• Computers and Concrete
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• 제15권4호
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• pp.687-707
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• 2015

A finite element computer code for short-term analysis of steel-concrete composite structures is extended to study long-term effects under service loads, in the present work. Long-term effects are important in engineering design because they influence stress and strain distribution of the structural system and therefore contribute to the increment of deflections in these structures. For creep analysis, a rheological model based on a Kelvin chain, with elements placed in series, was employed. The parameters of the Kelvin chain were obtained using Dirichlet series. Creep and shrinkage models, proposed by the CEB FIP 90, were used. The shear-lag phenomenon that takes place at the concrete slab is usually neglected or not properly taken into account in the formulation of beam-column finite elements. Therefore, in this work, a three-dimensional numerical model based on the assemblage of shell finite elements for representing the steel beam and the concrete slab is used. Stud shear connectors are represented for special beam-column elements to simulate the partial interaction at the slab-beam interface. The two-dimensional representation of the concrete slab permits to capture the non-uniform shear stress distribution in the horizontal plane of the slab due to shear-lag phenomenon. The model is validated with experimental results of two full-scale continuous composite beams previously studied by other authors. Results are given in terms of displacements, bending moments and cracking patterns in order to shown the influence of long-term effects in the structural response and also the potentiality of the present numerical code.

• Journal of Korean Society of Coastal and Ocean Engineers
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• 제4권2호
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• pp.130-137
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• 1992

The previous model of Inchon Bay (Choi 1980) was refined to hindcast/forecast the tides in the Inchon Bay by prescribing the 8 tidal constituents at the open boundaries. A series of hindcast was performed for the period of meterologically calm condition and the simulated results were compared with limited observation showing the reasonable agreements. Preliminary stage of real-time tidal prediction over the whole Inchon Bay were briefly outlined for practical purposes. The established model were further improved by dynamically interfacing, a one dimensional representation of the Han River system. With this model the tidal propagation in the Han River were computed and simulation of recent September. 1990 flood were performed. Discussion for further model development are also described.

• Journal of the Korean Institute of Intelligent Systems
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• 제11권4호
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• pp.293-301
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• 2001

This paper reviews three methods to compute a pointwise confidence interval of neuro-fuzzy models and compares their estimation perfonnanee through simulations. The eOITl.putation methods under consideration include stacked generalization using cross-validation, predictive error bar in regressive models, and local reliability measure for the networks employing a local representation scheme. These methods implemented on the neuro-fuzzy models are applied to the problems of simple function approximation and chaotic time series prediction. The results of reliability estimation are compared both quantitatively and qualitatively.

• Atmosphere
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• 제17권4호
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• pp.393-405
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• 2007

Simulation results of WRF for the case of typhoon 'Rusa' were analyzed, comparing with observed data especially forjavascript:confirm_mark('abe', '1'); the Gangneung area around to examine its ability in numerical simulation. From the hourly precipitation time series, two peaks were found at Gangneung and Daegwallyeong, while only one peak was found from those of inland regions else. Especially, for the Yeongdong region, the first peak was directly related to spiral bands generated in front of the typhoon. Convective cells that were developed within the spiral bands moved to the eastern coastal area from the sea so that local heavy rainfall occurred in the Yeongdong region. The second peak was mainly related to the accompanying rain band of typhoon itself, topographic effect and the convergence near Gangneung area. Precipitation in Gangneung was simulated as much as about 30% of observed one. The main reason of this result came from a poor representation of wind directions in Gangneung area of WRF model. Observed wind direction was northwesterly but simulated one was nearly easterly in the area. This might shift a local heavy rainfall area downstream to the mountain area rather than the coastal area.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.1161-1164
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• 1996

In this paper, a parameter optimization technique for a mobile robot navigation is discussed. Authors already have proposed a navigation algorithm for mobile robots with sonar sensors using fuzzy decision making theory. Fuzzy decision making selects the optimal via-point utilizing membership values of each via-point candidate for fuzzy navigation goals. However, to make a robot successfully navigate through an unknown and cluttered environment, one needs to adjust parameters of membership function, thus changing shape of MF, for each fuzzy goal. Furthermore, the change in robot configuration, like change in sensor arrangement or sensing range, invokes another adjusting of MFs. To accomplish an intelligent way to adjust these parameters, we adopted a genetic algorithm, which does not require any formulation of the problem, thus more appropriate for robot navigation. Genetic algorithm generates the fittest parameter set through crossover and mutation operation of its string representation. The fitness of a parameter set is assigned after a simulation run according to its time of travel, accumulated heading angle change and collision. A series of simulations for several different environments is carried out to verify the proposed method. The results show the optimal parameters can be acquired with this method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권5호
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• pp.2539-2554
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• 2017

Regression-based image super resolution (SR) methods have shown great advantage in time consumption while maintaining similar or improved quality performance compared to other learning-based methods. In this paper, we propose a novel single image SR method based on hierarchical regression to further improve the quality performance. As an improvement to other regression-based methods, we introduce a hierarchical scheme into the process of learning multiple regressors. First, training samples are grouped into different clusters according to their geometry similarity, which generates the structure layer. Then in each cluster, a compact dictionary can be learned by Sparse Coding (SC) method and the training samples can be further grouped by dictionary atoms to form the detail layer. Last, a series of projection matrixes, which anchored to dictionary atoms, can be learned by linear regression. Experiment results show that hierarchical scheme can lead to regression that is more precise. Our method achieves superior high quality results compared with several state-of-the-art methods.

• Journal of KIISE:Computer Systems and Theory
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• 제33권1_2호
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• pp.62-68
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• 2006

The important arithmetic operations over finite fields include multiplication and exponentiation. An exponentiation operation can be implemented using a series of squaring and multiplication operations over GF($2^m$) using the binary method. Hence, it is important to develop a fast algorithm and efficient hardware for multiplication. This paper presents an efficient bit-serial systolic array for MSB-first multiplication in GF($2^m$) based on the polynomial representation. As compared to the related multipliers, the proposed systolic multiplier gains advantages in terms of input-pin and area-time complexity. Furthermore, it has regularity, modularity, and unidirectional data flow, and thus is well suited to VLSI implementation.

• 기호학연구
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• 제56호
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• pp.45-70
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• 2018

Criticism of the postmodern philosophy of the system of representation, which has continued since the Renaissance, is based on a critique of the dichotomy that separates the subjects and objects and the environment from the human being. Interactivity, highlighted in a series of works emerging as postmodern trends in the 1960s, was transmitted to an interactive aspect of digital art in the late 1990s. The key feature of digital art is the possibility of infinite variations reflecting unpredictable changes based on public participation on the spot. In this process, the importance of computer programs is highlighted. Instead of using the existing program as it is, more and more artists are creating and programming their own algorithms or creating unique algorithms through collaborations with programmers. We live in an era of paradigm shift in which programming itself must be considered as a creative act. Simulation technology and VR technology draw attention as a technique to represent the meaning of reality. Simulation technology helps artists create experimental works. In fact, Baudrillard's concept of Simulation defines the other reality that has nothing to do with our reality, rather than a reality that is extremely representative of our reality. His book Simulacra and Simulation refers to the existence of a reality entirely different from the traditional concept of reality. His argument does not concern the problems of right and wrong. There is no metaphysical meaning. Applying the concept of simulation to algorithmic art, the artist models the complex attributes of reality in the digital system. And it aims to build and integrate internal laws that structure and activate the world (specific or individual), that is to say, simulate the world. If the images of the traditional order correspond to the reproduction of the real world, the synthesized images of algorithmic art and simulated space-time are the forms of art that facilitate the experience. The moment of seeing and listening to the work of Ian Cheng presented in this article is a moment of personal experience and the perception is made at that time. It is not a complete and closed process, but a continuous and changing process. It is this active and situational awareness that is required to the audience for the comprehension of post-representation's forms.

• KIPS Transactions on Software and Data Engineering
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• 제12권8호
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• pp.355-364
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• 2023

As advanced cyber threats continue to increase in recent years, it is difficult to detect new types of cyber attacks with existing pattern or signature-based intrusion detection method. Therefore, research on anomaly detection methods using data learning-based artificial intelligence technology is increasing. In addition, supervised learning-based anomaly detection methods are difficult to use in real environments because they require sufficient labeled data for learning. Research on an unsupervised learning-based method that learns from normal data and detects an anomaly by finding a pattern in the data itself has been actively conducted. Therefore, this study aims to extract a latent vector that preserves useful sequence information from sequence log data and develop an anomaly detection learning model using the extracted latent vector. Word2Vec was used to create a dense vector representation corresponding to the characteristics of each sequence, and an unsupervised autoencoder was developed to extract latent vectors from sequence data expressed as dense vectors. The developed autoencoder model is a recurrent neural network GRU (Gated Recurrent Unit) based denoising autoencoder suitable for sequence data, a one-dimensional convolutional neural network-based autoencoder to solve the limited short-term memory problem that GRU can have, and an autoencoder combining GRU and one-dimensional convolution was used. The data used in the experiment is time-series-based NGIDS (Next Generation IDS Dataset) data, and as a result of the experiment, an autoencoder that combines GRU and one-dimensional convolution is better than a model using a GRU-based autoencoder or a one-dimensional convolution-based autoencoder. It was efficient in terms of learning time for extracting useful latent patterns from training data, and showed stable performance with smaller fluctuations in anomaly detection performance.

• Nuclear Engineering and Technology
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• 제52권7호
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• pp.1429-1435
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• 2020

Applying an accurate parametric prediction model to identify abnormal or false pressurizer water levels (PWLs) is critical to the safe operation of marine pressurized water reactors (PWRs). Recently, deep-learning-based models have proved to be a powerful feature extractor to perform high-accuracy prediction. However, the effectiveness of models still suffers from two issues in PWL prediction: the correlations shifting over time between PWL and other feature parameters, and the example imbalance between fluctuation examples (minority) and stable examples (majority). To address these problems, we propose a cost-sensitive mechanism to facilitate the model to learn the feature representation of later examples and fluctuation examples. By weighting the standard mean square error loss with a cost-sensitive factor, we develop a Cost-Sensitive Long Short-Term Memory (CSLSTM) model to predict the PWL of PWRs. The overall performance of the CSLSTM is assessed by a variety of evaluation metrics with the experimental data collected from a marine PWR simulator. The comparisons with the Long Short-Term Memory (LSTM) model and the Support Vector Regression (SVR) model demonstrate the effectiveness of the CSLSTM.