• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.65-73
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• 2023

In this paper, we propose a data augmentation method based on CNN(Convolutional Neural Network) learning for efficiently obtaining concrete crack image datasets. Real concrete crack images are not only difficult to obtain due to their unstructured shape and complex patterns, but also may be exposed to dangerous situations when acquiring data. In this paper, we solve the problem of collecting datasets exposed to such situations efficiently in terms of cost and time by using vector and thickness-based data augmentation techniques. To demonstrate the effectiveness of the proposed method, experiments were conducted in various scenes using U-Net-based crack detection, and the performance was improved in all scenes when measured by IoU accuracy. When the concrete crack data was not augmented, the percentage of incorrect predictions was about 25%, but when the data was augmented by our method, the percentage of incorrect predictions was reduced to 3%.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.

• Journal of IKEEE
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• v.27 no.4
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• pp.601-609
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• 2023

In this paper, we propose an artificial intelligence-based high-speed fault diagnosis method using an FPGA in the event of a ground fault in a DC system. When applying artificial intelligence algorithms to fault diagnosis, a substantial amount of computation and real-time data processing are required. By employing an FPGA with AI-based high-speed fault diagnosis, the DC breaker can operate more rapidly, thereby reducing the breaking capacity of the DC breaker. therefore, in this paper, an intelligent high-speed diagnosis algorithm was implemented by collecting fault data through fault simulation of a DC system using Matlab/Simulink. Subsequently, the proposed intelligent high-speed fault diagnosis algorithm was applied to the FPGA, and performance verification was conducted.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.821-830
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• 2023

The advancement of Internet network technology has triggered the emergence of new OTT video content platforms, increasing demand for content and altering consumption patterns. This trend is bringing positive changes to the South Korean animation industry, where diverse and high-quality animation content is becoming increasingly important. As investment in technology grows, video production technology continues to advance. Specifically, 3D animation and VFX production technologies are enabling effects that were previously unthinkable, offering detailed and realistic graphics. The Fourth Industrial Revolution is providing new opportunities for this technological growth. The rise of Artificial Intelligence (AI) is automating repetitive tasks, thereby enhancing production efficiency and enabling innovations that go beyond traditional production methods. Cutting-edge technologies like 3D animation and VFX are being continually researched and are expected to be more actively integrated into the production process. Digital technology is also expanding the creative horizons for artists. The future of AI and advanced technologies holds boundless potential, and there is growing anticipation for how these will elevate the video content industry to new heights.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.418-426
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• 2022

Unlike OSR(observation space representation), the SSR(state space representation) augmentation system is suitable for a one-way broadcasting service because it provides the same corrections to all users in the service area. Due to this advantage, several GNSS(global navigation system) systems such as Galileo, BDS(beidou navigation satellite system), QZSS(quasi zenith satellite system) are establishing PPP (precise point positioning)/PPP-RTK precision positioning services based on SSR messages. Therefore, in this paper, we try to understand the trends of satellite-based PPP/PPP-RTK correction services by analyzing the system configurations, characteristics, and precise positioning performance of satellite-based SSR correction broadcasting services.

• Atmosphere
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• v.33 no.5
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• pp.561-570
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• 2023

In order to improve the predictability of marine high-impacts weather such as typhoon and high waves, the marine observation network is an essential because it could be rapidly changed by strong air-sea interaction. In this regard, the National Institute of Meteorological Sciences, Korea Meteorological Administration (NIMS/KMA) has promoted the Argo float observation program since 2001 to participate in the International Argo program. In this study, current status and future direction of the NIMS/KMA Argo program are presented through the internal meeting and external expert forum. To date, a total of 264 Argo floats have been deployed into the offshore around the Korean Peninsula and the Northwestern Pacific Ocean. The real-time and delayed modes quality control (QC) system of Argo data was developed, and an official regional data assembling center (call-sign 'KM') was run. In 2002, the Argo homepage was established for the systematic management and dissemination of Argo data for domestic and international users. The future goal of the NIMS/KMA Argo program is to improve response to the marine high-impacts weather through a marine environment monitoring and observing system. The promotion strategy for this is divided into four areas: strengthening policy communication, developing observation strategies, promoting utilization research, and activating international cooperation.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.189-196
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• 2024

Frequent false positives alarm from the Intelligent Selective Control System have raised significant concerns. These persistent issues have led to declines in operational efficiency and market credibility among agents. Developing a new model or replacing the existing one to mitigate false positives alarm entails substantial opportunity costs; hence, improving the quality of the training dataset is pragmatic. However, smaller organizations face challenges with inadequate capabilities in dataset collection and refinement. This paper proposes an automatic human pose data collection system centered around a human pose estimation model, utilizing camera-based sensor fusion techniques and edge devices. The system facilitates the direct collection and real-time processing of field data at the network periphery, distributing the computational load that typically centralizes. Additionally, by directly labeling field data, it aids in constructing new training datasets.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.111-119
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• 2024

In this paper, we propose a big data platform that can collect information from various sources collected at ocean. Currently operating ocean-related big data platforms are focused on storing and sharing created data, and each data provider is responsible for data collection and preprocessing. There are high costs and inefficiencies in collecting and integrating data in a marine environment using communication networks that are poor compared to those on land, making it difficult to implement related infrastructure. In particular, in fields that require real-time data collection and analysis, such as weather information, radar and sensor data, a number of issues must be considered compared to land-based systems, such as data security, characteristics of organizations and ships, and data collection costs, in addition to communication network issues. First, this paper defines these problems and presents solutions. In order to design a big data platform that reflects this, we first propose a data source, hierarchical MEC, and data flow structure, and then present an overall platform structure that integrates them all.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.121-130
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• 2024

In this paper, we propose an installation approach for Naval Combat Management System(CMS) software that identifies potential data anomalies during installation. With the popularization of wireless communication methods, such as Low Earth Orbit(LEO) satellite communications, various utilization methods using wireless networks are being discussed in CMS. One of these methods includes the use of wireless network communications for installation, which is expected to enhance the real-time performance of the CMS. However, wireless networks are relatively more vulnerable to security threats compared to wired networks, necessitating additional security measures. This paper presents a method where files are transmitted to multiple nodes using encryption, and after the installation of the files, a validity check is performed to determine if there has been any tampering or alteration during transmission, ensuring proper installation. The feasibility of applying the proposed method to Naval Combat Systems is demonstrated by evaluating transmission performance, security, and stability, and based on these evaluations, results sufficient for application to CMS have been derived.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.79-86
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• 2024

In this paper, a novel visual tracking method which can utilize the context of object regions is presented. Conventional methods have the inherent problem of treating all candidate regions independently, where the tracker could not successfully discriminate regions with similar appearances. This was due to lack of contextual modeling in a given scene, where all candidate object regions should be taken into consideration when choosing a single region. The goal of the proposed method is to encourage feature exchange between candidate regions to improve the discriminability between similar regions. It improves upon conventional methods that only consider a single region, and is implemented by employing the MLP-Mixer model for enhanced feature exchange between regions. By implementing channel-wise, inter-region interaction operation between candidate features, contextual information of regions can be embedded into the individual feature representations. To evaluate the performance of the proposed tracker, the large-scale LaSOT dataset is used, and the experimental results show a competitive AUC performance of 0.560 while running at a real-time speed of 65 fps.