• Journal of Information Processing Systems
• /
• v.15 no.2
• /
• pp.305-319
• /
• 2019

The prediction of the sum of container is very important in the field of container transport. Many influencing factors can affect the prediction results. These factors are usually composed of many variables, whose composition is often very complex. In this paper, we use gray relational analysis to set up a proper forecast index system for the prediction of the sum of containers in foreign trade. To address the issue of the low accuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factors and other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP) neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalized by the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residual correction calculation for the results based on the preliminary data. The results of practical examples show that the overall relative error of the combined prediction model is no more than 1.5%, which is less than the relative error of the single prediction models. It is hoped that the research can provide a useful reference for the prediction of the sum of container and related studies.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2000.10b
• /
• pp.23-28
• /
• 2000

The DGPS is used to improve the accuracy of the GPS by determining the positioning error data known locatio and subsequently transmitting the determined error, or corrective factors, to users of the GPS operating in the same geographical area. The accuracy of the DGPS available to real0time users is published in the Federal Radio-Navigation Plan to be better than 10 meters, although users often experience accuracies of better than 3 meters. USA recently decided to expand the marine DGPS service for land applications and initiated the Nationwide DGPS chain. Korea is processing for improving the marine DGPS. We have not achieved Full Operational Capability. But after announcing FOC, we have to begin expansion of DGPS into land. This paper investigate the availability of marine DGPS in land. The results will provide a basic guide for estimation of a propagation loss of the marine DGPS frequency in land.

• Proceedings of the IEEK Conference
• /
• 2004.06a
• /
• pp.55-58
• /
• 2004

This this paper, we propose vertical Bell laboratories layered space time (V-BLAST) system based on variable rate Low-Density Parity Check (LDPC) codes to improve performance of receiver when QR decomposition interference suppression combined with interference cancellation is used over independent Rayleigh fading channel. The different rate LDPC codes can be made by puncturing some rows of a given parity check matrix. This allows to implement a single encoder and decoder for different rate LDPC codes. The performance can be improved by assigning stronger LDPC codes in lower layer than upper layer because the poor SNR of first detected data streams makes error propagation. Keeping the same overall code rates, the V-BLAST system with different rate LDPC codes has the better performance (in terms of Bit Error Rate) than with constant rate LDPC code in fast fading channel.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.12
• /
• pp.4584-4599
• /
• 2021

In IEEE 802.11p-based wireless access in vehicular environments (WAVE) communication systems, channel estimation (CE) is one of the important issues to provide stable communication service. It is hard to apply conventional CE schemes based on data pilot to real systems, because error propagation occurs in high mobility and modulation order environments, resulting in degrading the CE performance. In this paper, we propose one data pilot using multiple receive antennas (ODPM) CE scheme based on the weighted sum using update matrix (WSUM) with time-domain averaging (TDA) to overcome this problem. Within the process of WSUM-TDA in the proposed scheme, the maximum ratio combining (MRC) technique is applied so as to create more accurate one data pilot. Moreover, a new reliability test criterion is proposed as the fashion of utilizing MRC, which makes it possible to apply selective TDA that guarantees performance improvement. In simulation results, the packet error rate (PER) performance of the proposed ODPM is compared with that of conventional CE methods and its superiority is demonstrated.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 2005.03a
• /
• pp.18-25
• /
• 2005

Stochastic ground-motion method is adopted to simulate horizontal PGA values for the offshore Uljin earthquake recorded at nationwide seismic stations. For this purpose, the Fourier spectra are calculated at every stations based on comprehensive results of wave propagation and site effect which were previously revealed through inversion process applied to large accumulated spectral D/B. In addition, the apparent source spectrum of the offshore Uljin earthquake is estimated by removing the path and site response from the observed spectra. The distance dependent time-duration model is revised by iteratively fitting the PGA values generated by using the raw spectra data to the observed PGA data. The stochastic ground-motion method predicts the observed PGA values within a error of ${\sigma}_{log10}=0.1$. Transfer functions of a site relative to another site are estimated based on the error residual of the inversion results and used to convert PGA values at multiple stations to expected PGA values at a reference station of TJN. The converted PGA values can be used as basic data to evaluate the ground-motion attenuation relations developed for seismic hazard analysis that concerns the large damaging earthquakes.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.34C no.6
• /
• pp.71-78
• /
• 1997

In this paper, a new neural network training algorithm will be devised for function approximator with good generalization characteristics and tested with the time series prediction problem using santaFe competition data sets. To enhance the generalization ability a constraint term of hidden neuraon activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. A hybrid learning algorithm of the error-back propagation and Hebbian learning algorithm with weight decay constraint will be naturally developed by the steepest decent algorithm minimizing the proposed cost function without much increase of computational requriements.

• Proceedings of the KIEE Conference
• /
• 2001.11c
• /
• pp.420-423
• /
• 2001

In this paper, the Multi-FNNs(Fuzzy-Neural Networks) architecture is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNNs architecture uses simplified inference and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNNs according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNNs model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model we use the time series data for gas furnace and the NOx emission process data of gas turbine power plant.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.383-386
• /
• 1997

In this paper, We proposed a parallel structure of the Neural Network model to nonlinear complex system. Neural Network was used as basic model which has learning ability and high tolerence level. This paper, we used Neural Network which has BP(Error Back Propagation Algorithm) model. But it sometimes has difficulty to append characteristic of input data to nonlinear system. So that, I used HCM(hard c-Means) method of clustering technique to append property of input data. Clustering Algorithms are used extensively not only to organized categorize data, but are also useful for data compression and model construction. Gas furance, a sewage treatment process are used to evaluate the performance of the proposed model and then obtained higher accuracy than other previous medels.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.15 no.9
• /
• pp.738-744
• /
• 1990

This paper proposed a real-time memory storage method and an improved sample data method from given data of the speech signal, so, the isolated word recognition system using a back-propagation learning algorithm of the neural netwrok is studied. The recognition rate and the error rate are compared with the new sample data sets generated from small sets of given sample data by the node nunber variatiion of each layer. In this result, the recognition rate of 95.1% was achived.

• Proceedings of the KIEE Conference
• /
• 1989.11a
• /
• pp.186-190
• /
• 1989

A Study for Bad Data Processing in state estimation by a Neural Network is presented. State estimation is the process of assigning a value to an unknown system state variable based on measurement from that system according to some criteria. In this case, the ability to detect and identify bad measurements is extremely valuable, and much time in oder to achieve the state estimation is needed. This paper proposed new bad data processing using Neural Network in order to settle it. The concept of neural net is a parallel distributed processing. In this paper, EBP (Error Back Propagation) algorithm based on three layered feed forward network is used.