• Wind and Structures
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• v.38 no.1
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1186-1207
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• 2024

Bearings are one of the main components of mechanical equipment and one of the primary components prone to faults. Therefore, conducting fault diagnosis on bearings is a key issue in mechanical equipment research. Belief rule base (BRB) is essentially an expert system that effectively integrates qualitative and quantitative information, demonstrating excellent performance in fault diagnosis. However, class imbalance often occurs in the diagnosis task, which poses challenges to the diagnosis. Models with interpretability can enhance decision-makers' trust in the output results. However, the randomness in the optimization process can undermine interpretability, thereby reducing the level of trustworthiness in the results. Therefore, a hierarchical BRB model based on extreme gradient boosting (XGBoost) feature selection with interpretability (HFS-IBRB) is proposed in this paper. Utilizing a main BRB alongside multiple sub-BRBs allows for the conversion of a multi-classification challenge into several distinct binary classification tasks, thereby leading to enhanced accuracy. By incorporating interpretability constraints into the model, interpretability is effectively ensured. Finally, the case study of the actual dataset of bearing fault diagnosis demonstrates the ability of the HFS-IBRB model to perform accurate and interpretable diagnosis.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.116-130
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• 2024

In the Internet-of-Things (IoT) or blockchain-based network systems, secure keys may be stored in individual devices; thus, individual devices should protect data by performing secure operations on the data transmitted and received over networks. Typically, secure functions, such as a physical unclonable function (PUF) and fully homomorphic encryption (FHE), are useful for generating safe keys and distributing data in a network. However, to provide these functions in embedded devices for IoT or blockchain systems, proper inspection is required for designing and implementing embedded system-on-chip (SoC) modules through overhead and performance analysis. In this paper, a virtual platform (SoC VP) was developed that includes a secure key generation module with a PUF and FHE. The SoC VP platform was implemented using SystemC, which enables the execution and verification of various aspects of the secure key generation module at the electronic system level and analyzes the system-level execution time, memory footprint, and performance, such as randomness and uniqueness. We experimentally verified the secure key generation module, and estimated the execution of the PUF key and FHE encryption based on the unit time of each module.

• Steel and Composite Structures
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• v.50 no.5
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• pp.583-594
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• 2024

In this paper, a Guided Simulated Annealing (GSA) algorithm is presented to optimize 2D and 3D steel frames against Progressive Collapse. Considering the nature of structural optimization problems, a number of restrictions and improvements have been applied to the decision mechanisms of the algorithm without harming the randomness. With these improvements, the algorithm aims to focus relatively on the flawed variables of the analyzed frame. Besides that, it is intended to be more rational by instituting structural constraints on the sections to be selected as variables. In addition to the LRFD restrictions, the alternate path method with nonlinear dynamic procedure is used to assess the risk of progressive collapse, as specified in the US Department of Defense United Facilities Criteria (UFC) Design of Buildings to Resist Progressive Collapse. The entire optimization procedure was carried out on a C# software that supports parallel processing developed by the authors, and the frames were analyzed in SAP2000 using OAPI. Time history analyses of the removal scenarios are distributed to the processor cores in order to reduce computational time. The GSA produced 3% lighter structure weights than the SA (Simulated Annealing) and 4% lighter structure weights than the GA (Genetic Algorithm) for the 2D steel frame. For the 3D model, the GSA obtained 3% lighter results than the SA. Furthermore, it is clear that the UFC and LRFD requirements differ when the acceptance criteria are examined. It has been observed that the moment capacity of the entire frame is critical when designing according to UFC.

• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.2
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• pp.33-44
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• 2008

Most of the civil structure - bridges, offshore structures, plant, etc. - have been designed by the classical approaches which deal with all the design parameters as deterministic variables. However, some more advanced techniques are required to evaluate the inherent randomness and uncertainty of each design variable. In this research, a seismic safety assessment algorithm based on the structural reliability analysis has been formulated and computerized for more reasonable seismic design of turbine-generator foundations. The formulation takes the design parameters of the system and loading properties as random variables. Using the proposed method, various kinds of parametric studies have been performed and probabilistic characteristics of the resulted structural responses have been evaluated. Afterwards, the probabilistic safety of the system has been quantitatively evaluated and finally presented as the reliability indexes and failure probabilities. The proposed procedure is expected to be used as a fundamental tool to improve the existing design techniques of turbine-generator foundations.

• Journal of the Korea Concrete Institute
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• v.20 no.1
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• pp.51-58
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• 2008

Analytical probabilistic vulnerability analysis requires extensive computing effort as a result of the randomness in both input motion and response characteristics. In this study, a new methodology whereby a set of vulnerability curves are derived based on the fundamental response quantities of stiffness, strength and ductility is presented. A response database of coefficients describing lognormal vulnerability relationships is constructed by employing aclosed-form solution for a generalized single-degree-of-freedom system. Once the three fundamental quantities of a wide range of structural systems are defined, the vulnerability curves for various limit states can be derived without recourse to further simulation. Examples of application are given and demonstrate the extreme efficiency of the proposed approach in deriving vulnerability relationships.

• Tunnel and Underground Space
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• v.13 no.5
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• pp.397-402
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• 2003

Generally it is likely that rock mass properties are expressed not by a mean value but by values with variation due to its characteristic uncertainty. This characteristic is one of the most important parts for the design of undergound structures, but yet to be fully examined. Stochastic finite element method (SFEM) is contrary to deterministic finite element method in its concept as the former has been developed in order to take the randomness of structural systems into account. Using SFEM, the response variability of structural system can be obtained and it leads probabilistic stability of structure to be analyzed. In this study, displacement response variability of circular opening with hydrostatic stress field are analyzed in terms of rock mass properties having a certain mean and a standard deviation using the SFEM. The analyzed response variability shows that the necessity of probabilistic stability analysis of underground structures using reliable mean value and standard deviation of deformation modulus.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.3
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• pp.133-140
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• 2018

This paper is concerned with the 2-D micostructure generation for $Ni-A{\ell}_2O_3$ dual-phase composite materials and the numerical analysis of mechanical characteristic of hierarchical models of microstructure which are defined in terms of the scale of microstructure. The microstructures of dual-phase composite materials were generated by applying the mathematical RMDF(random morphology description functions) technique to a 2-D RVE of composite materials. And, the hierarchical models of microstructure were defined by the number of Gaussian points. Meanwhile, the volume fractions of metal and ceramic particles were set by adjusting the level of RMD functions. The microstructures which were generated by RMDF technique are definitely random even though the total number of Gaussian points is the same. The randomly generated microstructures were applied to a 2-D beam model, and the variation of normal and shear stresses to the scale of microstructure was numerically investigated. In addition, through the crack analyses, the influence of RMDF randomness and Gauss point number on the crack-tip stress is investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1146-1165
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• 2013

Collaborative awareness between persons with various smart multimedia devices is a new trend in the Internet of Things (IoT). Because of the mobility, randomness, and complexity of persons, it is difficult to achieve complete data awareness and data transmission in IoT. Therefore, research must be conducted on mobile-aware service models. In this work, we first discuss and quantify the social relationships of mobile nodes from multiple perspectives based on a summary of social characteristics. We then define various decision factors (DFs). Next, we construct a directed and weighted community by analyzing the activity patterns of mobile nodes. Finally, a mobile-aware service routing algorithm (MSRA) is proposed to determine appropriate service nodes through a trusted chain and optimal path tree. The simulation results indicate that the model has superior dynamic adaptability and service discovery efficiency compared to the existing models. The mobile-aware service model could be used to improve date acquisition techniques and the quality of mobile-aware service in the IoT.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.961-972
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• 2016

For securing password-based authentication, a user must select and manage a strong password that has sufficient length and randomness. Unfortunately, however, it is known that many users are likely to choose easy-to-remember weak passwords and very poorly manage them. In this paper, we study a domestic user case of password selection and management. We conducted a survey on 327 domestic users and analyzed their tendency on password creation and update strategies, and also on the password structure and account management. We then analyzed an effect of a server's password creation rule on a structure of a user-chosen password. Our findings include that there are password structures and special characters that users significantly prefer while the effect of server's password creation rule is insignificant.