• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.3
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• pp.303-309
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• 2004

In this paper, an adaptive Bayesian approach to image segmentation was developed for boundary detection. Both image intensities and texture information were used for obtaining better quality of the image segmentation by using the C programming language. Fuzzy c-mean clustering was applied fer the conditional probability density function, and Gibbs random field model was used for the prior probability density function. To simply test the algorithm, a synthetic image (256$\times$256) with a set of low gray values (50, 100, 150 and 200) was created and normalized between 0 and 1 n double precision. Results have been presented that demonstrate the effectiveness of the algorithm in segmenting the synthetic image, resulting in more than 99% accuracy when noise characteristics are correctly modeled. The algorithm was applied to the Antarctic mosaic that was generated using 1963 Declassified Intelligence Satellite Photographs. The accuracy of the resulting vector map was estimated about 300-m.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8B
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• pp.716-729
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• 2006

The information systems in the most enterprise environments are distributed locally and are comprised with various heterogeneous data sources, so that it is difficult to obtain necessary and integrated information for supporting user decision. For solving 'this problems efficiently, it provides uniform interface to users and constructed database systems between heterogeneous systems make a consistence each independence and need to provide transparency like one interface. This paper presents XMDR that consists of category, standard ontology, location ontology and knowledge base. Standard ontology solves heterogeneous problem about naming, attributes, relations in data expression. Location ontology is a mediator that connects each legacy systems. Knowledge base defines the relation for sharing glossary. Adaptive retrieve proposes integrated retrieve system through reflecting site weight by location ontology, information sharing of various forms of knowledge base and integration and propose conceptual domain model about how to share unstructured knowledge.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.9A
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• pp.1395-1404
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• 2000

In this paper, we propose a heuristic wavelength routing algorithm for IP datagrams in WDM (Wavelength-Division Multiplexing) networks which operates in a distributed manner, while most previous works have focused centralized algorithms. We first present an efficient construction method for a loose virtual topology with a connectivity property, which reserves a few wavelength to cope with dynamic traffic demands properly. This connectivity property assures that data from any source node could reach any destination node by hopping one or multiple lightpaths. We then develop a high-speed distributed wavelength routing algorithm adaptive to dynamic traffic demands by using such a loose virtual topology and derive the general bounds on average utilization in the distributed wavelength routing algorithms. Finally, we show that the performance of the proposed algorithms is better than that of the FSP(Fixed Shortest-Path) wavelength routing algorithms through simulation using the NSFNET[1] and a dynamic hot-spot traffic model, and that the algorithms is a good candidate in distributed WDM networks in terms of the blocking performance, the control traffic overhead, and the computation complexity.

• Smart Media Journal
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• v.6 no.3
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• pp.41-48
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• 2017

Event detection using social media has been widespread since social network services have been an active communication channel for connecting with others, diffusing news message. Especially, the real-time characteristic of social media has created the opportunity for supporting for real-time applications/systems. Social network such as Twitter is the potential data source to explore useful information by mining messages posted by the user community. This paper proposed a novel system for temporal event detection by analyzing social data. As a result, this information can be used by first responders, decision makers, or news agents to gain insight of the situation. The proposed approach takes advantages of deep learning methods that play core techniques on the main tasks including informative data identifying from a noisy environment and temporal event detection. The former is the responsibility of Convolutional Neural Network model trained from labeled Twitter data. The latter is for event detection supported by Recurrent Neural Network module. We demonstrated our approach and experimental results on the case study of earthquake situations. Our system is more adaptive than other systems used traditional methods since deep learning enables to extract the features of data without spending lots of time constructing feature by hand. This benefit makes our approach adaptive to extend to a new context of practice. Moreover, the proposed system promised to respond to acceptable delay within several minutes that will helpful mean for supporting news channel agents or belief plan in case of disaster events.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.9
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• pp.1492-1512
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• 2011

Due to multiple hops, mobility and time-varying channel, supporting delay sensitive real-time traffic in wireless local area network-based (WLAN) mesh networks is a challenging task. In particular for real-time traffic subject to medium access control (MAC) layer control overhead, such as preamble, carrier sense waiting time and the random backoff period, the performance of real-time flows will be degraded greatly. In order to support real-time traffic, an efficient adaptive packet scheduling (APS) scheme is proposed, which aims to improve the system performance by guaranteeing inter-class, intra-class service differentiation and adaptively adjusting the packet length. APS classifies incoming packets by the IEEE 802.11e access class and then queued into a suitable buffer queue. APS employs strict priority service discipline for resource allocation among different service classes to achieve inter-class fairness. By estimating the received signal to interference plus noise ratio (SINR) per bit and current link condition, APS is able to calculate the optimized packet length with bi-dimensional markov MAC model to improve system performance. To achieve the fairness of intra-class, APS also takes maximum tolerable packet delay, transmission requests, and average allocation transmission into consideration to allocate transmission opportunity to the corresponding traffic. Detailed simulation results and comparison with IEEE 802.11e enhanced distributed channel access (EDCA) scheme show that the proposed APS scheme is able to effectively provide inter-class and intra-class differentiate services and improve QoS for real-time traffic in terms of throughput, end-to-end delay, packet loss rate and fairness.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.329-340
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• 2019

Recently, broadcasting technologies have evolved as high-quality AV services such as domestic terrestrial UHD(Ultra-High Definition) broadcasting have been increasing, and broadcasting standards have been newly defined. Also, as network technology develops, contents are consumed not only in the country but also the world. Accordingly, content insertion technology, which is a method of providing suitable contents in accordance with the national and local environments, will be needed. This paper proposes a content insertion service system model and synchronization scheme using ATSC(Advanced Television Systems Committee) 3.0 Event Signaling standard under heterogeneous network environment of broadcasting network and internet network based on transmission standard DASH(Dynamic Adaptive Streaming over HTTP)/ROUTE(Real time Object delivery Over Unidirectional Transport) and MMT(MPEG Media Transport) of terrestrial UHD broadcasting. It also verifies that the service operates in an environment that meets the broadcast standard.

• Wind and Structures
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• v.28 no.1
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• pp.31-47
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• 2019

The accurate prediction of snow distributions under the wind action on roofs plays an important role in designing structures in civil engineering in regions with heavy snowfall. Affected by some factors such as building shapes, sizes and layouts, the snow drifting on roofs shows more three-dimensional characteristics. Thus, the research on three-dimensional snow distribution is needed. Firstly, four groups of stepped flat roofs are designed, of which the width-height ratio is 3, 4, 5 and 6. Silica sand with average radius of 0.1 mm is used to model the snow particles and then the wind tunnel test of snow drifting on stepped flat roofs is carried out. 3D scanning is used to obtain the snow distribution after the test is finished and the mean mass transport rate is calculated. Next, the wind velocity and duration is determined for numerical simulations based on similarity criteria. The adaptive-mesh method based on radial basis function (RBF) interpolation is used to simulate the dynamic change of snow phase boundary on lower roofs and then a time-marching analysis of steady snow drifting is conducted. The overall trend of numerical results are generally consistent with the wind tunnel tests and field measurements, which validate the accuracy of the numerical simulation. The combination between the wind tunnel test and CFD simulation for three-dimensional typical roofs can provide certain reference to the prediction of the distribution of snow loads on typical roofs.

• The Journal of Korean Institute of Information Technology
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• v.18 no.5
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• pp.55-64
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• 2020

Estimating the state of charge (SOC) of a battery is essential for increasing the stability and reliability of a photovoltaic system. In this study, battery SOC estimation methods were proposed using artificial neural networks (ANNs) with gradient descent (GD), Levenberg-Marquardt (LM), and scaled conjugate gradient (SCG), and an adaptive neuro-fuzzy inference system (ANFIS). The charge start voltage and the integrated charge current were used as input data and the SOC was used as output data. Four models (ANN-GD, ANN-LM, ANN-SCG, and ANFIS) were implemented for battery SOC estimation and compared using MATLAB. The experimental results revealed that battery SOC estimation using the ANFIS model had both the highest accuracy and highest convergence speed.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• The Journal of the Acoustical Society of Korea
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• v.40 no.6
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• pp.571-577
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• 2021

This study is a study on System On Chip (SOC) that implements virtual engine sound in electric vehicles without engines, and realizes vivid engine sound by combining Adaptive Difference PCM (ADPCM) method and frequency modulation method for satisfaction of driver's needs and safety of pedestrians. In addition, by proposing an electronic sound synthesis algorithm applying Musical Instrument Didital Interface (MIDI), an engine sound synthesis method and a constitutive model of an engine sound generation system are presented. In order to satisfy both drivers and pedestrians, this study uses Controller Area Network (CAN) communication to receive information such as Revolution Per Minute (RPM), vehicle speed, accelerator pedal depressed amount, torque, etc., transmitted according to the driver's driving habits, and then modulates the frequency according to the appropriate preset parameters We implemented an interaction algorithm that accurately reflects the intention of the system and driver by using interpolation for the system, ADPCM algorithm for reducing the amount of information, and MIDI format information for making engine sound easier.