• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.3
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• pp.541-548
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• 2007

The workflow, one of core features for modern business operation, models common elements and their relationships in business processes. The research is focused on how to adapt the workflow model to the healthcare examination which becomes popular these days. Especially, we intend to have fully automated process for healthcare examination by providing customers with PDA with RFTD chip. In order to realize this goal, this paper proposes the design of software architectures the class structures, and the definition of messages used for the system interactions. In other word, the paper shows the analysis of healthcare application in terms of service scenario and the design of workflow engine ant ubiquitous healthcare application under the Object-Oriented programming environment. the The result of the study may also enable the unmanned healthcare examination as well as ubiquitous based healcare examination sooner or later.

• Journal of Soil and Groundwater Environment
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• v.24 no.2
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• pp.8-22
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• 2019

Among numerous chemicals used globally, the number of emerging contaminants is increasing. Numerical modeling for contaminant fate and transport in the subsurface is critical to evaluate environmental and health risk. In general, such models require physicochemical properties of contaminants as input values, which can be found in numerous chemical databases (DB). However, there exist lack of information specific to recently emerging contaminants, which requires estimation of physicochemical properties using regression programs. The purpose of the study is to introduce the workflow for identifying physicochemical properties of potential contaminants utilizing numerous chemical DBs, which frequently lists up potential contaminants for estimating chemical behavior. In this review paper, details of several chemical DBs such as KISChem, TOXNET, etc. and regression programs including EPI $Suite^{TM}$, ChemAxon, etc. were summarized and also benefit of using such DBs were explained. Finally, a few examples were introduced to estimate predominant phase, removal ratio, partitioning, and eco-toxicities by searching or regressing physicochemical properties.

• Advances in Energy Research
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• v.6 no.1
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• pp.75-90
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• 2019

Anthropogenic greenhouse gas emissions are rising rapidly despite efforts to curb release of such gases. One long term potential solution to offset these destructive emissions is the capture and storage of carbon dioxide. Partially depleted hydrocarbon reservoirs are attractive targets for permanent carbon dioxide disposal due to proven storage capacity and seal integrity, existing infrastructure. Optimum well completion design in depleted reservoirs requires understanding of prominent geomechanics issues with regard to rock-fluid interaction effects. Geomechanics plays a crucial role in the selection, design and operation of a storage facility and can improve the engineering performance, maintain safety and minimize environmental impact. In this paper, an integrated geomechanics workflow to evaluate reservoir caprock integrity is presented. This method integrates a reservoir simulation that typically computes variation in the reservoir pressure and temperature with geomechanical simulation which calculates variation in stresses. Coupling between these simulation modules is performed iteratively which in each simulation cycle, time dependent reservoir pressure and temperature obtained from three dimensional compositional reservoir models in ECLIPSE were transferred into finite element reservoir geomechanical models in ABAQUS and new porosity and permeability are obtained using volumetric strains for the next analysis step. Finally, efficiency of this approach is demonstrated through a case study of oil production and subsequent carbon storage in an oil reservoir. The methodology and overall workflow presented in this paper are expected to assist engineers with geomechanical assessments for reservoir optimum production and gas injection design for both natural gas and carbon dioxide storage in depleted reservoirs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1217-1233
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• 2017

Effective monitoring and testing during each step are essential for document verification in research and development (R&D) projects. In software development, proper testing is required to verify it carefully and constantly because of the invisibility features of software. However, not enough studies on test processes for R&D projects have been done. Thus, in this paper, we introduce a Test Maturity Model integration (TMMi)-based software field R&D test process that offers five integrity levels and makes the process compatible for different types of projects. The Software & Systems Process Engineering Metamodel (SPEM) is used widely in the software process-modeling context, but it lacks built-in enactment capabilities, so there is no tool or process engine that enables one to execute the process models described in SPEM. Business Process Model and Notation (BPMN)-based workflow engines can be a solution for process execution, but process models described in SPEM need to be converted to BPMN models. Thus, we propose an approach to support enactment of SPEM-based process models by converting them into business processes. We show the effectiveness of our approach through converting software R&D test processes specified in SPEM in a case study.

• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.408-417
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• 2005

Increasing numbers of value added chemicals are being produced using microbial fermentation strategies. Computational modeling and simulation of microbial metabolism is rapidly becoming an enabling technology that is driving a new paradigm to accelerate the bioprocess development cycle. In particular, constraint-based modeling and the development of genome-scale models of industrial microbes are finding increasing utility across many phases of the bioprocess development workflow. Herein, we review and discuss the requirements and trends in the industrial application of this technology as we build toward integrated computational/experimental platforms for bioprocess engineering. Specifically we cover the following topics: (1) genome-scale models as genetically and biochemically consistent representations of metabolic networks; (2) the ability of these models to predict, assess, and interpret metabolic physiology and flux states of metabolism; (3) the model-guided integrative analysis of high throughput 'omics' data; (4) the reconciliation and analysis of on- and off-line fermentation data as well as flux tracing data; (5) model-aided strain design strategies and the integration of calculated biotransformation routes; and (6) control and optimization of the fermentation processes. Collectively, constraint-based modeling strategies are impacting the iterative characterization of metabolic flux states throughout the bioprocess development cycle, while also driving metabolic engineering strategies and fermentation optimization.

• The Journal of Advanced Prosthodontics
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• v.11 no.5
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• pp.271-279
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• 2019

PURPOSE. The newest technologies for digital implant impression (DII) taking are developing rapidly and showing acceptable clinical results. However, scientific literature is lacking data from clinical studies about the accuracy of DII. The aim of this study was to compare digital and conventional dental implant impressions (CII) in a clinical environment. MATERIALS AND METHODS. Twenty-four fixed zirconia restorations supported by 2 implants were fabricated using conventional open-tray impression technique with splinted transfers (CII group) and scan with Trios 3 IOS (3Shape) (DII group). After multiple verification procedures, master models were scanned using laboratory scanner D800 (3Shape). 3D models from conventional and digital workflow were imported to reverse engineering software and superimposed with high resolution 3D CAD models of scan bodies. Distance between center points, angulation, rotation, vertical shift, and surface mismatch of the scan bodies were measured and compared between conventional and digital impressions. RESULTS. Statistically significant differences were found for: a) inter-implant distance, b) rotation, c) vertical shift, and d) surface mismatch differences, comparing DII and CII groups for mesial and distal implant scan bodies ($P{\leq}.05$). CONCLUSION. Recorded linear differences between digital and conventional impressions were of limited clinical significance with two implant-supported restorations.

• Progress in Medical Physics
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• v.32 no.4
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• pp.172-178
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• 2021

Purpose: This study aimed to develop a deep learning architecture combining two task models to generate synthetic computed tomography (sCT) images from low-tesla magnetic resonance (MR) images to improve metallic marker visibility. Methods: Twenty-three patients with cervical cancer treated with intracavitary radiotherapy (ICR) were retrospectively enrolled, and images were acquired using both a computed tomography (CT) scanner and a low-tesla MR machine. The CT images were aligned to the corresponding MR images using a deformable registration, and the metallic dummy source markers were delineated using threshold-based segmentation followed by manual modification. The deformed CT (dCT), MR, and segmentation mask pairs were used for training and testing. The sCT generation model has a cascaded three-dimensional (3D) U-Net-based architecture that converts MR images to CT images and segments the metallic marker. The performance of the model was evaluated with intensity-based comparison metrics. Results: The proposed model with segmentation loss outperformed the 3D U-Net in terms of errors between the sCT and dCT. The structural similarity score difference was not significant. Conclusions: Our study shows the two-task-based deep learning models for generating the sCT images using low-tesla MR images for 3D ICR. This approach will be useful to the MR-only workflow in high-dose-rate brachytherapy.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.129-135
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• 2022

Real human digital models appear more and more frequently in VR/AR application scenarios, in which real-time markerless face capture animation of personalized virtual human faces is an important research topic. The traditional way to achieve personalized real human facial animation requires multiple mature animation staff, and in practice, the complex process and difficult technology may bring obstacles to inexperienced users. This paper proposes a new process to solve this kind of work, which has the advantages of low cost and less time than the traditional production method. For the personalized real human face model obtained by 3D reconstruction technology, first, use R3ds Wrap to topology the model, then use Avatary to make 52 Blend-Shape model files suitable for AR-Kit, and finally realize real-time markerless face capture 3D real human on the UE4 platform facial motion capture, this study makes rational use of the advantages of software and proposes a more efficient workflow for real-time markerless facial motion capture of personalized 3D real human models, The process ideas proposed in this paper can be helpful for other scholars who study this kind of work.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.151-158
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• 2003

Recently, the off-line processes in the construction industry are being changed to the electronic processes by several information technology (IT) tools including CALS. To improve the business processes to on-line processes, the business process reengineering (BPR) is a representative methodology for reviewing unnecessary and additional processes from the established business procedure. This study suggests a new process modeling method for construction BPR (CBPR) that consists of object-related process model (ORPM). In suggested modeling method, the connection with IT tools such as Workflow, enterprise resource planning (ERP), construction CALS, and business breakdown structure (BBS) was considered. The ORPM in the study is the main model for representing the detailed construction processes. It consists of nine relationship and six diagrams to represent the relationship between construction activities and the direction within the process model. Those models suggested from this study can be a useful tool for the CBPR because the researches and the practical applications related to the BPR of construction project are insufficient in our construction industry.