• Earthquakes and Structures
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• v.11 no.2
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• pp.245-264
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• 2016

A suitable ground motion intensity measure (IM) plays a crucial role in the seismic performance assessment of a structure. In this paper, we introduce a scalar IM for use in evaluating the seismic response of single-layer reticulated domes. This IM is defined as the weighted geometric mean of the spectral acceleration ordinates at the periods of the dominant vibration modes of the structure considered, and the modal strain energy ratio of each dominant vibration mode is the corresponding weight. Its applicability and superiority to 11 other existing IMs are firstly investigated in terms of correlation with the nonlinear seismic response, efficiency and sufficiency using the results of incremental dynamic analyses which are performed for a typical single-layer reticulated dome. The hazard computability of this newly proposed IM is also briefly discussed and illustrated. A conclusion is drawn that this dominant vibration mode-based scalar IM has the characteristics of strong correlation, high efficiency, good sufficiency as well as hazard computability, and thereby is appropriate for use in the prediction of seismic response of single-layer reticulated domes.

• Structural Engineering and Mechanics
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• v.52 no.6
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• pp.1099-1120
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• 2014

In this paper, reliability-based design optimization (RBDO) of structures is addressed. For this purpose, the global search and optimization capabilities of genetic algorithm (GA) are combined with the efficiency and reasonable accuracy of an advanced moment-based finite element reliability method. For performing RBDO, three variants of GA including a real-coded, a binary-coded and an improved binary-coded GA are developed. In these methods, GA performs (finite element) reliability analyses to evaluate reliability constraints. For truss structures which include finite element modeling, reliability constraints are evaluated using finite element reliability analysis. Response sensitivity required for finite element reliability analysis is obtained by direct differentiation method (DDM) rather than finite difference method (FDM). The proposed methods are examined within four standard test examples and real-world design problems. The results illustrate the superiority and efficiency of the improved binary-coded GA. Results also illustrate that DDM significantly reduces the computational cost and improves the efficiency of the optimization procedure.

• Journal of Power Electronics
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• v.18 no.2
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• pp.522-532
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• 2018

The cascaded H Bridge (CHB) multilevel inverter (MLI) is popular among the classical MLI topologies due to its modularity and reliability. Although space vector modulation (SVM) is the most suitable modulation scheme for MLIs, it has not been used widely in industry due to the higher complexity involved in its implementation. In this paper, a simple and novel generalized SVM algorithm is proposed, which has both reduced time and space complexity. The proposed SVM involves the generalization of both the duty cycle calculation and switching sequence generation for any n-level inverter. In order to generate the gate pulses for an inverter, a generalized switch matrix (SM) for the CHB inverter is also introduced, which further simplifies the algorithm. The algorithm is tested and verified for three-phase, three-level and five-level CHB inverters in simulations and hardware implementation. A comparison of the proposed method with existing SVM schemes shows the superiority of the proposed scheme.

• Bulletin of the Korean Chemical Society
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• v.34 no.3
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• pp.801-809
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• 2013

We investigate sensitivity and limit of detection (LOD) of trace copper (Cu) metal using pristine carbon nanotube (CNT) and acidified CNT (ACNT) electrodes. Squarewave based anodic stripping voltammetry (SWASV) is used to determine the stripped Cu concentration. Prior to performing the SWASV measurements, its optimal conditions are determined and with that, effects of potential scan rate and $Cu^{2+}$ concentration on stripping current are evaluated. The measurements indicate that (1) ACNT electrode shows better results than CNT electrode and (2) stripping is controlled by surface reaction. In the given $Cu^{2+}$ concentration range of 25-150 ppb, peak stripping current has linearity with $Cu^{2+}$ concentration. Quantitatively, sensitivity and LOD of Cu in ACNT electrode are 9.36 ${\mu}A\;{\mu}M^{-1}$ and 3 ppb, while their values are 3.99 ${\mu}A\;{\mu}M^{-1}$ and 3 ppb with CNT electrode. We evaluate the effect of three different water solutions (deionized water, tap water and river water) on stripping current and the confirm types of water don't affect the sensitivity of Cu. It turns out by optical inspection and cyclic voltammetry that superiority of ACNT electrode to CNT electrode is attributed to exfoliation of CNT bundles and improved interfacial adhesion occurring during oxidation of CNTs.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.285-302
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• 2010

Active aeroelastic control is an emerging technology aimed at providing solutions to structural systems that under the action of aerodynamic loads are prone to instability and catastrophic failures, and to oscillations that can yield structural failure by fatigue. The purpose of the aeroelastic control among others is to alleviate and even suppress the vibrations appearing in the flight vehicle subcritical flight regimes, to expand its flight envelope by increasing the flutter speed, and to enhance the post-flutter behavior usually characterized by the presence of limit cycle oscillations. Recently adaptive and robust control strategies have demonstrated their superiority to classical feedback strategies. This review paper discusses the latest development on the topic by the authors. First, the available control techniques with focus on adaptive control schemes are reviewed, then the attention is focused on the advanced single-input and multi-input multi-output adaptive feedback control strategies developed for lifting surfaces operating at subsonic and supersonic flight speeds. A number of concepts involving various adaptive control methodologies, as well as results obtained with such controls are presented. Emphasis is placed on theoretical and numerical results obtained with the various control strategies.

• Journal of Power Electronics
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• v.16 no.2
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• pp.610-620
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• 2016

The compound active clamp zero voltage soft switching (CACZVS) three-phase power factor correction (PFC) converter has many advantages, such as high efficiency, high power factor, bi-directional energy flow, and soft switching of all the switches. Triple closed-loop PI controllers are used for the three-phase power factor correction converter. The control objectives of the converter include a fast transient response, high accuracy, and unity power factor. There are six parameters of the controllers that need to be tuned in order to obtain multi-objective optimization. However, six of the parameters are mutually dependent for the objectives. This is beyond the scope of the traditional experience based PI parameters tuning method. In this paper, an improved chaotic particle swarm optimization (CPSO) method has been proposed to optimize the controller parameters. In the proposed method, multi-dimensional chaotic sequences generated by spatiotemporal chaos map are used as initial particles to get a better initial distribution and to avoid local minimums. Pareto optimal solutions are also used to avoid the weight selection difficulty of the multi-objectives. Simulation and experiment results show the effectiveness and superiority of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.736-752
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• 2010

We propose a novel collision-avoidance algorithm for the active type RFID regarding an indoor tracking system. Several well-known collision avoidance algorithms are analyzed considering the adequacy for the indoor tracking system. We prove the superiority of the slotted ALOHA in comparison with CSMA for short and fixed length packets like an ID message in RFID. Observed results show that they are not applicable for active type RFID in terms of energy efficiency. Putting these all together, we propose a dedicated collision avoidance algorithm considering the unique features of the indoor tracking system. The proposed method includes a scheduled tag access period (STAP) as well as a random tag access period (RTAP) to address both of the static and dynamic characteristics of the system. The system parameters are determined through a quantitative analysis of the throughput and energy efficiency. Especially, some mathematical techniques have been deployed to obtain the optimal slot count for RTAP. Finally, simulation results are provided to illustrate the performance of the proposed method with variations of the parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1682-1701
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• 2015

In this paper, we propose an intergraded unified imperfect CSI model and investigate the joined effects of feedback delay and channel estimation errors (CEE) for two-hop relaying systems with transmit beamforming and relay selection. We derived closed-form expressions for important performance measures including the exact analysis and lower bounds of outage probability as well as error performance. The ergodic capacity is also included with closed-form results. Furthermore, diversity and coding gains based on the asymptotic analysis at high SNRs are also presented, which are simple and concise and provide new analytical insights into the corresponding power allocation scheme. The analysis indicates that delay effect results in the coding gain loss and the diversity order loss, while CEE will merely cause the coding gain loss. Numerical results verify the theoretical analysis and illustrate the system is more sensitive to transmit beamforming delay compared with relay selection delay and also verify the superiority of optimum power allocation. We further investigate the outage loss due to the CEE and feedback delays, which indicates that the effect of the CEE is more influential at low-to-medium SNR, and then it will hand over the dominate role to the feedback delay.

• ETRI Journal
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• v.42 no.2
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• pp.258-271
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• 2020

A correlation exists between luminance samples and chrominance samples of a color image. It is beneficial to exploit such interchannel redundancy for color image compression. We propose an algorithm that predicts chrominance components Cb and Cr from the luminance component Y. The prediction model is trained by supervised learning with Laplacian-regularized least squares to minimize the total prediction error. Kernel principal component analysis mapping, which reduces computational complexity, is implemented on the same point set at both the encoder and decoder to ensure that predictions are identical at both the ends without signaling extra location information. In addition, chrominance subsampling and entropy coding for model parameters are adopted to further reduce the bit rate. Finally, luminance information and model parameters are stored for image reconstruction. Experimental results show the performance superiority of the proposed algorithm over its predecessor and JPEG, and even over JPEG-XR. The compensation version with the chrominance difference of the proposed algorithm performs close to and even better than JPEG2000 in some cases.

• Advances in Energy Research
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• v.6 no.2
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.