• Structural Engineering and Mechanics
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• v.62 no.6
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• pp.749-758
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• 2017

In structural mechanics, traditional analyses methods usually employ matrix operations for obtaining displacement and internal forces of the structure under the external effects, such as distributed loads, earthquake or wind excitations, and temperature changing inter alia. These matrices are derived from the well-known principle of mechanics called minimum potential energy. According to this principle, a system can be in the equilibrium state only in case when the total potential energy of system is minimum. A close examination of the expression of the well-known equilibrium condition for linear problems, $P=K{\Delta}$, where P is the load vector, K is the stiffness matrix and ${\Delta}$ is the displacement vector, it is seen that, basically this principle searches the displacement set (or deformed shape) for a system that minimizes the total potential energy of it. Instead of using mathematical operations used in the conventional methods, with a different formulation, meta-heuristic algorithms can also be used for solving this minimization problem by defining total potential energy as objective function and displacements as design variables. Based on this idea the technique called Total Potential Optimization using Meta-heuristic Algorithms (TPO/MA) is proposed. The method has been successfully applied for linear and non-linear analyses of trusses and truss-like structures, and the results have shown that the approach is much more successful than conventional methods, especially for analyses of non-linear systems. In this study, the application of TPO/MA, with Harmony Search as the selected meta-heuristic algorithm, to cables net system is presented. The results have shown that the method is robust, powerful and accurate.

• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.461-467
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• 2011

Accurate classification of input signals is the key prerequisite for variable bit-rate coding, which has been introduced in order to effectively utilize limited communication bandwidth. Especially, recent surge of multimedia services elevate the importance of speech/music classification. Among many speech/music classifier, the ones based on support vector machine (SVM) have a strong selling point, high classification accuracy, but their computational complexity and memory requirement hinder their way into actual implementations. Therefore, techniques that reduce the computational complexity and the memory requirement is inevitable, particularly for embedded systems. We first analyze implementation of an SVM-based classifier on embedded systems in terms of execution time and energy consumption, and then propose two techniques that alleviate the implementation requirements: One is a technique that removes support vectors that have insignificant contribution to the final classification, and the other is to skip processing some of input signals by virtue of strong correlations in speech/music frames. These are post-processing techniques that can work with any other optimization techniques applied during the training phase of SVM. With experiments, we validate the proposed algorithms from the perspectives of classification accuracy, execution time, and energy consumption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5015-5038
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• 2018

In this paper, a novel spectral-spatial joint sparse representation algorithm for hyperspectral image classification is proposed based on multi-layer superpixels in various scales. Superpixels of various scales can provide complete yet redundant correlated information of the class attribute for test pixels. Therefore, we design a joint sparse model for a test pixel by sampling similar pixels from its corresponding superpixels combinations. Firstly, multi-layer superpixels are extracted on the false color image of the HSI data by principal components analysis model. Secondly, a group of discriminative sampling pixels are exploited as reconstruction matrix of test pixel which can be jointly represented by the structured dictionary and recovered sparse coefficients. Thirdly, the orthogonal matching pursuit strategy is employed for estimating sparse vector for the test pixel. In each iteration, the approximation can be computed from the dictionary and corresponding sparse vector. Finally, the class label of test pixel can be directly determined with minimum reconstruction error between the reconstruction matrix and its approximation. The advantages of this algorithm lie in the development of complete neighborhood and homogeneous pixels to share a common sparsity pattern, and it is able to achieve more flexible joint sparse coding of spectral-spatial information. Experimental results on three real hyperspectral datasets show that the proposed joint sparse model can achieve better performance than a series of excellent sparse classification methods and superpixels-based classification methods.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.3
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• pp.272-275
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• 2014

An improved fuzzy Kalman filtering speed estimation scheme was proposed by means of measuring stator side voltage and current value based on vector control state equation of induction motor. The designed fuzzy adaptive controller conducted recursive online correction of measurement noise covariance matrix by monitoring the ratio of theory residuals and actual residuals to make it approach real noise level gradually, allowing the filter to perform optimal estimation to improve estimation accuracy of EKF. Meanwhile, co-simulation scheme based on MATLAB and Ansoft was proposed in order to improve simulation accuracy. Field-circuit coupling problems of induction motor under the action of vector control were solved and the parameter optimization accuracy was improved dramatically. The simulation and experimental results show that this algorithm has a strong ability to inhibit the random measurement noise. It is able to estimate motor speed accurately, and has superior static and dynamic characteristics.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.3
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• pp.449-457
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• 2017

In this paper, we analyze the security and performance of the Quantis Quantum random number generator in terms of cryptography through experiments. The Quantis' post-processing is designed to output full-entropy via bit-matrix-vector multiplication based on mathematical background, and we used the min-entropy estimating test of NIST SP 800-90B so as to verify whether the output is full-entropy. Quantis minimizes the effect on the random bit rate by using an optimization technique for bit-matrix-vector multiplication, and compared the performance to conditioning functions of NIST SP 800-90B by measuring the random bit rate. Also, we have distinguished what is in Quantis' post-processing to the standard model of NIST in USA and BSI in Germany, and in case of applying Quantis to cryptographic systems in accordance with the CMVP standard, it is recommended to use the output of Quantis as the seed of the approved DRBG.

• Structural Engineering and Mechanics
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• v.72 no.4
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• pp.421-432
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• 2019

In high-speed railway (HSR) system, the structure-borne noise inside viaduct at low frequency has been extensively investigated for its mitigation as a research hotspot owing to its harm to the nearby residents. This study proposed a novel acoustic optimization method for declining the structure-borne noise in viaduct-like structures by separating the acoustic contribution of each structural component in the measured acoustic field. The structural vibration and related acoustic sourcing, propagation, and radiation characteristics for the viaduct box girder under passing vehicle loading are studied by incorporating Finite Element Method (FEM) with Modal Acoustic Vector (MAV) analysis. Based on the Modal Acoustic Transfer Vector (MATV), the structural vibration mode that contributes maximum to the structure-borne noise shall be hereinafter filtered for the acoustic radiation. With vibration mode shapes, the locations of maximum amplitudes for being ribbed to mitigate the structure-borne noise are then obtained, and the structure-borne noise mitigation performance shall be eventually analyzed regarding to the ribbing conduction. The results demonstrate that the structural vibration and structure-borne noise of the viaduct box girder mainly occupy both in the range within 100 Hz, and the dominant frequency bands both are [31.5, 80] Hz. The peak frequency for the structure-borne noise of the viaduct box girder is mainly caused by $16^{th}$ and $62^{th}$ vibration modes; these two mode shapes mainly reflect the local vibration of the wing plate and top plate. By introducing web plate at the maximum amplitude of main mode shapes that contribute most to the acoustic modal contribution factors, the acoustic pressure peaks at the field-testing points are hereinafter obviously declined, this implies that the structure-borne noise mitigation performance is relatively promising for the viaduct.

• Journal of Industrial Technology
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• v.39 no.1
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• pp.15-19
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• 2019

This work focused on characterization of the starch degradation activity from extremophile strain Deinococcus geothermalis. Glucoamylase gene from D. geothermalis was cloned and overexpressed by pET-21a vector using E. coli BL21 (DE3). In order to characterize starch degrading activity of recombinant glucoamylase, enzyme was purified using HisPur Ni-NTA column. The recombinant glucoamylase from D. geothermalis exhibited the optimum temperature as $45^{\circ}C$ for starch degradation activity. And highly acido-stable starch degrading activity was shown at pH 2. For further optimization of starch degrading activity with metal ion, various metal ions ($AgCl_2$, $HgCl_2$, $MnSO_4{\cdot}4H_2O$, $CoCl_2{\cdot}6H_2O$, $MgSO_4$, $ZnSO_4{\cdot}7H_2O$, $K_2SO_4$, $FeCl_2{\cdot}4H_2O$, NaCl, or $CuSO_4$) were added for enzyme reaction. As results, it was found that $FeCl_2{\cdot}4H_2O$ or $MnSO_4{\cdot}4H_2O$ addition resulted in 17% and 9% improved starch degrading activity, respectively. The recombinant glucoamylase from D. geothermalis might be used for simultaneous saccharification and fermentation (SSF) process at high acidic conditions.

• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.49-56
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• 2023

Machine learning methods diversely applied to the Internet of Things (IoT) field have been successful due to the enhancement of computer processing power. They offer an effective way of detecting malicious intrusions in IoT because of their high-level feature extraction capabilities. In this paper, we proposed a novel feature selection method for malicious intrusion detection in IoT by using an evolutionary technique - Genetic Algorithm (GA) and Machine Learning (ML) algorithms. The proposed model is performing the classification of BoT-IoT dataset to evaluate its quality through the training and testing with classifiers. The data is reduced and several preprocessing steps are applied such as: unnecessary information removal, null value checking, label encoding, standard scaling and data balancing. GA has applied over the preprocessed data, to select the most relevant features and maintain model optimization. The selected features from GA are given to ML classifiers such as Logistic Regression (LR) and Support Vector Machine (SVM) and the results are evaluated using performance evaluation measures including recall, precision and f1-score. Two sets of experiments are conducted, and it is concluded that hyperparameter tuning has a significant consequence on the performance of both ML classifiers. Overall, SVM still remained the best model in both cases and overall results increased.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1587-1592
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• 2023

Time-encoded imagers (TEI) are important class of instruments to search for potential radioactive sources to prevent illicit transportation and trafficking of nuclear materials and other radioactive sources. The energy of the radiation cannot be known in advance due to the type and shielding of source is unknown in practice. However, the response function of the time-encoded imagers is related to the energy of neutrons or gamma-rays. An improved image reconstruction method based on MLEM was proposed to correct for the energy induced response difference. In this method, the count vector versus time was first smoothed. Then, the preset response function was adaptively corrected according to the measured counts. Finally, the smoothed count vector and corrected response were used in MLEM to reconstruct the source distribution. A one-dimensional dual-particle time-encode imager was developed and used to verify the improved method through imaging an Am-Be neutron source. The improvement of this method was demonstrated by the image reconstruction results. For gamma-ray and neutron images, the angular resolution improved by 17.2% and 7.0%; the contrast-to-noise ratio improved by 58.7% and 14.9%; the signal-to-noise ratio improved by 36.3% and 11.7%, respectively.