• Electronics and Telecommunications Trends
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• v.33 no.5
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• pp.33-41
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• 2018

The paradigm of mobile communication technology has changed from an increase in transmission capacity to service-based technologies satisfying various types of service requirements. One of the new paradigms is a service that provides users with a QoE (Quality Of Experience (QoE), at anytime and anywhere. 5G defines various technologies such as dense network structures and beam selection for increasing the transmission capacity and ensuring the quality of experience so as to satisfy this requirement, and related research is underway. In this paper, we describe the definition of a 5G small cell and 5G network structure as well as research trends of standardization and related technologies for constructing optimal solutions for mobile users in dense networks based on small cells.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.691-694
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• 2020

최근 원격 탐사 영상의 발달로 인해 작지만 중요한 객체에 대한 탐지 가능성이 커져 건물 변화 탐지에 대한 관심이 높아지고 있다. 본 논문은 건물 변화 탐지 방법 중 가장 좋은 성능을 가진 PGA-SiamNet 의 세부 변화 탐지의 정확도가 낮은 한계점을 개선시키기 위해 DensNet 기반의 Dense Siamese Network 를 제안한다. 제안하는 방법은 공개된 WHU 데이터 세트에 대해 변화 탐지 측정 지표인 TPR, OA, F1, Kappa 에 대해 97.02%, 99.5%, 97.44%, 97.16%의 성능을 얻었다. 기존 PGA-SiamNet 에 비해 TPR 은 0.83%, F1 은 0.02%, Kappa 는 0.02% 증가하였으며, 세부 변화 탐지의 성능이 우수함을 확인할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4122-4143
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• 2018

Because of the increment in network scale and test expenditure, simulators gradually become main tools for research on key problems of wireless networking, such as radio resource management (RRM) techniques. However, existing simulators are generally event-driven, causing unacceptably large simulation time owing to the tremendous number of events handled during a simulation. In this article, a mass-processing framework for RRM simulations is proposed for the scenarios with a massive amount of terminals of Internet of Things accessing 5G communication systems, which divides the time axis into RRM periods and each period into a number of mini-slots. Transmissions within the coverage of each access point are arranged into mini-slots based on the simulated RRM schemes, and mini-slots are almost fully occupied in dense scenarios. Because the sizes of matrices during this process are only decided by the fixed number of mini-slots in a period, the time expended for performance calculation is not affected by the number of terminals or packets. Therefore, by avoiding the event-driven process, the proposal can simulate dense scenarios in a quite limited time. By comparing with a classical event-driven simulator, NS2, we show the significant merits of our proposal on low time and memory costs.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.25-31
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• 2022

In ultra dense networks (UDNs), macro user equipments (MUEs) have significant interference from small-cell access points (SAPs) since a number of SAPs are deployed in the coverage of macro base stations of 5G mobile communication systems. In this paper, we propose a dynamic channel assignment scheme to increase the performance of MUEs for the uplink of UDNs even though the number of SAPs is increased. The target of the proposed dynamic channel assignment scheme is that the signal-to-interference and noise ratio (SINR) of MUEs is above a given SINR threshold assigning different subchannels to SUEs from those of MUEs. Simulation results show that the proposed dynamic channel assignment scheme outperforms others in terms of the mean MUE capacity even though the mean SUE capacity is decreased a little lower.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.04a
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• pp.51-54
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• 2003

Three kinds of experiments were conducted to test existing methods and develop an effective methodology for the remediation of DNAPL trapped in vertical dead-end fractures. A water-flushing method failed to remove TCE from vertical dead-end fractures where no fluid flow occurs. A water-flushing experiment implies that existing remediation methods, utilizing water-based remedial fluid such as surfactant-enhanced method, have difficulty in removing DNAPL trapped from the vertical downward dead-end fractures, because of no water flow through dead-end fractures, capillary, and gravity forces. Fluid denser than TCE was injected into the fracture network, but did not displace TCE from the vertical dead-end fractures. Base(B on the analysis of the experiments, the increase in the density of the dense fluid and the addition of surfactant to the dense fluid were suggested, and this composite dense fluid with surfactant effectively removed TCE from the vertical dead-end fractures.

• Journal of Animal Science and Technology
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• v.63 no.2
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• pp.367-379
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• 2021

The objectives of this study were to evaluate convolutional neural network models and computer vision techniques for the classification of swine posture with high accuracy and to use the derived result in the investigation of the effect of dietary fiber level on the behavioral characteristics of the pregnant sow under low and high ambient temperatures during the last stage of gestation. A total of 27 crossbred sows (Yorkshire × Landrace; average body weight, 192.2 ± 4.8 kg) were assigned to three treatments in a randomized complete block design during the last stage of gestation (days 90 to 114). The sows in group 1 were fed a 3% fiber diet under neutral ambient temperature; the sows in group 2 were fed a diet with 3% fiber under high ambient temperature (HT); the sows in group 3 were fed a 6% fiber diet under HT. Eight popular deep learning-based feature extraction frameworks (DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, MobileNet, VGG16, VGG19, and Xception) used for automatic swine posture classification were selected and compared using the swine posture image dataset that was constructed under real swine farm conditions. The neural network models showed excellent performance on previously unseen data (ability to generalize). The DenseNet121 feature extractor achieved the best performance with 99.83% accuracy, and both DenseNet201 and MobileNet showed an accuracy of 99.77% for the classification of the image dataset. The behavior of sows classified by the DenseNet121 feature extractor showed that the HT in our study reduced (p < 0.05) the standing behavior of sows and also has a tendency to increase (p = 0.082) lying behavior. High dietary fiber treatment tended to increase (p = 0.064) lying and decrease (p < 0.05) the standing behavior of sows, but there was no change in sitting under HT conditions.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.45-55
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• 2021

This paper has proposed an algorithm that detecting for dense small vehicle in large image efficiently. It is consisted of two Ensemble Deep-Learning Network algorithms based on Coarse to Fine method. The system can detect vehicle exactly on selected sub image. In the Coarse step, it can make Voting Space using the result of various Deep-Learning Network individually. To select sub-region, it makes Voting Map by to combine each Voting Space. In the Fine step, the sub-region selected in the Coarse step is transferred to final Deep-Learning Network. The sub-region can be defined by using dynamic windows. In this paper, pre-defined mapping table has used to define dynamic windows for perspective road image. Identity judgment of vehicle moving on each sub-region is determined by closest center point of bottom of the detected vehicle's box information. And it is tracked by vehicle's box information on the continuous images. The proposed algorithm has evaluated for performance of detection and cost in real time using day and night images captured by CCTV on the road.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.343-364
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• 2021

In the 5G era, the communication networks tend to be ultra-densified, which will improve the accuracy of indoor positioning and further improve the quality of positioning service. In this study, we propose an indoor three-dimensional (3D) dynamic reconstruction fingerprint matching algorithm (DSR-FP) in a 5G ultra-dense network. The first step of the algorithm is to construct a local fingerprint matrix having low-rank characteristics using partial fingerprint data, and then reconstruct the local matrix as a complete fingerprint library using the FPCA reconstruction algorithm. In the second step of the algorithm, a dynamic base station matching strategy is used to screen out the best quality service base stations and multiple sub-optimal service base stations. Then, the fingerprints of the other base station numbers are eliminated from the fingerprint database to simplify the fingerprint database. Finally, the 3D estimated coordinates of the point to be located are obtained through the K-nearest neighbor matching algorithm. The analysis of the simulation results demonstrates that the average relative error between the reconstructed fingerprint database by the DSR-FP algorithm and the original fingerprint database is 1.21%, indicating that the accuracy of the reconstruction fingerprint database is high, and the influence of the location error can be ignored. The positioning error of the DSR-FP algorithm is less than 0.31 m. Furthermore, at the same signal-to-noise ratio, the positioning error of the DSR-FP algorithm is lesser than that of the traditional fingerprint matching algorithm, while its positioning accuracy is higher.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.1
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• pp.33-38
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• 2005

A cost-effective dense WDM-PON employing wavelength-locked Fabry-Perot lasers has been demonstrated. We have successfully demonstrated a dense WDM transmission of 4×622 Mb/s upstream signal with 50-GHz channel spacing over 30-km conventional single mode fiber. We have also investigated the heating noise characteristics of a wavelength-locked Fabry-Perot laser and showed the wavelength-locked Fabry-Perot laser suppresses the intensity noise of the incoherent light injected which enables a dense WDM transmission with a channels spacing of 50 GHz.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4108-4122
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• 2016

To address challenges of an unprecedented growth in mobile data traffic, the ultra-dense network deployment is a cost efficient solution to off-load the traffic over other small cells. However, the real traffic is often much lower than the peak-hour traffic and certain small cells are superfluous, which will not only introduce extra energy consumption, but also impose extra interference onto the radio environment. In this paper, an elastic energy efficient cell management scheme is proposed based on flow scheduling among multi-layer ultra-dense cells by a SDN controller. A significant power saving was achieved by a cell-level energy manager. The scheme is elastic for energy saving, adaptive to the dynamic traffic distribution in the office or campus environment. In the end, the performance is evaluated and demonstrated. The results show substantial improvements over the conventional method in terms of the number of active BSs, the handover times, and the switches of BSs.