• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.5
• /
• pp.751-763
• /
• 2022

With the recent increase in spending growth in the IoT sector worldwide, the importance of lightweight block ciphers to encrypt them is also increasing. The lightweight block cipher PIPO algorithm proposed in ICISC 2020 is an SPN-structured cipher using an unbalanced bridge structure. The white box attack model refers to a state in which an attacker may know the intermediate value of the encryption operation. As a technique to cope with this, Chow et al. proposed a white box implementation technique and applied it to DES and AES in 2002. In this paper, we propose a white box PIPO applying a white box implementation to a lightweight block cipher PIPO algorithm. In the white box PIPO, the size of the table decreased by about 5.8 times and the calculation time decreased by about 17 times compared to the white box AES proposed by Chow and others. In addition, white box PIPO was used for mobile security products, and experimental results for each test case according to the scope of application are presented.

• Nuclear Engineering and Technology
• /
• v.27 no.5
• /
• pp.701-709
• /
• 1995

The realistic discharge coefficient for the critical How model of RELAP5/AOD3/KAERI are determined for the subcooled and too-phase critical flow by assessments of nine MARVIKEN Critical flew Test(CFT). The selected test runs include a high initial subcooling and large nozzle aspect rat-io(L/D). The code assessment results show that RELAP5/MOD3/KAERI over-predicts the subcooled critical flow and under-predicts the two-phase critical flow. Using these result, the realistic discharge coefficients of critical flow models are quantified by an iterative method. The realistic discharge coefficients are determined to be 0.89 for the subcooled critical How and 1.07 for the two-phase critical flow, and the associated standard deviations are 0.0349 and 0.1189, respectively. The results obtained from this study can be applied to calculate the realistic system response of Large Break Loss of Coolant Accident and to evaluate the realistic Emergency Core Cooling System performance.

• Journal of IKEEE
• /
• v.26 no.1
• /
• pp.62-66
• /
• 2022

Recently, there have been many research efforts based on data-based deep learning technologies to deal with the interference problem between heterogeneous wireless communication devices in unlicensed frequency bands. However, existing approaches are commonly based on the use of complex neural network models, which require high computational power, limiting their efficiency in resource-constrained network interfaces and Internet of Things (IoT) devices. In this study, we address the problem of classifying heterogeneous wireless technologies including Wi-Fi and ZigBee in unlicensed spectrum bands. We focus on a data-driven approach that employs a supervised-learning method that uses received signal strength indicator (RSSI) data to train Deep Convolutional Neural Networks (CNNs). We propose a simple measurement methodology for collecting RSSI training data which preserves temporal and spectral properties of the target signal. Real experimental results using an open-source 2.4 GHz wireless development platform Ubertooth show that the proposed sampling method maintains the same accuracy with only a 10% level of sampling data for the same neural network architecture.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.328-329
• /
• 2021

Newly, the fourth industrial revolution is a way of describing the blurring of boundaries between the physical, digital, and biological worlds. It's a fusion of advances in AI (artificial intelligence), robotics, the IoT (Internet of Things), 3d printing, genetic engineering, quantum computing, and other technologies. At the world economic forum in Davos, switzerland, in january 2016, chairman professor klaus schwab proposed the fourth industrial revolution for the first time. In order to apply the AHP (analytic hierarchy process) analysis method, the first stage factors were designed as Natural, Water, Earth and Atom energy. In addition, the second stage factors were organized into 9 detailed energies presented in the conceptual model. Thus, we present the theoretical and practical implications of these results.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2020.07a
• /
• pp.577-578
• /
• 2020

본 연구의 목적은 흰점박이꽃무지 사육농가 및 사육환경에 대해 일반농가의 실태를 조사·분석하여 식용곤충 사육시설에 대한 기초 연구자료를 수집하기 위한 것이다. 전국에 곤충사육 농가로 등록한 업체 중에서 흰점박이꽃무지를 사육하는 농가 17농가를 대상으로 설문조사를 실시하여 농장운영 및 시설·장치 현황 등을 조사하였으며, 주요 결과는 다음과 같다. 일반적으로 곤충사육 농가는 대부분 농가형으로 운영되고 있지만, 생산업, 유통업 및 가공업 등으로 신고하여 곤충사육 농가에서 직접 생산, 가공 및 제품 개발 등을 하고 있는 것으로 나타났다. 그리고 대부분의 곤충사육 농가는 판넬 형식의 건축물 내에서 냉·난방기를 가동하여 곤충의 생육환경을 조성하고 있으며, 필요에 따라 IoT 기반의 사양관리 장치 및 운영관리 시스템을 활용하고 있는 것으로 파악되었는데, 식용곤충 대량생산을 위한 사양관리 장치 및 생산 기반시설 구축은 여전히 부족한 상황으로 개선이 필요해 보인다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.51-51
• /
• 2020

본 연구에서는 기수역에서의 GPS 전자부자를 활용한 계측 데이터와 염분측정 데이터를 비교분석하여 GPS 전자부자의 거동 특성이 담수의 실제 거동특성과 같이 일치하게 나타나는지 확인하고, GPS 전자부자에 대한 활용성을 증명하고자 하였다. 기수역에서의 담수와 해수의 분포를 확인하기 위해서는 많은 인력과 장비를 소모하며 수리량과 염분도에 대한 측정을 통해 분석을 할 수 있는데, 대표적인 수리량 측정장비인 ADCP 또는 ADV는 각종 측정 한계들이 존재한다. 따라서 이러한 기수역에서의 수체 구간별 흐름 특성이 어떻게 나타나는지 확인하기 위해서 GPS 전자부자를 활용하고자 하였으며. GPS 전자부자의 데이터와 수직방향으로 측정한 염분도 데이터와 비교하고자 하였다. 수영강 좌수영교 상류 1km지점에서 주입하였으며 간조 시와 만조 시에 유하시켜 수영강에 위치한 좌수영교와 수영교에서 각각 좌측, 중간, 우측에서 수심방향으로 측정한 염분도를 상층 중층 하층으로 평균하여 비교하였다. 본 연구에서 사용한 GPS 전자부자는 표면부자이기 때문에 만조 시 하류방향으로 더 뻗어 나가는것으로 확인하였고, 간조 시에는 흐름이 더디거나 정체하는 것으로 나타났다. 이러한 계측 결과는 담수의 흐름 특성과 일치하게 나타나는 것을 확인하였고 이러한 GPS Floater를 활용한다면 굳이 각종 수질을 측정하지 않아도 흐름 상황으로 간조인지 만조인지에 대한 상황과 염분 분포를 대략적으로 알 수 있다. 이러한 GPS 전자부자의 위치데이터와 시간을 잘 이용한다면 ADCP의 유속 측정에 대한 단점을 보완할 수 있는 Lagrangian 타입의 유속을 측정 할 수 있다. 이러한 GPS 전자부자를 하천에서 선주입으로 활용한다면 각 구간별로 수체에 대한 흐름과 유속들을 분석할 수 있을 것으로 사료된다. 또한 GPS 전자부자를 좀더 활용하여 IoT 기반 수질센서 등과 같은 각종 센서가 GPS Floater에 결합이 된다면, 흘러가면서 각종 데이터 정보취득을 통해 수체내 흐름을 분석하는 것뿐만 아니라 수환경 유출오염사고들에 대한 초기대응에 많은 도움이 될 것으로 사료 된다.

• Smart Media Journal
• /
• v.12 no.10
• /
• pp.47-54
• /
• 2023

As understanding sudden climate change and agricultural productivity becomes increasingly important due to global warming, soil moisture prediction is emerging as a key topic in agriculture. Soil moisture has a significant impact on crop growth and health, and proper management and accurate prediction are key factors in improving agricultural productivity and resource management. For this reason, soil moisture prediction is receiving great attention in agricultural and environmental fields. In this paper, we collected and analyzed open field environmental data using a pilot field through random forest, a machine learning algorithm, obtained the correlation between data characteristics and soil moisture, and compared the actual and predicted values of soil moisture. As a result of the comparison, the prediction rate was about 92%. It was confirmed that the accuracy was . If soil moisture prediction is carried out by adding crop growth data variables through future research, key information such as crop growth speed and appropriate irrigation timing according to soil moisture can be accurately controlled to increase crop quality and improve productivity and water management efficiency. It is expected that this will have a positive impact on resource efficiency.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.6
• /
• pp.175-182
• /
• 2024

In wireless sensor networks, batteries limit lifespan, and heavy data transmission around the sink causes the hotspot problem. To address this, data collection amounts are allocated to child nodes to limit transmission. However, this approach has issues with nodes far from the sink having excessive energy and failing to transmit the allocated amount due to data transmission errors. This paper proposes a method to prevent sensor data loss through error recovery via retransmission. The method ensures that each node's retransmission volume stays within its allocated data amount and energy limits, using excess energy for error recovery. Simulations show that this technique effectively recovers data transmission errors, collects data, minimizes energy depletion around the sink, and increases data collection rates.

• Information Systems Review
• /
• v.21 no.3
• /
• pp.87-110
• /
• 2019

The Fintech services provide innovation to financial services users using various mobile devices and computers in wired and wireless communication environments. In this study, we develope a theoretical research framework to explain the psychology of Fintech services users based on a cognitive, affective, and conative framework. Using this framework, this study analyzes the relationships between the cognitive characteristics (i.e., innovation, relative advantage, ease of use, and usefulness), emotional characteristic (i.e., attitude), and behavioral characteristic (i.e., actual use) toward Fintech services users. This study conducted an online survey of people who have experienced using Fintech services. And the data of the collected Fintech services users was analyzed using structural equation model software (i.e., SMART PLS 2.0 M3). The results of the empirical analysis show the relationships between innovation, relative advantage, perceived usefulness, perceived ease of use, attitude, and actual use of Fintech service users. The results of this study provide useful information to improve the practical use of Fintech services users in the Internet of Things (IoT) environment.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.3
• /
• pp.107-116
• /
• 2024

In this paper, we analyze the performance of MicroVM when running AI applications on an edge computing environment and whether it can replace current container technology and traditional virtual machines. To achieve this, we set up Docker container, Firecracker MicroVM and KVM virtual machine environments on a Raspberry Pi 4 and executed representative AI applications in each environment. We analyze the inference time, total CPU usage and trends over time and file I/O performance on each environment. The results show that there is no significant performance difference between MicroVM and container when running AI applications. Moreover, on average, a stable inference time over multiple trials was observed on MicroVM. Therefore, we can confirm that executing AI applications using MicroVM instead of container or heavy-weight virtual machine is suitable for an edge computing.