• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.961-964
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• 1995

For geometric accuracy in the net shape machining, the problem of tool deflection should be resolved in some fashion. In particular, this is crucial in finish cut operation where slim tools are used. The purpose of this paper is to verify the validity and effectiveness of the prediction model of the machined surface. Experimental results are presented for the cut of steel material with HSS endmill of diameter 6mm on machining center. The results shows that 1) the machining error due totool deflection is serious even in the low cutting load, 2) by using the mechanistic simulation model with experimental coefficients, the machining error was predicted with maximum prediction error of 10% which was significantly reduced to the desired level by the path modification method.

• Design & Manufacturing
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• v.17 no.4
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• pp.24-32
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• 2023

In this study, we introduce research aimed at minimizing machining errors without compromising productivity by compensating for the machining errors caused by tool deformation. Our approach experimentally establishes the direct correlation between cutting depth and machining error, and creates predictive models using mathematical functions. This method allows for the prediction of compensated cutting depths to obtain the desired cutting profiles, thereby maximizing the compensation of machining errors in the cutting process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.183-189
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• 2013

This paper proposes a novel lossless image compression scheme composed of direction-adaptive prediction and context-based entropy coding. In the prediction stage, we analyze the directional property with respect to the current coding pixel and select an appropriate prediction pixel. In order to further reduce the prediction error, we propose a prediction error compensation technique based on the context model defined by the activities and directional properties of neighboring pixels. The proposed scheme applies a context-based Golomb-Rice coding as the entropy coding since the coding efficiency can be improved by using the conditional entropy from the viewpoint of the information theory. Experimental results indicate that the proposed lossless image compression scheme outperforms the low complexity and high efficient JPEG-LS in terms of the coding efficiency by 1.3% on average for various test images, specifically for the images with a remarkable direction the proposed scheme shows better results.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.2
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• pp.121-127
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• 2008

Micro end-milling has been becoming an important machining process to manufacture a number of small products such as micro-devices, bio-chips, micro-patterns and so on. Despite the importance of micro end-milling, many related researches have given grand efforts to micro end-milling phenomenon, for example, micro end-milling mechanism, cutting force modeling and machinability. This paper strongly concerned actual problem, micro tool deflection, which causes excessive machining errors on the workpiece. To solve this problem, machining error prediction method was proposed through a series of test micro cutting and analysis of their SEM images. An iterative algorithm was applied in order to obtain corrected tool path which allows reducing machining errors in spite of tool deflection. Experiments are carried out to validate the proposed approaches. In result, remarkable error reduction could be obtained.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.4C
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• pp.384-391
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• 2003

Blind channel identification and equalization attempt to identify the communication channel and to remove the inter-symbol interference caused by a communication channel without using any known trainning sequences. In this paper, we propose a blind adaptive channel identification and equalization algorithm with phase offset compensation for single-input multiple-output (SIMO) channel. It is based on the one-step forward multichannel linear prediction error method and can be implemented by an RLS algorithm. Phase offset problem, we use a blind adaptive algorithm called the constant modulus derotator (CMD) algorithm based on condtant modulus algorithm (CMA). Moreover, unlike many known subspace (SS) methods or cross relation (CR) methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.7
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• pp.754-761
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• 2013

Thermal displacement is an important issue in machine tool systems. During the last several decades, thermal error compensation technology has significantly reduced thermal distortion error; this success has been attributed to the development of a precise, robust thermal error model. A major advantage of using the thermal error model is instant compensation for the control variables during the modeling process. However, successful application of thermal error modeling requires correct determination of the temperature sensor placement. In this paper, a procedure for predicting thermal-mode-based thermal error is introduced. Based on this thermal analysis, temperature sensors were positioned for multiple heat-source models. The performance of the sensors based on thermal-mode error analysis, was compared with conventional methods through simulation and experiments, for the case of a slide table in a transient state. Our results show that for predicting thermal error the proposed thermal model is more accurate than the conventional model.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.10
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• pp.45-52
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• 2004

This Paper presents a modified hard decision Viterbi decoder with an error prediction circuit enhancing performance for the burst error inputs. Viterbi decoder employs the maximum likelihood decoding algorithm which shows excellent error correction capability for the random error inputs. Viterbi decoders, however, suffer poor error correction performance for the burst error inputs under the fading channel. The proposed error prediction algorithm increases error correction capability for the burst errors. The algorithm estimaties the burst error data area using the maximum path metric for the erroneous inputs, It calculates burst error intervals based on increases in the maximum values of a path metric. The proposed decoder keeps a performance the same as the conventional decoders on AWGN channels for the IEEE802.l1a WLAN system. It shows performance inproving 15% on the burst error of multi-path fading channels, widely used in mobile systems.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.173-180
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• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.5
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• pp.108-115
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• 2003

This paper is concerned with the development of Internet-based robot system controlled on the remote site via the Internet. In order to draw the public attention into this exciting system, we built the simple system by which a robot is moved in response to answer for the given OX quizzes. As the primary research fer Internet-based robot control, this study focuses on the development of user-friendly interface by which a beginner achieves information for a robot on the remote site from the 3D virtual simulator and the real camera image. for the compensation of Internet time delay, position prediction simulator is implemented in the user interface.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.14-19
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• 2008

The paper presents an intelligent time series model to predict uncertain electricity market price in the deregulated industry environment. Since the price of electricity in a deregulated market is very volatile, it is difficult to estimate an accurate market price using historically observed data. The parameter of an intelligent time series model is obtained based on the simultaneous perturbation stochastic approximation (SPSA). The SPSA is flexible to use in high dimensional systems. Since prediction models have their modeling error, an error compensator is developed as compensation. The SPSA based intelligent model is applied to predict the electricity market price in the Pennsylvania-New Jersey-Maryland (PJM) electricity market.