• Computers and Concrete
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• v.31 no.5
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• pp.457-468
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• 2023

Buildings, bridges, and dams are examples of civil infrastructure that play an important role in public life. These structures are prone to structural variations over time as a result of external forces that might disrupt the operation of the structures, cause structural integrity issues, and raise safety concerns for the occupants. Therefore, monitoring the state of a structure, also known as structural health monitoring (SHM), is essential. Owing to the emergence of the fourth industrial revolution, next-generation sensors, such as wireless sensors, UAVs, and video cameras, have recently been utilized to improve the quality and efficiency of building forensics. This study presents a method that uses a target-based system to estimate the dynamic displacement and its corresponding dynamic properties of structures using UAV-based video. A laboratory experiment was performed to verify the tracking technique using a shaking table to excite an SDOF specimen and comparing the results between a laser distance sensor, accelerometer, and fixed camera. Then a field test was conducted to validate the proposed framework. One target marker is placed on the specimen, and another marker is attached to the ground, which serves as a stationary reference to account for the undesired UAV movement. The results from the UAV and stationary camera displayed a root mean square (RMS) error of 2.02% for the displacement, and after post-processing the displacement data using an OMA method, the identified natural frequency and damping ratio showed significant accuracy and similarities. The findings illustrate the capabilities and reliabilities of the methodology using UAV to evaluate the dynamic properties of structures.

• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1011-1020
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• 2007

It is difficult to assemble independently developed software components because of discrepancies between their interfaces. In order to resolve such problem, we propose a new component model, Active Binding Technology, in which each component has its own independently-defined interface for service request that is revealed in its metadata, instead of passively following the interface of a service-providing component. This model Includes the use of the glue component, an interface mediating place, whose template code is automatically generated by reading in the metadata of the components to be combined. We also propose a runtime framework that holds the pool of component instances, completes the assembly of components in the manner of dependency injection, and performs middleware services and real-time system monitoring through glue components. In order to test the practical value of Active Binding Technology, we have made a tool, which supports the development and assembly of Active Binding components.

• Journal of Environmental Health Sciences
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• v.38 no.5
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• pp.398-406
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• 2012

Objectives: In order to investigate the relation between environmental pollution and its effect on human health in large-scale industrial complex regions, monitoring studies have been conducted since 2003. The main purpose of this Gangneung study is to produce background data which can be compared with several large-scale industrial complex regions such as Ulsan, Sihwa, Banwol, Gwangyang, Yeosu, Pohang and Cheongju Daesan industrial complexes. Methods: In this Gangneung study, 1,007 local residents were recruited and the framework of this study was designed to evaluate and monitor health effects associated with low-level but long-term exposure to environmental pollutants. A survey was performed based on personal questionnaires, medical check-ups, and allergy skin prick tests for twelve common allergens. The authors reviewed the prevalence rate of skin allergens in Gangneung compared with in large-scale industrial areas. The results are summarized as follows. Results: The diagnosis prevalence and medical treatment prevalence of asthma were lower than in industrial complex regions, but allergic rhinitis was higher. The allergy skin test prevalence was 26.0% and the most common allergens were dust mites (D. pteronysinus 16.7% and D. farinae 16.3%). Conclusions: The positive findings of the skin prick test were similar with large-scale industrial complex regions in Korea. Further study is required to find the cause of the regional differences in skin prick prevalence.

• The KIPS Transactions:PartD
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• v.19D no.2
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• pp.203-210
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• 2012

Capture-and-replay technique is a common automatic method for GUI testing. Testing applications on Android platform cannot use directly capture-and-replay technique due to the testing framework which is already set up and technical supported by Google and lack of automatic linking GUI elements to actions handling widget events. Without capture-and-replay testing tools testers must design and implement testing scenarios according to the specification, and make linking every GUI elements to event handling parts all by hand. This paper proposes a more improved and optimized approach than common capture-and-replay technique for automatic testing Android GUI widgets. XML is applied to extract GUI elements from applications based on tracing the actions to handle widget events. After tracing click events using monitoring in capture phase test cases will be created by communicating status of activated widget in replay phase with API events.

• Journal of Advanced Navigation Technology
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• v.14 no.1
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• pp.127-133
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• 2010

The process for establishment of Operational Continuity Management Plan is organized repeatedly of Business Risk Assessment, Crisis Analysis, Business Impact Analysis, Establishing Business Recovery Strategies, Detailed Planning, Plan Execution, Test and Maintenance(Including Monitoring). Therefore, in this paper in response to global environmental change and the construction and operation of social security systems to maximize operational continuity management, crisis management and crisis management systems, building integrated systems for building technology in general and operational continuity management within an organization to understand developed to provide a framework for implementing operational continuity management in terms of crisis management has proposed to build an integrated system.

• Earthquakes and Structures
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• v.8 no.4
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• pp.843-857
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• 2015

System identification is regarded as the most basic technique for structural health monitoring to evaluate structural integrity. Although many system identification techniques extracting mode information (e.g., mode frequency and mode shape) have been proposed so far, it is also desired to identify physical parameters (e.g., stiffness and damping). As for high-rise buildings subjected to long-period ground motions, system identification for evaluating only the shear stiffness based on a shear model does not seem to be an appropriate solution to the system identification problem due to the influence of overall bending response. In this paper, a system identification algorithm using a shear-bending model developed in the previous paper is revised to identify both shear and bending stiffnesses. In this algorithm, an ARX (Auto-Regressive eXogenous) model corresponding to the transfer function for interstory accelerations is applied for identifying physical parameters. For the experimental verification of the proposed system identification framework, vibration tests for a 3-story steel mini-structure are conducted. The test structure is specifically designed to measure horizontal accelerations including both shear and bending responses. In order to obtain reliable results, system identification theories for two different inputs are investigated; (a) base input motion by a modal shaker, (b) unknown forced input on the top floor.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.277-286
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• 2021

This paper examines the readiness of Jordanian insurance companies to apply the International Financial Reporting Standards (IFRS 17), and the challenges of its application. The study developed a questionnaire based on prior related studies, and in the light of IFRS 17, the study used different statistical methods and techniques such as means, standard deviation, and t-test to achieve its goals. The results indicate that Jordanian insurance companies are not ready to apply IFRS 17, for several reasons. Most importantly, insurance companies have a low level of ability to define the scope of IFRS 17, study the impact of IFRS 17 application to financial reports, and develop new internal monitoring methods to apply IFRS 17. As for the challenges for applying IFRS 17, the biggest is the data challenge, followed by the challenges of first-time implementation, systems, and results and presentation. Finally, this paper advocates that it has become important for Jordanian insurance companies and supervisory bodies to enhance their readiness to apply IFRS 17 within a scheduled time framework and by taking several preparatory steps: performing simulations consisting of procedures to deal with IFRS 17 requirements and the impact on financial reports, and helping human resources with familiarization and application of IFRS 17.

• Smart Structures and Systems
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• v.29 no.1
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• pp.221-235
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• 2022

Manual inspection of steel box girders on long span bridges is time-consuming and labor-intensive. The quality of inspection relies on the subjective judgements of the inspectors. This study proposes an automated approach to detect and segment cracks in high-resolution images. An end-to-end cascaded framework is proposed to first detect the existence of cracks using a deep convolutional neural network (CNN) and then segment the crack using a modified U-Net encoder-decoder architecture. A Naïve Bayes data fusion scheme is proposed to reduce the false positives and false negatives effectively. To generate the binary crack mask, first, the original images are divided into 448 × 448 overlapping image patches where these image patches are classified as cracks versus non-cracks using a deep CNN. Next, a modified U-Net is trained from scratch using only the crack patches for segmentation. A customized loss function that consists of binary cross entropy loss and the Dice loss is introduced to enhance the segmentation performance. Additionally, a Naïve Bayes fusion strategy is employed to integrate the crack score maps from different overlapping crack patches and to decide whether a pixel is crack or not. Comprehensive experiments have demonstrated that the proposed approach achieves an 81.71% mean intersection over union (mIoU) score across 5 different training/test splits, which is 7.29% higher than the baseline reference implemented with the original U-Net.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.21-30
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• 2014

Hypertension is one of the major causes of death in the world as it is related with cardiovascular or cerebrovascular disease, so it is needed to provide continuos management for blood pressure. This study selected Health Level 7 Fast Health Interoperability Resources (HL7 FHIR) as a bio-signal data exchange service model that can provide constant blood pressure management in the rapidly growing mobile health care environment. The HL7 FHIR framework developed communicates with the IEEE 11073-10407 Personal Health Device (PHD) protocol through the bluetooth Health Device Profile (HDP) between the manager (smart phone) and the agent (hemomanometer) and acquires information about blood pressure. According to the test results, it performed its tasks successfully including hypertension patients' blood pressure monitoring, management on measured records, generation of document, or transmission of measured information. Because in the actual, clinical environment, it is possible to transmit measured information through the TCP/IP protocol, it will be needed to conduct constant research on it and vitalize it in the field of mobile health care afterwards.