• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1549-1552
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• 2003

Semiconductor gas sensor using tin oxide as sensing material has been used to detect gases based on the fact that impedance of the sensing material varies when the gas sensor is exposed to the gases. This variation comprises of two parts. The first one is variation in resistance of the sensing material and the other is expressed in terms of the sensor capacitance variation. Normally, only variation of the sensor resistance is considered. In this paper, using AC measurement with a capacitor-coupled inverting amplifier circuit, both changes in the sensor resistance and variations in the sensor capacitance were investigated. These characteristics were represented as magnitude gain and phase shift of AC signal at a specific frequency after passing it through the sensor and the designed circuit. A two-stage artificial neural network, which utilized the information above, was employed to identify and quantify three combustible gases: methane, propane and butane. The network outputs were approximately proportional to concentrations of test gases with reasonable level of error.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3145-3162
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• 2023

As an important technology of the internetwork, wireless sensor network technique plays an important role in indoor localization. Non-line-of-sight (NLOS) problem has a large effect on indoor location accuracy. A location algorithm based on improved particle filter and directional probabilistic data association (IPF-DPDA) for WSN is proposed to solve NLOS issue in this paper. Firstly, the improved particle filter is proposed to reduce error of measuring distance. Then the hypothesis test is used to detect whether measurements are in LOS situations or NLOS situations for N different groups. When there are measurements in the validation gate, the corresponding association probabilities are applied to weight retained position estimate to gain final location estimation. We have improved the traditional data association and added directional information on the original basis. If the validation gate has no measured value, we make use of the Kalman prediction value to renew. Finally, simulation and experimental results show that compared with existing methods, the IPF-DPDA performance better.

• Wind and Structures
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• v.36 no.4
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• pp.237-247
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• 2023

The emergence of high-rise buildings has necessitated frequent structural health monitoring and maintenance for safety reasons. Wind causes damage and structural changes on tall structures; thus, safe structures should be designed. The pressure developed on tall buildings has been utilized in previous research studies to assess the impacts of wind on structures. The wind tunnel test is a primary research method commonly used to quantify the aerodynamic characteristics of high-rise buildings. Wind pressure is measured by placing pressure sensor taps at different locations on tall buildings, and the collected data are used for analysis. However, sensors may malfunction and produce erroneous data; these data losses make it difficult to analyze aerodynamic properties. Therefore, it is essential to generate missing data relative to the original data obtained from neighboring pressure sensor taps at various intervals. This study proposes a deep learning-based, deep convolutional generative adversarial network (DCGAN) to restore missing data associated with faulty pressure sensors installed on high-rise buildings. The performance of the proposed DCGAN is validated by using a standard imputation model known as the generative adversarial imputation network (GAIN). The average mean-square error (AMSE) and average R-squared (ARSE) are used as performance metrics. The calculated ARSE values by DCGAN on the building model's front, backside, left, and right sides are 0.970, 0.972, 0.984 and 0.978, respectively. The AMSE produced by DCGAN on four sides of the building model is 0.008, 0.010, 0.015 and 0.014. The average standard deviation of the actual measures of the pressure sensors on four sides of the model were 0.1738, 0.1758, 0.2234 and 0.2278. The average standard deviation of the pressure values generated by the proposed DCGAN imputation model was closer to that of the measured actual with values of 0.1736,0.1746,0.2191, and 0.2239 on four sides, respectively. In comparison, the standard deviation of the values predicted by GAIN are 0.1726,0.1735,0.2161, and 0.2209, which is far from actual values. The results demonstrate that DCGAN model fits better for data imputation than the GAIN model with improved accuracy and fewer error rates. Additionally, the DCGAN is utilized to estimate the wind pressure in regions of buildings where no pressure sensor taps are available; the model yielded greater prediction accuracy than GAIN.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.69-80
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• 2019

This paper proposes a multimodal sensor platform which supports plug and play (PnP) technology. PnP technology automatically recognizes a connected sensor module and an application program easily controls a sensor. To verify a multimodal platform for PnP technology, we build up a firmware and have the experiment on a sensor system. When a sensor module is connected to the platform, a firmware recognizes the sensor module and reads sensor data. As a result, it provides PnP technology to simply plug sensors without any software configuration. Measured sensor raw data suffer from various distortions such as gain, offset, and non-linearity errors. Therefore, we introduce a polynomial calculation to compensate for sensor distortions. To find the optimal coefficients for sensor calibration, we apply a genetic algorithm which reduces the calibration time. It achieves reasonable performance using only a few data points with reducing 97% error in the worst case. The platform supports various protocols for multimodal sensors, i.e., UART, I2C, I2S, SPI, and GPIO.

• Journal of Korean Society of Transportation
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• v.13 no.4
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• pp.133-151
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• 1995

The collective of highly reliable traffic data is necessary for traffic signal control. This study is to test application of RTMS sensor to traffic signal control. In order to find out the possibility of its application th traffic signal control, 5 types of experiments were performed. The major findings are as follows ; -The detection are a has been changing according to degree and gain. -At the results of experiments for interference are a measure, Degree 60 is stable condition. -At the results of reliability test for volume and speed. the error rate decreases as speed increases and that of Zone 1 is lower than that of Zone 3. -Two modes are set up for reliability test of traffic volume. It founds that the detection reliability of the stopped vehicles are higher than that of the passing vehicles at sidefire-intersection mode. It founds that the results are vice-versa at sidefire-highway mode. Conclusively, this sensor cannot directly apply to colection of traffic data for traffic signal control. However, this sensor can be substituted for a loop detector which is used popularly for signal control, and freeway traffic control if above faults are made up.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.10
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• pp.1007-1014
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• 2004

This paper presents a study of low frequencies volume velocity vibration control of a smart panel in order to reduce sound transmission. A distributed piezoelectric quadratically shaped polyvinylidene fluoride (PVDF) polymer film is used as a uniform force actuator and an array of $4\;{\times}\;4$ accelerometer is used as a volume velocity sensor for the implementation of a single-input single-output control system. The theoretical and experimental study of sensor-.actuator frequency response function shows that this sensor-actuator arrangement provides a required strictly positive real frequency response function below about 900 Hz. Direct velocity feedback could therefore be implemented with a limited gain which gives reductions of about 15 dB in vibration level and about 8 dB in acoustic power level at the (1,1) mode of the smart panel. It has been also shown that the shaping error of PVDF actuator could limit the stability and performance of the control system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.368-373
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• 2003

This paper presents a study of low frequencies volume velocity vibration control of a smart panel in order to reduce sound transmission. A distributed piezoelectric quadratically shaped polyvinylidene fluoride (PVDF) polymer film is used as a uniform force actuator and an array of 4$\times$4 accelerometer is used as a volume velocity sensor for the implementation of a single-input single-output con rot system. The theoretical and experimental study of sensor-actuator frequency response function sho vs that this sensor-actuator arrangement provides a required strictly positive real frequency response function below about 900Hz. Direct velocity feedback could therefore be implemented with a limited gain which gives reductions of about 15㏈ in vibration level and about 8 ㏈ in acoustic power level at the (1, 1) mode of the smart Panel. It has been also shown that the shaping error of PVDF actuator could limit he stability and performance of the control system.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.195-201
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• 2020

This paper implemented quadcopter-type drone system and proposed the heuristic method for obtaining the optimum gain coefficients in order to minimize the settling time. Control system for quadcopter posture stabilization reads the posture data from accelerator and gyro sensor, revises the original posture data using Mahony filter, and drives 4 DC motors using PID controller. The first step of the proposed method is to obtain the gain coefficients using the Ziegler-Nichols method, and then determine the optimum gain coefficients using the heuristic method at the next 3 steps. The experimental result shows that the maximum overshoot decreases from 44.3 to 29.8 degrees and the settling time decreases from 2.6 to 1.7 seconds compared to the Ziegler-Nichols method. Therefore, we proved that the proposed method works well in quadcopter flight system with high motor noise while reducing trial and error to obtain the optimal PID gain coefficients.

• Journal of IKEEE
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• v.13 no.4
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• pp.63-68
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• 2009

The power management integrated circuit(PMIC) for CCD image sensor is presented in this study. A CCD image sensor is very sensitive against temperature. The temperature, that is heat, is generally generated by the PMIC with low efficiency. Since the generated heat influences performance of CCD image sensor, it should be minimized by using a PMIC which has a high efficiency. In order to develop the PMIC with high efficiency, the input stage is designed with synchronous type step down DC-DC converter. The operating range of the converter is from 5V to 15V and the converter is controlled using PWM method. The PWM control circuit consists of a saw-tooth generator, a band-gap reference circuit, an error amplifier and a comparator circuit. The saw-tooth generator is designed with 1.2MHz oscillation frequency. The comparator is designed with the two stages OP Amp. And the error amplifier has 40dB DC gain and $77^{\circ}$ phase margin. The output of the step down converter is connected to input stage of the charge pump. The output of the charge pump is connected to input of the LDO which is the output stage of the PMIC. Finally, the PMIC, based on the PWM control circuit and the charge pump and the LDO, has output voltage of 15V, -7.5V, 3.3V and 5V. The PMIC is designed with a 0.35um process.

• Proceedings of the KIPE Conference
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• 2004.11a
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• pp.32-36
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• 2004

In order to achieve high performance vector control, it is essential to measure accurate ac current. The errors generated from current path are inevitable, and they could be divided into two categories: offset error and scaling error. The current data including these errors cause periodic speed ripples which are one and two times of stator electrical frequency respectively. Since these undesirable ripples bring about bad influences to motor driving system, a compensation algorithm must be needed in the control algorithm of the motor drive. In this paper, a new compensation algorithm is proposed. The signal of the integrator output of the d-axis current regulator is chosen and processed to compensate the current measurement errors. The compensation of the current measurement errors is easily implemented to smooth the signal of the integrator output of the d-axis current regulator by subtracting the DC offset value or rescaling the gain of the hall sensor. Therefore, the proposed algorithm has several features: the robustness of the variation of the mechanical parameters, the application of the steady and transient state, the easy implementation, and less computation time.