• International Journal of Aeronautical and Space Sciences
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• 제1권2호
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• pp.50-67
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• 2000

The spacecraft simulator is used for command validation, operational check of the Satellite Operation Subsystem (SOS), spacecraft anomaly analysis support, satellite operator training etc. In this paper, S/W design features and modeling characteristics of the KOMPSAT Spacecraft Simulator Subsystem (SIM) are described. Validation procedures and simulation results are also provided. The SIM provides extensive simulation capabilities by including models for most of the spacecraft subsystems. The software structure of the SIM was designed and implemented so as to support operations not only in real-time but also in non real-time by utilizing the Hewlett Packard (HP) UNIX functions. The SIM incorporates as many user-friendly Man Machine Interface (MMI) windows as possible so that all the SIM normal operations can be executed through the MMI windows.

• Smart Structures and Systems
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• 제11권5호
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• pp.477-496
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• 2013

Due to their cost-effectiveness and ease of installation, wireless smart sensors (WSS) have received considerable recent attention for structural health monitoring of civil infrastructure. Though various wireless smart sensor networks (WSSN) have been successfully implemented for full-scale structural health monitoring (SHM) applications, monitoring of low-level ambient strain still remains a challenging problem for WSS due to A/D converter (ADC) resolution, inherent circuit noise, and the need for automatic operation. In this paper, the design and validation of high-precision strain sensor board for the Imote2 WSS platform and its application to SHM of a cable-stayed bridge are presented. By accurate and automated balancing of the Wheatstone bridge, signal amplification of up to 2507-times can be obtained, while keeping signal mean close to the center of the ADC span, which allows utilization of the full span of the ADC. For better applicability to SHM for real-world structures, temperature compensation and shunt calibration are also implemented. Moreover, the sensor board has been designed to accommodate a friction-type magnet strain sensor, in addition to traditional foil-type strain gages, facilitating fast and easy deployment. The wireless strain sensor board performance is verified through both laboratory-scale tests and deployment on a full-scale cable-stayed bridge.

• Smart Structures and Systems
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• 제6권5_6호
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• pp.661-673
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• 2010

This paper presents recent developments in an extremely compact, wireless impedance sensor node (the WID3, $\underline{W}$ireless $\underline{I}$mpedance $\underline{D}$evice) for use in high-frequency impedance-based structural health monitoring (SHM), sensor diagnostics and validation, and low-frequency (< ~1 kHz) vibration data acquisition. The WID3 is equipped with an impedance chip that can resolve measurements up to 100 kHz, a frequency range ideal for many SHM applications. An integrated set of multiplexers allows the end user to monitor seven piezoelectric sensors from a single sensor node. The WID3 combines on-board processing using a microcontroller, data storage using flash memory, wireless communications capabilities, and a series of internal and external triggering options into a single package to realize a truly comprehensive, self-contained wireless active-sensor node for SHM applications. Furthermore, we recently extended the capability of this device by implementing low-frequency analog-to-digital and digital-to-analog converters so that the same device can measure structural vibration data. The compact sensor node collects relatively low-frequency acceleration measurements to estimate natural frequencies and operational deflection shapes, as well as relatively high-frequency impedance measurements to detect structural damage. Experimental results with application to SHM, sensor diagnostics and low-frequency vibration data acquisition are presented.

• Structural Engineering and Mechanics
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• 제49권4호
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• pp.509-521
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• 2014

In this paper two protocols of Wireless Sensor Networks (WSN) are examined through both a simulation and a case study. The simulation was performed with the optimized network (OPNET) simulator while comparing the performance of the Ad-Hoc on demand Distance Vector (AODV) and the Dynamic Source Routing (DSR) protocols. This is compared and shown with real-world measurement of deflection from eight wireless sensor nodes. The wireless sensor response results were compared with accelerometer sensors for validation purposes. It was found that although the computer simulation suggests the AODV protocol is more accurate, in the case study no distinct difference was found. However, it was shown that AODV is still more beneficial in the field as it has a longer battery life enabling longer surveying times. This is a significant finding as a large factor in determining the use of wireless network sensors as a method of assessing structural response has been their short battery life. Thus if protocols which enhance battery life, such as the AODV protocol, are employed it may be possible in the future to couple wireless networks with solar power extending their monitoring periods.

• 소음진동
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• 제9권1호
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• pp.70-76
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• 1999

This paper presents a pseudo-sensor-output-feedback(PSOF) method for the vibration suppression of the flexible piezoelectric smart structures. This method reduces the modeling errors using pseudo sensors in the output equation formulation. It also reduces computation time in practice. since the output equation does not need the state observer required in the state space equation. Experimental works are performed for the validation of theoretical predictions with the piezoelectric sensor and actuator bonded on the cantilever beam. An algorithm based on the sliding mode control theory is developed and analyzed for the robustness to the modeling errors and parameter uncertainties. This study also discusses the characteristics of the active and semi-active systems.

• 대한전기학회논문지
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• 제43권7호
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• pp.1169-1177
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• 1994

The function estimation characteristics of neural networks can be used sensor signal estimation of the nuclear power plants. In case of applying the neural network to the signal estimation of redundant sensors, it is an important problem that the redundant sensor signals used as the input signals of neural network should be validated. In this paper, we simplify the conventional parity space method in order to input the validated signal to the neural network and lso propose the sensor signal validation method, which estimates the reliable sensor output combining the neural network with the simplified parity space method. The acceptability of the proposed process variable estimation method is demonstrated by using the simulation data in safety injection accident of the nuclear power plant.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.227-236
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• 2019

In this study, we develop a Korotkoff sound based automatic blood pressure measurement device including sensor, hardware, and analysis algorithm. PVDF-based sensor pattern was developed to function as a vibration sensor to detect of Korotkoff sounds, and the film's output was connected to an impedance-matching circuit. An algorithm for determining starting and ending points of the Korotkoff sounds was established, and clinical data from subjects were acquired and analyzed to find the relationship between the values obtained by the auscultatory method and from the developed device. The results from 86 out of 90 systolic measurements and 84 out of 90 diastolic measurements indicate that the developed device pass the validation criteria of the international protocol. Correlation coefficients for the values obtained by the auscultatory method and from the developed device were 0.982 and 0.980 for systolic and diastolic blood pressure, respectively. Blood pressure measurements based on Korotkoff sound signals obtained by using the developed PVDF film-based sensor module are accurate and highly correlated with measurements obtained by the traditional auscultatory method.

• Smart Structures and Systems
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• 제6권5_6호
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• pp.561-578
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• 2010

This study proposes a multi-level damage localization strategy to achieve an effective damage detection system for civil infrastructure systems based on wireless sensors. The proposed system is designed for use of distributed computation in a wireless sensor network (WSN). Modal identification is achieved using the frequency-domain decomposition (FDD) method and the peak-picking technique. The ASH (angle-between-string-and-horizon) and AS (axial strain) flexibility-based methods are employed for identifying and localizing damage. Fundamentally, the multi-level damage localization strategy does not activate all of the sensor nodes in the network at once. Instead, relatively few sensors are used to perform coarse-grained damage localization; if damage is detected, only those sensors in the potentially damaged regions are incrementally added to the network to perform finer-grained damage localization. In this way, many nodes are able to remain asleep for part or all of the multi-level interrogations, and thus the total energy cost is reduced considerably. In addition, a novel distributed computing strategy is also proposed to reduce the energy consumed in a sensor node, which distributes modal identification and damage detection tasks across a WSN and only allows small amount of useful intermediate results to be transmitted wirelessly. Computations are first performed on each leaf node independently, and the aggregated information is transmitted to one cluster head in each cluster. A second stage of computations are performed on each cluster head, and the identified operational deflection shapes and natural frequencies are transmitted to the base station of the WSN. The damage indicators are extracted at the base station. The proposed strategy yields a WSN-based SHM system which can effectively and automatically identify and localize damage, and is efficient in energy usage. The proposed strategy is validated using two illustrative numerical simulations and experimental validation is performed using a cantilevered beam.

• 대한원격탐사학회지
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• 제38권1호
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• Journal of Mechanical Science and Technology
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• 제17권12호
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• pp.1844-1854
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• 2003

This paper discusses details of modeling and robust control of an AMB (active magnetic bearing) spindle, and part I presents a modeling and validation process of the AMB spindle. There are many components in AMB spindle : electromagnetic actuator, sensor, rotor, power amplifier and digital controller. If each component is carefully modeled and evaluated, the components have tight structured uncertainty bounds and achievable performance of the system increases. However, since some unknown dynamics may exist and the augmented plant could show some discrepancy with the real plant, the validation of the augmented plant is needed through measuring overall frequency responses of the actual plant. In addition, it is necessary to combine several components and identify them with a reduced order model. First, all components of the AMB spindle are carefully modeled and identified based on experimental data, which also render valuable information in quantifying structured uncertainties. Since sensors, power amplifiers and discretization dynamics can be considered as time delay components, such dynamics are combined and identified with a reduced order. Then, frequency responses of the open-loop plant are measured through closed-loop experiments to validate the augmented plant. The whole modeling process gives an accurate nominal model of a low order for the robust control design.