• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.916-920
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• 2008

The personal workspace consists of user- specified computing environment such as user profile, applications and their configurations, and user data. Mobile computing devices (i.e., cellular phones, PDAs, laptop computers, and Ultra Mobile PC) are getting smaller and lighter to provide personal work-space ubiquitously. However, various personal work-space mobility solutions (c.f. VMWare Pocket ACE[1], Mojopac[2], u-PC[3], etc.) are appeared with the advance of virtualization technology and portable storage technology. The personal workspace can be loaded at public PC using above solutions. Especially, we proposed a framework called ubiquitous personal computing environment (u-PC) that supports mobility of personal workspace based on wireless iSCSI network storage in our previous work. However, previous u-PC could support limited applications, because it uses IRP (I/O Request Packet) forwarding technique at filter driver level on Windows operating system. In this paper, we implement OS-level virtualization technology using system call hooking on Windows operating system. It supports personal workspace mobility and covers previous u-PC limitation. Also, it overcomes personal workspace loading overhead that is limitation of other solutions (i.e., VMWare Pocket ACE, Mojopac, etc). We implement a prototype consisting of Windows XP-based host PC and Linux-based mobile device connected via WiNET protocol of UWB. We leverage several use~case models of our framework for proving its usability.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.373-380
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• 2022

This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.319-323
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• 2007

실세계의 여러 문제들은 마르코프 결정 문제(Markov decision problem, MDP)로 표현될 수 있고, 이 MDP는 모델이 알려진 경우에는 평가치 반복(value iteration) 이나 모델이 알려지지 않은 경우에도 강화 학습(reinforcement learning) 알고리즘 등을 사용하여 풀 수 있다. 하지만 이들 알고리즘들은 시간 복잡도가 높아 크기가 큰 실세계 문제에 적용하기 쉽지 않아, MDP를 계층적으로 분할하거나, 여러 단계를 묶어서 수행하는 등의 시간적 추상화(temporal abstraction) 방법이 제안되어 왔다. 이러한 시간적 추상화 방법들의 문제점으로는 시간적 추상화의 디자인에 따라 MDP의 풀이 성능이 크게 달라질 수 있으며, 많은 경우 사용자가 이 디자인을 직접 제공해야 한다는 것들이 있다. 최근 사용자의 간섭이 필요 없이 자동적으로 시간적 추상화를 만드는 방법들이 제안된 바 있으나, 이들 방법들 역시 결과물에 대한 이론적인 성능 보장(performance guarantee)은 제공하지 못하고 있다. 본 연구에서는 이러한 문제점을 해결하기 위해 MDP의 구조와 그 풀이 성능을 연관짓는 복잡도 척도에 대해 살펴본다. 이를 위해 MDP로부터 얻은 상태 경로 그래프(state trajectory graph)의 위상적 성질들을 여러 네트워크 척도(network measurements) 들을 이용하여 측정하고, 이와 MDP의 풀이 성능과의 관계를 다양한 상황에 대해 실험적, 이론적으로 분석해 보았다.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.980-984
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• 2010

Exploring microRNA (miRNA) and mRNA regulatory interactions may give new insights into diverse biological phenomena. Recently, miRNAs have been discovered as important regulators that play a major role in various cellular processes. Therefore, it is essential to identify functional interactions between miRNAs and mRNAs for understanding the context- dependent activities of miRNAs in complex biological systems. While elucidating complex miRNA-mRNA interactions has been studied with experimental and computational approaches, it is still difficult to infer miRNA-mRNA regulatory modules. Here we present a novel method, termed layered hypernetworks (LHNs), for identifying functional miRNA-mRNA interactions from heterogeneous expression data. In experiments, we apply the LHN model to miRNA and mRNA expression profiles on multiple cancers. The proposed method identifies cancer-specific miRNA-mRNA interactions. We show the biological significance of the discovered miRNA- mRNA interactions.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.125-131
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• 2021

In recent years, Convolutional Neural Networks (CNNs) have achieved outstanding performance in the fields of computer vision such as image classification, object detection, visual quality enhancement, etc. However, as huge amount of computation and memory are required in CNN models, there is a limitation in the application of CNN to low-power environments such as mobile or IoT devices. Therefore, the need for neural network compression to reduce the model size while keeping the task performance as much as possible has been emerging. In this paper, we propose a method to compress CNN models by combining matrix decomposition methods of LR (Low-Rank) approximation and CP (Canonical Polyadic) decomposition. Unlike conventional methods that apply one matrix decomposition method to CNN models, we selectively apply two decomposition methods depending on the layer types of CNN to enhance the compression performance. To evaluate the performance of the proposed method, we use the models for image classification such as VGG-16, RestNet50 and MobileNetV2 models. The experimental results show that the proposed method gives improved classification performance at the same range of 1.5 to 12.1 times compression ratio than the existing method that applies only the LR approximation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.1
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• pp.118-130
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• 2013

With the advent of a new services environment based on high-speed mobile networks and high-performance mobile devices, users in real life require content-centric services that provide personalized information conveniently and efficiently. These services are defined as ambient services. To implement and support sustainable ambient services, there is a critical need to conduct research regarding practicable models and methodologies. This paper proposes an effective model for ambient services based on the personalization of real-life space. The model consists of Public Info-space, Universal Info-space and Private Info-space. We also show a methodology for implementing the model with currently available techniques in order to prove that the model and methodology constitute an applicable solution to developing true ambient services. Finally, a kind of role-playing game which is built on a real university campus is presented to show the model to be available, where the test bed infrastructure consists of wireless mesh networks and real-time location systems (RTLSes).

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.37-47
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• 2018

The number of capabilities of Internet of Things (IoT) devices will exponentially grow over the next years. These devices may generate a vast amount of time-constrained data. In the context of IoT, data management should act as a layer between the objects and devices generating the data and the applications accessing the data for analysis purposes and services. In addition, most of IoT services will be content-centric rather than host centric to increase the data availability and the efficiency of data delivery. IoT will enable all the communication devices to be interconnected and make the data generated by or associated with devices or objects globally accessible. Also, fog computing keeps data and computation close to end users at the edge of network, and thus provides a new breed of applications and services to end users with low latency, high bandwidth, and geographically distributed. In this paper, we propose Edge-Fog cloud-based Hierarchical Data Delivery ($EFcHD^2$) method that effectively and reliably delivers IoT data to associated with IoT applications with ensuring time sensitivity. The proposed $EFcHD^2$ method stands on basis of fully decentralized hybrid of Edge and Fog compute cloud model, Edge-Fog cloud, and uses information-centric networking and bloom filters. In addition, it stores the replica of IoT data or the pre-processed feature data by edge node in the appropriate locations of Edge-Fog cloud considering the characteristic of IoT data: locality, size, time sensitivity and popularity. Then, the performance of $EFcHD^2$ method is evaluated through an analytical model, and is compared to fog server-based and Content-Centric Networking (CCN)-based data delivery methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.4697-4706
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• 2010

This paper suggested the optimal operating design method using simulation and ANP(Analytic Network Process) for mass-customization in the automotive component manufacturing industry. For this, first of all, we built the simulation model including various and complex factors in the field, and estimated the meta-model by RSM(Response Surface Method). Secondly using ANP, we calculated the weight of relative importance of evaluation factors gathered from decision makers. And then, we proposed the optimal operation designs by MOGA(Multi-Objective Genetic Algorithm), analyzed results of them. Moreover, by comparing the results with the consequences using AHP(Analytic Hierarchy Process), we showed its superiority of suggested method to the manner using AHP, because it reflects inner, outer dependency, and inter-relation among judgement factors. In conclusion, through this process, we can present the better way to serve mover effective, precise, and accurate information to decision makers when they build operation design for mass-customization system as automotive parts production system.

• Journal of Technology Innovation
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• v.23 no.3
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• pp.43-65
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• 2015

Technology standard continues to exist as an social agreement throughout all industry. With development of Information and Communication, it is recently considered as a strategic factor for enhancing interoperability and increasing market dominance. Hence, technology standard research(standardization research) have a significance in analyzing the process of standard adoption and its economic effect and deriving theoretical and policy implications. However, as existing relative researches are lack in consideration on indigenous value of technology and its interoperability, there exists limitation in drawing the result of technology centered analysis. The goal of this study is to suggest new technology centered standard method by implying function of technology differentiation rate and technology preference that are deduced by technology network analysis into two stage game theory. As an example of empirical case, we selected wireless pan technology of Internet of Things, and derive its technological structure and implications related to standard.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.103-119
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• 2018

This study explored news coverage on the sex crimes and analyzed news by network analysis and frame analysis based on the layered model to compare news coverage on the celebrity. As a result, in case of celebrity the broadcasting focused more and the tone of news is more sensational. The news in ground wave broadcasting more detailed on the sex crimes. It blamed the An, the governor of Chungnam more and the news is more sensational by interviewing marginal man. In #Metoo case, broadcasting news focused on the offender. The title of case name and the headline are framed based on the offender. Especially consensual relationship frame is dominated in the sex crime news. This study also can see the offender blaming frame and in the viewpoint of agenda-setting. It is difficult to find the cause of #Metoo movement and the structural approach on the case. This study highlighted the importance of layed model when analyzing the sex-crime news related with #Metoo movement.