• Smart Structures and Systems
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• v.6 no.5_6
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• pp.423-438
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• 2010

Wireless smart sensors enable new approaches to improve structural health monitoring (SHM) practices through the use of distributed data processing. Such an approach is scalable to the large number of sensor nodes required for high-fidelity modal analysis and damage detection. While much of the technology associated with smart sensors has been available for nearly a decade, there have been limited numbers of fulls-cale implementations due to the lack of critical hardware and software elements. This research develops a flexible wireless smart sensor framework for full-scale, autonomous SHM that integrates the necessary software and hardware while addressing key implementation requirements. The Imote2 smart sensor platform is employed, providing the computation and communication resources that support demanding sensor network applications such as SHM of civil infrastructure. A multi-metric Imote2 sensor board with onboard signal processing specifically designed for SHM applications has been designed and validated. The framework software is based on a service-oriented architecture that is modular, reusable and extensible, thus allowing engineers to more readily realize the potential of smart sensor technology. Flexible network management software combines a sleep/wake cycle for enhanced power efficiency with threshold detection for triggering network wide operations such as synchronized sensing or decentralized modal analysis. The framework developed in this research has been validated on a full-scale a cable-stayed bridge in South Korea.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.813-826
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• 2022

AI-based image processing technologies in various fields have been widely studied. However, the lighter the board, the more difficult it is to reduce the weight of image processing algorithm due to a lot of computation. In this paper, we propose a method using deep learning for object recognition algorithm in lightweight embedded boards. We can determine the area using a deep neural network architecture algorithm that processes semantic segmentation with a relatively small amount of computation. After masking the area, by using more accurate deep learning algorithm we could operate object detection with improved accuracy for efficient neural network (ENet) and You Only Look Once (YOLO) toward executing object recognition in real time for lightweighted embedded boards. This research is expected to be used for autonomous driving applications, which have to be much lighter and cheaper than the existing approaches used for object recognition.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.290-293
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• 2021

In this paper, we implement a learning efficiency verification system to inspire learning motivation and help improve concentration by detecting the situation of the user studying. To this aim, data on learning attitude and concentration are measured by extracting the movement of the user's face or body through a real-time camera. The Jetson board was used to implement the real-time embedded system, and a convolutional neural network (CNN) was implemented for image recognition. After detecting the feature part of the object using a CNN, motion detection is performed. The captured image is shown in a GUI written in PYQT5, and data is collected by sending push messages when each of the actions is obstructed. In addition, each function can be executed on the main screen made with the GUI, and functions such as a statistical graph that calculates the collected data, To do list, and white noise are performed. Through learning efficiency checking system, various functions including data collection and analysis of targets were provided to users.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.spc
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• pp.23-29
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• 2022

To detect harmful chemical substances in seawater, we fabricated a prototype sensor and evaluated its performance. The prototype sensor consisted of a detector, housing, and driving circuit. We built the detector by printing an Indium-Tin-Oxide (ITO) nanoparticle film on a flexible substrate, and it had two detection parts for simultaneous detection of temperature and HNS concentration. The housing connected the detector and the driving circuit and was made of Teflon material to prevent chemical reactions that may affect sensor performance. The driving circuit supplied electric power, and display measured data using a bridge circuit and an Arduino board. We evaluated the sensor performances such as response (ΔR), the limit of detection (LOD), response time, and errors to confirm the specification.

• Publications of The Korean Astronomical Society
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• v.27 no.4
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• pp.153-159
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• 2012

We constructed an unbiased asteroid catalog from the mid-infrared part of the All-Sky Survey with the Infrared Camera (IRC) on board AKARI. About 20% of the point source events recorded in the IRC All-Sky Survey observations were not used for the IRC Point Source Catalog in its production process because of a lack of multiple detection by position. Asteroids, which are moving objects on the celestial sphere, are included in these "residual events" We identified asteroids out of the residual events by matching them with the positions of known asteroids. For the identified asteroids, we calculated the size and albedo based on the Standard Thermal Model. Finally we had a new brand of asteroid catalog, which contains 5,120 objects, about twice as many as the IRAS asteroid catalog.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.11
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• pp.49-54
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• 2005

This paper proposes the low power MDCT algorithm for precise FV with minimum size of sub-window in mobile phone. Proposed algerian uses the coefficient at the middle of whole result process requiring the least number of calculations, since it has a good characteristic when used as standard of the FV and needs minimum amount of operation. In addition, using the DCT result related to the middle frequency makes the characteristic of FV more superior because it suppresses the impulsive noise and difference of focus values is larger than any others. The proposed algorithm is implemented using Verilog HDL and verified using Excalibur-ARM board.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.214-220
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• 2019

Hearing impaired people are apt to be in danger because they can't detect danger with sound. Hearing impaired people have less risk-detection ability than non-disabled people because of lack of hearing. There are many devices to help the hearing impaired, such as hearing aids. A hearing aids can be helpful, but it may not be available depending on the degree or type of hearing loss for example, to the hearing-impaired people with little remaining hearing of high frequencies, ordinary hearing aids are not very useful for understanding the high frequency consonants and it requires a high cost, from thousands to tens of thousands of dollars. Also, it is difficult for the underprivileged, such as the low-income bracket and the elderly, to use them because they are difficult to manage. Therefore, this paper describes the development of low-cost wearable device to assistant a hearing-impaired people using Arduino. Also, it accepts values from switches or sensors and can control external electronic devices such as LEDs and motors to create objects that can interact with the environment. In this is paper, through sound sensors, the ambient sound was taken as an analogue value and transmitted to the aduino board, and the vibration motor was operated when the noise was generated, so that the user could be aware of the occurrence of danger.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.339-348
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• 2017

Due to recent improvements in computer processing speed and image processing technology, researches are being actively carried out to combine information from a camera with existing GNSS (Global Navigation Satellite System) and dead reckoning. In this study, the mathematical model based on SPR (Single Photo Resection) is derived for image-based assistant algorithm for vehicle positioning. Simulation test is performed to analyze factors affecting SPR. In addition, GNSS/on-board vehicle sensor/image based positioning algorithm is developed by combining image-based positioning algorithm with existing positioning algorithm. The performance of the integrated algorithm is evaluated by the actual driving test and landmark's position data, which is required to perform SPR, based on simulation. The precision of the horizontal position error is 1.79m in the case of the existing positioning algorithm, and that of the integrated positioning algorithm is 0.12m at the points where SPR is performed. In future research, it is necessary to develop an optimized algorithm based on the actual landmark's position data.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.735-742
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• 2004

We present a new use of common graphics hardware to perform a faster artificial neural network. And we examine the use of GPU enhances the time performance of the image processing system using neural network, In the case of parallel computation of multiple input sets, the vector-matrix products become matrix-matrix multiplications. As a result, we can fully utilize the parallelism of GPU. Sigmoid operation and bias term addition are also implemented using pixel shader on GPU. Our preliminary result shows a performance enhancement of about thirty times faster using ATI RADEON 9800 XT board.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.63-69
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• 2020

In this paper, a small microwave sensor that can be applied to a wearable device is proposed because it can detect the heartbeat signal of a human body moving irregularly at low speed. It consist of balanced microstrip radiation patches in the 2.4 GHz ISM band, self-oscillation detection circuit, and feedback circuit. Based on the theoretical development and simulation, the validity of the proposed structure was confirmed and the manufactured prototype was tested. The board size of the circuit is as small as 65mm × 85㎟, and has a low power consumption of 60mW thanks to the simple RF circuit structure. Finally heartbeat signal has been obtained from a human body moving at low speed (0.5Hz) within a linear distance of 2 to 30mm close to the sensor and a lateral distance of ±20mm.