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

• International conference on construction engineering and project management
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• 2024.07a
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• pp.909-916
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• 2024

The emergence of Building Information Modelling (BIM), reality data, Virtual Reality (VR), and Augmented Reality (AR) has significantly enhanced the collaboration between stakeholders in construction management. The utilization of VR/AR devices holds considerable potential for monitoring safety in complex and constrained working environments on the construction site. On the other hand, near-miss incidents remain an important early sign of struck-by accidents. However, research on early warning and prevention methods for this risk is still limited. This paper, therefore, presents a framework for on-site and off-site collaborative safety monitoring framework using augmented and virtual reality for near-miss incidents. In the proposed framework, three phases to develop a VR/AR-based safety monitoring system include (1) construction safety simulation environment, (2) localization-based interaction system, and (3) safety monitoring system. The system can undertake the processing of data and enables communication among disparate VR/AR devices. VR clients are observational tools and offer guidance, while the AR client stays onsite for construction tasks. All clients connect to a processing computer, which also works as a host. The system embedded in the AR device can trigger an alarm or receive signals from the VR client when a near-miss issue happens. Additionally, all device clients possess the capability to share data acquired from onsite monitoring cameras, thereby fostering effective discussions and decision-making. The efficacy of this cross-platform system has been validated through the implementation of an outdoor coordination case study.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.6
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• pp.684-689
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• 2017

We examined the possibility of developing an early monitoring system using the shell valve movement activity of Pacific oyster (Crassostrea gigas) for early detection of low salinity water in coastal areas. At salinity levels of 30 psu and 20 psu, SVMs were detected $7.32{\pm}3.21times/hr$ and $7.11{\pm}3.90times/hr$, respectively, The patterns and times of SVMs were not significantly different between the two experiment phases. However, at 10 psu and 5 psu, shell valves were observed to be permanently closed in all experiments. Under combined condition (Group 1: temperature $15^{\circ}C$ ${\times}$ salinity 15 psu), SVMs were observed from 20 psu to 30 psu over a 2 - 3 hr period, and then remained closed. In Group 2 (temperature $30^{\circ}C$ ${\times}$ salinity 15 psu), SVMs were observed, which indicated that the physiological condition of the oysters reached a critical point. Thus, it may be possible to utilize SVMs as an early warning signal for low salinity water.

• Information Systems Review
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• v.12 no.2
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• pp.89-104
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• 2010

Recently, ubiquitous sensor network (USN) has been applied to many areas including environment monitoring. A few studies applied the USN to disaster prevention and emergency management, in particular, aiming to conserve cultural heritage. USN is an useful technology to do online real-time monitoring for the purpose of early detection of the fire which is a critical cause of damage and destruction of cultural heritages. It is necessary to online monitor the cultural heritages that human has a difficulty to access or their external appearance and beauty are important, by using the USN. However, there exists false warning from USN-based monitoring systems without human intervention. In this paper, we presented an alternative to resolve the problem by applying ontology. Our intelligent fire early detection systems for conserving cultural heritages are based on ontology and inference rules, and tested under laboratory environments.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1998.04a
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• pp.321-327
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• 1998

This paper presented a case study on the application of on-line wear monitoring technique to a high duty air-turbo-compressor system. Main objects monitored were a gear unit and metal bearings, both shown frequent troubles due to the severe operation conditions at heavy dynamic load. The air-turbo-compressor system needs secure condition monitoring because it is one of the main utilities in steel making industry. Temperature and vibration characteristics have been mainly on-line monitored in this system for a predictive maintenance; however, it has been shown that they are not fairly good enough to give an early warning prior to the machine failure. In this work, an on-line Opto Magnetic Detector(OMD) was implemented for an on-line wear monitoring, which quantitatively measured the contamination level of both ferrous and non-ferrous wear particles by detecting the change in optical density of used oil. Results showed that the application of on-line OMD system was satisfactory in diagnosis of the machine system.

• Smart Structures and Systems
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• v.21 no.5
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• pp.537-548
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• 2018

A tunnel deformation monitoring system is developed with the use of fiber Bragg grating (FBG) sensing technique, aiming at providing continuous monitoring of railway tunnel deformation in the long term, and early warning for the rail service maintainers and authorities to avoid catastrophic consequences when significant deformation occurs. Specifically, a set of FBG bending gauges with the ability of angle measurement and temperature compensation is designed and manufactured for the purpose of online monitoring of tunnel deformation. An overall profile of lateral tunnel displacement along the longitudinal direction can be obtained by implementing an array of the FBG bending gauges interconnected by rigid rods, in conjunction with a proper algorithm. The devised system is verified in laboratory experiments with a test setup enabling to imitate various patterns of tunnel deformation before the implementation of this system in an in-service high-speed railway (HSR) tunnel.

• The Journal of Society for e-Business Studies
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• v.12 no.2
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• pp.219-231
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• 2007

Based on a complex event processing technique, an event analysis method for Business Activity Monitoring(BAM) is developed to provide an early warning for on-going events so that process managers effectively detect and monitor potential risks prior to the completion of the events. In this study, process-based event monitoring procedures to extract events with significant risks are presented; Complex event patterns are defined from historical event log data and risks of events are evaluated based on the patterns. A process-based event monitoring architecture for BAM is also presented. The proposed method has been applied to a service process of a home shopping company.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.123-126
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• 2005

Wireless Sensor Networks will revolutionize applications such as environmental monitoring, home automation, and logistics. We developed forest fire surveillance system. In this paper, Considering the fact that in Korea, during November to May, forest fires occur very frequently causing catastrophic damages on the valuable environment, Although exists other forest fire surveillance system such as surveillance camera tower, infrared ray sensor system and satellite system. Preexistence surveillance system can't real-time surveillance, monitoring, database and automatic alarm. But, forest fire surveillance system(FFSS) support above. In this paper, we describes a system development approach for a wireless sensor network based FFSS that is to be used to measure temperature and humidity as well as being fitted with a smoke detector. Such a device can be used as an early warning fire detection system and real-time surveillance in the area of a bush fire or endangered public infrastructure. Once the system has being development, a mesh network topology will be implemented with the chosen sensor node with the aim of developing a sophisticated mesh network.

• Environmental Analysis Health and Toxicology
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• v.25 no.1
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• pp.1-10
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• 2010

Monitoring toxicity levels in specific biological compartments is necessary to evaluate the ecotoxicological risk associated with soil environmental pollution. Gene expression, as potential biomarker, is increasingly used as rapid early warning systems in environmental monitoring and ecological risk assessment procedures. Various representative species are currently used for the purpose of assessing soil toxicity, however, investigations on toxicological assessments using endpoint based on gene-level have been limited. In this review, we will present the current trends in organisms and endpoints used in soil toxicity study and report gene expression related to toxicity using soil organism, and C. elegans as promising organisms for this approach.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.4
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• pp.49-58
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• 2008

This paper proposes a methodology to measure the risk level in real-time for Business Activity Monitoring (BAM). A decision-tree methodology was employed to analyze the effect of process attributes on the result of the process execution. In the course of process execution, the level of risk is monitored in real-time, and an early warning can be issued depending on the change of the risk level. An algorithm for estimating the risk of ongoing processes in real-time was formulated. Comparison experiments were conducted to demonstrate the effectiveness of our method. The proposed method detects the risks of business processes more precisely and even earlier than existing approaches.