• Journal of Appropriate Technology
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• v.6 no.2
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• pp.136-145
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• 2020

Agriculture is the backbone of the economy of most developing countries. In these countries, agriculture or farming is mostly done manually with little integration of machinery, intelligent systems and data monitoring. Irrigation is an essential process that directly influences crop production. The fluctuating amount of rainfall per year has led to the adoption of irrigation systems in most farms. The absence of smart sensors, monitoring methods and control, has led to low harvests and draining water sources. In this research paper, we introduce a 433 MHz Radio Frequency and 2G based Smart Irrigation Meter System and a water prepayment system for rural areas of Tanzania with no reliable internet coverage. Specifically, Ngurudoto area in Arusha region where it will be used as a case study for data collection. The proposed system is hybrid, comprising of both weather data (evapotranspiration) and soil moisture data. The architecture of the system has on-site weather measurement controllers, soil moisture sensors buried on the ground, water flow sensors, a solenoid valve, and a prepayment system. To achieve high precision in linear and nonlinear regression and to improve classification and prediction, this work cascades a Dynamic Regression Algorithm and Naïve Bayes algorithm.

• Journal of the korean academy of Pediatric Dentistry
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• v.49 no.3
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• pp.253-263
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• 2022

A buccal pit is a prominent point-like depression that appears at the cervical end of the mandibular molar developmental grooves. A defective buccal pit can be defined as a buccal pit in which the continuity of the dentinoenamel junction is broken and the pit extends to the dentinal level. This study aimed to determine the frequency of buccal pits and defective buccal pits in un-erupted mandibular first and second molars using cone-beam computed tomography (CBCT). The analysis was performed on CBCT images taken from 417 Korean children and adolescents who visited the Department of Pediatric Dentistry, Yonsei University Dental Hospital between 2004 and 2020. Based on cross-sectional views of CBCT images, buccal pits were categorized into 4 classes according to the depth of the pits. The expression rate of the buccal pits was 29.1%. The prevalence of defective buccal pits was 7.9%. The buccal pits tended to develop bilaterally. To date, this is the most comprehensive study on the frequency of buccal pits with the largest sample size. This was the first attempt worldwide to analyze the depth of the buccal pit using CBCT images and to define a defective buccal pit worldwide.

• Ocean and Polar Research
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• v.44 no.4
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• pp.355-364
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• 2022

When the 60-ton-class patrol boat '72' of the Korea Coast Guard (KCG) was on duty and she accidentally collided with another patrol boat ('207', 200-ton-class) and sank. A month-long search found a small amount of lost items, but neither the crew nor the ship was found. For the first time in 39 years since the accident, the Korea Institute of Ocean Science and Technology (KIOST) searched the boat 72 using the latest integrated geophysical techniques. A number of sonar images presumed to be of a sunken ship was acquired using a combined system of side scan sonar and marine magnetometer, operated at an altitude of approximately 30 m from the seabed. At the same time, a strong magnetic anomaly (100 nT) was detected in one place, indicating the presence of an iron ship. A video survey using a remotely operated underwater vehicle (ROV) confirmed the presence of a shielding part of a personal firearm at the stern of the sunken vessel. Based on these comprehensive data, the sunken vessel discovered in this exploration was assumed to be '72'. This result is meaningful in terms of future ocean exploration and underwater archaeology, as the integrated system of various geophysical methods is an efficient means of identifying objects present in the water.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.39-48
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• 2022

Cyberattacks around the world continue to increase, and their damage extends beyond government facilities and affects civilians. These issues emphasized the importance of developing a system that can identify and detect cyber anomalies early. As above, in order to effectively identify cyber anomalies, several studies have been conducted to learn BGP (Border Gateway Protocol) data through a machine learning model and identify them as anomalies. However, BGP data is unbalanced data in which abnormal data is less than normal data. This causes the model to have a learning biased result, reducing the reliability of the result. In addition, there is a limit in that security personnel cannot recognize the cyber situation as a typical result of machine learning in an actual cyber situation. Therefore, in this paper, we investigate BGP (Border Gateway Protocol) that keeps network records around the world and solve the problem of unbalanced data by using SMOTE. After that, assuming a cyber range situation, an autoencoder classifies cyber anomalies and visualizes the classified data. By learning the pattern of normal data, the performance of classifying abnormal data with 92.4% accuracy was derived, and the auxiliary index also showed 90% performance, ensuring reliability of the results. In addition, it is expected to be able to effectively defend against cyber attacks because it is possible to effectively recognize the situation by visualizing the congested cyber space.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.367-374
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• 2022

The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.5
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• pp.303-314
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• 2018

In the past few years, government agencies and corporations have succumbed to stealthy, tailored cyberattacks designed to exploit vulnerabilities, disrupt operations and steal valuable information. Security Information and Event Management (SIEM) is useful tool for cyberattacks. SIEM solutions are available in the market but they are too expensive and difficult to use. Then we implemented basic SIEM functions to research and development for future security solutions. We focus on collection, aggregation and analysis of real-time logs from host. This tool allows parsing and search of log data for forensics. Beyond just log management it uses intrusion detection and prioritize of security events inform and support alerting to user. We select Elastic Stack to process and visualization of these security informations. Elastic Stack is a very useful tool for finding information from large data, identifying correlations and creating rich visualizations for monitoring. We suggested using vulnerability check results on our SIEM. We have attacked to the host and got real time user activity for monitoring, alerting and security auditing based this security information management.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5B
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• pp.409-417
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• 2009

In this study it was assessed the accuracy of the Chukwooki rainfall data in Seoul by comparing with tree-ring width index data, sunspot numbers, southern oscillation index (SOI) and global temperature anomaly. And it was investigated the correlations of climatic change and change characteristics in past north-east asia by comparisons of tree-ring width index data in near Korea. The results of this study shows that Chukwooki rainfall data has the strong reliance since the trends and depths of change are very well matched with other comparative data. And with the results by compared with tree-ring width index data in six sites of near Korea, climates of north-east asia are changed with strong correlations as being temporal and spatial and longterm periodic possibility of reproducing are exist on those changes. However characteristics of climate change post 1960 A.D. are investigated as represented differently to past although statistical moving characteristics or changing criterion are within the limitations of reproducing phase in the past since they represent the different trends and irregularity and their frequencies are increase. The results of this study are widely used on long-term forecasting for climate change in north-east asia.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• Journal of Dental Rehabilitation and Applied Science
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• v.40 no.1
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• pp.31-38
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• 2024

Dens in dente is a developmental anomaly resulting from infolding of the enamel organ into dental papilla prior to calcification of dental tissue. The pulpal tissue of the tooth can be vulnerable for bacterial invasion through direct exposure to the oral cavity or through defective enamel and dentin of the infolding part, thereby increasing the possibility of pulpal necrosis and subsequent apical periodontitis. Treatment planning of teeth with dens invaginatus may be difficult due to the complex root canal morphology. Therefore, thorough knowledge of anatomical variations of dens invaginatus is of great importance for proper treatment planning. The focus of this case report is on Oehler's type II and III dens invaginatus. The infolding of type III dens invaginatus extends beyond the crown and CEJ. Bacterial invasion through the infolding can easily cause inflammation of the pulpal and periradicular tissue. This case report presents endodontic treatment of type II and III dens invaginatus with the aid of CBCT.

• Journal of Industrial Convergence
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• v.22 no.4
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• pp.1-9
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• 2024

Smart factories represent production facilities where cutting-edge information and communication technologies are fused with manufacturing processes, reflecting rapid advancements and changes in the global manufacturing sector. They capitalize on the integration of robotics and automation, the Internet of Things (IoT), and the convergence of artificial intelligence technologies to maximize production efficiency in various manufacturing environments. However, the smart factory environment is prone to security threats and vulnerabilities due to various attack techniques. When security threats occur in smart factories, they can lead to financial losses, damage to corporate reputation, and even human casualties, necessitating an appropriate security response. Therefore, this paper proposes a security authentication mechanism for safe communication in the smart factory environment. The components of the proposed authentication mechanism include smart devices, an internal operation management system, an authentication system, and a cloud storage server. The smart device registration process, authentication procedure, and the detailed design of anomaly detection and update procedures were meticulously developed. And the safety of the proposed authentication mechanism was analyzed, and through performance analysis with existing authentication mechanisms, we confirmed an efficiency improvement of approximately 8%. Additionally, this paper presents directions for future research on lightweight protocols and security strategies for the application of the proposed technology, aiming to enhance security.