• 제목/요약/키워드: artificial hand

검색결과 487건 처리시간 0.034초

기모가공 조건에 따른 트리코 기포 인조 스웨이드의 태와 물성 (Subjective Hand and Physical Properties of Tricot based Artificial Suede according to Raising Finish)

  • 노의경;오경화
    • 한국의류산업학회지
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    • 제16권1호
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    • pp.153-159
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    • 2014
  • This study evaluates the changes of the subjective hand, preference, comfort and mechanical properties of tricot based artificial suede made from sea-island type micro fibers according to raising condition. The subjective hand and the preference of raised suede for jacket were rated by the 20's and 30's women experts according to raising cycles. Comfort properties were evaluated by air permeability, water vapor transmission, and thermal transmission. Mechanical properties were measured by the KES-FB system. The subjective hand of artificial suede was categorized into three hand factors: smoothness, warmness and thickness. Smoothness, warmness and thickness perception increased with raising cycles which affected hand preference and luxuriousness perception. The thickness and wale density of suede increased with the number of raising. Suede became more compact and less pliable and less stretchable due to increased fabric thickness; in addition, the surface of suede became smoother and compressive since the surface evenness of suede improved with smaller fiber fineness and an increased amount of naps covered the base fabric. Furthermore, water vapor transmission decreased and thermal insulation increased. The best raising conditions for artificial suede was four cycles in which artificial suede was preferred without changes in physical properties.

The Subjective Hand and Preferences Evaluation of Artificial Leather by Use

  • Roh, Eui Kyung;Oh, Kyung Wha
    • 한국의류산업학회지
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    • 제19권1호
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    • pp.79-89
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    • 2017
  • Sensory attributes and preferences that contribute to consumer satisfaction with artificial leather were measured by subjective evaluation, and subjective hand and preferences were analyzed in relation to its use. Using tactile and visual senses, 50 experts in fashion and textile industry evaluated leathers classified into two categories, suede and polyurethane coated, according to different manufacturing methods. They answered questions on subjective hand and preferences of different artificial leathers of various fashion items (jackets, purses, bags, shoes, boots, furniture, etc.), using specific adjectives to describe the hand properties. As a result, it was found that the subjective hand properties of artificial leathers were related to 'Thickness', 'Fullness/softness', 'Surface contour', 'Stickiness', and 'Elasticity'. The leather type from different manufacturing methods influenced their perceived hand and preferences relating to use. By use, different hands were preferred. The preferences for jackets and furniture of suede type leathers were related to their surface properties, whereas the preferences for items of the other type of leathers were associated with their resilience. On the other hand, in the case of polyurethane coated leathers, the preferences for jackets were significantly affected by their thickness, while those for the other items were influenced by their resilience and surface properties.

인공신경망을 이용한 한복지 태의 평가에 관한 연구 (A Study on the Evaluation of the Hand Value of Korean Fabrics using the Artificial Neural Network)

  • 문명희
    • 한국생활과학회지
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    • 제12권1호
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    • pp.63-73
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    • 2003
  • The purpose of this study was to quantify the hands of fabrics for the Korean folk clothes using both a KES-FB and an artificial neural network. In order to select the proper input parameters, we calculated the correlation using step-wise regression between mechanical properties and the hand value of fabrics. For the classification, the primary hand values and total hand value, five neural networks with three-layered structure were constructed using the error back propagation algorithm and, in order to reduce errors and to speed up learning, the momentum method was selected. From the analysis of the primary and total hands using a self-constructed artificial intelligence system, the error rates of sleekness, stiffness, silkiness, and roughness compared with the judgement of expert panels were found to be 3.3%, 3.3%, 1.6%, and 4.9%, respectively, while that of the total hand was 9.83%.

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A Skeletal Framework Artificial Hand Actuated by Micro Pneumatic Artificial Muscles

  • Lee, Young-Kwun;Oh, Yeon-Taek;Sung, Hak-Kyung
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2002년도 ICCAS
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    • pp.36.2-36
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    • 2002
  • .Developing a skeletal framework artificial hand similar to real human hand. .Developing a micro artificial muscle actuated by pneumatic power. .Building a micro pneumatic system including micro atuators and its control devices. .Building a soft driving system for Service robots. .Designning and Fabricating a multi-channel micro pneumatic valve using MEMS technology.

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효율적인 중고거래 메타서비스 (An Efficient Second-hand transaction meta-services)

  • 황세웅;임민택;홍현기;황훈태;박성현;최영규;황석형;김수환
    • 한국컴퓨터정보학회:학술대회논문집
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    • 한국컴퓨터정보학회 2023년도 제67차 동계학술대회논문집 31권1호
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    • pp.469-471
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    • 2023
  • 본 논문에서는 기존 중고거래 플랫폼들의 불편한 점들을 해소하고 사용자들이 효율적이고 편리한 중고거래를 할 수 있도록 도와주는 플랫폼을 개발했다. 조사를 통해 기존 중고거래 플랫폼은 허위 매물, 시세 파악의 어려움, 사기 피해 등의 문제점이 존재한다는 사실을 인식했다. 문제 해결을 위해 파이썬을 활용하여 주요 중고거래 플랫폼의 상품 데이터를 수집했다. 이에 IQR을 적용하여 가격의 이상치를 판별했다. 가격 비교와 허위 매물 판별이 용이하게 되는 장점이 있다. 또한 이상치를 제거한 상품들의 시세를 계산하여 데이터를 차트로 시각화했다. 플랫폼과 지역마다 상이한 중고 상품의 신뢰성 있는 시세를 파악할 수 있고 중고거래 사기 피해를 방지할 수 있도록 사용자에게 주요 사기 수법, 뉴스 등의 정보를 제공한다.

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안정적 로봇 파지를 위한 인공신경망 (Artificial Neural Network for Stable Robotic Grasping)

  • 김기서;김동언;박진현;이장명
    • 로봇학회논문지
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    • 제14권2호
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    • pp.94-103
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    • 2019
  • The optimal grasping point of the object varies depending on the shape of the object, such as the weight, the material, the grasping contact with the robot hand, and the grasping force. In order to derive the optimal grasping points for each object by a three fingered robot hand, optimal point and posture have been derived based on the geometry of the object and the hand using the artificial neural network. The optimal grasping cost function has been derived by constructing the cost function based on the probability density function of the normal distribution. Considering the characteristics of the object and the robot hand, the optimum height and width have been set to grasp the object by the robot hand. The resultant force between the contact area of the robot finger and the object has been estimated from the grasping force of the robot finger and the gravitational force of the object. In addition to these, the geometrical and gravitational center points of the object have been considered in obtaining the optimum grasping position of the robot finger and the object using the artificial neural network. To show the effectiveness of the proposed algorithm, the friction cone for the stable grasping operation has been modeled through the grasping experiments.

다중센서를 이용한 로봇 손의 파지 제어

  • 이양희;서동수;박민용;이종원
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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    • pp.694-697
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    • 1996
  • The aim of this work for 5 years from 1994 is to develop a multi-fingered robot hand and its control system for grasp and manipulation of objects dexterously. Since the robot hand is still being developed, a commercialized robot hand from Barrett Company is utilized to implement a hand controller and control algorithm. For this, VME based motion control and interface boards are developed and multi-sensors such as encoder, force/torque sensor, dynamic sensor and artificial skin sensor are partly developed and employed for the grasping control algorithm. In oder to handle uncertainties such as mechanical idleness and backlash, a fuzzy rule based grasping algorithm is also considered and tested with the developed control system.

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웨어러블 센서를 활용한 경량 인공신경망 기반 손동작 인식기술 (A Light-weight ANN-based Hand Motion Recognition Using a Wearable Sensor)

  • 이형규
    • 대한임베디드공학회논문지
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    • 제17권4호
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    • pp.229-237
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    • 2022
  • Motion recognition is very useful for implementing an intuitive HMI (Human-Machine Interface). In particular, hands are the body parts that can move most precisely with relatively small portion of energy. Thus hand motion has been used as an efficient communication interface with other persons or machines. In this paper, we design and implement a light-weight ANN (Artificial Neural Network)-based hand motion recognition using a state-of-the-art flex sensor. The proposed design consists of data collection from a wearable flex sensor, preprocessing filters, and a light-weight NN (Neural Network) classifier. For verifying the performance and functionality of the proposed design, we implement it on a low-end embedded device. Finally, our experiments and prototype implementation demonstrate that the accuracy of the proposed hand motion recognition achieves up to 98.7%.

파나막스 중고선가치 추정모델 연구 (Panamax Second-hand Vessel Valuation Model)

  • 임상섭;이기환;양혁준;윤희성
    • 한국항해항만학회지
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    • 제43권1호
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    • pp.72-78
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    • 2019
  • 중고선은 신조선과 달리 시장참여자에게 즉각적인 시장 진출입 기회를 제공하기 때문에 해운산업에서 중요한 시장이라 할 수 있다. 중고선 거래 시 정확한 선가 추정은 향후 장기적인 자본비용의 부담과 직접적인 관련이 있기 때문에 투자의사결정에서 상당히 중요한 요소가 된다. 기존의 중고선시장과 관련된 연구들은 시장의 효율성검증에 치우쳐 있어 정확한 중고선가 추정을 위한 연구는 부족한 실정이다. 본 연구에서는 중고선박 가치추정에 전통적인 계량모델보다 기존연구에서 시도되지 않았던 인공신경망모델을 새롭게 제안하였다. 문헌연구를 통해 중고선 가격에 영향을 미치는 6개 요인(운임, 신조선가격, 총 선복대비 발주량, 해체선 가격, 선령, 사이즈)을 선정하였고, 데이터는 2016년 1월부터 2018년 12월까지 Clarkson에 보고된 파나막스 중고선의 실거래 기록 366건을 이용하였다. 변수선정을 위하여 상관분석과 단계적 회귀분석 실시한 결과 최종적으로 운임, 선령, 사이즈 3개의 변수가 채택되었다. 모델의 설계는 10분할 교차검증으로 인공신경망모델의 파라미터들을 추정하여 진행되었다. 인공신경망 모델의 중고선 가치추정치를 단순 단계적 회귀모형과 비교한 결과 인공신경망모델의 성능이 우수함을 확인하였다. 이 연구는 중고선 선가추정에 미치는 요인들에 대한 통계적인 검증, 성능개선을 위한 기계학습기반의 인공신경망 모델활용이라는 측면에서 차별적 의미가 있다. 또한 정확한 선가 추정이 요구되는 실무에서 통계적인 합리성과 결과의 정확성이 동시에 만족되는 과학적 모델을 제시하여 실무적으로도 도움이 될 것으로 기대한다.

Control of IPMC-based Artificial Muscle for Myoelectric Hand Prosthesis

  • Lee Myoung-Joon;Jung Sung-Hee;Moon Inhyuk;Lee Sukmin;Mun Mu-Seong
    • 대한의용생체공학회:의공학회지
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    • 제26권5호
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    • pp.257-264
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    • 2005
  • This paper proposes an ionic polymer metal composite (IPMC) based artificial muscle to be applicable to the Myoelectric hand prosthesis. The IPMC consists of a thin polymer membrane with metal electrodes plated chemically on both faces, and it is widely applying to the artificial muscle because it is driven by relatively low input voltage. The control commands for the IPMC-based artificial muscle is given by electromyographic (EMG) signals obtained from human forearm. By an intended contraction of the human flexor carpi ulnaris and extensor carpi ulnaris muscles, we investigated the actuation behavior of the IPMC-based artificial muscle. To obtain higher actuation force of the IPMC, the single layered as thick as $800[{\mu}m]$ or multi-layered IPMC of which each layer can be as thick as $178[{\mu}m]$ are prepared. As a result, the bending force was up to the maximum 12[gf] from 1[gf] by actuating the single layered IPMC with $178[{\mu}m]$, but the bending displacement was reduced to 6[mm] from 30[mm]. The experimental results using an implemented IPMC control system show a possibility and a usability of the bio-mimetic artificial muscle.