• Communications for Statistical Applications and Methods
• /
• v.25 no.5
• /
• pp.501-512
• /
• 2018

We propose Sequential Patient Recruitment Monitoring (SPRM), a new monitoring procedure for patient recruitment in a clinical trial. Based on the sequential probability ratio test using improved stopping boundaries by Woodroofe, the method allows for continuous monitoring of the rate of enrollment. It gives an early warning when the recruitment is unlikely to achieve the target enrollment. The packet data approach combined with the Central Limit Theorem makes the method robust to the distribution of the recruitment entry pattern. A straightforward application of the counting process framework can be used to estimate the probability to achieve the target enrollment under the assumption that the current trend continues. The required extension of the recruitment period can also be derived for a given confidence level. SPRM is a new, continuous patient recruitment monitoring tool that provides an opportunity for corrective action in a timely manner. It is suitable for the modern, centralized data management environment and requires minimal effort to maintain. We illustrate this method using real data from two well-known, multicenter, phase III clinical trials.

• Smart Structures and Systems
• /
• v.12 no.2
• /
• pp.209-233
• /
• 2013

With an annual growth rate of about 30%, wind energy systems, such as wind turbines, represent one of the fastest growing renewable energy technologies. Continuous structural health monitoring of wind turbines can help improving structural reliability and facilitating optimal decisions with respect to maintenance and operation at minimum associated life-cycle costs. This paper presents an integrated monitoring system that is designed to support structural assessment and life-cycle management of wind turbines. The monitoring system systematically integrates a wide variety of hardware and software modules, including sensors and computer systems for automated data acquisition, data analysis and data archival, a multiagent-based system for self-diagnosis of sensor malfunctions, a model updating and damage detection framework for structural assessment, and a management module for monitoring the structural condition and the operational efficiency of the wind turbine. The monitoring system has been installed on a 500 kW wind turbine located in Germany. Since its initial deployment in 2009, the system automatically collects and processes structural, environmental, and operational wind turbine data. The results demonstrate the potential of the proposed approach not only to ensure continuous safety of the structures, but also to enable cost-efficient maintenance and operation of wind turbines.

• Smart Structures and Systems
• /
• v.19 no.6
• /
• pp.587-599
• /
• 2017

This paper introduces an approach to the realization of an ICT-based bridge remote monitoring system that enables real-time monitoring and controlled adjustments for unexpected heavy loads and also for damaging earthquakes or typhoons. In this paper, an integrated bridge remote monitoring system called the "Intelligent Bridge", which consists of a stand-alone monitoring system (SMS) and a web-based Internet monitoring system(IMS), was developed for not only bridge maintenance but also as an application for a para-stressing bridge system. To verify the possibility of controlling the actual structural performance of an "Intelligent Bridge", a model 2-span continuous cable-stayed bridge with adjustable cables was constructed. The experimental results demonstrate that the implemented monitoring system supplies detailed and accurate information about bridge behaviour for further evaluation and diagnosis, and it also opens up prospects for future application of a web-based remote system to actually adjust in-service bridges under field conditions.

• Structural Engineering and Mechanics
• /
• v.81 no.5
• /
• pp.647-664
• /
• 2022

The powerful data mapping capability of computational deep learning methods has been recently explored in academic works to develop strategies for structural health monitoring through appropriate characterization of dynamic responses. In many cases, these studies concern laboratory prototypes and finite element models to validate the proposed methodologies. Therefore, the present work aims to investigate the capability of a deep learning algorithm called Sparse Autoencoder (SAE) specifically focused on detecting structural alterations in real-case studies. The idea is to characterize the dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the Shewhart T control chart, calculated with SAE extracted features. The anomaly detection approach is exemplified using data from the Z24 bridge, a classical benchmark, and data from the continuous monitoring of the San Vittore bell-tower, Italy. In both cases, the influence of temperature is also evaluated. The proposed approach achieved good performance, detecting structural changes even under temperature variations.

• Computers and Concrete
• /
• v.20 no.5
• /
• pp.555-562
• /
• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• Industrial Engineering and Management Systems
• /
• v.15 no.4
• /
• pp.432-445
• /
• 2016

A random profile is the result of a process, the output of which is a function instead of a scalar or vector quantity. In the nature of these objects, two main dimensions of "functionality" and "randomness" can be recognized. Valuable researches have been conducted to present control charts for monitoring such processes in which a regression approach has been applied by focusing on "randomness" of profiles. Performing other statistical techniques such as hypothesis testing for different parameters, comparing parameters of two populations, ANOVA, DOE, etc. has been postponed thus far, because the "functional" nature of profiles is ignored. In this paper, first, some needed theorems are proven with an applied approach, so that be understandable for an engineer which is unfamiliar with advanced mathematical analysis. Then, as an application of that, a statistical test is designed for mean of continuous random profiles. Finally, using experimental operating characteristic curves obtained in computer simulation, it is demonstrated that the presented tests are properly able to recognize deviations in the null hypothesis.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2004.05b
• /
• pp.209-214
• /
• 2004

Time Domain Reflectometry with multiplex system has been installed to configure the spatial and temporal characteristics of soil moisture in a mountainous hillslope. An intensive surveying was performed to build a refined digital elevation model and flow determination algorithms with inverse surveying have been applied to establish an efficient soil monitoring system. Steady state wetness index, quasi-dynamic wetness index and fully dynamic wetness index have been calculated. Continuous monitoring of soil moisture data were analyized with wetness indices. Limitations and hydrological interpretations of this approach have beer discussed.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.28 no.1
• /
• pp.70-82
• /
• 2000

This is a study to generate a theoretical base for the development of wildlife corridor as a solution to the problems of wildlife population size reduction and declining bio-diversity resulting from the fragmented habitats caused by road constructions. This study seeks to examine and define techniques in very aspect of wildlife corridor including planning, site selection, design and development, and maintenance through an actual application. The results of this study are as follows. 1) The wildlife corridor should be developed in an approach supplementing a landscape ecological approach and a restoration ecological approach bilaterally. To this end, systematic methodology and process are required. 2) It was restored an ecosystem as close as possible go to the forest ecosystem before road development. In addition, in order to allow it to function as an ecological corridor, topography restoration, stream development, and ecological plantation were implemented. 3) The result of monitoring activities that continued for one year since the development confirmed the migration and inhabitation of a number of animals including small mammals. It is judged that it functions as a corridor in fragmented habitats as initially expected. 4) Through continuous monitoring in the future, the effects of corridor development on ecological restoration need to be reviewed in a long-term perspective. There is also a need to develop and refine a comprehensive maintenance plan for wildlife corridors and their surroundings. Based on such study results, actual data on the development of wildlife corridor should be accumulated. In follow-up studies, after continuous monitoring for a long period of time, the effects of wildlife corridor development should be evaluated comprehensively and wildlife corridor applicable to Korea should be standardized by correcting disclosed problems.

• International Journal of Computer Science & Network Security
• /
• v.21 no.2
• /
• pp.221-228
• /
• 2021

Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.

• Korean Chemical Engineering Research
• /
• v.48 no.4
• /
• pp.475-482
• /
• 2010

In this work, soft sensor based on image anlaysis is proposed for quantitatively estimating the visual appearance of manufactured products and is applied to quality monitoring. The methodology consists of three steps; (1) textural feature extraction from product images using wavelet transform, (2) numerical estimation of the product appearance through projection of the textural features on subspace, and (3) use of latent variables of textural features (i.e., numerical estimates of product appearance). The focus of this approach is on the consistent and quantitative estimation of continuous variations in visual appearance rather than on classification into discrete classes. This approach is illustrated through the application to the estimation and monitoring of the appearance of engineered stone countertops.