• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.470-473
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• 2021

In this study, we developed the biometric ultrasound transducer, residual thickness measurement algorithm and optimized ultrasound operation methods to diagnose precise conditions of implanted medical prosthetic material inserted during total hip artificial joint replacement. In detail, ultrasound transducers having 8 MHz and 20 MHz center frequencies with similar sensitivity and bandwidth were fabricated to measure various thicknesses of commercial polyethylene-based artificial hip liners, resulting in a comparative analysis of signal-to-noise ratio and axial resolution to conduct an optimization study of ultrasound operations in vivo.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.141-147
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• 2006

Soil losses are occurred by rainfall has caused productivity decline of a fertile surface soil and inflow sediment on Dam reservoir which are the main reasons of the decrease of storage volume and difficulty of water management. In this study, the amount and location of soil losses which were evaluated using USLE(Universal Soil Loss Equation) were applied on soil, landcover, and topographical conditions on the basis of satellite images and GIS. Furthermore, it was possible to evaluate the amount of riverbed sediments using echo-sounder and sediment rate were analyzed by comparing with soil losses.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.340-351
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• 2023

Wearable sensors designed for strain, pressure, and temperature measurements are essential for monitoring human movements, health status, physiological data, and responses to external stimuli. Notably, recent research has led to the development of high-performance wearable sensors using innovative materials and device structures that exhibit ultra-high sensitivity compared with their commercial counterparts. However, the quest for accurate sensing has identified a critical challenge. Specifically, the mechanical flexibility of the substrates in wearable sensors can introduce interference signals, particularly when subjected to varying external stimuli and environmental conditions, potentially resulting in signal crosstalk and compromised data fidelity. Consequently, the pursuit of non-interference sensing technology is pivotal for enabling independent measurements of concurrent input signals related to strain, pressure, and temperature, ensuring precise signal acquisition. In this comprehensive review, we present an overview of the recent advances in noninterference sensing strategies. We explore various fabrication methods for sensing strain, pressure, and temperature, emphasizing the use of hybrid composite materials with distinct mechanical properties. This review contributes to the understanding of critical developments in wearable sensor technology that are vital for their ongoing application and evolution in numerous fields.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.277-283
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• 2023

Vital signals provide essential information regarding the health status of individuals, thereby contributing to health management and medical research. Present monitoring methods, such as ECGs (Electrocardiograms) and smartwatches, demand proximity and fixed postures, which limit their applicability. To address this, Non-contact vital signal measurement methods, such as CW (Continuous-Wave) radar, have emerged as a solution. However, unwanted signal components and a stepwise processing approach lead to errors and limitations in heart rate detection. To overcome these issues, this study introduces an integrated neural network approach that combines noise removal, demodulation, and dominant-frequency detection into a unified process. The neural network employed for signal processing in this research adopts a MLP (Multi-Layer Perceptron) architecture, which analyzes the in-phase and quadrature signals collected within a specified time window, using two distinct input layers. The training of the neural network utilizes CW radar signals and reference heart rates obtained from the ECG. In the experimental evaluation, networks trained on different datasets were compared, and their performance was assessed based on loss and frequency accuracy. The proposed methodology exhibits substantial potential for achieving precise vital signals through non-contact measurements, effectively mitigating the limitations of existing methodologies.

• Journal of the Korean Society of Radiology
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• v.85 no.3
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• pp.549-565
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• 2024

MRI is a crucial tool for diagnosing adenomyosis and identifying its related pathologies. To accurately diagnose adenomyosis, it is necessary to recognize both the typical MRI findings and atypical features of the condition. Recently, a standardized classification system has been developed to facilitate precise presurgical diagnosis of adenomyosis and to determine the appropriate treatment method. Differentiating between various subtypes based on MRI-based classification and identifying different MRI phenotypes can aid in categorizing patients with adenomyosis into specific treatment groups and monitoring their response to therapy.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.688-706
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• 2024

Austenitic stainless steels (ASS) are extensively employed in various sectors such as nuclear, power, petrochemical, oil and gas because of their excellent structural strength and resistance to corrosion. SS304 and SS316 are the predominant choices for piping, pressure vessels, heat exchangers, nuclear reactor core components and support structures, but they are susceptible to stress corrosion cracking (SCC) in chloride-rich environments. Over the course of several decades, extensive research efforts have been directed towards evaluating SCC using diverse methodologies and models, albeit some uncertainties persist regarding the precise progression of cracks. This review paper focuses on the application of Acoustic Emission Technique (AET) for assessing SCC damage mechanism by monitoring the dynamic acoustic emissions or inelastic stress waves generated during the initiation and propagation of cracks. AET serves as a valuable non-destructive technique (NDT) for in-service evaluation of the structural integrity within operational conditions and early detection of critical flaws. By leveraging the time domain and time-frequency domain techniques, various Acoustic Emission (AE) parameters can be characterized and correlated with the multi-stage crack damage phenomena. Further theories of the SCC mechanisms are elucidated, with a focus on both the dissolution-based and cleavage-based damage models. Through the comprehensive insights provided here, this review stands to contribute to an enhanced understanding of SCC damage in stainless steels and the potential AET application in nuclear industry.

• The KIPS Transactions:PartA
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• v.11A no.4
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• pp.243-250
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• 2004

Monitoring is used to see if a real-time system provides a service on time. Generally, monitoring for real-time focuses on investigating the current status of a real-time system. To support a stable performance of a real-time system, it should have not only a function to see the current status of real-time process but also a function to predict executions of real-time processes, however. The legacy prediction model has some limitation to apply it to a real-time monitoring. First, it performs a static prediction after a real-time process finished. Second, it needs a statistical pre-analysis before a prediction. Third, transition probability and data about clustering is not based on the current data. We propose the execution prediction model based on learning algorithm to solve these problems and apply it to real-time monitoring. This model gets rid of unnecessary pre-processing and supports a precise prediction based on current data. In addition, this supports multi-level prediction by a trend analysis of past execution data. Most of all, We designed the model to support dynamic prediction which is performed within a real-time process' execution. The results from some experiments show that the judgment accuracy is greater than 80% if the size of a training set is set to over 10, and, in the case of the multi-level prediction, that the prediction difference of the multi-level prediction is minimized if the number of execution is bigger than the size of a training set. The execution prediction model proposed in this model has some limitation that the model used the most simplest learning algorithm and that it didn't consider the multi-regional space model managing CPU, memory and I/O data. The execution prediction model based on a learning algorithm proposed in this paper is used in some areas related to real-time monitoring and control.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.3
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• pp.220-237
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• 2021

Since the functional importance of marine phytoplankton was firstly advocated from early 1880s massive data on the species composition and abundance were produced by classical microscopic observation and the advanced auto-imaging technologies. Recently, pigment composition resulted from direct chemical analysis of phytoplankton samples or indirect remote sensing could be used for the group-specific quantification, which leads us to more diversified data production methods and for more improved spatiotemporal accessibilities to the target data-gathering points. In quite a few cases of many long-term marine ecosystem monitoring programs the phytoplankton species composition and abundance was included as a basic monitoring item. The phytoplankton data could be utilized as a crucial evidence for the long-term change in phytoplankton community structure and ecological functioning at the monitoring stations. Usability of the phytoplankton data sometimes is restricted by the differences in data producers throughout the whole monitoring period. Methods for sample treatments, analyses, and species identification of the phytoplankton species could be inconsistent among the different data producers and the monitoring years. In-depth study to determine the precise quantitative values of the phytoplankton species composition and abundance might be begun by Victor Hensen in late 1880s. International discussion on the quality assurance of the marine phytoplankton data began in 1969 by the SCOR Working Group 33 of ICSU. Final report of the Working group in 1974 (UNESCO Technical Papers in Marine Science 18) was later revised and published as the UNESCO Monographs on oceanographic methodology 6. The BEQUALM project, the former body of IPI (International Phytoplankton Intercomparison) for marine phytoplankton data QA/QC under ISO standard, was initiated in late 1990. The IPI is promoting international collaboration for all the participating countries to apply the QA/QC standard established from the 20 years long experience and practices. In Korea, however, such a QA/QC standard for marine phytoplankton species composition and abundance data is not well established by law, whereas that for marine chemical data from measurements and analysis has been already set up and managed. The first priority might be to establish a QA/QC standard system for species composition and abundance data of marine phytoplankton, then to be extended to other functional groups at the higher consumer level of marine food webs.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.545-554
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• 2013

Augustine is an active stratovolcano located in southwest of Cook Inlet, about 290 kilometers southwest of Anchorage, Alaska. Between January 11 and 28, 2006, the volcano erupted explosively 14 times. We collected twelve permanent GPS stations operating by Plate Boundary Observatory (PBO) from 2005 to 2011. All data processing was carried out using Bernese GPS Software V5.0 with IGS precise orbit. Static baseline processing by fixing AC59 station was applied for the volcano activity monitoring. AC59 is the nearest (about 24.5 km) station to Augustine volcano, and located on North America Plate including Augustine Island. The test results show inflation (9.7 cm/yr) and deflation (-9.2 cm/yr) of volcano before and after eruption around crater clearly. After volcano activity has reached a plateau, some of the GPS stations installed north of the volcano show ground subsidence phenomenon caused by compaction of pyroclastic flows. These results indicate the possibility of using surface deformation observed by GPS for monitoring and prediction of volcano activity.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.181-190
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• 2011

Since the radar satellite missions such as TerraSAR-X and COSMO-SkyMed were launched in 2007, the spatial resolution of spaceborne SAR(Synthetic Aperture Radar) images reaches about 1 meter at spotlight mode. In 2011, the first Korean SAR satellite, KOMPSAT-5, will be launched, operating at X-band with the highest spatial resolution of 1 m as well. The improved spatial resolution of state-of-the-art SAR sensor suggests expanding InSAR(Interferometric SAR) analysis in urban monitoring. By the way, the shadow and layover phenomena are more prominent in urban areas due to building structure because of inherent side-looking geometry of SAR system. Up to date the most conventional algorithms do not consider the return signals at the frontage of building during InSAR phase and SAR intensity simulation. In this study the new algorithm introducing multi-scattering in layover region is proposed for phase and intensity simulation, which is utilized a precise LIDAR DSM(Digital Surface Model) in urban areas. The InSAR phases simulated by the proposed method are compared with TerraSAR-X spotlight data. As a result, both InSAR phases are well matched, even in layover areas. This study will be applied to urban monitoring using high resolution SAR data, in terms of change detection and displacement monitoring at the scale of building unit.