• Particle and aerosol research
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• v.14 no.4
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• pp.107-119
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• 2018

In this study, we have developed a real-time monitoring device for measuring PM10 and PM2.5 of ambient aerosol particles. The real-time PM monitor (SENTRY Dust Monitor) uses the optical scattering method and has 16 channels in particle size. The laboratory and field tests were carried out to evaluate the developed SENTRY Dust Monitor. Arizona Test Dust particles were used as test particles in the laboratory test and the field test was carried out at the Jongno-gu Observatory in Seoul. The measurements of PM10 and PM2.5 concentrations obtained by SENTTRY Dust Monitor were compared with Grimm Dust Monitor (Model 1.108) and a beta ray gauge. It was shown that the PM10 and PM2.5 concentrations obtained by SENTRY Dust Monitor agree well with that of the reference devices. Based on the results obtained in this study, it could be concluded that the SENTRY Dust Monitor can be used as a PM monitoring device for real-time monitoring of the ambient aerosols.

• Smart Structures and Systems
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• v.30 no.3
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• pp.273-286
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• 2022

In recent years, vision-based monitoring has received great attention. However, structural identification using vision-based displacement measurements is far less established. Especially, simultaneous identification of structural systems and unknown excitation using vision-based displacement measurements is still a challenging task since the unknown excitations do not appear directly in the observation equations. Moreover, measurement accuracy deteriorates over a wider field of view by vision-based monitoring, so, only a portion of the structure is measured instead of targeting a whole structure when using monocular vision. In this paper, the identification of structural system and excitations using vision-based displacement measurements is investigated. It is based on substructure identification approach to treat of problem of limited field of view of vision-based monitoring. For the identification of a target substructure, substructure interaction forces are treated as unknown inputs. A smoothing extended Kalman filter with unknown inputs without direct feedthrough is proposed for the simultaneous identification of substructure and unknown inputs using vision-based displacement measurements. The smoothing makes the identification robust to measurement noises. The proposed algorithm is first validated by the identification of a three-span continuous beam bridge under an impact load. Then, it is investigated by the more difficult identification of a frame and unknown wind excitation. Both examples validate the good performances of the proposed method.

• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2873-2878
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• 2023

The purpose of this study is to perform radiation monitoring by acquiring gamma images and real-time optical images for ^99mTc vial source using charge couple device (CCD) cameras equipped with the proposed compact gamma camera. The compact gamma camera measures 86×65×78.5 mm³ and weighs 934 g. It is equipped with a metal 3D printed diverging collimator manufactured in a 45 field of view (FOV) to detect the location of the source. The circuit's system uses system-on-chip (SoC) and field-programmable-gate-array (FPGA) to establish a good connection between hardware and software. In detection modules, the photodetector (multi-pixel photon counters) is tiled at 8×8 to expand the activation area and improve sensitivity. The gadolinium aluminium gallium garnet (GAGG) measuring 0.5×0.5×3.5 mm³ was arranged in 38×38 arrays. Intrinsic and extrinsic performance tests such as energy spectrum, uniformity, and system sensitivity for other radioisotopes, and sensitivity evaluation at edges within FOV were conducted. The compact gamma camera can be mounted on unmanned equipment such as drones and robots that require miniaturization and light weight, so a wide range of applications in various fields are possible.

• Smart Structures and Systems
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• v.32 no.5
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• pp.297-308
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• 2023

Dynamic deflection is important for evaluating the performance of a long-span cable-stayed bridge, and its continuous measurement is still cumbersome. This study proposes a dynamic deflection monitoring method for cable-stayed bridge based on Bi-directional Long Short-term Memory (BiLSTM) neural network taking advantages of the characteristics of spatial variation of cable acceleration response (CAR) and main girder deflection response (MGDR). Firstly, the relationship between the spatial and temporal variation of the CAR and the MGDR is described based on the geometric deformation of the bridge. Then a data-driven relational model based on BiLSTM neural network is established using CAR and MGDR data, and it is further used to monitor the MGDR via measuring the CAR. Finally, numerical simulations and field test are conducted to verify the proposed method. The root mean squared error (RMSE) of the numerical simulations are less than 4 while the RMSE of the field test is 1.5782, which indicate that it provides a cost-effective and convenient method for real-time deflection monitoring of cable-stayed bridges.

• Journal of Sensor Science and Technology
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• v.33 no.1
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• pp.30-33
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• 2024

The occurrence of harmful algae on the coast of Korea has been a cause of damage to the aquaculture industry and deterioration of the coastal ecosystem environment. A method is required to predict their outbreak in real-time at the site. Therefore, this study attempted to develop a small hybrid optical sensor and real-time monitoring system based on LiDAR that can be used in the field and laboratory and can be applied to various platforms. FMS-L specifically suggested the amount of Chlorophyll a (Chl a) in the sample by measuring and analyzing the fluorescence emitted by the irradiating light. The accuracy of FMS-L was verified by measuring the concentrations of standard Chlorophyll a substances and Margalfidinium polykirkoids. In addition, the precision was verified by comparing the measurement results of FMS-L using commercial equipment Phyto-PAM-II. This equipment is compact and easy to move. Therefore, it can be easily applied to field surveys, allows short time measurements (10 s), and can be applied at a distance of 10 m from the measurement site.

• Biotechnology and Bioprocess Engineering:BBE
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• v.5 no.6
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• pp.395-399
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• 2000

Five different freeze-dried recombinant bioluminescent bacteria were used for the detection of cellular stresses caused by endocrine disrupting chemicals. These strains were DPD2794 (recA::luxCDABE), which is sensitive to DNA damage, DPD2540 (fabA::luxCDABE), sensitive to cellular membrane damage, DPD2511 (katG::luxCDABE), sensitive to oxidative damage, and TV1061 (grpE::luxCDABE), sensitive to protein damage. GC2, which emits bioluminescence constitutively, was also used in this study. The toxicity of several chemicals was measured using GC2. Damage caused by known endocrine disrupting chemicals, such as nonyl phenol, bisphenol A, and styrene, was detected and classified according to toxicity mode, while others, such as phathalate and DDT, were not detected with the bacteria. These results suggest that endocrine disrupting chemicals are toxic in bacteria, and do not act via an estrogenic effect, and that toxicity monitoring and classification of some endocrine disrupting chemicals may be possible in the field using these freeze-dried recombinant bioluminescent bacteria.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.225-233
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• 2010

Recently, in the field of Smart Grid, Smart Home Network, Ubiquitous Computing, etc. we have continued to study Smart Information Monitoring Technology(SIMT) which exchange, control and monitor information collected and processed by need in real-time and two-way. In this paper, we understand application products or recent trends of SIMT for Energy, U-Farm, Vehicle Information and Home Network. Specially, we explain Google PowerMeter which exchange information with Smart Meter of core part of the smart grid at real-time, Real-time Monitoring System(RMS) for U-Farm, RMS for vehicle status Information. we subscribe Smart Information Monitoring Technology application based on ZigBee of low price, low power or related work. Finally we subscribe actual proof construction situation of Jesu for smart grid.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.276-284
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• 2009

In this paper, we develop the image transfer remote maintenance monitoring system for hand held device which can compensate defects of human mistake. The human mistakes always happen when the worker communicate information each other to check and maintenance the equipment of the power plant under bad circumstance such as small place and long distance in power plant. A worker couldn't converse with other when in noisy place like Power plant. So, we make some hand device for handy size and able to converse in noisy place. The developed system can have improvement of productivity through increasing plant operation time. And developed system is composed of advanced H/W(hard ware) system and S/W(soft ware)system. The H/W system consist of media server unit, communication equipment with hand held device, portable camera, mike and head set. The advanced s/w system consist of data base system, client pc(personal computer) real time monitoring system which has server GUI(graphic user interface) program, wireless monitoring program and wire ethernet communication program. The client GUI program is composed of total solution program as pc camera program, and phonetic conversation program etc.. We analyzed the required items and investigated applicable part in the image transfer remote maintenance monitoring system with hand held device. Also we investigated linkage of communication protocol for developed prototype, developed software tool of two-way communication and realtime recording skill of voice with image. We confirmed the efficiency by the field test in preventive maintenance of plant power.

• Structural Engineering and Mechanics
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• v.15 no.3
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• pp.285-297
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• 2003

Complementing recent advances made in the field of structural health monitoring and damage detection, the concept of a wireless sensing network with distributed computational power is proposed. The fundamental building block of the proposed sensing network is a wireless sensing unit capable of acquiring measurement data, interrogating the data and transmitting the data in real time. The computational core of a prototype wireless sensing unit can potentially be utilized for execution of embedded engineering analyses such as damage detection and system identification. To illustrate the computational capabilities of the proposed wireless sensing unit, the fast Fourier transform and auto-regressive time-series modeling are locally executed by the unit. Fast Fourier transforms and auto-regressive models are two important techniques that have been previously used for the identification of damage in structural systems. Their embedment illustrates the computational capabilities of the prototype wireless sensing unit and suggests strong potential for unit installation in automated structural health monitoring systems.