• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.83-84
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• 2009

• Journal for History of Mathematics
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• v.15 no.2
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• pp.113-124
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• 2002

The main purpose of this study is to classify types of class using computer in the school mathematics classroom. For this purpose, we will first survey the Tyle's theory relate to the curriculum model and its details. Then we will investigate the crucial points using computer in the school mathematics classroom in the viewpoint of Glaser's teaching model. From this, we will device several types of mathematics class using computer.

• ETRI Journal
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• v.36 no.3
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• pp.479-489
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• 2014

This paper presents a model-based image steganography method based on Watson's visual model. Model-based steganography assumes a model for cover image statistics. This approach, however, has some weaknesses, including perceptual detectability. We propose to use Watson's visual model to improve perceptual undetectability of model-based steganography. The proposed method prevents visually perceptible changes during embedding. First, the maximum acceptable change in each discrete cosine transform coefficient is extracted based on Watson's visual model. Then, a model is fitted to a low-precision histogram of such coefficients and the message bits are encoded to this model. Finally, the encoded message bits are embedded in those coefficients whose maximum possible changes are visually imperceptible. Experimental results show that changes resulting from the proposed method are perceptually undetectable, whereas model-based steganography retains perceptually detectable changes. This perceptual undetectability is achieved while the perceptual quality - based on the structural similarity measure - and the security - based on two steganalysis methods - do not show any significant changes.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.355-357
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• 2001

HTTP/1.1 standard reduces latencies and overhead from closing and re-establishing connections by supporting persistent connections as a default, which encourage multiple transfers of objects over one connection. HTTP/1.1, however, does not define explicitly connection-closing time but specifies a certain fixed holding time model. This model may induce wasting server’s resource when server maintains connection with the idle-state client that requests no data for a certain time. This paper proposes the mechanism of a heuristic connection management supported by the client-side under persistent HTTP, in addition to HTTP/1.1’s fixed holding time model on server-side. The client exploits the tag information within transferred HTML page so that decides connection-closing time. As a result, the mechanism allows server to use server’s resource more efficiently without server’s efforts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2121-2142
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• 2011

In this paper, we propose an e-Learning process control model that aims to graphically describe and automatically generate the manifest of sequencing prerequisites in packaging SCORM's content aggregation models. In specifying the e-Learning activity sequencing, SCORM provides the concept of sequencing prerequisites to be manifested on each e-Learning activity of the corresponding tree-structured content organization model. However, the course developer is required to completely understand the SCORM's complicated sequencing prerequisites and other extensions. So, it is necessary to achieve an efficient way of packaging for the e-Learning content organization models. The e-Learning process control model proposed in this paper ought to be an impeccable solution for this problem. Consequently, this paper aims to realize a new concept of process-driven e-Learning content aggregating approach supporting the e-Learning process control model and to implement its e-Learning process modeling system graphically describing and automatically generating the SCORM's sequencing prerequisites. Eventually, the proposed model becomes a theoretical basis for implementing a SCORM-based e-Learning process management system satisfying the SCORM's sequencing prerequisite specifications. We strongly believe that the e-Learning process control model and its modeling system achieve convenient packaging in SCORM's content organization models and in implementing an e-Learning management system as well.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.47-54
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• 2023

For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. But previous researches had the flaws of low accuracy and overfitting problem. In this research, attention-based Conv1D and BiGRU model is designed for music emotion classification and comparative experimentation shows that the proposed model is classifying emotions more accurate. We have proposed a Conv1D and Bi-GRU with the attention-based model for emotion classification of our Bengali music dataset. The model integrates attention-based. Wav preprocessing makes use of MFCCs. To reduce the dimensionality of the feature space, contextual features were extracted from two Conv1D layers. In order to solve the overfitting problems, dropouts are utilized. Two bidirectional GRUs networks are used to update previous and future emotion representation of the output from the Conv1D layers. Two BiGRU layers are conntected to an attention mechanism to give various MFCC feature vectors more attention. Moreover, the attention mechanism has increased the accuracy of the proposed classification model. The vector is finally classified into four emotion classes: Angry, Happy, Relax, Sad; using a dense, fully connected layer with softmax activation. The proposed Conv1D+BiGRU+Attention model is efficient at classifying emotions in the Bengali music dataset than baseline methods. For our Bengali music dataset, the performance of our proposed model is 95%.

• Journal of Biosystems Engineering
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• v.29 no.3
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• pp.275-280
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• 2004

The main objective of this study was to develop and evaluate a model for estimating pigs weight using computer vision for improving the management in Korean swine farms in Korea. This research was carried out in two steps: 1) to find a model that relates the projection area with the weight of a pig; 2) to implement the model in a computer vision system mainly consisted of a monochrome CCD camera, a frame grabber and a computer system for estimating the weight of pigs in a non-contact, real-time manner. The model was developed under an important assumption there were no observable genetic differences among the pigs. The main results were: 1) The relationship between the projection area and the weight of pigs was W = 0.0569 ${\times}$ A - 32.585($R^2$ = 0.953), where W is the weight in kg; A is the projection area of a pig in $\textrm{cm}^2$; 2) The model could estimate the weight of pigs with an error less than 3.5%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3507-3522
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• 2022

Model pruning methods have attracted huge attention owing to the increasing demand of deploying models on low-resource devices recently. Most existing methods use the weight norm of filters to represent their importance, and discard the ones with small value directly to achieve the pruning target, which ignores the contribution of the small norm filters. This is not only results in filter contribution waste, but also gives comparable performance to training with the random initialized weights [1]. In this paper, we point out that the small norm filters can harm the performance of the pruned model greatly, if they are discarded directly. Therefore, we propose a novel filter contribution recycle (FCR) method for structured model pruning to resolve the fore-mentioned problem. FCR collects and reassembles contribution from the small norm filters to obtain a mixed contribution collector, and then assigns the reassembled contribution to other filters with higher probability to be preserved. To achieve the target FLOPs, FCR also adopts a weight decay strategy for the small norm filters. To explore the effectiveness of our approach, extensive experiments are conducted on ImageNet2012 and CIFAR-10 datasets, and superior results are reported when comparing with other methods under the same or even more FLOPs reduction. In addition, our method is flexible to be combined with other different pruning criterions.

• Journal of The Korean Association of Information Education
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• v.5 no.1
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• pp.145-156
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• 2001

The purpose of this study is to set up the situated learning model for computer education and to investigate which method has better effect on the students' computer skill and learning attitude among the situated learning model and the traditional learning model. The result of this study is as follows. In order to investigate the effect of the students' learning attitude, students had been tested on six factors: the Understanding, the Interest, the Achievement, the Concentration, the Applicability, and the Spontaneity. As for the Understanding, the traditional learning model has better effect on students than the situated learning model. But the situated learning model was much superior in the other factors to the traditional learning model. Next, it had been examined how much students improved their computer skills under the situated learning model and under the traditional learning model. The study showed that the traditional learning model resulted in a little bit higher scores than the situated learning model. However, it was a great success to find out that the situated learning model is superior in the students' learning attitude to the traditional learning model.

• Journal of the Korea Computer Graphics Society
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• v.21 no.3
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• pp.11-16
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• 2015

We present a system for generating 3D personalized figures. This system provides 3D figures model modification and combination functions based on the content-awareness. The integrity of the 3D model must be guaranteed at the time of slicing of the 3D model for 3D printing. In addition to this, with 3D printing, we generally have to print a hollow model in order to save money, time, and the integrity of the print. This paper proposes the automatic algorithm that creates the 3D individual figures with depth sensor and the easy UI functions for deformation, thickness adjustment, and combination of the generated 3D figures model based on the content-awareness. Our proposed method maintains the unique features of the generated 3D figures and ensures the successful 3D printing.