• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.79-89
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• 2022

With the popularity and the advanced graphics hardware technology of mobile devices, various mobile applications that help children with physical activities have been studied. This paper presents SandUp, a mobile application that guides the play of building sand castles using artificial intelligence and augmented reality(AR) technology. In the process of building the sandcastle, children can interactively explore the target virtual sandcastle through the smartphone display using AR technology. In addition, to help children complete the sandcastle, SandUp informs the sand shape and task required step by step and provides visual and auditory feedback while recognizing progress in real-time using the phone's camera and deep learning classification. We prototyped our SandUp app using Flutter and TensorFlow Lite. To evaluate the usability and effectiveness of the proposed SandUp, we conducted a questionnaire survey on 50 adults and a user study on 20 children aged 4~7 years. The survey results showed that SandUp effectively helps build the sandcastle with proper interactive guidance. Based on the results from the user study on children and feedback from their parents, we also derived usability issues that can be further improved and suggested future research directions.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.1
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• pp.41-49
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• 2023

A convolutional neural network (CNN) is a representative algorithm for implementing artificial neural networks. CNNs have improved on the issues of rapid increase in calculation amount and low object classification rates, which are associated with a conventional multi-layered fully-connected neural network (FNN). However, because of the rapid development of IT devices, the maximum resolution of images captured by current smartphone and tablet cameras has reached 108 million pixels (MP). Specifically, a traditional CNN algorithm requires a significant cost and time to learn and process simple, high-resolution images. Therefore, this study proposes an improved CNN algorithm for implementing an object classification learning model for simple, high-resolution images. The proposed method alters the adjacency matrix value of the pooling layer's max pooling operation for the CNN algorithm to reduce the high-resolution image learning model's creation time. This study implemented a learning model capable of processing 4, 8, and 12 MP high-resolution images for each altered matrix value. The performance evaluation result showed that the creation time of the learning model implemented with the proposed algorithm decreased by 36.26% for 12 MP images. Compared to the conventional model, the proposed learning model's object recognition accuracy and loss rate were less than 1%, which is within the acceptable error range. Practical verification is necessary through future studies by implementing a learning model with more varied image types and a larger amount of image data than those used in this study.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1120-1128
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• 2022

In the event of a marine accident, the longer the exposure time to the sea increases, the faster the chance of survival decreases. However, because the search area of the sea is extremely wide compared to that of land, marine object detection technology based on the sensor mounted on a satellite or an aircraft must be applied rather than ship for an efficient search. The purpose of this study was to rapidly detect an object in the ocean using a hyperspectral image sensor mounted on an aircraft. The image captured by this sensor has a spatial resolution of 8,241 × 1,024, and is a large-capacity data comprising 127 spectra and a resolution of 0.7 m per pixel. In this study, a marine object detection model was developed that combines a seawater identification algorithm using DBSCAN and a density-based land removal algorithm to rapidly analyze large data. When the developed detection model was applied to the hyperspectral image, the performance of analyzing a sea area of about 5 km² within 100 s was confirmed. In addition, to evaluate the detection accuracy of the developed model, hyperspectral images of the Mokpo, Gunsan, and Yeosu regions were taken using an aircraft. As a result, ships in the experimental image could be detected with an accuracy of 90 %. The technology developed in this study is expected to be utilized as important information to support the search and rescue activities of small ships and human life.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.87-96
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• 2022

The traffic vulnerable, including elderly pedestrians, have a relatively low walking speed and slow cognitive response time due to reduced physical ability. Although a smart crossing system has been developed and operated to improve problem, it is difficult to operate a signal that reflects the appropriate walking speed for each pedestrian. In this study, a neural network model and a multiple regression model-based traversing speed estimation model were developed using image information collected in an area with a high percentage of traffic vulnerability. to support the provision of optimal walking signals according to real-time traffic weakness. actual traffic data collected from the urban traffic network of Paju-si, Gyeonggi-do were used. The performance of the model was evaluated through seven selected indicators, including correlation coefficient and mean absolute error. The multiple linear regression model had a correlation coefficient of 0.652 and 0.182; the neural network model had a correlation coefficient of 0.823 and 0.105. The neural network model showed higher predictive power.

• Journal of the Korean Society of Radiology
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• v.17 no.2
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• pp.201-206
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• 2023

In order to improve the spatial resolution of the gamma camera, the size of the hole in the collimator must be reduced, so the sensitivity is reduced. In order to improve the sensitivity, the size of the hole must be increased, and thus the spatial resolution is reduced. In other words, spatial resolution and sensitivity show opposite characteristics. In this study, a gamma camera was designed to improve both spatial resolution and sensitivity. In order to obtain higher sensitivity in gamma cameras with the same spatial resolution, the structure of the scintillator was designed differently from the existing system. A scintillation pixel was used, and a partition wall was placed between the scintillation pixels to prevent incident gamma rays from being transmitted to other scintillation pixels to interact. Geant4 Application for Tomographic Emission (GATE) simulation was performed to evaluate the performance of the designed gamma camera. When the same sensitivity as the block-type scintillator was obtained, the spatial resolution increased by 16.5%, and when the same spatial resolution was obtained, the sensitivity increased by 61.5%. It is considered that the use of the gamma camera designed in this study can improve the sensitivity compared to the existing system while securing excellent spatial resolution.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.221-232
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• 2023

This paper presents a novel convolutional neural network (CNN)-based approach for predicting bolt clamping force in the early bolt loosening state of bolted structures. The approach entails tightening eight bolts with different clamping forces and generating frequency responses, which are then used to create a similarity map. This map quantifies the magnitude and shape similarity between the frequency responses and the initial model in a fully fastened state. Krylov subspace-based model order reduction is employed to efficiently handle the large amount of frequency response data. The CNN model incorporates a regression output layer to predict the clamping forces of the bolts. Its performance is evaluated by training the network by using various amounts of training data and convolutional layers. The input data for the model are derived from the magnitude and shape similarity map obtained from the frequency responses. The results demonstrate the diagnostic potential and effectiveness of the proposed approach in detecting early bolt loosening. Accurate bolt clamping force predictions in the early loosening state can thus be achieved by utilizing the frequency response data and CNN model. The findings afford valuable insights into the application of CNNs for assessing the integrity of bolted structures.

• Clean Technology
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• v.29 no.3
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• pp.178-184
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• 2023

Transition metal chalcogenides have garnered significant attention as anode materials for sodium-ion batteries due to their high theoretical capacity. Nevertheless, their practical application is impeded by their limited lifespan resulting from substantial volume expansion during cycling and their low electrical conductivity. To tackle these issues, this study devised a solution by synthesizing a nanostructured anode material composed of porous CNT (carbon nanotube) spheres and (Ni,Co)Se₂ nanocrystals. By employing spray pyrolysis and subsequent heat treatments, a porous-structured (Ni,Co)Se₂-CNT composite microsphere was successfully synthesized, and its electrochemical properties as an anode for sodium-ion batteries were evaluated. The synthesized (Ni,Co)Se₂-CNT microsphere possesses a porous structure due to the nanovoids that formed as a result of the decomposition of the polystyrene (PS) nanobeads during spray pyrolysis. This porous structure can effectively accommodate the volume expansion that occurs during repeated cycling, while the CNT scaffold enhances electronic conductivity. Consequently, the (Ni,Co)Se₂-CNT anode exhibited an initial discharge capacity of 698 mA h g^-1 and maintained a high discharge capacity of 400 mA h g^-1 after 100 cycles at a current density of 0.2 A g^-1.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.403-411
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• 2023

Functional ultrasound imaging, such as elasticity imaging and micro-blood flow Doppler imaging, enhances diagnostic capability by providing useful mechanical and functional information about tissues. However, the implementation of functional ultrasound imaging poses limitations such as the storage of vast amounts of data in Radio Frequency (RF) data acquisition and processing. In this paper, we propose a sub-Nyquist approach that reduces the amount of acquired axial samples for efficient shear-wave elasticity imaging. The proposed method acquires data at a sampling rate one-third lower than the conventional Nyquist sampling rate and tracks shear-wave signals through RF signals reconstructed using band-pass filtering-based interpolation. In this approach, the RF signal is assumed to have a fractional bandwidth of 67 %. To validate the approach, we reconstruct the shear-wave velocity images using shear-wave tracking data obtained by conventional and proposed approaches, and compare the group velocity, contrast-to-noise ratio, and structural similarity index measurement. We qualitatively and quantitatively demonstrate the potential of sub-Nyquist sampling-based shear-wave elasticity imaging, indicating that our approach could be practically useful in three-dimensional shear-wave elasticity imaging, where a massive amount of ultrasound data is required.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.8
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• pp.253-262
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• 2023

E-voting is a concept that includes actions such as kiosk voting at a designated place and internet voting at an unspecified place, and has emerged to alleviate the problem of consuming a lot of resources and costs when conducting offline voting. Using E-voting has many advantages over existing voting systems, such as increased efficiency in voting and ballot counting, reduced costs, increased voting rate, and reduced errors. However, centralized E-voting has not received attention in public elections and voting on corporate agendas because the results of voting cannot be trusted due to concerns about data forgery and modulation and hacking by others. In order to solve this problem, recently, by designing an E-voting system using blockchain, research has been actively conducted to supplement concepts lacking in existing E-voting, such as increasing the reliability of voting information and securing transparency. In this paper, we proposed an electronic voting system that introduced hybrid blockchain that uses public and private blockchains in convergence. A hybrid blockchain can solve the problem of slow transaction processing speed, expensive fee by using a private blockchain, and can supplement for the lack of transparency and data integrity of transactions through a public blockchain. In addition, the proposed system is implemented as BaaS to ensure the ease of type conversion and scalability of blockchain and to provide powerful computing power. BaaS is an abbreviation of Blockchain as a Service, which is one of the cloud computing technologies and means a service that provides a blockchain platform ans software through the internet. In this paper, in order to evaluate the feasibility, the proposed system and domestic and foreign electronic voting-related studies are compared and analyzed in terms of blockchain type, anonymity, verification process, smart contract, performance, and scalability.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.555-566
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• 2023

In this study, a composite phase change material (CPCM) produced using the SOL-GEL technique was developed as a thermal energy storage medium for low-temperature applications. Tetradecane and activated carbon (AC) were used as the core and supporting materials, respectively. The tetradecane phase change material (PCM) was impregnated into the porous structure of AC using the vacuum impregnation method, and a thin layer of silica gel was coated on the prepared composite using the SOL-GEL process, where tetraethyl orthosilicate (TEOS) was used as the silica source. The thermal performance of the CPCM was analysed using differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). DSC results showed that the pure tetradecane PCM had melting and freezing temperatures of 6.4℃ and 1.3℃ and corresponding enthalpies 226 J/g and 223.8 J/g, respectively. The CPCM exhibited enthalpy of 32.98 J/g and 27.7 J/g during the melting and freezing processes at 7.1℃ and 2.4℃, respectively. TGA test results revealed that the AC is thermally stable up to 500℃, which is much higher than the decomposition temperature of the pure tetradecane, which is around 120℃. Moreover, in the case of AC-PCM and CPCM thermal degradation started at 80℃ and 100℃, respectively. The chemical stability of the CPCM was studied using Fourier-transform infrared (FT-IR) spectroscopy, and the results confirmed that the developed composite is chemically stable. Finally, the surface morphology of the AC and CPCM was analysed using scanning electron microscopy (SEM), which confirmed the presence of a thin layer of silica gel on the AC surface after the SOL-GEL process.