• International journal of advanced smart convergence
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• v.8 no.3
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• pp.115-122
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• 2019

Recent and ongoing discoveries of mycoviruses with new properties demand the development of an appropriate research infrastructure to analyze their evolution and classification. In particular, the discovery of negative-sense single-stranded mycoviruses is worth noting in genome types in which double-stranded RNA virus and positive-sense single-stranded RNA virus were predominant. In addition, some genomic properties of mycoviruses are more interesting because they have been reported to have similarities with the pathogenic virus family that infects humans and animals. Genetic information on mycoviruses continues to accumulate in public repositories; however, these databases have some difficulty reflecting the latest taxonomic information and obtaining specialized data for mycoviruses. Therefore, in this study, we developed a bioinformatics-based pipeline to efficiently utilize this genetic information. We also designed a schema for data processing and database construction and an algorithm to keep taxonomic information of mycoviruses up to date. The pipeline and database (termed 'mycoVDB') presented in this study are expected to serve as useful foundations for improving the accuracy and efficiency of future research on mycoviruses.

• Journal of the Korea Institute of Building Construction
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• v.21 no.3
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• pp.231-239
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• 2021

Entering the era of the fourth industrial revolution, information and communication technologies such as CCTV, home network systems and equipment are being used in the construction industry. In particular, in order to increase the autonomy of information and communication technologies in apartments, the government has announced an administrative revision of information and communication-related laws, and companies are focusing on developing technologies such as smart home services. In addition, most domestic and foreign studies on the information and communication work were mainly conducted on technology and management. However there is a lack of research on physical defects affecting the quality of ICT. Therefore, this study collected the defect data registered in the project management system of three domestic construction companies and classified them according to the standards of the Enforcement Decree of the Apartment House Management Act. According to the analysis of the frequency of defects work type, 88.10% of defects occurred in home network equipment work. In addition, analysis of defects type in the four detailed works showed the highest number of operation error. The cause was analyzed and prevention measures and countermeasures were presented in parts of design, construction, and maintenance. The results of this study will improve the quality of apartment housing and be used as basic data for future research on practical defect minimization and prevention measures.

• Smart Structures and Systems
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• v.26 no.5
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• pp.667-680
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• 2020

This study aims at reporting a systematic procedure for evaluating the static and dynamic structural performance of steel bridges based on a short-period structural health monitoring measurement. Sungsu bridge located in Korea is considered as a case study presenting the most recent tests carried out to examine the bridge condition. Short-period measurements of Structural Health Monitoring (SHM) system were used during the bridge testing phase. A novel symmetry index is introduced using statistical analyses of deflection and strain measurements. Frequency Domain Decomposition (FDD) is implemented to the strain measurements to estimate the bridge mode shapes and damping ratios. Furthermore, Markov Chain Monte Carlo (MCMC) is also implemented to examine the reliability of bridge performance while ambient design trucks are in static or moving at different speeds. Strain, displacement and acceleration were measured at selected locations on the bridge. The results show that the symmetry index can be an efficient and useful measure in assessing the steel bridge performance. The results from the used method reveal that the performance of the Sungsu bridge is safe under operational conditions.

• Smart Structures and Systems
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• v.32 no.1
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.619-630
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• 2022

The concrete mix design and compressive strength evaluation are used as basic data for the durability of sustainable structures. However, the recent diversification of mixing factors has created difficulties in calculating the correct mixing factor or setting the reference value concrete mixing design. The purpose of this study is to design a predictive model of bidirectional analysis that calculates the mixing elements of ternary concrete using deep learning, one of the artificial intelligence techniques. For the DNN-based predictive model for calculating the concrete mixing factor, performance evaluation and comparison were performed using a total of 8 models with the number of layers and the number of hidden neurons as variables. The combination calculation result was output. As a result of the model's performance evaluation, an average error rate of about 1.423% for the concrete compressive strength factor was achieved. and an average MAPE error of 8.22% for the prediction of the ternary concrete mixing factor was satisfied. Through comparing the performance evaluation for each structure of the DNN model, the DNN5L-2048 model showed the highest performance for all compounding factors. Using the learned DNN model, the prediction of the ternary concrete formulation table with the required compressive strength of 30 and 50 MPa was carried out. The verification process through the expansion of the data set for learning and a comparison between the actual concrete mix table and the DNN model output concrete mix table is necessary.

• Journal of KIBIM
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• v.12 no.3
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• pp.1-17
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• 2022

As the technology of Smart City and Digital Twin is developing, techniques to integrate BIM data of infrastructure facilities into GIS are becoming more critical. Hence, this study aims to manage BIM data representing bridge and tunnel structures through the Industry Foundation Classes (IFC) standard and to develop a method to link these IFC-compliant files to the GIS standard CityGML without loss of information. We analyze the criteria for creating BIM data for bridges and tunnels by reviewing the BIM guidelines set by each client. We use these criteria to suggest methods for data management based on InfraBIM as a specific IFC class standard. Furthermore, we perform model analysis to determine the necessary design and construction field-appropriate model process and Level of Detail (LOD). From the model analysis, we conclude that the classified BIM models can be used as base data to generate BIM models of bridges and tunnels for facility management.

• Journal of Fashion Business
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• v.20 no.4
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• pp.153-171
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• 2016

With the advent of smart clothing for health care and sports, the sophisticated designs with curved seams are drawing attention. One of the problems in those clothing is to determine the design curves in 2D pattern, such that it corresponds to the lines on the intended 3D body. Moreover, the difficulty increases when the original pattern needs to be changed for various sizes and body types. We compare two methods of pattern enlargement in this paper: one is the offset/projection type, and the other is the split grading type. For the enlarged pattern with offset/projection type, the 3D surface offset was first adopted to transform the standard lower body to the target larger size; next, the design lines were projected to the new 3D surface, following which the 3D pattern was developed from the newly transformed 3D surface. In the second method, the enlarged pant patterns were developed by the split grading method. Here, a 3D pattern was developed from the initial body, and then enlarged to the target size by the conventional split grading method. Two feminine pants patterns were examined by 3D virtual fitting. We observed that the 3D offset/projection pants pattern was well fitted, having an evenly distributed surplus, as compared with the sample developed using the split grading method. The difference between the two patterns were apparent at the location where several curved lines merged.

• Smart Structures and Systems
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• v.6 no.4
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• pp.405-421
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• 2010

This paper proposes a method for early warning of hazard for pipelines. Many pipelines transport dangerous contents so that any damage incurred might lead to catastrophic consequences. However, most of these damages are usually a result of surrounding third-party activities, mainly the constructions. In order to prevent accidents and disasters, detection of potential hazards from third-party activities is indispensable. This paper focuses on recognizing the running of construction machines because they indicate the activity of the constructions. Acoustic information is applied for the recognition and a novel pipeline monitoring approach is proposed. Principal Component Analysis (PCA) is applied. The obtained Eigenvalues are regarded as the special signature and thus used for building feature vectors. One-class Support Vector Machine (SVM) is used for the classifier. The denoising ability of PCA can make it robust to noise interference, while the powerful classifying ability of SVM can provide good recognition results. Some related issues such as standardization are also studied and discussed. On-site experiments are conducted and results prove the effectiveness of the proposed early warning method. Thus the possible hazards can be prevented and the integrity of pipelines can be ensured.

• The Journal of the Convergence on Culture Technology
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• v.2 no.1
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• pp.87-91
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• 2016

This study is to develop a standard model in order to establish IOT production information system and to analyze the effect. Professional IT industry and SMEs that want to build a production information system can be applied to standard models to build the system more effectively. It provides ease of construction and reliability for IOT production information system with removing irrational elements, product quality and reducing production cost. In addition, it can be applied to standardize management of raw materials supply and demand aggregation processes of production and constructed a system more effectively using standard module.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.2
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• pp.153-167
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• 2024

Recently the ground settlement has been increasing in urban area according to development. And, this may attribute a groundwater level drawdown. This study presents an analysis of groundwater level drawdown for circular vertical shaft excavation of ｢◯◯◯◯ double track railway build transfer operate project｣. And, in-situ monitoring data and numerical analysis were compared. So, if we examine the groundwater level drawdown in design, ground conditions should be applied so that the site situation can be reflected. And, groundwater level should be considered a seasonal measurement in order to apply the appropriate groundwater level. It was confirmed a similar predicted value to groundwater level drawdown of in-situ monitoring data.