• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.119-124
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• 2023

Deep learning has recently demonstrated conspicuous efficacy across diverse domains than traditional machine learning techniques, as the most popular approach for pattern recognition. The classification problems for tabular data, however, are remain for the area of traditional machine learning. This paper introduces a novel network module designed to tabular data into high-dimensional tensors. The module is integrated into conventional deep learning networks and subsequently applied to the classification of structured data. The proposed method undergoes training and validation on four datasets, culminating in an average accuracy of 90.22%. Notably, this performance surpasses that of the contemporary deep learning model, TabNet, by 2.55%p. The proposed approach acquires significance by virtue of its capacity to harness diverse network architectures, renowned for their superior performance in the domain of computer vision, for the analysis of tabular data.

• Journal of Information Technology Services
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• v.6 no.3
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• pp.111-128
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• 2007

The purpose of the research is to analyze the status and problems of the e-Learning quality assurance system on e-Learning contents and service provider(institutes) in the field of enterprise education. In addition, the research is to suggest the direction and strategies for revising and developing the system. The research put emphasis on two systems of the e-Learning quality assurance(contents, service provider) which directly influence financial support of government. This study depended mostly on literature review, supplemented by expert panel meetings. In the case of the quality assurance system on e-Learning contents, the followings are suggested; (1)admitting the contents made of the combination of modules in the approved module set, (2)making easier the qualifying of modified contents for maintenance, (3)revising evaluation criteria, (4)providing substantial feedback. In the field of service provider, the followings are requested; (1)differentiating of qualifying system by industry and scale of company, (2)extending the qualifying cycle, (3)improving the feedback and sharing system.

• The KIPS Transactions:PartA
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• v.14A no.6
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• pp.383-390
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• 2007

This paper describes the e-learning system for science and engineering mathematics using computer algebra system and Bayesian inference network. The best feature of this system is using one of the most recent mathematical dynamic web content authoring model which is called client independent dynamic web content authoring model and using the Bayesian inference network for diagnosing student's learning. The authoring module using computer algebra system provides teacher-user with easy way to make dynamic mathematical web contents. The diagnosis module using Bayesian inference network helps students know the weaker parts of their learning, in this way our system determines appropriate next learning sequences in order to provide supplementary learning feedback.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.37-44
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• 2021

Automatic classification of brain MRI images play an important role in early diagnosis of brain tumors. In this work, we present a deep learning-based brain tumor classification model in MRI images using ensemble of deep features. In our proposed framework, three different deep features from brain MR image are extracted using three different pre-trained models. After that, the extracted deep features are fed to the classification module. In the classification module, the three different deep features are first fed into the fully-connected layers individually to reduce the dimension of the features. After that, the output features from the fully-connected layers are concatenated and fed into the fully-connected layer to predict the final output. To evaluate our proposed model, we use openly accessible brain MRI dataset from web. Experimental results show that our proposed model outperforms other machine learning-based models.

• Journal of the Korean Society for information Management
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• v.18 no.4
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• pp.37-55
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• 2001

The purpose of this study is to present a text summarization system using a knowledge base containing information about verbs and their arguments that are statistically obtained from a subject domain. The system consists of two modules: the training module and the summarization module. The training module is to extract cue verbs and their basic sentence patterns by counting the frequency of verbs and case markers respectively, and the summarization module is substantiate basic sentence patterns and to generate summaries. Basic sentence patterns are substantiated by applying substantiation rules to the syntactics structure of sentences. A summary is then produced by connecting simple sentences that the are generated through the substantiation module of basic sentence patterns. ‘robbery’in the daily newspapers are selected for a test collection. The system generates natural summaries without losing any essential information by combining both cue verbs and essential arguments. In addition, the use of statistical techniques makes it possible to apply this system to other subject domains through its learning capability.

• The KIPS Transactions:PartA
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• v.19A no.3
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• pp.147-154
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• 2012

The higher thinking skills, such as creativity and problem-solving about a given problem, are difficult to assess and diagnose. For an accurate diagnosis of these higher thinking abilities, we need to fully observe learner's problem-solving process or learner's individual reports. However, in an online learning or virtual class environments, evaluation of learner's problem-solving process becomes more difficult to diagnose. The best way to solve this problem is through reporting by tracking learner's actions when he tries to solve a problem. In this study, we developed a module which can evaluate and diagnose student's problem-solving ability by tracking actions in MS-Office suite, which is used by students to solve a given problem. This module performs based on the learner's job history through user tracking. To evaluate the effectiveness of this diagnostic module, we conducted satisfaction survey from students who were preparing the actual MOS exams. As a result, eighty-one (81) of the participants were positive on the effectiveness of the learning system with the use of this module.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1402-1408
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• 2017

Recently, services for personal biometric data analysis based on real-time monitoring systems has been increasing and many of them have focused on recognition of emotions. In this paper, we propose a classification model to classify anxiety emotion using biometric data actually collected from people. We propose to deploy the support vector machine to build a classification model. In order to improve the classification accuracy, we propose two data pre-processing procedures, which are normalization and data deletion. The proposed algorithms are actually implemented based on Real-time Traffic Flow Measurement structure, which consists of data collection module, data preprocessing module, and creating classification model module. Our experiment results show that the proposed classification model can infers anxiety emotions of people with the accuracy of 65.18%. Moreover, the proposed model with the proposed pre-processing techniques shows the improved accuracy, which is 78.77%. Therefore, we can conclude that the proposed classification model based on the pre-processing process can improve the classification accuracy with lower computation complexity.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1540-1551
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• 2020

Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.

• ETRI Journal
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• v.44 no.6
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• pp.1034-1046
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• 2022

The learning-based multiview stereo (MVS) methods for three-dimensional (3D) reconstruction generally use 3D volumes for depth inference. The quality of the reconstructed depth maps and the corresponding point clouds is directly influenced by the spatial resolution of the 3D volume. Consequently, these methods produce point clouds with sparse local regions because of the lack of the memory required to encode a high volume of information. Here, we apply the atrous spatial pyramid pooling (ASPP) module in MVS methods to obtain dense feature maps with multiscale, long-range, contextual information using high receptive fields. For a given 3D volume with the same spatial resolution as that in the MVS methods, the dense feature maps from the ASPP module encoded with superior information can produce dense point clouds without a high memory footprint. Furthermore, we propose a 3D loss for training the MVS networks, which improves the predicted depth values by 24.44%. The ASPP module provides state-of-the-art qualitative results by constructing relatively dense point clouds, which improves the DTU MVS dataset benchmarks by 2.25% compared with those achieved in the previous MVS methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1369-1389
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• 2024

In the era of 6G, the rapid increase in communication data volume poses higher demands on traditional channel estimation techniques and those based on deep learning, especially when processing large-scale data as their computational load and real-time performance often fail to meet practical requirements. To overcome this bottleneck, this paper introduces quantum computing techniques, exploring for the first time the application of Quantum Generative Adversarial Networks (QGAN) to broadband channel estimation challenges. Although generative adversarial technology has been applied to channel estimation, obtaining instantaneous channel information remains a significant challenge. To address the issue of instantaneous channel estimation, this paper proposes an innovative QGAN with a dual-module design in the generator. The adversarial loss function and the Mean Squared Error (MSE) loss function are separately applied for the parameter updates of these two modules, facilitating the learning of statistical channel information and the generation of instantaneous channel details. Experimental results demonstrate the efficiency and accuracy of the proposed dual-module QGAN technique in channel estimation on the Pennylane quantum computing simulation platform. This research opens a new direction for physical layer techniques in wireless communication and offers expanded possibilities for the future development of wireless communication technologies.