• Journal of IKEEE
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• v.19 no.2
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• pp.276-281
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• 2015

Engineers increasingly rely on computers and their computer programming skills for their works. As a result, most engineering curricula have introduced a computer programming courses. However, students consider the subject to be unrelated to their core interests and often feel uncomfortable when learning to program for the first time. To overcome these difficulties, several studies have proposed the use of physical computing paradigm. This paradigm takes the computational concepts out of the PC screen and into the real world so that the student can interact with them. This paper proposes Arduino platform as a tool for attracting interest of the programming and reports the results of questionnaire survey analysis.

• International Journal of Advanced Culture Technology
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• v.8 no.2
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• pp.58-67
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• 2020

In this paper, the maintenance training manual of EV vehicle was produced by utilizing digital twin technology and various sensors such as IR-based light house tracking and head tracker. In addition, through digital twin technology and VR to provide high immersiveness to users, sensory content creation technology was secured through animation and effect realization suitable for EV vehicle maintenance situation. EV vehicle maintenance training manual is 3D engine programming and real-time creation of 3D objects and minimization of screen obstacles and selection of specific menus in virtual space in the form of training simulation. In addition, automatic output from the Head Mount Display (HUD), EV vehicle maintenance and inspection, etc., user can easily operate content was produced. This technology development can enhance immersion to users through implementation of detailed scenarios for maintenance / inspection of EV vehicles" and 3D parts display by procedure, realization of animations and effects for maintenance situations. Through this study, familiarity with improving the quality of education and safety accidents and correct maintenance process and the experienced person was very helpful in learning how to use equipment naturally and how to maintain EV vehicles.

• The Journal of Korean Association of Computer Education
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• v.12 no.2
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• pp.13-22
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• 2009

In this paper, we propose a new dynamic syllabus composition system to solve the problems of a static syllabus which can appear in the field of computer education, where the relationship between pre-post study subjects is clear and teachers may grasp easily the degree of understanding of learners in real time. Our dynamic syllabus composition system is designed to be adjusted according to the physical change of the amount of education and the level of learners, which is based on the priority of educational objects. The result of instance performed on two groups of different entering behavior shows that the proposed method enhances the degree of transfer of education and helps us teach a class around the representative subject which has to be dealt with in the class, so that it is very effective for the achievement of educational objects prior to others.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.539-544
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• 1998

Generally, welding gap is a serious factor of a falling-off in weld quality among various kind of weld defect. Welding gap is created between two work piece in GMAW(Gas Metal Arc Welding) of horizontal fillet weld because surface of workpiece is not flat by cutting process. In these days, there were many attempts to detect welding gap. though we prevalently use the vision sensor or arc sensor in welding process, it is difficult to detect welding gap for improvement of welding quality. But we have a trouble to find relationship between welding gap and many welding parameters due to non-linearity of welding process. As mentioned about the various difficult problem, we can detect welding gap precisely using neural networks which are able to model non-linear function. Also, this paper was proposed real-time monitoring of weld bead shape to find effect of welding gap and to estimate weld quality. Monitoring of weld bead shape examined the correlation of welding parameters with bead eometry using learning ability of neural networks. Finally, the developed system, welding gap detecting system and bead shape monitoring system, is expected to the successful capability of automation of welding process by result of simulation.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.224-237
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• 2020

Recognition of banknote serial number is one of the important functions for intelligent banknote counter implementation and can be used for various purposes. However, the previous character recognition method is limited to use due to the font type of the banknote serial number, the variation problem by the solid status, and the recognition speed issue. In this paper, we propose an aspect ratio based character region segmentation and a convolutional neural network (CNN) based banknote serial number recognition method. In order to detect the character region, the character area is determined based on the aspect ratio of each character in the serial number candidate area after the banknote area detection and de-skewing process is performed. Then, we designed and compared four types of CNN models and determined the best model for serial number recognition. Experimental results showed that the recognition accuracy of each character was 99.85%. In addition, it was confirmed that the recognition performance is improved as a result of performing data augmentation. The banknote used in the experiment is Indian rupee, which is badly soiled and the font of characters is unusual, therefore it can be regarded to have good performance. Recognition speed was also enough to run in real time on a device that counts 800 banknotes per minute.

• International Journal of Automotive Technology
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• v.6 no.2
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• pp.95-105
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• 2005

Diesel engines have low specific fuel consumption, but high particulate emissions, mainly soot. Diesel soot is suspected to have significant effects on the health of living beings and might also affect global warming. Hence stringent measures have been put in place in a number of countries and will be even stronger in the near future. Diesel engines require either advanced integrated exhaust after treatment systems or modified engine models to meet the statutory norms. Experimental analysis to study the emission characteristics is a time consuming affair. In such situations, the real picture of engine control can be obtained by the modeling of trend prediction. In this article, an effort has been made to predict emissions smoke and NO$_{x}$ using cylinder combustion derived parameters and diesel particulate filter data, with artificial neural network techniques in MATLAB environment. The model is based on three layer neural network with a back propagation learning algorithm. The training and test data of emissions were collected from experimental set up in the laboratory for different loads. The network is trained to predict the values of emission with training values. Regression analysis between test and predicted value from neural network shows least error. This approach helps in the reduction of the experimentation required to determine the smoke and NO$_{x}$ for the catalyst coated filters.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.4
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• pp.587-595
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• 2002

Recent trend changes a criterion for evaluation of humors that environmental problems are raised as a global issue. Burners with higher thermal efficiency and lower oxygen in the exhaust gas, evaluated better. To comply with environmental regulations, burners must satisfy the NO/sub x/ and CO regulation. Consequently, 'good burner'means one whose thermal efficiency is high under the constraint of NO/sub x/ and CO consistency. To make existing burner satisfy recent criterion, it is highly recommended to develop a feedback control scheme whose output is the consistency of NO/sub x/ and CO. This paper describes the development of a real time flame diagnosis technique that evaluate and diagnose the combustion states, such as consistency of components in exhaust gas, stability of flame in the quantitative sense. In this paper, it was proposed on the flame diagnosis technique of burner using Neuro-Fuzzy algorithm. This study focuses on the relation of the color of the flame and the state of combustion. Neuro-Fuzzy loaming algorithm is used in obtaining the fuzzy membership function and rules. Using the constructed inference algorithm, the amount of NO/sub x/ and CO of the combustion gas was successfully inferred.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4204-4222
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• 2015

Cloud is the latest buzz word in the internet community among developers, consumers and security researchers. There have been many attacks on the cloud in the recent past where the services got interrupted and consumer privacy has been compromised. Denial of Service (DoS) attacks effect the service availability to the genuine user. Customers are paying to use the cloud, so enhancing the availability of services is a paramount task for the service provider. In the presence of DoS attacks, the availability is reduced drastically. Such attacks must be detected and prevented as early as possible and the power of computational approaches can be used to do so. In the literature, machine learning techniques have been used to detect the presence of attacks. In this paper, a novel approach is proposed, where intelligent rule based feature selection and classification are performed for DoS attack detection in the cloud. The performance of the proposed system has been evaluated on an experimental cloud set up with real time DoS tools. It was observed that the proposed system achieved an accuracy of 98.46% on the experimental data for 10,000 instances with 10 fold cross-validation. By using this methodology, the service providers will be able to provide a more secure cloud environment to the customers.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.74-81
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• 2017

In learning stage of the preliminary Cut-off indoor positioning scheme, RSSI and UUID data received from beacons at each reference point(RP) are stored in fingerprint map. The fingerprint map and real-time beacon information are compared to identify the nearest K reference points through which the user position is estimated. If the number of K is zero, this scheme cannot estimate user position. We have improved the preliminary Cut-off scheme to get the estimated user position even in the case. The improved scheme excludes the beacon of the weakest signal received by user mobile device and identifies neighborhood reference points using the other beacon information. This procedure are performed repetitively until K > 0. The simulation results confirm that the proposed scheme outperforms K-Nearest-Neighbor (KNN), Cluster KNN and the conventional Cut-off scheme in terms of accuracy while the constraints are guaranteed to be satisfied.

• Journal of the Korea Society of Computer and Information
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• v.7 no.1
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• pp.174-183
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• 2002

In this paper a control chart pattern recognition methodology based on the back propagation algorithm and Multi layer perceptron, a neural computing theory, is presented. This pattern recognition algorithm, suitable for real time statistical process control. evaluates observations routinely collected for control charting to determine whether a Pattern, such as a cycle. trend or shift, which is exists in the data. This approach is promising because of its flexible training and high speed computation with low-end workstation. The artificial neural network methodology is developed utilizing the delta learning rule, sigmoid activation function with two hidden layers. In a computer integrated manufacturing environment, the operator need not routinely monitor the control chart but, rather, can be alerted to patterns by a computer signal generated by the proposed system.