Single Image Super-Resolution Using a Deep Encoder-Decoder Symmetrical Network with Iterative Back Projection

Image super-resolution (SR) usually refers to reconstructing a high resolution (HR) image from a low resolution (LR) image without losing high frequency details or reducing the image quality. Recently, image SR based on convolutional neural network (SRCNN) was proposed and has received much attention due to its end-to-end mapping simplicity and superior performance. This method, however, only using three convolution layers to learn the mapping from LR to HR, usually converges slowly and leads to the size of output image reducing significantly. To address these issues, in this work, we propose a novel deep encoder-decoder symmetrical neural network (DEDSN) for single image SR. This deep network is fully composed of symmetrical multiple layers of convolution and deconvolution and there is no pooling (down-sampling and up-sampling) operations in the whole network so that image details degradation occurred in traditional convolutional frameworks is prevented. Additionally, in view of the success of the iterative back projection (IBP) algorithm in image SR, we further combine DEDSN with IBP network realization in this work. The new DEDSN-IBP model introduces the down sampling version of the ground truth image and calculates the simulation error as the prior guidance. Experimental results on benchmark data sets demonstrate that the proposed DEDSN model can achieve better performance than SRCNN and the improved DEDSN-IBP outperforms the reported state-of-the-art methods

Single Image Super-Resolution Using a Deep Encoder-Decoder Symmetrical Network with Iterative Back Projection

Image super-resolution (SR) usually refers to reconstructing a high resolution (HR) image from a low resolution (LR) image without losing high frequency details or reducing the image quality. Recently, image SR based on convolutional neural network (SRCNN) was proposed and has received much attention due to its end-to-end mapping simplicity and superior performance. This method, however, only using three convolution layers to learn the mapping from LR to HR, usually converges slowly and leads to the size of output image reducing significantly. To address these issues, in this work, we propose a novel deep encoder-decoder symmetrical neural network (DEDSN) for single image SR. This deep network is fully composed of symmetrical multiple layers of convolution and deconvolution and there is no pooling (down-sampling and up-sampling) operations in the whole network so that image details degradation occurred in traditional convolutional frameworks is prevented. Additionally, in view of the success of the iterative back projection (IBP) algorithm in image SR, we further combine DEDSN with IBP network realization in this work. The new DEDSN-IBP model introduces the down sampling version of the ground truth image and calculates the simulation error as the prior guidance. Experimental results on benchmark data sets demonstrate that the proposed DEDSN model can achieve better performance than SRCNN and the improved DEDSN-IBP outperforms the reported state-of-the-art methods

Efficient android electronic nose design for recognition and perception of fruit odors using Kernel Extreme Learning Machines

This study presents a novel android electronic nose construction using Kernel Extreme Learning Machines (KELMs). The construction consists of two parts. In the first part, an android electronic nose with fast and accurate detection and low cost are designed using Metal Oxide Semiconductor (MOS) gas sensors. In the second part, the KELMs are implemented to get the electronic nose to achieve fast and high accuracy recognition. The proposed algorithm is designed to recognize the odor of six fruits. Fruits at two concentration levels are placed to the sample chamber of the electronic nose to ensure the features invariant with the concentration. Odor samples in the form of time series are collected and preprocessed. This is a newly introduced simple feature extraction step that does not use any dimension reduction method. The obtained salient features are imported to the inputs of the KELMs. Additionally, K-Nearest Neighbor (K-NN) classifiers, the Support Vector Machines (SVMs), Least-Squares Support Vector Machines (LSSVMs), and Extreme Learning Machines (ELMs) are used for comparison. According to the comparative results for the proposed experimental setup, the KELMs produced good odor recognition performance in terms of the high test accuracy and fast response. In addition, odor concentration level was visualized on an android platform.TUBITA

Otimização de parâmetros de projeto de amortecedores de massa sintonizados para controle de vibrações em passarelas metálicas

O projeto de estruturas mais resistentes e econômicas é um fator importante para a indústria, e a constante evolução tecnológica permite aprimorar a forma como se projeta. Na área de construção civil, em especial de passarelas, uma das principais preocupações é a minimização de vibrações provenientes de carregamentos dinâmicos como o tráfego de pedestres e de veículos, ventos, sismos, etc. Esses carregamentos podem colocar em risco tanto a estabilidade da estrutura quanto a segurança dos pedestres. Para minimizar a amplitude das vibrações, uma das alternativas é a instalação de dispositivos de controle, como o amortecedor de massa sintonizado (AMS). Sendo um sistema passivo, o AMS não necessita controle externo, e sua estrutura simples facilita a instalação e diminui custos com manutenção. Mesmo com elevada capacidade de redução de vibrações, é possível melhorar a eficiência de AMSs por meio da otimização de seus parâmetros. Neste contexto, o processo de otimização realizado neste trabalho tem por objetivo reduzir a resposta dinâmica de duas passarelas submetidas à carga de pedestres, por meio da instalação de AMSs. Para isso, definiram-se dois parâmetros como variáveis de projeto: a rigidez e a constante de amortecimento dos AMSs. Com os parâmetros obtidos pelo algoritmo de otimização, o Backtracking Search Optimization Algorithm (BSA), determinam-se as respostas dinâmicas otimizadas das estruturas. Foram estudados três casos de otimização para cada passarela, considerando 1, 2 e 3 AMSs posicionados nos nós centrais dessas estruturas. Para a passarela Warren, as reduções por otimização foram em torno de 15%, 40% e 50% maiores em relação ao dimensionamento sem otimização, em termos de deslocamento, velocidade e aceleração, respectivamente. Para a passarela Pratt essas reduções foram acima de 20%, 10% e 5%. Os resultados demonstram a efetividade do método proposto, visto que foi capaz de otimizar os parâmetros dos AMSs, reduzindo a resposta dinâmica das estruturas e assim minimizando os efeitos de vibração sobre as passarelas, o que por sua vez reduz o risco de falhas estruturais. Além disso, após a otimização, a resposta em termos de aceleração se situou dentro da faixa estabelecida nas normas consultadas, o que garante, além da segurança, também o conforto dos pedestres.The design of more resistant and economical structures is an important factor for the industry, and the constant technological evolution allows to improve the way it is projected. In the area of civil construction, especially structures as footbridges, one of the main concerns is the minimization of vibrations from dynamic loads such as pedestrian and vehicle traffic, winds, earthquakes, among others. Such loads can endanger both the stability of the structure and the safety of pedestrians. To reduce vibration amplitudes, one of the alternatives is the installation of control devices, such as the tuned mass damper (TMD). As a passive system, the TMD does not require external control, and its simple construction structure facilitates installation and reduces maintenance costs. Even with high vibration reduction capacity, it is possible to improve the efficiency of TMDs by optimizing their design parameters. In this context, the optimization process performed in this work aims to reduce the dynamic response of two footbridges under pedestrian load, through the installation of TMDs. For this, two parameters were defined as design variables: stiffness and damping coefficient of the TMDs. With the parameters obtained by the optimization algorithm, the Backtracking Search Optimization Algorithm (BSA), the optimized dynamic responses of the structures are determined. Three optimization cases were studied for each footbridge, considering 1, 2 and 3 TMDs positioned in the central nodes of these structures. For the Warren footbridge, optimization reduced dynamic response above 15%, 40% and 50% more than non-optimized TMDs, in terms of displacement, speed and acceleration, respectively. For the Pratt footbridge, these reductions were above 20%, 10% and 5% higher. The results demonstrate the effectiveness of the proposed method, since it was able to optimize the parameters of the AMSs, reducing the dynamic response of the structures and thus minimizing the effects of vibration on the footbridges, which in turn reduces the risk of structural failures. In addition, after optimization, the response in terms of acceleration was within the range established in the consulted standards, which guarantees, in addition to safety, also pedestrian comfort

Machine Learning Prediction of Shear Capacity of Steel Fiber Reinforced Concrete

The use of steel fibers for concrete reinforcement has been growing in recent years owing to the improved shear strength and post-cracking toughness imparted by fiber inclusion. Yet, there is still lack of design provisions for steel fiber-reinforced concrete (SFRC) in building codes. This is mainly due to the complex shear transfer mechanism in SFRC. Existing empirical equations for SFRC shear strength have been developed with relatively limited data examples, making their accuracy restricted to specific ranges. To overcome this drawback, the present study suggests novel machine learning models based on artificial neural network (ANN) and genetic programming (GP) to predict the shear strength of SFRC beams with great accuracy. Different statistical metrics were employed to assess the reliability of the proposed models. The suggested models have been benchmarked against various soft-computing models and existing empirical equations. Sensitivity analysis has also been conducted to identify the most influential parameters to the SFRC shear strength