• Steel and Composite Structures
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• v.20 no.6
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• pp.1345-1368
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• 2016

This paper presents the probabilistic-based assessment of composite steel-concrete structures through an innovative framework. This framework combines model identification and reliability assessment procedures. The paper starts by describing current structural assessment algorithms and the most relevant uncertainty sources. The developed model identification algorithm is then presented. During this procedure, the model parameters are automatically adjusted, so that the numerical results best fit the experimental data. Modelling and measurement errors are respectively incorporated in this algorithm. The reliability assessment procedure aims to assess the structure performance, considering randomness in model parameters. Since monitoring and characterization tests are common measures to control and acquire information about those parameters, a Bayesian inference procedure is incorporated to update the reliability assessment. The framework is then tested with a set of composite steel-concrete beams, which behavior is complex. The experimental tests, as well as the developed numerical model and the obtained results from the proposed framework, are respectively present.

• Journal of Information Technology and Architecture
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• v.9 no.1
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• pp.1-10
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• 2012

To promote process effectiveness and efficiency, it is necessary that port logistics employ automated equipments for handling containers. There exists a system for automatically managing the container flow, called Control Module. However, it has limitation to assign the execution order to the machine and monitor the container flow in real time process. Business process management (BPM) provides a suitable and effective framework to address this problem including controlling and monitoring the flow of each container. Since the nature of container handling process is different with the common process in BPM that is conducted by human performer, it is necessary to adjust the BPM framework in the domain of port logistic management. This study presents a BPM framework corresponds with both human-based and machine-based activity to enhance the efficiency of port process flow including container flow. This framework is introduced as an integrated approach and mechanism of BPM application into the container handling system for the purpose of port logistics process automation.

• Structural Monitoring and Maintenance
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• v.9 no.4
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• pp.337-357
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• 2022

The objective of this study is to present a data-driven machine learning (ML) framework for predicting ultimate shear strength and failure modes of reinforced concrete ledge beams. Experimental tests were collected on these beams with different loading, geometric and material properties. The database was analyzed using different ML algorithms including decision trees, discriminant analysis, support vector machine, logistic regression, nearest neighbors, naïve bayes, ensemble and artificial neural networks to identify the governing and critical parameters of reinforced concrete ledge beams. The results showed that ML framework can effectively identify the failure mode of these beams either web shear failure, flexural failure or ledge failure. ML framework can also derive equations for predicting the ultimate shear strength for each failure mode. A comparison of the ultimate shear strength of ledge failure was conducted between the experimental results and the results from the proposed equations and the design equations used by international codes. These comparisons indicated that the proposed ML equations predict the ultimate shear strength of reinforced concrete ledge beams better than the design equations of AASHTO LRFD-2020 or PCI-2020.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.558-565
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• 2013

The process and efficiency of monitoring building and construction violations is a concern of the construction industry. The detection of violations requires appropriate and sufficiently accurate spatial information to manage and support a comprehensive inspection process and monitor compliance. A building inspection workflow must extract appropriate spatial and measurement in-formation from a variety of sources, identify potential violations across a range of compliance criteria and determine the quality of resulting inspection reports. This paper presents a framework for supporting building inspections using spatial information and methods to detect construction violations and compliance. Current inspection processes involve issues around the identification of building violations, access to building regulations and existing spatial information, integration of a range of spatial and non-spatial information, and the quality of decisions within the inspection workflows. A survey of building inspectors was conducted and used together with the issues identified to establish the requirements for a spatial inspection framework. The results demonstrate how such a framework can support improved decision-making and reduced fieldwork effort in detecting and measuring the accuracy of building violations involving building placements, street offsets and footprint areas.

• Smart Structures and Systems
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• v.26 no.4
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• pp.495-506
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• 2020

Monitoring of complex processes faces several challenges mainly due to the lack of relevant sensory information or insufficient elaborated decision-making strategies. These challenges motivate researchers to adopt complex data processing and analysis in order to improve the process representation. This paper presents the development and implementation of quality monitoring framework based on a model-driven approach using embedded artificial intelligence strategies. In this work, the strategies are applied to the supervision of a microfabrication process aiming at showing the great performance of the framework in a very complex system in the manufacturing sector. The procedure involves two methods for modelling a representative quality variable, such as surface roughness. Firstly, the hybrid incremental modelling strategy is applied. Secondly, a generalized fuzzy clustering c-means method is developed. Finally, a comparative study of the behavior of the two models for predicting a quality indicator, represented by surface roughness of manufactured components, is presented for specific manufacturing process. The manufactured part used in this study is a critical structural aerospace component. In addition, the validation and testing are performed at laboratory and industrial levels, demonstrating proper real-time operation for non-linear processes with relatively fast dynamics. The results of this study are very promising in terms of computational efficiency and transfer of knowledge to manufacturing industry.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1239-1242
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• 2003

The target of this paper is the manufacturing system of mould plant and the object of system development is the reduction of lead times and the improvement of product quality. From the existing MES framework. we found the module to apply in the mould plant, designed and developed the function of the module. Also. as the environment of development is based on internet, we can check and analysis correctly the status of mould plant from remote site.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1427-1429
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• 2003

In our contribution, the new idea of parking/moving discrimination is proposed by using Three Line Scanner Imagery. The framework of our study consists of three main stages: preprocessing, vehicle detection and parking/moving detection respectively. First two stages of framework have been done in our previous work. Parking/ Moving Discrimination algorithm have been developed by using generic vehicle characteristics and some principle of photogrammetry. By using detected vehicles from our previous work, stopping/moving vehicles are able to discriminate. Moving vehicle is detected by detecting generic moving vehicle in TLS, inter-band gap. Stopping vehicle is verified by 3 dimensional viewing of Stereoscopic measurement. Finally, the conceptual framework has been done and the result will been realized soon.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.180-186
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• 2021

In cloud computing service it is important to share the system resource among multiple instances according to user requirements. In particular, the issue of efficiently distributing I/O resources across multiple instances is paid attention due to the rise of emerging data-centric technologies such as big data and deep learning. However, it is difficult to evaluate the I/O resource distribution of a Linux container, which is one of the core technologies of cloud computing, since conventional I/O benchmarks does not support features related to container management. In this paper, we propose a new I/O performance benchmarking framework that can easily evaluate the resource distribution of Linux containers using existing I/O benchmarks by supporting container-related features and integrated user interface. According to the performance evaluation result with trace-replay benchmark, the proposed benchmark framework has induced negligible performance overhead while providing convenience in evaluating the I/O performance of multiple Linux containers.

• IE interfaces
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• v.23 no.3
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• pp.246-256
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• 2010

As wireless and mobile technologies have advanced significantly, lots of large sized healthcare organizations have implemented so called mobile hospital (m-Hospital) which provides a location independent and point of care (POC) clinical environment. Implementation of m-Hospital enhances quality of care because health professionals such as physicians and nurses can use hospital information systems at the very place where patients are located without any delay. This paper presents a real-time patient monitoring system based on wireless network technologies. A general framework for the patient monitoring process is introduced and the architecture and components of the proposed monitoring system is described. The system collects and analyzes biometric signals of in-patients who suffer from cancer. Specifically, it continuously monitors oxygen saturation of patients in bed and alarms health professionals instantly when an abnormal status of the patient is detected. The monitoring system has been used and clinically verified in a university hospital.

• Structural Monitoring and Maintenance
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• v.4 no.1
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• pp.85-105
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• 2017

Monitoring is the most important part of the construction and operation of the embankment dams. Applied instruments in these dams should be determined based on dam requirements and specifications. Instruments selection considered as one of the most important steps of monitoring plan. Competent instruments selection for dams is very important, as inappropriate selection causes irreparable loss in critical condition. Lack of a systematic method for determining instruments has been considered as a problem for creating an efficient selection. Nowadays, decision making methods have been used widely in different sciences for optimal determination and selection. In this study, the Multi-Attribute Decision Making is applied by considering 9 criteria and categorisation of 8 groups of geotechnical instruments. Therefore, the Analytic Hierarchy Process and Multi-Criteria Optimisation and Compromise Solution methods are employed in order to determine the attributes' importance weights and to prioritise of instruments for embankment dams, respectively. This framework was applied for a rock fill with clay core dam. The results indicated that group decision making optimizes the selection and prioritisation of monitoring instruments for embankment dams, and selected instruments are reliable based on the dam specifications.