• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.321-322
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• 2008

CSS(Chirp Spread Spectrum) technology adopt SDS-TWR algorithms of TOA(Time of Arrival) using velocity of specific medium and ToF(Time of Flight) to measure a distance, but this method always has a regular error on distance regardless of a real distance, as a result, in far distance, it decrease a error on distance relatively, but in near distance, it increase a error on distance relatively. in this paper, we propose and test new method measuring a distance more precisely in near distance using CSS and RSSI

• 한국통신학회논문지
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• 제36권3C호
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• pp.170-174
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• 2011

본 논문은 양방향 중계 채널(Two-Way Relay Channel) 에서 기존에 연구되었던 협력 통신(Cooperative Communication) 에서 네트워크 부호화(Network Coding)에 대해 간단히 소개한다. 협력 통신 시스템에서의 직교성을 이용한 새로운 네트워크 부호화 기법을 제안한다. 제안된 직교 사상 네트워크 부호화 기법은 기존에 널리 사용되고 있는 DF(Decode-and-Forward) 모듈러 합 기법의 전송률을 유지하면서 DF 기법의 단점인 오류 전파(Error Propagation) 현상을 제거하여 향상된 BER(Bit Error Rate) 성능을 보인다.

• Journal of Advanced Marine Engineering and Technology
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• 제23권4호
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• pp.480-487
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• 1999

In this paper we propose a neural network precompensated PID(NNP PID) controller for load frequency control of 2-area power system. While proportional integral derivative(PID) controllers are used in power system they have many problems because of high nonlinearities of the power system So a neural network-based precompensation scheme is adopted into a conventional PID controller to obtain a robust control to the nonlinearities. The applied neural network precompen-sator uses an error back-propagation learning algorithm having error and change of error as inputand considers the changing component of forward term of weighting factor for reducing of learning time. Simulation results show that the proposed control technique is superior to a conventional PID controller and an optimal controller in dynamic responses about load disturbances. The pro-posed technique can be easily implemented by adding a neural network precompensator to an existing PID controller.

• 한국측량학회지
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• 제13권1호
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• pp.13-20
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• 1995

수평위치 결정에서 오차해석은 중요하다. 오차해석의 경우 표준오차타원이 정밀도의 척도로써 사용되나, 오차한계의 설정이나 측정치의 합격기준과 상대오차를 비교하는데 한계가 있어, 캐나다나 미국에서는 작업규정 에 95%신뢰타원을 오차한계로 설정하고 있다. 본 연구에서는 수평위치 결정시 신뢰타원을 오차해석에 적용하여 분석하였다. 최소제곱법과 오차해석이론에 대해 검토하였으며 , 컴퓨터 프로그램으로 E.D.M 삼변망에 대해 오차분석을 하였다. 본 연구결과 오차한계의 설정 , 측정치의 합격기준, 상대오차를 비교할 때 95%신뢰타원이 합리적이므로 작업규정에 95％신뢰타원을 도입하는 것이 필요하다. 또한 경제성있는 최적망 설계를 위해 측량망의 예비분석에도 95%신뢰타원이 효과적으로 적용될 수 있다고 사료된다.

• 한국지능시스템학회논문지
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• 제5권3호
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• pp.83-91
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• 1995

본 연구에서는 종래에 학습법으로 널리 이용되고 있는 역전파학습법의 문제점으로 지적되어 온 학습에 많은 시간이 걸리는 점과 국소적 최적해에 해가 수렴하여 오차가 충분히 작게 되지 않는 등의 문제점을 해결하기 위해, Hu에 의해 고안된 random tabu 탐색법을 이용하여 신경회로망의 연결강도를 최적화하는 학습알고리즘을 새로이 제안하였다. 그리고 이 방법을 배타적 논리합 문제에 적용하여 기존의 역전파학습법과 학습상수 $, $에 tabu탐색법을 이용한 결과와 비교 검토하여 본 방법이 국소적 최적해에 수렴하지 않고 수렴정도를 개선할 수 있음을 확인하였다.

• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권8호
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• pp.510-516
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• 2005

Rapid advances in telecommunication make emergency telemedicine possible that specialist offers medical care to an emergency case in moving vehicle. Although there were many telemedicine projects delivering the image or video of patient over several wireless networks, none of them considered effective solutions for optimizing video transmission over error-prone environments, such like wireless links. To alleviate the effect of channel errors on compressed video bit-stream, this paper analyzed the error resilient features of MPEG-4 standard and measured the quality of transmitted MPEG-4 encoded video over commercially available CDMA2000 1xEV-DO networks, transmitting different IP packet sizes and RM positions. we propose an error resilient transmission methods for emergency telemedicine over real 3G network.

• 한국정밀공학회지
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• 제16권7호
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• pp.66-72
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• 1999

Robot calibration is very important to improve the accuracy of robot manipulators. However, the calibration procedure is very time consuming and laborious work for users. In this paper, we propose a method of relative error compensation to make the calibration procedure easier. The method is completed by a Pi-Sigma network architecture which has sufficient capability to approximate the relative relationship between the accuracy compensations and robot configurations while maintaining an efficient network learning ability. By experiment of 4-DOF SCARA robot, KIRO-3, it is shown that both the error of joint angles and the positioning error of end effector are drop to 15$\%$. These results are similar to those of other calibration methods, but the number of measurement is remarkably decreased by the suggested compensation method.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권6호
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• pp.296-301
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• 2006

Even though the emergency telecommunication make possible that specialist offers medical care over emergency cases in moving vehicle, we still have many problems in transmitting the image or video of patient over several wireless networks. To alleviate the effect of channel errors on compressed video bit-stream, this paper analyzed the error resilient features of JPEG2000 standard and measured the quality of transmission over noisy wireless channel, CDMA2000 1xEV-DO networks, compared to the features of error resilient tool of MPEG-4. We also proposed the optimum solution of transmitting images over real 3G network using JPEG2000 error resilient tool.

• Journal of Positioning, Navigation, and Timing
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• 제4권1호
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• pp.33-41
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• 2015

Compact Network Real-Time Kinematic (RTK) is a method that combines compact RTK and network RTK, and it can effectively reduce the time and spatial de-correlation errors. A network RTK user receives multiple correction information generated from reference stations that constitute a network, calculates correction information that is appropriate for one's own position through a proper combination method, and uses the information for the estimation of the position. This combination method is classified depending on the method for modeling the GPS error elements included in correction information, and the user position accuracy is affected by the accuracy of this modeling. Among the GPS error elements included in correction information, tropospheric delay is generally eliminated using a tropospheric model, and a combination method is then applied. In the case of a tropospheric model, the estimation accuracy varies depending on the meteorological condition, and thus eliminating the tropospheric delay of correction information using a tropospheric model is limited to a certain extent. In this study, correction information modeling accuracy performances were compared focusing on the Low-Order Surface Model (LSM), which models the GPS error elements included in correction information using a low-order surface, and a modified LSM method that considers tropospheric delay characteristics depending on altitude. Both of the two methods model GPS error elements in relation to altitude, but the second method reflects the characteristics of actual tropospheric delay depending on altitude. In this study, the final residual errors of user measurements were compared and analyzed using the correction information generated by the various methods mentioned above. For the performance comparison and analysis, various GPS actual measurement data were collected. The results indicated that the modified LSM method that considers actual tropospheric characteristics showed improved performance in terms of user measurement residual error and position domain residual error.