• Journal of the Korea Institute of Information Security & Cryptology
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• v.11 no.1
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• pp.13-23
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• 2001

This paper implemented into hardware SEED which is the KOREA standard 128-bit block cipher. First, at the respect of hardware implementation, we compared and analyzed SEED with AES finalist algorithms - MARS, RC6, RIJNDAEL, SERPENT, TWOFISH, which are secret key block encryption algorithms. The encryption of SEED is faster than MARS, RC6, TWOFISH, but is as five times slow as RIJNDAEL which is the fastest. We propose a SEED hardware architecture which improves the encryption speed. We divided one round into three parts, J1 function block, J2 function block J3 function block including key mixing block, because SEED repeatedly executes the same operation 16 times, then we pipelined one round into three parts, J1 function block, J2 function block, J3 function block including key mixing block, because SEED repeatedly executes the same operation 16 times, then we pipelined it to make it more faster. G-function is implemented more easily by xoring four extended 4 byte SS-boxes. We tested it using ALTERA FPGA with Verilog HDL. If the design is synthesized with 0.5 um Samsung standard cell library, encryption of ECB and decryption of ECB, CBC, CFB, which can be pipelined would take 50 clock cycles to encrypt 384-bit plaintext, and hence we have 745.6 Mbps assuming 97.1 MHz clock frequency. Encryption of CBC, OFB, CFB and decryption of OFB, which cannot be pipelined have 258.9 Mbps under same condition.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.2
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• pp.3-10
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• 2008

The choice of basis for representation of element in $GF(2^m)$ affects the efficiency of a multiplier. Among them, a multiplier using redundant representation efficiently supports trade-off between the area complexity and the time complexity since it can quickly carry out modular reduction. So time of a previous multiplier using redundant representation is faster than time of multiplier using others basis. But, the weakness of one has a upper space complexity compared to multiplier using others basis. In this paper, we propose a new efficient multiplier with consideration that polynomial exponentiation operations are frequently used in cryptographic hardwares. The proposed multiplier is suitable fer left-to-right exponentiation environment and provides efficiency between time and area complexity. And so, it has both time delay of $T_A+({\lceil}{\log}_2m{\rceil})T_x$ and area complexity of (2m-1)(m+s). As a result, the proposed multiplier reduces $2(ms+s^2)$ compared to the previous multiplier using equally-spaced polynomials in area complexity. In addition, it reduces $T_A+({\lceil}{\log}_2m+s{\rceil})T_x$ to $T_A+({\lceil}{\log}_2m{\rceil})T_x$ in the time complexity.($T_A$:Time delay of one AND gate, $T_x$:Time delay of one XOR gate, m:Degree of equally spaced irreducible polynomial, s:spacing factor)

• Environmental and Resource Economics Review
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• v.30 no.2
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• pp.299-323
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• 2021

This study investigated the impact of information and communication technology (ICT), trade openness, financial development, and economic growth on CO₂ emissions in Korea from 1990 to 2016. The cointegration relationship of the variables was confirmed by an autoregressive distributed lag (ARDL) bounds test. In the long-run, economic growth was statistically significant factor in the increase in CO₂ emissions, while other factors, as well as ICT, did not significant factors in the changes in CO₂ emissions. In the long-run, a link between economic growth and CO₂ emissions has been confirmed, but other factors, including ICT, have not been able to confirm the link between CO₂ emissions in the long-run. Meanwhile, in the short-run, economic growth and ICT increased CO₂ emissions, and financial development led to a decrease in CO₂ emissions. Trade openness did not have a significant effect on CO₂ emissions in the short-run as in the long-run. In particular, ICT did not contribute to the reduction of CO₂ emissions in the short-run as well as the long-run. In order to induce CO₂ mitigation through ICT, the development and deployment of technology that efficiently save energy by using ICT should be further promoted.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.115-126
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• 2021

Central banks communicate with the market through a statement on the direction of monetary policy while implementing monetary policy. The rapid contraction of the global economy due to the recent Covid-19 pandemic could be compared to the crisis situation during the 2008 global financial crisis. In this paper, we analyzed the text data from the monetary policy statements of the Bank of Korea and Fed reflecting monetary policy directions focusing on how they were affected in the face of a global crisis. For analysis, we collected the text data of the two countries' monetary policy direction reports published from October 1999 to September 2020. We examined the semantic features using word cloud and word embedding, and analyzed the trend of the similarity between two countries' documents through a piecewise regression tree model. The visualization result shows that both the Bank of Korea and the US Fed have published the statements with refined words of clear meaning for transparent and effective communication with the market. The analysis of the dissimilarity trend of documents in both countries also shows that there exists a sense of synchronization between them as the rapid changes in the global economic environment affect monetary policy.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.640-645
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• 2021

The deep learning based end-to-end TTS system consists of Text2Mel module that generates spectrogram from text, and vocoder module that synthesizes speech signals from spectrogram. Recently, by applying deep learning technology to the TTS system the intelligibility and naturalness of the synthesized speech is as improved as human vocalization. However, it has the disadvantage that the inference speed for synthesizing speech is very slow compared to the conventional method. The inference speed can be improved by applying the non-autoregressive method which can generate speech samples in parallel independent of previously generated samples. In this paper, we introduce FastSpeech, FastSpeech 2, and FastPitch as Text2Mel technology, and Parallel WaveGAN, Multi-band MelGAN, and WaveGlow as vocoder technology applying non-autoregressive method. And we implement them to verify whether it can be processed in real time. Experimental results show that by the obtained RTF all the presented methods are sufficiently capable of real-time processing. And it can be seen that the size of the learned model is about tens to hundreds of megabytes except WaveGlow, and it can be applied to the embedded environment where the memory is limited.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.104-112
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• 2021

For extrusion processes in the process industry, the need to build smart factories is increasing. However, in most extrusion production sites, the production method is continuous, and because the properties of the data are undeed, it is difficult to process the data. In order to solve this problem, we present a methodology utilizing a near field communication (NFC) sensor rather than water-based data entry. To this end, a wireless network environment was built, and a data management method was designed. A non-contact NFC method was studied for the production performance-data input method, and an analysis method was implemented using the pivot function of the Excel program. As a result, data input using NFC was automated, obtaining a quantitative effect from reducing the operator's data processing time. In addition, using the input data, we present a case where a bottleneck is improved due to quality problems.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.245-256
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• 2021

The Fourth Industrial Revolution brought the quantitative value of data across the industry and entered the era of 'Big Data'. This is due to both the rapid development of information & communication technology and the diversity & complexity of customer purchasing tendencies. An enterprise's core competence in the Big Data Era is to analyze and utilize the data to make strategic decisions for enterprise. However, most of traditional studies on Big Data have focused on technical issues and future potential values. In addition, these studies lacked interest in managing the quality and utilization levels of internal & external customer Big Data held by the entity. To overcome these shortages, this study attempted to derive influential factors by recognizing the quality management information systems and quality management of the internal & external Big Data. First of all, we conducted a survey of 204 executives & employees to determine whether Big Data quality management, Big Data utilization, and level management have a significant impact on corporate work efficiency & corporate management performance. For the study for this purpose, hypotheses were established, and their verifications were carried out. As a result of these studies, we found that the reasons that significantly affect corporate management performance are support from the management class, individual innovation, changes in the management environment, Big Data quality utilization metrics, and Big Data governance system.

• Journal of Advanced Navigation Technology
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• v.26 no.3
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• pp.172-178
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• 2022

In this paper, water meter AMR(automatic meter reading), one of the core technologies of smart water grid, using LoRa IoT network is studied. The main content of the research is to develop the network system and show the test results that one PC server receives the readings of water meters from multiple households through LoRa communication and stores them in the database, and at the same time sends the data to the web server database through internet. The system also allows users to monitor the meter readings using their smartphones. The hardware and firmware of the main board of the digital water meter are developed. For a PC server program, MDMS(meter data management system) is developed using Visual C#. The app program running on the user's smartphone is also developed using Android Studio. By connecting each developed parts, the total network system is mounted on a flow test bench in the laboratory and tested. For the fields test, 5 places around the university are selected and the transmission distances are tested. The test result show that the developed system can be applied into the real field. The developed system can be expanded to various social safety nets such as monitoring the living alone or elderly with dementia.

• Journal of Korea Foundry Society
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• v.41 no.6
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• pp.550-557
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• 2021

With the advent of the fourth industrial revolution, the interest in the internet of things (IoT) in manufacturing is growing, even at foundries. There are several types of process data that can be automatically collected at a foundry, but considerable amounts of process data are still managed based on handwriting for reasons such as the limited functions of outdated production facilities and process design based on operator know-how. In particular, despite recognizing the importance of converting process data into big data, many companies have difficulty adopting these steps willingly due to the burden of system construction costs. In this study, the field applicability of IoT-based devices was examined by manufacturing devices and applying them directly to the site of a centrifugal foundry. For the centrifugal casting process, the temperature and humidity of the working site, the molten metal temperature, and mold rotation speed were selected as process parameters to be collected. The sensors were selected in consideration of the detailed product specifications and cost required for each process parameter, and the circuit was configured using a NodeMCU board capable of wireless communication for IoT-based devices. After designing the circuit, PCB boards were prepared for each parameter, and each device was installed on site considering the working environment. After the on-site installation process, it was confirmed that the level of satisfaction with the safety of the workers and the efficiency of process management increased. Also, it is expected that it will be possible to link process data and quality data in the future, if process parameters are continuously collected. The IoT-based device designed in this study has adequate reliability at a low cast, meaning that the application of this technique can be considered as a cornerstone of data collecting at foundries.