• International conference on construction engineering and project management
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• 2020.12a
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• pp.87-95
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• 2020

Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.6
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• pp.661-668
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• 2012

Wireless sensor network is one of prospective methods for railway monitoring due to the long-term operation and low-maintenance performances. How to supply power to the wireless sensor nodes has drawn much attention recently. In railway monitoring, the idea of converting ambient vibration energy from vibration of railway track induced by passing trains to electric energy has made it a potential way for powering the wireless sensor nodes. In this paper, a bimorph cantilever piezoelectric energy harvester was designed based on a single degree-of-freedom model. Experimental test was also performed to validate the design. The first natural frequency of the bimorph piezoelectric energy harvester was decreased from 117.1 Hz to 65.2 Hz by adding 4 gram tip mass to the free end of the 8.6 gram energy harvester. In addition, the power generation of the piezoelectric energy harvester with 4 gram tip mass at resonant frequency was increased from 0.14 mW to 0.74 mW from $2.06m/s^2$ base excitation compared to stand-alone piezoelectric energy harvester without tip mass.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.108-115
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• 1999

Since grinding is a more complicated process than any other machining process, it is hard for operators to setup a grinding machine properly and to find out correctly abnormal grinding states resulting in damages to products. Abnormalities would be caused by improper setup, improper dressing/grinding conditions which are likely to be occurred without skilled operators' attention. In this study, an AE monitoring unit is developed to help operators conduct with ease setup, and set properly dressing/grinding conditions. AErms(root-mean-square) signal being monitored, on-going process states during grinding and dressing is visualized for machine operators to judge whether the processes are in good condition. Evaluation tests are carried out on centerless grinding machines-both cylindrical and internal. The developed AE monitoring system is verified to be useful to check grinding/dressing states in process even in the centerless grinding of which process is most unknown among various grinding methods because of the complex structure.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.892-897
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• 2018

Due to population aging, an increase in the number of patients with chronic illnesses, and an increase in the number of single-person households, monitoring of health status in everyday life without the need for a hospital has become very important. For this reason, researches on various health care devices have been attempted, among which wearable devices are attracting much attention. In this paper, we propose a new ring-type wearable device for next generation healthcare. On the inner side of the ring, a metal electrodes for GSR measurement and an optical sensor for measurement of pulse wave signals of two wavelengths of red and near-infrared light were mounted. In addition, it was equipped with an acceleration sensor, and information about the degree of motion could be obtained. In this paper, it is shown that a health monitoring device can be implemented in the form of a ring, and the measured signals can be used to calculate heart rate, oxygen saturation, sleep time and sleep efficiency. Through the advanced algorithm, it is expected that we can extract various health information, especially sleep related health information by using the ring device, and it is also expected that it can contribute to the implementation of wearable healthcare effectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2167-2173
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• 2013

Recently, people are paying huge attention to problems caused by indoor air pollution. Also, the need for the system that monitors environmental information for indoor and warns a danger is growing increasingly. In this regard, we designed and implemented environment monitoring system for indoor. In this paper, we describe the system with hardware elements and it's communication method. In order to transfer the data from environment sensors, we use short-range wireless communication in smart phone. The proposed system is possible to know the indoor environment information in installed sensor node. If the risk is found, For the proposed system, there is an advantage that may be appropriate evacuation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.448-452
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• 2007

Many industrial operations require continuous or nearly-continuous operation of machines, which if interrupted can result in significant financial loss. The condition monitoring of these machines has received considerable attention recent years. Rapid developments in semiconductor, computing, and communication with a remote site have led to a new generation of sensor called "smart" sensors which are capable of wireless communication with a remote site. The purpose of this research is the development of smart sensor using which can on-line perform condition monitoring. This system is addressed to detect conditions that may lead to equipment failure when it is running. Moreover it will reduce condition monitoring expense using low cost MEMS accelerometer. This sensor can receive data in real-time or periodic time from MEMS accelerometer. Furthermore, this system is capable for signal preprocessing task (High Pass Filter, Low Pass Filter and Gain Amplifier) and analog to digital converter (A/D) which is controlled by CPU. A/D converter that converts 10bit digital data is used. This sensor communicates with a remote site PC using TCP/IP protocols. Wireless LAN contain IEEE 802.11i-PSK or WPA (PSK, TKIP) encryption. Developed sensor executes performance tests for data acquisition accuracy estimations.

• Industrial Engineering and Management Systems
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• v.4 no.2
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• pp.176-183
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• 2005

The theory of signal detection has been applied to a wide range of practical situation for a long time, including sonar detection, air traffic control and so on. In general, in this theory, sensitivity parametric index d' and bias parametric index $\beta$ are used to evaluated the performance of vigilance. These indices use observer's response "hit" and "false alarm" to explain and evaluate vigilance, but not considering reaction time. However, the reaction time of detecting should be considered in measuring vigilance in some supervisory tasks such as unlimited monitoring tasks (e.g., supervisors in nuclear plant). There are some researchers have used the segments of reaction time to generate a pair of probabilities of hit and false alarm probabilities and plot the receiver operating characteristic curve. The purpose of this study was to develop a quantitative vigilance-measuring model by fuzzy sets, which combined the concepts of hit, false alarm and reaction time. The model extends two-values logic to multi-values logic by membership functions of fuzzy sets. A simulated experiment of monitoring task in nuclear plant was carried out. Results indicated that the new vigilance-measuring model is more efficient than traditional indices; the characteristics of vigilance would be realized more clearly in unlimited monitoring task.

• Smart Structures and Systems
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• v.14 no.5
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• pp.917-942
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• 2014

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

• Computers and Concrete
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• v.25 no.1
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• pp.15-27
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• 2020

During the past decades, the application of acoustic emission techniques (AET) through the diagnosis and monitoring of the fracture process in materials has been attracting considerable attention. AET proved to be operative among the other non-destructive testing methods for various reasons including their practicality and cost-effectiveness. Concrete and rock structures often demand thorough and real-time assessment to predict and prevent their damage nucleation and evolution. This paper presents an overview of the work carried out on the use of AE as a monitoring technique to form a comprehensive insight into its potential application in brittle materials. Reported properties in this study are crack growth behavior, localization, damage evolution, dynamic character and structures monitoring. This literature review provides practicing engineers and researchers with the main AE procedures to follow when examining the possibility of failure in civil/resource structures that rely on brittle materials.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.22 no.4
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• pp.349-354
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• 2009

To rationally secure and maintain the safety and serviceability of a high-rise building, monitoring of structural responses of members is necessary. As such health monitoring of large-scale building structures has received growing attention by researchers in recent years. However, due to a very large number of members complexity of structural responses of a high-rise building structure, practical difficulties exist in selection of structural members to be sensored for assessment of structural safety of a structure. In this paper, a selection technique for active members for safety monitoring of a high-rise building based on displacement participation factor and strain energy density of a member is investigated.