• Journal of the Korea Society of Computer and Information
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• v.18 no.2
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• pp.107-116
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• 2013

In this paper, we propose a hybrid music recommendation system combining users' listening habits and tag information in a social music site. Most of commercial music recommendation systems recommend music items based on the number of plays and explicit ratings of a song. However, the approach has some difficulties in recommending new items with only a few ratings or recommending items to new users with little information. To resolve the problem, we use tag information which is generated by collaborative tagging. According to the meaning of tags, a weighted value is assigned as the score of a tag of an music item. By combining the score of tags and the number of plays, user profiles are created and collaborative filtering algorithm is executed. For performance evaluation, precision, recall, and F-measure are calculated using the listening habit-based recommendation, the tag score-based recommendation, and the hybrid recommendation, respectively. Our experiments show that the hybrid recommendation system outperforms the other two approaches.

• Elastomers and Composites
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• v.55 no.1
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• pp.59-66
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• 2020

Herein, we investigated the thermal conductivity and thermal stability of natural rubber composite systems containing hybrid fillers of boron nitride (BN) and aluminum nitride (AlN). In the hybrid system, the bimodal distribution of polygonal AlN and planar BN particles provided excellent filler-packing efficiency and desired energy path for phonon transfer, resulting in high thermal conductivity of 1.29 W/mK, which could not be achieved by single filler composites. Further, polyethylene glycol (PEG) was compounded with a commonly used naphthenic oil, which substantially increased thermal conductivity to 3.51 W/mK with an excellent thermal stability due to facilitated energy transfer across the filler-filler interface. The resulting PEG-incorporated hybrid composite showed a high thermal degradation temperature (T₂) of 290℃, a low coefficient of thermal expansion of 26.4 ppm/℃, and a low thermal distortion parameter of 7.53 m/K, which is well over the naphthenic oil compound. Finally, using the Fourier's law of conduction, we suggested a modeling methodology to evaluate the cooling performance in thermal management system.

• Animal cells and systems
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• v.6 no.4
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• pp.317-322
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• 2002

To improve conventional yeast one-hybrid screening, we have developed an efficient mammalian one-hybrid system that allows rapid isolation of com-plementary DNAs which are able to induce human p14$^{ARF}$. tumor suppressor gene. A 1.5 kb promoter region of p14$^{ARF}$ was fused to EGFP to generate ARF promoter-EGFP reporter vector. This reporter plasmid was stably trans-fected into NIH3T3 cells for generation of reporter cell line. When the reporter cell line was infected with E2F-1 together with excess amounts of empty vector, the cells that received the positive modulator were readily identifiable by green fluorescence using FACS. The GFP-positive cells were cloned directly from the cultured cells and expanded in bulk culture. The genomic DNAs from GFP-positive cells were prepared and the CDNA insert in integrated retroviral genome was recovered by PCR using primers annealing to the retroviral vector sequences flanking the insert-cloning site. This system should be useful for efficient screening of expression CDNA libraries in mammalian cells to identify novel upstream regulators for spe-cific genes by one-hybrid interaction.ion.

• Journal of the Korea Society for Simulation
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• v.27 no.3
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• pp.1-11
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• 2018

Discrete event system specification (DEVS) has been used in many simulations including hybrid systems featuring both discrete and continuous behavior that require a lot of time to get results. Therefore, in this study, we proposed the acceleration of a DEVS-based hybrid system simulation using multi-cores and GPUs tightly coupled computing. We analyzed the proposed heterogeneous computing of the simulation in terms of the configuration of the target device, changing simulation parameters, and power consumption for efficient simulation. The result revealed that the proposed architecture offers an advantage for high-performance simulation in terms of execution time, although more power consumption is required. With these results, we discovered that our approach is applicable in hybrid system simulation, and we demonstrated the possibility of optimized hardware distribution in terms of power consumption versus execution time via experiments in the proposed architecture.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2045-2049
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• 2018

An antenna arrays for a satellite navigation systems require more antenna elements to mitigate multiple jamming signals. In order to maintain the small array size while increasing the number of antenna elements, miniaturization technique is essential for antenna design. In this work, an electrically small circular microstrip patch antenna with a 3 dB hybrid coupler is designed as an element antenna, where the 3 dB hybrid coupler can yield the circularly polarized radiation characteristic. The miniaturized element antenna typically has too large capacitance in GPS L1 and GLONASS G1 bands, making it difficult to match with a single stand-alone non-Foster matching circuit (NFMC) in a stable state. Therefore, we propose a new matching technique, referred to as the hybrid matching method, which consists of a NFMC and a passive circuit. This passive tuning circuit manages reactance of antenna elements at an appropriate capacitance without a pole in the operating frequency range. The antenna array is fabricated, and the measured results show a reflection coefficient of less than -10 dB and an isolation of greater than 50 dB. In addition, peak gain of the proposed antenna is increased by 22.3 dB compared to the antenna without the hybrid matching network.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.2
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• pp.45-54
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• 2013

In this paper, a theoretical weight-reduction method was suggested to substitute an underframe material of a box-type bodyshell having cut-outs with an alternative light-weight material. To utilize the material substitution method previously developed for a box-type hybrid bodyshell not having cut-outs, we derived a box-type baseline model without cut-outs which is similar to the stiffness condition of a box-type bodyshell having cut-outs. To do this, the thicknesses of roof and walls of the baseline model were determined such that the deflection of the baseline model under a distributed vertical load condition is equal to the sum of the theoretical section deflections of the original box model with cut-outs. Next, to derive a hybrid bodyshell by under-frame material substitution, the material substitution method for a box-type hybrid bodyshell without cut-outs was applied to the box-type baseline model. Finally, we compared the FE simulation results of the derived hybrid bodyshells having cut-outs for various materials with the theoretical results of the suggested method, and we obtained their good correlations.

• Ocean Systems Engineering
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• v.8 no.4
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• pp.381-407
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• 2018

This paper presents a hybrid numerical approach, which combines a two-phase Navier-Stokes model (NS) and the fully nonlinear potential theory (FNPT), for modelling wave-structure interaction. The former governs the computational domain near the structure, where the viscous and turbulent effects are significant, and is solved by OpenFOAM/InterDyMFoam which utilising the finite volume method (FVM) with a Volume of Fluid (VOF) for the phase identification. The latter covers the rest of the domain, where the fluid may be considered as incompressible, inviscid and irrotational, and solved by using the Quasi Arbitrary Lagrangian-Eulerian finite element method (QALE-FEM). These two models are weakly coupled using a zonal (spatially hierarchical) approach. Considering the inconsistence of the solutions at the boundaries between two different sub-domains governed by two fundamentally different models, a relaxation (transitional) zone is introduced, where the velocity, pressure and surface elevations are taken as the weighted summation of the solutions by two models. In order to tackle the challenges associated and maximise the computational efficiency, further developments of the QALE-FEM have been made. These include the derivation of an arbitrary Lagrangian-Eulerian FNPT and application of a robust gradient calculation scheme for estimating the velocity. The present hybrid model is applied to the numerical simulation of a fixed horizontal cylinder subjected to a unidirectional wave with or without following current. The convergence property, the optimisation of the relaxation zone, the accuracy and the computational efficiency are discussed. Although the idea of the weakly coupling using the zonal approach is not new, the present hybrid model is the first one to couple the QALE-FEM with OpenFOAM solver and/or to be applied to numerical simulate the wave-structure interaction with presence of current.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.2
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• pp.65-72
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• 2021

Recently, attacks on the network environment have been rapidly escalating and intelligent. Thus, the signature-based network intrusion detection system is becoming clear about its limitations. To solve these problems, research on machine learning-based intrusion detection systems is being conducted in many ways, but two problems are encountered to use machine learning for intrusion detection. The first is to find important features associated with learning for real-time detection, and the second is the imbalance of data used in learning. This problem is fatal because the performance of machine learning algorithms is data-dependent. In this paper, we propose the HSF-DNN, a network intrusion detection model based on a deep neural network to solve the problems presented above. The proposed HFS-DNN was learned through the NSL-KDD data set and performs performance comparisons with existing classification models. Experiments have confirmed that the proposed Hybrid Feature Selection algorithm does not degrade performance, and in an experiment between learning models that solved the imbalance problem, the model proposed in this paper showed the best performance.

• 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.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.27-35
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• 2021

With the development of the Internet, various IT technologies such as IoT, Cloud, etc. have been developed, and various systems have been built in countries and companies. Because these systems generate and share vast amounts of data, they needed a variety of systems that could detect threats to protect the critical data contained in the system, which has been actively studied to date. Typical techniques include anomaly detection and misuse detection, and these techniques detect threats that are known or exhibit behavior different from normal. However, as IT technology advances, so do technologies that threaten systems, and these methods of detection. Advanced Persistent Threat (APT) attacks national or companies systems to steal important information and perform attacks such as system down. These threats apply previously unknown malware and attack technologies. Therefore, in this paper, we propose a hybrid intrusion detection system that combines anomaly detection and misuse detection to detect unknown threats. Two detection techniques have been applied to enable the detection of known and unknown threats, and by applying machine learning, more accurate threat detection is possible. In misuse detection, we applied Classification based on Association Rule(CBA) to generate rules for known threats, and in anomaly detection, we used One-Class SVM(OCSVM) to detect unknown threats. Experiments show that unknown threat detection accuracy is about 94%, and we confirm that unknown threats can be detected.