• Annual Conference of KIPS
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• 2013.05a
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• pp.610-613
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• 2013

Vehicular Ad Hoc NETwork (VANET), a subclass of Mobile Ah Hoc NETwork (MANET) has been a tech-buzz for the last couple of decades. VANET, yet not deployed, promises the ease, comfort, and safety to both drivers and passengers once deployed. The by far most important factor in successful VANET application is the data dissemination scheme. Such data includes scheduled beacons that contain whereabouts information of vehicles. In this paper, we aim at regularly broadcasted beacons and devise an algorithm to disseminate the beacon information up to a maximum distance and alleviate the broadcast storm problem at the same time. According to the proposed scheme, a vehicle before re-broadcasting a beacon, takes into account the current vehicular density in its neighborhood. The re-broadcasters are chosen away from the source of the beacon and among the candidate re-broadcasters, if the density in the neighborhood is high, then the candidate rebroadcaster re-broadcasts the beacon with high probability and with low probability, otherwise. We also performed thorough simulations of our algorithms and the results are sound according to the expectations.

• Smart Structures and Systems
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• v.29 no.3
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• pp.513-522
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• 2022

Due to the importance of accurate analysis of bearing capacity in civil engineering projects, this paper studies the efficiency of two novel metaheuristic-based models for this objective. To this end, black hole algorithm (BHA) and multi-verse optimizer (MVO) are synthesized with an artificial neural network (ANN) to build the proposed hybrid models. Based on the settlement of a two-layered soil (and a shallow footing) system, the stability values (SV) of 0 and 1 (indicating the stability and failure, respectively) are set as the targets. Each model predicted the SV for 901 stages. The results indicated that the BHA and MVO can increase the accuracy (i.e., the area under the receiving operating characteristic curve) of the ANN from 94.0% to 96.3 and 97.2% in analyzing the SV pattern. Moreover, the prediction accuracy rose from 93.1% to 94.4 and 95.0%. Also, a comparison between the ANN's error decreased by the BHA and MVO (7.92% vs. 18.08% in the training phase and 6.28% vs. 13.62% in the testing phase) showed that the MVO is a more efficient optimizer. Hence, the suggested MVO-ANN can be used as a reliable approach for the practical estimation of bearing capacity.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.440-449
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• 2022

In recent years, the air pollution and Air Quality Index (AQI) has been a pivotal point for researchers due to its effect on human health. Various research has been done in predicting the AQI but most of these studies, either lack dense temporal data or cover one or two air pollutant elements. In this paper, a hybrid Convolutional Neural approach integrated with recurrent neural network architecture (CNN-LSTM), is presented to find air pollution inference using a multivariate air pollutant elements dataset. The aim of this research is to design a robust and real-time air pollutant forecasting system by exploiting a neural network. The proposed approach is implemented on a 24-month dataset from Seoul, Republic of Korea. The predicted results are cross-validated with the real dataset and compared with the state-of-the-art techniques to evaluate its robustness and performance. The proposed model outperforms SVM, SVM-Polynomial, ANN, and RF models with 60.17%, 68.99%, 14.6%, and 6.29%, respectively. The model performs SVM and SVM-Polynomial in predicting O3 by 78.04% and 83.79%, respectively. Overall performance of the model is measured in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE).

• Fourth Industrial Review
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• v.3 no.2
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• pp.11-17
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• 2023

Purpose - Recently, in the field of language education, interest in edutech has increased due to difficulties in classroom teaching due to COVID-19. Accordingly, we would like to analyze research topics related to e-learning before and after COVID-19 and examine the implications for the future Korean language education field. Research design, data, and methodology - This study organized a list of papers to be analyzed by searching for e-learning terms applicable to Korean language education in RISS. The collected data was electronically documented, keywords were extracted using text mining techniques, and word frequencies were checked, and then viewed through cloud visualization. Result - It was confirmed that research on e-learning in the field of Korean language education has increased rapidly in 2021 and 2022. In particular, extensive research on online learning methods has been actively conducted due to the difficulties of face-to-face learning in the COVID-19 era. There have been many studies on teaching and learning methods, such as flipped learning, hybrid learning, blended learning, mobile learning, and smart learning. Conclusion - Since the research so far has mainly focused on online class management methods. Therefore, future research suggests that efforts should be made to develop educational contents and teaching methods using specific ICT technologies. These efforts will contribute to advancing smart education that future education aims for.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.624-626
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• 2022

It is possible to manage individual exercise information using a smartphone application that may be attached to exercise equipment. We propose a service platform that provides effective exercise techniques and management information to athletes by establishing an AI module to analyze and present the current user's exercise volume and exercise intensity direction through analysis of exercise data. To this end, it can be effectively managed by establishing a system based on a cloud environment and builds a hybrid health model that utilizes air and magnetic technologies at the same time.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.53-63
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• 2023

Many researchers are trying hard to minimize the incidence of cancers, mainly Gastric Cancer (GC). For GC, the five-year survival rate is generally 5-25%, but for Early Gastric Cancer (EGC), it is almost 90%. Predicting the onset of stomach cancer based on risk factors will allow for an early diagnosis and more effective treatment. Although there are several models for predicting stomach cancer, most of these models are based on unbalanced datasets, which favours the majority class. However, it is imperative to correctly identify cancer patients who are in the minority class. This research aims to apply three class-balancing approaches to the NHS dataset before developing supervised learning strategies: Oversampling (Synthetic Minority Oversampling Technique or SMOTE), Undersampling (SpreadSubsample), and Hybrid System (SMOTE + SpreadSubsample). This study uses Naive Bayes, Bayesian Network, Random Forest, and Decision Tree (C4.5) methods. We measured these classifiers' efficacy using their Receiver Operating Characteristics (ROC) curves, sensitivity, and specificity. The validation data was used to test several ways of balancing the classifiers. The final prediction model was built on the one that did the best overall.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• Smart Structures and Systems
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• v.32 no.3
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

• Korean Journal of Materials Research
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• v.34 no.3
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• pp.163-169
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• 2024

As the limitations of Moore's Law become evident, there has been growing interest in advanced packaging technologies. Among various 3D packaging techniques, Cu-SiO₂ hybrid bonding has gained attention in heterogeneous devices. However, certain issues, such as its high-temperature processing conditions and copper oxidation, can affect electrical properties and mechanical reliability. Therefore, we studied depositing only a heterometal on top of the Cu in Cu-SiO₂ composite substrates to prevent copper surface oxidation and to lower bonding process temperature. The heterometal needs to be deposited as an ultra-thin layer of less than 10 nm, for copper diffusion. We established the process conditions for depositing a Co film using a Co(EtCp)₂ precursor and utilizing plasma-enhanced atomic layer deposition (PEALD), which allows for precise atomic level thickness control. In addition, we attempted to use a growth inhibitor by growing a self-assembled monolayer (SAM) material, octadecyltrichlorosilane (ODTS), on a SiO₂ substrate to selectively suppress the growth of Co film. We compared the growth behavior of the Co film under various PEALD process conditions and examined their selectivity based on the ODTS growth time.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.1
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• pp.65-72
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• 2008

The use of hybrid FRP-concrete structures with a dual purpose of both permanent formwork and reinforcement, has been considered in some studies recently. For the FRP plank and the concrete to act as a composite structural member a satisfactory bond at the interface between the smooth surface of the pultruded plank and the cast-in-place concrete must be developed. Sand was bonded to the pultruded FRP plank using a commercially available epoxy system. In applying general analysis techniques to evaluate the performance of composite structures with FRP stay-in-place forming, it is essential that characteristics of the bond stress-slip relation be identified. In this study I would like to propose a simplified bilinear bond stress-slip model for FRP composite structures.