• Korean Journal of Materials Research
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• v.30 no.11
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• pp.627-635
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• 2020

3D printing technology is a processing technology in which 3D structures are formed by fabricating multiple 2D layers of materials based on 3D designed digital data and stacking them layer by layer. Although layers are stacked using inkjet printing to release various materials, it is still rare for research to successfully form a product as an additive manufacture of multi-materials. In this study, dispersion conditions are optimized by adding a dispersant to an acrylic monomer suitable for inkjet printing using Co₃O₄ and Al₂O₃. 3D structures having continuous composition composed of a different ceramic material are manufactured by printing using two UV curable ceramic inks whose optimization is advanced. After the heat treatment, the produced structure is checked for the formation and color of the desired crystals by comparing the crystalline analysis according to the characteristics of each part of the structure with ceramic pigments made by solid phase synthesis method.

• Journal of Powder Materials
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• v.27 no.4
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• pp.305-310
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• 2020

In this study, partially dry transfer is investigated to solve the problem of fully dry transfer. Partially dry transfer is a method in which multiple layers of graphene are dry-transferred over a wet-transferred graphene layer. At a wavelength of 550 nm, the transmittance of the partially dry-transferred graphene is seen to be about 3% higher for each layer than that of the fully dry-transferred graphene. Furthermore, the sheet resistance of the partially dry-transferred graphene is relatively lower than that of the fully dry-transferred graphene, with the minimum sheet resistance being 179 Ω/sq. In addition, the fully dry-transferred graphene is easily damaged during the solution process, so that the performance of the organic photovoltaics (OPV) does not occur. In contrast, the best efficiency achievable for OPV using the partially dry-transferred graphene is 2.37% for 4 layers.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.775-779
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• 2002

Currently, high-resolution satellite imagery such as KOMPSAT and IKONOS has been tentatively utilized to various types of urban engineering problems such as transportation planning, site planning, and utility management. This approach aims at software development and followed applications of remotely sensed imagery to transportation geographic analysis. At first, GDPA (Gradient Direction Profile Algorithm) and main modules in it are overviewed, and newly implemented results under MS visual programming environment are presented with main user interface, input imagery processing, and internal processing steps. Using this software, road network are automatically generated. Furthermore, this road network is used to transportation geographic analysis such as gamma index and road pattern estimation. While, this result, being produced to do-facto format of ESRI-shapefile, is used to several types of road layers to urban/transportation planning problems. In this study, road network using KOMPSAT EOC imagery and IKONOS imagery are directly compared to multiple road layers with NGI digital map with geo-coordinates, as ground truth; furthermore, accuracy evaluation is also carried out through method of computation of commission and omission error at some target area. Conclusively, the results processed in this study is thought to be one of useful cases for further researches and local government application regarding transportation geographic analysis using remotely sensed data sets.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.787-800
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• 2021

Three-dimensional (3D) object classification tasks using point clouds are widely used in 3D modeling, face recognition, and robotic missions. However, processing raw point clouds directly is problematic for a traditional convolutional network due to the irregular data format of point clouds. This paper proposes a pointwise convolution neural network (CNN) structure that can process point cloud data directly without preprocessing. First, a 2D convolutional layer is introduced to percept coordinate information of each point. Then, multiple 2D convolutional layers and a global max pooling layer are applied to extract global features. Finally, based on the extracted features, fully connected layers predict the class labels of objects. We evaluated the proposed pointwise CNN structure on the ModelNet10 dataset. The proposed structure obtained higher accuracy compared to the existing methods. Experiments using the ModelNet10 dataset also prove that the difference in the point number of point clouds does not significantly influence on the proposed pointwise CNN structure.

• Structural Engineering and Mechanics
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• v.69 no.2
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• pp.131-143
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• 2019

In this study, the nonlinear vibration analysis of the composite nanoplate is studied. The composite nanoplate is fabricated by the functional graded (FG) core and lipid face sheets. The material properties in the FG core vary in three directions. The Kelvin-Voigt model is used to study the viscoelastic effect of the lipid layers. By using the Von-Karman assumptions, the nonlinear differential equation of the vibration analysis of the composite nanoplate is obtained. The foundation of the system is modeled by the nonlinear Pasternak foundation. The Bubnov-Galerkin method and the multiple scale method are used to solve the nonlinear differential equation of the composite nanoplate. The free and force vibration analysis of the composite nanoplate are studied. A comparison between the presented results and the reported results is done and good achievement is obtained. The reported results are verified by the results which are obtained by the Runge-Kutta method. The effects of different parameters on the nonlinear vibration frequencies, the primary, the super harmonic and subharmonic resonance cases are investigated. This work will be useful to design the nanosensors with high biocompatibility.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1540-1551
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• 2020

Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.

• Wind and Structures
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• v.34 no.1
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• pp.101-113
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• 2022

We experimentally investigate the high-Reynolds flow around a rectangular cylinder of aspect ratio 5:1. This configuration is the object of the international BARC benchmark. Wind tunnel tests have been carried out for the flow at zero angle of attack and a Reynolds number, based on the crossflow cylinder length and on the freestream velocity, equal, to 40 000. Velocity measurements are obtained by using hot-wire anemometry along 50 different cross-flow traverses on the cylinder side and in the near wake. Differential pressure measurements are acquired on multiple streamwise sections of the model. The obtained measurements are in a good agreement with the state-of-the-art experiments. For the first time among the several contributions to the BARC benchmark, detailed flow measurements are acquired in the region near the cylinder side and in the near-wake flow. The edges and the thickness of the shear layers detaching from the upstream edges are derived from velocity measurements. Furthermore, we compute the flow frequencies characterizing the roll-up of the shear layers, the evolution of vortical structures near the cylinder side and the vortex shedding in the wake.

• Structural Engineering and Mechanics
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• v.84 no.6
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• pp.767-782
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• 2022

In this paper, the nonlinear vibration behavior of the spiral stiffened multilayer functionally graded (SSMFG) cylindrical shells exposed to the thermal environment and a uniformly distributed harmonic loading using a semi-analytical method is investigated. The cylindrical shell is surrounded by a nonlinear viscoelastic foundation consisting of a two-parameter Winkler-Pasternak foundation augmented by a Kelvin-Voigt viscoelastic model with a nonlinear cubic stiffness. The distribution of temperature and material constitutive of the stiffeners are continuously changed through the thickness direction. The cylindrical shell has three layers consisting of metal, FGM, and ceramic. The interior layer of the cylindrical shell is rich in metal, while the exterior layer is rich in ceramic, and the FG material is located between two layers. The nonlinear vibration problem utilizing the smeared stiffeners technique, the von Kármán equations, and the Galerkin method has been solved. The multiple scales method is utilized to examine the nonlinear vibration behavior of SSMFG cylindrical shells. The considered resonant case is 1:3:9 internal resonance and subharmonic resonance of order 1/3. The influences of different material and geometrical parameters on the vibration behavior of SSMFG cylindrical shells are examined. The results show that the angles of stiffeners, temperature, and elastic foundation parameters have a strong effect on the vibration behaviors of the SSMFG cylindrical shells.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.128-134
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• 2023

The widespread usage of blockchain technology in cryptocurrencies has led to the adoption of the blockchain concept in data storage management systems for secure and effective data storage and management. Several innovative studies have proposed solutions that integrate blockchain with distributed databases. In this article, we review current blockchain databases, then focus on two well-known blockchain databases-BigchainDB and FalconDB-to illustrate their architecture and design aspects in more detail. BigchainDB is a distributed database that integrates blockchain properties to enhance immutability and decentralization as well as a high transaction rate, low latency, and accurate queries. Its architecture consists of three layers: the transaction layer, consensus layer, and data model layer. FalconDB, on the other hand, is a shared database that allows multiple clients to collaborate on the database securely and efficiently, even if they have limited resources. It has two layers: the authentication layer and the consensus layer, which are used with client requests and results. Finally, a comparison is made between the two blockchain databases, revealing that they share some characteristics such as immutability, low latency, permission, horizontal scalability, decentralization, and the same consensus protocol. However, they vary in terms of database type, concurrency mechanism, replication model, cost, and the usage of smart contracts.

• Restorative Dentistry and Endodontics
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• v.46 no.3
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• pp.32.1-32.10
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• 2021

Objectives: This study aimed to evaluate the effect of the application method of universal adhesives on the shear bond strength (SBS) of repaired composites, applied with different thicknesses. Materials and Methods: The 84 specimens (Filtek Z350 XT) were prepared, stored in distilled water for a week and thermocycled (5,000 cycles, 5℃ to 55℃). They were roughened using 400-grit sandpapers and etched with phosphoric acid. Then, specimens were equally divided into 2 groups; Single Bond Universal (SU) and Prime&Bond Universal (PB). Each group was subdivided into 3 subgroups according to application methods (n = 14); UC: 1 coat + uncuring, 1C: 1 coat + curing, 3C: 3 coats + curing. After storage of the repaired composite for 24 hours, specimens were subjected to the SBS test and the data were statistically analyzed by 2-way analysis of variance and independent t-tests. Specimens were examined with a stereomicroscope to analyze fracture mode and a scanning electron microscope to observe the interface. Results: Adhesive material was a significant factor (p = 0.001). Bond strengths with SU were higher than PB. The highest strength was obtained from the 1C group with SU. Bonding in multiple layers increased adhesive thicknesses, but there was no significant difference in SBS values (p = 0.255). Failure mode was predominantly cohesive in old composites. Conclusions: The application of an adequate bonding system plays an important role in repairing composite resin. SU showed higher SBS than PB and the additional layers increased the adhesive thickness without affecting SBS.