The quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by machine learning models. Employing a neural Encoder-Decoder structure to extract informative features from complex input time-series has proved very effective in other popular tasks like neural machine translation and video captioning. In this paper, a novel end-to-end model based on the neural Encoder-Decoder Framework combined with deep reinforcement learning is proposed to learn single instrument trading strategies from a long sequence of raw prices of the instrument. In addition, the effects of different structures for the Encoder and various forms of the input sequences on the performance of the learned strategies are investigated. Experimental results showed that the proposed model outperforms other state-of-the-art models in highly dynamic environments.

Image captioning is an interdisciplinary research field in machine vision and natural language processing. Most of the proposed methods for generating image captions follow an Encoder-Decoder Framework. In this way, each word is generated based on the image features and previously generated words. Recently the attention mechanism, which usually creates a spatial map that highlights the image regions associated with each word, has been widely used in research. In this paper, we propose a new method that integrates the Encoder-Decoder Framework with the attention on attention mechanism. The Encoder part of the model uses ResNet to extract global features of the image, and the Decoder consists of three important parts: Attention-LSTM, Language-LSTM, and Attention on attention-layer. The attention mechanism uses local evidence to enhance the demonstration of the features and reasoning in the generation of image descriptions. The method was able to improve the generation of captions and improve METEOR, ROUGH evaluation metric well. And also it generates better captions compared to modern methods on the Flickr8k, dataset.

Concrete is one of the major materials used in modern structures. Concrete members are used as the main structural parts of various infrastructures such as dams, tunnels, bridges, and skyscrapers. However, this wide application requires some accurate and efficient inspection system during the structure’s life. Cracks are classified as the earliest symptoms of degradation in concrete members. Although manual inspection is a common method in structural health monitoring and crack detection in civil engineering structures, serious limitations caused by implementing human resources degraded the efficiency of the proposed method. In recent years, many studies tried to automate the inspection of these structures by using different sensors such as Ultrasonic and Piezo-electric sensors, seeming to be costly and insufficient in some cases. With recent development in computer vision techniques, especially deep-learning-based methods, there is an opportunity for researchers to come with autonomous visual inspection systems for structural health monitoring of concrete members. This study proposes a deep-learning-based model for automatic crack detection on the concrete surface. The proposed model is an Encoder-Decoder model that uses ResNet101, a well-known convolutional neural network, as the Encoder and the U-Net’s expansion path as the Decoder. To minimize the training time and maximize the accuracy, we use transfer learning in our approach. The dataset implemented for this study includes 458 images of the cracked surface of concrete members which come with corresponding segmentation label masks. Data augmentation techniques strongly increased the robustness of the proposed model encountering different imaging conditions and noises. The proposed model was trained using the backpropagation algorithm and it achieved 99.39% precision and 84.99% recall, which led to a 91.38% F1 score on the unseen test dataset. The accuracy and speed of the present model outperform the existing methods and different crack types composing the dataset help generalize the model for prediction of different crack types and different imaging conditions.

Introduction: Natural convection is an important phenomenon in porous media problems. It is encountered in a variety of applications, including in enhanced oil recovery systems and geothermal reservoirs. Physics-based numerical models are widely used to simulate natural convection in porous media. Although these models are usually effective, they commonly suffer from high computational costs. This is notably problematic in repetitive runs at large time and space scales, as in uncertainty analysis, data assimilation, and sensitivity analysis. In recent years, at least four different methods have been proposed to overcome this challenge, including optimizing the numerical solution algorithm, parallel computing, cloud computing, and data-driven methods. In most cases, while data-driven models are capable of handling low-dimensional problems, they have not been very successful in dealing with high-dimensional problems, both accurately and time efficient. To overcome these challenges, we propose using the Encoder-Decoder convolutional neural networks (ED-CNNs) for heterogeneous porous media. We apply the ED-CNN in the context of ‘image-to-image’ regression in the following two use cases in the context of natural convection simulations: (1) as a meta-model to estimate the heat map from the Rayleigh number distribution, and (2) as an optimizer to estimate the Rayleigh number distribution from the heat map. Methodology: The proposed ED-CNN is employed to model the hypothetical example of a square porous enclosure filled with a saturated porous medium. The boundaries are impermeable, and temperatures at two opposite side walls are different, resulting in the formation of natural convection. Heterogeneity in the Rayleigh number across the problem domain is applied through zonation.A numerical modeling tool is used to generate steady-state heat maps based on a number of randomly selected Rayleigh numbers. The numerical model input-outputs are transformed into square-shaped jpg images of 64 × 64 resolution. Two ED-CNNs are trained, one as a meta-model and the other as an optimizer. Different numbers of training input-output images (including 1000, 2000, 4000, and 5000) generated from the numerical model are employed to evaluate the performance of proposed networks. Two evaluation criteria are used to assess the performance of the developed ED-CNN models: (1) the root mean squared error (RMSE), and (2) the coefficient of determination (R^2-score). The ED-CNNs have been developed using Keras and Tensorflow python libraries. Results and discussion: Results show that the ED-CNN accuracy, both as a meta-model and as an optimizer, is satisfactory. For the meta-model case (i.e. prediction of the temperature distribution from the Rayleigh map), the RMSE is mostly smaller than 0.15, and the R^2-score is around 0.92. In the case of ED-CNN as optimizer (i.e. estimation of the Rayleigh distribution from the heat map), RMSE is mostly in the interval [0.017-0.034], while the R^2-score is around 0.89. Acceptable results can be obtained using 2000 input-output image pairs and 150 epochs for the meta-model case, and 4000 image pairs and 200 epochs for the optimizer case. Analysis of the spatial distribution of errors shows that maximum errors occur in the middle of the problem domain where the heat map is least sensitive to the Rayleigh number. The ED-CNN model is also evaluated as an uncertainty analysis tool by comparing maps of mean and standard deviation based on the numerical model and ED-CNN predictions, showing a significant agreement with estimation error between them.Conclusion: In this paper, we examine the performance of ED-CNNs, as a specialized architecture of deep neural networks, to solve the forward and inverse problems of natural convection in porous media. For this purpose, we frame the problem as one of image-to-image regression and show that the developed model is able to provide high accuracy approximations with limited training samples, effectively solving the curse of dimensionality problem associated with heterogeneous domains. In practice, the proposed methodology can be applied to image datasets obtained from not only numerical modeling, but also high-resolution imaging and non-destructive scanning techniques, to either estimate the temperature distribution due to natural convection, or to characterize the porous media based on the temperature distribution.

Recent researches on pixel-wise semantic segmentation use deep neural networks to improve accuracy and speed of these networks in order to increase the efficiency in practical applications such as automatic driving. These approaches have used deep architecture to predict pixel tags, but the obtained results seem to be undesirable. The reason for these unacceptable results is mainly due to the existence of max pooling operators, which reduces the resolution of the feature maps. In this paper, we present a convolutional neural network composed of Encoder-Decoder segments based on successful SegNet network. The Encoder section has a depth of 2, which in the first part has 5 convolutional layers, in which each layer has 64 filters with dimensions of 3×3. In the decoding section, the dimensions of the decoding filters are adjusted according to the convolutions used at each step of the encoding. So, at each step, 64 filters with the size of 3×3 are used for coding where the weights of these filters are adjusted by network training and adapted to the educational data. Due to having the low depth of 2, and the low number of parameters in proposed network, the speed and the accuracy improve compared to the popular networks such as SegNet and DeepLab. For the CamVid dataset, after a total of 60, 000 iterations, we obtain the 91% for global accuracy, which indicates improvements in the efficiency of proposed method.

Building segmentation is a difficult task due to the need for rich semantic features. Differences in the shape, color and size of buildings and their proximity to other features such as parking lots and streets make their recognition in high resolution images challenging. In this research, with the aim of extracting buildings from high-resolution images, deep convolutional neural network architecture of the Encoder-Decoder type based on the modified DeepLabV3+ model has been used. In the Atrous module of this modified model, convolution layers are applied with lower rates compared to the original module, in order to achieve the goal of performing a more powerful semantic segmentation of small and large building objects. The performance of the proposed model in this research was evaluated using two data sets, WHU and INRIA, and the results showed that using lower Atrous rates and changing them to 4, 8, and 12 significantly improved the segmentation performance in both data sets. The proposed modified model was able to improve the IOU and F-Score indices compared with other advanced models in the WHU data set by 0. 39 and 0. 53, respectively. In addition, the modified method in the INRIA dataset improved both of the above indices by 0. 35. The proposed model in this research, based on the reduction of Atros rates to 4, 8 and 12 and the change in ResNet-50 layers, was able to achieve an IOU equal to 89. 51 in the WHU dataset and 76. 64 in the INRIA dataset in the extraction of construction charges.

The portable video-on-demand system is a video compression system for transmitting video in a wireless network, it is sent in smaller packets and uses Wi-Fi and Bluetooth standards to connect to mobile devices and tablets at any time and place. There is a method for transmitting video in a portable wireless network. Here, we use the VQ pyramid decoding method, a video compression method that we used to transmit video in wireless networks with limited bandwidth. This method is able to reduce the amount of data and preserve In this method, the video quality is divided into two small frames. This algorithm uses a set of quantized codes to select the closest code to each block of the frame, and then this code is used as a representative of the block in the frame. The method instead of transmitting all the block information, only the block representative code information is transmitted, which reduces the data volume. In fact, VQ pyramid decoding is an efficient method for video compression and transmission in wireless networks, which reduces the data volume and maintains the video quality. It provides the possibility of transmitting and watching high-quality video in a network with limited bandwidth. Overall, literature reviews in this field analyze and examine these techniques and their applications for optimizing video systems, and they aim to find the best methods for compressing videos with high quality at low bit rates.

This paper is concerned with state tracking as well as reference tracking of nonlinear dynamic systems with process and measurement noise over the Additive White Gaussian Noise (AWGN) channel which is subject to transmission noise and transmission power constraints. The AWGN channel is a continuous alphabet channel. Therefore, this channel is very suitable for controlling dynamic systems over wireless communication links. To address these problems, a novel Encoder, Decoder and controller by implementing a novel linearization method for linearizing the nonlinear dynamic systems on operating points are proposed. This method compensates for communication imperfections and maintains real-time state and reference tracking at the end of the communication link. For identifying the exact time of linearization in the Encoder and Decoder, the Monte-Carlo approximation is applied. Using the Monte-Carlo approximator provides a possible approximation of the estimation error in the Encoder and Decoder at the same time. The linearization method is based on the variable (optimal) linearization rate. A proper Encoder, Decoder and controller for real-time state estimation and reference tracking are proposed. The nonlinear dynamic systems which were considered in this paper have process and measurement noises. Simulation results illustrate the satisfactory state tracking and reference tracking performances of the proposed technique; while variable linearization technique is used.