• Journal of the Korea Convergence Society
• /
• v.12 no.3
• /
• pp.141-149
• /
• 2021

3D printers, which emerged in the late 20th century, have become a key part of the fourth industrial revolution in the 21st century. Although 3D printers, the key equipment of the maker movement and the starting point of the new cottage industry in the 21st century, still reveal the limitations of mass production with low output speed and limited filament materials, the use of 3D printers by ceramic craftsmen has recently increased exponentially. However, as part of a way to overcome the discord between craftsmanship and the new technology, which has been repeated over and over in the past in craft history, the study focused on the 'plaster mold casting' technique using 3D printers. Therefore, after analyzing casting techniques of Tony Hansen, Webe van Gansbeck, Jade Crompton, and Ryu Hee-do, the potters who actively developed gypsum techniques in the world's ceramic crafts field and applied them to their own designs, I tried to find the point of convergence between 3D printers and ceramic crafts by presenting examples of effective 3D modeling methods and optimal slip casting methods using 3D printers.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.1
• /
• pp.668-673
• /
• 2021

As the fourth industrial revolution begins in earnest, related technologies are becoming a hot topic. Hardware development is accelerating to make the most of technologies such as high-speed wireless communication, and related companies are growing rapidly. Artificial intelligence often uses desktops in general for related research, but it is mainly used for the learning process of deep learning and often transplants the generated models into devices to be used by including them in programs, etc. However, it is difficult to produce results for devices that do not have sufficient power or performance due to excessive learning or lack of power due to the use of models built to the desktop's performance. In this paper, we analyze efficiency using boards with several Neural Process Units on sale before developing the performance of deep learning to match embedded boards, and deep learning accelerators that can increase deep learning performance with USB, and present a simple development direction possible using embedded boards.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.11
• /
• pp.1330-1336
• /
• 2019

As the Fourth Industrial Revolution era began, a variety of devices are running on the cloud. These various devices continue to generate various types of data or large amounts of multimedia data. To handle this situation, a large amount of storage is required, and big data technology is required to process stored data and obtain accurate information. NAS (Network Attached Storage) or SAN (Storage Area Network) technology is typically used to build high-speed, high-capacity storage in a network-based environment. In this paper, we propose a method to construct a mass storage device using Network-DAS which is an extension technology of DAS (Direct Attached Storage). Benchmark experiments were performed to verify the scalability of the storage system with 76 HDD. Experimental results show that the proposed high performance mass storage system is scalable and reliable.

• The Journal of the Acoustical Society of Korea
• /
• v.40 no.6
• /
• pp.571-577
• /
• 2021

This study is a study on System On Chip (SOC) that implements virtual engine sound in electric vehicles without engines, and realizes vivid engine sound by combining Adaptive Difference PCM (ADPCM) method and frequency modulation method for satisfaction of driver's needs and safety of pedestrians. In addition, by proposing an electronic sound synthesis algorithm applying Musical Instrument Didital Interface (MIDI), an engine sound synthesis method and a constitutive model of an engine sound generation system are presented. In order to satisfy both drivers and pedestrians, this study uses Controller Area Network (CAN) communication to receive information such as Revolution Per Minute (RPM), vehicle speed, accelerator pedal depressed amount, torque, etc., transmitted according to the driver's driving habits, and then modulates the frequency according to the appropriate preset parameters We implemented an interaction algorithm that accurately reflects the intention of the system and driver by using interpolation for the system, ADPCM algorithm for reducing the amount of information, and MIDI format information for making engine sound easier.

• Fisheries and Aquatic Sciences
• /
• v.25 no.3
• /
• pp.151-157
• /
• 2022

Various approaches have been applied to transform aquaculture from a manual, labour-intensive industry to one dependent on automation technologies in the era of the fourth industrial revolution. Technologies associated with the monitoring of physical condition have successfully been applied in most aquafarm facilities; however, real-time biological monitoring systems that can observe fish condition and behaviour are still required. In this study, we used a video recorder placed on top of a fish tank to observe the swimming patterns of rock bream (Oplegnathus fasciatus), first one fish alone and then a group of five fish. Rock bream in the video samples were successfully identified using the you-only-look-once v3 algorithm, which is based on the Darknet-53 convolutional neural network. In addition to recordings of swimming behaviour under normal conditions, the swimming patterns of fish under abnormal conditions were recorded on adding an anaesthetic or lowering the salinity. The abnormal conditions led to changes in the velocity of movement (3.8 ± 0.6 cm/s) involving an initial rapid increase in speed (up to 16.5 ± 3.0 cm/s, upon 2-phenoxyethanol treatment) before the fish stopped moving, as well as changing from swimming upright to dying lying on their sides. Machine learning was applied to datasets consisting of normal or abnormal behaviour patterns, to evaluate the fish behaviour. The proposed algorithm showed a high accuracy (98.1%) in discriminating normal and abnormal rock bream behaviour. We conclude that artificial intelligence-based detection of abnormal behaviour can be applied to develop an automatic bio-management system for use in the aquaculture industry.

• Journal of Engineering Education Research
• /
• v.26 no.4
• /
• pp.22-35
• /
• 2023

As information and communication technology has developed remarkably, it has become possible to analyze various types of large-volume data generated at a speed close to real time, and based on this, reliable value creation has become possible. Such big data analysis is becoming an important means of supporting decision-making based on scientific figures. The purpose of this study is to develop a big data analysis tool that can analyze large amounts of data generated through engineering education. The tasks of this study are as follows. First, a database is designed to store the information of entries in the National Creative Capstone Design Contest. Second, the pre-processing process is checked for analysis with big data analysis tools. Finally, analyze the data using the developed big data analysis tool. In this study, 1,784 works submitted to the National Creative Comprehensive Design Contest from 2014 to 2019 were analyzed. As a result of selecting the top 10 words through topic analysis, 'robot' ranked first from 2014 to 2019, and energy, drones, ultrasound, solar energy, and IoT appeared with high frequency. This result seems to reflect the current core topics and technology trends of the 4th Industrial Revolution. In addition, it seems that due to the nature of the Capstone Design Contest, students majoring in electrical/electronic, computer/information and communication engineering, mechanical engineering, and chemical/new materials engineering who can submit complete products for problem solving were selected. The significance of this study is that the results of this study can be used in the field of engineering education as basic data for the development of educational contents and teaching methods that reflect industry and technology trends. Furthermore, it is expected that the results of big data analysis related to engineering education can be used as a means of preparing preemptive countermeasures in establishing education policies that reflect social changes.

• Journal of agriculture & life science
• /
• v.50 no.2
• /
• pp.187-194
• /
• 2016

The purpose of this study was to analyze temperature distribution characteristics using a model swine housing for temperature sensor adjustable positioning and developed a sensor and controller embedded exhaust fans utilizing ICT fusion technology for windowless swine housing. Temperature measured by the sensor attached on the exhaust fan was also determined that there is no problem, the temperature is located in the upper fan given the measured errors shown in the 1℃ temperature difference between the lower temperature than the other positions in the model swine housing. The performance of the exhaust fan at maximum output was found to be 1920rpm, air flow rate 125㎥/min. When the open area ratio of 70% one proper air volume of the exhaust fan was found to be 75㎥/min, 60pa. Maximum efficiency in all of the output of the exhaust fan is exhibited at about 70% open area ratio of the damper. The number of revolution of the exhaust fan was 1920rpm when the output was a maximum of 100%. AC output phase of the pulse duty ratio change of the controller was shown to change without delay. It was determined that the instant fan speed control is possible.

• Smart Structures and Systems
• /
• v.31 no.3
• /
• pp.275-281
• /
• 2023

In recent years, technological developments have rapidly increased the number of complex structures and equipment in the industrial. Accordingly, the prognostics and health monitoring (PHM) technology has become significant. The safety assessment of industrial sites requires data obtained by installing a number of sensors in the structure. Therefore, digital twin technology, which forms the core of the Fourth Industrial Revolution, is attracting attention in the safety field. The research on digital twin technology of structures subjected to seismic loads has been conducted recently. Hence, this study proposes a digital twin system that estimates the responses and arbitrary load in real time by utilizing the minimum sensor to a pipe that receives a seismic and arbitrary load. To construct the digital twin system, a finite-element model was created considering the dynamic characteristics of the pipe system, and then updating the finite-element model. In addition, the calculation speed was improved using a finite-element model that applied the reduced-order modeling (ROM) technology to achieve real-time performance. The constructed digital twin system successfully and rapidly estimated the load and the point where the sensor was not attached. The accuracy of the constructed digital twin system was verified by comparing the response of the digital twin model with that derived by using the load estimated from the digital twin model as input in the finite-element model.

• Journal of Aerospace System Engineering
• /
• v.17 no.2
• /
• pp.45-55
• /
• 2023

This study conducted aeromechanics modeling and structural load analyses of Tilt Rotor Aeroacoustic Model (TRAM), a 25% scaled V-22 tiltrotor model used in wind tunnel tests. A rotorcraft comprehensive analysis code, CAMRAD II, was used. Analysis results of this study in low-speed forward flights were compared with DNW test and previous analysis results. Blade flap bending moments were in good agreement with measured data. Mean values and oscillatory loads for lead-lag bending and torsion moments were slightly different from measured data. However, when mean values were removed, results of structural loads for one rotor revolution were moderately compared with wind tunnel tests and previous analyses. Total forces and half peak-to-peak forces of the pitch link reasonably well matched with previous analysis results and measured data. Finally, harmonic magnitudes of blade structural loads were investigated.

• Journal of the Society of Naval Architects of Korea
• /
• v.60 no.5
• /
• pp.367-374
• /
• 2023

As the Fourth Industrial Revolution accelerating, countries worldwide are developing technologies to digitize and automate various industrial sectors. Building smart factories not only reduces costs through improved process productivity but also allows for preemptive identification and removal of risk factors through the practice of Health, Safety, and Environment (HSE) management, thereby reducing industrial accident risks. In this study, we visualized pressure, temperature, power, and wind speed data measured in real-time via a monitoring GUI, enabling field managers and workers to easily access related information. Through the work environment monitoring system developed in this study, it is possible to conduct economic analysis on per-unit basis, based on the digitization of production management elements and the tracking of required resources. By implementing HSE in shipyards, potential risk factors can be improved, and gas and electrical leaks can be identified, which are expected to reduce production costs.