• Journal of Navigation and Port Research
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• v.29 no.5 s.101
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• pp.439-445
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• 2005

Recently, owing to the increasing demand on the simplification of arrival and departure procedures, IMO's (International Maritime Organization) Facilitation Committee (FAL) is carrying out the standardization project of arrival and departure formalities and clearance form. Also, many port authorities of developed countries are making active researches for the smooth flow and efficiency of the information inbound and outbound ships by way of simplifying their formalities or through electronic means. However, this standardization project cannot be done by one country but by mutual cooperation among related nations. And to carry out this task, the first thing to be done is to standardize the formalities and document form, and to integrate information. To this end, this study has reviewed the model cases of advanced ports of developed countries with regard to their simplification and standardization efforts. And also we have analyzed the formalities and clearance form of the three countries Korea, China, and Japan. And then for the solution of common problems of three countries, this paper has suggested an ebXML-based Global Port B2B framework. Through this framework, we can reuse and automate the necessary information on the arrival and departure of ships, consequently realizing simplification, and laying a foundation for the introduction of e-commerce to the port industry.

• Journal of the Korean Institute of Gas
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• v.3 no.1
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• pp.48-57
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• 1999

Safe plants are maintained by the systematic identification of potential hazards. Various hazard evaluation methods have been developed in pursuit of safe plants. Recently, much efforts has been made for the automation of hazard evaluation system by introducing expert system to get rid of weak points of the conventional hazard evaluation methods. HAZOP study is recognized as one of the most systematic and logical hazard evaluation methods. However, it has several disadvantages; experts should participate simultaneously, the detailed study is time-consuming, and the quality of the results largely depends on the quality of the experts. Therefore, the automation of HAZOP Study is highly beneficial for reducing the required time and obtaining the consistent evaluation results. In this study, a framework for the automation of HAZOP study is suggested. Based on the suggested framework, HAxSYM, an expert system to automate HAZOP study, has been developed. The case study validates the performance of the developed system, and the results are compared with the results from the conventional HAZOP study.

• Journal of the Society of Naval Architects of Korea
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• v.53 no.6
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• pp.503-513
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• 2016

In order to reduce the green-house gas exhaustion, International Maritime Organization (IMO) has been reinforcing carbon gas regulations. Due to the regulations, a lot of competitions for designing Eco ship in the shipbuilding industry are progressing now. It is faced with the necessity of reducing hull weight by combining automated systems for optimal compartment arrangement with hull structural design. Most researches on optimum structural design method have been consistently in progress and applied to minimize weight and cost of mid-ship section in preliminary ship design stage based on analytical structural analysis method on fixed compartment arrangement. In order to reduce design period and to improve international technical competitiveness by shortening the period of hull structural design and enhancing design accuracy, it has been felt necessity to combine optimized compartment arrangement with optimum design of ship structure based on the international regulations and rules. So in this study, the automated design algorithm for longitudinal members has been developed to combine automated algorithm of compartment arrangement with hull structural design system for oil tanker. The SeaTrust-Hullscan software developed by Korean Register is used to perform ship structural design for mother ship and selected design cases. The effect of weight reduction is verified with comparison of ship weight between mother ship and the cases suggested in this study.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.63-70
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• 2021

Generating a super-resolution meteological data by using a high-resolution deep neural network can provide precise research and useful real-life services. We propose a new technique of generating improved training data for super-resolution deep neural networks. To generate high-resolution meteorological data with domain specific knowledge, Lambert conformal conic projection and objective analysis were applied based on observation data and ERA5 reanalysis field data of specialized institutions. As a result, temperature and humidity analysis data based on domain specific knowledge showed improved RMSE by up to 42% and 46%, respectively. Next, a super-resolution generative adversarial network (SRGAN) which is one of the aritifial intelligence techniques was used to automate the manual data generation technique using damain specific techniques as described above. Experiments were conducted to generate high-resolution data with 1 km resolution from global model data with 10 km resolution. Finally, the results generated with SRGAN have a higher resoltuion than the global model input data, and showed a similar analysis pattern to the manually generated high-resolution analysis data, but also showed a smooth boundary.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.889-898
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• 2021

Currently, due to the demographic cliff phenomenon in Republic of Korea, A serious defense vacuum could occur due to the lack of South Korean military's personal strength. As a result, The South Korean military has a possibility to implement the polices the prepare for military provocations and preemptive strikes by the North Korean military while resolving the South Korean defense vacuum caused by the shrinking population. It seems like that the only way for the South Korean military to solve the shortage of personal strength due to the population decline is to reduce the number of Mechanized Units(MU) other than, infantry and automate, and autonomous driving the weapons system of the Mechanized Units(MU). In this paper, we propose the use of the virtual autonomous driving of the self propelled artillery K-9's in self selection of the position and occupation of position and self positioning in the position. At the same time in this paper, the self propelled artillery K-9 model robot is used to simulate and the explain about the operation method, necessity and feasibility in the self propelled artillery K-9. In addition, this paper predicted the problems that would arise if the South Korean military deployed autonomous driving self propelled K-9, in real combat.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.504-509
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• 2019

Recently, both wireless communications technology and the performance of small devices have developed exponentially, while the number of services using various types of Internet of Things (IoT) devices has also massively increased in line with the ongoing technological and environmental changes. Furthermore, ever more devices that were previously used in the offline environment-including small-size sensors and CCTV-are being connected to the Internet due to the huge increase in IoT services. However, many IoT devices are not equipped with security functions, and use vulnerable open source software as it is. In addition, conventional network equipment, such as switches and gateways, operates with vulnerabilities, because users tend not to update the equipment on a regular basis. Recently, the simple vulnerability of IoT devices has been exploited through the distributed denial of service (DDoS) from attackers creating a large number of botnets. This paper proposes a system that is capable of identifying Internet-connected devices quickly, analyzing and managing the vulnerability of such devices using Internet-wide scan technology. In addition, the vulnerability analysis rate of the proposed technology was verified through collected banner information. In the future, the company plans to automate and upgrade the proposed system so that it can be used as a technology to prevent cyber attacks.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.441-447
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• 2021

Construction sites are complex and dangerous because heavy equipment and workers perform various operations simultaneously within limited working areas. Solitary works of heavy equipment in complex job sites can cause fatal accidents, and thus they should interact with spotters and obtain information about surrounding environments during operations. Recently, many computer vision technologies have been developed to automatically monitor construction equipment and detect their interactions with other resources. However, previous methods did not take into account the interactions between equipment and spotters, which is crucial for identifying solitary works of heavy equipment. To address the drawback, this research develops a computer vision-based solitary work detection model that considers interactive operations between heavy equipment and spotters. To validate the proposed model, the research team performed experiments using image data collected from actual construction sites. The results showed that the model was able to detect workers and equipment with 83.4 % accuracy, classify workers and spotters with 84.2 % accuracy, and analyze the equipment-to-spotter interactions with 95.1 % accuracy. The findings of this study can be used to automate manual operation monitoring of heavy equipment and reduce the time and costs required for on-site safety management.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.93-106
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• 2021

Many recent studies on deep learning-based road crack detection have shown significantly more improved performances than previous works using algorithm-based conventional approaches. However, many deep learning-based studies are still focused on classifying the types of cracks. The classification of crack types is highly anticipated in that it can improve the crack detection process, which is currently relying on manual intervention. However, it is essential to calculate the severity of the cracks as well as identifying the type of cracks in actual pavement maintenance planning, but studies related to road crack detection have not progressed enough to automated calculation of the severity of cracks. In order to calculate the severity of the crack, the type of crack and the area of the crack in the image must be identified together. This study deals with a method of using Mobilenet-SSD that is deep learning-based object detection techniques to effectively automate the simultaneous detection of crack types and crack areas. To improve the accuracy of object-detection for road cracks, several experiments were conducted to combine the U-Net for automatic segmentation of input image and object-detection model, and the results were summarized. As a result, image masking with U-Net is able to maximize object-detection performance with 0.9315 mAP value. While referring the results of this study, it is expected that the automation of the crack detection functionality on pave management system can be further enhanced.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.133-139
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• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.260-269
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• 2022

In this paper, We propose an anomaly detection model using deep neural network to automate the identification of outliers of the national air pollution measurement network data that is previously performed by experts. We generated training data by analyzing missing values and outliers of weather data provided by the Institute of Environmental Research and based on the BeatGAN model of the unsupervised learning method, we propose a new model by changing the kernel structure, adding the convolutional filter layer and the transposed convolutional filter layer to improve anomaly detection performance. In addition, by utilizing the generative features of the proposed model to implement and apply a retraining algorithm that generates new data and uses it for training, it was confirmed that the proposed model had the highest performance compared to the original BeatGAN models and other unsupervised learning model like Iforest and One Class SVM. Through this study, it was possible to suggest a method to improve the anomaly detection performance of proposed model while avoiding overfitting without additional cost in situations where training data are insufficient due to various factors such as sensor abnormalities and inspections in actual industrial sites.