• Geomechanics and Engineering
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• 제18권3호
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• pp.277-289
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• 2019

In this study, an innovative anchoring approach has been developed dealing with all relevant aspects in consideration of previous works. An ultimate pulling force calculation of anchor is presented from a geotechnical point of view. The proposed umbrella anchor focuses not only on the friction resistance capacity, but also on the axial capacity of the composite end structure and the friction capacity occurring around the wedge. Even though the theoretical background is proposed, in-situ application requires high-level mechanical design. Hence, the required parts have been carefully improved and are composed of anchor body, anchor cap, connection brackets, cutter vanes, open-close ring, support elements and grouting system. Besides, stretcher element made of aramid fabric, interior grouting system, guide tube and cable-locking apparatus are the unique parts of this design. The production and placement steps of real sized anchors are explained in detail. Experimental results of 52 pullout tests on the weak dry soils and 12 in-situ tests inside natural soil indicate that the proposed approach is conservative and its peak pullout value is directly limited by a maximum strength of anchored soil layer if other failure possibilities are eliminated. Umbrella anchor is an alternative to conventional anchor applications used in all types of soils. It not only provides time and workmanship benefits, but also a high level of economic gain and safe design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권1호
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• pp.110-132
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• 2019

To ensure reliable and secure communication in heterogeneous cellular network (HCN) with imperfect channel state information (CSI), we proposed a coordinated multipoint (CoMP) transmission scheme based on dual-threshold optimization, in which only base stations (BSs) with good channel conditions are selected for transmission. First, we present a candidate BSs formation policy to increase access efficiency, which provides a candidate region of serving BSs. Then, we design a CoMP networking strategy to select serving BSs from the set of candidate BSs, which degrades the influence of channel estimation errors and guarantees qualities of communication links. Finally, we analyze the performance of the proposed scheme, and present a dual-threshold optimization model to further support the performance. Numerical results are presented to verify our theoretical analysis, which draw a conclusion that the CoMP transmission scheme can ensure reliable and secure communication in dense HCNs with imperfect CSI.

• Structural Engineering and Mechanics
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• 제77권1호
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• pp.57-74
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• 2021

The thermal eigenvalue responses of the graded sandwich shell structure are evaluated numerically under the variable thermal loadings considering the temperature-dependent properties. The polynomial type rule-based sandwich panel model is derived using higher-order type kinematics considering the shear deformation in the framework of the equivalent single-layer theory. The frequency values are computed through an own home-made computer code (MATLAB environment) prepared using the finite element type higher-order formulation. The sandwich face-sheets and the metal core are discretized via isoparametric quadrilateral Lagrangian element. The model convergence is checked by solving the similar type published numerical examples in the open domain and extended for the comparison of natural frequencies to have the final confirmation of the model accuracy. Also, the influence of each variable structural parameter, i.e. the curvature ratios, core-face thickness ratios, end-support conditions, the power-law indices and sandwich types (symmetrical and unsymmetrical) on the thermal frequencies of FG sandwich curved shell panel model. The solutions are helping to bring out the necessary influence of one or more parameters on the frequencies. The effects of individual and the combined parameters as well as the temperature profiles (uniform, linear and nonlinear) are examined through several numerical examples, which affect the structural strength/stiffness values. The present study may help in designing the future graded structures which are under the influence of the variable temperature loading.

• 한국정보시스템학회지:정보시스템연구
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• 제30권4호
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• pp.203-226
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• 2021

Purpose The purpose of this study is to enhance the accuracy score of personal credit scoring using the convolutional neural networks and secure the transparency of the deep learning model using eXplainalbe Artifical Inteligence(XAI) technique. Design/methodology/approach This study built a classification model by using the convolutional neural networks(CNN) and applied a methodology that is transformation of numerical data to imaged data to apply CNN on personal credit data. Then layer-wise relevance propagation(LRP) was applied to model we constructed to find what variables are more influenced to the output value. Findings According to the empirical analysis result, this study confirmed that accuracy score by model using CNN is highest among other models using logistic regression, neural networks, and support vector machines. In addition, With the LRP that is one of the technique of XAI, variables that have a great influence on calculating the output value for each observation could be found.

• Current Optics and Photonics
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• 제6권3호
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• pp.270-281
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• 2022

Based on the transfer-matrix method (TMM), we report the characteristics of the interface states in one-dimensional (1D) composite structures consisting of two photonic crystals (PCs) composed of binary dielectrics A and B, with unit-cell configurations ABA (PC I) and BAB (PC II). The dependence of the interface states on the number of unit cells N and the boundary factor x are displayed. It is verified that the interface states are independent of N when the PC has inversion symmetry (x = 0.5). Besides, the composite structures support the formation of interface states independent of the PC symmetry, except that the positions of the interface states will be varied within the photonic band gaps. Moreover, the robustness of the interface states against nonuniformities is investigated by adding Gaussian noise to the layer thickness. In the case of inversion symmetry (x = 0.5) the most robust interface states are achieved, while for the other cases (x ≠ 0.5) interface states decay linearly with position inside the band gap. This work could shed light on the development of robust photonic devices.

• Geomechanics and Engineering
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• 제30권3호
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• pp.247-258
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• 2022

A field velocity resistivity probe (FVRP) can measure compressional waves, shear waves and electrical resistivity in boreholes. The objective of this study is to perform the soil classification through a machine learning technique through elastic wave velocity and electrical resistivity measured by FVRP. Field and laboratory tests are performed, and the measured values are used as input variables to classify silt sand, sand, silty clay, and clay-sand mixture layers. The accuracy of k-nearest neighbors (KNN), naive Bayes (NB), random forest (RF), and support vector machine (SVM), selected to perform classification and optimize the hyperparameters, is evaluated. The accuracies are calculated as 0.76, 0.91, 0.94, and 0.88 for KNN, NB, RF, and SVM algorithms, respectively. To increase the amount of data at each soil layer, the synthetic minority oversampling technique (SMOTE) and conditional tabular generative adversarial network (CTGAN) are applied to overcome imbalance in the dataset. The CTGAN provides improved accuracy in the KNN, NB, RF and SVM algorithms. The results demonstrate that the measured values by FVRP can classify soil layers through three kinds of data with machine learning algorithms.

• Earthquakes and Structures
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• 제22권1호
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• pp.53-64
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• 2022

This study proposes an intelligent semi-active isolation system combining a variable-stiffness control device and ground motion characteristic prediction. To determine the optimal control parameter in real-time, a genetic algorithm (GA)-fuzzy control law was developed in this study. Data on various types of ground motions were collected, and the ground motion characteristics were quantified to derive a near-fault (NF) characteristic ratio by employing an on-site earthquake early warning system. On the basis of the peak ground acceleration (PGA) and the derived NF ratio, a fuzzy inference system (FIS) was developed. The control parameters were optimized using a GA. To support continuity under near-fault and far-field ground motions, the optimal control parameter was linked with the predicted PGA and NF ratio through the FIS. The GA-fuzzy law was then compared with other control laws to verify its effectiveness. The results revealed that the GA-fuzzy control law could reliably predict different ground motion characteristics for real-time control because of the high sensitivity of its control parameter to the ground motion characteristics. Even under near-fault and far-field ground motions, the GA-fuzzy control law outperformed the FPEEA control law in terms of controlling the isolation layer displacement and the superstructure acceleration.

• 한국멀티미디어학회논문지
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• 제25권8호
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• pp.1075-1084
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• 2022

Internet of Things(IoT) has become a key available technology for efficiently implementing device to device(D2D) services in various domains such as smart home, healthcare, smart city, agriculture, energy, logistics, and transportation. A lightweight publish/subscribe(Pub/Sub) messaging protocol not only establishes data dissemination pattern but also supports connectivity between IoT devices and their applications. Also, a Pub/Sub broker is deployed to facilitate data exchange among IoT devices. A scalable edge-based publish/subscribe (Pub/Sub) broker overlay networks support latency-sensitive IoT applications. In this paper, we design a hypercube grid quorum(HGQ) for distributed Pub/Sub systems based IoT applications. In designing HGQ, the network of hypercube structures suitable for the publish/subscribe model is built in the edge layer, and the proposed HGQ is designed by embedding a mesh overlay network in the hypercube. As their applications, we propose an HGQ-based mechansim for dissemination of the data of sensors or the message/event of IoT devices in IoT environments. The performance of HGQ is evaluated by analytical models. As the results, the latency and load balancing of applications based on the distributed Pub/Sub system using HGQ are improved.

• Applied Microscopy
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• 제50권
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• pp.17.1-17.9
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• 2020

Recent advances in fabrication have enabled radial-junction architectures for cost-effective and high-performance optoelectronic devices. Unlike a planar PN junction, a radial-junction geometry maximizes the optical interaction in the three-dimensional (3D) structures, while effectively extracting the generated carriers via the conformal PN junction. In this paper, we report characterizations of radial PN junctions that consist of p-type Si micropillars created by deep reactive-ion etching (DRIE) and an n-type layer formed by phosphorus gas diffusion. We use electron-beam induced current (EBIC) microscopy to access the 3D junction profile from the sidewall of the pillars. Our EBIC images reveal uniform PN junctions conformally constructed on the 3D pillar array. Based on Monte-Carlo simulations and EBIC modeling, we estimate local carrier separation/collection efficiency that reflects the quality of the PN junction. We find the EBIC efficiency of the pillar array increases with the incident electron beam energy, consistent with the EBIC behaviors observed in a high-quality planar PN junction. The magnitude of the EBIC efficiency of our pillar array is about 70% at 10 kV, slightly lower than that of the planar device (≈ 81%). We suggest that this reduction could be attributed to the unpassivated pillar surface and the unintended recombination centers in the pillar cores introduced during the DRIE processes. Our results support that the depth-dependent EBIC approach is ideally suitable for evaluating PN junctions formed on micro/nanostructured semiconductors with various geometry.

• 스마트미디어저널
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• 제11권11호
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• pp.32-39
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• 2022

AI 서비스를 제공하는 IoT 응용이 늘어나면서 자율적인 학습 및 추론을 지원하는 다양한 하드웨어와 소프트웨어들이 개발되고 있다. 하지만 하드웨어마다 특성 및 제약조건이 상이하여 IoT 응용 개발에 어려움이 가중됨에 따라 통합된 플랫폼의 개발이 요구되고 있다. 본 논문에서는 IoT 기술뿐만 아니라 인공 신경망 및 스파이킹 신경망 기반의 컴포넌트를 오픈 플랫폼과 호환되도록 자동 생성하는 도구를 제안한다. 제안하는 컴포넌트 자동 생성 도구는 IoT 및 AI의 가상 컴포넌트 계층을 통해 다양한 하드웨어의 특성에 맞는 컴포넌트 생성을 용이하게 하고 자동으로 오픈 플랫폼에 적용할 수 있도록 지원한다.