• Journal of Korea Multimedia Society
• /
• v.11 no.6
• /
• pp.755-763
• /
• 2008

Silhouette is useful in computer graphics for a number of techniques such as non-photorealistic rendering, silhouette clipping, and blueprint generating. Methods for generating silhouette are classified into three categories: image-based, object-based, and hybrid-based. Hybrid-based method is effective in terms of time complexity but spatial coherence problem still remains. In this paper, we proposed a new hybrid-based method which produces 3D data for silhouette and also guarantees no spatial coherence problem. To verify the efficiency of the proposed algorithm, several experiments are conducted for various 3D models from simple to quite complex. Results show that our algorithm generates no gap between any two consecutive silhouette lines when the silhouette model is magnified significantly.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 1998.10a
• /
• pp.913-928
• /
• 1998

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.19 no.5
• /
• pp.454-461
• /
• 2009

Internal aeroacoustics of an axial fan used for circulating cold air in refrigerators are computed by using the hybrid method where CFD, acoustic analogy and BEM techniques are utilized. The unsteady flow field around the axial fan is predicted by solving the incompressible RANS equations with the conventional CFD techniques. Then, main noise sources are extracted from this unsteady flow field predictions using Acoustic Analogy. Lastly, BPF noise generated from an axial fan are predicted using these modeled sources combined with the tailed Green function techniques, which are numerically solved by the BEM technique. This hybrid model is validated by comparing the prediction with the experiment. Then, parameter studies are carried out, which suggest a capability of the current method as a design tool for the low-noise of the current axial fan system in a refrigerator.

• IEIE Transactions on Smart Processing and Computing
• /
• v.3 no.4
• /
• pp.181-190
• /
• 2014

This paper reports a cognitive radio network architecture based on overloaded multicarrier direct sequence code division multiple access (O-MC-DS-CDMA). The O-MC-DSCDMA technique combines CDMA with a multicarrier modulation technique to overcome the channel fading effects. In this technique, secondary users are enabled to share the available bandwidth with the existing primary users. Two sets of orthogonal Gold codes are used to support the primary and secondary users simultaneously. The orthogonality between the spreading codes is lost due to the non-zero cross correlation between the codes and the timing synchronization error in the uplink transmission, which causes interference between primary and secondary users. This paper proposes two modified hybrid parallel/successive interference cancellation techniques for primary and secondary user base station receivers with multiple antennas to suppress the interference among users. Interference among the same group of users is cancelled by parallel interference cancellation and the interference among groups is cancelled using successive interference cancellation. The simulation results confirmed that the proposed modified interference cancellation techniques show better BER performance over conventional interference cancellation techniques.

• Nuclear Engineering and Technology
• /
• v.46 no.6
• /
• pp.725-736
• /
• 2014

It is very important to detect and identify small anomalies and component failures for the safe operation of complex and large-scale artifacts such as nuclear power plants. Each diagnostic technique has its own advantages and limitations. These facts inspire us not only to enhance the capability of diagnostic techniques but also to integrate the results of diagnostic subsystems in order to obtain more accurate diagnostic results. The article describes the outline of four diagnostic techniques developed for the condition monitoring of the fast breeder reactor "Monju". The techniques are (1) estimation technique of important state variables based on a physical model of the component, (2) a state identification technique by non-linear discrimination function applying SVM (Support Vector Machine), (3) a diagnostic technique applying WT (Wavelet Transformation) to detect changes in the characteristics of measurement signals, and (4) a state identification technique effectively using past cases. In addition, a hybrid diagnostic system in which a final diagnostic result is given by integrating the results from subsystems is introduced, where two sets of values called confidence values and trust values are used. A technique to determine the trust value is investigated under the condition that the confidence value is determined by each subsystem.

• Steel and Composite Structures
• /
• v.29 no.6
• /
• pp.749-758
• /
• 2018

This article derived a hybrid coupling technique using the higher-order displacement polynomial and three soft computing techniques (teaching learning-based optimization, particle swarm optimization, and artificial bee colony) to predict the optimal stacking sequence of the layered structure and the corresponding frequency values. The higher-order displacement kinematics is adopted for the mathematical model derivation considering the necessary stress and stain continuity and the elimination of shear correction factor. A nine noded isoparametric Lagrangian element (eighty-one degrees of freedom at each node) is engaged for the discretisation and the desired model equation derived via the classical Hamilton's principle. Subsequently, three soft computing techniques are employed to predict the maximum natural frequency values corresponding to their optimum layer sequences via a suitable home-made computer code. The finite element convergence rate including the optimal solution stability is established through the iterative solutions. Further, the predicted optimal stacking sequence including the accuracy of the frequency values are verified with adequate comparison studies. Lastly, the derived hybrid models are explored further to by solving different numerical examples for the combined structural parameters (length to width ratio, length to thickness ratio and orthotropicity on frequency and layer-sequence) and the implicit behavior discuss in details.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2013.02a
• /
• pp.93-93
• /
• 2013

In recent years, multifunctional ternary nitride thin films have received extenstive attention due to its versatility in many applications. In particular, noble metal based ternary nitride thin films showed a promising properties in the application of Multifunctional heating resistor films because its good electrical properties and excellent resistance against oxidation and corrosion. In this study, we prepared multifunctional noble metal based ternary nitride thin films by atomic layer deposition (ALD) and plasma-enhanced ALD (PEALD) method. ALD and PEALD techniques were used due to their inherent merits such as a precise composition control and large area uniformity, which is very attractive for preparing multicomponent thin films on large area substrate. Here, we will demonstrate the design concept of multifunctional noble metal based ternary thin films. And, the relationship between microstructural evolution and electrical resistivity in noble metal based ternary thin films will be systemically presented. The useful properties of noble metal based ternary thin films including anti-corrosion and anti-oxidation will be discussed in terms of hybrid functionality.

• Journal of Korea Multimedia Society
• /
• v.23 no.1
• /
• pp.31-42
• /
• 2020

During the 4th Industrial Revolution, service platforms utilizing diverse contents are emerging, and research on recommended systems that can be customized to users to provide quality service is being conducted. hybrid recommendation systems that provide high accuracy recommendations are being researched in various domains, and various filtering techniques, machine learning, and deep learning are being applied to recommended systems. However, in a recommended service environment where data must be analyzed and processed real time, the accuracy of the recommendation is important, but the computational speed is also very important. Due to high level of model complexity, a hybrid recommendation system or a Deep Learning-based recommendation system takes a long time to calculate. In this paper, a Cascade-hybrid recommended algorithm is proposed that can reduce the computational time while maintaining the accuracy of the recommendation. The proposed algorithm was designed to reduce the complexity of the model and minimize the computational speed while processing sequentially, rather than using existing weights or using a hybrid recommendation technique handled in parallel. Therefore, through the algorithms in this paper, contents can be analyzed and recommended effectively and real time through services such as SNS environments or shared economy platforms.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.53 no.4
• /
• pp.479-485
• /
• 2020

We evaluated the hatchability of fertilized eggs from six hybrid combinations of highly valued grouper species inhabiting temperate and warm waters, with the goal of establishing a novel hybrid with enhanced growth and viability during the culturing period in the temperate waters of Korea. Hybrid combinations with red-spotted grouper females exhibited high hatchability with high a fertilization and hatching rate of fertilized eggs and a low deformity rate of hatched larvae. Conversely, hybrid combinations with kelp grouper females had very low hatching rates and very high deformity rates; commercial production of seed from such crosses would be difficult without improving hatchability. The hatchabilities of convict grouper ♀×giant grouper ♂ and kelp grouper ♀×red-spotted grouper ♂ were lower than those of maternal purebreds, but these two hybrid combinations were expected to produce potentially large quantities of hatched larvae. In the above evaluation, promising hybrid combinations were identified for commercial production of seed. For these hybrids to contribute to the development of Korea's mariculture industry, mass production of fertilized eggs and seeds is necessary, along with the development of advanced rearing techniques, such as the identification of a suitable rearing temperature.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.10
• /
• pp.9-21
• /
• 2020

The aim of this paper is to predict the Borsa Istanbul (BIST) 30 index movements to determine the most accurate buy and sell decisions using the methods of Artificial Neural Networks (ANN) and Genetic Algorithm (GA). We combined these two methods to obtain a hybrid intelligence method, which we apply. In the financial markets, over 100 technical indicators can be used. However, several of them are preferred by analysts. In this study, we employed nine of these technical indicators. They are moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI), momentum, directional movement index (DMI), stochastic oscillator, on-balance volume (OBV), average directional movement index (ADX), and simple moving averages (3-day moving average, 5-day moving average, 10-day moving average, 14-day moving average, 20-day moving average, 22-day moving average, 50-day moving average, 100-day moving average, 200-day moving average). In this regard, we combined these two techniques and obtained a hybrid intelligence method. By applying this hybrid model to each of these indicators, we forecast the movements of the Borsa Istanbul (BIST) 30 index. The experimental result indicates that our best proposed hybrid model has a successful forecast rate of 75%, which is higher than the single ANN or GA forecasting models.