• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.99-104
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• 2019

Recently, modern people are increasing the incidence of various musculoskeletal diseases due to wrong posture. Prevention is possible through proper posture habit, but it is not easy to recognize posture by oneself. Various studies have been conducted to monitor persistent posture in daily life, but most studies using constrained measurement methods and high-cost measurement equipment are not suitable for daily life. In this paper, we implemented a posture discrimination system using a FSR sensor array that can induce posture correction spontaneously through sitting posture monitoring in daily life. The implemented system is designed as a cushion type so it is easy to apply to existing chair. In addition, it can identify five most common postures in everyday life, and can monitor real-time through Android-based smart-phone monitoring application. For the performance evaluation of the implemented system, each posture was measured 50 times repeatedly. As a result, 97.6% accuracy was confirmed.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.2
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• pp.86-91
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• 2010

We constructed a quasi-distributed fiber-optic sensor network for the safety monitoring in power systems. It is possible to construct many of FBG sensors in a line and to be immune from electromagnetic noise. For demodulation analysis of reflected wavelength from FBG sensor, we proposed a simple and fast system using a InGaAs photo-diode array and a holographic diffraction grating. For accuracy improvement of the proposed demodulation system, we applied a Gaussian line-fitting algorithm. We obtained about 4[pm] of wavelength resolution and stability.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.431-435
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• 2013

Aroma has been used for healing for a long time. The healing effects depend on aroma used. We made gas sensor array system to classify aromas systematically. We used outputs of sensors as the input to IAFC neural network. Results show that the neural network successfully classified jasmine, orange, roman chamomile, and lavender into 4 classes, and classified without any error.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.1
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• pp.19-29
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• 2003

This paper proposes a CMOS readout circuit for uncooled micro-bolometer arrays adopting a four-point step calibration technique. The proposed readout circuit employing an 11b analog-to-digital converter (ADC), a 7b digital-to-analog converter (DAC), and an automatic gain control circuit (AGC) extracts minute infrared (IR) signals from the large output signals of uncooled micro-bolometer arrays including DC bias currents, inter-pixel process variations, and self-heating effects. Die area and Power consumption of the ADC are minimized with merged-capacitor switching (MCS) technique adopted. The current mirror with high linearity is proposed at the output stage of the DAC to calibrate inter-pixel process variations and self-heating effects. The prototype is fabricated on a double-poly double-metal 1.2 um CMOS process and the measured power consumption is 110 ㎽ from a 4.5 V supply. The measured differential nonlinearity (DNL) and integrat nonlinearity (INL) of the 11b ADC show $\pm$0.9 LSB and $\pm$1.8 LSB, while the DNL and INL of the 7b DAC show $\pm$0.1 LSB and $\pm$0.1 LSB.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.06a
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• pp.356-360
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• 2002

Polluting the air with such pollutants as CO, H₂S and SO₂, industrial development huts increased the danger of gas toxication. Futhermore, as the: living standard goes higher, the consumption of explosive hydrocarbonic gases such as butane(C₄H/sub 10/) or propane(C₃H/sub 8/) has been soaring, which results in the danger of a gas explosion. As measures to cope with such dangers, the development of highly sensitive gas sensors, gas detectors adopting gas-sensing technologies, and gas recognition systems are urgently required. The objective of the present research is to develop a gas recognition system that is capable of identifying specific types of selected gases by formulating a semiconductor-typed gas sensor array, which not only improves the selectivity of semiconductor-typed gas sensors but also minimizes the erect of drifts on a single sensor signal, and applying the input pattern data of gases detected by the array to a neural network.

• Journal of Sensor Science and Technology
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• v.8 no.6
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• pp.461-468
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• 1999

In this paper, we have implemented a gas recognition system for classification and identification of explosive gases such as methane, propane, and butane using a sensor array and an artificial neural network. Such explosive gases which can be usually detected in the oil factory and LPG pipeline are very dangerous for a human being. We analyzed the characteristics of a multi-dimensional sensor signals obtained from the nine sensors using the principal component analysis(PCA) technique. Also, we implemented a gas pattern recognizer using a multi-layer neural network with error back propagation learning algorithm, which can classify and identify the sorts of gases and concentrations for each gas. The simulation and experimental results show that the proposed gas recognition system is effective to identify the explosive gases. And also, we used DSP board(TMS320C31) to implement the proposed gas recognition system using the neural network for real time processing.

• Journal of Sensor Science and Technology
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• v.9 no.6
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• pp.396-403
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• 2000

The low- frequency acoustic sensor using the recently developed FBG has an excellent merits which the existing fiber-optic sensor has and also it has an excellent signal sensing effect in the environment of low-frequency($30Hz{\sim}300Hz$). Furthermore, we can expect the utilization of low-frequency signal defection instead of existing microphones in the environment of electric noise and also it can be developed as the high-sensibility multiplexing through the sensor array system.

• The Journal of the Acoustical Society of Korea
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• v.27 no.5
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• pp.221-228
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• 2008

To estimate array shape with reference sources in SONAR systems, nearfield signal modeling is required for the reference sources near a towed array. Array shape estimation method based on the nearfield signal modeling generally exploits the spatial covariance matrix of the received reference sources. Among those method, nearfield eigenvector method uses the eigenvector corresponding to the maximum eigenvalue as a steering vector of the reference source. In this paper, we propose a simplified subspace fitting method based on the nearfield signal modeling with spherical wave modeling. Furthermore, we analyze performance of the array shape estimation methods based on the nearfield signal modeling for various environments. The results of the numerical experiments indicate that the simplified subspace fitting method and the nearfield eigenvector method with single reference source shows almost similar performance. Furthermore, the simplified subspace fitting method with 2 reference sources consistently estimates the shape of the array regardless of the incident angle of the reference sources, whereas the nearfield eigenvector method cannot apply for the case of 2 reference sources.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.9
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• pp.31-39
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• 2011

In this paper, we propose a signal readout system with small area and low power consumption for CNT sensor arrays. The proposed system consists of signal readout circuitry, a digital controller, and UART I/O. The key components of the signal readout circuitry are 64 transimpedance amplifiers (TIA) and SAR-ADC with 11-bit resolution. The TIA adopts an active input current mirror (AICM) for voltage biasing and current amplification of a sensor. The proposed architecture can reduce area and power without sampling rate degradation because the 64 TIAs share a variable gain amplifier (VGA) which needs large area and high power due to resistive feedback. In addition, the SAR-ADC is designed for low power with modified algorithm where the operation of the lower bits can be skipped according to an input voltage level. The operation of ADC is controlled by a digital controller based on UART protocol. The data of ADC can be monitored on a computer terminal. The signal readout circuitry was designed with 0.13${\mu}m$ CMOS technology. It occupies the area of 0.173 $mm^2$ and consumes 77.06${\mu}W$ at the conversion rate of 640 samples/s. According to measurement, the linearity error is under 5.3% in the input sensing current range of 10nA - 10${\mu}A$. The UART I/O and the digital controller were designed with 0.18${\mu}m$ CMOS technology and their area is 0.251 $mm^2$.

• Applied Biological Chemistry
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• v.47 no.3
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• pp.300-306
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• 2004

Eight cation or anion-responsive polymer membranes were prepared by a casting procedure employing polyvinyl chloride, Bis (2-ethylhexyl)sebacate and each electroactive material in the ratio of 66 : 33 : 1. The resulting membranes were separately installed onto the sensitive area of the ionic electrodes to produce an 8-channel taste sensor array. The taste sensors of the array were connected to a high-input impedance amplifier and the amplified sensor signals were interfaced to a PC via an A/D converter. The taste evaluation system was applied to a discriminant analysis on six groups of marketed beverages like sikhye, sujunggwa, tangerine juice, ume juice, ionic drink and green tea. When the signal data from the sensor array were analyzed by principal component analysis after normalization, the 1st, 2nd and 3rd principal component explained most of the total data variance. The six groups of the analyzed beverages were discriminated well in the three dimensional principal component space. The half of the five groups of the analyzed beverages was also discriminated in the two dimensional principal component plane.