• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.584-591
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• 2014

This paper presents a novel aided navigation method for AUV (Autonomous Underwater Vehicles). The navigation system for AUV includes several sensors such as IMU (Inertial Measurement Unit), DVL (Doppler Velocity Log) and depth sensor. In general, the $13^{th}$ order INS error model, which includes depth error, velocity error, attitude error, and the accelerometer and gyroscope biases as state variables is used with measurements from DVL and depth sensors. However, the model may degrade the estimation performance of the heading state. Therefore, the $11^{th}$ INS error model is proposed. Its validity is verified by using a degree of observability and analyzing steady state error. The performance of the proposed model is shown by the computer simulation. The results show that the performance of the reduced $11^{th}$ order error model is better than that of the conventional $13^{th}$ order error model.

• ETRI Journal
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• v.39 no.4
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• pp.605-613
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• 2017

Simulated fault injection (SFI) is widely used to assess the effectiveness of fault tolerance mechanisms in safety-critical embedded systems (SCESs) because of its advantages such as controllability and observability. However, the long test time of SFI due to the large number of test cases and the complex simulation models of modern SCESs has been identified as a limiting factor. We present a method that can accelerate an SFI tool using a checkpoint forwarding (CF) technique. To evaluate the performance of CF-based SFI (CF-SFI), we have developed a CF mechanism using Verilog fault-injection tools and two systems under test (SUT): a single-core-based co-simulation model and a triple modular redundant co-simulation model. Both systems use the Verilog simulation model of the OpenRISC 1200 processor and can execute the embedded benchmarks from MiBench. We investigate the effectiveness of the CF mechanism and evaluate the two SUTs by measuring the test time as well as the failure rates. Compared to the SFI with no CF mechanism, the proposed CF-SFI approach reduces the test time of the two SUTs by 29%-45%.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.547-555
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• 2002

This paper proposes a stabilizing controller for the multirate sampled-data systems, which have a periodic output measurement scheme, in case of the open-loop unstable plant. A sufficient condition for maintaining observability in the multirate sampled-data systems is derived and a design strategy for filtered disturbance rejection is proposed. We also propose a design method for the plant output estimator. It is shown that the proposed pre-stabilizing controllers can stabilize the plant through the simulations. The proposed controller has IMC structure, and can be decomposed into the pre-stabilizing controller, the plant output estimator, the filtered disturbance estimator and the inverse of the fast pre-stabilized plant model. We assume that the plant is open-loop unstable and the disturbance consists of a sum of finite number of sinusoids with different frequencies. Some examples are presented for illustrations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.108-121
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• 2019

This paper presents the study results on the design of the correction filter to improve the azimuth error estimation of the high-speed guided missile launched from the warship after the transfer alignment. We theoretically proved that the transfer alignment performance is determined by the accuracy of the marine inertial navigation system and the observability of the attitude error state variable in the transfer alignment filter, and that most of navigation errors in high-speed guided missile are caused by azimuth error. In order to improve the azimuth estimation performance of the correction filter, the multiple adaptive estimation method and the adaptive filters adapting the measurement noise covariance or the process noise covariance are proposed. The azimuth estimation performance of the proposed adaptive filter and the existing Kalman filter are compared and analyzed each other for 8 different transfer alignment accuracy cases. As a result of comparison and analysis, it was confirmed that the adaptive filter adapting the process noise covariance has the best azimuth estimation performance. These results can be applied to the design of correction filters for high-speed guided missile.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.733-749
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• 2018

Sample selection arises as a result of the partial observability of the outcome of interest in a study. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. Recently sample selection models for binomial and Poisson response variables have been proposed. Based on the theory of symmetry-modulated distribution, we extend these to a model for overdispersed count data. This type of data with no sample selection is often modeled using negative binomial distribution. Hence we propose a sample selection model for overdispersed count data using the negative binomial distribution. A real data application is employed. Simulation studies reveal that our estimation method based on profile log-likelihood is stable.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.10
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• pp.836-843
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• 2017

In lunar exploration, the necessity of utilizing rover is verified by the examples of the Soviet Union and China and the similar Mars missions of the United States. In order to achieve the successful management of a lunar rover, a high precision navigation technique is required, and accordingly, high precision initial alignment is essential. Even though it is general to perform initial alignment in a steady state, a multiposition alignment technique is applied when high performance is needed. On the lunar surface, however, the performance of initial alignment decreases from that on Earth, and it cannot be improved by applying multiposition alignment method owing to certain constraints of lunar environment. In this paper, a sun sensor aided multiposition alignment technique is proposed. The measurement model for a sun vector is established, and its observability analysis is performed. The performance of the proposed algorithm is verified through computer simulations, and the results show the estimation performance is improved dramatically.

• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.10
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• pp.1087-1099
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• 2012

This study investigated the purchase intention of green fashion products based on Rogers' Diffusion of Innovation theory and compared the differences between Korean and British consumers. In order to identify the impact of personal characteristics, this study also examined the effects of fashion innovativeness and LOHAS tendency on perceived attributes of innovation and intention to purchase. With a convenience sampling method, a survey questionnaire was distributed at popular fashion streets in each country. A total of 426 data were obtained, 203 from the UK and 223 from Korea. About 52% were females, and 69% were in their twenties. A factor analysis generated two LOHAS factors (health concerns and eco concerns) and four attributes of green fashion products (image improvement, symbolic superiority, observability, and compatibility). Two types of green fashion products (organic cotton t-shirts and organic cotton t-shirts with an environmental message) were provided to measure the purchase intention, respectively. The findings were as follows. British consumers were more likely to show LOHAS tendency and to perceive positive advantages of green products compared to Koreans; in addition, British consumers presented higher mean scores on the purchase intentions of organic cotton products. Fashion innovativeness was significant to predict image improvement and symbolic superiority, while eco concerns were significant in compatibility for both nations. Compatibility was important for both countries in order to explain the intention to adopt two types of organic products. In addition, image improvement was another predictor for purchase intention of organic t-shirts with an environmental message. Managerial implications were provided.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.11
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• pp.997-1003
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• 2012

GNSS and ARS are the most common sensors in low-end UAVs. However, these sensors are vulnerable to built-in errors and cannot measure the body heading independently. The GNSS/INS cannot fully compensate the IMU errors in initial alignment process and rectilinear flights. For an unmanned helicopter, a magnetometer can be more useful than any other sensors to obtain heading information. However, the electric motor which drives small helicopter UAV keeps the magnetometer from reading the pure magnetotelluric vector. This paper shows the effects of electric motor on the magnetometer readings, and presents a method to compensate the effects. The results are verified with flight test data. The simulation and experimental results in this paper proves that aiding GNSS/INS with magnetometer increases observability and improves accuracy.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.419-424
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• 2012

This paper discusses the state-of-the-art techniques in real-time state estimation for the Smart Microgrids. The most popular method used in traditional power system state estimation is a Weighted Least Square(WLS) algorithm which is based on Maximum Likelihood(ML) estimation under the assumption of static system state being a set of deterministic variables. In this paper, we present a survey of dynamic state estimation techniques for Smart Microgrids based on Belief Propagation (BP) when the system state is a set of stochastic variables. The measurements are often too sparse to fulfill the system observability in the distribution network of microgrids. The BP algorithm calculates posterior distributions of the state variables for real-time sparse measurements. Smart Microgrids are modeled as a factor graph suitable for characterizing the linear correlations among the state variables. The state estimator performs the BP algorithm on the factor graph based the stochastic model. The factor graph model can integrate new models for solar and wind correlation. It provides the Smart Microgrids with a way of integrating the distributed renewable energy generation. Our study on Smart Microgrid state estimation can be extended to the estimation of unbalanced three phase distribution systems as well as the optimal placement of smart meters.