• Journal of Welding and Joining
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• v.32 no.2
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• pp.43-47
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• 2014

Famous artificial neural network (ANN) is applied to predict proper process window of arc welding. Target weldment is variously combined lap joint fillet welding of automotive steel plates. ANN's system variable such as number of hidden layers, perceptrons and transfer function are carefully selected through case by case test. Input variables are welding condition and steel plate combination, for example, welding machine type, shield gas composition, current, speed and strength, thickness of base material. The number of each input variable referred in welding experiment is counted and provided to make it possible to presume the qualitative precision and limit of prediction. One of experimental process windows is excluded for predictability estimation and the rest are applied for neural network training. As expected from basic ANN theory, experimental condition composed of frequently referred input variables showed relatively more precise prediction while rarely referred set showed poorer result. As conclusion, application of ANN to arc welding process window derivation showed comparatively practical feasibility while it still needs more training for higher precision.

• Journal of the HCI Society of Korea
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• v.11 no.3
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• pp.13-22
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• 2016

Vocal and instrument apprenticeship in applied music has been accepted without significant changes since the introduction of applied music education to South Korea. Few discussions or suggestions about other types of teaching than 'one-to-one lessons' or 'education of apprentices by assigned specialists' have been made. Since the introduction of applied music education to South Korea late in the 1980s, the CAI(Computer Aided Instruction) courseware development for applied music education has not actively been under way. The area of applied music has also made rapid progress in terms of music producing or music videos using computers. Actually, the improved computer program is not positively applied to applied music education. This study aimed to present learning methods using the improved functions of music production softwares to improve the traditional apprenticeship system in the area of vocal training in applied music. In particular, it used the technique of auto tune-pitch shift developed for interval correction in sound sources. By giving real-time feedbacks concerning intervals or monitors visually after recording, it intended to present a learning method to induce improvement in accuracy of intervals in vocal training. This study is expected to present a method that allows vocal trainers to overcome temporal and spatial limitations in applied music and make their vocal training more efficient.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.246-247
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• 2006

전력계통에 발생되는 사고 및 이상 현상에 대해한 계통운용자의 빠르고 정확한 복구 조치 능력 향상을 위해서 사전에 모의 훈련을 통한 교육이 절실히 필요하고 교육시스템의 활용을 통해 광역정전 시 전력계통운용원의 사고 조치 능력의 향상을 도모하고 이를 통해 대규모 정전으로 인한 경제적 손실과 사회적 혼란 감소하고자 고장복구 교육시스템이 필요하다. 본 논문에서는 지역급전원이 사전모의 할 수 있는 전력계통 고장복구 교육시스템의 효과적인 GUI(Graphical User Interface) 대해서 설명한다.

• New & Renewable Energy
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• v.2 no.2 s.6
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• pp.37-43
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• 2006

In this paper, the first forecasting system of wind power generation, KIER Forecaster is presented. KIER Forecaster has been constructed based on statistical models and was trained with wind speed data observed at Gosan Weather Station nearby Walryong Site. Due to short period of measurements at Walryong Site for training the model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict(MCP) technique. The results of One to Three-hour advanced forecasting models are consistent with the measurement at Walryong site. In particular, the multiple regression model by classification of wind speed pattern, which has been developed in this work, shows the best performance comparing with neural network and auto-regressive models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3C
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• pp.189-194
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• 2007

This paper derives and computes the detection probability of timing synchronization in an orthogonal frequency division multiplexing (OFDM) system encountered with a multipath Rayleigh fading channel and imperfect noise estimation. The timing synchronization scheme using a simple repeated constant amplitude zero auto-correlation (CAZAC) training symbol and correlation techniques is adopted. With this provision, we focus on the numerical analysis for OFDM timing synchronization scheme employing a preadvancement technique to reduce the inter-symbol interference (ISI). For measuring system performance, the detection performance derived in the considered system is presented in a multipath Rayleigh fading channel.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.6
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• pp.358-365
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• 2021

Crabbing motion is a pure sway motion with only sway velocity. The ship's crabbing motion is essential for an ideal berthing/unberthing process. The unberthing situation proceeds in sequential order such as crabbing motion section, pivoting section, and outer port section. For the berthing situation, the sequence has a reverse order: the inner port section, pivoting section, and crabbing motion section. In this paper, the berthing/unberthing data of the reference ship, Pukyong National University research ship "NARA", was analyzed to develop a sway velocity reference model. Several constraints were defined to derive the crabbing motion section during berthing/unberthing. The sway velocity reference model for the auto-berthing/unberthing was developed using the estimated sway velocity. A reproduction simulation of the ship was performed to compare the designed reference model and the reference ship data.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.106-118
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• 1998

The study concerns a design procedure of rule-based systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient from of "IF..., THEN..." statements, and exploits the theory of system optimization and fuzzy implication rules. The method for rule-based fuzzy modeling concerns the from of the conclusion part of the the rules that can be constant. Both triangular and Gaussian-like membership function are studied. The optimization hinges on an autotuning algorithm that covers as a modified constrained optimization method known as a complex method. The study introduces a weighted performance index (objective function) that helps achieve a sound balance between the quality of results produced for the training and testing set. This methodology sheds light on the role and impact of different parameters of the model on its performance. The study is illustrated with the aid of two representative numerical examples.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.2
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• pp.41-45
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• 2010

It is often misled that Multi Pilot License is introduced by International Civil Aviation Organization for the shortage of pilots. The truth is, however, that the license is focused on efficient training of co-pilots in the airline transportation system which an autopilot system is increasing in the Multi Crew environment. ICAO has been researching on the license since 1982, and made it international standard on 2006. Currently, co-pilots trained under Multi-Crew Pilot License courses are continuously increasing over the world. Although the license has introduced to Korea in September 10, 2009, it has not won popular support yet. This paper will lead people to precise understanding of Multi-Crew Pilot License and suggest its introduction plan.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.991-1000
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• 1994

To control nonlinear systems adaptively, we improve learning speed of neural networks and present a novel control algorithm characterized by compensation of control inputs. In an error-backpropagation algorithm for tranining multilayer neural networks(MLNN's) the effect of the slope of activation functions on learning performance is investigated and the learning speed of neural networks is improved by auto-adjusting the slope of activation functions. The control system is composed of two MLNN's, one for control and the other for identification, with the weights initialized by off-line training. The control algoritm is modified by a control strategy which compensates the control error induced by the indentification error. Computer simulations show that the proposed control algorithm is efficient in controlling a nonlinear system with abruptly changing parameters.