• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.5
• /
• pp.2680-2700
• /
• 2017

Finite field arithmetic over GF($2^m$) is used in a variety of applications such as cryptography, coding theory, computer algebra. It is mainly used in various cryptographic algorithms such as the Elliptic Curve Cryptography (ECC), Advanced Encryption Standard (AES), Twofish etc. The multiplication in a finite field is considered as highly complex and resource consuming operation in such applications. Many algorithms and architectures are proposed in the literature to obtain efficient multiplication operation in both hardware and software. In this paper, a modified serial multiplication algorithm with interleaved modular reduction is proposed, which allows for an efficient realization of a sequential polynomial basis multiplier. The proposed sequential multiplier supports multiplication of any two arbitrary finite field elements over GF($2^m$) for generic irreducible polynomials, therefore made versatile. Estimation of area and time complexities of the proposed sequential multiplier is performed and comparison with existing sequential multipliers is presented. The proposed sequential multiplier achieves 50% reduction in area-delay product over the best of existing sequential multipliers for m = 163, indicating an efficient design in terms of both area and delay. The Application Specific Integrated Circuit (ASIC) and the Field Programmable Gate Array (FPGA) implementation results indicate a significantly less power-delay and area-delay products of the proposed sequential multiplier over existing multipliers.

• Nuclear Engineering and Technology
• /
• v.50 no.1
• /
• pp.88-96
• /
• 2018

Concerns over reliability assessments of the main components in nuclear power plants (NPPs) related to aging and continuous operation have increased. The conventional reliability assessment for main components uses experimental correlations under general conditions. Most NPPs have been operating in Korea for a long time, and it is predictable that NPPs operating for the same number of years would show varying extent of aging and degradation. The conventional reliability assessment does not adequately reflect the characteristics of an individual plant. Therefore, the reliability of individual components and an individual plant was estimated according to operating data and conditions. It is essential to reflect aging as a characteristic of individual NPPs, and this is performed through prognostics. To handle this difficulty, in this paper, the general path model/Bayes, a data-based prognostic method, was used to update the reliability estimated from the generic database. As a case study, the authors consider the aging for steam generator tubes in NPPs and demonstrate the suggested methodology with data obtained from the probabilistic algorithm for the steam generator tube assessment program.

• Transactions on Control, Automation and Systems Engineering
• /
• v.3 no.1
• /
• pp.43-50
• /
• 2001

In this paper, we propose the design procedure of advance Polynomial Neural Networks(PNN) architecture for optimal model identification of complex and nonlinear system. The proposed PNN architecture is presented as the generic and advanced type. The essence of the design procedure dwells on the Group Method of Data Handling(GMDH). PNN is a flexible neural architecture whose structure is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated in a dynamic way. In this sense, PNN is a self-organizing network. With the aid of three representative numerical examples, compari-sons show that the proposed advanced PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and generalization capabilities of model is evaluated and also discussed.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.9 no.5
• /
• pp.472-479
• /
• 1999

In this paper, we propose a land cover pattern classifier for remote sensing image by using neuro-fuzzy algorithm. The proposed pattem classifier has a 3-layer feed-forward architecture that is derived from generic fuzzy perceptrons, and the weights are con~posed of h u y sets. We also implement a neuro-fuzzy pattern classification system in the Visual C++ environment. To measure the performance of this, we compare it with the conventional neural networks with back-propagation learning and the Maximum-likelihood algorithms. We classified the remote sensing image into the eight classes covered the majority of land cover feature, selected the same training sites. Experimental results show that the proposed classifier performs well especially in the mixed composition area having many classes rather than the conventional systems.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.27 no.7C
• /
• pp.670-676
• /
• 2002

The object of ATM network is to the guarantee quality of service(QoS). Therefore, various of traffic management schemes have been proposed. Among these schemes, call admission control(CAC) is very important to provide real-time services and ON-OFF model, which is single source traffic model, has been used. But ON-OFF model differ from GCRA(Generic Cell Rate Algorithm) controlled traffic in ATM network. In this paper, we analyze the traffic, which is controlled as dual GCRA, and propose TWM(Three-state Worst-case Model), which is new single source traffic model. We also proposed CAC to guarantee peak-to-peak CDV(Cell Delay Variation) based on the TWM. In experiments, ON-OFF model and TWM are compared to show that TWM is superior to ON-Off model in terms of QoS guaranteeing.

• Smart Structures and Systems
• /
• v.20 no.6
• /
• pp.669-682
• /
• 2017

The normalised version of bispectrum, the so-called bicoherence, has often proved a reliable method of damage detection on engineering applications. Indeed, higher-order spectral analysis (HOSA) has the advantage of being able to detect non-linearity in the structural dynamic response while being insensitive to ambient vibrations. Skewness in the response may be easily spotted and related to damage conditions, as the majority of common faults and cracks shows bilinear effects. The present study tries to extend the application of HOSA to damage localisation, resorting to a neural network based classification algorithm. In order to validate the approach, a non-linear finite element model of a 4-meters-long cantilever beam has been built. This model could be seen as a first generic concept of more complex structural systems, such as aircraft wings, wind turbine blades, etc. The main aim of the study is to train a Neural Network (NN) able to classify different damage locations, when fed with bispectra. These are computed using the dynamic response of the FE nonlinear model to random noise excitation.

• Journal of the Korean Solar Energy Society
• /
• v.33 no.4
• /
• pp.89-93
• /
• 2013

In recent years, many investigations about photovoltaic power systems have been significantly carried out in the fields of renewable power energy. Such research area generally includes developments of highly efficient solar cells, advanced power conversion systems, and smart monitoring systems. A generic objective of fault detection and diagnosis techniques is to timely recognize unexpected faulty of dynamic systems so that economic demage occurred by such faulty is decreased by means of engineering techniques. This paper presents a novel fault detection approach for photovoltaic power arrays which are electrically connected in series and parallels. In the proposed fault detection scheme, we first measure all of photovoltaic modules located in each array by using electronic sense systems and then compare each measurement in turn to detect location of fault module through statistic computation algorithm. We accomplish real-time experiments to demonstrate our proposed fault detection methodology by using a test-bed system including two 20 watt photovoltaic modules.

• Explosives and Blasting
• /
• v.39 no.3
• /
• pp.44-64
• /
• 2021

With the upcoming 4^th industrial revolution era, the applications of artificial intelligence(AI) and big data in engineering are increasing. In the field of blasting, there have been various reported cases of the application of AI. In this paper, AI techniques, such as artificial neural network, fuzzy logic, generic algorithm, swarm intelligence, and support vector machine, which are widely applied in blasting area, are introduced, The studies about the application of AI for the prediction of ground vibration, rock fragmentation, fly rock, air overpressure, and back break are surveyed and summarized. It is for providing starting points for the discussion of active application of AI on effective and safe blasting design, enhancing blasting performance, and minimizing the environmental impact due to blasting.

• Coupled systems mechanics
• /
• v.12 no.5
• /
• pp.409-418
• /
• 2023

A coupled algorithm is proposed which first considers the creation of porous structure of the material and then the simulations of response of mechanical components with porous structure to a variable load history. The simulations are carried out by the Prandtl operator approach in the finite element method (FEM) which enables structural simulations of mechanical components subjected to variable thermomechanical loads. Temperature-dependent material properties and multilinear kinematic hardening of the material can be taken into account by this approach. Several simulations are then performed for a tensile-compressive specimen made of a generic porous structure and mechanical properties of Aluminium alloy AlSi9Cu3. Variable mechanical load history has been applied to the specimens under constant temperature conditions. Comparison of the simulation results shows a considerable elastoplastic stress-strain response in the vicinity of pores whilst the surface of the gauge-length of the specimen remains in the elastic region of the material. Moreover, the distribution of the pore sizes seems more influential to the stress-strain field during the loading than their radial position in the gauge-length.

• The Journal of the Acoustical Society of Korea
• /
• v.22 no.7
• /
• pp.562-572
• /
• 2003

A matched-target model inversion method was developed for a passive sonar to estimate the position of moving targets. Based on the well known matched-field processing in underwater acoustics, the method finds target position by matching the measured target directions and frequencies with the corresponding values of the proposed target model. For the efficient and accurate estimations, the parameter searching was accomplished using a hybrid optimizing method, which first starts with a global optimization such as generic algorithm or simulated annealing then applies a local optimization of a simple down hill algorithm. The suggested method was testified using simulations for three different moving scenarios. The simulation results showed that the method is robust in convergence, even under the situation of over 5 times standard deviation of Gaussian distribution of measured error, and is practical in calculation time as well.