• Korean Journal of Optics and Photonics
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• v.29 no.1
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• pp.7-12
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• 2018

In the field of machine vision, a variety of lenses are used to inspect a product for defects. Only part of the appearance of an object can be photographed with a general lens. Optical components such as mirrors, multiple lenses and cameras are required to inspect the entire exterior. This increases the size of the optical system, and has the disadvantage of high cost. In this paper, we design a hypercentric lens, which can photograph the top and side of an object, and various sizes of objects while maintaining the image size. Also, the validity of the design is verified through the performance analysis of the product.

• Geomechanics and Engineering
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• v.30 no.1
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• pp.11-26
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• 2022

One of the most important issues in tunneling, is the squeezing phenomenon. Squeezing can occur during excavation or after the construction of tunnels, which in both cases could lead to significant damages. Therefore, it is important to predict the squeezing and consider it in the early design stage of tunnel construction. Different empirical, semi-empirical and theoretical-analytical methods have been presented to determine the squeezing. Therefore, it is necessary to examine the ability of each of these methods and identify the best method among them. In this study, squeezing in a part of the Alborz service tunnel in Iran was estimated through a number of empirical, semi- empirical and theoretical-analytical methods. Among these methods, the most robust model was used to obtain a database including 300 data for training and 33 data for testing in order to develop a machine learning (ML) method. To this end, three ML models of Gaussian process regression (GPR), artificial neural network (ANN) and support vector regression (SVR) were trained and tested to propose a robust model to predict the squeezing phenomenon. A comparative analysis between the conventional and the ML methods utilized in this study showed that, the GPR model is the most robust model in the prediction of squeezing phenomenon. The sensitivity analysis of the input parameters using the mutual information test (MIT) method showed that, the most sensitive parameter on the squeezing phenomenon is the tangential strain (ε_θ^α) parameter with a sensitivity score of 2.18. Finally, the GPR model was recommended to predict the squeezing phenomenon in tunneling projects. This work's significance is that it can provide a good estimation of the squeezing phenomenon in tunneling projects, based on which geotechnical engineers can take the necessary actions to deal with it in the pre-construction designs.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.359-364
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• 2021

Recently, research on predictive systems using deep learning and machine learning of artificial intelligence is being actively conducted. Due to the development of artificial intelligence, the role of the investment manager is being replaced by artificial intelligence, and due to the higher rate of return than the investment manager, algorithmic trading using artificial intelligence is becoming more common. Algorithmic trading excludes human emotions and trades mechanically according to conditions, so it comes out higher than human trading yields when approached in the long term. The deep learning technique of artificial intelligence learns past time series data and predicts the future, so it learns like a human and can respond to changing strategies. In particular, the LSTM technique is used to predict the future by increasing the weight of recent data by remembering or forgetting part of past data. fbprophet, an artificial intelligence algorithm recently developed by Facebook, boasts high prediction accuracy and is used to predict stock prices and cryptocurrency prices. Therefore, this study intends to establish a sound investment culture by providing a new algorithm for automatic cryptocurrency trading by analyzing the actual value and difference using fbprophet and presenting conditions for accurate prediction.

• Journal of Adhesion and Interface
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• v.23 no.1
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• pp.8-16
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• 2022

In this study, self-healable waterborne polyurethane (SH-WPU) as shoes and coating materials with self-healable disulfide functionalities was synthesized by mixing polyether polyol to impart excellent durability and heat resistance and polycarbonate polyol to impart excellent mechanical properties. The synthesized SH-WPU was characterized by fourier transform-infrared spectroscopy (FT-IR), and physical and self-healing properties were confirmed through universal testing machine (UTM) and scanning electron microscope (SEM) measurements. Tensile strength and hardness were increased and elongation was decreased by using polycarbonate polyol. In addition, as a result of comparison of thermal properties, thermal stability has been increased as the content of polycarbonate polyol increased. The healing efficiency showed the highest efficiency when poly(tetramethylene ether)glycol : polycarbonate polyol = 0.75 : 0.25, and it was confirmed that the damaged part was healed through surface observation using a microscope and SEM.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.124-130
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• 2023

In this paper, we intend to improve the network lifetime by improving the energy efficiency of sensor nodes in a wireless sensor network by utilizing machine learning using K-means clustering algorithm. A wireless sensor network is a wireless network composed of physical devices including batteries as physical sensors. Due to the characteristics of sensor nodes, all resources must be efficiently used to minimize energy consumption to maximize network lifetime. A cluster based approach is used to manage groups of relatively large numbers of nodes. In the proposed protocol, by improving the existing LEACH algorithm, we propose a clustering algorithm that selects a cluster head using a cluster based approach and a location based approach. The performance results to be improved were measured using Matlab simulation. Through the experimental results, K-means clustering was applied to the energy efficiency part. By utilizing K-means, it is confirmed that energy efficiency is improved and the lifetime of the entire network is extended.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.129-136
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• 2022

The most important part of the smart livestock building system is to maintain a breeding environment so that livestock can grow to high quality despite changes in the internal and external atmospheric environment. Especially, it is very important to maintain the temperature and humidity in the livestock building because various diseases occur during the summer and winter. To manage the environment suitable for livestock, a smartfarm system for livestock building is applied, but it is very expensive. In this study, we propose a hardware design and control method for low cost system based on HMI and fuzzy control. To evaluate the performance of the proposed system, we did a simulation experiment in the atmospheric conditions of summer and winter. As a result, it showed the performance of minimizing the temperature and humidity stress of livestock. And when applied to the livestock building, the proposed system showed stable control performance even in the change of the external atmospheric environment. Therefore, as with these results, if proposed system in this study is applied to the smart farm system, it will be effective in managing the environment of livestock building.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1377-1393
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• 2023

Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.569-582
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• 2023

Lead-acid Battery is the oldest rechargeable battery system and has maintained its position in the rechargeable battery field. The battery causes thermal runaway for various reasons, which can lead to major accidents. Therefore, preventing thermal runaway is a key part of the battery management system. Recently, research is underway to categorize thermal runaway battery cells into machine learning. In this paper, we present a thermal runaway hazard cell detection and verification algorithm using DBSCAN and statistical method. An experiment was conducted to classify thermal runaway hazard cells using only the resistance values as measured by the Battery Management System (BMS). The results demonstrated the efficacy of the proposed algorithms in accurately classifying thermal runaway cells. Furthermore, the proposed algorithm was able to classify thermal runaway cells between thermal runaway hazard cells and cells containing noise. Additionally, the thermal runaway hazard cells were early detected through the optimization of DBSCAN parameters using a grid search approach.

• Korea Trade Review
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• v.48 no.2
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• pp.27-43
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• 2023

The maritime industry is playing an increasingly vital part in global economic expansion. Specifically, the Baltic Dry Index is highly correlated with global commodity prices. Hence, the importance of BDI prediction research increases. But, since the global situation has become more volatile, it has become methodologically more difficult to predict the BDI accurately. This paper proposes an integrated machine-learning strategy for accurately forecasting BDI trends. This study combines the benefits of a convolutional neural network (CNN) and long short-term memory neural network (LSTM) for research on prediction. We collected daily BDI data for over 27 years for model fitting. The research findings indicate that CNN successfully extracts BDI data features. On this basis, LSTM predicts BDI accurately. Model R2 attains 94.7 percent. Our research offers a novel, machine-learning-integrated approach to the field of shipping economic indicators research. In addition, this study provides a foundation for risk management decision-making in the fields of shipping institutions and financial investment.