• Computers and Concrete
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• v.5 no.5
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• pp.461-473
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• 2008

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.5-9
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• 1998

In this paper, we propose an error-resilient transmission technique for the H.263 video data stream over wireless networks. The proposed algorithm employs bit rearrangement hierarchically, providing the robust and exact synchronization against the bit errors, without requiring extra redundant information. In addition, we propose the recovery algorithm for the lost or erroneous motion vectors. We implement the encoder and decoder, based on the H.263 standard, and evaluate the proposed algorithm through intensive computer simulation. The experimental results demonstrate that the proposed algorithm yields good image quality, in spite of the channel errors, and prevents the error propagation both in the spatial and the temporal domain efficiently.

• The Transactions of the Korea Information Processing Society
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• v.3 no.1
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• pp.144-152
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• 1996

Recently, as the demands for radio spectrum are growing and the number of cell sites is increasing rapidly, the spectrum engineering plays an important role in estimating frequency sharing and reuse. The radio propagation analysis is essential in the basic technology of radio network design such as deciding the service area and selecting the position of the base station. But, domestic propagation environment in which mountainous region is occupying over 70% of our terrain does not allow us to apply foreign studies which are deduced in highly different environments. Therefore, we need to have our propagation analysis system derived from our own terrain condition. In this paper, we propose the propagation prediction model which issuitable toour propagation environment, and also usinghis model, we implement thesimulator based on GIS(Geographic Information System)which can be applied to both spectrum engineering and radio propagation analysis. We showed that this simulator can well be applied to frequency assignment, propagation network design as well as other radio services. Considering the results of our analysis, we could guarantee the standard deviation of error between the measured data and predicted results as 5 to 7 dB.

• ETRI Journal
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• v.45 no.6
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• pp.929-938
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• 2023

The detection of all the symbols transmitted simultaneously in multiuser systems using limited wireless resources is challenging. Traditional model-based methods show high performance with perfect channel state information (CSI); however, severe performance degradation will occur if perfect CSI cannot be acquired. In contrast, data-driven methods perform slightly worse than model-based methods in terms of symbol error ratio performance in perfect CSI states; however, they are also able to overcome extreme performance degradation in imperfect CSI states. This study proposes a novel deep learning-based method by improving a state-of-the-art data-driven technique called deep soft interference cancellation (DSIC). The enhanced DSIC (EDSIC) method detects multiuser symbols in a fully sequential manner and uses an efficient neural network structure to ensure high performance. Additionally, error-propagation mitigation techniques are used to ensure robustness against channel uncertainty. The EDSIC guarantees a performance that is very close to the optimal performance of the existing model-based methods in perfect CSI environments and the best performance in imperfect CSI environments.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.92-94
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• 2004

We become an industry information society which is advanced to the altitude with the today. The information to be loading various goods each other together at a circumstance environment is increasing extremely. The restriction recognizes the data of many Quantity and it follows because the human deals the task to classify. The development of a mathematical formulation for solving a problem like this is often very difficult. But Artificial intelligent systems such as neural networks have been successfully applied to solving complex problems in the area of pattern recognition and classification. So, in this paper a neural network approach is used to recognize and classification problem was broken into two steps. The first step consist of using a neural network to recognize the existence of purpose pattern. The second step consist of a neural network to classify the kind of the first step pattern. The neural network leaning algorithm is to use error back-propagation algorithm and to find the weight and the bias of optimum. Finally two step simulation are presented showing the efficacy of using neural networks for purpose recognition and classification.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.104-110
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• 2014

This paper considers two reconstruction schemes of structured-sparse signals, turbo inference and Markov chain Monte Carlo (MCMC) inference, in compressed sensing(CS) technique that is recently getting an important issue for an efficient video wireless transmission system using multi-copter as an unmanned aerial vehicle. Proposed reconstruction algorithms are setting importance on reduction of image data sizes, fast reconstruction speed and errorless reconstruction. As a result of experimentation with twenty kinds of images, we can find turbo reconstruction algorithm based on loopy belief propagation(BP) has more excellent performances than MCMC algorithm based on Gibbs sampling as aspects of average reconstruction computation time, normalized mean squared error(NMSE) values.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.6
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• pp.39-47
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• 2008

In this research, a web-based design support system is constructed for the design process of automotive steel pulley to gather engineering knowledge from pulley design data. In the design search module, a clustering tool for design data is proposed using K-means clustering algorithm. To obtain correlational patterns between design and FEA (Finite Element Analysis) data, a Multi-layer Back Propagation Network (MBPN) is applied. With the analyzed patterns from a number of simulation data, an estimation of minimum von mises can be provided for given design parameters of pulleys. The case study revealed fast estimation of minimum stress in the pulley within 12% error.

• Journal of The Korean Association For Science Education
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• v.31 no.1
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• pp.115-127
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• 2011

Analysis frame for undergraduate physics laboratory reports in collecting, processing, and analyzing data was developed. Using the frame and questionaries, we analyzed what difficulties students have in the concepts of error and uncertainty in writing laboratory reports. Students considered repetitive measurement for collecting data, but they didn't express it distinctly in their reports. They also had difficulties in measuring data around the extreme value or the large slope. Especially, most students have had difficulties with error and uncertainty. They can't apply the basic formulation to propagation of error and uncertainty. They also had the difficulties in analyzing data with concepts of error and uncertainty. While most students responded that error and uncertainty is important, there were few students who analyzed the influence of the cause of error on the results quantitatively. The result of the study showed that students have difficulties in writing the laboratory reports because they didn't have the correct concept of the error and uncertainty. So, it is needed to not only teach the physics concept about experiment but to teach basic concept of data collecting, processing, and analyzing specially about error and uncertainty for students as well.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.194-196
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• 2005

The command processor in satellite handles the capability of the process of command transmitted from ground station and deliver the processed data to on board computer in satellite. The command processor is consisted of redundant box to increase the reliability and availability of the capability. At each command processor, the processing time of each command processor is different, so the mismatch of processing time makes it difficult to timely synchronize the reception to on board computer and even will be became worse under the command processor's fault. To minimize the tine loss induced by the command processor's fault on board computer must analyze the time distribution of command propagation. This paper presents the logic of minimizing the delay error of command propagation the logic of analyzing the output of command processor.

• Structural Engineering and Mechanics
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• v.70 no.6
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• pp.671-681
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• 2019

This paper examines the applicability of artificial neural network (ANN) and multivariate adaptive regression splines (MARS) to predict the compressive strength of bacteria incorporated geopolymer concrete (GPC). The mix is composed of new bacterial strain, manufactured sand, ground granulated blast furnace slag, silica fume, metakaolin and fly ash. The concentration of sodium hydroxide (NaOH) is maintained at 8 Molar, sodium silicate ($Na_2SiO_3$) to NaOH weight ratio is 2.33 and the alkaline liquid to binder ratio of 0.35 and ambient curing temperature ($28^{\circ}C$) is maintained for all the mixtures. In ANN, back-propagation training technique was employed for updating the weights of each layer based on the error in the network output. Levenberg-Marquardt algorithm was used for feed-forward back-propagation. MARS model was developed by establishing a relationship between a set of predictors and dependent variables. MARS is based on a divide and conquers strategy partitioning the training data sets into separate regions; each gets its own regression line. Six models based on ANN and MARS were developed to predict the compressive strength of bacteria incorporated GPC for 1, 3, 7, 28, 56 and 90 days. About 70% of the total 84 data sets obtained from experiments were used for development of the models and remaining 30% data was utilized for testing. From the study, it is observed that the predicted values from the models are found to be in good agreement with the corresponding experimental values and the developed models are robust and reliable.