc-tan function. But the variation of the air gap between magnetic position sensor and the stator and the error in manufacturing process can cause the variation in offset, phase and amplitude of the generated waveforms when the linear motor moves. These variations in sine and cosine waveforms are changed according to the current linear motor position, and it is very difficult to compensate the errors using constant value. In this paper, the generated sine and cosine waveforms from the magnetic position sensor are compensated on-line using the RLSM with forgetting factor. And the speed observer is introduced to reduce the effect of uncompensated harmonic component. The approaches are verified by some simulations and experiments.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.41 no.2
• /
• pp.95-102
• /
• 2004

In this paper, Experience Sensitive Cumulative Neural Network (ESCNN) is introduced, which can cumulate the same or similar experiences. As the same or similar training patterns are cumulated in the network, the system recognizes more important information in the training patterns. The functions of forgetting less important information and attending more important information resided in the training patterns are surveyed and implemented by simulations. The system behaves well under the noisy circumstances due to its forgetting and/or attending properties, even in 50 percents noisy environments. This paper also describes the creation of the generalized patterns for the input training patterns.

• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.7
• /
• pp.663-671
• /
• 2008

This paper presents a real-time haptic rendering method for multi-contact interaction with virtual environments. Haptic systems often employ physics-based deformation models such as finite-element models and mass-spring models which demand heavy computational overhead. The haptic system can be designed to have two sampling times, T and JT, for the haptic loop and the graphic loop, respectively. A multi-rate output-estimation with an exponential forgetting factor is proposed to implement real-time haptic rendering for the haptic systems with two sampling rates. The computational burden of the output-estimation increases rapidly as the number of contact points increases. To reduce the computation of the estimation, the multi-rate output-estimation with reduced parameters is developed in this paper. Performance of the new output-estimation with reduced parameters is compared with the original output-estimation with full parameters and an exponential forgetting factor. Estimated outputs are computed from the estimated input-output model at a high rate, and trace the analytical outputs computed from the deformation model. The performance is demonstrated by simulation with a linear tensor-mass model.

• Proceedings of the KIEE Conference
• /
• 1999.07c
• /
• pp.1063-1066
• /
• 1999

This paper presents an extended integral control with the PID controller by introducing the delay and decaying factors. The convolution integral control scheme is developed by substituting proportional convolution integral controls for the proportional-integral control. So far, the integral part of the PI controller produces a signal that is proportional to the time integral of the input of the controller. The steady-state operation points are affected forever by the errors in the past due to the input signal containing the information of the errors in the past. These phenomina may cause some disturbances for other control purposes related to the given PI control. Introduction of forgetting factors of the error in the past can resolve the disturbance problems. Various forgetting factors are developed using the delay, the decaying factors, and the combination of the delay and the decaying factors. The proposed various extended integral control schemes can be applicable to corresponding PI control designs in which the error in the past may badly affect to the current steady-state operation points and may cause some disturbances for other control purposes.

• The Journal of the Acoustical Society of Korea
• /
• v.25 no.4E
• /
• pp.154-158
• /
• 2006

In this paper, we suggest a noise estimation method for speech enhancement in nonstationary noisy environments. The proposed method consists of the following two main processes. First, in order to receive fewer affect of variable signals, a best fitting regression line is used, which is obtained by applying a least squares method to coefficient magnitudes in a node with a uniform wavelet packet transform. Next, in order to update the noise estimation efficiently, a differential forgetting factor and a correlation coefficient per subband are used, where subband is employed for applying the weighted value according to the change of signals. In particular, this method has the ability to update the noise estimation by using the estimated noise at the previous frame only, without utilizing the statistical information of long past frames and explicit nonspeech frames by voice activity detector. In objective assessments, it was observed that the performance of the proposed method was better than that of the compared (minima controlled recursive averaging, weighted average) methods. Furthermore, the method showed a reliable result even at low SNR.