• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.562-569
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• 2022

The alarm monitoring technology applied to existing operating ships manages data items such as temperature and pressure with AMS (Alarm Monitoring System) and provides an alarm to the crew should these sensing data exceed the normal level range. In addition, the maintenance of existing ships follows the Planned Maintenance System (PMS). whereby the sensing data measured from the equipment is monitored and if it surpasses the set range, maintenance is performed through an alarm, or the corresponding part is replaced in advance after being used for a certain period of time regardless of whether the target device has a malfunction or not. To secure the reliability and operational safety of ship engine operation, it is necessary to enable advanced diagnosis and prediction based on real-time condition monitoring data. To do so, comprehensive measurement of actual ship data, creation of a database, and implementation of a condition diagnosis monitoring system for condition-based predictive maintenance of auxiliary equipment and piping must take place. Furthermore, the system should enable management of auxiliary equipment and piping status information based on a responsive web, and be optimized for screen and resolution so that it can be accessed and used by various mobile devices such as smartphones as well as for viewing on a PC on board. This update cost is low, and the management method is easy. In this paper, we propose CBM (Condition Based Management) technology, for autonomous ships. This core technology is used to identify abnormal phenomena through state diagnosis and monitoring of pumps and purifiers among ship auxiliary equipment, and seawater and steam pipes among pipes. It is intended to provide performance diagnosis and failure prediction of ship auxiliary equipment and piping for convergence analysis, and to support preventive maintenance decision-making.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.565-575
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• 2022

Thanks to the latest developments in digital architectural technologies, free-form designs that maximize the creativity of architects have rapidly increased. However, there are a lot of difficulties in forming various free-form curved surfaces. In panelizing to produce free forms, the methods of mesh, developable surface, tessellation and subdivision are applied. The process of applying such panelizing methods when producing free-form panels is complex, time-consuming and requires a vast amount of manpower when extracting production data. Therefore, algorithms are needed to quickly and systematically extract production data that are needed for panel production after a free-form building is designed. In this respect, the purpose of this study is to propose mathematical algorithms for the automatic generation of production data of free-form panels in consideration of the building model, performance of production equipment and pattern information. To accomplish this, mathematical algorithms were suggested upon panelizing, and production data for a CNC machine were extracted by mapping as free-form curved surfaces. The study's findings may contribute to improved productivity and reduced cost by realizing the automatic generation of data for production of free-form concrete panels.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.675-681
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• 2021

With the recent growth of the AI speaker market, the demand for speech synthesis technology that enables natural conversation with users is increasing. Therefore, there is a need for a multi-speaker speech synthesis system that can generate voices of various tones. In order to synthesize natural speech, it is required to train with a large-capacity. high-quality speech DB. However, it is very difficult in terms of recording time and cost to collect a high-quality, large-capacity speech database uttered by many speakers. Therefore, it is necessary to train the speech synthesis system using the speech DB of a very large number of speakers with a small amount of training data for each speaker, and a technique for naturally expressing the tone and rhyme of multiple speakers is required. In this paper, we propose a technology for constructing a speaker encoder by applying the deep learning-based x-vector technique used in speaker recognition technology, and synthesizing a new speaker's tone with a small amount of data through the speaker encoder. In the multi-speaker speech synthesis system, the module for synthesizing mel-spectrogram from input text is composed of Tacotron2, and the vocoder generating synthesized speech consists of WaveNet with mixture of logistic distributions applied. The x-vector extracted from the trained speaker embedding neural networks is added to Tacotron2 as an input to express the desired speaker's tone.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1074-1081
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• 2021

Nitrous oxide (N₂O) is one of the six major greenhouse gases and is known to produce a greenhouse ef ect by absorbing infrared radiation in the atmosphere. In particular, its global warming potential (GWP) is 310 times higher than that of CO₂, making N₂O a global concern. Accordingly, strong environmental regulations are being proposed. N₂O reduction technology can be classified into concentration recovery, catalytic decomposition, and pyrolysis according to physical methods. This study intends to provide information on temperature conditions and reaction time required to reduce nitrogen oxides with cost. The high-temperature ranges selected for pyrolysis conditions were calculated at intervals of 100 K from 1073 K to 1373 K. Under temperatures of 1073 K and 1173 K, the N₂O reduction rate and nitrogen monoxide concentration were observed to be proportional to the residence time, and for 1273 K, the N₂O reduction rate decreased due to generation of the reverse reaction as the residence time increased. Particularly for 1373 K, the positive and reverse reactions for all residence times reached chemical equilibrium, resulting in a rather reduced reaction progression to N₂O reduction.

• Environmental and Resource Economics Review
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• v.31 no.2
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• pp.291-317
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• 2022

This study tried to analyze the factors affecting consumers' vehicle selection for the spread of eco-friendly vehicles. We used the energy census data for this purpose, and although the energy census collects useful information from a large number of samples, it has been limitedly used to create simple statistics in many cases. Based on 2,771 transport sector microdata from the 2017 Energy Census, we collected vehicle price, fuel efficiency, and number of vehicle models, which are alternative characteristic variables that change according to consumers' choice, and converted and analyzed data to enable conjoint analysis. The analysis results in two-folds. First, it was confirmed that the official fuel efficiency of a vehicle and the fuel cost, which is affected by changes in the relative price of each fuel, are important variables in selecting an eco-friendly vehicle. In order to achieve the goal of spread of eco-friendly vehicles, it is necessary to develop technologies to improve fuel efficiency and set appropriate electric rates for charging electric vehicles. Second, an increase in the number of vehicle models through the expansion of the eco-friendly car industry and market also affects consumers' choice of eco-friendly vehicles, so efforts to expand the supply of eco-friendly vehicles will be an important factor. In addition, it is also significant that this study showed that the use of the energy census can be diversified by deriving meaningful policy implications using the results of the energy census periodically conducted in the country without a separate survey.

• Science of Emotion and Sensibility
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• v.25 no.2
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• pp.71-78
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• 2022

This study investigates the recommendation for tax accounting services used in many companies. In particular, it aims to create guidelines for small businesses with fewer than 100 employees, which are relatively difficult to manage in terms of cost or time. We surveyed 100 corporate officials on basic business information, such as the number of employees, job titles, and business type, as well as the type of tax accounting service, the recommended score for the service, the reason for the score, and other opinions related to tax accounting services. In particular, the recommendation score seeks to obtain more effective results by using the Net Promoter Score method, which is known to be more effective in understanding customer opinions than general customer satisfaction surveys. The survey revealed a Net Promoter Score for a recommendation of -33 points, lower than the general Net Promoter Score. It also indicated that tax accounting services need improvement. Specifically, the opinions of the respondents who gave a non-recommendation score were as follows: "Not inconvenient or comfortable," "It was just okay," "I don't know if it would be helpful," and "There is no differentiation and there are no special alternatives." We concluded that an improved service for raising recommendation scores was necessary. This survey focused on recommendations for companies with fewer than 100 employees; future studies should incorporate larger companies and more variables.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.6
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• pp.181-188
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• 2023

Block cipher is not expected to be safe for quantum computer, as Grover's algorithm reduces the security strength by accelerating brute-force attacks on symmetric key ciphers. So it is necessary to check the post-quantum security strength by implementing quantum circuit for the target cipher. In this paper, we propose the optimal quantum circuit implementation result designed as a technique to minimize the use of quantum resources (qubits, quantum gates) for SIMECK lightweight cryptography, and explain the operation of each quantum circuit. The implemented SIMECK quantum circuit is used to check the estimation result of quantum resources and calculate the Grover attack cost. Finally, the post-quantum strength of SIMECK lightweight cryptography is evaluated. As a result of post-quantum security strength evaluation, all SIMECK family cipher failed to reach NIST security strength. Therefore, it is expected that the safety of SIMECK cipher is unclear when large-scale quantum computers appear. About this, it is judged that it would be appropriate to increase the block size, the number of rounds, and the key length to increase the security strength.

• Smart Media Journal
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• v.12 no.7
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• pp.43-51
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• 2023

As online transactions increase, the image of clothing has a great influence on consumer purchasing decisions. The importance of image information for clothing materials has been emphasized, and it is important for the fashion industry to analyze clothing images and grasp the materials used. Textile materials used for clothing are difficult to identify with the naked eye, and much time and cost are consumed in sorting. This study aims to classify the materials of textiles from clothing images based on deep learning algorithms. Classifying materials can help reduce clothing production costs, increase the efficiency of the manufacturing process, and contribute to the service of recommending products of specific materials to consumers. We used machine vision-based deep learning algorithms ResNet and Vision Transformer to classify clothing images. A total of 760,949 images were collected and preprocessed to detect abnormal images. Finally, a total of 167,299 clothing images, 19 textile labels and 20 fabric labels were used. We used ResNet and Vision Transformer to classify clothing materials and compared the performance of the algorithms with the Top-k Accuracy Score metric. As a result of comparing the performance, the Vision Transformer algorithm outperforms ResNet.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.105-126
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• 2023

The domestic used car market continues to grow along with the used car online platform service. The used car online platform service discloses vehicle specifications, accident history, inspection history, and detailed options to service consumers. Most of the preceding studies were predictions of used car prices using vehicle specifications and some options for vehicles. As a result of the study, it was confirmed that there was a nonlinear relationship between used car prices and some specification variables. Accordingly, the researchers tried to solve the nonlinear problem by executing a Machine Learning model. In common, the Regression based Machine Learning model had the advantage of knowing the actual influence and direction of variables, but there was a disadvantage of low Cost Function figures compared to the Decision Tree based Machine Learning model. This study attempted to predict used car prices of six domestic brands by utilizing both vehicle specifications and vehicle options. Through this, we tried to collect the advantages of the two types of Machine Learning models. To this end, we sequentially conducted a regression based Machine Learning model and a decision tree based Machine Learning model. As a result of the analysis, the practical influence and direction of each brand variable, and the best tree based Machine Learning model were selected. The implications of this study are as follows. It will help buyers and sellers who use used car online platform services to predict approximate used car prices. And it is hoped that it will help solve the problem caused by information inequality among users of the used car online platform service.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.557-564
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• 2023

In this study, 3D printing technology was used to compensate for the shortcomings of the use of lead, which has proven to have excellent shielding performance, and to control unnecessary human exposure. 3D printers can implement three-dimensional shapes and can immediately apply individual ideas, which has great advantages in maintaining technology supplementation while reducing the cost and duration of prototyping. Among the various special 3D printers, the FDM method was adopted, and the filament used for output was manufactured using a research extruder by mixing two materials, PLA (Poly-Lactic-Acid) and tungsten. The purpose was to verify the validity through dose evaluation and to provide basic information on the production of chapezones of various materials. The mixed filament was implemented as a morphological shield. Filaments made of a research extruder by mixing PLA and tungsten were divided into 10 %, 20 %, 30 %, 40 %, and 50 % according to the tungsten content ratio. Through the process of 3D Modeling, STL File storage, G-code generation, and output, 10 cm × 10 cm × 0.5 cm was manufactured, respectively, and dose and shielding ability were evaluated under the conditions of tube voltages of 60 kVp, 80 kVp, 100 kVp, 120 kVp, and tube currents of 20 mAs and 40 mAs.