• Geophysics and Geophysical Exploration
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• v.27 no.1
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• pp.57-68
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• 2024

Offshore wind power is increasingly regarded as a viable solution for reducing greenhous emissions due to the construction of wind farms and their superior power generation efficiency. Submarine power cables play a crucial role in transmitting the electricity generated offshore to land. To monitor cables and identify points of failure, analyzing the location or depth of burial of submarine cables is necessary. This study reviewed the technology and research for detecting submarine power cables, which were categorized into seismic/acoustic, electromagnetic, and magnetic exploration. Seismic/acoustic waves are primarily used for detecting submarine power cables by installing equipment on ships. Electromagnetic and magnetic exploration detects cables by installing equipment on unmanned underwater vehicles, including autonomous underwater vehicles (AUV) and remotely operated vihicles (ROV). This study serves as a foundational resource in the field of submarine power cable detection.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.60 no.1
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• pp.71-79
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• 2024

In compliance with environmental regulations at sea and the introduction of unmanned autonomous ships, electric propulsion ships are garnering significant attention. Induction machines used as propulsion electric motor (PEM) have maintenance advantages, but speed control is very complicated and difficult. One of the most commonly used techniques for speed control is DTC (direct torque control). DTC is simple in the reference frame transformation and the stator flux calculation. Meanwhile, two-level and three-level voltage source inverters (VSI) are predominantly used. The three-level VSI has more flexibility in voltage space vector selection compared to the two-level VSI. In this paper, speed is controlled using the DTC method based on the specifications of the PEM. The speed controller employs a PI controller with anti-windup functionality. In addition, the characteristics of the two-level VSI and three-level VSI are compared under identical conditions. It was confirmed through simulation that proper control of speed and torque has been achieved. In particular, the torque ripple was small and control was possible with a low DC voltage at low speed in the three-level VSI. The study confirmed that the application of DTC, using a three-level VSI, contributes to enhancing the system's response performance.

• Journal of Advanced Navigation Technology
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• v.28 no.2
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• pp.238-245
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• 2024

In this paper, we propose a framework for improving the performance of semantic segmentation of agricultural multispectral image using feature fusion techniques. Most of the semantic segmentation models being studied in the field of smart farms are trained on RGB images and focus on increasing the depth and complexity of the model to improve performance. In this study, we go beyond the conventional approach and optimize and design a model with multispectral and attention mechanisms. The proposed method fuses features from multiple channels collected from a UAV along with a single RGB image to increase feature extraction performance and recognize complementary features to increase the learning effect. We study the model structure to focus on feature fusion and compare its performance with other models by experimenting with favorable channels and combinations for crop images. The experimental results show that the model combining RGB and NDVI performs better than combinations with other channels.

• Computers and Concrete
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• v.33 no.4
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• pp.341-348
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• 2024

Ultra-high-performance concrete (UHPC) has received remarkable attentions in civil infrastructure due to its unique mechanical characteristics and durability. UHPC gains increasingly dominant in essential structural elements, while its unique properties pose challenges for traditional inspection methods, as damage may not always manifest visibly on the surface. As such, the need for robust inspection techniques for detecting cracks in UHPC members has become imperative as traditional methods often fall short in providing comprehensive and timely evaluations. In the era of artificial intelligence, computer vision has gained considerable interest as a powerful tool to enhance infrastructure condition assessment with image and video data collected from sensors, cameras, and unmanned aerial vehicles. This paper presents a computer vision-based approach employing deep learning to detect cracks in UHPC beams, with the aim of addressing the inherent limitations of traditional inspection methods. This work leverages computer vision to discern intricate patterns and anomalies. Particularly, a convolutional neural network architecture employing transfer learning is adopted to identify the presence of cracks in the beams. The proposed approach is evaluated with image data collected from full-scale experiments conducted on UHPC beams subjected to flexural and shear loadings. The results of this study indicate the applicability of computer vision and deep learning as intelligent methods to detect major and minor cracks and recognize various damage mechanisms in UHPC members with better efficiency compared to conventional monitoring methods. Findings from this work pave the way for the development of autonomous infrastructure health monitoring and condition assessment, ensuring early detection in response to evolving structural challenges. By leveraging computer vision, this paper contributes to usher in a new era of effectiveness in autonomous crack detection, enhancing the resilience and sustainability of UHPC civil infrastructure.

• Computers and Concrete
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• v.33 no.4
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• pp.425-443
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• 2024

Floods are among the most common natural hazards in urban areas. To mitigate the problems caused by flooding, unstructured data such as images and videos collected from closed circuit televisions (CCTVs) or unmanned aerial vehicles (UAVs) have been examined for flood management (FM). Many computer vision (CV) techniques have been widely adopted to analyze imagery data. Although some papers have reviewed recent CV approaches that utilize UAV images or remote sensing data, less effort has been devoted to studies that have focused on CCTV data. In addition, few studies have distinguished between the main research objectives of CV techniques (e.g., flood depth and flooded area) for a comprehensive understanding of the current status and trends of CV applications for each FM research topic. Thus, this paper provides a comprehensive review of the literature that proposes CV techniques for aspects of FM using ground camera (e.g., CCTV) data. Research topics are classified into four categories: flood depth, flood detection, flooded area, and surface water velocity. These application areas are subdivided into three types: urban, river and stream, and experimental. The adopted CV techniques are summarized for each research topic and application area. The primary goal of this review is to provide guidance for researchers who plan to design a CV model for specific purposes such as flood-depth estimation. Researchers should be able to draw on this review to construct an appropriate CV model for any FM purpose.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.101-120
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• 2024

As the number of smart libraries is continuously increasing, activation measures are necessary to expand user engagement and enhance service quality. This study analyzed the usage patterns of four smart libraries in A Library in Incheon, from June 2020 to December 2023. It also conducted user satisfaction surveys and question-and-answer sessions with a librarian. The number of books and loans increased, and two smart libraries in areas without public libraries showed high usage rates. Although users were satisfied with the smart libraries, they pointed out inconveniences such as the limited number of loan books and insufficient books. Consequently, it is suggested to improve user-centered service quality, continuous monitoring and evaluation, as well as active promotion.

• Journal of Drive and Control
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• v.21 no.2
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• pp.53-64
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• 2024

Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R²) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

• Journal of The Korean Institute of Defense Technology
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• v.5 no.2
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• pp.10-16
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• 2023

This study establishes the operation concept of the 4th industrial revolution defense technology and communication facility-based 5G MUM-T system, and diagnoses our current situation, focusing on the case of US government technology policy, which is a leading country in 5G MUM-T system and operation. And to advance the operation of the 5G MUM-T system, reflect combat robots and drones in the detailed classification of weapon systems, early introduction of low-orbit 5G satellite communication, expansion of the use of 5G specialized networks and wholesale provision for demonstration and verification, establishment of a defense AI governance system, Suggests the necessity of a 3-class method for radiological weapon systems. For future research, it is important to respond to the technological evolution of 6G MUM-T and 6G NTN and compare and analyze each country's policy cases, such as China, Germany, the United Kingdom, and Japan.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.229-235
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• 2023

Recently, for drones to be used for agricultural applications, it is necessary to increase the payload and extending flight time. Currently, the payload and extending flight time are limited by the battery technology for solar paper drone. In addition, charging or replacing the batteries may be a practical solution at the field that requires near continuous operation. In this paper, the procedure to optimize the main power system of an electric hybrid drone that consists of a battery and electric motor is presented. As a result, the solar paper drone flied successfully for 2-3%. The developed solar paper drone consumes and average of 55W when cruising and can receive up to 25W of energy during the day, and its extending flight time was verified through flight tests.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.176-189
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• 2024

Vertical takeoff and landing (VTOL) is a core feature of unmanned aerial vehicles (UAVs), which are commonly referred to as drones. In emerging smart logistics, drones are expected to play an increasingly important role as mobile platforms. Therefore, research on last-mile delivery using drones is on the rise. There is a growing trend toward providing drone delivery services, particularly among retailers that handle small and lightweight items. However, there is still a lack of research on a structural definition of the VTOL drone flight model for multi-point delivery service. This paper describes a VTOL drone flight route structure for a multi-drone delivery service using rotary-wing type VTOL drones. First, we briefly explore the factors to be considered when providing drone delivery services. Second, a VTOL drone flight route model is introduced using the idea of the nested graph. Based on the proposed model, we describe various time-related attributes for delivery services using drones and present corresponding calculation methods. Additionally, as an application of the drone route model and the time attributes, we comprehensively describe a simple example of the multi-drone delivery for first-come-first-served (FCFS) services.