• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.6
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• pp.83-88
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• 2016

Over the last five years, 568 people have died while hiking according to 2015 statistics from the public safety ministry. Among those deaths, approximately 33% were due to loss of footing or falling. In this respect, the highly advanced functions of hiking boots should be considered to prevent these unfortunate accidents. For example, by utilizing the Internet of Things (IoT) and Information and Communications Technology (ICT), hiking boots equipped with a Global Positioning System (GPS) or vital signs monitoring systems should be considered. In addition, many challenges remain for the production of 3D printed hiking boots, because the functions of hiking boots are variable, which is important when handling changing terrains and situations. The design of customized hiking boots was introduced in this paper, and 3D printing applications for midsoles using a Porous Polymer Printing (PPP) method was also suggested to verify the possibility of manufacturing hiking boots.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.41-56
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• 2019

Cloud manufacturing is a new concept of manufacturing process that works like a single factory with connected multiple factories. The cloud manufacturing system is a kind of large-scale CPS that produces products through the collaboration of distributed manufacturing facilities based on technologies such as cloud computing, IoT, and virtualization. It utilizes diverse and distributed facilities based on centralized information systems, which allows flexible composition user-centric and service-oriented large-scale systems. However, the cloud manufacturing system is composed of a large number of highly heterogeneous subsystems. It has difficulties in interconnection, data exchange, information processing, and system verification for system construction. In this paper, we derive the user requirements of various aspects of the cloud manufacturing system, such as functional, human, trustworthiness, timing, data and composition, based on the CPS Framework, which is the analysis methodology for CPS. Next, by analyzing the user requirements we define the system requirements including scalability, composability, interactivity, dependability, timing, interoperability and intelligence. We map the defined CPS system requirements to the requirements of oneM2M, which is the platform standard for IoT, so that the support of the system requirements at the level of the IoT platform is verified through Mobius, which is the implementation of oneM2M standard. Analyzing the verification result, finally, we propose a large-scale cloud manufacturing platform based on oneM2M that can meet the cloud manufacturing requirements to support the overall features of the Cloud Manufacturing CPS with dependability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.208-213
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• 2016

In the fourth industrial revolution, customization is becoming a conversation topic in various domains. Industry 4.0 applies cyber-physical systems (CPS), the Internet of Things (IoT), and cloud computing to manufacturing businesses. One of the main phrases in Industry 4.0 is mass customization. Optimized products or services are developed and provided through customization. Therefore, the competitiveness of a product can be enhanced, and satisfaction is improved. In particular, as IoT technology spreads, customization is an essential aspect of smooth service connections between various devices or things. Customized services in mobile applications are assembled and operate in various mobile devices in the mobile environment. Therefore, this paper proposes a method for improving customized cloud server-based mobile architectures, processes, and metrics, and for measuring the performance improvement of the customized architectures operating in various mobile devices based on the Android or IOS platforms. We reduce the total time required for customization in half as a result of applying the proposed customized architectures, processes, and metrics in various devices.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1175-1182
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• 2017

Recent trend in the manufacturing industry is focused on the convergence with IoT and Big Data, by emergence of the 4th Industrial Revolution. To realize a smart factory, the proposed system based on MTConnect technology collects and integrates various status information of machines from many production facilities including heterogeneous devices. Also it can distribute the acquisited status of heterogeneous manufacturing machines to the remote devices. As a key technology of a flexible automated production line, the proposed system can provide much possibility to manage important information such as error detection and processing state management in the unmanned automation line.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.147-155
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• 2016

Industry 4.0's goal is the 'Smart Factory' that integrates and controls production process, procurement, distribution and service based on the fundamental technology such as internet of the things, cyber physical system, sensor, etc. Basic requirement for successful promotion of this Industry 4.0 is the large supply of semiconductor. However, company I who produces dicing blades has difficulty to meet the increasing demand and has hard time to increase revenue because its raw material includes high price diamond, and requires very complex and sensitive process for production. Therefore, this study is focused on understanding the problems and presenting optimal plan to increase productivity of dicing blade manufacturing processes. We carried out a study as follows to accomplish the above purposes. First, previous researches were investigated. Second, the bottlenecks in manufacturing processes were identified using simulation tool (Arena 14.3). Third, we calculate investment amount according to added equipments purchase and perform economic analysis according to cost and sales increase. Finally, we derive optimum plan for productivity improvement and analyze its expected effect. To summarize these results as follows : First, daily average blade production volume can be increased two times from 60 ea. to 120 ea. by performing mixing job in the day before. Second, work flow can be smoother due to reduced waiting time if more machines are added to improve setting process. It was found that average waiting time of 23 minutes can be reduced to around 9 minutes from current process. Third, it was found through simulation that the whole processing line can compose smoother production line by performing mixing process in advance, and add setting and sintering machines. In the course of this study, it was found that adding more machines to reduce waiting time is not the best alternative.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.503-509
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• 2019

Order-revealing encryption which enables a range query over encrypted data is attracting attention as one of the important security technologies in industry such as IoT, smart manufacturing, and cloud computing. In 2015, an ideally-secure order-revealing encryption whose ciphertexts reveal no additional information beyond the order of the underlying plaintexts has been proposed. However, their construction is too inefficient for practical use and some security analysis of multilinear maps, which their construction relies on, have been proposed. Recently, more practical schemes have been proposed, focusing on achieving practically usable efficiency rather than the ideal security. In this paper, we propose a more storage-efficient order-revealing encryption scheme than the Lewi et al.'s scheme most recently published by presenting an idea that can generate shorter ciphertexts without any security loss.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.179-188
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• 2018

Recently, blockchain technology has been recognized as one of the most important issues for the 4th Industrial Revolution which can be represented by Artificial Intelligence and Internet of Things. Cryptocurrency, named Bitcoin, was the first successful implementation of blockchain, and it triggered the emergence of various cryptocurrencies. In addition, blockchain technology has been applied to various applications such as finance, healthcare, manufacturing, logistics as well as public services. Distributed consensus algorithm is an essential component in blockchain, and it enables all nodes belonging to blockchain network to make an agreement, which means all nodes have the same information. For example, Bitcoin uses a consensus algorithm called Proof-of-Work (PoW) that gives possession of block generation based on the computational volume committed by nodes. However, energy consumption for block generation in PoW has drastically increased due to the growth of computational performance to prove the possession of block. Although many other distributed consensus algorithms including Proof-of-Stake are suggested, they have their own advantages and limitations, and new research works should be proposed to overcome these limitations. For doing this, above all things, we need to establish an evaluation method existing distributed consensus algorithms. Based on this motivation, in this work, we suggest and analyze assessment items by classifying them as efficiency and safety perspectives for investigating existing distributed consensus algorithms. Furthermore, we suggest new assessment criteria and their implementation methods, which can be used for a baseline for improving performance of existing distributed consensus algorithms and designing new consensus algorithm in future.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.99-108
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• 2023

According to the COVID-19, development of various medical software based on IoT(Internet of Things) was accelerated. Especially, interest in a central software system that can remotely monitor and control ventilators is increasing to solve problems related to the continuous increase in severe COVID-19 patients. Since medical device software is closely related to human life, this study aims to develop central monitoring system that can remotely monitor and control multiple ventilators in compliance with medical device software development standards and to verify performance of system. In addition, to ensure the safety and reliability of this central monitoring system, this study also specifies risk management requirements that can identify hazardous situations and evaluate potential hazards and confirms the implementation of cybersecurity to protect against potential cyber threats, which can have serious consequences for patient safety. As a result, we obtained medical device software manufacturing certificates from MFDS(Ministry of Food and Drug Safety) through technical documents about performance verification, risk management and cybersecurity application.