• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Journal of the Korean Society of Systems Engineering
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• v.11 no.1
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• pp.25-31
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• 2015

Under the relevant regulations, it is required to develop a system architecture for the research and development of the information and communication infra system ; however, the national defense architecture development guides and MND-AF, which provide the instructions on the development and utilization of a system architecture, are still limited to the ITA level and merely providing the guidelines for developing the information system centric architecture. Thus, it is evident that we need a suitable architecture development methodology that corresponds to the growing needs for the communication infra system architecture, as well as the general weapon system architecture. Improving a meta-model is the core of improving a architecture framework. It determines a contents of a architecture and it influences a efficiency and a effectiveness of a architecture. The meta-model of the architecture framework must reflect concerns of various stakeholders and provide a traceability among them. Also, it should be easy to develop and use the architecture by securing the feasibility of the logical relationships and eliminating the duplication of the data inside the architecture. It is implemented through the development of the data-centric architecture and achieved through the "Fit-for-purpose" concept.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.1-6
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• 2008

Data grid shares distributed large data via wide-band network. Such grid environment consumes much time for large data transmission. Because it is implemented on internet as physical network. Many replication strategies were proposed for solving this problem, but they are not optimal in real Data grid environments. Because they were proposed that based on logical topology without consideration of real internet architecture. Grid data access time is largely influenced by internet architecture as physical network of Data grid. In this paper, we propose a new data replication strategy RSIA(Replication Strategy based on Internet Architecture) based on internet architecture. The RSIA places replicas considering structural hierarchy in each element of internet, and avoid the performance bottlenecks to reduce system performance degradation when a data transfer. Through simulation, we show that the proposed RSIA data replication strategy improves the performance of Data Grid environment compared with previous strategies.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1294-1294
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• 2024

Flood risk maps are used in urban flooding to understand the spatial extent and depth of inundation damage. To construct these maps, hydrodynamic modeling capable of simulating flood waves is necessary. Flood waves are typically fast, and inundation patterns can significantly vary depending on the terrain, making it essential to accurately represent the terrain of the flood source in flood wave analysis. Recently, methods using UAVs for terrain data construction through Structure-from-Motion or LiDAR have been utilized. These methods are crucial for UAV operations, and thus, still require a lot of time and manpower, and are limited when UAV operations are not possible. Therefore, for efficient nationwide monitoring, this study developed a model that can automatically generate terrain data by estimating depth information from a single image using c-GAN (Conditional Generative Adversarial Networks) and BBDM (Brownian Bridge Diffusion Model). The training, utilization, and validation datasets employed images from the ISPRS (2018) and directly aerial photographed image sets from five locations in the territory of the Republic of Korea. Compared to the ground truth of the test data set, it is considered sufficiently usable as terrain data for flood wave analysis, capable of generating highly accurate and precise terrain data with high reproducibility.

• Journal of Information Technology and Architecture
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• v.9 no.3
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• pp.233-241
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• 2012

Architecture framework (AF) is a guideline to define components needed to develop and operate enterprise architecture (EA), and to define relationships among the components. There are many architecture frameworks to operate EA of governments and businesses such as Zachman framework, DoDAF, TOGAF, FEAF, and TEAF. DoDAF is the most representative AF to support the development of the EA in the military domain. DoDAF is composed of eight viewpoints and 40 views that are affiliated with the viewpoints. To develop an AF for a specific goal, system architects decide a set of views. Furthermore, they determine data that are needed for a view modeling. However, views and data in DoDAF are structurally inter-related explicitly and/or implicitly. So, developing an AF for a specific goal is going to be a project to be carried out over a long haul. To reduce the burden of its development, in this paper, we develop ONT-SAF (Ontology for DoDAF) that can infer inter-relationships like referential and transitive relationships and the sequences among the views. Furthermore, to promote reusability and consistency of the views and the data within an AF, we adopt the view-data separation strategy. ONT-DAT contains classes like 'viewpoint', 'view', 'data', 'expression method', and 'reference model', and 11 properties including 'hasView.' To prove the effectiveness of ONT-SAF, we perform a case study.

• Asia pacific journal of information systems
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• v.8 no.3
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• pp.1-20
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• 1998

We propose an Executive Information Systems (EIS) Architecture for chief officers of the Air Force using data warehousing method. This architecture has two main proposes. The one is to provide the information for chief officers to control and command their organizations effectively by analyzing operation data at normal times. The other is to provide chief officers with the information about current situation so that they may make right and rapid decisions at emergency. The architecture introduced here is one that analyzes operational trends as well as current trends in a hierarchical organization environment. System analysis and design techniques for the Air Force EIS such as data flow diagram, system structure, entity-relational diagram, and third normal form of relational database were presented. After prototype screens are demonstrated, benefits of new EIS architecture were also discussed as a conclusion.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.9
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• pp.207-212
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• 2020

The amount of data has increased significantly due to advances in technology, and various big data processing platforms are emerging, to handle it. Among them, the most widely used platform is Hadoop developed by the Apache Software Foundation, and Hadoop is also used in the IoT field. However, the existing Hadoop-based IoT sensor data collection and analysis environment has a problem of overloading the name node due to HDFS' Small File, which is Hadoop's core project, and it is impossible to update or delete the imported data. This paper uses Apache Kudu and Impala to design Lambda Architecture. The proposed Architecture classifies IoT sensor data into Cold-Data and Hot-Data, stores it in storage according to each personality, and uses Batch-View created through Batch and Real-time View generated through Apache Kudu and Impala to solve problems in the existing Hadoop-based IoT sensor data collection analysis environment and shorten the time users access to the analyzed data.

• Smart Structures and Systems
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• v.24 no.4
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• pp.507-524
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• 2019

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.

• Asia pacific journal of information systems
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• v.7 no.1
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• pp.7-24
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• 1997

Executive information system (EIS) is a computer-based information system that supports decision makings ana management activities for senior executives. Data warehouse is a database that receives data copies from legacy systems and external data sources. Data warehouse is typically optimized for decision supports and can be an attractive solution for EIS implementation. This paper proposes an architecture for implementing EIS by the use of data warehouse. The architecture consists of ten implementation layers. Interrelationships among these layers are investigated for an effective EIS implementation. An EIS prototype for a real-fife enterprise is implemented to demonstrate the usefulness of the proposed architecture.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.254-257
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• 1996

Executive information system (EIS) is a computer-based information system that supports decision makings and management activities for senior executives. Data warehouse is a database that receives data copies from legacy systems and external data sources. Data warehouse is typically optimized for decision supports and can be an attractive solution for EIS implementation. This paper proposes an architecture for implementing EIS by the use of data warehouse. The architecture consists of ten implementation layers. Interrelationships among these layers are investigated for an effective EIS implementation. An EIS prototype for a real-life enterprise is implemented to demonstrate the usefulness of the proposed architecture.