• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.577-584
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• 2021

Electrical energy is an essential component in present societies, which is an important basis for our technological society. In the design of new power infrastructure, it is important to consider the psychological aspects of how our culture considers and aspects its development as an integral component of the community environment. The construction of new high voltage overhead transmission lines has become a controversial issue for public policy of government due to social opposition. The members of community are concerned about how these power lines may have an impact on their lives, basically caused by their effects on health and safety. The landscape and visual impact is one of the most impact that can be easily perceived for local community. The computer 3D simulation of new landscape is illustrated by a real life use corresponding to the selection of the power line route with least observability for local community. This paper used ArcGIS(geographic information system tool) for planning, survey, basic route and detailed route, route for implementation of transmission line corridor. Also, the paper showed the map of natural environment, living environment, safety and altitude using database of power line corridor, and transmission siting model was developed by this study. The suggested landscape of computer simulation with lowest visibility on a power line zone can contribute to reducing oppositions of local community and accelerating the construction of new power lines.

• Korean Journal of Agricultural Science
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• v.48 no.2
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• pp.299-310
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• 2021

Glycerol is a non-volatile compound with no aromatic properties that contributes significantly to the quality of wine by providing sweetness and richness of taste. In addition, it is also the third most significant byproduct of alcoholic fermentation in terms of quantity after ethanol and carbon dioxide. In this study, Fourier transform infrared (FT-IR) spectroscopy was employed as a fast non-destructive method in conjugation with multivariate regression analysis to build a model for the quantitative analysis of glycerol concentration in wine samples. The samples were prepared by using three varieties of red wine samples (i.e., Shiraz, Merlot, and Barbaresco) that were adulterated with glycerol in concentration ranges from 0.1 to 15% (v·v^-1), and subjected to analysis together with pure wine samples. A net analyte signal (NAS)-based methodology, called hybrid linear analysis in the literature (HLA/GO), was applied for predicting glycerol concentrations in the collected FT-IR spectral data. Calibration and validation sets were designed to evaluate the performance of the multivariate method. The obtained results exhibited a high coefficient of determination (R²) of 0.987 and a low root mean square error (RMSE) of 0.563% for the calibration set, and a R² of 0.984 and a RMSE of 0.626% for the validation set. Further, the model was validated in terms of sensitivity, selectivity, and limits of detection and quantification, and the results confirmed that this model can be used in most applications, as well as for quality assurance.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.269-279
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• 2022

Purpose: The purpose of this study is to develop a deep learning-based personal protective equipment detection model for disaster prevention at construction sites, and to apply it to actual construction sites and to analyze the results. Method: In the method of conducting this study, the dataset on the real environment was constructed and the developed personal protective equipment(PPE) detection model was applied. The PPE detection model mainly consists of worker detection and PPE classification model.The worker detection model uses a deep learning-based algorithm to build a dataset obtained from the actual field to learn and detect workers, and the PPE classification model applies the PPE detection algorithm learned from the worker detection area extracted from the work detection model. For verification of the proposed model, experimental results were derived from data obtained from three construction sites. Results: The application of the PPE recognition model to construction site brings up the problems related to mis-recognition and non-recognition. Conclusions: The analysis outcomes were produced to apply the object recognition technology to a construction site, and the need for follow-up research was suggested through representative cases of worker recognition and non-recognition, and mis-recognition of personal protective equipment.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.62-77
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• 2022

A cooperative driving automation system is imperative to overcome the limitation of the stand-alone automated driving technology. By definition, a cooperative driving automation system refers to a technology in which an automated vehicle cooperates with other vehicles or infrastructure to increase driving efficiency and safety. Specifically, in this study, the technical elements necessary for the cooperative driving automation technology and the technological research trends were investigated. Subsequently, implications for future cooperative driving automation technology development were drawn through the research trends. Finally, the importance of cooperative driving automation technology and infra-guidance service for automated vehicles were discussed.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.382-390
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• 2023

The Korea market for LUAV (Light Unmanned Aerial Vehicle) weighing less than 150 kg is growing rapidly. As a result, the market for manufacturing and operating LUAV is expanding, and domestic development of parts and finished products is actively taking place. However, the flight control system and onboard software, which are key components of domestic LUAV, are largely dependent on overseas products due to the excessive cost and period required for development. This paper presented a domestic software development and certification procedure using DO-178C, a guideline for aircraft software development, and the Model-based Development method, and conducted a survey of those involved in the development, manufacturing, and certification of LUAV and analyzed the results. In addition, a case study was conducted to apply the software development plan to the helicopter FCC (Flight Control Computer).

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.456-461
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• 2023

The Korea Maritime Environment Corporation is conducting a comprehensive survey of the national marine ecosystem under the commission of the Ministry of Oceans and Fisheries (MOF) to ensure continuous use of the ocean, preserve and manage the marine ecosystem. The survey has set major peaks to investigate changes in the marine ecosystem around the Korean Peninsula. However as the peak has been set around the coast, it is necessary to expand the scope of investigation to encompass offshore areas. Meanwhile, the Aids to Navigation Division of the MOF supports a comprehensive national marine ecosystem survey providing photographs of fouling organisms during the Aids to Navigation lifting inspection, however, the photographs are provided only in consultation with the Korea Maritime Environment Corporation. Therefore, a study was conducted to generate information on fouling organisms using deep learning-based image processing algorithms by the lifting Aids to Navigation and dorsal buoys so that Aids to Navigation could be used as the major component of a comprehensive national marine ecosystem. If the Aids to Navigation are used as the peak of the survey, they could serve as fundamental data to enhance their own value as well as analyze abnormal marine conditions and ecosystem changes in Korea.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.916-923
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• 2023

Purpose: Looking at the status of fatal accidents in the construction industry in the 2022 Industrial Accident Status Supplementary Statistics, 27.8% of all fatal accidents in the construction industry are caused by construction equipment. In order to overcome the limitations of tours and inspections caused by the enlargement of sites and high-rise buildings, we plan to build a model that can extract construction equipment using computer vision technology and analyze the model's accuracy and field applicability. Method: In this study, deep learning is used to learn image data from excavators, dump trucks, and mobile cranes among construction equipment, and then the learning results are evaluated and analyzed and applied to construction sites. Result: At site 'A', objects of excavators and dump trucks were extracted, and the average extraction accuracy was 81.42% for excavators and 78.23% for dump trucks. The mobile crane at site 'B' showed an average accuracy of 78.14%. Conclusion: It is believed that the efficiency of on-site safety management can be increased and the risk factors for disaster occurrence can be minimized. In addition, based on this study, it can be used as basic data on the introduction of smart construction technology at construction sites.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.653-658
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• 2023

In systems like the combat management system of a naval ship or smart city of civilians, where many sensors and actuators are connected, the middle-ware DDS (Data Distribution Service) is mainly used to transmit large amounts of data. It is scalable and can effectively respond to the increase in sensors or equipment connected to the system in the future. The engineering control system (ECS), which plays an important role similar to the combat management system of a naval ship, still uses Server-Client model with industrial protocols such as Modbus and CAN (Controller Area Network) bus, to transmit data, which is unfavorable in terms of scalability. However, as automation and unmanned systems advance, more sensors and actuators are expected to be added, necessitating substantial program modification. DDS can effectively address such situations. The purpose of this study is to confirm the development possibility of an integrated monitoring and control system of a ship by using OpenDDS, which follows the OMG (Object Management Group) standard among the middle-ware DDS used in the combat management system. To achieve this goal, field equipment simulators and an ECS server were configured to perform field equipment data input/output and simulation using DDS was performed. The ECS prototype successfully handled data transmission, confirming that DDS is capable of serving as the middle-ware for the ECS of a ship.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.15-29
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• 2014

Increasing the smart instrument based social and economical activity, problems of electronic business's safety, reliability and user's privacy are be on the rise. so variety standard cryptography algorithms for information security have been developed in korea and How to efficiently implement them in a variety of environments is issued. ARIA and SEED, developed in Korea, are standard block cipher algorithm to encrypt the 128-bit plaintext, are each configured Feistel, SPN structure. In this paper, SEED and ARIA were implemented using the GEZEL language that can be used easily in the software designer because grammar is simple compared to other hardware description language. In particular, in this paper, will be described in detail the characteristics and design method using GEZEL as the first paper that implements 128bits ARIA and SEED and it showed the flexibility and efficiency of development using GEZEL. SEED designed GEZEL is occupied 69043 slice, is operating Maximum frequency 146.25Mhz and ARIA is occupied 7282 slice, is operating Maximum frequency 286.172Mhz. Also, Speed of SEED designed and implemented signal flow method is improved 296%.