• Proceedings of the KSRS Conference
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• v.2
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• pp.578-581
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• 2006

Usually to achieve precise geolocation of satellite images, we need to get GCPs (Ground control points) from individual scenes. This requirement greatly increases the cost and processing time for satellite mapping. In this article, we focus on finding appropriate sensor models for entire image strips composing of several adjacent scenes. We tested the feasibility of modelling whole satellite image strips by establishing sensor models of one scene with GCPs and by applying the models to neighboring scenes without GCPs. For this, we developed two types of sensor models: collinearity-based type and orbit-based type and tested them using different sets of unknowns. Results indicated that although the performance of two types was very similar, for modelling individual scenes, it was not for modelling the whole strips. Moreover, the performance of sensor models was remarkably sensitive to different sets of unknowns. It was found that the orbit-based model using attitude biases as unknowns can be used to model SPOT image strips of 420 Km in length.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.91-94
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• 2005

A numerical formula that presents relationship between a point of a satellite image and its ground position is called a sensor model. For precise geolocation of satellite images, we need an error-free sensor model. However, the sensor model based on GOES ephemeris data has some error, in particular after Image Motion Compensation (IMC) mechanism has been turned off. To solve this problem, we investigate three sensor models: Collinearity model, Direct Linear Transform (DLT) model and Orbit-based model. We apply matching between GOES images and global coastline database and use successful results as control points. With control points we improve the initial image geolocation accuracy using the three models. We compare results from three sensor models that are applied to GOES-9 images. As a result, a suitable sensor model for precise geolocation of GOES-9 images is proposed.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.217-220
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• 2005

Currently, a number of control points are required in order to achieve accurate geolocation of satellite images. Control points can be generated from existing maps or surveying, or, preferably, from GPS measurements. The requirement of control points increase the cost of satellite mapping, let alone it makes the mapping over inaccessible areas troublesome. This paper investigates the possibilities of modeling an entire imaging strip with control points obtained from a small portion of the strip. We tested physical sensor models that were based on satellite orbit and attitude angles. It was anticipated that orbit modeling needed a sensor model with good accuracy of exterior orientation estimation, rather then the accuracy of bundle adjustment. We implemented sensor models with various parameter sets and checked their accuracy when applied to the scenes on the same orbital strip together with the bundle adjustment accuracy and the accuracy of estimated exterior orientation parameters. Results showed that although the models with good bundle adjustments accuracy did not always good orbit modeling and that the models with simple unknowns could be used for orbit modeling.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1307-1313
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• 2017

Sensor networks are composed of provide low powered, inexpensive distributed devices which can be deployed over enormous physical spaces. Coordination between sensor devices is required to achieve a common communication. In low cost, low power and short-range wireless environment, sensor networks cope with significant resource constraints. Security is one of main issues in wireless sensor networks because of potential adversaries. Several security protocols and models have been implemented for communication on computing devices but deployment these models and protocols into the sensor networks is not easy because of the resource constraints mentioned. Memory intensive encryption algorithms as well as high volume of packet transmission cannot be applied to sensor devices due to its low computational speed and memory. Deployment of sensor networks without security mechanism makes sensor nodes vulnerable to potential attacks. Therefore, attackers compromise the network to accept malicious sensor nodes as legitimate nodes. This paper provides the different security models as a metric, which can then be used to make pertinent security decisions for securing wireless sensor network communication.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.2
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• pp.137-145
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• 2005

In this paper, we investigate the accuracy of various sensor models developed for linear pushbroom satellite images. We define the accuracy of a sensor model in two aspects: the accuracy of bundle adjustments and the accuracy of estimating exterior orientation parameters. The first accuracy has been analyzed and reported frequently whereas the second accuracy has somewhat been neglected. We argue that the second accuracy is as important as the first one. The second accuracy describes a model's ability to predict satellite orbit and attitude, which has many direct and indirect applications. Analysis was carried out on the traditional collinearity-based sensor models and orbit-based sensor models. Collinearity-based models were originally developed for aerial photos and modified for linear pushbroom-type satellite images. Orbit-based models have been used within satellite communities for satellite control and orbit determination. Models were tested with two Kompsat-1 EOC scenes and GPS-driven control points. Test results showed that orbit-based models produced better estimation of exterior orientation parameters while maintained comparable accuracy on bundle adjustments.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6D
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• pp.1025-1032
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• 2006

Sensor modeling of high-resolution satellites is a prerequisite procedure for mapping and GIS applications. Sensor models, describing the geometric relationship between scene and object, are divided into two main categories, which are rigorous and approximate sensor models. A rigorous model is based on the actual geometry of the image formation process, involving internal and external characteristics of the implemented sensor. However, approximate models require neither a comprehensive understanding of imaging geometry nor the internal and external characteristics of the imaging sensor, which has gathered a great interest within photogrammetric communities. This paper described a comparison between rigorous and various approximate sensor models that have been used to determine three-dimensional positions, and proposed the appropriate sensor model in terms of the satellite imagery usage. Through the case study of using IKONOS satellite scenes, rigorous and approximate sensor models have been compared and evaluated for the positional accuracy in terms of acquirable number of ground controls. Bias compensated RFM(Rational Function Model) turned out to be the best among compared approximate sensor models, both modified parallel projection and parallel-perspective model were able to be modelled with a small number of controls. Also affine transformation, one of the approximate sensor models, can be used to determine the planimetric position of high-resolution satellites and perform image registration between scenes.

• Korean Journal of Geomatics
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• v.4 no.1
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• pp.31-37
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• 2004

The aim of this research is comparing the existing approximation models (e.g. Affine Transformation and Direct Linear Transformation) with Rational Function Model as a substitute of rigorous sensor model of linear array scanner, especially push-broom sensor. To do so, this research investigates the mathematical model of each approximation method. This is followed by the assessments of accuracy of transformation from object space to image space by using simulated data generated by collinearity equations which incorporate or depict the physical aspects of linear array sensor.

• Nuclear Engineering and Technology
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• v.39 no.2
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• pp.133-142
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• 2007

The Electric Power Research Institute (EPRI) demonstrated a method for monitoring the performance of instrument channels in Topical Report (TR) 104965, 'On-Line Monitoring of Instrument Channel Performance.' This paper presents the results of several models originally developed by EPRI to monitor three nuclear plant sensor sets: Pressurizer Level, Reactor Protection System (RPS) Loop A, and Reactor Coolant System (RCS) Loop A Steam Generator (SG) Level. The sensor sets investigated include one redundant sensor model and two non-redundant sensor models. Each model employs an Auto-Associative Kernel Regression (AAKR) model architecture to predict correct sensor behavior. Performance of each of the developed models is evaluated using four metrics: accuracy, auto-sensitivity, cross-sensitivity, and newly developed Error Uncertainty Limit Monitoring (EULM) detectability. The uncertainty estimate for each model is also calculated through two methods: analytic formulas and Monte Carlo estimation. The uncertainty estimates are verified by calculating confidence interval coverages to assure that 95% of the measured data fall within the confidence intervals. The model performance evaluation identified the Pressurizer Level model as acceptable for on-line monitoring (OLM) implementation. The other two models, RPS Loop A and RCS Loop A SG Level, highlight two common problems that occur in model development and evaluation, namely faulty data and poor signal selection

• Journal of National Security and Military Science
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• s.1
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• pp.191-229
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• 2003

Multi-sensor data fusion techniques combine evidences from multiple sensors in order to get more accurate and efficient meaningful information through several process levels that may not be possible from a single sensor alone. One of the most important parts in the data fusion system is the identification fusion, and it can be categorized into physical models, parametric classification and cognitive-based models, and parametric classification technique is usually used in multi-sensor data fusion system by its characteristic. In this paper, we propose a novel heuristic identification fusion method in which we adopt desirable properties from not only parametric classification technique but also cognitive-based models in order to meet the realtime processing requirements.

• Structural Monitoring and Maintenance
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• v.5 no.2
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• pp.273-295
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• 2018

In this study, an investigation of a damage detection methodology for global condition assessment is presented. A particular emphasis is put on the utilization of wireless sensors for more practical, less time consuming, less expensive and safer monitoring and eventually maintenance purposes. Wireless sensors are deployed with a sensor roving technique to maintain a dense sensor field yet requiring fewer sensors. The time series analysis method called ARX models (Auto-Regressive models with eXogeneous input) for different sensor clusters is implemented for the exploration of artificially induced damage and their locations. The performance of the technique is verified by making use of the data sets acquired from a 4-span bridge-type steel structure in a controlled laboratory environment. In that, the free response vibration data of the structure for a specific sensor cluster is measured by both wired and wireless sensors and the acceleration output of each sensor is used as an input to ARX model to estimate the response of the reference channel of that cluster. Using both data types, the ARX based time series analysis method is shown to be effective for damage detection and localization along with the interpretations and conclusions.