• Journal of the Korean Institute of Landscape Architecture
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• v.36 no.5
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• pp.1-12
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• 2008

Since the late 20th century, post-modern society has needed new styles in environmental design. The land art begun during that time supplied the momentum for the birth of the hybrid environmental design. The new design approach, focusing on land form and landscaping begun with land-form architecture, raised a powerful current of hybridization in the environmental design genres. The new picturesque landscape design distinguished by manipulated land forms and sublimated aesthetics appeared under the influence of land art and land-form architecture. Similarly, landscape urbanism was formed by the fusion of landscape architecture and urbanism. Therefore, the representative hybrid styles in environmental design appear as new picturesque landscape design, land-form architecture and landscape urbanism. With the new, strong interest in land and landscape, this same new interest was given to 'time' on account of the dynamics and indeterminacy of urban society. This new interest in land and time gave rise to new hybrid methodologies for environmental design such as mapping, diagramming and folding. These three tools have been applied most comprehensively in landscape urbanism. The 'fold' is the most popular design tool for most of the hybrid genres. The 'diagram' is the second-most popular design tool mostly for landscape urbanism and land-form architecture. Mapping is being actively applied to landscape urbanism and passively applied in new picturesque landscape design. In general, landscape urbanism seems to be a timely and suitable alternative for contemporary urban society. It displays very high potentials in the regeneration of the locality through the comprehensive hybrid methodology. It is necessary to actively engage in and develop landscape urbanism fit the local needs.

• Computers and Concrete
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• v.16 no.6
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• pp.919-932
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• 2015

This paper presents a new method for assessing the three-point-bending (3PB) strength of mortar beams in a non-destructive manner, based on neural network (NN) models. The models are based on the radial basis function (RBF) architecture and the fuzzy means algorithm is employed for training, in order to boost the prediction accuracy. Data for training the models were collected based on a series of experiments, where the cement mortar beams were subjected to various bending mechanical loads and the resulting pressure stimulated currents (PSCs) were recorded. The input variables to the NN models were then calculated by describing the PSC relaxation process through a generalization of Boltzmannn-Gibbs statistical physics, known as non-extensive statistical physics (NESP). The NN predictions were evaluated using k-fold cross-validation and new data that were kept independent from training; it can be seen that the proposed method can successfully form the basis of a non-destructive tool for assessing the bending strength. A comparison with a different NN architecture confirms the superiority of the proposed approach.

• Journal of Biomedical Engineering Research
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• v.28 no.6
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• pp.803-810
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• 2007

Although synthetic aperture focusing techniques can improve the spatial resolution of ultrasound imaging, they have not been employed in a commercial product because they require a real-time N-channel beamformer with a tremendously increased hardware complexity for simultaneous beamforming along M multiple lines. In this paper, a hardware-efficient beamformer architecture for synthetic aperture focusing is presented. In contrast to the straightforward design using NM delay calculators, the proposed method utilizes only M delay calculators by sharing the same values among the focusing delays which should be calculated at the same time between the N channels for all imaging points along the M scan lines. In general, synthetic aperture beamforming requires M 2-port memories. In the proposed beamformer, the input data for each channel is first upsampled with a 4-fold interpolator and each polyphase component of the interpolator output is stored into a 2-port memory separately, requiring 4M 2-port memories for each channel. By properly limiting the area formed with the synthetic aperture focusing, the input memory buffer can be implemented with only 4 2-port memories and one short multi-port memory.

• Architectural research
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• v.19 no.3
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• pp.53-61
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• 2017

This study aims to understand and analyze the social recognition and debate shown during the progress of the Asian Culture Complex (ACC) public project as well as the environmental evaluation performed by provincial community members. A 30-question survey based on a Likert scale was conducted with 150 experts. The study showed, first, that despite controversies over landmarks and historical locations, the quality of the architect's first draft was considered fairly high. However, vibrant landmarks were greatly desired. Furthermore, respondents reported conflicting recognitions of the same concept, which were defined differently depending on the terms and concepts used by the architect. Second, historical locations were considered important. The architects' final design was modified based on quality and in response to social pressure, resulting in the partial retention of the provincial government annex building. Although the first draft was considered to be of high quality, the modified plan was considered even better than the original design, revealing the 2-fold characteristics of the identical phenomenon in the environmental evaluation. Third, many groups held opinions that were balanced between the architects' originality and the value of community-based recognition; hence, the social inclination and evaluation of the project differed across the public.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.5
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• pp.304-308
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• 2014

As users generate lots of contents anytime and anywhere with an explosive growth of the number of mobile devices, Content centric networking (CCN) has emerged as a new networking architecture. However, the efficient CCN routing scheme is required for ad hoc network support because of its one to one message exchange characteristics. So, this paper proposes the new CCN ad hoc routing scheme using on-demand approach, which includes the secure routing configuration scheme based on multiple hash operation. It is shown from the simulation that the proposed method can provide lower control overhead because of its two-fold routing configuration architecture.

• Structural Engineering and Mechanics
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• v.84 no.1
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• pp.1-16
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• 2022

An efficient and accurate classification method for failure modes of reinforced concrete (RC) columns was proposed based on key characteristic parameters. The weight coefficients of seven characteristic parameters for failure modes of RC columns were determined first based on the support vector machine-recursive feature elimination. Then key characteristic parameters for classifying flexure, flexure-shear and shear failure modes of RC columns were selected respectively. Subsequently, a support vector machine with key characteristic parameters (SVM-K) was proposed to classify three types of failure modes of RC columns. The optimal parameters of SVM-K were determined by using the ten-fold cross-validation and the grid-search algorithm based on 270 sets of available experimental data. Results indicate that the proposed SVM-K has high overall accuracy, recall and precision (e.g., accuracy>95%, recall>90%, precision>90%), which means that the proposed SVM-K has superior performance for classification of failure modes of RC columns. Based on the selected key characteristic parameters for different types of failure modes of RC columns, the accuracy of SVM-K is improved and the decision function of SVM-K is simplified by reducing the dimensions and number of support vectors.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.436-443
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• 2023

With the advancement of robot intelligence, the concept of man and unmanned teaming (MUM-T) has garnered considerable attention in military research. In this paper, we present a transformer-based architecture for predicting the health status of agents, with the help of multi-head attention mechanism to effectively capture the dynamic interaction between friendly and enemy forces. To this end, we first introduce a framework for generating a dataset of battlefield situations. These situations are simulated on a virtual simulator, allowing for a wide range of scenarios without any restrictions on the number of agents, their missions, or their actions. Then, we define the crucial elements for identifying the battlefield, with a specific emphasis on agents' status. The battlefield data is fed into the transformer architecture, with classification headers on top of the transformer encoding layers to categorize health status of agent. We conduct ablation tests to assess the significance of various factors in determining agents' health status in battlefield scenarios. We conduct 3-Fold corss validation and the experimental results demonstrate that our model achieves a prediction accuracy of over 98%. In addition, the performance of our model are compared with that of other models such as convolutional neural network (CNN) and multi layer perceptron (MLP), and the results establish the superiority of our model.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.395-408
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• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

• Architectural research
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• v.15 no.1
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• pp.25-33
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• 2013

The amount of construction waste increased to 176,447 ton/day in 2008 from 28,400 ton/day in 2000, a 6-fold increase in just 10 years. Such dramatic increase in the waste of concrete, asphalt concrete, wood and metals was due to demolition of old buildings as well as a great number of building redevelopment projects and hurried city industrialization. Many buildings targeted for demolition today were built in line with the government policy to provide affordable housing to citizens in the shortest time possible and consequently, said buildings underwent rapid deterioration and required periodic repairs and reconstruction. Based on the above, we predict that construction waste will continue to increase for the foreseeable future. In particular, due to limited availability of suitable space to construct apartments and residential buildings in the city, old buildings are being torn down to make space for new development, further increasing construction waste. In light of that, efforts to recycle as well as reduce generated waste are urgently required.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.173-178
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• 2019

Mongolian historical and cultural monuments on settlement areas of stone inscriptions, stone images, rock-drawings, remains of cities, architecture are still telling us their stories. These monuments depict the understanding of the word, philosophical and artistic outlook, beliefs, religion, national art, language, culture and traditions of Mongols [1]. Nowadays computer science, especially computer vision is applying in the other science fields. The main problem is how to apply and which algorithm can detect and classify the objects correctly. In this paper, we propose a method to detect object from Mongolian nomadic environment images. This work proposes a method for object detection that is the combination of the binary operations in the edge detection results. We found out the best method and parameters of state-of-the-art machine learning algorithms. In experimental result, we evaluate our results with 10-fold cross validation and split 66% strategies.