• ETRI Journal
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• v.45 no.2
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• pp.203-212
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• 2023

In this study, we developed a co-located and space-shared multiple-input multiple-output (MIMO) antenna module with a modular design and high integration level. The proposed antenna pair includes a half-wavelength loop antenna and a dipole-type antenna printed on the front and back sides of a compact modular board. Owing to their modal orthogonality, these two independent antenna elements are highly self-isolated and free of additional decoupling components, even though they are assembled at the same location and within the same space. Thus, the proposed antenna is attractive in 5G MIMO systems. Furthermore, the proposed co-located and space-shared MIMO antenna module was employed in a 5G smartphone to verify their radiation and diversity performances. A 12 × 12 MIMO antenna system was simulated and fabricated using the proposed module. Based on the results, the proposed module can be employed in large-scale MIMO antenna systems for current and future terminal devices owing to its high integration, compactness, simple implementation, and inherent isolation.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.164-168
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• 2023

There are many possible ways to configure database management systems (DBMSs) have challenging to manage and set.The problem increased in large-scale deployments with thousands or millions of individual DBMS that each have their setting requirements. Recent research has explored using machine learning-based (ML) agents to overcome this problem's automated tuning of DBMSs. These agents extract performance metrics and behavioral information from the DBMS and then train models with this data to select tuning actions that they predict will have the most benefit. This paper discusses two engineering approaches for integrating ML agents in a DBMS. The first is to build an external tuning controller that treats the DBMS as a black box. The second is to incorporate the ML agents natively in the DBMS's architecture.

• Asia pacific journal of information systems
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• v.28 no.4
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• pp.308-319
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• 2018

This paper aims at providing valuable insights on Financial Fraud Detection on a mobile money transactional activity. We have predicted and classified the transaction as normal or fraud with a small sample and massive data set using Azure and Spark ML, which are traditional systems and Big Data respectively. Experimenting with sample dataset in Azure, we found that the Decision Forest model is the most accurate to proceed in terms of the recall value. For the massive data set using Spark ML, it is found that the Random Forest classifier algorithm of the classification model proves to be the best algorithm. It is presented that the Spark cluster gets much faster to build and evaluate models as adding more servers to the cluster with the same accuracy, which proves that the large scale data set can be predictable using Big Data platform. Finally, we reached a recall score with 0.73, which implies a satisfying prediction quality in predicting fraudulent transactions.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.5
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• pp.266-274
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• 2004

This paper presents a scheme for the interconnection of dispersed generator systems(DGs) based on load .unbalance and load model in composite distribution systems. Groups of each individual load model consist of residential, industrial, commercial, official and agricultural load. The unbalance is involved with many single-phase line segment. . Voltage profile improvement and system loss minimization by installation of DGs depend greatly on how they are placed and operated in the distribution systems. So, DGs can reduce distribution real power losses and replace large-scale generators if they are placed appropriately in the distribution systems. The main idea of solving fuzzy goal programming is to transform the original objective function and constraints into the equivalent multi-objectives functions with fuzzy sets to evaluate their imprecise nature for the criterion of power loss minimization, the number or total capacity of DGs and the bus voltage deviation, and then solve the problem using genetic algorithm. The method proposed is applied to IEEE 13 bus and 34 bus test systems to demonstrate its effectiveness.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.3 s.192
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• pp.108-116
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• 2007

To overcome many defects such as the high product cost, large energy consumption, and big space capacity in conventional mechanical machining, the miniaturization of machine tool and micro factory systems has been envisioned recently. The object of this paper is to research the effect of dynamic characteristic parameters in bolted-joint beams, which is widely applied to the joining of mechanical structures in order to identify structural system characteristics and to predict dynamic behavior according to scale-down from macro to micro system as the development of micro/meso-scale machine tool and micro factories. Modal parameters such as the natural frequency, damping ratio, and mode shape from modal testing and dynamic characteristics from finite element analysis are extracted with all 12 test beam models by materials, by size, and by joining condition, and then the results obtained by both methods are compared.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.5
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• pp.572-581
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• 2005

This research is conducted to develop predictable method of real scale nanofiltration treatability with small scale nanofiltration experiments. As a result of comparing calculated values with measured values, they are in a good agreement for the concentrations in filtered water and concentrated water. The results of that are not affected by change of system recovery from 20% to 95%. The proposed method is produced using constant recovery of elements, that is, no considering the pressure change. we can predict filtrated flux and contaminant concentrations with the method. The method has the following steps. (1) Calculate recovery of each element with water quality level after fixing recovery elements, (2) Predict system recovery with recovery of elements in 1, 2, 3, and 4 banks, (3) Run small scale nanofiltration experiments in predicted water quality and (4) Simulate large scale nanofiltration system for forecasting actual water quality. As the cost for nanofiltration pretest will reduced if we use the proposed method, it will be a promising method for introducing nanofiltration to supply safe drinking water.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1083-1094
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• 2020

Recognizing plant species based on leaf images is challenging because of the large inter-class variation and inter-class similarities among different plant species. The effective extraction of leaf descriptors constitutes the most important problem in plant leaf recognition. In this paper, a multi-scale angular description method is proposed for fast and accurate leaf recognition and retrieval tasks. The proposed method uses a novel scale-generation rule to develop an angular description of leaf contours. It is parameter-free and can capture leaf features from coarse to fine at multiple scales. A fast Fourier transform is used to make the descriptor compact and is effective in matching samples. Both support vector machine and k-nearest neighbors are used to classify leaves. Leaf recognition and retrieval experiments were conducted on three challenging datasets, namely Swedish leaf, Flavia leaf, and ImageCLEF2012 leaf. The results are evaluated with the widely used standard metrics and compared with several state-of-the-art methods. The results and comparisons show that the proposed method not only requires a low computational time, but also achieves good recognition and retrieval accuracies on challenging datasets.

• Journal of the Korean Solar Energy Society
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• v.28 no.6
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• pp.87-92
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• 2008

Buildings are responsible for approximately 50% of current carbon dioxide emissions. Energy planning at a town and city scale needs a strategic approach, supported by strong planning policies. The purpose of this study was to investigate the urban scale grid-connected photovoltaic(PV) system for urban residential and commercial sector applications. The integration of PV technology into roof of houses is an approach that is being championed in Germany, Japan and United states etc. In the Korea, PV roofing systems already are given the large number of houses which are projected to be built by 2012. However unlike germany and Japan, urban scale grid-connected PV system is not yet installed. The solar city which is installed building-integrated photovoltaic system is available to use of renewable energy sources such as solar to meet demand, instead of fossil fuels, with the goal of realizing an ecologically oriented energy supply.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1293-1308
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• 2020

In fine-tuning-based transfer learning, the size of the dataset may affect learning accuracy. When a dataset scale is small, fine-tuning-based transfer-learning methods use high computing costs, similar to a large-scale dataset. We propose a mid-level feature extractor that retrains only the mid-level convolutional layers, resulting in increased efficiency and reduced computing costs. This mid-level feature extractor is likely to provide an effective alternative in training a small-scale medical image dataset. The performance of the mid-level feature extractor is compared with the performance of low- and high-level feature extractors, as well as the fine-tuning method. First, the mid-level feature extractor takes a shorter time to converge than other methods do. Second, it shows good accuracy in validation loss evaluation. Third, it obtains an area under the ROC curve (AUC) of 0.87 in an untrained test dataset that is very different from the training dataset. Fourth, it extracts more clear feature maps about shape and part of the chest in the X-ray than fine-tuning method.

• Nuclear Engineering and Technology
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• v.47 no.1
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• pp.11-25
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• 2015

The accident at Japan's Fukushima Daiichi nuclear power plant in March 2011, caused by an earthquake and a subsequent tsunami, resulted in a failure of the power systems that are needed to cool the reactors at the plant. The accident progression in the absence of heat removal systems caused Units 1-3 to undergo fuel melting. Containment pressurization and hydrogen explosions ultimately resulted in the escape of radioactivity from reactor containments into the atmosphere and ocean. Problems in containment venting operation, leakage from primary containment boundary to the reactor building, improper functioning of standby gas treatment system (SGTS), unmitigated hydrogen accumulation in the reactor building were identified as some of the reasons those added-up in the severity of the accident. The Fukushima accident not only initiated worldwide demand for installation of adequate control and mitigation measures to minimize the potential source term to the environment but also advocated assessment of the existing mitigation systems performance behavior under a wide range of postulated accident scenarios. The uncertainty in estimating the released fraction of the radionuclides due to the Fukushima accident also underlined the need for comprehensive understanding of fission product behavior as a function of the thermal hydraulic conditions and the type of gaseous, aqueous, and solid materials available for interaction, e.g., gas components, decontamination paint, aerosols, and water pools. In the light of the Fukushima accident, additional experimental needs identified for hydrogen and fission product issues need to be investigated in an integrated and optimized way. Additionally, as more and more passive safety systems, such as passive autocatalytic recombiners and filtered containment venting systems are being retrofitted in current reactors and also planned for future reactors, identified hydrogen and fission product issues will need to be coupled with the operation of passive safety systems in phenomena oriented and coupled effects experiments. In the present paper, potential hydrogen and fission product issues raised by the Fukushima accident are discussed. The discussion focuses on hydrogen and fission product behavior inside nuclear power plant containments under severe accident conditions. The relevant experimental investigations conducted in the technical scale containment THAI (thermal hydraulics, hydrogen, aerosols, and iodine) test facility (9.2 m high, 3.2 m in diameter, and $60m^3$ volume) are discussed in the light of the Fukushima accident.