• Wind and Structures
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• v.28 no.3
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• pp.171-180
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• 2019

This paper presents a restart iterative approach for time-domain flutter analysis of long-span bridges using the commercial FE package ANSYS. This approach utilizes the recursive formats of impulse-response-function expressions for bridge's aeroelastic forces. Nonlinear dynamic equilibrium equations are iteratively solved by using the restart technique in ANSYS, which enable the equilibrium state of system to get back to last moment absolutely during iterations. The condition for the onset of flutter instability becomes that, at a certain wind velocity, the amplitude of vibration is invariant with time. A long-span suspension bridge was taken as a numerical example to verify the applicability and accuracy of the proposed method by comparing calculated results with wind tunnel tests. The proposed method enables the bridge designers and engineering practitioners to carry out time-domain flutter analysis of bridges in commercial FE package ANSYS.

• Earthquakes and Structures
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• v.17 no.1
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• pp.101-113
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• 2019

Earthquake-induced pounding is one of the major reasons for structural failure in earthquake prone cities. An accurate description of the pounding phenomenon of two buildings requires the consideration of systems with a large number of degrees of freedom including adequate contact impact formulations. In this paper, firstly, a node to surface formulation for the realization of state-of-the-art pounding models for structural beam elements is presented. Secondly, a hierarchical substructure technique is introduced, which is adapted to the structural pounding problem. The numerical accuracy and efficiency of the method, especially for the contact forces, are verified on an academic example, applying four different impact elements. Error estimations are carried out and compared with the classical modal truncation method. It is demonstrated that the hierarchical substructure method is indeed able to significantly speed up the numeric integration procedure by preserving a required level of accuracy.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.211-221
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• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

• Journal of the Korea Institute of Building Construction
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• v.17 no.3
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• pp.295-303
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• 2017

Over the past 7 years, the number of victims of construction disasters has been gradually increasing. Compared with projects in other industries, construction projects are highly exposed to safety risks. For this reason, the research methods of predicting and managing the risk of construction disasters are urgently needed that can be applied to a construction site. This study aims to propose a prediction model for a construction disaster using the decision tree technique. The developed the model is reviewed the applicability by evaluating its accuracy based on disaster data. The top three of the prediction values obtained from the proposed model were enumerated, and then the cumulative accuracy were also calculated. The prediction accuracy was 40 percent for the first value, but the cumulative accuracy was 80 percent. Thus, as more disaster data was accumulated, the cumulative accuracy appeared to be higher. If utilized in construction sites, the model proposed in this study would contribute to a reduction in the rate of construction disasters.

• Ocean Systems Engineering
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• v.9 no.3
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• pp.329-347
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• 2019

The shipbuilding industry is characterized by order production, and various processes are performed simultaneously in the construction of ships. Therefore, effective management of the production process and productivity improvement form important key factors in the industry. For decades, researchers and process managers have attempted to improve processes by using business process analysis (BPA). However, conventional BPA is time-consuming, expensive, and mainly based on subjective results generated by employees, which may not always correspond to the actual conditions. This paper proposes a method to improve the production process of offshore plant modules by analysing the process mining data obtained from the shipbuilding industry. Process mining uses information accumulated from the system-provided event logs to generate a process model and determine the values hidden within the process. The discovered process is visualized as a process model. Subsequently, alternatives are proposed by brainstorming problems (such as bottlenecks or idle time) in the process. The results of this study can aid in productivity improvement (idle time or bottleneck reduction in the production process) in conjunction with a six-sigma technique or ERP system. In future, it is necessary to study the standardization of the module production processes and development of the process monitoring system.

• Journal of KIBIM
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• v.10 no.2
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• pp.21-28
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• 2020

As the number of aging bridges increases, more studies are being conducted on developing effective and reliable methods for the assessment and maintenance of bridges. With the advancement in new sensing systems and data learning techniques through AI technology, there is growing interests in how to evaluate bridges using these advanced techniques. This paper presents a CNN(Convolution Neural Network) deep learning based technique for evaluating the damage existence and for estimating the damage location in PSC bridges using static strain data. Simulation studies were conducted to investigate the proposed method with error analysis. Damage was simulated as the reduction in the stiffness of a finite element. A data learning model was constructed by applying the CNN technique as a type of deep learning. The damage status and its location were estimated using data set built through simulation. It was assumed that the strain gauges were installed in a regular interval under the PSC bridge girders. In order to increase the accuracy in evaluating damage, the squared error between the intact and measured strains are computed and applied for training the data model. Considering the damage occurring near the supports, the results of error analysis were compared according to whether strain data near the supports were included.

• Smart Structures and Systems
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• v.33 no.5
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• pp.325-332
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• 2024

This study proposes a novel long short-term memory (LSTM)-based approach for predicting carbonation depth, with the aim of enhancing the durability evaluation of concrete structures. Conventional carbonation depth prediction relies on statistical methodologies using carbonation influencing factors and in-situ carbonation depth data. However, applying in-situ data for predictive modeling faces challenges due to the lack of time-series data. To address this limitation, an LSTM-based carbonation depth prediction technique is proposed. First, training data are generated through random sampling from the distribution of carbonation velocity coefficients, which are calculated from in-situ carbonation depth data. Subsequently, a Bayesian theorem is applied to tailor the training data for each target bridge, which are depending on surrounding environmental conditions. Ultimately, the LSTM model predicts the time-dependent carbonation depth data for the target bridge. To examine the feasibility of this technique, a carbonation depth dataset from 3,960 in-situ bridges was used for training, and untrained time-series data from the Miho River bridge in the Republic of Korea were used for experimental validation. The results of the experimental validation demonstrate a significant reduction in prediction error from 8.19% to 1.75% compared with the conventional statistical method. Furthermore, the LSTM prediction result can be enhanced by sequentially updating the LSTM model using actual time-series measurement data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3884-3910
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• 2016

Intrusion Detection System (IDS) in general considers a big amount of data that are highly redundant and irrelevant. This trait causes slow instruction, assessment procedures, high resource consumption and poor detection rate. Due to their expensive computational requirements during both training and detection, IDSs are mostly ineffective for real-time anomaly detection. This paper proposes a dimensionality reduction technique that is able to enhance the performance of IDSs up to constant time O(1) based on the Principle Component Analysis (PCA). Furthermore, the present study offers a feature selection approach for identifying major components in real time. The PCA algorithm transforms high-dimensional feature vectors into a low-dimensional feature space, which is used to determine the optimum volume of factors. The proposed approach was assessed using HTTP packet payload of ISCX 2012 IDS and DARPA 1999 dataset. The experimental outcome demonstrated that our proposed anomaly detection achieved promising results with 97% detection rate with 1.2% false positive rate for ISCX 2012 dataset and 100% detection rate with 0.06% false positive rate for DARPA 1999 dataset. Our proposed anomaly detection also achieved comparable performance in terms of computational complexity when compared to three state-of-the-art anomaly detection systems.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.253-257
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• 2007

In case of the hydraulic turbine dynamo room at Dam, due to its big volume and reflexible finishing material, since the noise of electricity-generation is amplifying, it influences the difficulty of mutual communication among the workers, also it is causing both mental and physical damages to those workers in the neighboring office. Accordingly, after presentation of the optimized renovation model of the hydraulic turbine dynamo room using the acoustic simulation, this Research has compared and evaluated them using the auralizational technique between the present condition of "before improvement" and the acoustic condition of "after improvement". As the result of psycho-acoustics experiment, as the acoustic conditions at both "before & after Improvement" were apparently compared, it appeared that there is a considerable amount of noise-reduction effect at psycho-acoustics. It is considered that such material could be utilized as the valuable data hereafter for the time when any construction and renovation of the hydraulic turbine dynamo room and other similar workshop.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.1128-1133
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• 2003

A gas driven heat pump (GHP) core design comprises internal combustion engine, compressors incorporated to a cooling/heating system, rubber mountings and belt transmissions. Main excitation farces are generated by an engine, compressors themselves and belt fluctuation. It leads to high vibration level of the mount that can cause damage of GHP elements. Therefore an appropriate design of the mounting system is crucial in terms of reliability and vibration reduction. In this paper oscillation of the engine mount is explored both experimentally and analytically. Experimental analysis of natural frequencies and operational frequency response of the GHP engine mounting system enables to create simplified model for numerical and analytical investigations. It is worked out criteria f3r vibration abatement of the isolated structure. Influence of bracket stiffness between engine and compressors, suspension locations and damper performance is investigated. Ways to reduce excitation forces and improve dynamic performance of the engine-compressor mounting system are considered from these analyses. Implementation of the proposed approach permits to choose appropriate rubber mountings and their location as well as joining elements design A phase matching technique can be employed to control forces from main exciters. It enables to changing vibration response of the structure by control of natural modes contribution. Proposed changes lead to significant vibration reduction and can be easily utilized in engineering practice.