• International Journal of Safety
• /
• v.7 no.2
• /
• pp.17-22
• /
• 2008

Recent developments in contactless, air-coupled sensing of seismic and ultrasonic waves in concrete structures are presented. Contactless sensing allows for rapid, efficient and consistent data collection over a large volume of material. Two inspection applications are discussed: air-coupled impact-echo scanning of concrete structures using seismically generated waves, and air-coupled imaging of internal damages in concrete using ultrasonic tomography. The first application aims to locate and characterize shallow delamination defects within concrete bridge decks. Impact-echo method is applied to scan defected concrete slabs using air coupled sensors. Next, efforts to apply air-coupled ultrasonic tomography to concrete damage imaging are discussed. Preliminary results are presented for air-coupled ultrasonic tomography applied to solid elements to locate internal defects. The results demonstrate that, with continued development, air-coupled ultrasonic tomography may provide improved evaluation of unseen material defects within structures.

• Proceedings of the KIEE Conference
• /
• 2003.11b
• /
• pp.183-186
• /
• 2003

In this paper, we proposed fuzzy adaptive resonance theory(ART) combined with principle component analysis(PCA) to recognize and classify indoor environmental gases. In experiment Taguchi gas sensors(TGS) are used to detect VOCs. Using thermal modulation of operating temperature of two sensors, we extract patterns of gases from the voltage across the load resistance. We use the PCA algorithm to reduce dimension so it needs less memory and shortens calculation time. Simulation is accomplished to two directions for fuzzy ART with and without PCA.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2007.10a
• /
• pp.946-949
• /
• 2007

A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion at different locations. Environmental monitoring represent a class of sensor network applications with enormous potential benefits for scientific communities and society. In this paper we design and implement a novel platform for sensor networks to be used for monitoring of temperature, humidity, and light sensors.

• Journal of Animal Environmental Science
• /
• v.6 no.1
• /
• pp.23-30
• /
• 2000

A automated noncontact weight system for pigs consisted of a CCD-type video camera and 10 photo sensors connected to a computer. In the experiment 20 pigs(Large Yorkshire $\times$ Landrace breed) weighing from 95kg to 115kg were used. Pig's original image data was transformed to a binary image an image excluding head and tail portion from the whole binary image and the area of pig was calculated. Then pig's volume was calculated by multiplying the area by the body hight measured with photo sensors. The correlation equation between the above volume(x) and pig's weight was y=0.0007 x -9.2152($R^2$=0.9965) Performance of a automated noncontact weighing system for pigs was tested with this equation. The results showed $\pm$0.65kg average error and 1.63kg maximum error. It was concluded that performance of a automated noncontact weighing system is excellent.

• Journal of electromagnetic engineering and science
• /
• v.15 no.2
• /
• pp.104-110
• /
• 2015

As civil infrastructures continue to deteriorate, the demand for structural health monitoring (SHM) has increased. Despite its outstanding capability for damage identification, many conventional SHM techniques are restricted to huge structures because of their wired system for data and power transmission. Although wireless data transmission using radio-frequency techniques has emerged vis-$\grave{a}$-vis wireless sensors in SHM, the power supply issue is still unsolved. Normal batteries cannot support civil infrastructure for no longer than a few decades. In this study, we develop a magnetic resonance-based wireless power transmission system, and its performance is validated in three different mediums: air, unreinforced concrete, and reinforced concrete. The effect of concrete and steel rebars is analyzed.

• International conference on construction engineering and project management
• /
• 2015.10a
• /
• pp.321-323
• /
• 2015

Monitoring crane motion in real time is the first step to identifying and mitigating crane-related hazards on construction sites. However, no accurate and reliable crane motion capturing technique is available to serve this purpose. The objective of this research is to explore a method for real-time crane motion capturing and investigate an approach for assisting hazard detection. To achieve this goal, this research employed various techniques including: 1) a sensor-based method that accurately, reliably, and comprehensively captures crane motions in real-time; 2) computationally efficient algorithms for fusing and processing sensing data (e.g., distance, angle, acceleration) from different types of sensors; 3) an approach that integrates crane motion data with known as-is environment data to detect hazards associated with lifting tasks; and 4) a strategy that effectively presents crane operator with crane motion information and warn them with potential hazards. A prototype system was developed and tested on a real crane in a field environment. The results show that the system is able to continuously and accurately monitor crane motion in real-time.

• Proceedings of the Materials Research Society of Korea Conference
• /
• 1998.08a
• /
• pp.89.1-89
• /
• 1998

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.33 no.10B
• /
• pp.894-903
• /
• 2008

The position of sensors generally affects coverage, communication costs, and resource management of surveillance sensor networks. Thus we are required to place a sensor in the best location. However, it is difficult to consider that terrain and climate factors influencing sensors when sensor nodes are deployed in the real world, such as a mountain area or a downtown area. We therefore require a sensor deployment method for detecting effectively targets of interest in terms of surveillance area coverage in such environment. Thus in this paper, we analyze various environmental factors related to sensor deployment, and quantify these factors to use when we deploy sensors. By considering these quantified factors, we propose a practical and effective method for deploying sensors in terms of sensing coverage. We also demonstrate the propriety of the proposed method through implementing a sensor deployment management system according to the method.

• Journal of Sensor Science and Technology
• /
• v.23 no.1
• /
• pp.15-18
• /
• 2014

We present gas sensing performance based on $2{\times}2$ sensor array with four different elements ($TiO_2$, $SnO_2$, $WO_3$ and $In_2O_3$ thin films) fabricated by rf sputter. Each thin film was deposited onto the selected $SiO_2$/Si substrate with Pt interdigitated electrodes (IDEs) of $5{\mu}m$ spacing which were fabricated on a $SiO_2$/Si substrate using photolithography and dry etching. For 5 ppm $NO_2$ and 50 ppm CO, each thin film sensor has a different response to offers the distinguishable response pattern for different gas molecules. Compared with the conventional micro-fabrication technology, $2{\times}2$ sensor array with such remarkable response pattern will be open a new foundation for monolithic integration of high-performance chemoresistive sensors with simplicity in fabrication, low cost, high reliablity, and multifunctional smart sensors for environmental monitoring.

• Journal of Biosystems Engineering
• /
• v.40 no.2
• /
• pp.153-158
• /
• 2015

Purpose: The research presented in this paper applied the chemometric analysis to the near-infrared spectral data from line-scanned hyperspectral images of beef and fish meals in animal feeds. The chemometric statistical models were developed to distinguish beef meals from fish ones. Methods: The meal samples of 40 fish meals and 15 beef meals were line-scanned to obtain hyperspectral images. The spectral data were retrieved from each of 3600 pixels in the Region of Interest (ROI) of every sample image. The wavebands spanning 969 nm to 1551 nm (across 176 spectral bands) were selected for chemometric analysis. The partial least squares regression (PLSR) and the principal component analysis (PCA) methods of the chemometric analysis were applied to the model development. The purpose of the models was to correctly classify as many beef pixels as possible while misclassified fish pixels in an acceptable amount. Results: The results showed that the success classification rates were 97.9% for beef samples and 99.4% for fish samples by the PLSR model, and 85.1% for beef samples and 88.2% for fish samples by the PCA model. Conclusion: The chemometric analysis-based PLSR and PCA models for the hyperspectral image analysis could differentiate beef meals from fish ones in animal feeds.