• Journal of Biomedical Engineering Research
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• v.31 no.6
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• pp.464-471
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• 2010

Compressed sensing can be used to reduce scan time or to enhance spatial resolution in MRI. It is now recognized that compressed sensing works well in reconstructing magnitude images if the sampling mask and the sparsifying transform are well chosen. Phase images also play important roles in MRI particularly in chemical shift imaging and magnetic resonance electrical impedance tomography (MREIT). We reconstruct MRI phase images using the compressed sensing technique. Through computer simulation and real MRI experiments, we reconstructed phase images using the compressed sensing technique and we compared them with the ones reconstructed by conventional Fourier reconstruction technique. As compared to conventional Fourier reconstruction with the same number of phase encoding steps, compressed sensing shows better performance in terms of mean squared phase error and edge preservation. We expect compressed sensing can be used to reduce the scan time or to enhance spatial resolution of MREIT.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11b
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• pp.60-76
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• 2000

Early detection in real-time response of slope movements ensures tremendous saving of lives and repair costs from catastrophic disaster Therefore, it is essential to constantly monitor the performance and integrity of slope-stabilizing structures such as Rock bolt, Nail and Pile during or after installation. We developed a novel monitoring system using Fiber Bragg Grating (FBG)sensor. It's advantages are highly sensitivity, small dimension and electro-magnetic immunity. capability of multiplexing, system integrity, remote sensing - these serve real-time health monitoring of the structures. Real-time strain measurement by the signal processing program is shown graphically and it gives a warning sound when the monitored strain state exceeds a given threshold level so that any sign of abnormal disturbance on the spot can be easily perceived.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.133-138
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• 2002

In this paper, we propose an integrated GUI environment of parallel fuzzy inference system fur pattern classification of remote sensing data. In this, as 4 fuzzy variables in condition part and 104 fuzzy rules are used, a real time and parallel approach is required. For frost fuzzy computation, we use the scan line conversion algorithm to convert lines of each fuzzy linguistic term to the closest integer pixels. We design 4 fuzzy processor unit to be operated in parallel by using FPGA. As a GUI environment, PCI transmission, image data pre-processing, integer pixel mapping and fuzzy membership tuning are considered. This system can be used in a pattern classification system requiring a rapid inference time in a real-time.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.4
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• pp.437-446
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• 2018

This paper describes the real-time logic implementation of orthogonal matching pursuit(OMP) algorithm for compressive sensing digital receiver. OMP contains various complex-valued linear algebra operations, such as matrix multiplication and matrix inversion, in an iterative manner. Xilinx Vivado high-level synthesis(HLS) is introduced to design the digital logic more efficiently. The real-time signal processing is realized by applying dataflow architecture allowing functions and loops to execute concurrently. Compared with the prior works, the proposed design requires 2.5 times more DSP resources, but 10 times less signal reconstruction time of $1.024{\mu}s$ with a vector of length 48 with 2 non-zero elements.

• Wind and Structures
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• v.24 no.4
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• pp.333-350
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• 2017

Differing from the fixed-type, the dynamic motion of floating-type offshore wind turbines is very sensitive to wind and wave excitations. Thus, the sensing and monitoring of its motion is important to evaluate the dynamic responses to the external excitation. In this context, a monitoring system for sensing and processing the wind-induced dynamic motion of spar-type floating offshore wind turbine is developed in this study. It is developed by integrating a 1/00 scale model of 2.5MW spar-type floating offshore wind turbine, water basin equipped with the wind generator, sensing and data acquisition systems, real-time CompactRIO controller and monitoring program. The scale model with the upper rotatable blades is installed within the basin by means of three mooring lines, and its translational and rotational motions are detected by 3-axis inclinometer and accelerometers and gyroscope. The detected motion signals are processed using a real-time controller CompactRIO to calculate the acceleration and tilting angle of nacelle and the attitude of floating platform. The developed monitoring system is demonstrated and validated by measuring and evaluating the time histories and trajectories of nacelle and platform motions for three different wind velocities and for eight different fairlead positions.

• Smart Structures and Systems
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• v.4 no.6
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• pp.759-777
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• 2008

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

• Smart Structures and Systems
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• v.15 no.4
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• pp.1103-1120
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• 2015

A self-sensing magnetorheological (MR) damper with embedded piezoelectric force sensor has recently been devised to facilitate real-time close-looped control of structural vibration in a simple and reliable manner. The development and characterization of the self-sensing MR damper are presented based on experimental work, which demonstrates its reliable force sensing and controllable damping capabilities. With the use of experimental data acquired under harmonic loading, a nonparametric dynamic model is formulated to portray the nonlinear behaviors of the self-sensing MR damper based on NARX modeling and neural network techniques. The Bayesian regularization is adopted in the network training procedure to eschew overfitting problem and enhance generalization. Verification results indicate that the developed NARX network model accurately describes the forward dynamics of the self-sensing MR damper and has superior prediction performance and generalization capability over a Bouc-Wen parametric model.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1006-1008
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• 2003

In this paper, we describe the development of a Southeast Asia environmental information web portal based on near real time MODIS Level 2 and higher level products generated from the direct broadcast data received at the Centre for Remote Imaging, Sensing and Processing (CRISP). This web portal aims to deliver timely environmental information to interested users in the region. Interpreted data will be provided instead of raw satellite data to reduce operational requirements on our system, and to enable users with limited bandwidths to have access to the system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.4
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• pp.421-429
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• 2018

While IoT device sensing data or financial transaction data is transmitted in real time, huge data traffic is generated in processing it. This huge data can be effectively compressed or transmitted using universal code, which is a real-time lossless compressor. In this paper, we propose our BL-beta code, which is newly developed universal code for compressing stock trading data, which the maximum range of measured values is difficult to predict and is generated within a relatively constant range over a very short period of time. For compressing real-time stock trading data, Compared with the fixed length bit transmission, the compression efficiency is at least 49.5% higher than that of the fixed length bit transmission, and the compression transmission performance is 16.6% better than the Exponential Golomb code.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.721-730
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• 2010

In satellite remote sensing, irregular temporal sampling is a common feature of geophysical and biological process on the earth's surface. Lee (2008) proposed a feed-back system using a harmonic model of single period to adaptively reconstruct observation image series contaminated by noises resulted from mechanical problems or environmental conditions. However, the simple sinusoidal model of single period may not be appropriate for temporal physical processes of land surface. A complex model of multiple periods would be more proper to represent inter-annual and inner-annual variations of surface parameters. This study extended to use a multi-periodic harmonic model, which is expressed as the sum of a series of sine waves, for the adaptive system. For the system assessment, simulation data were generated from a model of negative errors, based on the fact that the observation is mainly suppressed by bad weather. The experimental results of this simulation study show the potentiality of the proposed system for real-time monitoring on the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather.