• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.433-440
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• 2009

As information technology of constructional area develops recently, web-based on-line system is rapidly increasing to provide information on diverse constructional materials so as to enhance productivity of constructional business and to reduce cost. Since the constructional materials information provided by these systems, i.e., quality, specification, etc are not standardized, however, the staffs on the constructional site suffer considerable difficulties in using materials information when acquiring information on specific materials, e.g., using diverse information systems or repeating similar jobs. Thus, this research typified information items of constructional materials on the basis of GDAS and designed multi system model to control integrated information on constructional materials. This system can efficiently control and utilize materials information by supporting automatic classification of constructional materials to which OmniClass Part-22 and UNSPSC are applied, conditional complex retrieval of materials information, real-time automatic embodiment of electronic catalog and retrieving/controlling RFID.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.29-36
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• 2021

In this paper, we classify ECG signal data for mobile devices using deep learning models. To classify abnormal heartbeats with high accuracy, three factors of the deep learning model are selected, and the classification accuracy is compared according to the changes in the conditions of the factors. We apply a CNN model that can self-extract features of ECG data and compare the performance of a total of 48 combinations by combining conditions of the depth of model, optimization method, and activation functions that compose the model. Deriving the combination of conditions with the highest accuracy, we obtained the highest classification accuracy of 97.88% when we applied 19 convolutional layers, an optimization method SGD, and an activation function Mish. In this experiment, we confirmed the suitability of feature extraction and abnormal beat detection of 1-channel ECG signals using CNN.

• New & Renewable Energy
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• v.7 no.1
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• pp.36-44
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• 2011

Technology is developed from the efficient interaction with other technology files while building up its own research field. This study analyzes the structure of solar cell research area and describes its paths of the technology development in terms of interdisciplinary diversity using the Co-Classification method during 1979-2009. As a results, 1,380 studies are determined as the interdisciplinary among the 2,605 studies. It shows that 52.98% of the solar cell researches have interdisciplinary relationships with two or more research fields. In addition, we show that the research area of solar cell technology is composed by Material Science, Multidisciplinary and Energy & Fuel, Physics, Applied, Chemistry, Physical from the Co-Classification matrix and network analysis. It means the complexity of the technological knowledge production increased with the concept of interdisciplinary. The results can be used for the planning of the efficient solar cell technology development.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.55 no.1
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• pp.39-49
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• 2019

Acoustics are increasingly regarded as a remote-sensing tool that provides the basis for classifying and mapping ocean resources including seabed classification. It has long been understood that details about the character of the seabed (roughness, sediment type, grain-size distribution, porosity, and material density) are embedded in the acoustical echoes from the seabed. This study developed a sophisticated yet easy-to-use technique to discriminate seabed characteristics using a split beam echosounder. Acoustic survey was conducted in Tongyeong waters, South Korea in June 2018, and the verification of acoustic seabed classification was made by the Van Veen grab sampler. The acoustic scattering signals extracted the seabed hardness and roughness components as well as various seabed features. The seabed features were selected using the principal component analysis, and the seabed classification was performed by the K-means clustering. As a result, three seabed types such as sand, mud, and shell were discriminated. This preliminary study presented feasible application of a sounder to classify the seabed substrates. It can be further developed for characterizing marine habitats on a variety of spatial scales and studying the ecological characteristic of fishes near the habitats.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.208-215
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• 2023

Deep learning techniques provide powerful solutions to several pattern-recognition problems, including Raman spectral classification. However, these networks require large amounts of labeled data to perform well. Labeled data, which are typically obtained in a laboratory, can potentially be alleviated by data augmentation. This study investigated various data augmentation techniques and applied multiple deep learning methods to Raman spectral classification. Raman spectra yield fingerprint-like information about chemical compositions, but are prone to noise when the particles of the material are small. Five augmentation models were investigated to build robust deep learning classifiers: weighted sums of spectral signals, imitated chemical backgrounds, extended multiplicative signal augmentation, and generated Gaussian and Poisson-distributed noise. We compared the performance of nine state-of-the-art convolutional neural networks with all the augmentation techniques. The LeNet5 models with background noise augmentation yielded the highest accuracy when tested on real-world Raman spectral classification at 88.33% accuracy. A class activation map of the model was generated to provide a qualitative observation of the results.

• The Journal of the Korean dental association
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• v.48 no.4
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• pp.263-274
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• 2010

I classified the bone graft material according to the component. Most bone graft material is composed of inorganic and organic constituent. Organic component such DBM is associated with osteoinduction. Inorganic components such as hydroxyapatite, $\beta$-TCP, calcium sulfate, bioactive glass, polymer are associated with osteoconduction. Autogenous bone graft is ideal material. We can select any biocompatible material for the restoration of small filling defect with intact bony wall. However, we should select first osteogenetic and osteoinductive material to regenerate the viable bone tissue.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.47-55
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• 2024

We spend a lot of time in indoor space, and the space has a huge impact on our lives. Interior design plays a significant role to make an indoor space attractive and functional. However, it should consider a lot of complex elements such as color, pattern, and material etc. With the increasing demand for interior design, there is a growing need for technologies that analyze these design elements accurately and efficiently. To address this need, this study suggests a deep learning-based design analysis system. The proposed system consists of a semantic segmentation model that classifies spatial components and an image classification model that classifies attributes such as color, pattern, and material from the segmented components. Semantic segmentation model was trained using a dataset of 30000 personal indoor interior images collected for research, and during inference, the model separate the input image pixel into 34 categories. And experiments were conducted with various backbones in order to obtain the optimal performance of the deep learning model for the collected interior dataset. Finally, the model achieved good performance of 89.05% and 0.5768 in terms of accuracy and mean intersection over union (mIoU). In classification part convolutional neural network (CNN) model which has recorded high performance in other image recognition tasks was used. To improve the performance of the classification model we suggests an approach that how to handle data that has data imbalance and vulnerable to light intensity. Using our methods, we achieve satisfactory results in classifying interior design component attributes. In this paper, we propose indoor space design analysis system that automatically analyzes and classifies the attributes of indoor images using a deep learning-based model. This analysis system, used as a core module in the A.I interior recommendation service, can help users pursuing self-interior design to complete their designs more easily and efficiently.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.2
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• pp.113-137
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• 2024

In this study, considering the National Memorial of the Korean Provisional Government as a "Larchiveum," the researchers attempted to develop a classification system that can comprehensively categorize various types of materials and propose a method of providing an online service. To this end, as a case study, the researchers examined the classification system structure and contents of the National Archives of Korea, National Assembly Archives, and Archives of Korean History of the National Institute of Korean History, which are the current material collection institutions of the Korean Provisional Government. Regarding online services, apart from the three institutions above, the Imperial War Museum and the Hoover Institution at Stanford University were also explored. Through the implications derived from the case analysis of domestic and foreign institutions, a basic hierarchical classification system by provenance for the materials held by the institution was established, and a multi-classification system was presented according to the classification criteria of "by type, by era, and by subject." In addition, methods of applying the developed classification system to online services were proposed.

• The Research Journal of the Costume Culture
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• v.32 no.1
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• pp.86-107
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• 2024

Although interest in eco-friendly fashion products is increasing among scholars and industry leaders, the concept of eco-friendly products remains unclear, preventing consistent assessment of which fashion products are eco-friendly. This study conducted a content analysis of eco-friendly product information from 87 domestic and 102 foreign brands to reveal key standards for categorizing eco-friendly fashion products. Product characteristic information was coded according to the four material-based standards (i.e., organic material, regenerative material, alternative material, and sustainably produced/upcycled material). Consistency between coders was confirmed by Cohen's kappa. In results, eco-friendly fashion products are categorized by four material-based standards and two certification standards (i.e., certified, not certified). Among the four material-based categories, the greatest number of domestic and foreign companies produced eco-friendly products that were classified as the regenerative material group. In addition, companies acquired eco-friendly certifications related to the use of organic, regenerative, and alternative materials. The greatest number of eco-friendly material brands used for eco-friendly fashion products belonged to the regenerative material group. Based on the study results, a typology of eco-friendly products was suggested. This typology can benefit practitioners and academics by highlighting a need for classification system for the eco-friendly fashion products, as well as by providing insight into the categorization of eco-friendly fashion products.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.107-111
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• 2010

The damage of tire by damage material classification method is researched as used ultrasonic wave signal to occur at a tire during vehicle driving. Auto-correlation function after having passed through an envelope detecting preprocess is used for detecting periodicity because of occurring periodic ultrasonic waves signal with tire revolution. One revolution cycle time of a damaged tire and period that calculated auto-correlation function appeared equally in experiment. The result that can classification whether or not there was a tire damage is established.