• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.77-82
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• 2021

There are three types of urinalysis: physical test, chemical test, and microscopic test. Among these, the chemical urinalysis is an easily accessible method of the general public to compare the chemical reaction of urinalysis strip with a standard colorimetric table by sight or purchase the portable urinalysis machine separately. Currently, with the popularization of smartphone, research on the urinalysis service using smartphone is increasing. The urinalysis screening application is one of the urinalysis services using a smartphone. However, the RGB values of the urinalysis pad taken by the urinalysis screening application have large deviations due to the effect of lighting. Deviation of RGB value debases the accuracy of urinalysis discrimination. Therefore, in this paper, the accuracy of urinaylsis pad image discrimination is improved through CNN after classifying urinalysis strips taken by the urinalysis screening application based on smartphone by urinalysis pad items. Urinalysis strip was taken from various backgrounds to generate CNN image, and urinalysis discrimination was analyzed using the ResNet-50 CNN model.

• The korean journal of orthodontics
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• v.49 no.1
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• pp.21-31
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• 2019

Objective: This study was performed to explore the effect of different bracket, archwire, and ligature combinations on resistance to sliding (RS) and rotational control in first-order angulation. Methods: Three types of brackets (multi-level low friction [MLF], self-ligating, and conventional brackets) coupled with four nickel-titanium archwires (0.012, 0.014, 0.016, and 0.018-inch diameter) and two stainless steel ligatures (0.20 and 0.25 mm) were tested in different first-order angulations ($0^{\circ}$, $2^{\circ}$, $4^{\circ}$, $6^{\circ}$, $8^{\circ}$, $10^{\circ}$, $15^{\circ}$, $20^{\circ}$) by using an Instron universal mechanical machine in the dry state at room temperature. RS value was evaluated and compared by one-way ANOVA. Results: Under the same angulation, the RS values showed the following order: conventional brackets > MLF brackets > self-ligating brackets. The RS was the highest for conventional brackets and showed a tendency to increase. The RS for MLF brackets coupled with thinner archwires and ligatures showed a similar tendency as the RS for the self-ligating bracket. In contrast, the RS for MLF brackets coupled with thicker archwires and ligatures increased like that for conventional brackets. MLF brackets showed the greatest range of critical contact angles in first-order angulation. Conclusions: The RS in first-order angulation is influenced by bracket design, archwire, and ligature dimension. In comparison with self-ligating and conventional brackets, MLF brackets could express low friction and rotational control with their greater range of critical contact angles.

• Journal of Fashion Business
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• v.23 no.1
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• pp.37-48
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• 2019

WKSF (Warp-knitted spacer fabrics) knitted using a double Raschel machine is the three-dimensional knit that has vertically connected separate layers in loop structures. Because of its unique structure, the fabric is light, compressible and breathable. Owing to the high production speed, the use of the fabric is increasing in various areas. The purpose of this study is to establish the design process in the utilization of WKSF program and analyze the difference between WKSF and Neoprene as garment materials.. The study on the design related to WKSF has rarely been carried out because of the complexity of WKSF structure and the difficulties encountered in analyzing the structure and thread. Therefore, checking beforehand the simulation results similar to a final knit using the CAD program for WKSF can only enhance the efficiency of the design for the light knits. The conclusion drawn after designing the light knits using the CAD program and analyzing the pros and cons of WKSF through the various property evaluation techniques is as follows. The tension characteristic analysis results indicated that Neoprene specimen has the elastic transformation and resilience, thus behaving like an elastic product such as rubber. By contrast, in the event that clothing and fashion accessories are designed with WKSF, these products are kept in a boxy style fit so that the fabric can be applied flexibly to a curvy body line. In addition, WKSF is good in forming noticeably around a curvy body, because its resistance shear deformation is lower than that of Neoprene.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.3
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• pp.282-290
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• 2019

Recently child abuse has occurred frequently in educational institutions such as daycare center and kindergarten. Therefore, government made it mandatory to install CCTVs, but it is not easy to inspect the CCTV images. In this paper, we propose a model for judging child abuse using CCTV images. First of all, child abuse is a physical abuse of children by adults, thus a model for classifying adults and children is needed. The existing Haar scheme uses the frontal image to classify adults and children. However, the OpenPose allows to classify adults and children regardless of frontal and side image. In this research, a child abuse judgment model was designed and implemented by applying characteristics of adult and child posture when a child was abused. Since the implemented system utilizes the currently installed CCTV image, it is possible to monitor the child abuse in real time without any additional installation, which enables us to cope with the abuse promptly.

• Structural Engineering and Mechanics
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• v.69 no.2
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• pp.221-230
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• 2019

In this paper, shear behavior of non-persistent joint surrounded in concrete and gypsum layers has been investigated using experimental test and numerical simulation. Two types of mixture were prepared for this study. The first type consists of water and gypsum that were mixed with a ratio of water/gypsum of 0.6. The second type of mixture, water, sand and cement were mixed with a ratio of 27%, 33% and 40% by weight. Shear behavior of a non-persistent joint embedded in these specimens is studied. Physical models consisting of two edge concrete layers with dimensions of 160 mm by 130 mm by 60 mm and one internal gypsum layer with the dimension of 16 mm by 13 mm by 6 mm were made. Two horizontal edge joints were embedded in concrete beams and one angled joint was created in gypsum layer. Several analyses with joints with angles of $0^{\circ}$, $30^{\circ}$, and $60^{\circ}$ degree were conducted. The central fault places in 3 different positions. Along the edge joints, 1.5 cm vertically far from the edge joint face and 3 cm vertically far from the edge joint face. All samples were tested in compression using a universal loading machine and the shear load was induced because of the specimen geometry. Concurrent with the experiments, the extended finite element method (XFEM) was employed to analyze the fracture processes occurring in a non-persistent joint embedded in concrete and gypsum layers using Abaqus, a finite element software platform. The failure pattern of non-persistent cracks (faults) was found to be affected mostly by the central crack and its configuration and the shear strength was found to be related to the failure pattern. Comparison between experimental and corresponding numerical results showed a great agreement. XFEM was found as a capable tool for investigating the fracturing mechanism of rock specimens with non-persistent joint.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.49-58
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• 2019

In this paper, we propose an edge cloud platform architecture for implementing smart factory. The edge cloud platform is one of edge computing architecture which is mainly focusing on the efficient computing between IoT devices and central cloud. So far, edge computing has put emphasis on reducing latency, bandwidth and computing cost in areas like smart homes and self-driving cars. On the other hand, in this paper, we suggest not only common functional architecture of edge system but also light weight cloud based architecture to apply to the specialized requirements of smart factory. Cloud based edge architecture has many advantages in terms of scalability and reliability of resources and operation of various independent edge functions compare to typical edge system architecture. To make sure the availability of edge cloud platform in smart factory, we also analyze requirements of smart factory edge. We redefine requirements from a 4M1E(man, machine, material, method, element) perspective which are essentially needed to be digitalized and intelligent for physical operation of smart factory. Based on these requirements, we suggest layered(IoT Gateway, Edge Cloud, Central Cloud) application and data architecture. we also propose edge cloud platform architecture using lightweight container virtualization technology. Finally, we validate its implementation effects with case study. we apply proposed edge cloud architecture to the real manufacturing process and compare to existing equipment engineering system. As a result, we prove that the response performance of the proposed approach was improved by 84 to 92% better than existing method.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.621-635
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• 2020

Water quality models are scientific tools that simulate and interpret the relationship between physical, chemical and biological reactions to external pollutant loads in water systems. They are actively used as a key technology in environmental water management. With recent advances in computational power, water quality modeling technology has evolved into a coupled three-dimensional modeling of hydrodynamics, water quality, and ecological inputs. However, there is uncertainty in the simulated results due to the increasing model complexity, knowledge gaps in simulating complex aquatic ecosystem, and the distrust of stakeholders due to nontransparent modeling processes. These issues have become difficult obstacles for the practical use of water quality models in the water management decision process. The objectives of this paper were to review the theoretical background, needs, and development status of water quality modeling technology. Additionally, we present the potential future directions of water quality modeling technology as a scientific tool for national environmental water management. The main development directions can be summarized as follows: quantification of parameter sensitivities and model uncertainty, acquisition and use of high frequency and high resolution data based on IoT sensor technology, conjunctive use of mechanistic models and data-driven models, and securing transparency in the water quality modeling process. These advances in the field of water quality modeling warrant joint research with modeling experts, statisticians, and ecologists, combined with active communication between policy makers and stakeholders.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.30 no.6
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• pp.271-276
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• 2020

In this study, an amorphous SiC block was manufactured using polycarbosilane (PCS), an organosilicon polymer. The dense SiC blocks were easily fabricated in various shapes via pyrolysis at 1100℃, 1200℃, 1300℃, 1400℃ after manufacturing a PCS molded body using cured PCS powder. Physical and chemical properties were analyzed using a thermogravimetric analyzer (TGA), scanning electron microscope (SEM), energy dispersive spectroscopy (EDS), and universal testing machine (UTM). The prepared SiC block was decomposed into SiO and CO gas as the temperature increased, and β-SiC crystal grains were grown in an amorphous structure. In addition, the density and flexural strength were the highest at 1.9038 g/㎤ and 6.189 MPa of SiC prepared at 1100℃. The manufactured amorphous silicon carbide block is expected to be applicable to other fields, such as the previously reported microwave assisted heating element.

• Earthquakes and Structures
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• v.20 no.1
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• pp.109-122
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• 2021

Significant progress in the oil and gas industry advances the application of pipeline into an intelligent era, which poses rigorous requirements on pipeline safety, reliability, and maintainability, especially when crossing seismic zones. In general, strike-slip faults are prone to induce large deformation leading to local buckling and global rupture eventually. To evaluate the performance and safety of pipelines in this situation, numerical simulations are proved to be a relatively accurate and reliable technique based on the built-in physical models and advanced grid technology. However, the computational cost is prohibitive, so one has to wait for a long time to attain a calculation result for complex large-scale pipelines. In this manuscript, an efficient and accurate surrogate model based on machine learning is proposed for strain demand prediction of buried X80 pipelines subjected to strike-slip faults. Specifically, the support vector regression model serves as a surrogate model to learn the high-dimensional nonlinear relationship which maps multiple input variables, including pipe geometries, internal pressures, and strike-slip displacements, to output variables (namely tensile strains and compressive strains). The effectiveness and efficiency of the proposed method are validated by numerical studies considering different effects caused by structural sizes, internal pressure, and strike-slip movements.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.