• Smart Structures and Systems
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• v.17 no.6
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• pp.957-980
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• 2016

Structural damage detection (SDD) is a challenging task in the field of structural health monitoring (SHM). As an exploring attempt to the SDD problem, a hybrid self-adaptive Firefly-Nelder-Mead (SA-FNM) algorithm is proposed for the SDD problem in this study. First of all, the basic principle of firefly algorithm (FA) is introduced. The Nelder-Mead (NM) algorithm is incorporated into FA for improving the local searching ability. A new strategy for exchanging the information in the firefly group is introduced into the SA-FNM for reducing the computation cost. A random walk strategy for the best firefly and a self-adaptive control strategy of three key parameters, such as light absorption, randomization parameter and critical distance, are proposed for preferably balancing the exploitation and exploration ability of the SA-FNM. The computing performance of the SA-FNM is evaluated and compared with the basic FA by three benchmark functions. Secondly, the SDD problem is mathematically converted into a constrained optimization problem, which is then hopefully solved by the SA-FNM algorithm. A multi-step method is proposed for finding the minimum fitness with a big probability. In order to assess the accuracy and the feasibility of the proposed method, a two-storey rigid frame structure without considering the finite element model (FEM) error and a steel beam with considering the model error are taken examples for numerical simulations. Finally, a series of experimental studies on damage detection of a steel beam with four damage patterns are performed in laboratory. The illustrated results show that the proposed method can accurately identify the structural damage. Some valuable conclusions are made and related issues are discussed as well.

• Proceedings of the SAREK Conference
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• 2008.11a
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• pp.227-232
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• 2008

The present study was investigated the local heat transfer characteristics and temperature distribution on the louver fin by using the expansion model. Heat transfer rate, frost mass and temperature distribution of the louver fin under frosting condition were experimentally investigated. Local heat transfer rate and heat flux on the louver were analyzed by the conduction heat transfer between top and lower part of the louver. The experimental key parameter was brine inlet temperature(-5, -10, $-15^{\circ}C$). The heat transfer performance and frost mass at brine temperature of $-15^{\circ}C$ were increased by maximum 3 time than the brine temperature of $-5^{\circ}C$. At all experimental case, local heat transfer rate and heat flux of the louver were almost symmetry at the louver number of 6. Especially, local heat transfer rate and heat flux were maximum increased on the louver number of 4 and 8.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.10
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• pp.1100-1108
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• 1992

The evaluation of coded error probabilities for antijam communication systems is usually difficult to do and, thus, easy-to-evaluate upper bounds are used. Since it is relatively easy to evaluate the cutoff rate for the coding channel, the coded bit error bounds for most antijam systems of interest can be easily expressed directly in terms of this cutoff rate parameter using the relationship between the bit error bounds and cutoff rate for AWGN channel. The key feature of these bounds is the decoupling of the coding aspects of the system from the remaining part of the communication system which includes jamming, suboptimum detectors, and arbitrary decoding metrics which may or may not use jammer state knowledge. In this paper the bit error bounds for the Trumpis coded FH/MFSK with an AWGN channel are translated into the corresponding bit error bounds for boradband and partial band noise jammer. And the impact of the side information about jammer state is also evaluated with these upper bounds. Although it is considered for the soft decision detector, it is also applicable to the hard decision detector.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.26 no.3
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• pp.198-203
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• 2013

Dye-sensitized solar cells (DSSCs) based on titanium dioxide ($TiO_2$) have been extensively studied because of their promising low-cost alternatives to conventional semiconductor based solar cells. DSSCs consist of molecular dye at the interface between a liquid electrolyte and a mesoporous wide-bandgap semiconductor oxide. Most efforts for high conversion efficiencies have focused on dye and liquid electrolytes. However, interface engineering between dye and electrode is also important to reduce recombination and improve efficiency. In this work, for interface engineering, we deposited semiconducting ferroelectric $BiFeO_3$ with bandgap of 2.8 eV on $TiO_2$ nanoparticles and nanotubes. Photovoltaic properties of DSSCs were characterized as a function of thickness of $BiFeO_3$. We showed that ferroelectric $BiFeO_3$-coated $TiO_2$ electrodes enable to increase overall efficiency of DSSCs, which was associated with efficient electron transport due to internal electric field originating from electric polarization. It was suggested that engineering the dye-$TiO_2$ interface using ferroelectric materials as inorganic modifiers can be key parameter for enhanced photovoltaic performance of the cell.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3984-4005
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• 2017

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.

• Smart Structures and Systems
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• v.24 no.5
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• pp.631-639
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• 2019

Finite element analysis is one of the important methods to study the structural performance. Due to the simplification, discretization and error of structural parameters, numerical model errors always exist. Besides, structural characteristics may also change because of material aging, structural damage, etc., making the initial finite element model cannot simulate the operational response of the structure accurately. Based on Bayesian methods, the initial model can be updated to obtain a more accurate numerical model. This paper presents the work on the field test, modal identification and model updating of a Chinese reinforced concrete pagoda. Based on the ambient vibration test, the acceleration response of the structure under operational environment was collected. The first six translational modes of the structure were identified by the enhanced frequency domain decomposition method. The initial finite element model of the pagoda was established, and the elastic modulus of columns, beams and slabs were selected as model parameters to be updated. Assuming the error between the measured mode and the calculated one follows a Gaussian distribution, the posterior probability density function (PDF) of the parameter to be updated is obtained and the uncertainty is quantitatively evaluated based on the Bayesian statistical theory and the Metropolis-Hastings algorithm, and then the optimal values of model parameters can be obtained. The results show that the difference between the calculated frequency of the finite element model and the measured one is reduced, and the modal correlation of the mode shape is improved. The updated numerical model can be used to evaluate the safety of the structure as a benchmark model for structural health monitoring (SHM).

• Tribology and Lubricants
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• v.37 no.6
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• pp.253-260
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• 2021

Metal pipes are used in a wide range of applications, from plumbing systems of large construction sites to small devices such as medical tools. When a liquid is enforced to flow through a metal pipe, a higher flow rate is beneficial for higher efficiency. Using high pressures can enhance the flow rate yet can be harmful for medical applications. Thus, we consider an optimal geometrical design to increase the flow rate in medical devices. In this study, we focus on cannulas, which are widely used small metal pipes for surgical procedures, such as liposuction. We characterize the internal flow rate driven by a negative pressure and explore its dependence on the key design parameters. We quantitatively analyze the suction characteristics for each design variable by conducting computational fluid dynamics simulations. In addition, we build a suction performance measurement system which enables the translational motion of cannulas with pre-programmed velocity for experimental validation. The inner diameter, section geometry, and hole configuration are the design factors to be evaluated. The effect of the inner diameter dominates over that of section geometry and hole configuration. In addition, the circular tube shape provides the maximum flow rate among the elliptical geometries. Once the flow rate exceeds a critical value, the rate becomes independent of the number and width of the suction holes. Finally, we introduce a slippery liquid-infused nanoporous surface (SLIPS) coating using nanoparticles and hydrophobic lubricants that effectively improves the flow rate and antifouling property of cannulas without altering the geometrical design parameter.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.379-395
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• 2021

For the complicated structural details in ships and offshore structures, the traditional hotspot stress approaches are known to be sensitive to the element variables of element topologies, sizes, and integration schemes. This motivated to develop a new approach for predicting reasonable hotspot stresses, which is less sensitive to the element variables and easy to be implemented the real marine structures. The three-point bending tests were conducted for the longitudinal attachments with the round and rectangular weld toes. The tests were reproduced in the numerical simulations using the solid and shell element models, and the simulation technique was validated by comparing the experimental stresses with the simulated ones. This paper considered three hotspot stress approaches: the ESM method based on surface stress extrapolation, the Dong's method based on nodal forces along a weld toe, and the proposed method based on nodal forces perpendicular to an imaginary vertical plane at a weld toe. In order to study the element sensitivities of each method, 16 solid element models and 8 shell element models were generated under the bending and tension loads, respectively. The element sensitivity was analyzed in terms of Stress Concentration Factors (SCFs) in viewpoints of two statistical quantities of mean and bias with respect to the reference SCFs. The average SCFs predicted by the proposed method were remarkably in good agreement with the reference SCFs based on the experiments and the ship rules. Negligibly small Coefficients of Variation (CVs) of the SCFs, which is measure of statistical bias, were drawn by the proposed method.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.2
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• pp.95-109
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• 2023

In the 4th industrial revolution, changes in the technological paradigm have had a direct impact on the maintenance system of ships. The 2-stroke low speed engine system integrates with the core equipment required for propulsive power. The Condition Based Management (CBM) is defined as a technology that predictive maintenance methods in existing calender-based or running time based maintenance systems by monitoring the condition of machinery and diagnosis/prognosis failures. In this study, we have established a framework for CBM technology development on our own, and are engaged in engineering-based failure analysis, data development and management, data feature analysis and pre-processing, and verified the reliability of failure mode DB using LSTM algorithms. We developed various simulated failure mode scenarios for 2-stroke low speed engine and researched to produce data on onshore basis test_beds. The analysis and pre-processing of normal and abnormal status data acquired through failure mode simulation experiment used various Exploratory Data Analysis (EDA) techniques to feature extract not only data on the performance and efficiency of 2-stroke low speed engine but also key feature data using multivariate statistical analysis. In addition, by developing an LSTM classification algorithm, we tried to verify the reliability of various failure mode data with time-series characteristics.

• Steel and Composite Structures
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• v.45 no.1
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• pp.133-145
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• 2022

Steel fiber composite bar (SFCB) is a novel type of reinforcement, which has good ductility and durability performance. Due to the unique pseudo strain hardening tensile behavior of SFCB, different flexural behavior is expected of SFCB reinforced concrete (SFCB-RC) beams from traditional steel bar reinforced concrete (S-RC) beams and FRP bar reinforced concrete (F-RC) beams. To investigate the flexural behavior of SFCB-RC beam, four points bending tests were carried out and different flexural behaviors between S/F/SFCB-RC beams were discussed. An flexural analytical model of SFCB-RC beams is proposed and proved by the current and existing experimental results. Based on the proposed model, the influence of the fiber volume ratio R of the SFCB on the flexural behavior of SFCB-RC beams is discussed. The results show that the proposed model is effective for all S/F/SFCB-RC flexural members. Fiber volume ratio R is a key parameter affecting the flexural behavior of SFCB-RC. By controlling the fiber volume ratio of SFCB reinforcements, the flexural behavior of the SFCB-RC flexural members such as bearing capacity, bending stiffness, ductility and repairability of SFCB-RC structures can be designed.