• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.875-876
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• 2013

• Journal of Oral Medicine and Pain
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• v.32 no.4
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• pp.421-429
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• 2007

Some researchers suggested that tactile sensor system would be useful in evaluating masticatory muscles of TMD patients, but there were few studies on the effects of chewing with time. The aim of this study was to investigate the change of elasticity and stiffness for masseter and temporal muscles of normal subjects before, during and after gum chewing and to obtain the baseline data for further researches on the elasticity and stiffness for masticatory muscles of TMD patients. Stiffness and elasticity of their anterior temporalis and inferior masseter muscle were measured bilaterally by a tactile sensor system. Each subject was instructed to sit on a chair for evaluation of masticatory muscles. Before operating the sensor, the thickest skin area over anterior temporalis and inferior masseter muscles were selected as the points to be pressed by a tactile sensor, and marked with a pen. While the teeth of subjects were lightly contacted, the probe of the tactile sensor was placed perpendicularly over the marked point over the skin, followed by computer-controlled movement including gently pressing straight down on the muscle for a second and retracting. All subjects were instructed to chew gum (Excellent Breath, Taiyo Co., Japan) bilaterally with a velocity of 2 times per second for 40 minutes after the first measurement had been performed for the baseline data of all subjects. The measurements had been repeated during chewing with 10 minutes of interval and continued for 40 minutes with same interval after chewing. Resultantly, the decrease of elasticity and the increase of stiffness in masticatory muscles can be seen significantly within 10 minutes after chewing and those were maintained during chewing without significant change with chewing time. The elasticity of muscles was recovered within 10 minutes after stopping chewing, but the stiffness was recovered more lately than elasticity by about 10 minutes. Based on these results, it can be concluded that elasticity and stiffness of muscles would be good indicators to evaluate the masticatory muscles objectively, when more supported by further researches.

• The Journal of Korea Robotics Society
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• v.4 no.1
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• pp.34-42
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• 2009

Rodents, specially rats, can recognize distance and shape of an object and also pattern of the textures by using their whiskers. Mechanoreceptors surrounding the root of whisker in their follicle measure deflection of the whisker. Rats can move their whisker back and forth freely. This ability, called active whisking or active sensing, is one of characteristics of rat behaviours. Many researches based on the mechanism have been progressed. In this paper, we test a simple and accurate method based on deflection of the whisker: we designed biomimetic whiskers modeling after a structure of follicle using the microphone. The microphone sensor measures a mechanical vibration. Attaching an artificial whisker beam to the microphone membrane, we can detect a vibration of whisker and this can show the deflection amount of whisker indirectly.

• The Journal of Korea Robotics Society
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• v.11 no.2
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• pp.100-107
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• 2016

To realize a robot hand interacting like a human hand, there are many tactile sensors sensing normal force, shear force, torque, shape, roughness and temperature. This sensing signal is essential to manipulate object accurately with robot hand. In particular, slip sensors make manipulation more accurate and breakless to object. Up to now several slip sensors were developed and applied to robot hand. Many of them used complicate algorithm and signal processing with vibration data. In this paper, we developed novel principle slip sensor using separation layer. These two layers are moved from each other when slip occur. Developed sensor can sense slip signal by measuring this relative displacement between two layers. Also our principle makes slip signal decoupled from normal force and shear force without other sensors. The sensor was fabricated using the NBR(acrylo-nitrile butadiene rubber) and the Ecoflex as substrate and a paper as dielectric. To verify our sensor, slip experiment and normal force decoupling test were conducted.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.365-372
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• 2022

The accurate object recognition is important for the precise and accurate manipulation. To enhance the recognition performance, we can use various types of sensors. In general, acquired data from sensors have a high sampling rate. So, in the past, the RNN-based model is commonly used to handle and analyze the time-series sensor data. However, the RNN-based model has limitations of excessive parameters. CNN-based model also can be used to analyze time-series input data. However, CNN-based model also has limitations of the small receptive field in early layers. For this reason, when we use a CNN-based model, model architecture should be deeper and heavier to extract useful global features. Thus, traditional methods like RN N -based and CN N -based model needs huge amount of learning parameters. Recently studied result shows that Fast Fourier Convolution (FFC) can overcome the limitations of traditional methods. This operator can extract global features from the first hidden layer, so it can be effectively used for feature extracting of sensor data that have a high sampling rate. In this paper, we propose the algorithm to recognize objects using tactile sensor data and the FFC model. The data was acquired from 11 types of objects to verify our posed model. We collected pressure, current, position data when the gripper grasps the objects by random force. As a result, the accuracy is enhanced from 84.66% to 91.43% when we use the proposed FFC-based model instead of the traditional model.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.279-285
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• 2022

Recently, 3D printing technology has gained increased attention in the manufacturing industry because it allows the manufacturing of complex but sophisticated structures as well as moderate production speed. Owing to advantages of 3D printers, such as flexible design, customization, rapid prototyping, and ease of access, can also be advantageous to sensor developments, 3D printing demands have increased in various active device fields, including sensor manufacturing. In particular, 3D printing technology is of significant interest in tactile sensor development where piezoelectric materials are typically embedded to acquire voltage signals from external stimuli. In regard with piezoelectricity, researchers have worked with various piezoelectric materials to achieve high piezoelectric response, but the structural approach is limited because ceramics have been regarded as challenging materials for complex design owing to their limited manufacturing methods. If appropriate piezoelectric materials and approaches to design are used, sensors can be fabricated with the improved piezoelectric response and high sensitivity that cannot be found in common bulk materials. In this study, various 3D printing technologies, material combinations, and applications of various piezoelectric sensors using the 3D printing method are reviewed.

• Prospectives of Industrial Chemistry
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• v.21 no.1
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• pp.3-18
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• 2018

전자피부(Electronic skin)는 외부 환경과의 상호작용하는 인간 피부의 기능을 대체하여 외부 자극 신호를 전기적 신호로 변환하는 센서들로 이루어진 인공피부로써, 최근 인간과 전자기기 간의 인터페이스에 대한 관심이 급증하면서 이에 대한 많은 연구들이 진행되고 있다. 그중에서도 피부의 주된 기능인 외부 물리적 자극을 인지하는 촉각을 모방하는 촉각센서는 많은 발전을 거쳐 왔으며, 한계를 극복하고자 다양한 연구들이 진행되고 있다. 촉각센서는 압력, 인장, 굽힘과 같은 물리적 자극에 반응하며, 물리적 자극 신호를 아날로그 및 디지털 신호로 변환하여 인지하는 연구들이 폭넓게 개발되고 있다. 또한, 소자의 구조에 따라 물리적 자극을 전달하는 다양한 변환 방식들이 있으며, 최근에는 각 신호 변환 방식의 민감도, 반응속도, 자극 인지 범위 등의 한계점을 극복하고, 소재의 기계적 물성을 향상시키기 위해 소재의 변형을 주거나 생체의 기관 구조 및 외부 자극 인지 원리 등을 모사한 연구들이 많은 관심을 받고 있다. 본 기고에서는 이러한 촉각센서의 물리적 자극 신호 변환 방식과 소재 변형 및 생체 모사를 통한 다양한 연구들을 소개하고자 하며, 이를 통하여 촉각센서의 나아갈 방향을 제시하고자 한다.

• Broadcasting and Media Magazine
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• v.22 no.1
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• pp.10-18
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• 2017

본 연구는 비접촉식 센서 기반의 웨어러블 디바이스를 이용한 딥러닝 기반의 제스처 인식에 대한 연구이다. 이를 위하여 Flexible MSG 센서를 기반으로 한 Flexible Epidermal Tactile Sensor를 사용하였으며, Flexible Epidermal Tactile Sensor는 손, 손가락 제스처를 취했을 때 손목, 손가락과 연결되어 있는 근육들의 움직임에 따라 발생하는 피부 표면의 전극을 취득하는 센서이다. 실험을 위하여 7가지 손, 손가락 제스처를 정의하였으며, 손목의 꺾임, 손목의 뒤틀림, 손가락의 오므림과 펴짐, 아무 동작도 취하지 않은 기본 상태에 대한 제스처로 정의하였다. 실험 데이터 수집에는 손목이나 손가락에 부상, 장애등이 없는 일반적인 8명의 참가자가 참가하였으며 각각 한 제스처에 대하여 20번씩 반복하여 1120개의 샘플을 수집하였다. 입력신호에 대한 제스처를 학습하기 위해 본 논문에서는 1차원 Convolutional Neural Network를 제안하였으며, 성능 비교를 위해 신호의 크기를 반영하는 특징벡터인 Integral Absolute Value와 Difference Absolute Mean Value를 입력신호에서 추출하고 Support Vector Machine을 사용하여 본 논문에서 제안한 1차원 CNN과 성능비교를 하였다. 그 결과 본 논문에서 제안한 1차원 CNN의 분류 정확도가 우수한 성능을 나타냈다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.21-22
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• 2006

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• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.21-25
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• 2004

최근 국가의 10대 신성장 동력산업에 포함 되어 있는 RT(robot technology)는 향후 포스트 반도체산업의 중요한 산업으로 국내 경제를 활성화 시키는 원동력이 될 것으로 전망된다. 이와 더불어 향후에는 현재의 산업용 로봇이 아닌 감각과 지능을 가진 인간친화적인 로봇이 출현할 것으로 기대된다. 즉 주변 환경을 인지하여 정보를 획득하고 지능적 판단, 행위 및 상호작용을 통하여 인간을 지원하는 지능형 로봇은 인간과의 상호작용을 통하여 감성을 이해하며 서비스 제공, 인간의 동작이나 작업을 지원 그리고 위험작업 수행, 인간이 불가능한 작업을 대신 할 수 있을 것이다. (중략)