• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.393-399
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• 2008

A modeling method for the modal analysis of a pre-twisted multi-packet blade system undergoing rotational motion is presented in this paper. Blades are idealized as pre-twisted cantilever beams that are fixed to a rotating disc. The stiffness coupling effects between blades due to the flexibilities of the disc and the shroud are modeled with discrete springs. The coupling effect between chordwise and flapwise bending deflection is also considered. Hybrid deformation variables are employed to derive the equations of motion. To obtain more general information, the equations of motion are transformed into dimensionless forms in which dimensionless parameters are identified. The effects of the dimensionless parameters and the number of packets as well as blades on the modal characteristics of the rotating multi-packet pre-twisted blade system are investigated with some numerical examples.

• Journal of Digital Convergence
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• v.12 no.2
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• pp.321-326
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• 2014

The needs for IT accessibility for disabled persons has increased for recent years. So, it is very important to support multi-modal interfaces, such as voice and vision recognition, TTS, etc. for disabled persons. In this paper, we deal with IT accessibility issues of home networks and show our implemented home network control system model with multi-modal interfaces including voice recognition and animated user interfaces.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.93-100
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• 2022

In this paper, we propose a machine learning-based method for supporting resource management of IoT software platforms in a multi-modal sensing scenario. We assume that an IoT device installed with a oneM2M-compatible software platform is connected with various sensors such as PIR, sound, dust, ambient light, ultrasonic, accelerometer, through different embedded system interfaces such as general purpose input output (GPIO), I2C, SPI, USB. Based on a collected dataset including CPU usage and user-defined priority, a machine learning model is trained to estimate the level of nice value required to adjust according to the resource usage patterns. The proposed method is validated by comparing with a rule-based control strategy, showing its practical capability in a multi-modal sensing scenario of IoT devices.

• Land and Housing Review
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• v.4 no.1
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• pp.89-98
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• 2013

In recent years, it was recognized that aurban policy paradigm of sustainable urban growth management and transportation policies have been shifted : from the era of automobile-oriented policy to the era of public transport policy. Against this backdrop, the introduction of the multi-modal transfer center is very consequential. Therefore, it is necessary for the introduction of wide-area and local-centric transfer facilities, as well as the center of the country-led national backbone transfer center. This study was applicable at the multi-modal transfer center plans to introduce guidelines to provide transit facilities, considering the regional and functional characteristics to classify the types of multi-modal transfer center. The final types of multi-modal transfer center were classified into six, and by considering the combination of the criteria to be classified. The multi-modal transfer center type classification based on the case analysis of the types of facilities at domestic and abroad. If the data of multi-modal transfer are accumulated continuously, can expect a more reliable type classification.

• Structural Engineering and Mechanics
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• v.39 no.2
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• pp.187-206
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• 2011

This paper uses dynamic computer simulation techniques to develop and apply a multi-criteria procedure using non-destructive vibration-based parameters for damage assessment in truss bridges. In addition to changes in natural frequencies, this procedure incorporates two parameters, namely the modal flexibility and the modal strain energy. Using the numerically simulated modal data obtained through finite element analysis of the healthy and damaged bridge models, algorithms based on modal flexibility and modal strain energy changes before and after damage are obtained and used as the indices for the assessment of structural health state. The application of the two proposed parameters to truss-type structures is limited in the literature. The proposed multi-criteria based damage assessment procedure is therefore developed and applied to truss bridges. The application of the approach is demonstrated through numerical simulation studies of a single-span simply supported truss bridge with eight damage scenarios corresponding to different types of deck and truss damage. Results show that the proposed multi-criteria method is effective in damage assessment in this type of bridge superstructure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.51-73
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• 2023

Social media play a significant role in communicating information across the globe, connecting with loved ones, getting the news, communicating ideas, etc. However, a group of people uses social media to spread fake information, which has a bad impact on society. Therefore, minimizing fake news and its detection are the two primary challenges that need to be addressed. This paper presents a multi-modal deep learning technique to address the above challenges. The proposed modal can use and process visual and textual features. Therefore, it has the ability to detect fake information from visual and textual data. We used EfficientNetB0 and a sentence transformer, respectively, for detecting counterfeit images and for textural learning. Feature embedding is performed at individual channels, whilst fusion is done at the last classification layer. The late fusion is applied intentionally to mitigate the noisy data that are generated by multi-modalities. Extensive experiments are conducted, and performance is evaluated against state-of-the-art methods. Three real-world benchmark datasets, such as MediaEval (Twitter), Weibo, and Fakeddit, are used for experimentation. Result reveals that the proposed modal outperformed the state-of-the-art methods and achieved an accuracy of 86.48%, 82.50%, and 88.80%, respectively, for MediaEval (Twitter), Weibo, and Fakeddit datasets.

• Journal of KSNVE
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• v.8 no.4
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• pp.632-642
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• 1998

A method based on finite element discretization is developed for optimizing the polarization profile of PVDF film to create the modal transducer for specific modes. Using this concept, one can design the modal transducer in two-dimensional structure having arbitrary geometry and boundary conditions. As a practical means for implementing this polarization profile without repoling the PVDF film the polarization profile is approximated by optimizing electrode patterns, lamination angles, and poling directions of the multi-layered PVDF transducer. This corresponds to the approximation of a continuous function using discrete values. The electrode pattern of each PVDF layer is optimized by deciding the electrode of each finite element to be used or not. Genetic algorithm, suitable for discrete problems, is used as an optimization scheme. For the optimization of each layers lamination angle, the continuous lamination angle is encoded into discrete value using binary 5 bit string. For the experimental demonstration, a modal sensor for first and second modes of cantilevered composite plate is designed using two layers of PVDF films. The actuator is designed based on the criterion of minimizing the system energy in the control modes under a given initial condition. Experimental results show that the signals from residual modes are successfully reduced using the optimized multi-layered PVDF sensor. Using discrete LQG control law, the modal peaks of first and second modes are reduced in the amount of 12 dB and 4 dB, resepctively.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.2
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• pp.137-147
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• 2008

A reliability analysis has been performed to investigate the systematic stability of multi-failure modes of rubble-mound breakwaters. The reliability functions of four different failure modes are established straightforwardly. AFDA(Approximate Full Distribution Approcah) reliability models for each failure modes are directly developed and satisfactorily calibrated through the comparison with CIAD's results. In the reliability analysis of single failure mode, the probabilities of failure are calculated and the influence coefficients of random variables in the failure modes are properly evaluated. Meanwhile, three different models such as uni-modal bounds, bimodal bounds, and PNET are applied to evaluate the probabilities of failure of multi-failure modes for rubble-mound breakwaters. It may be found that uni-modal bounds tend to overestimate the probability of failure of multi-failure modes. Therefore, for the systematic reliability analysis of multi-failure modes, it is recommended to use bi-modal bounds or PNET which consider the correlation between the failure modes for rubble-mound breakwaters. By introducing the reliability analysis of multi-failure modes, it could be possible to find out the additional probabilities of failure occurred by the multi-failure modes of a multi-component system such as rubble-mound breakwaters.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.250-253
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• 1998

This paper presents a multi-modal neural network composed of a preprocessing module and a multi-layer neural network module in order to enhance the nonlinear characteristics of neural network. The former module is based on spectral method using Chebyschev polynomials and transforms input data into spectra. The latter module identifies the system using the spectra generated by the preprocessing module. The omnibus numerical experiments show that the method is applicable to many a nonlinear dynamic system in the real world, and that preprocessing using Chebyschev polynomials reduces the number of neurons required for the multi-layer neural network.

• Journal of Korean Society of Transportation
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• v.28 no.5
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• pp.117-129
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• 2010

This study presents a sensitivity analysis method for stochastic user equilibrium of multi-modal network flows. We consider a multi-modal network consisting of a road network for passenger cars physically separated from a transit network for public transport. We first establish a sensitivity analysis method with respect to arbitrary link parameters and perform a sensitivity analysis with respect to link capacity and transit line frequency as practical link parameters. Next, We establish a sensitivity analysis method and perform the sensitivity analysis with respect to modal split by passenger car and public transit. As with the elasticity of economics, these results can be important information for analyzing changes in travel behavior due to the changes in operation and policy of transportation facilities, as well as for analyzing the effects of these operational changes and policies. These results also can be utilized as a tool to constitute a multi-modal network design problem by using cooperative game theory.