• Journal of Power Electronics
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• v.15 no.4
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• pp.1139-1147
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• 2015

In this paper, an algorithm based on a model analysis of the online calculation of the root-mean-square (RMS) value of welding current for single-phase AC resistance spot welding (RSW) was developed. The current is highly nonlinear and typically non-sinusoidal, which makes the measuring and controlling actions difficult. Though some previous methods focused on this issue, they were so complex that they could not be effectively used in general cases. The electrical model of a single-phase AC RSW was analyzed, and then an algorithm for online calculation of the RMS value of the welding current was presented. The description includes two parts, a model-dependent part and a model-independent part. Using a previous work about online measurement of the power factor angle, the first part can be solved. For the second part, although the solution of the governing equation can be directly obtained, a lot of CPU time must be consumed due to the fact that it involves a lot of complex calculations. Therefore, a neural network was employed to simplify the calculations. Finally, experimental results and a corresponding analysis showed that the proposed algorithm can obtain the RMS values with a high precision while consuming less time when compared to directly solving the equations.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.10a
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• pp.276-280
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• 2001

In the development of Chip Mounter(C/M), much interests have risen regarding how to decrease the operation time of mounting the different chips on the printed circuit board(PCB). The existing method to determine the time sequence of teaching C/M was to follow the procedure which was made by the operater. IN this study, a new but effective algorithm has been developed and employed in SCM-130 Chip Mounter and its online programming had reduced the mounting time significantly and provided the basis for the future online CAD/CAM system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.12-15
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• 2007

Online parallel machine scheduling problems have been studied by many researchers and enormous results are appeared in the last 40 years. With the development of scheduling theory and application, new online scheduling problems where the partial information is known in advance, that is, semi-online, gained much interest due to their increased application in practice. So we consider the online scheduling of three machines with general eligibility and its semi-online variant where the total processing time is known in advance. For the online and semi-online problems, we develop algorithms with competitive ratio of 5/2 which are shown to be optimal.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1029-1040
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• 2011

In this paper we propose an intelligent navigation algorithm for real world problem which can build a map without localization. Proposed algorithm operates online and furthermore does not require many memories for applying real world problem. After applying proposed algorithm to toy and huge data set, it does not require to calculate a whole eigenspace and need less memory compared to existing algorithm. Thus we can obtain that proposed algorithm is suitable for real world mobile navigation algorithm.

• Journal of Communications and Networks
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• v.7 no.1
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• pp.65-75
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• 2005

In this paper, we consider a scenario in which the source has been offered QoS guarantees subject to token-bucket regulation. The rate of the source should be controlled such that it conforms to the token-bucket regulation, and also the distortion obtained is the minimum. We have developed an optimal scheduling algorithm for offline (like pre-recorded video) sources with convex distortion function and which can not tolerate any delay. This optimal offline algorithm has been extended for the real-time online source by predicting the number of packets that the source may send in future. The performance of the online scheduler is not substantially degraded as compared to that of the optimal offline scheduler. A sub-optimal offline algorithm has also been developed to reduce the computational complexity and it is shown to perform very well. We later consider the case where the source can tolerate a fixed amount of delay and derive optimal offline algorithm for such traffic source.

• The Journal of Information Technology
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• v.9 no.4
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• pp.65-74
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• 2006

The back propagation of neural networks has the problems of falling into local minimum and delay of the speed by the iterative learning. An algorithm to solve the problem and improve the speed of the learning was already proposed in[8], which updates the learning parameter related with the connection weight. In this paper, we propose the algorithm generating initial weight to improve the efficiency of the algorithm by offering the difference between the input vector and the target signal to the generating function of initial weight. The algorithm proposed here can classify more than 98.75% of the handwritten digits and this rate shows 30% more effective than the other previous methods.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1464-1475
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• 2020

Due to the spread of COVID-19, the online lecture has become more prevalent. However, it was found that a lot of students and professors are experiencing lack of communication. This study is therefore designed to improve interactive communication between professors and students in real-time online lectures. To do so, we explore deep learning approaches for automatic recognition of students' facial expressions and classification of their understanding into 3 classes (Understand / Neutral / Not Understand). We use 'BlazeFace' model for face detection and 'ResNet-GRU' model for facial expression recognition (FER). We name this entire process 'Degree of Understanding (DoU)' algorithm. DoU algorithm can analyze a multitude of students collectively and present the result in visualized statistics. To our knowledge, this study has great significance in that this is the first study offers the statistics of understanding in lectures using FER. As a result, the algorithm achieved rapid speed of 0.098sec/frame with high accuracy of 94.3% in CPU environment, demonstrating the potential to be applied to real-time online lectures. DoU Algorithm can be extended to various fields where facial expressions play important roles in communications such as interactions with hearing impaired people.

• The Journal of Information Systems
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• v.31 no.4
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• pp.25-45
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• 2022

Purpose In this paper, we propose the method to extend topic modeling techniques in order to derive data-based research hypotheses when establishing research hypotheses for social sciences, As a concept in contrast to the existing deductive hypothesis establishment methodology for the social science research, the topic modeling technique was expanded to enable the so-called inductive hypothesis establishment methodology, and an analysis case of the Seongsan Ilchulbong online review based on the proposed methodology was presented. Design/methodology/approach In this paper, an extension architecture and extension algorithm in the form of extending the existing topic modeling were proposed. The extended architecture and algorithm include data processing method based on topic ratio in document, correlation analysis and regression analysis of processed data for topics derived by existing topic modeling. In addition, in this paper, an analysis case of the online review of Seongsan Ilchulbong Peak was presented by applying the extended topic modeling algorithm. An exploratory analysis was performed on the Seongsan Ilchulbong online reviews through the basic text analysis. The data was transformed into 5-point scale to enable correlation and regression analysis based on the topic ratio in each online review. A regression analysis was performed using the derived topics as the independent variable and the review rating as the dependent variable, and hypotheses could be derived based on this, which enable the so-called inductive hypothesis establishment. Findings This paper is meaningful in that it confirmed the possibility of deriving a causal model and setting an inductive hypothesis through an extended analysis of topic modeling.

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.7-17
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• 2017

Despite various types of online social networks having different topological and functional characteristics, the kinds of online social networks considered in social recommendations are highly restricted. The pervasiveness of social networks has brought scholarly attention to expanding the scope of social recommendations into more diverse and less explored types of online social networks. As a preliminary attempt, this study examined the information-sharing patterns of a new type of online social network - unilateral (directed) network - and assessed the feasibility of the network as a useful information source. Specifically, this study mainly focused on the presence of shared interests in unilateral networks, because the shared information is the inevitable condition for utilizing the networks as a feasible source of personalized recommendations. As the results, we discovered that user pairs with direct and distant links shared significantly more similar information than the other non-connected pairs. Individual users' social properties were also significantly correlated with the degree of their information similarity with social connections. We also found the substitutability of online social networks for the top cohorts anonymously chosen by the collaborative filtering algorithm.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.360-374
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• 2024

A personalized course recommendation algorithm based on deep learning in an online education cloud platform is proposed to address the challenges associated with effective information extraction and insufficient feature extraction. First, the user potential preferences are obtained through the course summary, course review information, user course history, and other data. Second, by embedding, the word vector is turned into a low-dimensional and dense real-valued vector, which is then fed into the compressed interaction network-deep neural network model. Finally, considering that learners and different interactive courses play different roles in the final recommendation and prediction results, an attention mechanism is introduced. The accuracy, recall rate, and F1 value of the proposed method are 0.851, 0.856, and 0.853, respectively, when the length of the recommendation list K is 35. Consequently, the proposed strategy outperforms the comparison model in terms of recommending customized course resources.