• Smart Structures and Systems
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• v.21 no.4
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• pp.435-448
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• 2018

This paper reports the development of a theoretically rigorous method for permanent way engineers to assess the condition of railway ballast under a concrete sleeper with the potential to be extended to a smart system for long-term health monitoring of railway ballast. Owing to the uncertainties induced by the problems of modeling error and measurement noise, the Bayesian approach was followed in the development. After the selection of the most plausible model class for describing the damage status of the rail-sleeper-ballast system, Bayesian model updating is adopted to calculate the posterior PDF of the ballast stiffness at various regions under the sleeper. An obvious drop in ballast stiffness at a region under the sleeper is an evidence of ballast damage. In model updating, the model that can minimize the discrepancy between the measured and model-predicted modal parameters can be considered as the most probable model for calculating the posterior PDF under the Bayesian framework. To address the problems of non-uniqueness and local minima in the model updating process, a two-stage hybrid optimization method was developed. The modified evolutionary algorithm was developed in the first stage to identify the important regions in the parameter space and resulting in a set of initial trials for deterministic optimization to locate all most probable models in the second stage. The proposed methodology was numerically and experimentally verified. Using the identified model, a series of comprehensive numerical case studies was carried out to investigate the effects of data quantity and quality on the results of ballast damage detection. Difficulties to be overcome before the proposed method can be extended to a long-term ballast monitoring system are discussed in the conclusion.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.989-1009
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• 2018

Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed 'big data'. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.

• Smart Structures and Systems
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• v.4 no.2
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• pp.195-208
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• 2008

This paper summarizes the experimental vibration-based structural health monitoring study on a historical monument in Jordan. In this work, and within the framework of the European Commission funded project "wide-Range Non-Intrusive Devices Toward Conservation of Historical Monuments in the Mediterranean Area", a seven and a half century old minaret located in Ajloun (73 km north of the capital Amman) is studied. Because of their cultural value, touristic importance and the desire to preserve them for the future, only non-destructive tests were allowed for the experimental investigation of such heritage structures. Therefore, after dimensional measurements and determination of the current state of damage in the selected monument, ambient vibration tests are conducted to measure the accelerations at strategic locations of the system. Output-only modal identification technique is applied to extract the modal parameters such as natural frequencies and mode shapes. A Non-linear version of SAP 2000 computer program is used to develop a three-dimensional finite element model of the minaret. The developed numerical model is then updated according to the modal parameters obtained experimentally by the ambient-vibration test-results and the measured characteristics of old stone and deteriorated mortar. Moreover, a parametric identification method using the N4Sid state space model is employed to model the dynamic behavior of the minaret and to build up a robust, immune and noise tolerant model.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.446-451
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• 2003

This paper presents some examples of active control of flow-induced vibration using piezoelectric actuators. The flutter phenomenon, which is the dynamic instability of structure due to mutual interaction among inertia, stiffness, and aerodynamic forces, may cause catastrophic structural failure, and therefore the active flutter suppression is one of the main objectives of the aeroelastic control. Active flutter control has been numerically and experimentally studied for swept-back lifting surfaces using piezoelectric actuation. A finite element method, a panel aerodynamic method, and the minimum state space realization are involved in the development of the governing equation, which is efficiently used for the analysis of the system and design of control laws with modern control framework. The active control suppressed flow-induced vibrations and extended the flutter speed around by 10%. Another representative flow-induced vibration phenomenon is the oscillation of blunt bodies due to the vortex shedding. In general, it is quite difficult to set up the numerical model because of the strong non-linearity of the vortex shedding structure. Therefore, we applied adaptive positive position feedback controller, which requires no pre-determined model of the plant, and successfully suppressed the flow-induced vibration.

• Smart Structures and Systems
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• v.22 no.4
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• pp.399-411
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• 2018

The difficulty in modeling complex nonlinear structures lies in the presence of significant sources of uncertainties mainly attributed to sudden changes in the structure's behavior caused by regular aging factors or extreme events. Quantifying these uncertainties and accurately representing them within the complex mathematical framework of Structural Health Monitoring (SHM) are significantly essential for system identification and damage detection purposes. This study highlights the importance of uncertainty quantification in SHM frameworks, and presents a comparative analysis between intrusive and non-intrusive techniques in quantifying uncertainties for SHM purposes through two different variations of the Kalman Filter (KF) method, the Ensemble Kalman filter (EnKF) and the Polynomial Chaos Kalman Filter (PCKF). The comparative analysis is based on a numerical example that consists of a four degrees-of-freedom (DOF) system, comprising Bouc-Wen hysteretic behavior and subjected to El-Centro earthquake excitation. The comparison is based on the ability of each technique to quantify the different sources of uncertainty for SHM purposes and to accurately approximate the system state and parameters when compared to the true state with the least computational burden. While the results show that both filters are able to locate the damage in space and time and to accurately estimate the system responses and unknown parameters, the computational cost of PCKF is shown to be less than that of EnKF for a similar level of numerical accuracy.

• Smart Structures and Systems
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• v.23 no.4
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• pp.359-371
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• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.

• Journal of KIISE:Software and Applications
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• v.34 no.9
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• pp.817-827
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• 2007

In the age of ubiquitous environment, huge number of devices and computing services are provided to users. Personalized service, which is modeled according to the character of each and every individual is of particular need. In order to provide various dynamic services according to user's movement, service unit and operating mode should be able to operate automatically with minimum user intervention. In this paper, we discuss the steps of offering approximate service based on user's request in ubiquitous environment. First, we present our simulator designed for modeling the physical resource and computing object in smart space - the infrastructure in ubiquitous. Second, intelligent agents, which we developed based on a FIPA specification compliant multi-agent framework will be discussed. These intelligent agents are developed for achieving the service goal through cooperation between distributed agents. Third, we propose an automated service discovery and composition method in heterogeneous environment using semantic message communication between agents, according to the movement by the user interacting with the service available in the smart space. Fourth, we provide personalized service through agent monitoring anytime, anywhere from user's profile information stored on handhold device. Therefore, our research provides high quality service more than general automated service operation.

• Asia pacific journal of information systems
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• v.18 no.1
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• pp.145-162
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• 2008

One of the big concerns in e-society is privacy issue. In special, in developing robust ubiquitous smart space and corresponding services, user profile and preference are collected by the service providers. Privacy issue would be more critical in context-aware services simply because most of the context data themselves are private information: user's current location, current schedule, friends nearby and even her/his health data. To realize the potential of ubiquitous smart space, the systems embedded in the space should corporate personal privacy preferences. When the users invoke a set of services, they are asked to allow the service providers or smart space to make use of personal information which is related to privacy concerns. For this reason, the users unhappily provide the personal information or even deny to get served. On the other side, service provider needs personal information as rich as possible with minimal personal information to discern royal and trustworthy customers and those who are not. It would be desirable to enlarge the allowable personal information complying with the service provider's request, whereas minimizing service provider's requiring personal information which is not allowed to be submitted and user's submitting information which is of no value to the service provider. In special, if any personal information required by the service provider is not allowed, service will not be provided to the user. P3P (Platform for Privacy Preferences) has been regarded as one of the promising alternatives to preserve the personal information in the course of electronic transactions. However, P3P mainly focuses on preserving the buyers' personal information. From time to time, the service provider's business data should be protected from the unintended usage from the buyers. Moreover, even though the user's privacy preference could depend on the context happened to the user, legacy P3P does not handle the contextual change of privacy preferences. Hence, the purpose of this paper is to propose a mutual P3P-based negotiation mechanism. To do so, service provider's privacy concern is considered as well as the users'. User's privacy policy on the service provider's information also should be informed to the service providers before the service begins. Second, privacy policy is contextually designed according to the user's current context because the nomadic user's privacy concern structure may be altered contextually. Hence, the methodology includes mutual privacy policy and personalization. Overall framework of the mechanism and new code of ethics is described in section 2. Pervasive platform for mutual P3P considers user type and context field, which involves current activity, location, social context, objects nearby and physical environments. Our mutual P3P includes the privacy preference not only for the buyers but also the sellers, that is, service providers. Negotiation methodology for mutual P3P is proposed in section 3. Based on the fact that privacy concern occurs when there are needs for information access and at the same time those for information hiding. Our mechanism was implemented based on an actual shopping mall to increase the feasibility of the idea proposed in this paper. A shopping service is assumed as a context-aware service, and data groups for the service are enumerated. The privacy policy for each data group is represented as APPEL format. To examine the performance of the example service, in section 4, simulation approach is adopted in this paper. For the simulation, five data elements are considered: $\cdot$ UserID $\cdot$ User preference $\cdot$ Phone number $\cdot$ Home address $\cdot$ Product information $\cdot$ Service profile. For the negotiation, reputation is selected as a strategic value. Then the following cases are compared: $\cdot$ Legacy P3P is considered $\cdot$ Mutual P3P is considered without strategic value $\cdot$ Mutual P3P is considered with strategic value. The simulation results show that mutual P3P outperforms legacy P3P. Moreover, we could conclude that when mutual P3P is considered with strategic value, performance was better than that of mutual P3P is considered without strategic value in terms of service safety.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.73-81
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• 2012

This study ultimately aims to suggest directions for development of U-City Competitiveness Index through thorough analyses of U-City competitiveness concepts and previous research in U-City-related index. In an attempt to reach the aforementioned goals, the concept of U-City competitiveness is presented based on existing publications on informatization age, future city, and U-City. Next, insights on the development of U-City Competitiveness Index are provided via analysis of research on indices regarding U-City and future city. Finally, the process of developing UCity Competitiveness Index and cautionary comments for each developmental step has been provided. Based on the research results, U-City competitiveness refers to the ability to respond to demands of residents in a timely manner as well as to manage U-City, ICT, U-City infrastructure, and U-City services that maximizes the efficiency of urban space management. In order for proper assessment of these criteria, development of assessment tools, selection of indices, standardization, allocation of weight per index, and construction of assessment framework are required.

• Smart Structures and Systems
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• v.29 no.1
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.