• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.3 s.309
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• pp.30-38
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• 2006

In this paper, we improved to drops recognition rate according to distance using distance and depth information with 3D from stereo face images. A monocular face image has problem to drops recognition rate by uncertainty information such as distance of an object, size, moving, rotation, and depth. Also, if image information was not acquired such as rotation, illumination, and pose change for recognition, it has a very many fault. So, we wish to solve such problem. Proposed method consists of an eyes detection algorithm, analysis a pose of face, md principal component analysis (PCA). We also convert the YCbCr space from the RGB for detect with fast face in a limited region. We create multi-layered relative intensity map in face candidate region and decide whether it is face from facial geometry. It can acquire the depth information of distance, eyes, and mouth in stereo face images. Proposed method detects face according to scale, moving, and rotation by using distance and depth. We train by using PCA the detected left face and estimated direction difference. Simulation results with face recognition rate of 95.83% (100cm) in the front and 98.3% with the pose change were obtained successfully. Therefore, proposed method can be used to obtain high recognition rate with an appropriate scaling and pose change according to the distance.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.3
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• pp.247-271
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• 2018

This research aims to explore the possibility of open source software for creating digital archives of small organizations or ordinary people that run short of budget and professional workforce and may easily create digital archives without the help of a professional. To do so, this study suggested three open source software, AtoM, ArchivesSpace, and Omeka, for such use, and conducted usability tests with system designers and users who had no experience with open source software. The results of the usability testing was that AtoM, which was developed to support the records management system and user services of small organizations, proved satisfactory to both system designers and users. ArchivesSpace had too many required fields with it to create archives. Omeka greatly satisfied the system designers because it is possible to create archives with simple inputs on the item level. However, Omeka, which focuses on exhibition functions while neglecting search functions, registered low satisfaction among the users. Based on the results of the usability testing, this study suggested selection criteria of open source software for small organizations or ordinary individuals, namely, purposes, license, characteristics, service creation environment, advantages and disadvantages, functions, metadata, file type, and interoperability.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1273-1283
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• 2019

This paper proposes an advanced crop classification model that combines a procedure for weighted combination of spatial features extracted from multi-scale input images with a conventional convolutional neural network (CNN) structure. The proposed model first extracts spatial features from patches with different sizes in convolution layers, and then assigns different weights to the extracted spatial features by considering feature-specific importance using squeeze-and-excitation block sets. The novelty of the model lies in its ability to extract spatial features useful for classification and account for their relative importance. A case study of crop classification with multi-temporal Landsat-8 OLI images in Illinois, USA was carried out to evaluate the classification performance of the proposed model. The impact of patch sizes on crop classification was first assessed in a single-patch model to find useful patch sizes. The classification performance of the proposed model was then compared with those of conventional two CNN models including the single-patch model and a multi-patch model without considering feature-specific weights. From the results of comparison experiments, the proposed model could alleviate misclassification patterns by considering the spatial characteristics of different crops in the study area, achieving the best classification accuracy compared to the other models. Based on the case study results, the proposed model, which can account for the relative importance of spatial features, would be effectively applied to classification of objects with different spatial characteristics, as well as crops.

• Journal of Digital Contents Society
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• v.9 no.2
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• pp.203-211
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• 2008

Due to the advance of H/W and S/W techniques to play 3D sound, the virtual space contented by 3D graphics and sounds can provide users more improved realities and vividness. However for the small 3D contents developers and companies, it is hard to implement 3D sound techniques because the implementation requires expensive sound engines, 3D sound technical understanding and 3D sound programming skills. Therefore 3D-sound-playing-interface is necessary to easy and cost-effective 3D sound implementation. Using this interface, graphics experts can easily add 3D sound techniques to their applications. In this paper, the followings are designed and implemented as a preliminary stage in the way of developing the 3D sound playing interface. First, we develop 3D sound S/W modules converting mono to 3D sound in PC based systems. Second, we develop the interconnection modules to map 3D graphic objects and sound sources. The developed modules in this paper can allow the user to percept sound source position and surround effect at the moving positions in the virtual world. In the coming works, we are going to develop the more completed 3D sound playing interface consisted of the synchronization technique for sound and moving objects, and HRTF.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.317-331
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• 2004

Recently software agent has been studied as a new abstraction unit of software engineering. The agent with autonomous, adaptability and cooperation attribute is accepted as a new abstraction unit especially in distributed systems, open systems, and complex systems. However, the progress of agent research has been slow and the realization of agent programming language seems to be far distant. Because the properties of agent are diverse, the opinions of researchers can not converge to one. In this situation, software agent framework is accepted more realistic alternative solution. However the knowledge for its development doesn't have been shared among developers. So they often have to make same errors. We will help sharing of knowledge and experience by using pattern language which has been used in object technology for long times. This paper proposes a reactive agent framework pattern language and validates it based on ATAM［l］ The increase of such indirect experience can reduce the waste of resource by preventing the same try and error. So agent framework developers are able to concentrate on more essential issues. Finally quality of software agent framework will be increased.

• Journal of The Korean Association For Science Education
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• v.40 no.2
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• pp.191-201
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• 2020

This study investigated verbal and physical interactions which appeared in collaborative science concept learning using augmented reality. Twelve 10th grade students participated in this study. After being organized into three four-member small groups, they participated in classes using smart device-based augmented reality application developed for the understanding of the chemical bonding concept. Their class activities were audio- and video-taped. Semi-structured interviews were also conducted. The results revealed that within individual statement units of verbal interaction, the proportions of information question/explanation and direction question/explanation were found to be high. Within interaction units, the proportions of reformative and cumulative interaction were relatively high. The proportions of progress were also found to be high within both individual statement units and interaction units of verbal interaction. Students' physical interactions were mainly conducted without meaningful verbal interactions. When their physical interactions were accompanied by knowledge construction-related verbal interactions, the proportions of gazing virtual objects and worksheet-related interactions were high. In contrast, various exploratory activities related to the manipulation of markers mainly appeared when they conducted physical interactions only, or when their physical interactions were accompanied by management-related verbal interactions. On the bases of the results, effective methods for collaborative concept learning using augmented reality in science education are discussed.

• Korean Journal of Construction Engineering and Management
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• v.17 no.2
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• pp.99-109
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• 2016

The utilization of Building Information Modeling (BIM) has increased in order to enhance the integration of information for management and resources throughout the construction projects. Therefore, various BIM softwares have been used under open BIM environments in the building and plant construction industry. However, it has obstructive factors due to the lack of interoperability. In order to address this problem, this study conducted an interoperability analysis of BIM software focused on user-defined properties for enhanced function and efficiency. Result of the analysis shows that authoring tools have more interoperability problems than viewer tools and simulation tools have. In terms of interoperability, user-defined properties outperforms than those of system basic properties and logic data. Therefore, it was found that functional improvement and workload minimization in BIM can be attained by applying the GBS (an user-defined property for automatic manipulation of BIM proposed by Jung et al. 2013) that enables automatic link between geometric data and non-geometric data. In this respect, this study concludes that the application of user-defined property (e.g. GBS) can be an effective method for information integration throughout construction projects.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.200-207
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• 2020

Recently, deep neural networks such as CNN are showing excellent performance in various fields such as image classification, object recognition, visual quality enhancement, etc. However, as the model size and computational complexity of deep learning models for most applications increases, it is hard to apply neural networks to IoT and mobile environments. Therefore, neural network compression algorithms for reducing the model size while keeping the performance have been being studied. In this paper, we apply few compression methods to CNN models and evaluate their performances in the embedded environment. For evaluate the performance, the classification performance and inference time of the original CNN models and the compressed CNN models on the image inputted by the camera are evaluated in the embedded board equipped with QCS605, which is a customized AI chip. In this paper, a few CNN models of MobileNetV2, ResNet50, and VGG-16 are compressed by applying the methods of pruning and matrix decomposition. The experimental results show that the compressed models give not only the model size reduction of 1.3~11.2 times at a classification performance loss of less than 2% compared to the original model, but also the inference time reduction of 1.2~2.21 times, and the memory reduction of 1.2~3.8 times in the embedded board.

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.616-626
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial feature information and the face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by combining the multiple frequency domains for each depth image and depth fusion using fuzzy integral. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. It is used as the reference point to normalize for orientated facial pose and extract multiple areas by the depth threshold values. In the second step, we adopt as features for the authentication problem the wavelet coefficient extracted from some wavelet subband to use feature information. The third step of approach concerns the application of eigenface and Linear Discriminant Analysis (LDA) method to reduce the dimension and classify. In the last step, the aggregation of the individual classifiers using the fuzzy integral is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) show the highest recognition rate among the regions, and the depth fusion method achieves 98.6% recognition rate, incase of fuzzy integral.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.103-116
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• 2017

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner, which can derive new qualitative spatial knowledge representing both topological and directional relationships between two arbitrary spatial objects in efficient way using Aparch Spark SQL. Apache Spark SQL is well known as a distributed parallel programming environment which provides both efficient join operations and query processing functions over a variety of data in Hadoop cluster computer systems. In our spatial reasoner, the overall reasoning process is divided into 6 jobs such as knowledge encoding, inverse reasoning, equal reasoning, transitive reasoning, relation refining, knowledge decoding, and then the execution order over the reasoning jobs is determined in consideration of both logical causal relationships and computational efficiency. The knowledge encoding job reduces the size of knowledge base to reason over by transforming the input knowledge of XML/RDF form into one of more precise form. Repeat of the transitive reasoning job and the relation refining job usually consumes most of computational time and storage for the overall reasoning process. In order to improve the jobs, our reasoner finds out the minimal disjunctive relations for qualitative spatial reasoning, and then, based upon them, it not only reduces the composition table to be used for the transitive reasoning job, but also optimizes the relation refining job. Through experiments using a large-scale benchmarking spatial knowledge base, the proposed reasoner showed high performance and scalability.