• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.283-290
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• 2021

We propose a task and motion planning algorithm for clearing obstacles and wiping surfaces, which is essential for surface disinfection during the pathogen disinfection process. The proposed task and motion planning algorithm determines task parameters such as grasping pose and placement location during the planning process without using pre-specified or discretized values. Furthermore, to quickly inspect many unit motions, we propose a motion feasibility prediction algorithm consisting of collision checking and an SVM model for inverse mechanics and self-collision prediction. Planning time analysis shows that the feasibility prediction algorithm can significantly increase the planning speed and success rates in situations with multiple obstacles. Finally, we implemented a hierarchical control scheme to enable wiping motion while following a planner-generated joint trajectory. We verified our planning and control framework by conducted an obstacle-clearing and surface wiping experiment in a simulated disinfection environment.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.803-814
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• 2016

Recent times have seen an exponential increase in the amount of spatial data, which is in many cases associated with temporal data. Recent advances in computer technology and computation of hierarchical Bayesian models have enabled to analyze complex spatio-temporal data. Our work aims at modeling data of daily average nitrogen dioxide (NO2) levels obtained from 25 air monitoring sites in Seoul between 2003 and 2010. We considered an independent Gaussian process model and an auto-regressive model and carried out estimation within a hierarchical Bayesian framework with Markov chain Monte Carlo techniques. A Gaussian predictive process approximation has shown the better prediction performance rather than a Hierarchical auto-regressive model for the illustrative NO2 concentration levels at any unmonitored location.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.509-523
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• 2005

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) for autonomous ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions while the upper levels utilize a probabilistic prediction approach based on situation recognition with an underlying cost model. The estimation-theoretic short-term prediction is via an extended Kalman filter-based algorithm using sensor data to predict the future location of moving objects with an associated confidence measure. The proposed estimation-theoretic approach does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory and is thus suited for short-term prediction in both on-road and off-road driving. In this article, we analyze the complementary role played by vehicle kinematic models in such short-term prediction of moving objects. In particular, the importance of vehicle process models and their effect on predicting the positions and orientations of moving objects for autonomous ground vehicle navigation are examined. We present results using field data obtained from different autonomous ground vehicles operating in outdoor environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6A
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• pp.550-557
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• 2007

We predict a context of various structures by using Bayesian Networks Algorithms, Three-Dimensional Structures Algorithms and Genetic Algorithms. However, these algorithms have unavoidable problems when providing a context-aware service in reality due to a lack of practicality and the delay of process time in real-time environment. As far as context-aware system for specific purpose is concerned, it is very hard to be sure about the accuracy and reliability of prediction. This paper focuses on reasoning and prediction technology which provides a stochastic mechanism for context information by incorporating various context information data. The objective of this paper is to provide optimum services to users by suggesting an intellectual reasoning and prediction based on hierarchical context information. Thus, we propose a design of user location prediction algorithm using sequential matching with n-size flexible window scheme by taking user's habit or behavior into consideration. This algorithm improves average 5.10% than traditional algorithms in the accuracy and reliability of prediction using the Flexible Window Scheme.

• Convergence Security Journal
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• v.12 no.4
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• pp.91-96
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• 2012

MANET is developed technique fairly among many field of wireless network. Nodes which consist of MANET transmit data using multi-hop wireless connection. Caching scheme is technique which can improve data access capacity and availability of nodes. Previous studies were achieved about dynamic routing protocol to improve multi-hop connection of moving nodes. But management and maintenance of effective cache information because of movement of nodes is not easy. In this study, we proposed cluster-based caching scheme to manage connection by decreasing overhead and moving of nodes as moving node discovers cache of wish information. And HLP was used to maintain effective cache table in each cluster head. Efficiency of proposed technique in this study was confirmed by experiment.

• Journal of the Korean Institute of Landscape Architecture
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• v.28 no.2
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• pp.18-27
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• 2000

The importance of constraints has been one of major issues in recreation for prediction of choice behavior; however, traditional conjoint choice model did not consider the effects of these variables or fail to integrate them into choice model adequately. The purposes of this research are (a) to estimate the effects of constraints in theme park choice behavior by the constraints-induced conjoint choice model, and (b) to test additional explanatory power of the additional constraints in this suggested model against the more parsimonious traditional model. A leading polling agency was employed to select respondents. Both alternative generating and choice set generating fractional factorial design were conducted to meet the necessary and sufficient conditions for calibration of the constraints-induced conjoint choice model. Th alternative-specific model was calibrated. The log-likelihood ratio test revealed that suggested model was accepted in the favor of the traditional model, and the goodness-of-fit($\rho$$^2$) of suggested and traditional model was 0.48427 and 0.47950, respectively. There was no difference between traditional and suggested model in estimates of attribute levels of car and shuttle bus because alternatives were created to estimate the effects of constraints independently from mode related variables. Most parameters values of constraints had the expected sign and magnitude: the results reflected the characteristics of the theme parks, such as abundance of natural attractions and poor accessibility in Everland, location of major fun rides indoor in Lotte World, city park like characteristics of Dream Land, and traffic jams in Seoul. Instead of the multinomial logit model, the nested logit model is recommended for future researches because this model more reasonably reflects the real decision-making process in park choice. Development of new methodology too integrate this hierarchical decision-making into choice model is anticipated.