• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.10
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• pp.103-108
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• 2016

With the growing trend of pervasive computing, (the idea that technology is moving beyond personal computers to everyday devices) there is a growing demand for lightweight ciphers to safeguard data in a network that is always available. For all block cipher applications, the AES is the preferred choice. However, devices used in pervasive computing have extremely constraint environment and as such the AES will not be suitable. In this paper we design and implement a new lightweight compact block cipher that takes advantage of both S-P network and the Feistel structure. The cipher uses the S-box of PRESENT algorithm and a key dependent one stage omega permutation network is used as the cipher's P-box. The cipher is implemented on iNEXT-V6 board equipped with virtex-6 FPGA. The design synthesized to 196 slices at 337 MHz maximum clock frequency.

• Journal of Internet Computing and Services
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• v.18 no.5
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• pp.1-8
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• 2017

Lightweight Encryption Algorithm(LEA) was developed by National Security Research Institute(NSRI) in 2013 and targeted to be suitable for environments for big data processing, cloud service, and mobile. LEA specifies the 128-bit message block size and 128-, 192-, and 256-bit key sizes. In this paper, block cipher LEA algorithm which can encrypt and decrypt 128-bit messages is designed using Verilog-HDL. The designed IP for encryption and decryption has a maximum throughput of 874Mbps in 128-bit key mode and that of 749Mbps in 192 and 656Mbps in 256-bit key modes on Xilinx Vertex5. The cryptographic IP of this paper is applicable as security module of the mobile areas such as smart card, internet banking, e-commerce and IoT.

• Structural Engineering and Mechanics
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• v.41 no.4
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• pp.495-508
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• 2012

The strength theory of concrete is significant to structure design and nonlinear finite element analysis of concrete structures because concrete utilized in engineering is usually subject to the action of multi-axial stress. Experimental results have revealed that lightweight aggregate (LWA) concrete exhibits plastic flow plateau under high compressive stress and most of the lightweight aggregates are crushed at this stage. For the purpose of safety, therefore, in the practical application the strength of LWA concrete at the plastic flow plateau stage should be regarded as the ultimate strength under multi-axial compressive stress state. With consideration of the strength criterion, the ultimate strength surface of LWA concrete under multi-axial stress intersects with the hydrostatic stress axis at two different points, which is completely different from that of the normal weight concrete as that the ultimate strength surface is open-ended. As a result, the strength criteria aimed at normal weight concrete do not fit LWA concrete. In the present paper, a multi-axial strength criterion for LWA concrete is proposed based on the Unified Twin-Shear Strength (UTSS) theory developed by Prof Yu (Yu et al. 1992), which takes into account the above strength characteristics of LWA under high compressive stress level. In this strength criterion model, the tensile and compressive meridians as well as the ultimate strength envelopes in deviatoric plane under different hydrostatic stress are established just in terms of a few characteristic stress states, i.e., the uniaxial tensile strength $f_t$, the uniaxial compressive strength $f_c$, and the equibiaxial compressive $f_{bc}$. The developed model was confirmed to agree well with experimental data under different stress ratios of LWA concrete.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.6
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• pp.119-126
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• 2015

The wall system has been also tending to shift from existing concrete wall to variable lightweight wall according to increasing use of column structure system in apartment construction. Therefore, wall needs certain amount of strength which also means the standard measurement of resistance against loading of wall attachments is needed. Nevertheless, there currently aren't enough researches of related standards for such measurement. For such reason, the research would be used as baseline data to development for test methods of load resistance about attachments on the lightweight wall, that presented improvements in the apparatus and maximum loads for domestic circumstances by researching current tests.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.1-11
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• 2011

In the development phase of a torpedo, the effectiveness analysis is carried out to predict the performance and to learn how to use the torpedo. In order to obtain reliable data, it is required to model the tactical situation closely to the actual one. Because the submarine is a target of a lightweight torpedo, the anti-torpedo countermeasures of a submarine such as evasive maneuvering and TACM (Torpedo Acoustic Counter Measure) should be modeled in detail. In this paper, the evasive maneuvering is modeled reflecting the movement characteristics of the submarine. Furthermore various TACMs such as a floating-type decoy, a self-propelled decoy and jammers are also modeled. Then, effectiveness of a lightweight torpedo is measured and analyzed using the simulation program which is developed through the above modeling procedure.

• Journal of the Korean Recycled Construction Resources Institute
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• v.7 no.3
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• pp.216-223
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• 2019

The objective of this study is to propose a reliable stress-strain model in compression for lightweight concrete using bottom ash aggregates and air foam(LWC-BF). The slopes of the ascending and descending branches in the fundamental equation form generalized by Yang et al. were determined from the regression analyses of different data sets(including the modulus of elasticity and strains at the peak stress and 50% peak stress at the post-peak performance) obtained from 9 LWC-BF mixtures. The proposed model exhibits a good agreement with test results, revealing that the initial slope decreases whereas the decreasing rate in the stress at the descending branch increases with the increase in foam content. The mean and standard deviation of the normalized root-square mean errors calculated from the comparisons of experimental and predicted stress-strain curves are 0.19 and 0.08, respectively, for the proposed model, which indicates significant lower values when compared with those(1.23 and 0.47, respectively) calculated using fib 2010 model.

• Structural Engineering and Mechanics
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• v.50 no.5
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• pp.635-652
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• 2014

Creep and shrinkage behaviour of an ultra lightweight cement composite (ULCC) up to 450 days was evaluated in comparison with those of a normal weight aggregate concrete (NWAC) and a lightweight aggregate concrete (LWAC) with similar 28-day compressive strength. The ULCC is characterized by low density < 1500 $kg/m^3$ and high compressive strength about 60 MPa. Autogenous shrinkage increased rapidly in the ULCC at early-age and almost 95% occurred prior to the start of creep test at 28 days. Hence, majority of shrinkage of the ULCC during creep test was drying shrinkage. Total shrinkage of the ULCC during the 450-day creep test was the lowest compared to the NWAC and LWAC. However, corresponding total creep in the ULCC was the highest with high proportion attributed to basic creep (${\geq}$ ~90%) and limited drying creep. The high creep of the ULCC is likely due to its low elastic modulus. Specific creep of the ULCC was similar to that of the NWAC, but more than 80% higher than the LWAC. Creep coefficient of the ULCC was about 47% lower than that of the NWAC but about 18% higher than that of the LWAC. Among five creep models evaluated which tend to over-estimate the creep coefficient of the ULCC, EC2 model gives acceptable prediction within +25% deviations. The EC2 model may be used as a first approximate for the creep of ULCC in the designs of steel-concrete composites or sandwich structures in the absence of other relevant creep data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3904-3922
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• 2022

As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.23-29
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• 2022

Recently, due to the increase in the use of Internet of Things (IoT) devices, the amount of data transmitted and processed to cloud computing servers has increased rapidly. As a result, network problems (delay, server overload and security threats) are emerging. In particular, edge computing with lower computational capabilities than cloud computing requires a lightweight authentication algorithm that can easily authenticate numerous IoT devices.In this paper, we proposed a key-agreement protocol of a lightweight algorithm that guarantees anonymity and forward and backward secrecy between IoT and edge devices. and the proposed algorithm is stable in MITM and replay attacks for edge device and IoT. As a result of comparing and analyzing the proposed key-agreement protocol with previous studies, it was shown that a lightweight protocol that can be efficiently used in IoT and edge devices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1433-1449
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• 2023

In recent years, the rapid development of social networks has led to a rapid increase in the amount of information available on the Internet, which contains a large amount of sensitive information related to pornography, politics, and terrorism. In the aspect of sensitive image detection, the existing machine learning algorithms are confronted with problems such as large model size, long training time, and slow detection speed when auditing and supervising. In order to detect sensitive images more accurately and quickly, this paper proposes a multiclassification sensitive image detection method based on lightweight Convolutional Neural Network. On the basis of the EfficientNet model, this method combines the Ghost Module idea of the GhostNet model and adds the SE channel attention mechanism in the Ghost Module for feature extraction training. The experimental results on the sensitive image data set constructed in this paper show that the accuracy of the proposed method in sensitive information detection is 94.46% higher than that of the similar methods. Then, the model is pruned through an ablation experiment, and the activation function is replaced by Hard-Swish, which reduces the parameters of the original model by 54.67%. Under the condition of ensuring accuracy, the detection time of a single image is reduced from 8.88ms to 6.37ms. The results of the experiment demonstrate that the method put forward has successfully enhanced the precision of identifying multi-class sensitive images, significantly decreased the number of parameters in the model, and achieved higher accuracy than comparable algorithms while using a more lightweight model design.