• International Journal of Advanced Culture Technology
• /
• v.4 no.1
• /
• pp.28-30
• /
• 2016

Due to the high increment in usage and built-in advanced technology of smartphones, human activity recognition relying on smartphone sensor data has become a focused research area. In order to reduce noise of collected data, most of previous studies assume that smartphones are fixed at certain positions. This strategy is impractical for real life applications. To overcome this issue, we here investigate a framework that allows detecting the status of a traveller as idle or moving regardless the position and the direction of smartphones. The application of our work is to estimate the total energy consumption of a traveller during a trip. A number of experiments have been carried out to show the effectiveness of our framework when travellers are not only walking but also using primitive vehicles like motorbikes.

• Proceedings of the International Microelectronics And Packaging Society Conference
• /
• 2004.11a
• /
• pp.3-4
• /
• 2004

A flexible Packaging scheme, which embedded chip packaging, has been developed using a thinned silicon chip. Mechanical characteristics of thinned silicon chips are examined by bending test and finite element analysis. Thinned silicon chips ($t<50{\mu}m$) are fabricated by chemical etching process to avoid possible surface damages on them. These technologies can be use for a real-time monitoring of blood pressure. Our research targets are implantable blood pressure sensor and its telemetric measurement. By winding round the coronary arteries, we can measure the blood pressure by capacitance variation of blood vessel.

• Advanced Composite Materials
• /
• v.16 no.2
• /
• pp.115-134
• /
• 2007

This study proposes two new approaches for identifying damage patterns in a holed CFRP cross-ply laminate using an embedded fiber Bragg grating (FBG) sensor. It was experimentally confirmed that the reflection spectrum from the embedded FBG sensor was significantly deformed as the damage near the hole (i.e. splits, transverse cracks and delamination) extended. The damage patterns were predicted using forward analysis (a damage analysis and an optical analysis) with strain estimation and the proposed damage-identification method as well as the forward analysis only. Forward analysis with strain estimation provided the most accurate damage-pattern estimation and the highest computational efficiency. Furthermore, the proposed damage identification significantly reduced computation time with the equivalent accuracy compared to the conventional identification procedure, by using damage analysis as the initial estimation.

• Journal of the Optical Society of Korea
• /
• v.8 no.4
• /
• pp.168-173
• /
• 2004

Brillouin scattering-based fiber optic sensors are useful to measure strain or temperature in a distributed manner. Since the Brillouin frequency of an optical fiber depends on both the strain and temperature, it is very important to know whether the Brillouin frequency shift is caused by the strain change or temperature change. This article presents a temperature compensation technique of a Brillouin scattering-based fiber optic strain sensor. Both the changes of the Brillouin frequency and the Brillouin gain power is observed for the temperature compensation using a BOTDA sensor system. Experimental results showed that the temperature compensated strain values were highly consistent with actual strain values.

• Journal of Sensor Science and Technology
• /
• v.32 no.6
• /
• pp.378-385
• /
• 2023

Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.42 no.11
• /
• pp.1-8
• /
• 2005

In this paper, we present design and prototyping of a low-cost, integrated multi-functional micro health sensor chip that can be used or embedded in widely consumer devices, such as cell phone and PDA, for monitoring environmental condition including air pressure, temperature and humidity. This research's scope includes basic individual sensor study, architecture for integrating sensors on a chip, fabrication process compatibility and test/evaluation of prototype sensors. The results show that the integrated TPH sensor has good characteristics of ${\pm}\;1\%FS$ of linearity and hysteresis for pressure sensor and temperature sensor and of ${\pm}\;5\%FS$ of linearity and hysteresis But if we use 3rd order approximation for humidity sensor, full scale error becomes much smaller and this will be one of our future study.

• Ceramist
• /
• v.23 no.1
• /
• pp.16-26
• /
• 2020

Recently, polymer-metal composite (IPMC)-based ionic artificial muscle has been drawing a huge attention for its excellent soft actuator performance having outstanding soft actuator performance with efficient conversion of electrical energy to mechanical energy under low working voltage. In addition, light, flexible and soft nature of IPMC and high bending strain response enabled development of versatile sensor application in association with soft actuator. In this paper, current issues of IPMC were discussed including standardizing preparation steps, relaxation under DC bias, inhibiting solvent evaporation, and improving poor output force. Solutions for these drawbacks of IPMC have recently been suggested in recent studies. After following explanation of the IPMC working mechanism, we investigate the main factors that affect the operating performance of the IPMC. Then, we reviewed the optimized IPMC actuator fabrication conditions especially for the preparation process, additive selection for a thicker membrane, water content, solvent substitutes, encapsulation, etc. Lastly, we considered the pros and cons of IPMCs for sensor application in a theoretical and experimental point of view. The strategies discussed in this paper to overcome such deficiencies of IPMCs are highly expected to provide a scope for IPMC utilization in soft robotics application.

• Journal of the Korean Ceramic Society
• /
• v.47 no.1
• /
• pp.92-96
• /
• 2010

Based on the differential electrode equilibria approach, potentiometric YSZ sensors with semiconducting oxide electrodes for CO detection are developed. To improve the selectivity, sensitivity and response-time of the sensor, our strategy includes (a) selection of an oxide with a semiconducting response to CO, (b) addition of other semiconducting materials, (c) addition of a catalyst (Pd), (d) utilization of combined p- and n-type electrodes in one sensor configuration, and (e) optimization of operating temperatures. Excellent sensing performance is obtained by a novel device structure incorporating $La_2CuO_4$ electrodes on one side and $TiO_2$-based electrodes on opposite substrate faces with Pt contacts. The resulting response produces additive effects for the individual $La_2CuO_4$ and $TiO_2$-based electrodes voltages, thereby realizing an even higher CO sensitivity. The device also is highly selective to CO versus NO with minor sensitivity for NO concentration, compared to a notably large CO sensitivity.

• Nuclear Engineering and Technology
• /
• v.54 no.11
• /
• pp.3996-4004
• /
• 2022

The data collected by the In-Situ Decommissioning (ISD) sensors are time-specific, age-specific, and developmental stage-specific. Research has been done on the stream data collected by ISD testbed in the recent few years to seek both frequent episodes and abnormal frequent episodes. Frequent episodes in the data stream have confirmed the daily cycle of the sensor responses and established sequences of different types of sensors, which was verified by the experimental setup of the ISD Sensor Network Test Bed. However, the discovery of abnormal frequent episodes remained a challenge because these abnormal frequent episodes are very small signals and may be buried in the background noise of voltage and current changes. In this work, we proposed Advanced Data Analytics (ADA) methods that are applied to the baseline data to identify frequent episodes and extended our approach by adding more features extracted from the baseline data to discover abnormal frequent episodes, which may lead to the early indicators of ISD system failures. In the study, we have evaluated our approach using the baseline data, and the performance evaluation results show that our approach is able to discover frequent episodes as well as abnormal frequent episodes conveniently.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2003.05a
• /
• pp.249-252
• /
• 2003

This paper propose an advanced circuit for fingerprint sensor signal processing. We increased the voltage between ridge and valley by modifying the parasitic capacitance eliminating circuit of sensor plate. The analog to comparator was designed for comparing the sensor signal voltage with the reference signal voltage. We also propose an exective isolation strategy for removing noise and signal coupling of each sensor pixel. The 128$\times$144 pixel fingerprint sensor circuit was designed and simulated, and the layout was performed.