• Journal of Korean Society for Quality Management
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• v.26 no.4
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• pp.143-150
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• 1998

We propose an adaptive monitoring a, pp.oach for serially correlated data. This algorithm uses the adaptive linear prediction lattice filter (ALPLF) which makes it compute process parameters in real time and recursively update their estimates. It involves computation of the forward and backward prediction errors. CUSUM control charts are a, pp.ied to prediction errors simulaneously in both directions as an omnibus method for detecting changes in process parameters. Results of computer simulations demonstrate that the proposed adaptive monitoring a, pp.oach has great potentials for real-time industrial a, pp.ications, which vary frequently in their control environment.

• International Journal of CAD/CAM
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• v.12 no.1
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• pp.9-19
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• 2012

Generation of optimum planar B-spline curve in terms of minimum deviation and required fairness to approximate a target shape defined by a strip-shaped unorganized 2D point cloud is studied. It is proposed to use the location of control points as variables within the geometric optimization framework of point distance minimization. An adaptive simulated annealing heuristic optimization algorithm is developed to iteratively update an initial approximate curve towards the target shape. The new implementation comprises an adaptive cooling procedure in which the temperature change is adaptively dependent on the objective function evolution. It is shown that the proposed method results in an improved convergence speed when compared to the standard simulated annealing method. A couple of examples are included to show the applicability of the proposed method in the surface model reconstruction directly from point cloud data.

• Journal of the Korea Computer Industry Society
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• v.2 no.4
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• pp.537-546
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• 2001

Griffth's and LCCMA which are Single-user detection adaptive algorithm are proposed for mitigate MAI(multiple access interference) and the near-far problem in direct-sequence spread-spectrum CDMA system and MOE Algorithm is proposed for MMSE(Minimum Mean-Square Error). This paper pertains to three types of Blind adaptive algorithms which can upgrade system functionality without the requirements from training sequence. It goes further to compare and analyze the functionalities of the algorithms as per number of interfering users or data update rate of the users. The simulation results was that LCCMA algorithm was superior to other algorithms in both areas. Blind application enabled a more flexible network design by eliminating the necessity of training sequence.

• The Journal of Information Technology
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• v.5 no.2
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• pp.55-67
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• 2002

This paper presents an adaptive back propagation algorithm that update the learning parameter by the generated error, adaptively and varies the number of hidden layer node. This algorithm is expected to escaping from the local minimum and make the best environment for convergence to be change the number of hidden layer node. On the simulation tested this algorithm on two learning pattern. One was exclusive-OR learning and the other was $7{\times}5$ dot alphabetic font learning. In both examples, the probability of becoming trapped in local minimum was reduce. Furthermore, in alphabetic font learning, the neural network enhanced to learning efficient about 41.56%~58.28% for the conventional back propagation. and HNAD(Hidden Node Adding and Deleting) algorithm.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.786-791
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• 2003

This paper proposes an adaptive control algorithm for nonholonomic mobile robots with unknown parameters and the proposed control method is used in numerical simulations for applying to a practical twowheeled welding mobile robot(WMR). The proposed adaptive controller to track an arbitrary given welding path is designed by using back-stepping technique and is derived for a nonlinear model under the assumption such that the system parameters are partially known. Moreover, the proposed adaptive control system is stable in the sense of Lyapunov stability. Inertia moments of system are considered to be unknown parameters and their values can be estimated simply by using update laws proposed in an adaptive control scheme of this research. The simulation results are provided to show the effectiveness of the accurate tracking capability of the proposed controller for two-wheeled welding mobile robot with a smooth curved reference welding path.

• Journal of Communications and Networks
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• v.6 no.1
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• pp.19-25
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• 2004

In this paper, we present a trellis-based blind channel estimation and equalization technique coupling two kinds of adaptive Viterbi algorithms. First, the initial blind channel estimation is accomplished by incorporating the list parallel Viterbi algorithm with the least mean square (LMS) updating approach. In this operation, multiple trellis mappings are preserved simultaneously and ranked in terms of path metrics. Equivalently, multiple channel estimates are maintained and updated once a single symbol is received. Second, the best channel estimate from the above operation will be adopted to set up the whole trellis. The conventional adaptive Viterbi algorithm is then applied to detect the signal and further update the channel estimate alternately. A small delay is introduced for the symbol detection and the decision feedback to smooth the noise impact. An automatic switch between the above two operations is also proposed by exploiting the evolution of path metrics and the linear constraint inherent in the trellis mapping. Simulation has shown an overall excellent performance of the proposed scheme in terms of mean square error (MSE) for channel estimation, robustness to the initial channel guess, computational complexity, and channel equalization.

• 한국생물공학회:학술대회논문집
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• 2001.11a
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• pp.81-84
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• 2001

A real time optimization algorithm for fed-batch cultures of recombinant yeast to determine the optimal substrate feed rate profile has been developed. Its development involved four key steps: (1) development of reliable adaptive model. (2) development of optimization algorithm. (3) design of on-line model update algorithm to be incorporated into the optimization algorithm and (4) experimental validation. A recombinant Saccharomyces cerevisiae producing human parathyroid hormone (hPTH) was chosen as the model strain. It was found to be very successful in maintaining cell growth and galactose consumption at leigh levels, thus resulting in significant improvements in the productivity (up to 2.1 times) and intact hPTH concentration (up to 1.5 times) compared with the case of an intermittent glucose and galactose, or galactose feeding.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.75-80
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• 2021

This paper relates with the Hybrid-DSE-MMA (Hybrid-Dithered Signed Error-MMA) that is possible to improving the equalization performance by using the adaptive modulus and adaptive stepsize in DSE-MMA adaptive equalizer. The DSE-MMA possible to improve the robustness performance to external noise of SE-MMA by using the sign after adding the dither signal for get the error signal in order to update the tap coefficient. But it has a drawback of performance degradation in convergence speed and residual isi by using the fixed modulus and fixed stepsize. In this paper, it was confirmed that this equalization performance degradation was improved by applying the adaptive modulus and stepsize in DSE-MMA propotional to the output power of equalizer by computer simulation. In order to compare the improved equalization performance to currently DSE-MMA, the recovered signal constellation that is the output of the equalizer, residual isi, Maximum Distortion, MSE and the SER were used as a performance index. As a result of computer simulation, the Hybrid-DSE-MMA improve the equalization performance in every index, but gives slower convergence speed compared to DSE-MMA.

• The Journal of Information Technology
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• v.4 no.1
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• pp.71-79
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• 2001

This paper presents an adaptive back propagation algorithm that update the learning parameter by the generated error, adaptively and configuration of the range for the initial connecting weight according to the different maximum target value from minimum target value. This algorithm is expected to escaping from the local minimum and make the best environment for the convergence. On the simulation tested this algorithm on three learning pattern. The first was 3-parity problem learning, the second was $7{\times}5$ dot alphabetic font learning and the third was handwritten primitive strokes learning. In three examples, the probability of becoming trapped in local minimum was reduce. Furthermore, in the alphabetic font and handwritten primitive strokes learning, the neural network enhanced to loaming efficient about 27%~57.2% for the standard back propagation(SBP).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.681-684
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• 2015

Power amplifier is an essential component for transmitting signals to a remote receiver in wireless communication systems. Power amplifier is a non-linear device in general, and the nonlinear distortion becomes severer as the output power increases. The nonlinearity results in spectral regrowth, which leads to adjacent channel interference, and decreases the transmit signal quality. To linearize power amplifiers, many techniques have been developed so far. Among the techniques, digital pre-distortion is known as the most cost and performance effective technique. However, the linearization performance falls down abruptly when the power amplifier operates in its saturation region. This is because of the severe nonlinearity. To relieve this problem, this paper proposes a new adaptive predistortion technique. The proposed technique controls the adaptive algorithm based on the power amplifier input level. Specifically, for small signals, the adaptive predistortion algorithm works normally. On the contrary, for large signals, the adaptive algorithm stops until small signals occur again. By doing this, wrong coefficient update by severe nonlinearity can be avoided. Computer simulation results show that the proposed method can improve the linearization performance compared with the conventional digital predistortion algorithms.