• Economic and Environmental Geology
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• v.41 no.2
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• pp.219-223
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• 2008

Many cross-over errors due to position errors, meter errors, observation errors, sea conditions and so on occur when marine geophysical data collected by own and other agencies are merged, and these errors can create artificial anomalies which cause an improper interpretation. Many methods have been introduced to reduce cross-over errors. However, most methods are designed to compare each point or segment data to find cross-over points, and require a long processing time. Therefore, FORTRAN program (FastXcorr) is presented to fast determine cross-over points using an overlap-sector, and to adjust cross-over errors using a weighted linear interpolation algorithm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.2
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• pp.23-28
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• 2009

This study describes an emotion classification method using two different feature parameters of four-channel EEG signals. One of the parameters is linear prediction coefficients based on AR modelling. Another one is cross-correlation coefficients on frequencies of ${\theta}$, ${\alpha}$, ${\beta}$ bands of FFT spectra. Using the linear predictor coefficients and the cross-correlation coefficients of frequencies, the emotion classification test for four emotions, such as anger, sad, joy, and relaxation is performed with an artificial neural network. The results of the two parameters showed that the linear prediction coefficients have produced the better results for emotion classification than the cross-correlation coefficients of FFT spectra.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.1
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• pp.58-67
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• 2020

A big data processing method to predict solar power generation using systems engineering approach is developed in this work. For developing analytical method, linear model (LM), support vector machine (SVN), and artificial neural network (ANN) technique are chosen. As evaluation indices, the cross-correlation and the mean square root of prediction error (RMSEP) are used. From multi-variable comparison test, it was found that ANN methodology provides the highest correlation and the lowest RMSEP.

• Journal of Electrical Engineering and Technology
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• v.1 no.4
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• pp.482-489
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• 2006

This paper presents the generalized dynamic modeling of self-excited induction generator (SEIG) using state-space approach. The proposed dynamic model consists of induction generator; self-excitation capacitance and load model are expressed in stationary d-q reference frame with the actual saturation curve of the machine. An artificial neural network model is implemented to estimate the machine magnetizing inductance based on the knowledge of magnetizing current. The dynamic performance of SEIG is investigated under no load, with the load, perturbation of load, short circuit at stator terminals, and variation of prime mover speed, variation of capacitance value by considering the effect of main and cross-flux saturation. During voltage buildup the variation in magnetizing inductance is taken into consideration. The performance of SEIG system under various conditions as mentioned above is simulated using MATLAB/SIMULINK and the simulation results demonstrates the feasibility of the proposed system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.2
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• pp.329-338
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• 1994

This paper presents a novel method of path planning for a quadruped walking robot on irregular terrain. In the previous study on the path planning problem of mobile robots, it has been usually focused on the collision-free path planning for wheeled robots. The path planning problem of legged roboth, however, has unique aspects from the point of viw that the legged robot can cross over the obstacles and the gait constraint should be considered in the process of planning a path. To resolve this unique problem systematically, a new concept of the artificial intensity field of light is numerically constructed over the configuration space of the robot including the transformed obstacles and a feasible path is sought in the field. Also, the efficiency of the proposed method is shown by various simulation results.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.1014-1021
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• 1999

PID controllers with constant gains have been widely used in various control systems. But it is difficult to have uniformly good control performance in all operating conditions. In this paper, we propose a variable PID controller for robot manipulators. We divide total workspace of manipulators into several subspaces. PID controllers in each subspace are optimized using evolution strategy which is a kind of global search algorithm. In real operation, the desired trajectories may cross several subspaces and we select the corresponding gains in each subspace. The gains may have large difference on the boundary of subspaces, which may cause oscillatory motion. So we use artificial neural network to have continuous smooth gain curves to reduce the oscillatory motion. From the experimental results, although the proposed variable PID controller for robots should pay for some computational burden, we have found that the controller is more superior to the conventional constant gain PID controller.

• Geomechanics and Engineering
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• v.1 no.4
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• pp.307-321
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• 2009

The paper employs a feed forward neural network with back-propagation algorithm for modeling time dependent swell in clays containing carbonate in the presence of sulfuric acid. The oedometer swell percent is estimated at a nominal surcharge pressure of 6.25 kPa to develop 612 data sets for modeling. The input parameters used in the network include time, sulfuric acid concentration, carbonate percentage, and liquid limit. Among the total data sets, 280 (46%) were assigned to training, 175 (29%) for testing and the remaining 157 data sets (25%) were relegated to cross validation. The network was programmed to process this information and predict the percent swell at any time, knowing the variable involved. The study demonstrates that it is possible to develop a general BPNN model that can predict time dependent swell with relatively high accuracy with observed data ($R^2$=0.9986). The obtained results are also compared with generated non-linear regression model.

• Journal of Smart Tourism
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• v.1 no.4
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• pp.9-18
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• 2021

The impacts that AI and robotics systems can and will have on our everyday lives are already making themselves manifest. However, there is a lack of research on the ethical impacts and means for amelioration regarding AI and robotics within tourism and hospitality. Given the importance of designing technologies that cross national boundaries, and given that the tourism and hospitality industry is fundamentally predicated on multicultural interactions, this is an area of research and application that requires particular attention. Specifically, tourism and hospitality have a range of context-unique stakeholders that need to be accounted for in the salient design of AI systems is to be achieved. This paper adopts a stakeholder approach to develop the conceptual framework to centralize human values in designing and deploying AI and robotics systems in tourism and hospitality. The conceptual framework includes several layers - 'Human-human-AI' interaction level, direct and indirect stakeholders, and the macroenvironment. The ethical issues on each layer are outlined as well as some possible solutions to them. Additionally, the paper develops a research agenda on the topic.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.29-37
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• 2017

The range of problems that can be handled by the activation of big data and the development of hardware has been rapidly expanded and machine learning such as deep learning has become a very versatile technology. In this paper, mnist data set is used as experimental data, and the Cross Entropy function is used as a loss model for evaluating the efficiency of machine learning, and the value of the loss function in the steepest descent method is We applied the Gradient Descent Optimize algorithm to minimize and updated weight and bias via backpropagation. In this way we analyze optimal reliability value corresponding to the number of exercises and optimal reliability value without overfitting. And comparing the overfitting time according to the number of data changes based on the number of training times, when the training frequency was 1110 times, we obtained the result of 92%, which is the optimal reliability value without overfitting.

• Steel and Composite Structures
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• v.24 no.5
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• pp.635-641
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• 2017

Safety service improvement and development of efficient maintenance strategies for corroded steel structures are undeniably essential. Therefore, understanding the influence of damage caused by corrosion on the remaining load-carrying capacities such as tensile strength is required. In this study, artificial neural network (ANN) approach is proposed in order to produce a simple, accurate, and inexpensive method developed by using tensile test results, material properties and finite element method (FEM) results to train the ANN model. Initially in reproducing corroded model process, FEM was used to obtain tensile strength of artificial corroded plates, for which surface is developed by a spatial autocorrelation model. By using the corroded surface data and material properties as input data, with tensile strength as the output data, the ANN model could be trained. The accuracy of the ANN result was then verified by using leave-one-out cross-validation (LOOCV). As a result, it was confirmed that the accuracy of the ANN approach and the final output equation was developed for predicting tensile strength without tensile test results and FEM in further work. Though previous studies have been conducted, the accuracy results are still lower than the proposed ANN approach. Hence, the proposed ANN model now enables us to have a simple, rapid, and inexpensive method to predict residual tensile strength more accurately due to corrosion in steel structures.