• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.7
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• pp.187-195
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• 2019

In December 2015, The Paris Agreement was adopted to undertake ambitious efforts to combat climate change. Korean government announced its goal of reducing the country's greenhouse gas emissions by up to 37% below business as usual projections by 2030 in 2015. The purpose of this study was to set up the calculation methodology of GHG emission($CO_{2e}$) in building sector and to estimate the annual GHG emission in building sector based on national energy consumption statistic. The GHG emission from buildings is about 135.8 million ton $CO_{2e}$ as of 2015, taking up about 19.6% of Korea's entire emission and is about 144.7 million ton $CO_{2e}$ in 2017. The GHG emission of building sector is increasing at annual rate of 2.0% from 2001 to 2017. The GHG emission from electricity consumption in buildings is 91.8 million ton $CO_{2e}$ in 2017, is the highest $CO_2$ emission by energy source. The results show that the intensity of GHG emission of residential building sector is $40.6kg-CO_{2e}/m^2{\cdot}yr$ and that of commercial building sector is $68.4kg-CO_{2e}/m^2{\cdot}yr$.

• Structural Engineering and Mechanics
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• v.77 no.2
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• pp.261-272
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• 2021

The energy-saving block and invisible multi-ribbed frame composite wall (EBIMFCW) is a new type of load-bearing wall. The study of this paper focus on it is hysteresis rule under horizontal cyclic loading. Firstly, based on the experimental data of the twelve specimens under horizontal cyclic loading, the influence of two important parameters of axial compression ratio and shear-span ratio on the restoring force model was analyzed. Secondly, a tetra-linear restoring force model considering four feature points and the degradation law of unloading stiffness was established by combining theoretical analysis and regression analysis of experimental data, and the theoretical formula of the peak load of the EBIMFCW was derived. Finally, the hysteretic path of the restoring force model was determined by analyzing the hysteresis characteristics of the typical hysteresis loop. The results show that the curves calculated by the tetra-linear restoring force model in this paper agree well with the experimental curves, especially the calculated values of the peak load of the wall are very close to the experimental values, which can provide a reference for the elastic-plastic analysis of the EBIMFCW.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.107-121
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• 2022

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f₁-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.

• Wind and Structures
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• v.35 no.6
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• pp.369-383
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• 2022

Wind tunnel experiment was carried out to study the cross-wind layer forces on a square cross-section building model using a synchronous multi-pressure sensing system. The stationarity of measured wind loadings are firstly examined, revealing the non-stationary feature of cross-wind forces. By converting the measured non-stationary wind forces into an energetically equivalent stationary process, the characteristics of local wind forces are studied, such as power spectrum density and spanwise coherence function. Mathematical models to describe properties of cross-wind forces at different layers are thus established. Then, a conditional simulation method, which is able to ex-tend pressure measurements starting from experimentally measured points, is proposed for the cross-wind loading. The method can reproduce the non-stationary cross-wind force by simulating a stationary process and the corresponding time varying amplitudes independently; in this way the non-stationary wind forces can finally be obtained by combining the two parts together. The feasibility and reliability of the proposed method is highlighted by an ex-ample of across wind loading simulation, based on the experimental results analyzed in the first part of the paper.

• Smart Structures and Systems
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• v.29 no.1
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3761-3779
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• 2022

The performance of existing recognition algorithms for binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals degrade under conditions of low signal-to-noise ratios (SNR). Hence, a novel recognition algorithm based on features in the graph domain is proposed in this study. First, the power spectrum of the squared candidate signal is truncated by a rectangular window. Thereafter, the graph representation of the truncated spectrum is obtained via normalization, quantization, and edge construction. Based on the analysis of the connectivity difference of the graphs under different hypotheses, the sum of degree (SD) of the graphs is utilized as a discriminate feature to classify BPSK and QPSK signals. Moreover, we prove that the SD is a Schur-concave function with respect to the probability vector of the vertices (PVV). Extensive simulations confirm the effectiveness of the proposed algorithm, and its superiority to the listed model-driven-based (MDB) algorithms in terms of recognition performance under low SNRs and computational complexity. As it is confirmed that the proposed method reduces the computational complexity of existing graph-based algorithms, it can be applied in modulation recognition of radar or communication signals in real-time processing, and does not require any prior knowledge about the training sets, channel coefficients, or noise power.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.36.2-36.2
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• 2019

Fe II emission is a prominent and ubiquitous feature in the spectra of broad-line Active Galactic Nuclei (AGN) by producing a pseudo-continuum from UV to optical with complex and strong blends of the numerous emission lines themselves, other emission lines, and continuum. Since theoretical modeling of such intricate Fe II emission is very difficult and still far from able to reproduce observed data in detail, an empirical iron emission template, derived from observations of a narrow-line Seyfert 1 galaxy, is an essential and practical tool to obtain accurate measurements of all the emission lines and continuum in AGN spectra. However, the existing iron templates, based on the single prototypical strong Fe II emitter I Zw 1, are suffering from inadequate S/N and non-simultaneous, inconsistent data with limited wavelength coverage, which consequently limit the accuracy of all the spectral measurements. To overcome the limitations and construct an improved iron template with wide spectral coverage, high-quality UV and optical spectra for the new and better identified template galaxy, Mrk 493, were successfully obtained from our HST STIS program (GO-14744). We will show the preliminary results for multicomponent spectral decomposition of the data and template construction with application tests to various AGN spectra and comparison with previous templates.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.78-81
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• 2021

Through the smart factory construction project, sensors can be installed in manufacturing production facilities and various process data can be collected in real time. Through this, research on real-time facility anomaly detection is being actively conducted to reduce production interruption due to facility abnormality in the manufacturing process. In this paper, to detect abnormalities in production facilities, the manufacturing data was applied to deep learning models Autoencoder(AE), VAE(Variational Autoencoder), and AAE(Adversarial Autoencoder) to derive the results. Manufacturing data was used as input data through a simple moving average technique and preprocessing process, and performance analysis was conducted according to the window size of the simple movement average technique and the feature vector size of the AE model.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.32 no.3
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• pp.180-188
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• 2024

The determination of runway length is critical for ensuring adequate airport size and stable operating conditions. This study aims to present a methodology for calculating the optimal length of the fifth runway at Incheon International Airport, considering the operating characteristics of the aircraft, airport conditions, and environmental factors. Domestic guidelines currently lack specificity in selecting design aircraft and applying aircraft weight standards, especially for large airports with multiple runways, posing a challenge for additional runway construction. This research conducts a case study on the length determination of Incheon International Airport's fifth runway, suggesting a procedure for large airports that takes into account various factors such as design aircraft selection, weight, and airfield characteristics. Our findings recommend an optimal runway length that accommodates all aircraft operating at the airport, ensuring efficient and economic expansion tailored to the future aviation landscape.