• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.273-277
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• 2009

Recently user-generated contents have been drastically increased. In this paper, we introduce the user reputation which can be used to evaluate quality of the content they created. First we have composed a social network that is based on user activity. And we have developed the algorithm to evaluate the users' reputation using this social network.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제22권6호
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• pp.1266-1272
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• 1997

Due to the price and performanceof uniprocessor workstations and off-the shelf networking, network of workstations(NOW) ae now a cost-effective parallel processing platform tht is competitive with supercomputers. Meanwhile, current network fiile system protocols rely heavily on a central server to coordinate file activity among client workstations. This central server can become a bottleneck that limits scalibility for environments with large numbers of clients. In this paper, we propsoe a highly reliable and effective software RAID file system on the network of workstation environment. We present results form a trace-driven simulation study that shows that the designed software RAID file system is more effective in the aspect of elapsed time when compared with client/server file systems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
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• pp.709-712
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• 1996

Generating a project network of a specific construction project is very time consuming and difficult task in the field. To effectiviely automate and support the planning process, we design a case-based project planning expert system inspired by the fact a human expert project planner uses previous cases for planning a new project. A construction project case consist of its specific characteristics and the corresponding project network (i.e. project plan). Using frame based representation. we represent the project features affecting the progress network and the entities composing the project plan such as the buildings, construction methods, WBS (work breakdown structure), activities, and resources. The project planning process runs through most similar case retrieval, case adaptation, and user requirement satisfaction. We represent the construction domain knowledge for each procedure using constraints and rules. We develop the methodology for constraint-based case adaption. Case adaptation process mainly consist of activity generation/deletion and predecence constraint satisfaction, for which we develop the dynamic constraint generation method and connect user-level requirement representation the system-level network modification knowledge. The methodology is being applied to the prototype for apartment construction project planning.

• Korean Management Science Review
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• 제32권4호
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• pp.175-192
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• 2015

Due to the emergence of open innovation driven by development of network service technologies and convergence in ICT service industry, It is necessary for ICT service firms to examine their capabilities for open innovation. The purpose of this paper is to empirically examine determinants affecting open innovation in Korean ICT service industry. In order to analyze, this paper uses logistic and multiple regression models based on survey data of Korean ICT service firms. Estimation results show that external network for collaboration is positive on the technological innovation activity regardless of the innovation type. Specifically, user networks are significant in all types of technology innovation, revealing that it is important to innovation activities of the ICT service firms.

• Proceedings of the KIEE Conference
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2461-2464
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• 2004

Recently, research about optimizing network of IBS(Intelligent Building System) is in progress activity. When we install existent building control network using general PC, system's efficiency drops. Because server that is charged with the task process is not and is expensive. Also, CapEx(Capital Expenditure) rises because of setting up the network equipment to integrate different protocol in building system. In this paper, we suggests the embedded web server that has various network structure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권1호
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• pp.50-67
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• 2015

The Cognitive Radio (CR) paradigm in tactical ad hoc networks is an important element of future military communications for network-centric warfare. This paper presents a novel Cognitive Link State Routing protocol for CR-based tactical ad hoc networks. The proposed scheme provides prompt and reliable routes for Primary User (PU) activity through procedures that incorporate two main functions: PU-aware power adaptation and channel switching. For the PU-aware power adaptation, closer multipoint relay nodes are selected to prevent network partition and ensure successful PU communication. The PU-aware channel switching is proactively conducted using control messages to switch to a new available channel based on a common channel list. Our simulation study based on the ns-3 simulator demonstrates that the proposed routing scheme delivers significantly improved performance in terms of average end-to-end delay, jitter, and packet delivery ratio.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 한국측량학회 2010년 춘계학술발표회 논문집
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• pp.363-365
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• 2010

In this study, based on numerical map GIS-T Dataset build and by using ArcGIS Network Analysis emergency vehicle's reach time were analyzed. AutoCad using 1: 50,000 based on roads and hospitals of numerical map were creating a Polyline and Point and Network Dataset made using ArcCatalog. ArcGIS Analysis setting the interval for the period reached 3 minutes, 5 minutes, 15 minutes was set and then U-Turn was set to not allow because U-turn takes a long time to calculate and does not happen often on the real road. Intersection of the passage of time, considering that the emergency vehicles were set to 3 seconds. To expand by taking advantage of this facility on Vulnerable area will be used as base material. If we focus on analyzing the emergency activity to convert little data, To prepare for disaster and disaster will be able to use the materials.

• Proceedings of the KIEE Conference
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.429-432
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• 2003

In this paper, a novel neural network approach is proposed for cleaning robot to complete coverage path planning with obstacle avoidance in stationary and dynamic environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location without any prior knowledge of the dynamic environment.

• Genomics & Informatics
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• 제18권4호
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• pp.39.1-39.8
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• 2020

Alzheimer's disease (AD) is a chronic, progressive brain disorder that slowly destroys affected individuals' memory and reasoning faculties, and consequently, their ability to perform the simplest tasks. This study investigated the hub genes of AD. Proteins interact with other proteins and non-protein molecules, and these interactions play an important role in understanding protein function. Computational methods are useful for understanding biological problems, in particular, network analyses of protein-protein interactions. Through a protein network analysis, we identified the following top 10 hub genes associated with AD: PTGER3, C3AR1, NPY, ADCY2, CXCL12, CCR5, MTNR1A, CNR2, GRM2, and CXCL8. Through gene enrichment, it was identified that most gene functions could be classified as integral to the plasma membrane, G-protein coupled receptor activity, and cell communication under gene ontology, as well as involvement in signal transduction pathways. Based on the convergent functional genomics ranking, the prioritized genes were NPY, CXCL12, CCR5, and CNR2.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.