• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.117-126
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• 2020

A maritime object detection system is an intelligent assistance system to maritime autonomous surface ship(MASS). It detects automatically floating debris, which has a clash risk with objects in the surrounding water and used to be checked by a captain with a naked eye, at a similar level of accuracy to the human check method. It is used to detect objects around a ship. In the past, they were detected with information gathered from radars or sonar devices. With the development of artificial intelligence technology, intelligent CCTV installed in a ship are used to detect various types of floating debris on the course of sailing. If the speed of processing video data slows down due to the various requirements and complexity of MASS, however, there is no guarantee for safety as well as smooth service support. Trying to solve this issue, this study conducted research on the minimization of computation volumes for video data and the increased speed of data processing to detect maritime objects. Unlike previous studies that used the Hough transform algorithm to find the horizon and secure the areas of interest for the concerned objects, the present study proposed a new method of optimizing a binarization algorithm and finding areas whose locations were similar to actual objects in order to improve the speed. A maritime object detection system was materialized based on deep learning CNN to demonstrate the usefulness of the proposed method and assess the performance of the algorithm. The proposed algorithm performed at a speed that was 4 times faster than the old method while keeping the detection accuracy of the old method.

• Journal of the Korean Geotechnical Society
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• v.39 no.3
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• pp.29-40
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• 2023

In this study, we investigated the reinforcing effects of waveform micropiles in a stratigraphic setting comprising buried soil, weathered soil, and weathered rock. We conducted a series of field load tests and determined that waveform micropiles exhibited sufficient bearing capacity through frictional resistance in the soil layer and demonstrated favorable constructability in conditions with deep bedrock layers. Moreover, the vertical stiffness of waveform micropiles was approximately 2.2 times higher than that of conventional micropiles when subjected to the same design load. Pile group load tests comprising conventional and waveform micropiles showed that micropiles with higher stiffness carried a greater proportion of the load. Although there was no significant difference in the bearing capacity between conventional and waveform micropiles under the same design load, waveform micropiles with higher stiffness showed a load-carrying capacity 1.7 to 3.2 times greater than that of conventional micropiles. These findings suggest that waveform micropiles can be effectively used for foundation reinforcement and reduce the risk of foundation failure when increased loads due to modifications such as expansion remodeling are expected.

• The Journal of Bigdata
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• v.7 no.2
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• pp.1-9
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• 2022

In the global era, the importance of imported food safety management is increasing. Address information of overseas food companies is key information for imported food safety management, and must be verified for prompt response and follow-up management in the event of a food risk. However, because each country's address system is different, one verification system cannot verify the addresses of all countries. Also, the purpose of address verification may be different depending on the field used. In this paper, we deal with the problem of classifying a given overseas food business address into the administrative district level of the country. This is because, in the event of harm to imported food, it is necessary to find the administrative district level from the address of the relevant company, and based on this trace the food distribution route or take measures to ban imports. However, in some countries the administrative district level name is omitted from the address, and the same place name is used repeatedly in several administrative district levels, so it is not easy to accurately classify the administrative district level from the address. In this study we propose a deep learning-based administrative district level classification model suitable for this case, and verify the actual address data of overseas food companies. Specifically, a method of training using a label powerset in a multi-label classification model is used. To verify the proposed method, the accuracy was verified for the addresses of overseas manufacturing companies in Ecuador and Vietnam registered with the Ministry of Food and Drug Safety, and the accuracy was improved by 28.1% and 13%, respectively, compared to the existing classification model.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.59-68
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• 2021

As artificial intelligence(AI) technologies, which have made rapid growth recently, began to be applied to the marine environment such as ships, there have been active researches on the application of CNN-based models specialized for digital videos. In E-Navigation service, which is combined with various technologies to detect floating objects of clash risk to reduce human errors and prevent fires inside ships, real-time processing is of huge importance. More functions added, however, mean a need for high-performance processes, which raises prices and poses a cost burden on shipowners. This study thus set out to propose a method capable of processing information at a high rate while maintaining the accuracy by applying Quantization techniques of a deep learning model. First, videos were pre-processed fit for the detection of floating matters in the sea to ensure the efficient transmission of video data to the deep learning entry. Secondly, the quantization technique, one of lightweight techniques for a deep learning model, was applied to reduce the usage rate of memory and increase the processing speed. Finally, the proposed deep learning model to which video pre-processing and quantization were applied was applied to various embedded boards to measure its accuracy and processing speed and test its performance. The proposed method was able to reduce the usage of memory capacity four times and improve the processing speed about four to five times while maintaining the old accuracy of recognition.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.707-721
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• 2022

In this study, a state-of-the-art review on urban inundation simulation technology was presented summarizing major achievements and limitations, and future research recommendations and challenges. More than 160 papers published in major domestic academic journals since the 2000s were analyzed. After analyzing the core themes and contents of the papers, the status of technological development was reviewed according to simulation methodologies such as physically-based and data-driven approaches. In addition, research trends for application purposes and advances in overseas and related fields were analyzed. Since more than 60% of urban inundation research used Storm Water Management Model (SWMM), developing new modeling techniques for detailed physical processes of dual drainage was encouraged. Data-based approaches have become a new status quo in urban inundation modeling. However, given that hydrological extreme data is rare, balanced research development of data and physically-based approaches was recommended. Urban inundation analysis technology, actively combined with new technologies in other fields such as artificial intelligence, IoT, and metaverse, would require continuous support from society and holistic approaches to solve challenges from climate risk and reduce disaster damage.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.63-73
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• 2024

Autonomous vehicles at Levels 3 to 5, currently under global research and development, seek to replace the driver's perception, judgment, and control processes with various sensors integrated into the vehicle. This integration enables artificial intelligence to autonomously perform the majority of driving tasks. However, autonomous vehicles currently obtain temporary driving permits, allowing them to operate on roads if they meet minimum criteria for autonomous judgment abilities set by individual countries. When autonomous vehicles become more widespread in the future, it is anticipated that buyers may not have high confidence in the ability of these vehicles to avoid hazardous situations due to the limitations of temporary driving permits. In this study, we propose a method for grading the judgment abilities of autonomous vehicles based on a driving simulator experiment comparing and evaluating drivers' abilities to avoid hazardous situations. The goal is to derive evaluation criteria that allow for grading based on specific scenarios and to propose a framework for grading autonomous vehicles. Thirty adults (25 males and 5 females) participated in the driving simulator experiment. The analysis of the experimental results involved K-means cluster analysis and independent sample t-tests, confirming the possibility of classifying the judgment abilities of autonomous vehicles and the statistical significance of such classifications. Enhancing confidence in the risk-avoidance capabilities of autonomous vehicles in future hazardous situations could be a significant contribution of this research.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.491-498
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• 2019

Digital twin is a technology that virtualizes physical objects of the real world on a computer. It is used by collecting sensor data through IoT, and using the collected data to connect physical objects and virtual objects in both directions. It has an advantage of minimizing risk by tuning an operation of virtual model through simulation and responding to varying environment by exploiting experiments in advance. Recently, artificial intelligence and machine learning technologies have been attracting attention, so that tendency to virtualize a behavior of physical objects, observe virtual models, and apply various scenarios is increasing. In particular, recognition of each robot's motion is needed to build digital twin for co-robot which is a heart of industry 4.0 factory automation. Compared with modeling based research for recognizing motion of co-robot, there are few attempts to predict motion based on sensor data. Therefore, in this paper, an experimental environment for collecting current and inertia data in co-robot to detect the motion of the robot is built, and a motion prediction model based on the collected sensor data is proposed. The proposed method classifies the co-robot's motion commands into 9 types based on joint position and uses current and inertial sensor values to predict them by accumulated learning. The data used for accumulating learning is the sensor values that are collected when the co-robot operates with margin in input parameters of the motion commands. Through this, the model is constructed to predict not only the nine movements along the same path but also the movements along the similar path. As a result of learning using SVM, the accuracy, precision, and recall factors of the model were evaluated as 97% on average.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.674-681
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• 2021

Cyber attacks targeting critical infrastructure are on the rise. Critical infrastructure is defined as core infrastructures within a country with a high degree of interdependence between the different structures; therefore, it is difficult to sufficiently protect it using outdated cybersecurity techniques. In particular, the distinction between the physical and logical risks of critical infrastructure is becoming ambiguous; therefore, risk management from a comprehensive perspective must be implemented. Accordingly, as a means of further actively protecting critical infrastructure, major countries have begun to apply their security and cybersecurity systems by design, as a more expanded concept is now being considered. This proactive security approach (CSbD, Cybersecurity by Design) includes not only securing the stability of software (SW) safety design and management, but also physical politics and device (HW) safety, precautionary and blocking measures, and overall resilience. It involves a comprehensive security system. Therefore, this study compares and analyzes security by design measures towards critical infrastructure that are leading the way in the US, Europe, and Singapore. It reflects the results of an analysis of optimal cybersecurity solutions for critical infrastructure. I would like to present a plan for applying by Design.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.100-103
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• 2023

Recently, development of maritime autonomoust surface ships and eco-friendly ships, production and evaluation research considering various marine environments is needed in the field of optimal routes as the demand for accurate and detailed real-time marine environment prediction information expands. An algorithm that can calculate the optimal route while reducing the risk of the marine environment and uncertainty in energy consumption in smart ships was developed in 2 stages. In the first stage, a profile was created by combining marine environmental information with ship location and status information within the Automatic Ship Identification System(AIS). In the second stage, a model was developed that could define the marine environment energy map using the configured profile results, A regression equation was generated by applying Random Forest among machine learning techniques to reflect about 600,000 data. The Random Forest coefficient of determination (R²) was 0.89, showing very high reliability. The Dijikstra shortest path algorithm was applied to the marine environment prediction at June 1 to 3, 2021, and to calculate the optimal safety route and express it on the map. The route calculated by the random forest regression model was streamlined, and the route was derived considering the state of the marine environment prediction information. The concept of route calculation based on real-time marine environment prediction information in this study is expected to be able to calculate a realistic and safe route that reflects the movement tendency of ships, and to be expanded to a range of economic, safety, and eco-friendliness evaluation models in the future.

• Journal of Digital Convergence
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• v.21 no.3
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• pp.49-62
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• 2023

Due to social and cultural changes and the growth of aging people living as a single because of aging, lonely deaths are steadily increasing, and each local government has begun to define them as a social problem. The legal basis began to be established. In order to explore policy measures to prevent lonely deaths, this study examined cases of lonely death prevention policies using smart digital information technology (AI, IOT), which is being promoted by Daegu Metropolitan City to promote non-face-to-face policies to prevent lonely deaths. Policies related to lonely deaths are divided into two axes: lonely death prevention projects and post-excavation support projects. In order to operate these businesses efficiently, the provision of non-face-to-face services through artificial intelligence and the Internet of Things is recognized as a new service delivery system, so the importance and necessity of non-face-to-face services is increasing. It is time that multifaceted changes and preparations are needed, such as establishing a system to expand the non-face-to-face industry at the national level. In order to respond to another national disaster situation in the future, the non-face-to-face smart care system is being expanded in various welfare policies such as preventing lonely deaths. It will have to be activated.