• Journal of Auto-vehicle Safety Association
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• v.13 no.2
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• pp.35-41
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• 2021

In this study, improved path tracking control algorithm based on pure pursuit algorithm is newly proposed by using improved lane detection algorithm through real time post-processing with interpolation methodology. Since the original pure pursuit works well only at speeds below 20 km/h, the look-ahead distance is implemented as a sigmoid function to work well at an average speed of 45 km/h to improve tracking performance. In addition, a smoothing filter was added to reduce the steering angle vibration of the original algorithm, and the stability of the steering angle was improved. The post-processing algorithm presented has implemented more robust lane recognition system using real-time pre/post processing method with deep learning and estimated interpolation. Real time processing is more cost-effective than the method using lots of computing resources and building abundant datasets for improving the performance of deep learning networks. Therefore, this paper also presents improved lane detection performance by using the final results with naive computer vision codes and pre/post processing. Firstly, the pre-processing was newly designed for real-time processing and robust recognition performance of augmentation. Secondly, the post-processing was designed to detect lanes by receiving the segmentation results based on the estimated interpolation in consideration of the properties of the continuous lanes. Consequently, experimental results by utilizing driving guidance line information from processing parts show that the improved lane detection algorithm is effective to minimize the lateral offset error in the diverse maneuvering roads.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.3
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• pp.23-32
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• 2008

The computing power of graphics processing units(GPU) has already surpassed that of CPU and the gap between their powers is getting wider. Thus, research on GPGPU which applies GPU to general purpose becomes popular and shows great success especially in the field of parallel data processing. Since the implementation of cryptographic algorithm using GPU was started by Cook et at. in 2005, improved results using graphic libraries such as OpenGL and DirectX have been published. In this paper, we present skills and results of implementing block ciphers using CUDA library announced by NVIDIA in 2007. Also, we discuss a general method converting source codes of block ciphers on CPU to those on GPU. On NVIDIA 8800GTX GPU, the resulting speeds of block cipher AES, ARIA, and DES are 4.5Gbps, 7.0Gbps, and 2.8Gbps, respectively which are faster than the those on CPU.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.6A
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• pp.3-15
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• 2008

In this paper we study exponentiation in finite fields $F{_p}{^{k}}$(k is odd) with very special exponents such as they occur in algorithms for computing square roots. Our algorithmic approach improves the corresponding exponentiation independent of the characteristic of $F{_p}{^{k}}$. To the best of our knowledge, it is the first major improvement to the Tonelli-Shanks algorithm, for example, the number of multiplications can be reduced to at least 60% on average when $p{\equiv}1$ (mod 16). Several numerical examples are given that show the speed-up of the proposed methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.5
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• pp.105-118
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• 2009

In modern society, as the rapid development of IT technology combined with the computer-based high-speed communication networks makes it possible to provide a wide spectrum of services and devices, we have been confronting a new cultural transformation era, referred to as the information society. However, the requirements to be considered in security aspect have became more complicated and diversified, and there remains the same security weaknesses as in the existing media or protocol. Particularly, the office network device with roaming is susceptible to the different kinds of attacks such as terminal hacking, virus attacks, and information leakage because the computing capacity is relatively low and the loading of already developed security functions is difficult. Although developed as one solution to this problems, PKI security authentication technology isn't suitable for multi-domain environments providing uonments proffice network service, and so the development of a novel authentication system is needed. Therefore, in this paper researched the roaming and device authentication/auth for multitechnology using an ID-based public key, authorization ticket, and Sub-device ticket with a purpose to contribute to the development of the secured and efficient technology.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.3
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• pp.111-117
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• 2003

The XTR public key system has advantage of short key length and fast computing speed. So, the XTR is used usefully in complicated operation. In this paper, we propose a new algorithm of double exponentiation operation and a proxy signature protocol based on the XTR. The double exponentiation operation should be executed to apply XTR for the proxy signature protocol. But this algorithm is inappropriate, because two secret key has to be blown in existent operation algorithm. New algorithm enable double exponentiation operation with proxy signer's secret key and public information. And the XTR is used to generation and verification of proxy at proxy signature protocol. Therefore proxy signature based on the XTR has basic advantage of the XTR. These advantage can be used in internet as well as mobile.

• Advances in nano research
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• v.10 no.2
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• pp.151-163
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• 2021

The main target of this study is to investigate nonlinear transient responses of moving polymer nano-size plates fortified by means of Graphene Platelets (GPLs) and resting on a Winkler-Pasternak foundation under a transverse pressure force and a temperature variation. Two graphene spreading forms dispersed through the plate thickness are studied, and the Halpin-Tsai micro-mechanics model is used to obtain the effective Young's modulus. Furthermore, the rule of mixture is employed to calculate the effective mass density and Poisson's ratio. In accordance with the first order shear deformation and von Karman theory for nonlinear systems, the kinematic equations are derived, and then nonlocal strain gradient scheme is used to reflect the effects of nonlocal and strain gradient parameters on small-size objects. Afterwards, a combined approach, kinetic dynamic relaxation method accompanied by Newmark technique, is hired for solving the time-varying equation sets, and Fortran program is developed to generate the numerical results. The accuracy of the current model is verified by comparative studies with available results in the literature. Finally, a parametric study is carried out to explore the effects of GPL's weight fractions and dispersion patterns, edge conditions, softening and hardening factors, the temperature change, the velocity of moving nanoplate and elastic foundation stiffness on the dynamic response of the structure. The result illustrates that the effects of nonlocality and strain gradient parameters are more remarkable in the higher magnitudes of the nanoplate speed.

• Smart Media Journal
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• v.10 no.1
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• pp.25-31
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• 2021

As the machine learning becomes more common, development of application using machine learning is actively increasing. In addition, research on machine learning platform to support development of application is also increasing. However, despite the increasing of research on machine learning platform, research on suitable load balancing for machine learning platform is insufficient. Therefore, in this paper, we propose a load balancing scheme that can be applied to machine learning distributed environment. The proposed scheme composes distributed servers in a level hash table structure and assigns machine learning task to the server in consideration of the performance of each server. We implemented distributed servers and experimented, and compared the performance with the existing hashing scheme. Compared with the existing hashing scheme, the proposed scheme showed an average 26% speed improvement, and more than 38% reduced the number of waiting tasks to assign to the server.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.406-412
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• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.87-93
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• 2022

Since 99% of PCs operating in the defense domain use the Windows operating system, detection and response of Window-based malware is very important to keep the defense cyberspace safe. This paper proposes a model capable of detecting malware in a Windows PE (Portable Executable) format. The detection model was designed with an emphasis on rapid update of the training model to efficiently cope with rapidly increasing malware rather than the detection accuracy. Therefore, in order to improve the training speed, the detection model was designed based on a Bidirectional LSTM (Long Short Term Memory) network that can detect malware with minimal sequence data without complicated pre-processing. The experiment was conducted using the EMBER2018 dataset, As a result of training the model with feature sets consisting of three type of sequence data(Byte-Entropy Histogram, Byte Histogram, and String Distribution), accuracy of 90.79% was achieved. Meanwhile, it was confirmed that the training time was shortened to 1/4 compared to the existing detection model, enabling rapid update of the detection model to respond to new types of malware on the surge.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.67-76
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• 2022

Power and signal transmission using electromagnetic waves are essential in modern times, and a guided structure is needed to transmit electromagnetic waves efficiently through the desired path. This paper performed an electromagnetic simulation using the in-house code for the 2-D/3-D waveguide using the finite element method. The accuracy of the analysis was verified by comparing it with the results of HFSS, a representative electromagnetic wave simulation software. In addition, the performance of the Absorbing Boundary Condition (ABC), which is essential to truncate the infinite computational domain for computational electromagnetics, was analyzed. Finally, the parallelization technique was applied to accelerate the simulation speed, demonstrating performance improvement.