• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.3
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• pp.233-239
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• 2013

This paper includes the algorithm of eliminating noise, the processing technique of sensor and the results of designing vital and environmental sensor, one of the survivability subsystem of Future Soldier System. In this paper, we propose the adaptive filtering, moving noise removal in order to detect signals stabilized. And these help that we get bio-signals the ECG calculating methods such as search back and ensemble method. It is made up the vital and environmental sensor including the flexible sensor. In that sense, this study can be applied when it is planned the modular type Future Soldier System.

• Smart Structures and Systems
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• v.28 no.6
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• pp.727-735
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• 2021

Recently, non-destructive health monitoring methods such as magnetic flux leakage (MFL) method, have become popular due to their advantages over destructive methods. Currently, numerical study on this field has been limited to simplified studies by only obtaining MFL instead of induced voltage inside coil sensor. In this study, it was proposed to perform a novel numerical simulation of MFL's coil sensor by considering vital parameters including specimen's motion with constant velocity and saturation status of specimen in time domain. A steel-rod specimen with two stepwise cross-sectional changes (i.e., 21% and 16%) was fabricated using low carbon steel. In order to evaluate the results of numerical simulation, an experimental test was also conducted using a magnetic probe, with same size specimen and test parameters, exclusively. According to comparative results of numerical simulation and experimental test, similar signal amplitude and signal pattern were observed. Thus, proposed numerical simulation method can be used as a reliable source to check efficiency of sensor probe when different size specimens with different defects should be inspected.

• Journal of Sensor Science and Technology
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• v.31 no.6
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• pp.433-440
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• 2022

Recently, owing to global warming, average summer temperatures are increasing and the number of hot days is increasing is increasing, which leads to an increase in heat stroke. In particular, outdoor workers directly exposed to the heat are at higher risk of heat stroke; therefore, preventing heat-related illnesses and managing safety have become important. Although various wearable devices have been developed to prevent heat stroke for outdoor workers, applying various sensors to the safety helmets that workers must wear is an excellent alternative. In this study, we developed a smart helmet that measures various vital signs of the wearer such as body temperature, heart rate, and sweat rate; external environmental signals such as temperature and humidity; and movement signals of the wearer such as roll and pitch angles. The smart helmet can acquire the various data by connecting with a smartphone application. Environmental data can check the status of heat wave advisory, and the individual vital signs can monitor the health of workers. In addition, we developed an algorithm that classifies the risk of heat-related illness as normal and abnormal by inputting a set of vital signs of the wearer using a support vector machine technique, which is a machine learning technique that allows for rapid binary classification with high reliability. Furthermore, the classified results suggest that the safety manager can supervise the prevention of heat stroke by receiving feedback from the control system.

• 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.

• Textile Coloration and Finishing
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• v.23 no.3
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• pp.219-226
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• 2011

Recently, study of functional clothing for vital sensing is focused on improving conductivity and decreasing resistance, in order to enhance the electrocardiogram(ECG) sensing accuracy and obtained stable environmental durability on operation condition. In this study, four ECG fabrics that having different componnt yarns and weaving structures were produced to analyze their environmental durabilities and electric properties under general operation conditions including different physical and chemical stimulation. For outstanding electric properties and physical properties, the optimized ECG sensing fabric should consist of a fabric of 2 up 3 down twill structure containing 210de silver-coated conductive yarns and polyester yarn in warp and weft directions respectively. The selected fabric has $0.11{\Omega}$ which is relative lower resistance than otherwisely produced fabrics under ECG measurement condition. And it has 7% stable resistance changes under 25% strain and repeated strain.

• Journal of KIISE:Information Networking
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• v.35 no.1
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• pp.76-90
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• 2008

Wireless sensor networks are expected to play a vital role in the upcoming age of ubiquitous computing such as home environmental, industrial, and military applications. Compared with the vivid utilization of the sensor networks, however, security and privacy issues of the sensor networks are still in their infancy because unique challenges of the sensor networks make it difficult to adopt conventional security policies. Especially, node compromise is a critical threat because a compromised node can drain out the finite amount of energy resources in battery-powered sensor networks by launching various insider attacks such as a false data injection. Even cryptographic authentication mechanisms and key management schemes cannot suggest solutions for the real root of the insider attack from a compromised node. In this paper, we propose a novel trust-based secure aggregation scheme which identifies trustworthiness of sensor nodes and filters out false data of compromised nodes to make resilient sensor networks. The proposed scheme suggests a defensible approach against the insider attack beyond conventional cryptographic solutions. The analysis and simulation results show that our aggregation scheme using trust evaluation is more resilient alternative to median.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.430-439
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• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.301-310
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• 2022

Accurate prediction of mixed ground conditions ahead of a tunnel face is of vital importance for safe excavation using tunnel boring machines (TBMs). Previous studies have primarily focused on electrical resistivity surveys from the ground surface for geotechnical investigation. In this study, an FE (finite element) numerical model was developed to simulate electrical resistivity surveys for the prediction of risky mixed ground conditions in front of a tunnel face. The proposed FE model is validated by comparing with the apparent electrical resistivity values obtained from the analytical solution corresponding to a vertical fault on the ground surface (i.e., a simplified model). A series of parametric studies was performed with the FE model to analyze the effect of geological and sensor geometric conditions on the electrical resistivity survey. The parametric study revealed that the interface slope between two different ground formations affects the electrical resistivity measurements during TBM excavation. In addition, a large difference in electrical resistivity between two different ground formations represented the dramatic effect of the mixed ground conditions on the electrical resistivity values. The parametric studies of the electrode array showed that the proper selection of the electrode spacing and the location of the electrode array on the tunnel face of TBM is very important. Thus, it is concluded that the developed FE numerical model can successfully predict the presence of a mixed ground zone, which enables optimal management of potential risks.

• Journal of Sensor Science and Technology
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• v.20 no.5
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• pp.311-316
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• 2011

Monitoring the carbon dioxide concentration in arterial blood is vital for the evaluation and prevention of pulmonary disease. Yet, domestic pure arterial blood carbon dioxide sensor technologies are not being developed, instead all sensors are imported. In this paper, we develop a real time monitoring system for arterial blood partial pressure of carbon dioxide($pCO_2$) gas from the wrist by using a carbon micro-heater. The micro-heater was fabricated with a thickness of 0.3 ${\mu}m$ in order to collect the carbon dioxide under the skin. The micro-heater has been designed to perform temperature compensation in order to prevent damage to the skin. Two clinical trials of the system were undertaken. As a result, we demonstrated that a portable, transcutaneous carbon dioxide analysis($TcpCO_2$) device produced domestically is possible. In addition, this system reduced the analysis time significantly. Carbon films could reduce the unit price of these sensors by replacing the gold film used in foreign models. Also, we developed a real time monitoring system which can be used with optical biosensors for medical diagnostics as well as gas sensors for environmental monitoring.

• Smart Structures and Systems
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• v.29 no.1
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.