• Journal of Internet Computing and Services
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• v.18 no.2
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• pp.53-60
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• 2017

Since processors are handling inputs and outputs independently on distributed memory computers, different file input/output methods are used. In this paper, we implemented and compared various file I/O methods to show their efficiency on distributed memory parallel computers. The implemented I/O systems are as following: (i) parallel I/O using NFS, (ii) sequential I/O on the host processor and domain decomposition, (iii) MPI-IO. For performance analysis, we used a separated file server and multiple processors on one or two computational servers. The results show the file I/O with NFS for inputs and sequential output with domain composition for outputs are best efficient respectively. The MPI-IO result shows unexpectedly the lowest performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.6
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• pp.125-134
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• 2004

When cryptosystem designers implement devices that computing power or memory is limited such as smart cards, PDAs and so on, not only he/she has to be careful side channel attacks(SCA) but also the cryptographic algorithms within the device has to be efficient using small memory. For this purpose, countermeasures such as Moiler's method, Okeya-Takagi's one and overlapping window method, based on window method to prevent SCA were proposed. However, Moiler's method and Okeya-Talngi's one require additional cost to prevent other SCA such as DPA, Second-Order DPA, Address-DPA, and so on since they are immune to only SPA. Also, overlapping window method has a drawback that requires big memory. In this paper, we analyze existing countermeasures and propose an efficient and secure countermeasure that is immune to all existing SCA using advantages of each countermeasure. Moreover, the proposed countermeasure can enhance the efficiency using mixed coordinate systems.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.443-451
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• 2023

Machine Learning has introduced many solutions in data science, but its application in IoT faces significant challenges, due to the limitations in memory size and processing capability of constrained devices. In this paper we design an automatic gamma radiation detection and identification embedded system that exploits the power of TinyML in a SiPM micro radiation sensor leveraging the Edge Impulse platform. The model is trained using real gamma source data enhanced by software augmentation algorithms. Tests show high accuracy in real time processing. This design has promising applications in general-purpose radiation detection and identification, nuclear safety, medical diagnosis and it is also amenable for deployment in small satellites.

• The Journal of the Korea Contents Association
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• v.15 no.9
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• pp.21-28
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• 2015

In the semi-conductor process, a simulation process is performed to detect defects by analyzing the behavior of the impurity through the physical quantity calculation of the inner element. In order to perform the simulation, Finite-Difference Time-Domain(FDTD) algorithm is used. The improvement of semiconductor which is composed of nanoscale elements, the size of simulation is getting bigger. Problems that a processor such as CPU or GPU cannot perform the simulation due to the massive size of matrix or a computer consist of multiple processors cannot handle a massive FDTD may come up. For those problems, studies are performed with parallel/distributed computing. However, in the past, only single type of processor was used. In GPU's case, it performs fast, but at the same time, it has limited memory. On the other hand, in CPU, it performs slower than that of GPU. To solve the problem, we implemented a computing model that can handle any FDTD simulation regardless of size on the cluster which consist of heterogeneous processors. We tested the simulation on processors using MPI libraries which is based on 'point to point' communication and verified that it operates correctly regardless of the number of node and type. Also, we analyzed the performance by measuring the total execution time and specific time for the simulation on each test.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.5
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• pp.69-79
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• 2010

In the current cloud environments, the applications are executed on the remote cloud server, and they also utilize computing resources of the remote cloud server such as physical memory and CPU. Therefore, if remote server is exposed to security threat, every applications in remote server can be victim by several security-attacks. Especially, despite many advantages, both individuals and businesses often have trouble to start the cloud services according to the malicious administrator of the cloud server. We propose a security-enhanced local process executing scheme resolving vulnerability of current cloud computing environments. Since secret data is stored in the local, we can protect secret data from security threats of the cloud server. By utilizing computing resource of local computer instead of remote server, high-secure processes can be set free from vulnerability of remote server.

• The Journal of the Acoustical Society of Korea
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• v.17 no.3E
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• pp.35-42
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• 1998

Transient acoustic and elastic wave propagation in inhomogeneous media are studied by using the Fourier method. It is known that the fourier method has advantages in memory requirements and computing speed over conventional methods such as FDM and FEM, because the Fourier method needs less grid points for achieving the same accuracy. To verify the proposed numerical scheme, several examples having analytic solutions are considered, where two different semi-infinite media are in contact along a plane boundary. The comparisons of numerical results by the Fourier method and analytic solutions show good agreements. In addition, the fourier method is applied to a layered half-plane, in which an elastic semi-infinite medium is covered by an elastic layer of finite thickness. It is showed how to derive the analytic solutions by using the Cagniard-de Hoop method. The numerical solutions are in excellent agreements with analytic results.

• International Journal of Contents
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• v.2 no.2
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• pp.29-31
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• 2006

Mobile Ad hoc network is a self configuring network of mobile nodes. It allows mobile nodes to configure network spontaneously and share their services. In these networks, service discovery is very important because all nodes do not have same resources in term of memory and computing power. Nodes need to use different services offered by different servers. Some service discovery protocols have been proposed in last couple of years but they include significant traffic overhead and for small scale MANETS. In this paper, we present extensible lookup service scheme based on distributed mechanism. In our scheme neighboring nodes of service provider monitor service provider and send notification to lookup server when the service provider terminates its services unexpectedly. Lookup server can find other service provider or other alternative services in advance because of advance notification method and can provide consistent lookup services. In our scheme neighboring nodes also monitor lookup server and send notification to network when lookup server terminates unexpectedly. Simulation results show that our scheme can reduce up to 70% and 30% lookup failure as compare to centralize and simple distributed mechanism respectively.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.79-84
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• 2016

In this paper, we propose reliable message transmission scheme based on CoAP, considering the constrained feature of IoT device, such as low power, the limited memory size and low computing capacity. Recently, the various kinds of application protocol has been studied to support IoT environments. In particular, CoAP protocol was developed as application protocol for IoT at the IETF core WG. However, because CoAP protocol is deigned to be used in constrained node, this protocol uses UDP at transport layer. Thus, data loss may occur frequently in network congestion environments. The proposed scheme, in this paper, is to overcome the problem of frequent data loss with low overhead. Also it includes the function which is to minimize the data loss in sleep mode of IoT device.

• International Journal of Computer Science & Network Security
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• v.24 no.10
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• pp.109-114
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• 2024

Wireless Sensor Networks (WSN) includes a large number of small sensor nodes and low cost, which are randomly located in a region. The wireless sensor network has attracted much attention from universities and industry around the world over the past decades, with features denser levels of node deployment, self-configuration, uncertainty of sensor nodes, computing, and memory constraints. Black-hole attack is one of the most known attacks on this network. In this study, the combination of fuzzy logic and particle swarm optimization (PSO) algorithms is proposed as an effective method for detecting black-hole attack in the AODV protocol. In the current study, a new function has been proposed in order to determine the membership of fuzzy parameters based on the particle swarm optimization algorithm. The proposed method was evaluated in different scenarios and was compared with other state of arts. The simulation result of this method proved the better performance in both detection rate and delivered packet rate.

• 전기의세계
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• v.23 no.3
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• pp.43-52
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• 1974

The matrix inversion is very inefficient for computing direct solutions of the large spare systems of linear equations that arise in many network problems as a large electrical power system. Optimally ordered triangular factorization of sparse matrices is more efficient and offers the other important computational advantages in some applications with this method. The direct solutions are computed from sparse matrix factors instead of a full inverse matrix, thereby gaining a significant advantage is speed and computer memory requirements. In this paper, it is shown that the sparse matrix method is superior to the inverse matrix method to solve the linear equations of large sparse networks. In addition, it is shown that the sparse matrix method is superior to the inverse matrix method to solve the linear equations of large sparse networks. In addition, it is shown that the solutions may be applied directly to sove the load flow in an electrical power system. The result of this study should lead to many aplications including short circuit, transient stability, network reduction, reactive optimization and others.