• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Structural Engineering and Mechanics
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• v.57 no.1
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• pp.105-126
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• 2016

In the present study, modelling and vibration control of axially moving laminated Carbon nanotubes/fiber/polymer composite (CNTFPC) plate under initial tension are investigated. Orthotropic visco-Pasternak foundation is developed to consider the influences of orthotropy angle, damping coefficient, normal and shear modulus. The governing equations of the laminated CNTFPC plates are derived based on new form of first-order shear deformation plate theory (FSDT) which is simpler than the conventional one due to reducing the number of unknowns and governing equations, and significantly, it does not require a shear correction factor. Halpin-Tsai model is utilized to evaluate the material properties of two-phase composite consist of uniformly distributed and randomly oriented CNTs through the epoxy resin matrix. Afterwards, the structural properties of CNT reinforced polymer matrix which is assumed as a new matrix and then reinforced with E-Glass fiber are calculated by fiber micromechanics approach. Employing Hamilton's principle, the equations of motion are obtained and solved by Hybrid analytical numerical method. Results indicate that the critical speed of moving laminated CNTFPC plate can be improved by adding appropriate values of CNTs. These findings can be used in design and manufacturing of marine vessels and aircrafts.

• Computers and Concrete
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• v.21 no.6
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• Current Optics and Photonics
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• v.2 no.2
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• pp.140-146
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• 2018

In this paper, mesoscale optical surface structures are found to possess both geometric and wave optics features. The study reveals that geometric optic analysis cannot correctly predict the experimental results of light transmission or reflection by mesoscale optical structures, and that, for reliable analyses, a hybrid approach incorporating both geometric and wave optic theories should be employed. By analyzing the transmission patterns generated by the mesoscale periodic pyramid prism plates, we show that the wave optic feature is mainly ascribed to the edge diffraction effect and we estimate the relative contributions of the wave optic diffraction effect and the geometric refraction effect to the total scattering field distribution with respect to the relative dimension of the structures.

• Korean Medical Education Review
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• v.23 no.3
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• pp.147-153
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• 2021

The coronavirus disease 2019 (COVID-19) pandemic made it necessary for medical schools to restructure their curriculum by switching from face-to-face instruction to various forms of flexible learning. Flexible learning is a student-centered approach to learning that has received interest in many educational sectors. It is a critical strategy for expanding access to higher education during the pandemic. As flexible learning includes online, blended, hybrid, and hyflex learning options, learners have the opportunity to select an instruction modality based on their needs and interests. The shift to flexible learning in medical education took place rapidly in response to the COVID-19 pandemic, and learners, instructors, and schools were not prepared for this instructional change. Through the lens of the technology acceptance model, human agency, and a social constructivist perspective, I examine students, instructors, and educational institutions' roles in successfully navigating the digital transformation era. The pandemic has also accelerated the use of advanced information and communication technologies, such as artificial intelligence and virtual reality, in learning. Through a review of the literature, this paper aimed to reflect on current flexible learning practices from the instructional design and educational technology perspective and explore emerging technologies that may be implemented in future medical education.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.87-96
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• 2021

The demand for e-learning through video lectures is rapidly increasing due to its diverse advantages over the traditional learning methods. This led to massive volumes of web-based lecture videos. Indexing and retrieval of a lecture video or a lecture video topic has thus proved to be an exceptionally challenging problem. Many techniques listed by literature were either visual or audio based, but not both. Since the effects of both the visual and audio components are equally important for the content-based indexing and retrieval, the current work is focused on both these components. A framework for automatic topic-based indexing and search depending on the innate content of the lecture videos is presented. The text from the slides is extracted using the proposed Merged Bounding Box (MBB) text detector. The audio component text extraction is done using Google Speech Recognition (GSR) technology. This hybrid approach generates the indexing keywords from the merged transcripts of both the video and audio component extractors. The search within the indexed documents is optimized based on the Naïve Bayes (NB) Classification and K-Means Clustering models. This optimized search retrieves results by searching only the relevant document cluster in the predefined categories and not the whole lecture video corpus. The work is carried out on the dataset generated by assigning categories to the lecture video transcripts gathered from e-learning portals. The performance of search is assessed based on the accuracy and time taken. Further the improved accuracy of the proposed indexing technique is compared with the accepted chain indexing technique.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• Journal of the Korean Electrochemical Society
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• v.21 no.4
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• pp.68-79
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• 2018

Currently, hybridization of energy generator and storage devices is considered to be one of the most important energy-related technologies due to the possibility of replacing batteries or extending the lifetime of a batteries in accordance with increasing battery demand. This review aims to describe current progress on the mechanical energy generator and hybridization of energy generator and energy storage devices for self-powered electronics. First, the research trends related to energy generation devices using piezoelectric and triboelectric effect that convert physical energy into electric energy is introduced. In addition, integration of energy generators and energy storage devices is introduced. In particular, self-charging energy cells provide an innovative approach to the direct conversion of mechanical energy into electrochemical energy to decrease energy conversion loss.

• Wind and Structures
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• v.29 no.2
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• pp.111-127
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• 2019

The tapered and set-back type of unconventional designs have been used earlier in many buildings. These shapes are aerodynamically efficient and offer a significant amount of damping against wind-induced forces and excitations. Various studies have been conducted on these shapes earlier. The present study adopts a hybrid approach of turbulence modelling i.e., Detached-eddy Simulation (DES) to investigate the effect of height modified tapered and set-back buildings on aerodynamic forces and their sensitivity towards pressure. The modifications in the flow field around the building models are also investigated and discussed. Three tapering ratios (T.R.=(Bottom width- Top width)/Height) i.e., 5%, 10%, 15% are considered for tapered and set-back buildings. The results show that, mean and RMS along-wind and across-wind forces are reduced significantly for the aerodynamically modified buildings. The extent of reduction in the forces increases as the taper ratio is increased, however, the set-back modifications are more worthwhile than tapered showing greater reduction in the forces. The pressure distribution on the surfaces of the buildings are analyzed and in the last section, the influence of the flow field on the forces is discussed.

• Journal of Power Electronics
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• v.19 no.1
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• pp.254-264
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• 2019

This paper proposes an indirect current control technique based on a proportional resonant (PR) approach for the seamless mode transfer of utility interactive inverters. Direct-current and voltage hybrid control methods have been used for inverter control under grid-connected and islanded modes. A large bandwidth can be selected due to the structure of single-loop control. However, this results in poor dynamic transients due to sudden changes of the controller during mode changes. Therefore, inverter control based on indirect current is proposed to improve the dynamic transients by consistently controlling the output voltage under all of the operation modes. A PR-based indirect current control topology is used in this study to maintain the load voltage quality under all of the modes. The design processes of the PR-based triple loop are analyzed in detail while considering the system stability and dynamic transients. The mode transfer techniques are described in detail for both sudden unintentional islanding and islanded mode voltage quality improvements. In addition, they are described using the proposed indirect control structure. The proposed method is verified by the PSiM simulations and laboratory-scale VDER-HILS experiments.