• Journal of the Korea Computer Graphics Society
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• v.23 no.4
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• pp.11-19
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• 2017

We propose a new pipeline of fluid surface reconstruction that incorporates hybrid SDF(signed distance fields) and adaptive fluid surface techniques to finely reconstruct liquid-solid mixed surfaces. Previous particle-based fluid simulation suffer from a noisy surface problem when the particles are distributed irregularly. If a smoothing scheme is applied to reduce the problem, sharp and detailed features can be lost by over-smoothing artifacts. Our method constructs a hybrid SDF by combining signed distance values from the solid and liquid parts of the object. We also proposed a method of adaptively reconstructing the surface of the fluid to further improve the overall efficiency. This not only shows the detailed surface of the solid and liquid parts, but also the detail of the solid surface and the smooth fluid surface when both materials are mixed. We introduce the concept of guiding shape and propose a method to get signed distance value quickly. In addition, the hybrid SDF and mesh reconstruction techniques are integrated in the adaptive framework. As a result, our method improves the overall efficiency of the pipeline to restore fluid surfaces.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.443-453
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• 2013

The hybrid test is one of the most advanced test methods to predict the structural dynamic behavior with the interaction between a physical substructure and a numerical modeling in the hybrid control system. The purpose of this study is to perform the multi-directional dynamic test of a steel frame structure with the real-time hybrid system and to evaluate the validation of the results. In this study, FEAPH, nonlinear finite element analysis program for hybrid only, was developed and the hybrid control system was optimized. The inefficient computational time was improved with a fixed number iteration method and parallel computational techniques used in FEAPH. Furthermore, the previously used data communication method and the interface between a substructure and an analysis program were simplified in the control system. As the results, the total processing time in real-time hybrid test was shortened up to 10 times of actual measured seismic period. In order to verify the accuracy and validation of the hybrid system, the linear and nonlinear dynamic tests with a steel framed structure were carried out so that the trend of displacement responses was almost in accord with the numerical results. However, the maximum displacement responses had somewhat differences due to the analysis errors in material nonlinearities and the occurrence of permanent displacements. Therefore, if the proper material model and numerical algorithms are developed, the real-time hybrid system could be used to evaluate the structural dynamic behavior and would be an effective testing method as a substitute for a shaking table test.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.539-544
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• 2012

Most of the man-made systems can be modeled as a hybrid system which consists of both the high-level and the low-level component model. High level model is responsible for decision-making and the low-level one takes control of the mechanical component parts. Since the two models requires different interpretation method according to their type, analysis of a hybrid system becomes a difficult job. For the Analysis of the high-level model, methods for discrete event system models such as FSM can be used. On the contrary, numerical analysis techniques are required for the low-level continuous-time system model. Since it becomes a difficult thing for a modeller specifies and develops both the two-level models altogether, we propose an efficient hybrid simulation method which employs a game physics engine that has been widely and successfully used in the area of game industry.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.893-898
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• 2003

This paper proposes a Truncated Type-II Hybrid ARQ scheme and coding techniques using an adaptive modulation system to achieve high throughput data transmission systems for wireless multimedia communication systems. In this paper, the adaptive modulation system analyzed in Nakagami (m-distribution) fading channel environment. The adaptive modulation system controls the modulation level and symbol rate according to the Nakagami fading parameter(m). When the received Eb/No is high or the Nakagami fading parameter m is high, the propose system selects higher modulation level and higher symbol rate to increase throughput. On the other hand, this system selects lower modulation level and lower symbol rate to prevent throughput performance degradation when the received Eb/No is low. The modulation method have been adopted QPSK(Quadrature Phase Shift Keying), 16QAM(Quadrature Amplitude Modulation), 64QAM, 256QAM. Therefore, adaptive modulation systems with truncated type-II hybrid ARQ scheme is proper for wireless multimedia communication system that require high reliability and delay-limited applications.

• The Journal of Society for e-Business Studies
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• v.15 no.4
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• pp.101-121
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• 2010

This paper presents an intelligent prefetching technique that significantly improves performance of hybrid fash-disk storage, a combination of flash memory and hard disk. Since flash memory embedded in a hybrid device is much faster than hard disk in terms of I/O operations, it can be utilized as a 'cache' space to improve system performance. The basic strategy for prefetching is to utilize sequential pattern mining, with which we can extract the access patterns of objects from historical access sequences. We use two techniques for enhancing the performance of hybrid storage with prefetching. One of them is to modify a FAST algorithm for mapping the flash memory. The other is to extend the unit of prefetching to a block level as well as a file level for effectively utilizing flash memory space. For evaluating the proposed technique, we perform the experiments using the synthetic data and real UCC data, and prove the usability of our technique.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• Steel and Composite Structures
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• v.43 no.1
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• pp.19-30
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• 2022

The static strength and fatigue crack resistance of the aircraft skin structures depend on the materials used and joint type. Most of the commercial aircraft's skin panel structures are made from aluminium alloy and carbon fibre reinforced epoxy. In this study, the fatigue resistance of four joint configurations (metal/metal, metal/composite, composite/composite and composite/metal) with riveted, adhesive bonded, and hybrid joining techniques are investigated with experiments and finite element analysis. The fatigue tests were tension-tension because of the typical nature of the loads on aircraft skin panels susceptible of experimenting fatigue. Experiment results suggest that the fatigue life of hybrid joints is superior to adhesive bonded joints, and these in turn much better than conventional riveted joints. Thanks to the fact that, for hybrid joints, the adhesive bond provides better load distribution and ensures load-carrying capacity in the event of premature adhesive failure while rivets induce compressive residual stresses in the joint. Results from FE tool ABAQUS analysis for adhesive bonded and hybrid joints agrees with the experiments. From the analysis, the energy release rate for adhesive bonded joints is higher than that of hybrid joints in both opening (mode I) and shear direction (mode II). Most joints show higher energy release rate in mode II. This indicates that the joints experience fatigue crack in the shear direction, which is responsible for crack opening.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• Advances in materials Research
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• v.13 no.5
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• pp.391-416
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• 2024

Hybrid composite materials are widely used in various load-bearing structural components of micro - mini UAVs. However, the design of thin laminates for better impact resistance remains a challenge, despite the strong demand for lightweight structures. This work aims to assess the low-velocity impact (LVI) behaviour of thin quasi-isotropic woven carbon/ aramid epoxy hybrid laminates using experimental and numerical techniques. Drop tower impact test with 10 J and 15 J impact energies is performed on carbon/epoxy laminates having aramid layers at different sequences and locations. The impact behaviour is experimentally evaluated using force-time, force-deformation, and energy-time histories considering delamination threshold load, peak load, and laminate deflection. Ultrasonic C-scan is performed on the post-impact samples to analyse the insidious damage profile at different impact energies. The experimental data is further utilized to numerically simulate LVI behaviour by employing the representative volume element model. The numerical results are in good agreement with the experimental data. Numerical and experimental approach predicts that the hybrid laminates with aramid layers at both impact and non-impact sides of the laminate exhibits significant improvement in the overall impact behaviour by having a subcritical damage morphology compared to carbon/epoxy laminate. A combined numerical-experimental approach is proposed for evaluating the effective impact performance.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.5
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• pp.454-462
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• 2006

To enhance the competitive edge of the material cost, various techniques lowering the material cost of inverter to drive Cold Cathode Fluorescent Lamp (CCFL) have been developed. In this paper, the theoretical analysis has been done for the existing techniques such as Jin Balance and O2Micro technique. Especially, How to design the value of magnetizing inductance to meet the specification of the lamp current tolerance between lamps has been disclosed. Based on this result, two kinds of hybrid type balancing techniques have been proposed and analyzed mathematically, Also, the accuracy of the proposed techniques has been verified through Pspice simulation.