• 한국터널공학회:학술대회논문집
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• 2005.04a
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• pp.348-358
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• 2005

Although the evaluation of the mechanical properties and behavior of discontinuous rock masses is very important for the design of underground openings, it has always been considered the most difficult problem. One of the difficulties in describing the rock mass behavior is assigning the appropriate constitutive model. This limitation may be overcome with the progress in discrete element software such as PFC, which does not need the user to prescribe a constitutive model for rock mass. Instead, the micro-scale properties of the intact rock and joints are defined and the macro-scale response results from those properties and the geometry of the problem. In this paper, a $30m{\times}30m{\times}30m$ jointed rock mass of road tunnel site was analyzed. A discrete fracture network was developed from the joint geometry obtained from core logging and surface survey. Using the discontinuities geometry from the DFN model, PFC simulations were carried out, starting with the intact rock and systematically adding the joints and the stress-strain response was recorded for each case. With the stress-strain response curves, the mechanical properties of discontinuous rock masses were determined and compared to the results of empirical methods such as RMR, Q and GSI. The values of Young's modulus, Poisson's ratio and peak strength are almost similar from PFC model and Empirical methods. As expected, the presence of joints had a pronounced effect on mechanical properties of the rock mass. More importantly, the mechanical response of the PFC model was not determined by a user specified constitutive model.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.10
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• pp.64-75
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• 2007

Service discovery protocols is software components to find specific services or resources on network. The SLP defined by IETF protocol is a framework for automatic service discovery on IP based networks. Automatic service discovery is an important component on ubiquitous computing environment. This paper proposes a service discovery architecture named as SLPA(Service Location Protocol based on AMAAM). AMAAM(Mobility Agent Advertisement Mechanism) is an aggregation-based Mobile IP implementation in MANET. In SLPA, the role of the directory agent is assigned to the mobility agent in AMAAM. The mobility agent periodically beacons an advertisement message which contains both the advertisement of the directory agent in SLP and the advertisement of the mobility agent in Mobile IP. For evaluating the functional correctness of SLPA and the overhead of maintaining a service directory of SLPA. We simulate SLPA using ns-2 and analyze the overhead of control overheads for the aggregation. Through the simulation experiments we show the functional correctness of the proposed architecture and analyze the performance results.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.3
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• pp.155-160
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• 2017

Recently, Deep learning has been used successfully to solve many recognition problems. It has many advantages over existing machine learning methods that extract feature points through hand-crafting. Deep neural networks for human activity recognition split video data into frame images, and then classify activities by analysing the connectivity of frame images according to the time. But it is difficult to apply to actual problems which has small-scale activity classes. Because this situations has a problem of overfitting and insufficient training data. In this paper, we defined 5 type of small-scale human activities, and classified them. We construct video database using 700 video clips, and obtained a classifying accuracy of 74.00%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1225-1234
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• 2019

The security of the firmware is more important because embedded devices have become popular. Network devices such as routers can be attacked by attackers through web application vulnerabilities in embedded firmware. Therefore, they must be found and removed quickly. The Firmadyne framework proposes a dynamic analysis method to find vulnerabilities after emulating firmware. However, it only performs vulnerability checks according to the analysis methods defined in the tool, thus limiting the scope of vulnerabilities that can be found. In this paper, fuzzing is performed in emulation-based environment through fuzzing, one of the software security test techniques. We also propose a Fabfuzz tool for efficient emulation based fuzzing. Experiments have shown that in addition to the vulnerabilities identified in existing tools, other types of vulnerabilities have been found.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.11
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• pp.801-805
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• 2009

Hardware based dynamic voltage and frequency scaling is a promising technique to reduce power consumption in a globally asynchronous locally synchronous system such as a homogeneous or heterogeneous multi-core system. In this paper, FPGA prototype design of hardware based dynamic frequency scaling is proposed. The proposed techniques are applied to a FIFO based multi-core system for a software defined radio and Network-on-Chip based hardware MPEG2 encoder. Compared with a references system using a single global clock, the first prototype design reduces the power consumption by 78%, but decreases the performance by 5.9%. The second prototype design shows that power consumption decreases by 29.1% while performance decreases by 0.36%.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.6
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• pp.385-392
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• 2022

Naval ship could face a variety of threats in isolated seas. In particular, fires and flooding are defined as disasters that are very likely to cause irreparable damage to ships. These disasters have a very high risk of personal injury as well. Therefore, when a disaster occurs, it must be quickly suppressed, but if there are people in the disaster area, the protection of life must be given priority. In order to quickly evacuate the ship crew in case of a disaster, we would like to propose a plan to quickly explore the evacuation route even in urgent situations. Using commercial escape simulation software, we obtain the data for deep neural network learning with simulations according to aisle characteristics and the properties and number of evacuation person. Using the obtained data, the passage prediction model is trained with a deep learning, and the passage time is predicted through the learned model. Construct a numerical map of a naval ship and construct a distance matrix of the vessel using predicted passage time data. The distance matrix configured in one of the path search algorithms, the Dijkstra algorithm, is applied to explore the evacuation path of naval ship.

• KNOM Review
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• v.24 no.2
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• pp.13-23
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• 2021

Recently, as networks become more complex due to the activation of IoT devices, research on long-term traffic prediction beyond short-term traffic prediction is being activated to predict and prepare for network congestion in advance. The recursive strategy, which reuses short-term traffic prediction results as an input, has been extended to multi-step traffic prediction, but as the steps progress, errors accumulate and cause deterioration in prediction performance. In this paper, an LSTM-based multi-step traffic prediction method using a multi-output strategy is introduced and its performance is evaluated. As a result of experiments based on actual DNS request traffic, it was confirmed that the proposed LSTM-based multiple output strategy technique can reduce MAPE of traffic prediction performance for non-stationary traffic by 6% than the recursive strategy technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2257-2285
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• 2022

Cyber-physical systems (CPS) have been growing exponentially due to improved cloud-datacenter infrastructure-as-a-service (CDIaaS). Incremental expandability (scalability), Quality of Service (QoS) performance, and reliability are currently the automation focus on healthy Tier 4 CDIaaS. However, stable QoS is yet to be fully addressed in Cyber-physical data centers (CP-DCS). Also, balanced agility and flexibility for the application workloads need urgent attention. There is a need for a resilient and fault-tolerance scheme in terms of CPS routing service including Pod cluster reliability analytics that meets QoS requirements. Motivated by these concerns, our contributions are fourfold. First, a Distributed Non-Recursive Cloud Model (DNRCM) is proposed to support cyber-physical workloads for remote lab activities. Second, an efficient QoS stability model with Routh-Hurwitz criteria is established. Third, an evaluation of the CDIaaS DCN topology is validated for handling large-scale, traffic workloads. Network Function Virtualization (NFV) with Floodlight SDN controllers was adopted for the implementation of DNRCM with embedded rule-base in Open vSwitch engines. Fourth, QoS evaluation is carried out experimentally. Considering the non-recursive queuing delays with SDN isolation (logical), a lower queuing delay (19.65%) is observed. Without logical isolation, the average queuing delay is 80.34%. Without logical resource isolation, the fault tolerance yields 33.55%, while with logical isolation, it yields 66.44%. In terms of throughput, DNRCM, recursive BCube, and DCell offered 38.30%, 36.37%, and 25.53% respectively. Similarly, the DNRCM had an improved incremental scalability profile of 40.00%, while BCube and Recursive DCell had 33.33%, and 26.67% respectively. In terms of service availability, the DNRCM offered 52.10% compared with recursive BCube and DCell which yielded 34.72% and 13.18% respectively. The average delays obtained for DNRCM, recursive BCube, and DCell are 32.81%, 33.44%, and 33.75% respectively. Finally, workload utilization for DNRCM, recursive BCube, and DCell yielded 50.28%, 27.93%, and 21.79% respectively.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.321-327
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• 2023

With the development of artificial intelligence, the prediction system has become one of the essential technologies in our lives. Despite the growth of these technologies, traffic congestion at intersections in the 21st century has continued to be a problem. This paper proposes a system that predicts intersection traffic jams using a Convolutional LSTM (Conv-LSTM) algorithm. The proposed system models data obtained by learning traffic information by time zone at the intersection where traffic congestion occurs. Traffic congestion is predicted with traffic volume data recorded over time. Based on the predicted result, the intersection traffic signal is controlled and maintained at a constant traffic volume. Road congestion data was defined using VDS sensors, and each intersection was configured with a Conv-LSTM algorithm-based network system to facilitate traffic.

• Management & Information Systems Review
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• v.36 no.3
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• pp.131-150
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• 2017

The purpose of this study is to observe the spread pattern of open source software (OSS) while establishing relations with surrounding actors during its operation period. In order to investigate the change pattern of participants in the OSS, we use a netnography on the basis of online data, which can trace the change patterns of the OSS depending on the passage of time. For this, the cases of three OSSs (e.g. jQuery, MooTools, and YUI), which are JavaScript frameworks, were compared, and the corresponding data were collected from the open application programming interface (API) of GitHub as well as blog and web searches. This research utilizes the translation process of the actor-network theory to categorize the stages of the change patterns on the OSS translation process. In the project commencement stage, we identified the type of three different OSS-related actors and defined associated relationships among them. The period, when a master commences a project at first, is refined through the course for the maintenance of source codes with persons concerned (i.e. project growth stage). Thereafter, the period when the users have gone through the observation and learning period by being exposed to promotion activities and codes usage respectively, and becoming to active participants, is regarded as the 'leap of participants' stage. Our results emphasize the importance of promotion processes in participants' selection of the OSS for participation and confirm the crowding-out effect that the rapid speed of OSS development retarded the emergence of participants.