• The Journal of Korean Association of Computer Education
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• v.7 no.5
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• pp.9-21
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• 2004

In spite of the strong request that Internet, a new IT technique, should be extensively used in classroom settings, WBI is not still wide spread. One possible reason is that most WBIs developed so far are not qualitatively different from the traditional classroom ones and various design models for WBI are unknown. Developing a WBI program according to Goal-based Scenarios (GBS), a constructivistic instructional design model, this study offers a useful case for the design and development of WBI to educators and implementers. The program developed embodied seven important components of the GBS model. The development process also included needs assessment, task analysis, and learner and environment analysis, which are the basic components of the domain of instructional design. The GBS development case in this article will provide concrete guidelines to educational practitioners who have an intention to create a new type of WBI in schools as well as in corporate.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.642-652
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• 2007

Many contracting firms and project managers in the construction industry have started to utilize multi objective optimization methods to handle multiple conflicting goals for completing the project within the stipulated time and budget with required quality and safety. These optimization methods have increased the pressure on decision makers to search for an optimal resources utilization plan that optimizes simultaneously the total project cost, completion time, and crashing cost by considering indirect cost, contractual penalty cost etc., practically charging them in terms of direct cost of the project which is fuzzy in nature. This paper presents a multiple fuzzy goal programming model (MFGP) that supports decision makers in performing the challenging task. The model incorporates the fuzziness which stems from the imprecise aspiration levels attained by the decision maker to these objectives that are quantified through fuzzy linear membership function. The membership values of these objectives are then maximized which forms the fuzzy decision. The problem is solved using LINGO 8 optimization solver and the best compromise solution is identified. Comparison between solutions of MFGP, fuzzy multi objective linear programming (FMOLP) and multiple goal programming (MGP) are also presented. Additionally, an interactive decision making process is developed to enable the decision maker to interact with the system in modifying the fuzzy data and model parameters until a satisfactory solution is obtained. A case study is considered to demonstrate the feasibility of the proposed model for optimization of project network parameters in the construction industry.

• Journal of the Korean Geographical Society
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• v.45 no.1
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• pp.144-164
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• 2010

This study aims to reveal path selection strategies and individual differences in a navigation task. Two experiments were presented that studied human route planning performance as well as the cognitive strategies and processes involved. For the outdoor task, university students were asked to select a route based on the instruction, i.e. to find the best route from the campus main gate to the Education Building for conference visitors by locating eight signposts. Results indicate (1) that locations of signposts were selected preferably at decision points where the traveler needs to make a choice and starting/ending points of the navigation task and (2) a variety of route planning strategies considering efficiency goal (e.g., the shortest path), environmental characteristics (e.g., fewest turns), and aesthetic purpose (e.g., most scenic) were used. It is notable that some participants took into account more than one path by locating one or two signposts on an alternative route while others preferred a linear route connecting signposts between the start point and the destination. Prior to the main experiment, the same participants were asked to complete the same task inside the classroom to investigate changes in strategies between two tasks. Participants often tend to place signposts at more regular intervals for the indoor navigation task than the same task conducted outside.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.4
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• pp.7-16
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• 2009

A robot must be able to generate various skills to achieve given tasks intelligently and reasonably. The robot must first learn affordances to generate the skills. An affordance is defined as qualities of objects or environments that induce actions. Affordances can be usefully used to generate skills. Most tasks require sequential and goal-oriented behaviors. However, it is usually difficult to accomplish such tasks with affordances alone. To accomplish such tasks, a skill is constructed with an affordance and a soft behavioral motivation switch for reflecting goal-oriented elements. A skill calculates a behavioral motivation as a combination of both presently perceived information and goal-oriented elements. Here, a behavioral motivation is the internal condition that activates a goal-oriented behavior. In addition, a robot must be able to execute sequential behaviors. We construct skill networks by using generated skills that make action selection feasible to accomplish a task. A robot can select sequential and a goal-oriented behaviors using the skill network. For this, we will first propose a method for modeling and learning Bayesian networks that are used to generate affordances. To select sequential and goal-oriented behaviors, we construct skills using affordances and soft behavioral motivation switches. We also propose a method to generate the skill networks using the skills to execute given tasks. Finally, we will propose action-selection-mechanism to select sequential and goal-oriented behaviors using the skill network. To demonstrate the validity of our proposed methods, "Searching-for-a-target-object", "Approaching-a-target-object", "Sniffing-a-target-object", and "Kicking-a-target-object" affordances have been learned with GENIBO (pet robot) based on the human teaching method. Some experiments have also been performed with GENIBO using the skills and the skill networks.

• Journal of Information Technology Application
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• v.2 no.1
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• pp.139-160
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• 2000

This paper suggests a technique for evaluating cognitive process when a working group performs its group tasks. First, it review a theory of distributed cognition which provides a theoretical background for investigating group's cognitive process. Then, it presents a procedure for DGOMS(Distributed GOMS) evaluation which is an extension from GOMS. GOMS is an analytica evalutation technique that has been used at the individual level. DGOMS analyzes task completion time and compares workload among group members on the basis of each member's task execution time, communication time, and cognitive workload. DGOMS can be applied to a situation where a group of people are working together for a common goal using a technical subsystem such as information systems.

• Phonetics and Speech Sciences
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• v.4 no.3
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• pp.3-12
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• 2012

The primary goal of the current study was to examine category solidity of Korean alveolar fricatives in the Busan and Seoul dialects of Korean. Considering the common belief of $/s^h/-/s^*/$ neutralization in Kyungsang speech, plain $/s^h/$ and fortis $/s^*/$ fricatives of Busan speakers were examined against the same fricatives of Seoul speakers. Perceptual distance between Korean $/s^h/$ and $/s^*/$ on the one hand and English /s/ on the other was investigated by use of across-linguistic mapping method. Two experiments of a perceptual mapping task of English /s/ to Korean $/s^h/$ and $/s^*/$ and a $/s^*/$-production task were conducted on users of the Busan and Seoul dialects of Korean. The results from the perception and production experiments suggested that at a micro-level, younger Busan speakers have less solid category stability for Korean $/s^*/$ compared with Seoul speakers, although their production of $/s^h/$ and $/s^*/$ was as highly distinctive from each other as that of Seoul speakers.

• Journal of Advanced Research in Ocean Engineering
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• v.3 no.3
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• pp.136-148
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• 2017

The paper presents dual arm ROV manipulation using deep reinforcement learning. The purpose of this underwater manipulator is to investigate and excavate natural resources in ocean, finding lost aircraft blackboxes and for performing other extremely dangerous tasks without endangering humans. This research work emphasizes on a self-learning approach using Deep Reinforcement Learning (DRL). DRL technique allows ROV to learn the policy of performing manipulation task directly, from raw image data. Our proposed architecture maps the visual inputs (images) to control actions (output) and get reward after each action, which allows an agent to learn manipulation skill through trial and error method. We have trained our network in simulation. The raw images and rewards are directly provided by our simple Lua simulator. Our simulator achieve accuracy by considering underwater dynamic environmental conditions. Major goal of this research is to provide a smart self-learning way to achieve manipulation in highly dynamic underwater environment. The results showed that a dual robotic arm trained for a 3DOF movement successfully achieved target reaching task in a 2D space by considering real environmental factor.

• The KIPS Transactions:PartB
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• v.18B no.2
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• pp.83-86
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• 2011

In this paper, we present the plan execution model for dynamic mobile computing environments, and then introduce the smart script system developed on these base models. The smart script system includes the smart script language, in which the task knowledge of a mobile personal assistant is represented, and the script execution engine, by which the scripts are dynamically executed in response to the given task goal and the environmental changes. In order to evaluate the utility and the performance of our system, we implement an application service called Smart Reservation and conduct some experiments.

• The Journal of Korean Physical Therapy
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• v.18 no.4
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• pp.1-9
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• 2006

Recently, several investigations revealed that after unilateral brain damage, movement abnormalities were exposed on the ipsilateral side as well as the upper extremity contralateral to the damaged hemisphere. Even the motor abilities had significantly recovered from ipsilateral motor deficits on not only simple sensoriomotor function, also clinical assessments since subacute stage, although could not completely returned. Such motor deficits were detected in a diversity of motor tasks depending on the interhemispheric specialization, further in clinical evaluation and a daily of activities. In the clinical features, muscular weakness, sensory loss and impaired manual dexterity were observed. In a laboratory experiment, there were increasing evidences that the kinematic processing deficits was founded in various-specific motor tasks, which ranged from simple basic element to complex tasks, such as tapping task, step-tracking, goal directional aiming task, and iso(and non-)directional interlimb coordination. In the point of view, the manifest understanding in related to ipsilateral deficits provide the clinicians with an important information for scientific management about brain injured patient's prognosis and therapeutic guidelines.

• Journal of the Korean Society of Industry Convergence
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• v.17 no.2
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• pp.42-53
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• 2014

Robust voice recognition (RVR) is essential for a robot to communicate with people. One of the main problems with RVR for robots is that robots inevitably real environment noises. The noise is captured with strong power by the microphones, because the noise sources are closed to the microphones. The signal-to-noise ratio of input voice becomes quite low. However, it is possible to estimate the noise by using information on the robot's own motions and postures, because a type of motion/gesture produces almost the same pattern of noise every time it is performed. In this paper, we propose an RVR system which can robustly recognize voice by adults and children in noisy environments. We evaluate the RVR system in a communication robot placed in a real noisy environment. Voice is captured using a wireless microphone. Navigation Strategy is shown Obstacle detection and local map, Design of Goal-seeking Behavior and Avoidance Behavior, Fuzzy Decision Maker and Lower level controller. The final hypothesis is selected based on posterior probability. We then select the task in the motion task library. In the motion control, we also integrate the obstacle avoidance control using ultrasonic sensors. Those are powerful for detecting obstacle with simple algorithm.