• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.177-186
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• 2015

This study aims to suggest the direction in which furniture design can contribute, keeping with the trend of small quantity batch production by analyzing cases of furniture design manufacturing. This study analyzed cases of furnitures and lights made by 3D printing with 3 classifications. They are 1st, classification by correlation between 3D printing method and materials, 2nd, classification by 8 formative characteristics of 3D printing furniture design, 3rd, comparison analysis of competitiveness between existing furniture design and 3D printing furniture design by practicality, usability and durability. The competitiveness 3D printing technique arouses in furniture design industry, which is investigated in this thesis, is as follows. 1st, small quantity batch production, which caters to personal taste, is made possible. 2nd, transmission and transportation via digital are became more convenient. 3rd, it brings about a breakthrough in furniture design manufacturing. 4th, there is room for development into the 'smart furniture design' industry through collaborative use of 3D printing and internet of things. 5th, an Eco-friendly method of furniture design is consistently facilitated.

• Korea Science and Art Forum
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• v.21
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• pp.31-43
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• 2015

Information and Communications Technology (ICT) is one of the technologies which represent the core value of the creative economy. It has served as a vehicle connecting the existing industry and corporate infrastructure, developing existing products and services and creating new products and services. In addition to the ICT, new devices including big data, mobile gadgets and wearable products are gaining a great attention sending an expectation for a new market-pioneering. Further, Internet of Things (IoT) is helping solidify the ICT-based social development connecting human-to-human, human-to-things and things-to-things. This means that the manufacturing-based hardware development needs to be achieved simultaneously with software development through convergence. The essential element the convergence between hardware and software is OS, for which world's leading companies such as Google and Apple have launched an intense development recognizing the importance of software. Against this backdrop, the status-quo of the software market has been examined for the study of the present report (Korea Evaluation Institute of Industrial Technology: Professional Design Technology Development Project). As a result, the software platform-based Google's android and Apple's iOS are dominant in the global market and late comers are trying to enter the market through various pathways by releasing web-based OS and similar OS to provide a new paradigm to the market. The present study is aimed at finding the way to utilize a smart content by which anyone can be a developer based on OS responding to such as social change, newly defining a smart content to be universally utilized and analyzing the market to deal with a rapid market change. The study method, scope and details are as follows: Literature investigation, Analysis on the app market according to a smart classification system, Trend analysis on the current content market, Identification of five common trends through comparison among the universal definition of smart content, the status-quo of application represented in the app market and content market situation. In conclusion, the smart content market is independent but is expected to develop in the form of a single organic body being connected each other. Therefore, the further classification system and development focus should be made in a way to see the area from multiple perspectives including a social point of view in terms of the existing technology, culture, business and consumers.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.21-26
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• 2020

An autonomous driving system is based on the deep learning system built by big data which are obtained by various IoT sensors. The miniaturization and high performance of the IoT sensors are needed for diverse devices including the autonomous driving system. Specially, the miniaturization of the sensors leads to compel the miniaturization of the fixer structures. In the viewpoint of the miniaturization, metallic structure is a best solution to attach the small IoT sensors to the main body. However, it is hard to manufacture the small metallic structure with a conventional machining process or manufacturing cost greatly increases. As one of solutions for the problems, in this work, metallic FDM (Fused depositon modeling) based on metallic filament was proposed and the FDM process was investigated to fabricate the small metallic structure. Final part was obtained by the post-process that consists of debinding and sintering. In this work, the relationship between infill rate and the density of the part after the post-process was investigated. The investigation of the relationship is based on the fact that the infill rate and the density obtained from the post-processing is not same. It can be said that this work is a fundamental research to obtain the higher density of the printed part.

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.75-80
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• 2020

Recently, the size of the IoT sensor has been decreased and the collecting direction of the IoT sensor for acquiring the data have been changed from 2D to 3D. It makes sensor structure complex. In the fabrication of the complex structure, 3D printing technology has more useful than traditional manufacturing technologies. Among 3D printing technologies, FDM (fused deposition modeling) is a candidate technology to fabricate a small IoT sensor because the price of the machine and the material is cheap. In the FDM process, a 3D shape is made by depositing the melted filament. Recently, the patent of FDM technology is expired and cheat machines are developed based on the open-source. In the FDM process, mechanical properties of a fabricated part is affected by a lots of factors such as the kind of material and process parameters. Among them, infill is affecting the mechanical properties and the production lead time as well. In this work, a new method to optimize the FDM process with the consideration of mechanical property and production lead time was proposed. To verify the method, the fabrications were performed with the different infill rates. The results of tensile tests were analyzed to verify the proposed method.

• Smart Media Journal
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• v.8 no.1
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• pp.74-81
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• 2019

Recently, machine learning technology has had a significant impact on society, particularly in the medical, manufacturing, marketing, finance, broadcasting, and agricultural aspects of human lives. In this paper, we study how to apply machine learning techniques to foods, which have the greatest influence on the human survival. In the field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus on optimizing the crop growth environment by monitoring the growth environment in real time. KT Smart Farm Solution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existing smart farm businesses mainly focus on controlling the growth environment and improving productivity. On the other hand, in this study, we are studying how to apply machine learning with respect to harvest time so that we will be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machine learning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, to apply accurate machine learning technology, it is necessary to continuously collect large data. Therefore, the color, value, internal temperature, and moisture of greenhouse-grown fruits are collected and secured in real time using color, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be used repeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulated continuously.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.383-392
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• 2014

The research for coatings is one of the most popular and active research in the polymer industry. For the coatings, electronics industry, medical and optical fields are growing more important. In particular, the trend is the increasing of the technical requirements for the performance and accuracy of the coatings by the development of automotive and electronic parts. In addition, the industry has a need of more intelligent and automated system in the industry is increasing by introduction of the IoT and big data analysis based on the environmental information and the context information. In this paper, we propose an optimization model for the design of experiments based coating formulation data objects using the Internet technologies and big data analytics. In this paper, the coating formulation was calculated based on the best data analysis is based on the experimental design, modify the operator with respect to the error caused based on the coating formulation used in the actual production site data and the corrected result data. Further optimization model to correct the reference value by leveraging big data analysis and Internet of things technology only existing coating formulation is applied as the reference data using a manufacturing environment and context information retrieval in color and quality, the most important factor in maintaining and was derived. Based on data obtained from an experiment and analysis is improving the accuracy of the combination data and making it possible to give a LOT shorter working hours per data. Also the data shortens the production time due to the reduction in the delivery time per treatment and It can contribute to cost reduction or the like defect rate reduced. Further, it is possible to obtain a standard data in the manufacturing process for the various models.

• Journal of Industrial Convergence
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• v.22 no.4
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• pp.1-9
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• 2024

Smart factories represent production facilities where cutting-edge information and communication technologies are fused with manufacturing processes, reflecting rapid advancements and changes in the global manufacturing sector. They capitalize on the integration of robotics and automation, the Internet of Things (IoT), and the convergence of artificial intelligence technologies to maximize production efficiency in various manufacturing environments. However, the smart factory environment is prone to security threats and vulnerabilities due to various attack techniques. When security threats occur in smart factories, they can lead to financial losses, damage to corporate reputation, and even human casualties, necessitating an appropriate security response. Therefore, this paper proposes a security authentication mechanism for safe communication in the smart factory environment. The components of the proposed authentication mechanism include smart devices, an internal operation management system, an authentication system, and a cloud storage server. The smart device registration process, authentication procedure, and the detailed design of anomaly detection and update procedures were meticulously developed. And the safety of the proposed authentication mechanism was analyzed, and through performance analysis with existing authentication mechanisms, we confirmed an efficiency improvement of approximately 8%. Additionally, this paper presents directions for future research on lightweight protocols and security strategies for the application of the proposed technology, aiming to enhance security.

• Journal of Convergence for Information Technology
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• v.11 no.4
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• pp.83-101
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• 2021

This study is to investigate, using AHP, what technologies should be applied first to Korean SMEs in order to respond to the 4th industrial revolution and change to a smart enterprise. To this end, technologies related to the 4th industrial revolution and smart factory are synthesized, and the classification criteria of Dae-Hoon Kim et al. (2019) are applied, but additional opinions of experts are collected and related technologies are converted to artificial intelligence (AI), Big Data, and Cloud Computing. As a base technology, mobile, Internet of Things (IoT), block chain as hyper-connected technology, unmanned transportation (autonomous driving), robot, 3D printing, drone as a convergence technology, smart manufacturing and logistics, smart healthcare, smart transportation and smart finance were classified as smart industrial technologies. As a result of confirming the priorities for technical use by AHP analysis and calculating the total weight, manufacturing companies have a high ranking in mobile, artificial intelligence (AI), big data, and robots, while service companies are in big data and robots, artificial intelligence (AI), and smart healthcare are ranked high, and in all companies, it is in the order of big data, artificial intelligence (AI), robot, and mobile. Through this study, it was clearly identified which technologies should be applied first in order to respond to the 4th industrial revolution and change to a smart company.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.57-64
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• 2017

Recent automobile manufacturing technology has improved not only the function and performance of cars, but also the audio systems in cars so as to increase their marketability. Automobile manufacturers always have the option of simply installing an expensive acoustic system to help customers enjoy a high-level sound quality car audio system. However, this also tends to increase the MSRP (Manufacturer's Suggested Retail Price) of the car. Therefore, it is desirable, where possible, to enhance the sound quality of plainer, less expensive audio devices to help customers feel as if they have a high-quality and expensive audio device in their car. In order to make this happen, the manufacturer must develop an optimal interior environment and audio system at a relatively lower cost. To this end, features of the car audio system can be enhanced by analyzing audio frequency response and using performance metrics to figure out the characteristics of the human auditory system. This study analyzed the sound field of Korean Recreation Vehicles (RVs) using the Internet of Things (IoT) sensor for the measurement of car audio system. As a result, high energy of sensitive bandwidth, one of the human auditory characteristics often makes annoying sound. This study also found that increasing the frequency response flatness is required by taking human auditory field into account when designing the car audio system for the future.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.4
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• pp.425-434
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• 2022

Objectives: This study analyzes TVOC concentrations in root industry workplaces in order to prevent probable occupational disease among workers. Root industry includes all the infrastructure of manufacturing, such as casting and molding. Methods: Real-time TVOC sensors were deployed in three root industry workplaces. We measured TVOC concentrations with these sensors and analyzed the results using a data-analysis tool developed with Python 3.9. Results: During the study period, the mean of the TVOC concentrations remained in an acceptable range, 0.30, 2.15, and 1.63 ppm across three workplaces. However, TVOC concentrations increased significantly at specific times, with respective maximum values of 4.98, 28.35, and 26.65 ppm for the three workplaces. Moreover, the analysis of hourly TVOC concentrations showed that during working hours or night shifts TVOC concentrations increased significantly to higher than twice the daily mean values. These results were scrutinized through classical decomposition results and autocorrelation indices, where seasonal graphs of the corresponding classical decomposition results showed that TVOC concentrations increased at a specific time. Trend graphs showed that TVOC concentrations vary by day. Conclusions: Deploying a real-time TVOC sensor should be considered to reflect irregularly high TVOC concentrations in workplaces in the root industry. It is expected that the real-time TVOC sensor with the presented data analysis methodology can eradicate probable occupational diseases caused by detrimental gases.