• Bulletin of the Society of Naval Architects of Korea
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• v.24 no.4
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• pp.45-52
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• 1987

In this paper, merits and demerits of Nelder and Mead Penalty Function Method(SUMTNM) and Flexible Tolerance Method(FTM) are investigated from the standpoint of generality, accuracy and efficiency. SUMTNM is combined with Nelder and Method and SUMT, but FTM improves the values of the objective function by using information provided by feasible points as well as certain nonfeasible points termed near-feasible points. Therefore, FTM uses more information than SUMTNM for minimizing object function. The structural analysis of a vertically corrugated bulkhead is performed by collapse mechanism and plate buckling analysis. Based on the results of this analysis, minimum structural weight design of a corrugated bulkhead by use of above two optimization techniques is carried out by investigating the effects of sizes of bulkhead on the structural weight.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.889-898
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• 2017

In this research multiple change-points estimation for South Korean electricity generation data is considered. We analyze the South Korean electricity data via deterministically trending dynamic time series model with multiple structural changes in trends in a Bayesian approach. The number of change-points and the timing are unknown. The goal is to find the best model with the appropriate number of change-points and the length of the segments. A genetic algorithm is implemented to solve this optimization problem with a variable dimension of parameters. We estimate the structural change-points for South Korean electricity generation data and Nile River flow data additionally.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.229-230
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• 2022

With the development of digital technology, construction companies at home and abroad are in the process of computerizing work and site information for the purpose of improving work efficiency. To this end, various technologies such as BIM, digital twin, and AI-based safety management have been developed, but the accuracy and completeness of the related technologies are insufficient to be applied to the field. In this paper, the learning data that has undergone a pre-processing process optimized for recognition of construction information based on structural members is trained on an existing artificial intelligence model to improve recognition accuracy and evaluate its effectiveness. The artificial intelligence model optimized for the structural member created through this study will be used as a base technology for the technology that needs to confirm the safety of the structure in the future.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.125-133
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• 2004

In this paper, we present a new method that combines the characteristics of edge in-formation and second-order neural networks for the classification of structural textures. The edges of a texture are extracted using an edge detection approach. From this edge information, classification features called second-order features are obtained. These features are fed into a second-order neural network for training and subsequent classification. It will be shown that the main disadvantage of using structural methods in texture classifications, namely, the difficulty of the extraction of texels, is overcome by the proposed method.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.05a
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• pp.197-205
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• 2004

A vocabulary for a understanding bridge has a different scope. There are the urban setting, landscape, lightness, from minimum to maximum, continuity, material, erection, motion and dynamic. Aesthetics criteria of footbridge design are movement and grace, space and experiment, symbolism, iconic, sculpture, innovation, spectacle, lighting, gemetry and wonder. New structural concepts of pedestrian bridges are presented on examples of recently built structures. The main characteristics of described structures are appropriateness, humanity, structural efficiency and aesthetics.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.48-60
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• 2004

XML documents consist of elements that are basic units of information. When the structure of XML documents is changed dynamically, we need to update structure information efficiently without changing the information of the index structure for fast retrieval. In this paper, we propose a dynamic indexing model scheme that updates the index structure in real time as the structure of XML documents is changed by insertion and deletion of elements. Our dynamic indexing model consists of a structure information representation method and a dynamic index structure. The structure information representation method supports various types of structure retrievals. Our dynamic index structure processes various structural queries efficiently. We show through various experiments that our method outperforms existing ones in processing various types of queries such as content based queries, structural queries and hybrid queries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.1080-1099
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• 2019

A better tradeoff between robustness and invisibility will be realized by using the just noticeable (JND) model into the quantization-based watermarking scheme. The JND model is usually used to describe the perception characteristics of human visual systems (HVS). According to the research of cognitive science, HVS can adaptively extract the structure features of an image. However, the existing JND models in the watermarking scheme do not consider the structure features. Therefore, a novel JND model is proposed, which includes three aspects: contrast sensitivity function, luminance adaptation, and contrast masking (CM). In this model, the CM effect is modeled by analyzing the direction features and texture complexity, which meets the human visual perception characteristics and matches well with the spread transform dither modulation (STDM) watermarking framework by employing a new method to measure edge intensity. Compared with the other existing JND models, the proposed JND model based on structural regularity is more efficient and applicable in the STDM watermarking scheme. In terms of the experimental results, the proposed scheme performs better than the other watermarking scheme based on the existing JND models.

• Smart Structures and Systems
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• v.30 no.3
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• pp.287-301
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• 2022

The machine-vision based structural displacement measurement methods are widely used due to its flexible deployment and non-contact measurement characteristics. The accuracy of vision measurement is directly related to the image resolution. In the field of computer vision, super-resolution reconstruction is an emerging method to improve image resolution. Particularly, the deep-learning based image super-resolution methods have shown great potential for improving image resolution and thus the machine-vision based measurement. In this article, we firstly review the latest progress of several deep learning based super-resolution models, together with the public benchmark datasets and the performance evaluation index. Secondly, we construct a binocular visual measurement platform to measure the distances of the adjacent corners on a chessboard that is universally used as a target when measuring the structure displacement via machine-vision based approaches. And then, several typical deep learning based super resolution algorithms are employed to improve the visual measurement performance. Experimental results show that super-resolution reconstruction technology can improve the accuracy of distance measurement of adjacent corners. According to the experimental results, one can find that the measurement accuracy improvement of the super resolution algorithms is not consistent with the existing quantitative performance evaluation index. Lastly, the current challenges and future trends of super resolution algorithms for visual measurement applications are pointed out.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3842-3855
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• 2018

A novel image completion method is proposed that uses the advantage of planar structural information to fill corrupted portions of an image. First, in estimating parameters of the projection plane, the image is divided into several planes, and their planar structural information is analyzed. Second, in calculating the a priori probability of patch and patch offset regularity, this information is converted into a constraint condition to guide the process of filling the hole. Experimental results show that the proposed algorithm is fast and effective, and ensures the structure continuity of the damaged region and smoothness of the texture.