• Journal of Communications and Networks
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• v.17 no.2
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• pp.100-109
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• 2015

Mobile phone carriers face challenges from three synergistic dimensions: Wireless, social, and mobile. Despite significant advances that have been made about social networks and human mobility, respectively, our knowledge about the interplay between two layers remains largely limited, partly due to the difficulty in obtaining large-scale datasets that could offer at the same time social and mobile information across a substantial population over an extended period of time. In this paper, we take advantage of a massive, longitudinal mobile phone dataset that consists of human mobility and social network information simultaneously, allowing us to explore the impact of human mobility patterns on the underlying social network. We find that human mobility plays an important role in shaping both local and global structural properties of social network. In contrast to the lack of scale in social networks and human movements, we discovered a characteristic distance in physical space between 10 and 20 km that impacts both local clustering and modular structure in social network. We also find a surprising distinction in trajectory overlap that segments social ties into two categories. Our results are of fundamental relevance to quantitative studies of human behavior, and could serve as the basis of anchoring potential theoretical models of human behavior and building and developing new applications using social and mobile technologies.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.93-101
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• 2020

Shipyards are potential environments for using IoT services, sensor networks, and delay tolerant networks. Simulations of those services and networks strongly rely on human mobility models. Results obtained with an unrealistic model may not reflect the true performance of applications, protocols, and algorithms in a shipyard. A lot of synthetic models for human movements have been studied but most of them are generic and focus on the daily movements of humans on city scales. Nevertheless, workers in shipyards have unique movement characteristics such as movement speed, pause time, and attractions places. For instance, workers usually move to some places, where they work, and rarely move to other places in the factory. Movement characteristics of workers not only depend on workers but also on tasks, which they do. For instance, workers, who paint ships, have similar movement speed and pause time. Hence, in this paper, human movements in shipyards are studied. We propose a new human mobility model called the human mobility mode in shipyards (MIS). In MIS, workers are classified into multiple types. Movement characteristics of a worker are similar to other workers in the same type. Based on the visiting probability, workers have some places, where they frequently visits, and some places, where they rarely visit. We analyze real mobility traces and studie to achieve human movement characteristics from real traces. The results show that MIS provides a well-match to the movement characteristic from real traces.

• BMB Reports
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• v.37 no.1
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• pp.53-58
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• 2004

With advances in determining the entire DNA sequence of the human genome, it is now critical to systematically identify the function of a number of genes in the human genome. These biological challenges, especially those in human diseases, should be addressed in human cells in which conventional (e.g. genetic) approaches have been extremely difficult to implement. To overcome this, several approaches have been initiated. This review will focus on the development of a novel 'chemical genetic/genomic approach' that uses small molecules to 'probe and identify' the function of genes in specific biological processes or pathways in human cells. Due to the close relationship of small molecules with drugs, these systematic and integrative studies will lead to the 'medicinal systems biology approach' which is critical to 'formulate and modulate' complex biological (disease) networks by small molecules (drugs) in human bio-systems.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.769-781
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• 2017

Deep learning networks like Convolutional Neural Networks (CNNs) show successful performances in many computer vision applications such as image classification, object detection, and so on. For implementation of deep learning networks in embedded system with limited processing power and memory, deep learning network may need to be simplified. However, simplified deep learning network cannot learn every possible scene. One realistic strategy for embedded deep learning network is to construct a simplified deep learning network model optimized for the scene images of the installation place. Then, automatic training will be necessitated for commercialization. In this paper, as an intermediate step toward automatic training under fisheye camera environments, we study more precise human localization in fisheye images, and propose an accurate human localization method, Automatic Ground-Truth Labelling Method (AGTLM). AGTLM first localizes candidate human object bounding boxes by utilizing GoogLeNet-LSTM approach, and after reassurance process by GoogLeNet-based CNN network, finally refines them more correctly and precisely(tightly) by applying saliency object detection technique. The performance improvement of the proposed human localization method, AGTLM with respect to accuracy and tightness is shown through several experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.941-946
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• 2005

In this paper an active random noise control using adaptive learning rate neural networks is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. It is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.

• Korean Journal of Human Ecology
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• v.22 no.3
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• pp.421-436
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• 2013

International marriage is composed over 10% among total marriage in Korea. This study tried to know what kinds of social networks, especially transnational networks, the immigrant wives use for the process of being married and for the adjusting to marriage and Korean culture, and how their Korean families also are affected by the transnational networks. For the purposes of this study FGI and the interviews were applied for the immigrant wives, the multicultural husbands and the specialist groups in metropolitan city DaeGu. 18 migrant interviewees from Vietnam, China, Philippine, etc. were collected by the snow-ball sampling. 5 husbands were collected from the self-help meeting in multicultural families support center. The transnational networks of the immigrant wives in DaeGu were deterritorialized and reterritorialized actively. Migrant wives managed the close relationship with their family members of motherland, and had the networks sticky with relatives, friends, and other fore-immigrant wives from the same countries. Their migrations are characterized as 'chain migration'. Even though they acquired the Korean nationality, they have the transnational identities. They and their Korean families are interrelated and internetworked in exchanging economic resources as goods and money, human beings, love, child caring, foods and culture over local boundaries.

• Biomaterials and Biomechanics in Bioengineering
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• v.2 no.3
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• pp.143-157
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• 2015

Successful integration of cementless femoral stems using porous surfaces relies on effective periimplant bone healing to secure the bone-implant interface. The initial stages of the healing process involve protein adsorption, fibrin clot formation and cell osteoconduction onto the implant surface. Modelling this process in vitro, the current work considered the effect of fibrin deposition on the responses of human mesenchymal stromal cells cultured on ferritic fibre networks intended for magneto-mechanical actuation of in-growing bone tissue. The underlying hypothesis for the study was that fibrin deposition would support early stromal cell attachment and physiological functions within the optimal regions for strain transmission to the cells in the fibre networks. Highly porous fibre networks composed of 444 ferritic stainless steel were selected due to their ability to support human osteoblasts and mesenchymal stromal cells without inducing untoward inflammatory responses in vitro. Cell attachment, proliferation, metabolic activity, differentiation and penetration into the ferritic fibre networks were examined for one week. For all fibrin-containing samples, cells were observed on and between the metal fibres, supported by the deposited fibrin, while cells on fibrin-free fibre networks (control surface) attached only onto fibre surfaces and junctions. Initial cell attachment, measured by analysis of deoxyribonucleic acid, increased significantly with increasing fibrinogen concentration within the physiological range. Despite higher cell numbers on fibrin-containing samples, similar metabolic activities to control surfaces were observed, which significantly increased for all samples over the duration of the study. It is concluded that fibrin deposition can support the early attachment of viable mesenchymal stromal cells within the inter-fibre spaces of fibre networks intended for magneto-mechanical strain transduction to in-growing cells.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.21-29
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• 2020

Accurate estimation of human pose relies on backbone method in which its role is to extract feature map. Up to dated, the method of backbone feature extraction is conducted by the plain convolutional neural networks named by CNN and the residual neural networks named by Resnet, both of which have various architectures and performances. The CNN family network such as VGG which is well-known as a multiple stacked hidden layers architecture of deep learning methods, is base and simple while Resnet which is a bottleneck layers architecture yields fewer parameters and outperform. They have achieved inspired results as a backbone network in human pose estimation. However, they were used then followed by different pose estimation networks named by pose parsing module. Therefore, in this paper, we present a comparison between the plain CNN family network (VGG) and bottleneck network (Resnet) as a backbone method in the same pose parsing module. We investigate their performances such as number of parameters, loss score, precision and recall. We experiment them in the bottom-up method of human pose estimation system by adapted the pose parsing module of openpose. Our experimental results show that the backbone method using VGG network outperforms the Resent network with fewer parameter, lower loss score and higher accuracy of precision and recall.

• ETRI Journal
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• v.38 no.6
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• pp.1229-1239
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• 2016

For efficient interaction between humans and robots, robots should be able to understand the meaning and intention of human behaviors as well as recognize them. This paper proposes an interactive human intention reading method in which a robot develops its own knowledge about the human intention for an object. A robot needs to understand different human behavior structures for different objects. To this end, this paper proposes a hierarchical behavior knowledge network that consists of behavior nodes and directional edges between them. In addition, a human intention reading algorithm that incorporates reinforcement learning is proposed to interactively learn the hierarchical behavior knowledge networks based on context information and human feedback through human behaviors. The effectiveness of the proposed method is demonstrated through play-based experiments between a human and a virtual teddy bear robot with two virtual objects. Experiments with multiple participants are also conducted.

• Journal of the Korean Geographical Society
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• v.41 no.5 s.116
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• pp.561-581
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• 2006

Machines, cities and bodies have been evolved together for a long time, and the recent development of information and communication technologies has transformed cities and bodies into new forms. Concerned with the relations between machines, cities and bodies, this paper explores how mobile networks are related with the physical space of the city and the psychological space of the body. The paper is organised into four main sections. First, it provides a theoretical review of the ways in which mobile networks transform urban spaces and human bodies. Secondly, it explains the generation of mobile networks through technological and institutional changes in Korea. Thirdly, it looks at the socio-spatial scales and time-space landscapes of mobile networks in relation to mobile users' motions and practices in their everyday lives. Finally, it attends to the ways in which mobile networks involve the production of paradoxical emotional spaces in relation to mobile users' emotions and desires to be dis/connected with mobile networks.