• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.243-262
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• 2022

In this study, eye tracking was used for the appearance of the robot during the social robot design study. During the research, each part of the social robot was designated as AOI (Areas of Interests), and the user's attitude was measured through a design evaluation questionnaire to construct a design research model of the social robot. The data used in this study are Fixation, First Visit, Total Viewed, and Revisits as eye tracking indicators, and AOI (Areas of Interests) was designed with the face, eyes, lips, and body of the social robot. And as design evaluation questionnaire questions, consumer beliefs such as Face-highlighted, Human-like, and Expressive of social robots were collected and as a dependent variable was attitude toward robots. Through this, we tried to discover the mechanism that specifically forms the user's attitude toward the robot, and to discover specific insights that can be referenced when designing the robot.

• Journal of Broadcast Engineering
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• v.17 no.4
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• pp.611-624
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• 2012

'Gesture' is the most intuitive means of communication except the voice. Therefore, there are many researches for method that controls computer using gesture input to replace the keyboard or mouse. In these researches, the method of user detection and main body parts estimation is one of the very important process. in this paper, we propose user objects detection and main body parts estimation method on inaccurate depth information for pose estimation. we present user detection method using 2D and 3D depth information, so this method robust to changes in lighting and noise and 2D signal processing 1D signals, so mainly suitable for real-time and using the previous object information, so more accurate and robust. Also, we present main body parts estimation method using 2D contour information, 3D depth information, and tracking. The result of an experiment, proposed user detection method is more robust than only using 2D information method and exactly detect object on inaccurate depth information. Also, proposed main body parts estimation method overcome the disadvantage that can't detect main body parts in occlusion area only using 2D contour information and sensitive to changes in illumination or environment using color information.

• Journal of the Korea Computer Graphics Society
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• v.21 no.2
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• pp.11-20
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• 2015

We present a new interactive technique for directing the motion sequences of an animated character by dragging its specific body part to a desired location in the three-dimensional virtual environment via a hand motion tracking device. The motion sequences of our character is synthesized by reordering subsequences of captured motion data based on a well-known graph representation. For each new input location, our system samples the space of possible future states by unrolling the graph into a spatial search tree, and retrieves one of the states at which the dragged body part of the character gets closer to the input location. We minimize the difference between each pair of successively retrieved states, so that the user is able to anticipate which states will be found by varying the input location, and resultantly, to quickly reach the desired states. The usefulness of our method is demonstrated through experiments with breakdance, boxing, and basketball motion data.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1202-1205
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• 2005

This paper describes a method for vision-based person identification that can detect, track, and recognize person from video using multiple cues: height and dressing colors. The method does not require constrained target's pose or fully frontal face image to identify the person. First, the system, which is connected to a pan-tilt-zoom camera, detects target using motion detection and human cardboard model. The system keeps tracking the moving target while it is trying to identify whether it is a human and identify who it is among the registered persons in the database. To segment the moving target from the background scene, we employ a version of background subtraction technique and some spatial filtering. Once the target is segmented, we then align the target with the generic human cardboard model to verify whether the detected target is a human. If the target is identified as a human, the card board model is also used to segment the body parts to obtain some salient features such as head, torso, and legs. The whole body silhouette is also analyzed to obtain the target's shape information such as height and slimness. We then use these multiple cues (at present, we uses shirt color, trousers color, and body height) to recognize the target using a supervised self-organization process. We preliminary tested the system on a set of 5 subjects with multiple clothes. The recognition rate is 100% if the person is wearing the clothes that were learned before. In case a person wears new dresses the system fail to identify. This means height is not enough to classify persons. We plan to extend the work by adding more cues such as skin color, and face recognition by utilizing the zoom capability of the camera to obtain high resolution view of face; then, evaluate the system with more subjects.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.53-60
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• 2013

This paper proposed visualization tool for motion of snowboarding using Skeleton data obtained by the Microsoft's Kinect sensor. The BBP(Balanced Body Position) posture is a most basic motion in the Snowboarding. This posture is the primary technology for stable turns. The implementation of visualization tool to analyse the BBP posture of snowboard. comparative analysis with standard postures to the ankles, knees, hips and spine angle of joints and body tracking using coordinate information obtained by the Kinect Sensor. Analysis of the final results of the screen through the OpenGL library. This research result could be used to analysis for turn postures of snowboarding.

• Journal of Sensor Science and Technology
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• v.31 no.3
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• pp.156-162
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• 2022

This study estimates the relative position between body segments using segment orientation and segment-to-joint center (S2J) vectors. In many wearable motion tracking technologies, the S2J vector is treated as a constant based on the assumption that rigid body segments are connected by a mechanical ball joint. However, human body segments are deformable non-rigid bodies, and they are connected via ligaments and tendons; therefore, the S2J vector should be determined as a time-varying vector, instead of a constant. In this regard, our previous study (2021) proposed a method for determining the time-varying S2J vector from the learning dataset using a regression method. Because that method uses a deformation-related variable to consider the deformation of S2J vectors, the optimal variable must be determined in terms of estimation accuracy by motion and segment. In this study, we investigated the effects of deformation-related variables on the estimation accuracy of the relative position. The experimental results showed that the estimation accuracy was the highest when the flexion and adduction angles of the shoulder and the flexion angles of the shoulder and elbow were selected as deformation-related variables for the sternum-to-upper arm and upper arm-to-forearm, respectively. Furthermore, the case with multiple deformation-related variables was superior by an average of 2.19 mm compared to the case with a single variable.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.6
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• pp.537-545
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• 2013

In this paper, the vibration for actuators having lens module, confined to lateral and torsional directions of suspensions, is described by mathematically analyzing its suspension configuration and motion. In order to prove the accuracy of this result, it is compared to a finite element analysis. Also it is shown that modal frequencies can be modified by changing design parameters in mathematical motion expressions.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.602-607
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• 2004

Accurate and quick positioning of the throttle valve in driving situation is required to implement the Traction Control System(TCS). Also, unlike a conventional throttle valve which is connected to the accelerator directly by a wire, an Electronic Throttle Valve(ETV) is driven by a DC motor and can move dependently upon the accurate position of the accelerator. In the research, the Electronic Throttle Body(ETB) and Controller for TCS application was developed. In order to drive the DC motor, the developed controller was built and interfaced to the ECU and ETB. The PID position control algorism and developed systems are designed to realize the robust tracking control of the ETV. Actual vehicle tests with these systems and PID position control algorithm. Finally, the performance of the proposed those are evaluated with the experimental studies.

• International journal of advanced smart convergence
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• v.6 no.2
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• pp.72-81
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• 2017

The developments in mobile technology and the increase in the production of smartphones have led to the growth of the mobile healthcare market. Moreover, the number of mobile healthcare apps is on the rise, and users are benefiting from using healthcare, exercise tracking, and body monitoring apps, as well as they have been searching for health information through mobile health apps. This paper evaluated the usability of a symptom checker application that provides information on diseases, treatment methods, and related hospitals through self-diagnosis of symptoms. Symptom checkers are currently being developed, and are widely used overseas. This paper sought to evaluate the usability of a symptom checker app, and suggested ways to introduce enhancements.

• Journal of Drive and Control
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• v.17 no.1
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• pp.44-50
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• 2020

For the requirement of accurate tracking control and the safety of physical human-robot interaction, torque control is basically desirable for humanoid robots. Because of the complexity of humanoid robot dynamics, the TDC (time-delay control) is practical because it does not require a dynamic model. However, there occurs a considerable error due to discontinuous non-linearities. To solve this problem, the TDC-FLC (fuzzy logic compensator) is applied to humanoid robots. The applied controller contains three factors: a TDE (time-delay estimation) factor, a desired error dynamic factor, and FLC to suppress the TDE error. The TDC-FLC is easy to execute because it does not require complicated humanoid dynamic calculations and the heuristic fuzzy control rules are intuitive. TDC-FLC is implemented on the whole body of a humanoid, not on biped legs even though it is performed by a virtual humanoid robot. The simulation results show the validity of the TDC-FLC for humanoid robots.