• Smart Structures and Systems
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• v.25 no.6
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• pp.765-778
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• 2020

The present study intends to find a proper solution for the estimation of the physical behaviors of enlarged piles through a combination of small-scale laboratory tests and a hybrid computational predictive intelligence process. In the first step, experimental program is completed considering various critical influential factors. The results of the best multilayer perceptron (MLP)-based predictive network was implemented through three mathematical-based solutions of dragonfly algorithm (DA), whale optimization algorithm (WOA), and ant lion optimization (ALO). Three proposed models, after convergence analysis, suggested excellent performance. These analyses varied based on neurons number (e.g., in the basis MLP hidden layer) and of course, the level of its complexity. The training R² results of the best hybrid structure of DA-MLP, WOA-MLP, and ALO-MLP were 0.996, 0.996, and 0.998 where the testing R² was 0.995, 0.985, and 0.998, respectively. Similarly, the training RMSE of 0.046, 0.051, and 0.034 were obtained for the training and testing datasets of DA-MLP, WOA-MLP, and ALO-MLP techniques, while the testing RMSE of 0.088, 0.053, and 0.053, respectively. This obtained result demonstrates the excellent prediction from the optimized structure of the proposed models if only population sensitivity analysis performs. Indeed, the ALO-MLP was slightly better than WOA-MLP and DA-MLP methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2356-2376
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• 2021

In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.382-388
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• 2011

Being aware of the risk in advance necessitates intricate processes but is feasible. Although previous studies have demonstrated high accuracy, their performance still leaves room for improvement. A self-organizing feature map (SOM) based neurofuzzy model is developed in this study to provide another alternative for forecasting corporate financial distress. The model is designed to yield high prediction accuracy, as well as reference rules for evaluating corporate financial status. As a database, the study collects all financial reports from listed construction companies during the latest decade, resulting in over 1000 effective samples. The proportion of "failed" and "non-failed" companies is approximately 1:2. Each financial report is comprised of 25 ratios which are set as the input variable s. The proposed model integrates the concepts of pattern classification, fuzzy modeling and SOM-based optimization to predict corporate financial distress. The results exhibit a high accuracy rate at 85.1%. This model outperforms previous tools. A total of 97 rules are extracted from the proposed model which can be also used as reference for construction practitioners. Users may easily identify their corporate financial status by using these rules.

• The Journal of the Korea Contents Association
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• v.17 no.7
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• pp.93-100
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• 2017

Recently, demands for high-performance flash-based storage devices (i.e., flash SSD) have rapidly grown in social network services, cloud computing, super-computing, and enterprise storage systems. The industry and academic communities made the NVMe specification for high-performance storage devices, and NVMe-based flash SSDs can be now obtained in the market. In this article, we evaluate performance of NoSQL databases that social network services and cloud computing services heavily adopt by using NVMe-based flash SSDs. To this end, we use NVMe SSD that Samsung Electronics recently developed, and the SSD used in this study has performance up to 3.5GB/s for sequential read/write operations. We use WiredTiger for NoSQL databases, and it is a default storage engine for MongoDB. Our experimental results show that log processing in NoSQL databases is a major overhead when high-performance NVMe-based flash SSDs are used. Furthermore, we optimize components of log processing and optimized WiredTiger show up to 15 times better performance than original WiredTiger.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.128-135
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• 2021

Thin-walled tubes have been widely used as energy absorbing devices because they are light and have high energy-absorption efficiency. However, the downside is that conventional thin-walled tubes usually exhibit an excessive initial peak crushing force (IPCF) and a large fluctuation in the load-displacement curve, and thus lack stability as energy absorbing devices. Corrugated tubes were introduced to reduce IPCF and to increase the stability of collision energy-absorbing devices. Since the performance of corrugated tubes is highly influence by geometry, design optimization methods can be utilized to optimize the performance of corrugated tubes. In this paper, we utilize shape design optimization based on an adaptive surrogate model for crashworthiness analysis. The amplitude and wavelength of the corrugation, as well as curvature changes in the features, are the design variables. A morphing methodology is adopted to perform shape design parameterization. Through numerical examples, we compare optimal design results based on the adaptive surrogate model, with optimal results based on conventional surrogate models, and we show that direct optimal design methods produce more efficient results.

• The KSFM Journal of Fluid Machinery
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• v.19 no.1
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• pp.18-23
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• 2016

A HVAC(Heating Ventilation and Air Conditioning) is adapted to increase the comfort of the cabin environment for train. The train HVAC duct system has very long duct and many outlets due to the shape of a train set. the duct cross section shape is limited by a roof structure and equipments. Therefore, the pressure distribution and flow uniformity is an important performance indicator for the duct system. In this study, the existing blow down type HVAC duct system for a train was supplemented to improve the flow uniformity by applying a design method combining design of experiment (DOE) with numerical analysis. The design variables and the test sets were selected and the performance for each test set was evaluated using CFD(Computational Fluid Dynamics). The influence of each design variable on the system performance was analysed based on the results of the performance evaluation on the test sets. Furthermore, the optimized model, whose the flow uniformity was improved was produced using the direct optimization(gradient-based method). Finally, the performance of the optimized model was evaluated using numerical analysis, and it was confirmed that its flow uniformity has indeed improved.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.5
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• pp.687-698
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• 2012

LADAR systems can rapidly acquire 3D point clouds by sampling the target surfaces using laser pulses. Such point clouds are widely used for diverse applications such as DSM/DTM generation, forest biomass estimation, target detection, wire avoidance and so on. Many kinds of LADAR systems have been developed with their respective purposes and applications. Particularly, Geiger mode imaging LADAR systems are increasingly utilized since they are energy efficient thank to extremely sensitive detectors incorporated into the systems. The purpose of this research is the performance assessment of a Geiger mode imaging LADAR system based on simulation with the real system parameters. We thus developed a simulation method of such a LADAR system by modeling its geometric, radiometric, optic and electronic aspects. Based on the simulation, we performed the performance assessment of a newly designed system to derive the outlier ratio and false alarm rate expected during its operation in almost real environment with reasonable system parameters. The proposed simulation and performance assessment method will be effectively utilized for system design and optimization, and test data generation.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.6
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• pp.331-344
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• 2020

Ship's hydrodynamic coefficients in manoeuvring equations are generally derived by captive model tests or numerical calculations. Empirical formulas have been also proposed in some previous researches, which were useful for practical predictions of hydrodynamic coefficients of a ship by using main dimensions only. In this study, ship's hydrodynamic coefficients based on empirical formulas were optimized by using its free running test data. Eight manoeuvring performance indices including steady turning radius, reach in zig-zag as well as well-known IMO criteria indices are selected in order to compare simulation results with free runs effectively. Sensitivities of hydrodynamic coefficients on manoeuvring performance indices are analyzed. And hydrodynamic coefficients are tuned within fixed bounds in order of sensitivity so that they are tuned as little as possible. Linear and nonlinear coefficients are successively tuned by using zig-zag and turning performance indices. Trajectories and velocity components by simulations with tuned hydrodynamic coefficients are in good agreements with free running tests. Tuned coefficients are also compared with coefficients by captive model tests or RANS calculations in other previous researches, and the magnitudes and signs of tunes are discussed.

• Steel and Composite Structures
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• v.51 no.5
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• pp.473-485
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• 2024

This paper proposes a composite form of fuzzy adaptive control plan based on a robust observer. The fuzzy 2D control gains are regulated by the parameters in the LMIs. Then, control and learning performance indices with weight matrices are constructed as the cost functions, which allows the regulation of the trade-off between the two performance by setting appropriate weight matrices. The design of 2D control gains is equivalent to the LMIs-constrained multi-objective optimization problem under dual performance indices. By using this proposed smart tracking design via fuzzy nonlinear criterion, the data link can be further extended. To evaluate the performance of the controller, the proposed controller was compared with other control technologies. This ensures the execution of the control program used to track position and trajectory in the presence of great model uncertainty and external disturbances. The performance of monitoring and control is verified by quantitative analysis. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and control theory.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.93-113
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• 2020

According to information theory, non-orthogonal transmission can achieve the multiple-user channel capacity with an onion-peeling like successive interference cancellation (SIC) based detection followed by a capacity approaching channel code. However, in multiple antenna system, due to the unideal characteristic of the SIC detector, the residual interference propagated to the next detection stage will significantly degrade the detection performance of spatial data layers. To overcome this problem, we proposed a modified power-domain non-orthogonal multiple access (P-NOMA) scheme joint designed with space-time coding for multiple input multiple output (MIMO) NOMA system. First, with proper power allocation for each user, inter-user signals can be separated from each other for NOMA detection. Second, a well-designed quasi-orthogonal space-time block code (QO-STBC) was employed to facilitate the SIC-based MIMO detection of spatial data layers within each user. Last, we proposed an optimization algorithm to assign channel coding rates to balance the bit error rate (BER) performance of those spatial data layers for each user. Link-level performance simulation results demonstrate that the proposed time-space-power domain joint transmission scheme performs better than the traditional P-NOMA scheme. Furthermore, the proposed algorithm is of low complexity and easy to implement.