• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.5
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• pp.475-482
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• 2007

Most golfers believe that knowing yardages will improve their score. Certainly it helps with club selection. But, simple "Graphic" yardage guides being notorious for error and inaccuracies, which a serious golfer will pick immediately, only serve to erode the players enjoyment and ultimately, golf course satisfaction. Someone believes with low-level aerial photographic images, golfer will be impressed with the accuracy of the depiction, helping them play a more confident game. But, there are no mapping products in true 3-D available in the world that allows a golfer to determine shot distances in yards or meters. So, we suggest an lenticular technology for real 3-D display as a viable alternative to conventional image map solution. This technology is an image display method for the generation of multi-image effects like 3D visualization or animation. This methodology is cutting edge stereoscopic image which overcomes the limitation of conventional photo tech by recomposing and producing 3 dimensional images. A significant strength of this methods its versatility concerning display effects. The main use of the hardcopy 3-D lenticular displays is in the fields of science, education, planning, and representation. This paper gives a concise overview of the lenticular foil technology and describes the production of the true 3-D yardage book of golf courses. For this study, 3-D effects are achieved and evaluated with the lenticular display by incorporation multiple synthetic images based on digital topographic terrain model and by using the two images of the actual stereopair.

• Journal of Computing Science and Engineering
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• v.9 no.4
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• pp.204-213
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• 2015

Under the conditional independence assumption among local features, the Naive Bayes Nearest Neighbor (NBNN) classifier has been recently proposed and performs classification without any training or quantization phases. While the original NBNN shows high classification accuracy without adopting an explicit training phase, the conditional independence among local features is against the compositionality of objects indicating that different, but related parts of an object appear together. As a result, the assumption of the conditional independence weakens the accuracy of classification techniques based on NBNN. In this work, we look into this issue, and propose a novel Bayesian network for an NBNN based classification to consider the conditional dependence among features. To achieve our goal, we extract a high-level feature and its corresponding, multiple low-level features for each image patch. We then represent them based on a simple, two-level layered Bayesian network, and design its classification function considering our Bayesian network. To achieve low memory requirement and fast query-time performance, we further optimize our representation and classification function, named relation-based Bayesian network, by considering and representing the relationship between a high-level feature and its low-level features into a compact relation vector, whose dimensionality is the same as the number of low-level features, e.g., four elements in our tests. We have demonstrated the benefits of our method over the original NBNN and its recent improvement, and local NBNN in two different benchmarks. Our method shows improved accuracy, up to 27% against the tested methods. This high accuracy is mainly due to consideration of the conditional dependences between high-level and its corresponding low-level features.

• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.4
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• pp.600-614
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• 2016

This study specifies the definition and characteristics of fake fashion by categorizing cases through an analytical framework that uses the concept of simulacre, which is one of the theories that explains the reproduction of images and symbols in a modern consumer society. The presentation stages of modern fake fashion based on Baudrillard's concept of simulacre are as follows: Stage 1 focuses on the realistic imitation of the original, Stage 2 maintains a similarity with the original while transforming through the distortion of shape or visual perception, Stage 3 is the reality of the original which has become significantly vague and actively involves the designer's creativity, and Stage 4 forms a new value and an independent aura beyond reproducing the original. The presentation techniques of modern fake fashion viewed in the concept of simulacre can be classified into optical illusions by reproduction, use of a fake object, use of unusual shapes, and re-signifying through borrowing. As a result of applying the collected cases to the analytical framework, image reproduction in Stage 1 with imitative nature is a counterfeit that cannot be regarded as fake fashion, and fake fashion in Stage 4 (that can be referred to as simulacre) is fashion with symbolic and multiple meanings with new and creative designs. Modern fake fashion analyzed in the concept of simulacre transforms or reproduces the preexisting original with the purpose of merely creating original designs as well as acts as a new symbolic signal that creates a new aura and sets a trend with a message.

• Trans-
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• v.7
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• pp.19-48
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• 2019

This study was described to examine the meaning of the media exhibition work <70mK>, which aims to capture the topography of the collective consciousness of the Korean people through large-scale interviews. <70mK> edits and organizes interview images of individual beings in mosaic-like layouts and forms, creating video exhibitions and holding exhibitions. The objects in the split frame show the continuity of differences that reveal their own thoughts and personalities. This is a synchronic and conscious collective typology in which the intrinsic nature of the individuals is embodied in a simultaneous and holistic image. Interview images reveal their own form as a actual being and convey the intrinsic nature of one's own as oral information. <70mK> constructs a new individualization by aesthetically structuring the forms and information of life individuals in the extension of a specific group. The beings in the frame are not communicating with each other and are looking straight ahead. it conveys to visitors their relationship and personality as the preindividual reality. It is the repetitive arrangement and composition of heterogeneity and difference that each individual shows, and is a chain operation that includes collective identity behind it. <70mK> constructs the direct images and sounds of individual interviewee, creating a new form of information transfer called Video Art Exhibition. This makes metaphors and perceptions of the meaning and process of transindividual relationships and the meaning of psychic individuation and collective individuation. This is an appropriate case to explain with modern technology and individualization of Gilbert Simondon thought together with the meaning of becoming and relation of individualization. The exhibition space constructed by <70mK> is an aesthetic methodology of the psychic and collective meaning and its relationship to a particular group of individuals through which they are connected. Simondon studied the meaning of the process of individualization and the meaning of becoming, and is a philosopher who positively considered the potential of modern technology. <70mK> is a new individual as structured and generated ethical reality mediated by modern technology mechanisms and network behaviors. It is an case of an aesthetic and practical methodology of how interviews function as 'transduction' in the process of individualization in which technology is cooperated. The direct images and sounds of <70mK> are systems in which the information of life individuals is carried, amplified, accumulated and transmitted. It is also a new individual as a psychic and collective landscape. It is a newly became exhibition art work through the multiple individualization, and is a representation of transindividual meanings and process. The media exhibition art of individualized metastable states leads to new relationships in which viewers perceive the same preindividual reality and feel affectivity. The exhibition space of <70mK> becomes a stage for preparing the actual possibility of the transindividual group beyond the representation of the semantic function.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.12C
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• pp.1288-1298
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• 2002

To implement elliptic curve cryptosystem in GF(2$\^$m/) at high speed, a fast divider is required. Although bit-parallel architecture is well suited for high speed division operations, elliptic curve cryptosystem requires large m(at least 163) to support a sufficient security. In other words, since the bit-parallel architecture has an area complexity of 0(m$\^$m/), it is not suited for this application. In this paper, we propose a new serial-in serial-out systolic array for computing division operations in GF(2$\^$m/) using the standard basis representation. Based on a modified version of tile binary extended greatest common divisor algorithm, we obtain a new data dependence graph and design an efficient bit-serial systolic divider. The proposed divider has 0(m) time complexity and 0(m) area complexity. If input data come in continuously, the proposed divider can produce division results at a rate of one per m clock cycles, after an initial delay of 5m-2 cycles. Analysis shows that the proposed divider provides a significant reduction in both chip area and computational delay time compared to previously proposed systolic dividers with the same I/O format. Since the proposed divider can perform division operations at high speed with the reduced chip area, it is well suited for division circuit of elliptic curve cryptosystem. Furthermore, since the proposed architecture does not restrict the choice of irreducible polynomial, and has a unidirectional data flow and regularity, it provides a high flexibility and scalability with respect to the field size m.