• Fashion & Textile Research Journal
• /
• v.16 no.1
• /
• pp.153-159
• /
• 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.

• Fashion & Textile Research Journal
• /
• v.19 no.1
• /
• pp.79-89
• /
• 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.

• Korean Journal of Human Ecology
• /
• v.12 no.1
• /
• pp.63-73
• /
• 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%.

• Korean Journal of Artificial Intelligence
• /
• v.12 no.3
• /
• pp.33-39
• /
• 2024

In this paper, we explore the application of Kodaly hand signs in enhancing children's music education, performances, and auditory assistance technologies. This research focuses on improving the recognition rate of Multilayer Perceptron (MLP) models in identifying Kodaly hand sign scales through the integration of Artificial Neural Networks (ANN). We developed an enhanced MLP model by augmenting it with additional parameters and optimizing the number of hidden layers, aiming to substantially increase the model's accuracy and efficiency. The augmented model demonstrated a significant improvement in recognizing complex hand sign sequences, achieving a higher accuracy compared to previous methods. These advancements suggest that our approach can greatly benefit music education and the development of auditory assistance technologies by providing more reliable and precise recognition of Kodaly hand signs. This study confirms the potential of parameter augmentation and hidden layers optimization in refining the capabilities of neural network models for practical applications.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.36.2-36
• /
• 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.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2023.01a
• /
• pp.469-471
• /
• 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.

• The Journal of Korea Robotics Society
• /
• v.14 no.2
• /
• pp.94-103
• /
• 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.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.694-697
• /
• 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.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.17 no.4
• /
• pp.229-237
• /
• 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%.

• Journal of Navigation and Port Research
• /
• v.43 no.1
• /
• pp.72-78
• /
• 2019

The second-hand ship market provides immediate access to the freight market for shipping investors. When introducing second-hand vessels, the precise estimate of the price is crucial to the decision-making process because it directly affects the burden of capital cost to investors in the future. Previous studies on the second-hand market have mainly focused on the market efficiency. The number of papers on the estimation of second-hand vessel values is very limited. This study proposes an artificial neural network model that has not been attempted in previous studies. Six factors, freight, new-building price, orderbook, scrap price, age and vessel size, that affect the second-hand ship price were identified through literature review. The employed data is 366 real trading records of Panamax second-hand vessels reported to Clarkson between January 2016 and December 2018. Statistical filtering was carried out through correlation analysis and stepwise regression analysis, and three parameters, which are freight, age and size, were selected. Ten-fold cross validation was used to estimate the hyper-parameters of the artificial neural network model. The result of this study confirmed that the performance of the artificial neural network model is better than that of simple stepwise regression analysis. The application of the statistical verification process and artificial neural network model differentiates this paper from others. In addition, it is expected that a scientific model that satisfies both statistical rationality and accuracy of the results will make a contribution to real-life practices.