• Journal of Information Management
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• v.34 no.3
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• pp.87-109
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• 2003

Continual attempts to accumulate and apply information eventually gave birth to the concept of the "Semantic Web". Thus, the "Semantic Web" can be defined as a product of mankind's desire to standardize information. At the same time, the term provides "a method that standardizes mankind's concept of linguistical expression", and can be noted as an effort to combine such methods into a standard web environment that may materialize to form a catalogue. This study introduced RDF schema, ontology languages for the semantic web, and ontology-based systems. The purpose of the study was to construct a system based on the semantic web environment's ontology by utilizing the ontology schema derived from the facettype Art and Architecture Thesaurus(AAT). The aforementioned ontology schema is based on the Web Ontology Language(OWL), which is being widely considered the standard ontology language for the W3C-centered semantic web environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.236-242
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• 2019

Recently, scanning projects have been carried out in various construction and construction fields for maintenance purposes. The point cloud generated by the scan results is composed of a number of points representing the object to be scanned. The process of extracting the necessary information, including dimensions, from such scan data is called paradox. The reverse engineering process of modeling a point cloud as BIM involves considerable manual work. Owing to the time-consuming reverse engineering nature of the work, the costs increase exponentially when rework requests are made, such as design changes. Reverse engineering automation technology can help improve these problems. On the other hand, the reverse design product is variable depending on the use, and the kind and detail level of the product may be different. This paper proposes the G2BM (Geometry-to-BIM mapping) rule definition method that automatically maps a BIM object from a primitive geometry to a BIM object. G2BM proposes a process definition and a customization method for reverse engineering BIM objects that consider the use case variability.

• Journal of IKEEE
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• v.25 no.3
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• pp.430-436
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• 2021

The non-contact body temperature measurement system is one of the key factors, which is manage febrile diseases in mass facilities using optical and thermal imaging cameras. Conventional systems can only be used for simple body temperature measurement in the face area, because it is used only a deep learning-based face detection algorithm. So, there is a limit to detecting abnormal symptoms of the infants and young children, who have difficulty expressing their opinions. This paper proposes an improved facial expression recognition algorithm for detecting abnormal symptoms in infants and young children. The proposed method uses an object detection model to detect infants and young children in an image, then It acquires the coordinates of the eyes, nose, and mouth, which are key elements of facial expression recognition. Finally, facial expression recognition is performed by applying a selective sharpening filter based on the obtained coordinates. According to the experimental results, the proposed algorithm improved by 2.52%, 1.12%, and 2.29%, respectively, for the three expressions of neutral, happy, and sad in the UTK dataset.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.309-317
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• 2019

The purpose of this study is to review the influence of 3D printing technology on the formability and artistic value of ceramic works through a theoretical review of 3D printing technology and a case study of ceramic works that incorporate them creatively. Thus, the following conclusions were drawn from the analysis of the ceramic works of seven artists and two design teams. First, digital production that incorporates 3D printing technology into works can be applied to data applications and changes, unlike the existing manual methods, and the artist's unique creative artwork is possible. Second, a new paradigm has emerged that expresses the new material, method, advanced digital technology, and more stereoscopic and colorful sculptures out of the traditional ceramic concepts. In the future, I hope to find new methodology that meets the developing digital technology through continuous research and utilization of 3D printing and realizing new value of ceramic design.

• Plant Journal
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• v.17 no.3
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• pp.37-41
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• 2021

P&ID((Piping and Instrument Diagram) is a key drawing in the engineering industry because it contains information about the units and instrumentation of the plant. Until now, simple repetitive tasks like listing symbols in P&ID drawings have been done manually, consuming lots of time and manpower. Currently, a deep learning model based on CNN(Convolutional Neural Network) is studied for drawing object detection, but the detection time is about 30 minutes and the accuracy is about 90%, indicating performance that is not sufficient to be implemented in the real word. In this study, the detection of symbols in a drawing is performed using 1-stage object detection algorithms that process both region proposal and detection. Specifically, build the training data using the image labeling tool, and show the results of recognizing the symbol in the drawing which are trained in the deep learning model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.1
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• pp.67-80
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• 2012

Recently as the number of smartphone user is growing rapidly, android is also getting more interest in digital forensic. However, there is not enough research on digital data acquisition and analysis based on android platform's unique characteristics so far. Android system stores all the related recent systemwide logs from the system components to applications in volatile memory, and therefore, the logs can potentially serve as important evidences. In this paper, we propose a digital data acquisition and analysis system for android which extracts meaningful information based on the correlation of android logs and user activities from a device at runtime. We also present an efficient search scheme to facilitate realtime analysis on site. Finally, we demonstrate how the proposed system can be used to reconstruct the sequence of user activities in a more intuitive manner, and show that the proposed search scheme can reduce overall search and analysis time approximately 10 times shorter than the normal regular search method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.459-466
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• 2020

The sound collected through the multi-rotor unmanned aerial vehicle (UAV) includes the ego noise generated by the motor or propeller, or the wind noise generated during the flight, and thus the quality is greatly impaired. In a multi-rotor UAV environment, both the magnitude and phase of the target sound are greatly corrupted, so it is necessary to enhance the sound in consideration of both the magnitude and phase. However, it is difficult to improve the phase because it does not show the structural characteristics. in this study, we propose a CNN-based complex spectrogram enhancement method that removes noise based on complex spectrogram that can represent both magnitude and phase. Experimental results reveal that the proposed method improves enhancement performance by considering both the magnitude and phase of the complex spectrogram.

• Korean Journal of Construction Engineering and Management
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• v.23 no.2
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• pp.3-15
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• 2022

In this study, we develop a simulation model for Supply Chain Management (SCM) of modular construction based steel bridge. To this end, first, Factory Production/Site Construction system data for the steel bridge construction were collected, and supply chain, entities, resources, processes were defined based on the collected data. After that, a steel bridge supply chain simulation model was developed by creating data, flowchart, and animation modules using Arena software. Finally, verification and validation of the model were performed by using animation check, extreme condition check, average value test, Little' s law test, and actual case value test. As a result, the developed simulation model appropriately expressed the processes and characteristics of the steel bridge supply chain without any logical errors, and provided accurate performance evaluation values for the target system. In the future, we expect that the model will faithfully play a role as a performance evaluation platform in developing management techniques for optimally operating the steel bridge supply chain.

• Journal of IKEEE
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• v.26 no.3
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• pp.349-354
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• 2022

In thermal power plants, boiler models have been used widely in evaluating logic configurations, performing system tuning and applying control theory, etc. Furthermore, proper plant models are needed to design the accurate controllers. Sometimes, mathematical models can not exactly describe a power plant due to time varying, nonlinearity, uncertainties and complexity of the thermal power plants. In this case, a neural network can be a useful method to estimate such systems. In this paper, the models of boiler steam system in a thermal power plant are developed by using a generalized regression neural network(GRNN). The models of the superheater, reheater, attemperator and drum are designed by using GRNN and the models are trained and validate with the real data obtained in 540[MW] power plant. The validation results showed that proposed models agree with actual outputs of the drum boiler well.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.11-18
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• 2022

De novo drug design is the process of developing new drugs that can interact with biological targets such as protein receptors. Traditional process of de novo drug design consists of drug candidate discovery and drug development, but it requires a long time of more than 10 years to develop a new drug. Deep learning-based methods are being studied to shorten this period and efficiently find chemical compounds for new drug candidates. Many existing deep learning-based drug design models utilize recurrent neural networks to generate a chemical entity represented by SMILES strings, but due to the disadvantages of the recurrent networks, such as slow training speed and poor understanding of complex molecular formula rules, there is room for improvement. To overcome these shortcomings, we propose a deep learning model for SMILES string generation using variational autoencoders with self-attention mechanism. Our proposed model decreased the training time by 1/26 compared to the latest drug design model, as well as generated valid SMILES more effectively.