• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.797-800
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• 2003

In the current study, the authors propose a method for extracting license plate regions by means of a neural network trained to output the plates center of gravity. The method is shown to be effective. Since the learning pattern presentation positions are defined by random numbers, a different pattern is submitted to the neural network for learning each time, which enables it to form a neural network with high universality of coverage. The article discusses issues of the optimal learning surface for a license plate revered by the learning pattern, the effort of suppression learning of the number and headlight sections, as well as the effect of learning pattern enlargement/reduction and of concentration value conversion. Results of evaluation tests based on pictures of 595 vehicles taken at an underground parking garage demonstrated detection rates of 98.5%.

• Archives of Plastic Surgery
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• v.47 no.4
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• pp.340-346
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• 2020

Background Adipofascial flaps covered with a skin graft address the challenges involved in reconstructing dorsal foot defects. The purpose of this study was to describe a large adipofascial flap based on the perforators of the dorsalis pedis artery for large foot defects. Methods Twelve patients aged 5-18 years with large soft tissue defects of the dorsal foot due to trauma were treated with an extended dorsalis pedis adipofascial flap from May 2016 to December 2018. The flap was elevated from the non-injured half of the dorsum of the foot. Its length was increased by fascial extension from the medial or lateral foot fascia to the plantar fascia to cover the defect. All perforators of the dorsalis pedis artery were preserved to increase flap viability. The dorsalis pedis artery and its branches were kept intact. Results The right foot was affected in 10 patients, and the left foot in two patients. All flaps survived, providing an adequate contour and durable coverage with a thin flap. Follow-up lasted up to 2 years, and patients were satisfied with the results. They were able to wear shoes. Donor-site morbidity was negligible. Two cases each of partial skin graft loss and superficial necrosis at the tip of the donor cutaneous flap occurred and were healed by a dressing. Conclusions The hinged multiperforator-based extended dorsalis pedis adipofascial flap described herein is a suitable method for reconstructing dorsal foot defects, as it provides optimal functional and aesthetic outcomes with minimal donor site morbidity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.9
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• pp.1395-1403
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• 1993

Protocol conformance is crucial to inter-operability and cost effective computer communication. Given a protocol specification, the task of checking whether an inplementation conforms to the specification is called conformance testing. The efficiency and fault coverage of conformance testing are largely dependent on how test cases are chosen. Some states may have more one UIO sequence when the protocol is represented by FSM (Finite State Machine). The length of test sequence can be minimized if the optimal test sequences are chosen. In this paper, we construct the depth-tree to find the maximum overlapping among the test sequence. By using the resulting depth-tree, we generate the minimum-length test sequence. We show the example of the minimum-length test sequence obtained by using the resulting depth-tree.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.53.1-53.1
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• 2016

With the official start of the operations of the three 1.6 m KMTNet telescope systems from 2015 October, we have initiated a program named KMTNet Supernova Program (KSP) from 2015 to 2019 aiming at searching for supernovae (SNe), other optical transients and related sources. Taking advantage of the 24-hour coverage, high cadence and multi-color monitoring observations, this is optimal for discovering early SNe and peculiar ones. From the start of the previous test observing runs of ~half a year, we have performed observations on several nearby galaxy groups and nearby galaxies with short separations on the sky. We have developed data reduction/variable object search pipelines, meanwhile we have discovered some interesting transient objects. We also stacked all the images for given fields, searched for new objects/galaxies, and discovered several new dwarf galaxies, e.g., in the NGC 2784 galaxy group field (H. S. Park et al.'s talk). We will report the current project status and the results obtained.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.62-65
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• 2018

This paper comes out with the study on sensing data collection strategy in a Software-Defined Mobile Edge vehicular networking. The two cooperative data dissemination are Direct Vehicular cloud mode and edge cell trajectory prediction decision mode. In direct vehicular cloud, the vehicle observe its neighboring vehicles and sets up vehicular cloud for cooperative sensing data collection, the data collection output can be transmitted from vehicles participating in the cooperative sensing data collection computation to the vehicle on which the sensing data collection request originate through V2V communication. The vehicle on which computation originate will reassemble the computation out-put and send to the closest RSU. The SDMEVN (Software Defined Mobile Edge Vehicular Network) Controller determines how much effort the sensing data collection request requires and calculates the number of RSUs required to support coverage of one RSU to the other. We set up a simulation scenario based on realistic traffic and communication features and demonstrate the scalability of the proposed solution.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.9
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• pp.780-785
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• 2012

In the current study, the authors propose a method for extracting license plate regions by means of a neural network trained to output the plates center of gravity. The method is shown to be effective. Since the learning pattern presentation positions are defined by random numbers, a different pattern is submitted to the neural network for learning each time, which enables it to form a neural network with high universality of coverage. The article discusses issues of the optimal learning surface for a license plate covered by the learning pattern, the effect of suppression learning of the number and headlight sections, as well as the effect of learning pattern enlargement/reduction and of concentration value conversion. Results of evaluation tests based on pictures of 595 vehicles taken at an underground parking garage demonstrated detection rates of 98.5%.

• Journal of Communications and Networks
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• v.18 no.1
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• pp.112-122
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• 2016

Cognitive radio (CR) is considered an attractive technology to deal with the spectrum scarcity problem. Multi-radio access technology (multi-RAT) can improve network capacity because data are transmitted by multiple RANs (radio access networks) concurrently. Thus, multi-RAT embedded in a cognitive radio network (CRN) is a promising paradigm for developing spectrum efficiency and network capacity in future wireless networks. In this study, we consider a new CRN model in which the primary user networks consist of heterogeneous primary users (PUs). Specifically, we focus on the energy-efficient resource allocation (EERA) problem for CR users with a special location coverage overlapping region in which heterogeneous PUs operate simultaneously via multi-RAT. We propose a two-tier crossover genetic algorithm-based search scheme to obtain an optimal solution in terms of the power and bandwidth. In addition, we introduce a radio environment map to manage the resource allocation and network synchronization. The simulation results show the proposed algorithm is stable and has faster convergence. Our proposal can significantly increase the energy efficiency.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6B
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• pp.738-743
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• 2011

This paper considers the cost minimization issue for sensor network systems where sensor energy is supplied by remote energy sources wirelessly. Assuming symmetric structures of sensor nodes and energy sources, cost minimization problem is formulated, where the cost of sensor networks is represented as a function of sensor node density and energy source coverage. The optimal solution for the problem is provided and simulation results show that the proposal scheme achieves around 19% cost reduction in comparision to a conventional scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3A
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• pp.281-289
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• 2009

Wireless relay attracts great attention as a core technology of next generation wireless communication systems since it enables reliable communications and extends cell coverage by supporting shadowed users. In this paper, we Propose transmit power and subcarrier allocation scheme for downlink OFDM systems with multiple decode and forward (DF) relays to increase data rate with fixed bit error rate (BER) and sum power constraint. In simulation results, average data rate based on the proposed schemes are evaluated and compared to that of the other schemes. It is also shown that the performance loss of the proposed scheme is negligible compared to the optimal scheme, while its computational complexity is reduced considerably.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.105-109
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• 2008

In the current study, the authors propose a method for extracting license plate regions by means of a neural network trained to output the plate.s center of gravity. The method is shown to be effective. Since the learning pattern presentation positions are defined by random numbers, a different pattern is submitted to the neural network for learning each time, which enables it to form a neural network with high universality of coverage. The article discusses issues of the optimal learning surface for a license plate covered by the learning pattern, the effect of suppression learning of the number and headlight sections, as well as the effect of learning pattern enlargement/reduction and of concentration value conversion. Results of evaluation tests based on pictures of 595 vehicles taken at an underground parking garage demonstrated detection rates of 98.5%.