• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.2
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• pp.96-105
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• 2021

Large-scale power converters consist of series or parallel module combinations. In these modular converter systems, the interleaving technique can be applied to improve capacitor reliability by reducing the ripple of the I/O current in which each module operates as a phase difference. However, when applying the interleaving technique for conventional three-level boost converters, the short-circuit period of the converter can be an obstacle. Such problem is caused by the absence of a low-level inductor of the conventional three-level boost converter. To solve this problem, a three-level boost converter with a low-level inductor is proposed and analyzed to enable interleaved operation. In the proposed circuit, the current ripple of the output capacitor depends on the neutral point connections between the modules. In this study, the ripple current is analyzed by the neutral point connections of the three-level boost converter that has a low-level inductor, and the effectiveness of the proposed circuit is proven by simulation and experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3714-3732
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• 2019

With the rapid development of network, Intrusion Detection System(IDS) plays a more and more important role in network applications. Many data mining algorithms are used to build IDS. However, due to the advent of big data era, massive data are generated. When dealing with large-scale data sets, most data mining algorithms suffer from a high computational burden which makes IDS much less efficient. To build an efficient IDS over big data, we propose a classification algorithm based on data clustering and data reduction. In the training stage, the training data are divided into clusters with similar size by Mini Batch K-Means algorithm, meanwhile, the center of each cluster is used as its index. Then, we select representative instances for each cluster to perform the task of data reduction and use the clusters that consist of representative instances to build a K-Nearest Neighbor(KNN) detection model. In the detection stage, we sort clusters according to the distances between the test sample and cluster indexes, and obtain k nearest clusters where we find k nearest neighbors. Experimental results show that searching neighbors by cluster indexes reduces the computational complexity significantly, and classification with reduced data of representative instances not only improves the efficiency, but also maintains high accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3182-3198
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• 2019

Vision-based Human Pose Estimation has been considered as one of challenging research subjects due to problems including confounding background clutter, diversity of human appearances and illumination changes in scenes. To tackle these problems, we propose to use a new multi-stage convolution machine for estimating human pose. To provide better heatmap prediction of body joints, the proposed machine repeatedly produces multiple predictions according to stages with receptive field large enough for learning the long-range spatial relationship. And stages are composed of various modules according to their strategic purposes. Pyramid stacking module and dilation module are used to handle problem of human pose at multiple scales. Their multi-scale information from different receptive fields are fused with concatenation, which can catch more contextual information from different features. And spatial and channel information of a given input are converted to gating factors by squeezing the feature maps to a single numeric value based on its importance in order to give each of the network channels different weights. Compared with other ConvNet-based architectures, we demonstrated that our proposed architecture achieved higher accuracy on experiments using standard benchmarks of LSP and MPII pose datasets.

• Smart Structures and Systems
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• v.24 no.1
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• pp.15-26
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• 2019

Over the past decades, a great variety of dampers have been developed and applied to mechanical, aerospace, and civil structures to control structural vibrations. This study is focused on two emerging damper types, namely, eddy current dampers (ECDs) and electromagnetic damper (EMDs), both of which are regarded as promising alternatives to commonly-applied viscous fluid dampers (VFDs) because of their similar mechanical behavior. This study aims to enhance the damping densities of ECDs and EMDs, which are typically lower than those of VFDs, by proposing new designs with multiple improvement measures. The design configurations, mechanisms, and experimental results of the new ECDs and EMDs are presented in this paper. The further comparison based on the experimental results revealed that the damping densities of the proposed ECD and EMD designs are comparable to those of market-available VFDs. Considering ECDs and EMDs are solid-state dampers without fluid leakage problems, the results obtained in this study demonstrate a great prospect of replacing conventional VFDs with the improved ECDs and EMDs in future large-scale applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.237-253
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• 2019

For massive multiple-input multiple-output (MIMO) circumstances with time division duplex (TDD) protocol, pilot contamination becomes one of main system performance bottlenecks. This paper proposes an uplink pilot sequence assignment to alleviate this problem for spatially correlated massive MIMO circumstances. Firstly, a single-cell TDD massive MIMO model with multiple terminals in the cell is established. Then a spatial correlation between two channel response vectors is established by the large-scale fading variables and the angle of arrival (AOA) span with an infinite number of base station (BS) antennas. With this spatially correlated channel model, the expression for the achievable system capacity is derived. To optimize the achievable system capacity, a problem regarding uplink pilot assignment is proposed. In view of the exponential complexity of the exhaustive search approach, a pilot assignment algorithm corresponding to the distinct channel AOA intervals is proposed to approach the optimization solution. In addition, simulation results prove that the main pilot assignment algorithm in this paper can obtain a noticeable performance gain with limited BS antennas.

• Journal of ILASS-Korea
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• v.23 no.4
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• pp.205-211
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• 2018

Y-jet nozzle has various advantages over other twin-fluid nozzles and are used in industrial boilers. However, it costs large energy consumption because of assisted air and its design is complex. The Y-jet nozzle is consisted of a liquid and gas port and a mixing chamber. The diameter of the port and the length of the mixing chamber greatly affect spray and atomization characteristics, therefore, they are the most important factors in nozzle design. In this study, The experimental setup is consisted of a laboratory scale spray system. The characteristics of the Y-jet nozzle according to the design parameters were observed. As a result, it was found that the length of the mixing chamber did not have effect on the flow rate and the choking condition. The droplet size was measured using a Malvern type measuring device. In addition, measurements were conducted in the front and the right directions of the nozzles. Based on the results, the SMD View Ratio is defined. It is the asymmetrical design characteristics of the Y-jet nozzle.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.440-448
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• 2019

The Internet of Things (IoT) is one of the main enablers for situation awareness needed in accomplishing smart cities. IoT devices, especially for monitoring purposes, have stringent timing requirements which may not be met by cloud computing. This deficiency of cloud computing can be overcome by fog computing for which fog nodes are placed close to IoT devices. Because of low capabilities of fog nodes compared to cloud data centers, fog nodes may not be deployed with all the services required by IoT devices. Thus, in this article, we focus on the issue of fog service placement and present the recent research trends in this issue. Most of the literature on fog service placement deals with determining an appropriate fog node satisfying the various requirements like delay from the perspective of one or more service requests. In this article, we aim to effectively place fog services in accordance with the pre-obtained service demands, which may have been collected during the prior time interval, instead of on-demand service placement for one or more service requests. The concept of the logical fog network is newly presented for the sake of the scalability of fog service placement in a large-scale smart city. The logical fog network is formed in a tree topology rooted at the cloud data center. Based on the logical fog network, a service placement approach is proposed so that services can be placed on fog nodes in a resource-effective way.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.350-353
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• 2014

The large scale of data and operating systems in the trading environment in the cloud. However, technology is not an easy trading system of cloud-based data interoperability. Partially meets the data transfer rate and also the timeliness of the best trading system on the difficulties. Thus various techniques have been introduced for improving the throughput and low latency minimization problem. But the reality is, and the limits of speed improvements like Socket Direct Protocol, Offload Engine with TCP/IP is the hardware, the introduction effect is also low. In this paper, the proposed trading of the cloud XMDR-DAI based stock system. The proposed Safe Proper Time Method for optimal transmission speed and reliability.

• Progress in Superconductivity and Cryogenics
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• v.21 no.3
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• pp.38-42
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• 2019

To protect the power systems from fault current, the rated protective equipment should be installed. However growth of power system scale and concentration of loads caused the large fault current in power transmission system and distribution system. And capacities of installed protective equipment have been exceeded the due to increase of fault current. This increase is not temporary phenomenon but will be steadily as long as the industry develops. The power system need a counter measurement for safety, so superconducting fault current limiter (SFCL) has been received attention as an effective solutions to reduce the fault current. For the above reasons various type SFCL is studied recently. In this paper, the operational characteristics and power burden of trigger type SFCL is studied. The trigger type SFCL has been used for real system research in many countries. And another trigger type SFCL (double quench trigger type SFCL) is also studied. For this paper, short circuit test is performed.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.97-110
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• 2021

Fog can cause large-scale human and economic damages, including traffic systems and agriculture. So, Korea Meteorological Administration is operating about 290 visibility meters to improve the observation level of fog. However, it is still insufficient to detect very localized fog. In this study, high-resolution grid-type visibility data were retrieved from irregularly distributed visibility data across the country. To this end, three objective analysis techniques (Inverse Distance Weighting (IDW), Ordinary Kriging (OK) and Universal Kriging (UK)) were used. To find the best method and parameters, sensitivity test was performed for the effective radius, power parameter and variogram model that affect the level of objective analysis. Also, the effect of data distribution characteristics (level of normality) on the performance level of objective analysis was evaluated. IDW showed a relatively high level of objective analysis in terms of bias, RMSE and correlation, and the performance is inversely proportional to the effective radius and power parameter. However, the two Krigings showed relatively low level of objective analysis, in particular, greatly weakened the variability of the variables, although the level of output was different depending on the variogram model used. As the level of objective analysis is greatly influenced by the distribution characteristics of data, power, and models used, care should be taken when selecting objective analysis techniques and parameters.