• Journal of Korea Multimedia Society
• /
• v.13 no.5
• /
• pp.691-701
• /
• 2010

We propose a haptic rendering technology for touchable video. Our touchable video technique allows users for feeling the sense of touch while probing directly on 2D objects in video scenes or manipulating 3D objects brought out from video scenes using haptic devices. In our technique, a server sends video and haptic data as well as the information of 3D model objects. The clients receive video and haptic data from the server and render 3D models. A video scene is divided into small grids, and each cell has its tactile information which corresponds to a specific combination of four attributes: stiffness, damping, static friction, and dynamic friction. Users can feel the sense of touch when they touch directly cells of a scene using a haptic device. Users can also examine objects by touching or manipulating them after bringing out the corresponding 3D objects from the screen. Our touchable video technique proposed in this paper can lead us to feel maximum satisfaction the haptic-audio-vidual effects directly on the video scenes of movies or home-shopping video contents.

• Korean Journal of Oriental Medicine
• /
• v.14 no.3
• /
• pp.103-111
• /
• 2008

Recently, skin diagnosis has been suggested as a promising tool for discrimination of Sasang Constitution, reported by examining the skin characteristics such as thickness, stiffness, slip, and skin textures like wrinkles and furrows. However, the works had a limitation in that clinical decision on the skin characteristics was made by relying upon oriental medicine doctors' subjective sense of touch. In order to objectify the skin diagnosis and claim its efficacy on the discrimination of the Sasang Constitutions, it is necessary to demonstrate its discrimination capability by providing numerical values in terms of physical quantities obtained from measurements using today's sensors and equipment technologies, which motivated this work as a priliminary step towards objectification of skin diagnosis. The skin characteristics focused in this work is the slip property of the back-hand skin that has been exploited using the dynamic friction measurement system. First, curved geometric effects of the back-hand skin on the measured lateral/vertical force signals were estimated using the artificially designed silicon coated structures, which led to a suggestion on a quality controlled experimental design based upon a empirical analysis model. Second, the experimental design thus suggested has been applied to the measurement of dynamic friction coefficients for two healthy male subjects of Taeumin (TE) and Soyangin (SY), respectively. The result shows that the dynamic friction coefficient is less for the SY subject than for the TE subject around the area of the skin used for diagnosis by the oriental medicine doctor, implying the TE subject's skin is more slippery than the SE subject's that is consistent with the oriental medicine doctor's diagnosis. Hopefully, this work can provide guidelines for obtaining quality data in friction measurement to be collected for discussion on the efficacy of the skin diagnosis and its objectification through statistical analysis.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2001.01a
• /
• pp.59-64
• /
• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to chose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state- action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem. we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL)as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.

• Journal of Fashion Business
• /
• v.27 no.6
• /
• pp.47-65
• /
• 2023

The purpose of this study is to derive the future-oriented characteristics Sonia Delaunay presented at the time based on the identity and modernity shown in her works and diverse activity. The scope of study spans from 1907 to the start of World War II and includes both applied and fine arts, with a focus on textiles and fashion. The research method is a literary study that includes old documents, exhibition booklets, and explanations from museum curators. The visual materials are actual works observed at exhibitions and digital images of various exhibitions. As a result of the research, she was a practitioner who expressed her identity in marriage, artwork, textiles, and fashion. In order to embody her design and express modernity, she showed geometric and modern motifs and she incorporated a sense of bright color and modernized light into her work by following the principle of simultaneous contrast in color. Additionally, she applied Hungarian embroidery techniques to simple materials and created geometric abstraction with her simultaneous colors, which contributed to both originality and the mass production of textile design. The future-oriented characteristics she presented are the dynamism of modern rhythm, the expansion of convergence and collaboration, the innovation of new production and exhibition, the media of consumer society, and the femininity of modern life. She recognized the mass consumer society and mass production of the early 20th century and actively utilized various media and genres to evoke a dynamic sense necessary for modern life and presented a design to be seen as a modern woman.

• Journal of the Korea Safety Management & Science
• /
• v.17 no.3
• /
• pp.163-171
• /
• 2015

The aim of this study was to investigate kinematic mechanism of gait different road conditions(dry vs. oil) in order women. For this study, twenty older women and ten young women participated in this research. twelve infrared cameras were used to collect data. It appeared that the gait strategies of older women were slower velocity and higher CoM than young women. Depending on road conditions, gait velocities of dominant muscle older women on dry surface were faster than dominant sense older women, but those of them were inverse on oil surface. The slip displacement of dominant muscle older women was less than young women, but the slip displacement of dominant sense older women was greater than young women. In case of blind during stance phase on oil surface, the rotational motion of the ankle and knee joints were increased. In conclusion, older women were subjected to self-organization theory and phase shift in dynamic theory.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2001.10a
• /
• pp.321-324
• /
• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement Beaming is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement loaming is different from supervised teaming in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement loaming algorithms like Q-learning do not require defining or loaming any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state-action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem, we suggest Adaptive Mediation-based Modular Q-Learning(AMMQL) as an improvement of the existing Modular Q-Learning(MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state space effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.

• Proceedings of the KSME Conference
• /
• 2001.06b
• /
• pp.501-506
• /
• 2001

Computer modeling is essential to evaluate possible design of suspension for a railway vehicles. By creating a simulation, the engineers are able to assess the feasibility of a given design and change the design factors to get a better design. But if one wishes to perform complex analysis on the simulation, such as railway vehicle dynamic, the computational time can become overwhelming. Therefore, many researchers have turned to surrogate modeling. A surrogate model is essentially a regression performed on a data sampling of the simulation. In the most general sense, metamodels(surrogate model) take the form $y(x)=f(x)+{\varepsilon}$, where y(x) is the true simulation output, f(x) is the metamodel output, and $\varepsilon$ is the error between the two. In this paper, a second order polynomial equation is partially used as a metamodel to represent the forty-six dynamic performances for high speed train. The number of factors as design variables of the metamodel is twenty-nine, which are composed the dynamic characteristics of suspension. This metamodel is used to search the optimum values of suspension characteristics which minimize the dynamic responses for high speed train. This optimization is a multi-objective problem which have many design variables. This paper shows that the response surface model which is made through the design of analysis of computer experiments method is very efficient to solve this complex optimization problem.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2001.01a
• /
• pp.151-155
• /
• 2001

Mobile agent technology has been the subject of much attention in the last few years, mainly due to the proliferation of distributed software technologies combined with the distributed AI research field. In this paper, we present a design of communication networks of agents that cooperate with each other for forwarding messages to the specific mobile agent in order to make the overall system location transparent. In order to make the material accessible to general intelligent system researchers, we present the general ideas abstractly in terms of the graph theory. In particular, a proxy network is defined as a directed acyclic graph satisfying some structural conditions. In turns out that the definition ensures some kind of reliability of the network, in the sense that as long as at most one proxy agent is abnormal, there agent exists a communication path, from every proxy agent to the target agent, without passing through the abnormal proxy. As the basis for the implementation of this scheme, an appropriate initial proxy network is specified and the dynamic nature of the network is represented by a set of graph transformation rules. It is shown that those rules are sound, in the sense that all graphs created from the initial proxy network by zero or more applications of the rules are guaranteed to be proxy networks. Finally, we will discuss some implementation issues.

• Journal of Welding and Joining
• /
• v.8 no.1
• /
• pp.31-42
• /
• 1990

Arc must be stable during welding first of all other factors for obtaining sound weldment, especially in the automation of welding process. Arc stability is somewhat sophisticated phenomenon which is not clearly defined yet. In consumable electrode welding, the voltage and current variation due to metal transfer enables to assess arc stability. Recently, statistical analyses of the voltage and current waveform factors are performed to assess the degress of arc stability which is assessed and controlled by operator's own experience by now. But, considering the increasing need and the trend of automation of welding process, it is necessary to monitor arc stability in real-time. In this sutdy, the modified stability index composed of two voltage and current wvaeform factors (arc time and short circuit time) reduced from four factors (arc time, short circuit time, average arc current and average short circuit current) in Mita's index by the welding electrical circuit modeling is proposed and verified by experiments to be well estimating arc stability in the static sense. Also, the recursive calculation form estimating present arc stability in the dynamic sense is developed for real-time estimation. The results of applying the recursive index during welding show good estimation of arc stability in real-time. Therefore, the results of this study offers the mean for real-time control arc stability.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.9
• /
• pp.3444-3457
• /
• 2021

In the field of anti-jamming based on dynamic spectrum, most methods try to improve the ability to avoid jamming and seldom consider whether the jammer would perceive the user's signal. Although these existing methods work in some anti-jamming scenarios, their long-term performance may be depressed when intelligent jammers can learn user's waveform or decision information from user's historical activities. Hence, we proposed a hidden anti-jamming method to address this problem by reducing the jammer's sense probability. In the proposed method, the action correlation between the user and the jammer is used to evaluate the hiding effect of the user's actions. And a deep reinforcement learning framework, including specific action correlation calculation and iteration learning algorithm, is designed to maximize the hiding and communication performance of the user synchronously. The simulation result shows that the algorithm proposed reduces the jammer's sense probability significantly and improves the user's anti-jamming performance slightly compared to the existing algorithms based on jamming avoidance.