• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.8
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• pp.671-677
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• 2017

A new INS/Vision integrated navigation system by using multi-vision sensors is addressed in this paper. When the total number of landmark measured by the vision sensor is smaller than the allowable number, there is possibility that the navigation filter can diverge. To prevent this problem, multi-vision concept is applied to expend the field of view so that reliable number of landmarks are always guaranteed. In this work, the orientation of camera installed are 0, 120, and -120degree with respect to the body frame to improve the observability. Finally, the proposed technique is verified by using numerical simulation.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.1 s.139
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• pp.189-197
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• 2005

The purpose of this study was to find factors affecting sun protective behavior and intention to buy UV-protective clothing among Korean golfers. Health belief (HB) model and diffusion theory(DT) were used for the study. Dependent variable of HB model was sun protective behaviors (SPBs) and dependent variable of DT model was intention to buy (ITB) UV-protective clothing. Independent variables for HB model were cancer perception, perceived benefits, behavioral/psychological barriers and cues to actions, while independent variables of DT model were relative advantage, compatibility, complexity, friability, and observability, besides demographic variables. Perceived benefits and cues to action variables in addition to gender and age were significant determinants of SPB for Korean golfers. Also, relative advantage and compatibility. behavioral barriers and cues to action were significant in affecting intention to buy UV-protective clothes. Both HB model and extended DT model were useful to predict sun protective behavior of Korean golfers.

• 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.

• 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.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.289-292
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• 2008

This paper presents a branch prediction algorithm and a 4-way set-associative cache for performance improvement of OpenRISC processor and a clock gating algorithm using ODC (Observability Don't Care) operation for a low-power processor. The branch prediction algorithm has a structure using BTB(Branch Target Buffer) and 4-way set associative cache has lower miss rate than direct-mapped cache. The clock gating algorithm reduces dynamic power consumption. As a result of estimation of performance and dynamic power, the performance of the OpenRISC processor using the proposed algorithm is improved about 8.9% and dynamic power of the processor using samsung $0.18{\mu}m$ technology library is reduced by 13.9%.

• 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.

• 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 Korea Institute of Information and Communication Engineering
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• v.14 no.2
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• pp.453-461
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• 2010

This paper presents a branch prediction algorithm and a 4-way set-associative cache for performance improvement of embedded RISC core and a clock-gating algorithm using ODC (Observability Don't Care) operation to improve the power consumption of the core. The branch prediction algorithm has a structure using BTB(Branch Target Buffer) and 4-way set associative cache has lower miss rate than direct-mapped cache. Pseudo-LRU Policy, which is one of the Line Replacement Policies, is used for decreasing the number of bits that store LRU value. The clock gating algorithm reduces dynamic power consumption. As a result of estimation of performance and dynamic power, the performance of the OpenRISC core applied the proposed architecture is improved about 29% and dynamic power of the core using Chartered $0.18{\mu}m$ technology library is reduced by 16%.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.10
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• pp.1002-1009
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• 2009

Precision guidance filter design for a tactical missile with a strapdown seeker aided by low-cost strapdown sensors has been addressed in this paper. The low-cost strapdown sensors consist of an IMU with 3-axis accelerometers and gyroscopes, 3-axis magnetometers, and a barometer. Missile's position, velocity, attitude, and bias error of the barometer are considered as state variables. Since the state and measurement equations are highly nonlinear, we adopt UKF(Unscented Kalman Filter). The proposed guidance filter has a function of a navigation filter if target position error is not considered. In the case that the target position error is introduced, the proposed filter can effectively estimate the relative states of the missile to the true target. For specific engagement scenarios, we can observe that observability problems occur.