• Smart Structures and Systems
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• 제6권5_6호
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• pp.461-480
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• 2010

This paper analyses the data collected from the $2^{nd}$ Jindo Bridge, a cable-stayed bridge in Korea that is a structural health monitoring (SHM) international test bed for advanced wireless smart sensors network (WSSN) technology. The SHM system consists of a total of 70 wireless smart sensor nodes deployed underneath of the deck, on the pylons, and on the cables to capture the vibration of the bridge excited by traffic and environmental loadings. Analysis of the data is performed in both the time and frequency domains. Modal properties of the bridge are identified using the frequency domain decomposition and the stochastic subspace identification methods based on the output-only measurements, and the results are compared with those obtained from a detailed finite element model. Tension forces for the 10 instrumented stay cables are also estimated from the ambient acceleration data and compared both with those from the initial design and with those obtained during two previous regular inspections. The results of the data analyses demonstrate that the WSSN-based SHM system performs effectively for this cable-stayed bridge, giving direct access to the physical status of the bridge.

• Smart Structures and Systems
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• 제6권5_6호
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• pp.439-459
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• 2010

Structural health monitoring (SHM) of civil infrastructure using wireless smart sensor networks (WSSNs) has received significant public attention in recent years. The benefits of WSSNs are that they are low-cost, easy to install, and provide effective data management via on-board computation. This paper reports on the deployment and evaluation of a state-of-the-art WSSN on the new Jindo Bridge, a cable-stayed bridge in South Korea with a 344-m main span and two 70-m side spans. The central components of the WSSN deployment are the Imote2 smart sensor platforms, a custom-designed multimetric sensor boards, base stations, and software provided by the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. In total, 70 sensor nodes and two base stations have been deployed to monitor the bridge using an autonomous SHM application with excessive wind and vibration triggering the system to initiate monitoring. Additionally, the performance of the system is evaluated in terms of hardware durability, software stability, power consumption and energy harvesting capabilities. The Jindo Bridge SHM system constitutes the largest deployment of wireless smart sensors for civil infrastructure monitoring to date. This deployment demonstrates the strong potential of WSSNs for monitoring of large scale civil infrastructure.

• Wind and Structures
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• 제11권1호
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• pp.35-50
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• 2008

Wind and temperature have been shown to be the critical sources causing changes in the modal properties of large-scale bridges. While the individual effects of wind and temperature on modal variability have been widely studied, the investigation about the effects of multiple environmental factors on structural modal properties was scarcely reported. This paper addresses the modeling of the simultaneous effects of wind and temperature on the modal frequencies of an instrumented cable-stayed bridge. Making use of the long-term monitoring data from anemometers, temperature sensors and accelerometers, a neural network model is formulated to correlate the modal frequency of each vibration mode with wind speed and temperature simultaneously. Research efforts have been made on enhancing the prediction capability of the neural network model through optimal selection of the number of hidden nodes and an analysis of relative strength of effect (RSE) for input reconstruction. The generalization performance of the formulated model is verified with a set of new testing data that have not been used in formulating the model. It is shown that using the significant components of wind speeds and temperatures rather than the whole measurement components as input to neural network can enhance the prediction capability. For the fundamental mode of the bridge investigated, wind and temperature together apply an overall negative action on the modal frequency, and the change in wind condition contributes less to the modal variability than the change in temperature.

• Smart Structures and Systems
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• 제19권1호
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• pp.105-113
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• 2017

A microstructure-dependent dynamic model for silicon nanobeams with axial motion is developed by considering the effects of nonlocal elasticity and surface energy. The nanobeam is considered to subject to both transverse and longitudinal loads arising from nanostructural surface effect and all positive directions of physical quantities are defined clearly prior to modeling so as to clarify the confusions of sign in governing equations of previous work. The nonlocal and surface effects are taken into consideration in the dynamic behaviors of silicon nanobeams with axial motion including circular natural frequency, vibration mode, transverse displacement and critical speed. Various supporting conditions are presented to investigate the circular frequencies by a numerical method and the effects of many variables such as nonlocal nanoscale, axial velocity and external loads on non-dimensional circular frequencies are addressed. It is found that both nonlocal and surface effects play remarkable roles on the dynamics of nanobeams with axial motion and cause the frequencies and critical speed to decrease compared with the classical continuum results. The comparisons of the non-dimensional calculation values by present and previous studies validate the correctness of the present work. Additionally, numerical examples for silicon nanobeams with axial motion are addressed to show the nonlocal and surface effects on circular frequencies intuitively. Results obtained in this paper are helpful for the design and optimization of nanobeam-like microstructures based sensors and oscillators at nanoscale with desired dynamic mechanical properties.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.11-16
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• 2021

At the present time, the number of accidents has enlarged speedily and in country like India per day there are about 204 accidents occurred. Accidents of two-wheeler compose a foremost segment of every accident and it can be true for the reason that two-wheelers like bikes not able to produce as many as security measurements normally incorporated in cars, truks and bus etc. General main rootcost of the two-wheeler accidents happen only when people community not remember to wearing a device helmet and during the driving time feels like sleep condition, alcohol disbursement, many of the drivers doesn't know heavy vehicles like Loory and buses approaching into very closer to their two wheelers, contravention of two wheelers in traffic rules and regulations. Let's overcome the above situations; our important objective is to develop an intelligent system device that can successfully facilitate in avoidance of every kind of problems. Suppose any of the above stated situations occurs, at that moment how system device identify and represents the commanders and community, and finally the stated situation be able to taken care of straight away without any further delay. A smart intelligent helmet system is a defending head covering used by rider for making bike riding safer than earlier. This is finished by incorporating sophisticated features like detecting the usage of helmet by the rider, connected Bluetooth module in helmet. In order to maintain the temperature inside the helmet device we need to include CPU fan module inside the device. RF based helmet prevents road accidents and identify whether people community is not using a component helmet or used. Main responsibility of the system is to detect accidents by vibration sensors, accelerometers and also with the help of modules global positioning system and global system for mobile commnicaiton module. A wireless communication device used to discover the accident area site location and likewise notifying the two-wheeler drived people's relatives and short message text information passed to the positioned hospitals.

• 대한임베디드공학회논문지
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• 제17권1호
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• pp.9-17
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• 2022

As the COVID-19 pandemic situation worsens, the time spent indoors increases, and the exposure to indoor environmental pollution such as indoor air pollution and noise also increases, causing problems such as deterioration of human health, stress, and discord between neighbors. This paper designs and implements a system that measures and monitors indoor air quality and noise, which are representative evaluation criteria of the indoor environment. The system proposed in this paper consists of a particulate matter measurement subsystem that measures and corrects the concentration of particulate matters to monitor indoor air quality, and a noise measurement subsystem that detects changes in sound and converts it to a sound pressure level. The concentration of indoor particulate matters is measured using a laser-based light scattering method, and an error caused by temperature and humidity is compensated in this paper. For indoor noise measurement, the voltage measured through a microphone is basically measured, Fourier transform is performed to classify it by frequency, and then A-weighting is performed to correct loudness equality. Then, the RMS value is obtained, high-frequency noise is removed by performing time-weighting, and then SPL is obtained. Finally, the equivalent noise level for 1 minute and 5 minutes are calculated to show the indoor noise level. In order to classify noise into direct impact sound and air transmission noise, a piezo vibration sensors is mounted to determine the presence or absence of direct impact transmitted through the wall. For performance evaluation, the error of particulate matter measurement is analyzed through TSI's AM510 instrument. and compare the noise error with CEM's noise measurement system.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 Proceedings ICPE 98 1998 International Conference on Power Electronics
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• pp.961-965
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• 1998

The rapid development of small-scale (1-10 MJ) Superconducting Magnetic Energy Storage Systems (SMES) can be explained by real perspective of practical implementation of these devices in electro power nets. However the serious problem of all high mechanically stressed superconducting coils-problem of training and degradation (decreasing) of operating current still exists. Moreover for SMES systems this problems is more dangerous because of pulsed origin of mechanical stresses-one of the major sources of local heat disturbances in superconducting coils. We investigated acoustic emission (AE) phenomenon on model and 0.5 MJ SMES coils taking into account close correlation of AE and local heat disturbances. Two-coils 0.5 MJ SMES system was developed, manufactured and tested at Russian Research Center in the frames of cooperation with Korean Electrical Engineering Company (KEPCO) [1]. The two-coil SMES operates with the stored energy transmitted between coils in the course of a single cycle with 2 seconds energy transfer time. Maximum operating current 1.55 kA corresponds to 0.5 MF in each coil. The Nb-Ti-based conductor was designed and used for SMES manufacturing. It represents transposed cable made of Nb-Ti strands in copper matrix, several cooper strands and several stainless steel strands. The coils are wound onto fiberglass cylindrical bobbins. To make AE event information more useful a real time instrumentation system was used. Two main measured and computer processed AE parameters were considered: the energy of AE events (E) and the accumulated energy of AE events (E ). Influence of current value in 0.5 MJ coils on E and E was studied. The sensors were installed onto the bobbin and the external surface of magnets. Three levels of initial current were examined: 600A, 1000A, 2450 A. An extraordinary strong dependence of the current level on E and E was observed. The specific features of AE from model coils, operated in sinusoidal vibration current changing mode were investigated. Three current frequency modes were examined: 0.012 Hz, 0.03 Hz and 0.12 Hz. In all modes maximum amplitude 1200 A was realized.

• 한국항행학회논문지
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• 제27권1호
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• pp.111-118
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• 2023

스마트팩토리의 구축을 위해서는 제조환경에서 여러 센서 및 기기 등을 연결하여 데이터를 수집하고, 데이터 분석을 통해 생산설비 등의 장애를 진단하거나 예측하여야 한다. 본 논문에서는 공작기계에서 제품을 가공하기 위해 사용되는 절삭용 인서트의 잔여 유효 수명을 예측하기 위해 진동 신호를 기반으로 한 가중화 k-최근접이웃(Weighted k-NN) 알고리즘, 의사결정나무(Decision Tree), 서포트벡터회귀(SVM), XGBoost, 랜덤포레스트(Random forest), 1차원 합성곱신경망(1D-CNN), 그리고 진동 신호를 FFT한 주파수 스펙트럼에 대해 알아보았다. 연구결과, 주파수 스펙트럼으로는 잔여 유효수명의 정확한 예측에 대해서는 신빙성있는 기준을 제공하지 못한다는 것을 알수 있었고, 예측 모델 중 가중화 k-최근접이웃 알고리즘이 MAE가 0.0013, MSE가 0.004, RMSE가 0.0192로 가장 우수한 성능을 나타내었다. 이는 가중화 k-최근접이웃 알고리즘에 의해 예측되는 인서트의 잔여 유효 수명의 오차가 0.001초 수준으로 평가되어, 실제 산업현장에 적용이 가능한 수준으로 사료된다.

• Smart Structures and Systems
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• 제31권5호
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• pp.469-483
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• 2023

The dynamic characteristics of wind turbine blades are usually monitored by contact sensors with the disadvantages of high cost, difficult installation, easy damage to the structure, and difficult signal transmission. In view of the above problems, based on computer vision technology and the improved YOLOv5 (You Only Look Once v5) deep learning model, a non-contact dynamic characteristic monitoring method for wind turbine blade is proposed. First, the original YOLOv5l model of the CSP (Cross Stage Partial) structure is improved by introducing the CSP2_2 structure, which reduce the number of residual components to better the network training speed. On this basis, combined with the Deep sort algorithm, the accuracy of structural displacement monitoring is mended. Secondly, for the disadvantage that the deep learning sample dataset is difficult to collect, the blender software is used to model the wind turbine structure with conditions, illuminations and other practical engineering similar environments changed. In addition, incorporated with the image expansion technology, a modeling-based dataset augmentation method is proposed. Finally, the feasibility of the proposed algorithm is verified by experiments followed by the analytical procedure about the influence of YOLOv5 models, lighting conditions and angles on the recognition results. The results show that the improved YOLOv5 deep learning model not only perform well compared with many other YOLOv5 models, but also has high accuracy in vibration monitoring in different environments. The method can accurately identify the dynamic characteristics of wind turbine blades, and therefore can provide a reference for evaluating the condition of wind turbine blades.

• 센서학회지
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• 제32권5호
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• pp.328-333
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• 2023

This study experimentally verified the etch rate of the SiO₂ sacrificial layer etching process with a single etch hole using vapor-phase hydrogen fluoride (VHF) etching. To fabricate small-sized polysilicon etch holes, both circular and triangular pattern masks were employed. Etch holes were fabricated in the polysilicon thin film on the SiO₂ sacrificial layer, and VHF etching was performed to release the polysilicon thin film. The lateral etch rate was measured for varying etch hole sizes and sacrificial layer thicknesses. Based on the measured results, we obtained an approximate equation for the etch rate as a function of the etch hole size and sacrificial layer thickness. The etch rates obtained in this study can be utilized to minimize structural damage caused by incomplete or excessive etching in sacrificial layer processes. In addition, the results of this study provide insights for optimizing sacrificial layer etching and properly designing the size and spacing of the etch holes. In the future, further research will be conducted to explore the formation of structures using chemical vapor deposition (CVD) processes to simultaneously seal etch hole and prevent adhesion owing to polysilicon film vibration.