• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.30 no.4 s.247
• /
• pp.409-419
• /
• 2006

The actuating performance of plate-type unimorph piezoelectric composite actuators having various stacking sequences was evaluated by three dimensional finite element analysis on the basis of thermal analogy model. Thermal residual stress distribution at each layer in an asymmetrically laminated plate with PZT ceramic layer and thermally induced dome height were predicted using classical laminated plate theory. Thermal analogy model was applied to a bimorph cantilever beam and LIPCA-C2 actuator in order to confirm its validity. Finite element analysis considering thermal residual deformation showed that the bending behavior of piezoelectric composite actuator subjected to electric loads was significantly different according to the stacking sequence, thickness of constituent PZT ceramic and boundary conditions. In particular, the increase of thickness of PZT ceramic led to the increase of the bending stiffness of piezoelectric composite actuator but it did not always lead to the decrease of actuation distance according to the stacking sequences of piezoelectric composite actuator. Therefore, it is noted that the actuating performance of unimorph piezoelectric composite actuator is rather affected by bending stiffness than actuation distance.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.40 no.8
• /
• pp.782-791
• /
• 1991

In the fields of pattern recognition such as speech recognition, several new techniques using Artifical Neural network Models have been proposed and implemented. In particular, the Multilayer Perception Model has been shown to be effective in static speech pattern recognition. But speech has dynamic or temporal characteristics and the most important point in implementing speech recognition systems using Artificial Neural Network Models for continuous speech is the learning of dynamic characteristics and the distributed cues and contextual effects that result from temporal characteristics. But Recurrent Multilayer Perceptron Model is known to be able to learn sequence of pattern. In this paper, the results of applying the Recurrent Model which has possibilities of learning tedmporal characteristics of speech to phoneme recognition is presented. The test data consist of 144 Vowel+ Consonant + Vowel speech chains made up of 4 Korean monothongs and 9 Korean plosive consonants. The input parameters of Artificial Neural Network model used are the FFT coefficients, residual error and zero crossing rates. The Baseline model showed a recognition rate of 91% for volwels and 71% for plosive consonants of one male speaker. We obtained better recognition rates from various other experiments compared to the existing multilayer perceptron model, thus showed the recurrent model to be better suited to speech recognition. And the possibility of using Recurrent Models for speech recognition was experimented by changing the configuration of this baseline model.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.2
• /
• pp.407-414
• /
• 2007

A sensor fault detection scheme(SFDS) for a class of nonlinear systems that can be represented by Takagi-Sugeno fuzzy model is proposed. Basically, the SFDS may be considered as a multiple observer scheme(MOS) in which the bank of state observers and the detection & isolation logic are included. However, the proposed scheme has two great differences from the conventional MOSs. First, the proposed scheme includes fuzzy fault detection observers(FFDO) that are constructed based on the T-S fuzzy model that provides very good approximation to nonlinear dynamic systems. Secondly, unlike the conventional MOS, the FFDOS are driven not parallelly but sequentially according to the predetermined sequence to avoid the massive computational burden, which is known to be the biggest obstacle to the practical application of the multiple observer based FDI schemes. During the operating time, each FFDO generates the residuals carrying the information of a specified fault, and the corresponding fault detection logic unit performs the logical operations to detect and isolate the fault of interest. The proposed scheme is applied to an inverted pendulum control system for sensor fault detection/isolation. Simulation study shows the practical feasibility of the proposed scheme.

• Korean Journal of Computational Design and Engineering
• /
• v.9 no.2
• /
• pp.122-132
• /
• 2004

Progressive mesh representation and generation have become one of the most important issues in network-based computer graphics. However, current researches are mostly focused on triangular mesh models. On the other hand, solid models are widely used in industry and are applied to advanced applications such as product design and virtual assembly. Moreover, as the demand to share and transmit these solid models over the network is emerging, the generation and the transmission of progressive solid models depending on specific engineering needs and purpose are essential. In this paper, we present a Cellular Topology-based approach to generating and transmitting progressive solid models from a feature-based solid model for internet-based design and collaboration. The proposed approach introduces a new scheme for storing and transmitting solid models over the network. The Cellular Topology (CT) approach makes it possible to effectively generate progressive solid models and to efficiently transmit the models over the network with compact model size. Thus, an arbitrary solid model SM designed by a set of design features is stored as a much coarser solid model SM/sup 0/ together with a sequence of n detail records that indicate how to incrementally refine SM/sup 0/ exactly back into the original solid model SM = SM/sup 0/.

• Journal of KIISE
• /
• v.44 no.1
• /
• pp.36-43
• /
• 2017

Syntactic information represents the dependency relation between predicates and arguments, and it is helpful for improving the performance of Semantic Role Labeling systems. However, syntax analysis can cause computational overhead and inherit incorrect syntactic information. To solve this problem, we exclude syntactic information and use only morpheme information to construct Semantic Role Labeling systems. In this study, we propose an end-to-end SRL system that only uses morpheme information with Stacked Bidirectional LSTM-CRFs model by extending the LSTM RNN that is suitable for sequence labeling problem. Our experimental results show that our proposed model has better performance, as compare to other models.

• Journal of Navigation and Port Research
• /
• v.28 no.8
• /
• pp.721-726
• /
• 2004

This study was performed to analyze the operation system of transfer crane to improve the reality of yard operation rules in container terminal and present the applicable method of operation rules to apply the operation priority. And we derived the procedure to estimate the maximum number of waiting truck based on the waiting of truck and the occupancy of driving lane in yard, and analyzed the constraint state of space. To solve the space constraint, we provided a multi-job principle to define the space resource and described the solution and sequence diagram for the principle.

• Journal of The Korean Association For Science Education
• /
• v.34 no.2
• /
• pp.135-146
• /
• 2014

The purpose of this study is to explore evaluation criteria that pre-service elementary teachers employ as they evaluate and select models to explain electric circuits. Thirty junior students in a university of education have participated in the study as a part of the science education course in which they were enrolled. The lessons for the participants have been organized as a cyclic sequence of different modeling pedagogies including the expressive, experimental, and evaluative modeling. The pre-service teachers have been given five electric circuits in order and asked to create models and further develop them through peer discussion. Their modeling activities have been video- or audio-recorded, and the recordings and their transcripts have been analyzed using a framework of model evaluation criteria. It reveals that the types and frequencies of evaluation criteria used are different between situations of model development and model selection. While empirical and theoretical criteria have been used dominantly in both situations, more various criteria have been employed in the situation where the pre-service teachers selected one model among alternatives. Implications for science education and science education research have been suggested.

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.4
• /
• pp.203-210
• /
• 2017

In this paper, we propose an effective neural network model for image caption generation and model transfer. This model is a kind of multi-modal recurrent neural network models. It consists of five distinct layers: a convolution neural network layer for extracting visual information from images, an embedding layer for converting each word into a low dimensional feature, a recurrent neural network layer for learning caption sentence structure, and a multi-modal layer for combining visual and language information. In this model, the recurrent neural network layer is constructed by LSTM units, which are well known to be effective for learning and transferring sequence patterns. Moreover, this model has a unique structure in which the output of the convolution neural network layer is linked not only to the input of the initial state of the recurrent neural network layer but also to the input of the multimodal layer, in order to make use of visual information extracted from the image at each recurrent step for generating the corresponding textual caption. Through various comparative experiments using open data sets such as Flickr8k, Flickr30k, and MSCOCO, we demonstrated the proposed multimodal recurrent neural network model has high performance in terms of caption accuracy and model transfer effect.

• The Journal of the Acoustical Society of Korea
• /
• v.10 no.2
• /
• pp.29-35
• /
• 1991

In this paper, we discuss the method of recognizing allophone unit isolated words using hidden Markov model(HMM). Frist we constructed allophone lexicon by extracting allophones from training data and by training allophone HMMs. And then to recognize isolated words using allophone HMMs, it is necessary to construct word dictionary which contains information of allophone sequence and inter-allophone transition probability. Allophone sequences are represented by allophone HMMs. To see the effects of inter-allophone transition probability and to determine optimal probabilities, we performend some experiments. And we showed that small number of traing data and simple train procedure is needed to train word HMMs of allophone sequences and that not less performance than word unit HMM is obtained.

• Journal of Korean Institute of Industrial Engineers
• /
• v.41 no.2
• /
• pp.173-184
• /
• 2015

The application of radio frequency identification (RFID) sensor-tags in cold chain systems has recently received a great deal of attention. To design cold chain systems with RFID sensor-tags that minimize the initial investment and operational cost while fulfilling the functional and operational requirements, simulation study is one of the preferable and effective approaches. To simulate the possible design configurations, the individual components in a cold chain system can be extracted and implemented as a DEVS (Discrete Event System Specification) model. Based on the proposed DEVS model, a new cold chain simulation model can be efficiently created by simply connecting each DEVS model around the RFID sensor-tag of interest in sequence according to the structure of the cold chain system, and then executed (or simulated) on Java programming environments by the DEVSJAVA simulator. As a result of simulation, some key performance indexes such as reliability, accuracy or timeliness can be calculated and used to choose better components or to compare different system configurations of cold chain systems.