• Journal of Korea Multimedia Society
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• v.14 no.5
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• pp.642-649
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• 2011

Among the various aspects of offline handwritten character patterns, it is the great variety of writing styles and variations that renders the task of computer recognition very hard. The immense variety of character shape has been recognized but rarely studied during the past decades of numerous research efforts. This paper tries to address the problem of measuring image distortions and handwritten character patterns with respect to reference patterns. This work is based on mass-spring mesh model with the introduction of simulated electric charge as a source of the external force that can aid decoding the shape distortion. Given an input image and a reference image, the charge is defined, and then the relaxation procedure goes to find the optimum configuration of shape or patterns of least potential. The relaxation process is based on the fourth order Runge-Kutta algorithm, well-known for numerical integration. The proposed method of modeling is rigorous mathematically and leads to interesting results. Additional feature of the method is the global affine transformation that helps analyzing distortion and finding a good match by removing a large scale linear disparity between two images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.751-770
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• 2019

Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction. The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video. Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data. In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data. Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a state-of-the-art detector for image classification). We broaden a semi-supervised learning method combined with an active learning method to improve overall performance. Numerous types of two-person interactions exist in the real world, which makes this a challenging task. In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex. We show that our trained model with an active semi-supervised learning architecture gradually improves the performance. In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy. Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset. We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.2
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• pp.175-186
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• 2021

In the field of automatic target recognition(ATR) with synthetic aperture radar(SAR) imagery, it is usually impractical to obtain SAR target images covering a full range of aspect views. When the database consists of SAR target images with limited angular diversity, it can lead to performance degradation of the SAR-ATR system. To address this problem, this paper proposes a deep learning-based method where channel attention modules(CAMs) are inserted to a convolutional neural network(CNN). Motivated by the idea of the squeeze-and-excitation(SE) network, the CAM is considered to help improve recognition performance by selectively emphasizing discriminative features and suppressing ones with less information. After testing various CAM types included in the ResNet18-type base network, the SE CAM and its modified forms are applied to SAR target recognition using MSTAR dataset with different reduction ratios in order to validate recognition performance improvement under the limited angular diversity condition.

• The Journal of the Acoustical Society of Korea
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• v.41 no.6
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• pp.647-654
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• 2022

Although the fields in which is utilized children's speech recognition is on the rise, the lack of quality data is an obstacle to improving children's speech recognition performance. This paper proposes a new method for improving children's speech recognition performance by additionally using adult speech data. The proposed method is a transformer based domain adversarial training using dynamically weighted loss to effectively address the data imbalance gap between age that grows as the amount of adult training data increases. Specifically, the degree of class imbalance in the mini-batch during training was quantified, and the loss function was defined and used so that the smaller the data, the greater the weight. Experiments validate the utility of proposed domain adversarial training following asymmetry between adults and children training data. Experiments show that the proposed method has higher children's speech recognition performance than traditional domain adversarial training method under all conditions in which asymmetry between age occurs in the training data.

• ETRI Journal
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• v.35 no.5
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• pp.869-879
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• 2013

This study is related to the acquisition of physiological signals of human emotions and the recognition of human emotions using such physiological signals. To acquire physiological signals, seven emotions are evoked through stimuli. Regarding the induced emotions, the results of skin temperature, photoplethysmography, electrodermal activity, and an electrocardiogram are recorded and analyzed as physiological signals. The suitability and effectiveness of the stimuli are evaluated by the subjects themselves. To address the problem of the emotions not being recognized, we introduce a methodology for a recognizer using prototype-based learning and particle swarm optimization (PSO). The design involves two main phases: i) PSO selects the P% of the patterns to be treated as prototypes of the seven emotions; ii) PSO is instrumental in the formation of the core set of features. The experiments show that a suitable selection of prototypes and a substantial reduction of the feature space can be accomplished, and the recognizer formed in this manner is characterized by high recognition accuracy for the seven emotions using physiological signals.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.11a
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• pp.64-65
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• 2015

As construction projects have increased in size and height recently, lifting accounts for increasingly greater portion and tower cranes are used more frequently. At present, the selection and arrangement of tower crane are depend on the experience of experts. However, since the number of experts is fairly limited and a database for tower cranes regarding lifting capacity, operation properties, rent, etc has not been widely employed, tower cranes are often not effectively selected and arranged which can cause cost overruns and delays in the lifting work. To address such issues, this study attempts to perform a basic study for development of automatic arrangement algorithm of tower crane using drawing recognition. If relevant database is established and the algorithm suggested in this study is refined more systematically, even beginning level engineers will be able to plan tower crane arrangement in a way comparable to experienced experts.

• International Journal of Contents
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• v.10 no.3
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• pp.26-34
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• 2014

Mobile learning is a practical learning method for busy adult learners because the mobility of digital devices can overcome the drawbacks of e-learning. However, research is strongly lacking in the theoretical exploration of mobile learning effects and functions and its empirical research. Moreover, the research of learning characteristics and learners' requirements must be considered before applying and disseminating mobile learning into the educational field. To address this shortcoming, this study conducted an online survey with 1,542 officers of the Ministry of Health and Welfare Affairs (MHWA) regarding learner recognition to mobile learning. The analysis of learners' attitudes toward mobile learning, based on age and position, indicated that subordinate workers appeared to place more value on mobile learning. Many participants preferred mobile learning because of its mobility and the effectiveness of anywhere and anytime. However, some participants continue to misunderstand mobile learning and its necessity. Therefore, consideration of learning effectiveness, the form of the content, and learner-centered learning must be reviewed in advance. This study could lead to practical implications of mobile learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4412-4428
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• 2018

The purpose of this work is to solve the problem of representing an entire video using Convolutional Neural Network (CNN) features for human action recognition. Recently, due to insufficient GPU memory, it has been difficult to take the whole video as the input of the CNN for end-to-end learning. A typical method is to use sampled video frames as inputs and corresponding labels as supervision. One major issue of this popular approach is that the local samples may not contain the information indicated by the global labels and sufficient motion information. To address this issue, we propose a binary hashing method to enhance the local feature extractors. First, we extract the local features and aggregate them into global features using maximum/minimum pooling. Second, we use the binary hashing method to capture the motion features. Finally, we concatenate the hashing features with global features using different normalization methods to train the classifier. Experimental results on the JHMDB and MPII-Cooking datasets show that, for these new local features, binary hashing mapping on the sparsely sampled features led to significant performance improvements.

• Journal of Arbitration Studies
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• v.25 no.2
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• pp.49-70
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• 2015

There are three categories of arbitration - domestic arbitration, foreign-related arbitration and foreign arbitration. Although the meaning of foreign arbitration and International Commercial Arbitration is different, they are used to mean the same in practice. In fact, there is significant controversy about the meaning of non-domestic arbitration because it is too difficult to distinguish between non-domestic arbitration and domestic arbitration. In the Chinese arbitration system, there are two main laws,Chinese Arbitration Law and Chinese Civil Procedure Law. Chinese Arbitration Law regulates the internal matters, while Chinese Civil Procedure Law regulates the external legal regulations. After the 2012 revised Chinese Civil Procedure Law, a number of laws and regulations have been revised, and almost every Arbitrations Rules have been revised, and will be in effect in 2015. Depending on the nationality of arbitration, the applicable laws will be different. The nationality of arbitration is so important that this paper will pay more attention to it. Although the case in China has no precedent effect, it is so important to the parties that this paper will address it. This paper will analyze the process and the cases of the recognition and enforcement of the award system in China.

• Journal of Arbitration Studies
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• v.27 no.3
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• pp.53-76
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• 2017

Korean businesses, and their legal representatives, have observed the improvements of enforcement of commercial judgments through arbitration over traditional collections litigation in U.S. Courts-due to quicker proceedings, exceptional cost savings and more predictable outcomes-in attaching assets within U.S. jurisdictions. But how are the 2016 interim measures implemented by the Arbitration Act of Korea utilized to avoid jurisdictional and procedure pitfalls of enforcement proceedings in the Federal Courts of the United States? Authors examine the necessary prerequisites of the U.S. Federal Arbitration Act as adopted through the New York Convention, to which Korea and the U.S. are signatories, as distinguished from the Panama Convention. Five common U.S. arbitration institutions address U.S. "domestic" disputes, preempting U.S. state law arbitrations, while this article focuses on U.S. enforcement of "international" arbitration awards. Seeking U.S. recognition and enforcement of Korean arbitral awards necessitates avoiding common defenses involving due process, public policy or documentary formality challenges. Provisional and conservatory injunctive relief measures are explored. A variety of U.S. cases involving Korean litigants are examined to illustrate the legal challenges involving non?domestic arbitral awards, foreign arbitral awards and injunctive relief. Suggestions aimed toward further research are focused on typical Korean business needs such as motions to confirm foreign arbitration awards, enforce such awards or motions to compel arbitration.