Parameter Evaluations for Vertical Wells with Hydraulic Fracture Using Well-Testing and Deep Learning Method

The reliability of well-testing interpretation largely depends on the experience of reservoir engineers, which make the issue of non-unique solution serious and increase its application threshold. Virtually, deep learning assistive techniques are good strategies in well-testing interpretation. Although some work has been done based on automatic interpretation techniques, there is still a lack of an automatic interpretation model with wide applicability and fast interpretation on parameter evaluation of vertically fractured well. To improve this situation and make the well-testing interpretation easier to apply, this paper uses deep learning methods to build an automatic interpretation model of well-testing data for vertically fractured well. The model can automatically identify the corresponding parameters. The results in the validation set show that the median relative error of the curve parameter inversion is less than 10%. In addition, the accuracy of parameter prediction can be improved by increasing the weight of some important parameters in deep learning model training, such as permeability and fracture half-length. Finally, the automatic interpretation model is tested on a field case. The test results prove that the model has high accuracy and interpretation speed.

GPR Data Interpretation Approaches in Archaeological Prospection

This article focuses on the possible drawbacks and pitfalls in the GPR data interpretation process commonly followed by most GPR practitioners in archaeological prospection. Standard processing techniques aim to remove some noise, enhance reflections of the subsurface. Next, one has to calculate the instantaneous envelope and produce C-scans which are 2D amplitude maps showing high reflectivity surfaces. These amplitude maps are mainly used for data interpretation and provide a good insight into the subsurface but cannot fully describe it. The main limitations are discussed while studies aiming to overcome them are reviewed. These studies involve integrated interpretation approaches using both B-scans and C-scans, attribute analysis, fusion approaches, and recent attempts to automatically interpret C-scans using Deep Learning (DL) algorithms. To contribute to the automatic interpretation of GPR data using DL, an application of Convolutional Neural Networks (CNNs) to classify GPR data is also presented and discussed.

Autoencoder-Based Anomaly Detection in Industrial X-ray Images

Within many quality-critical industries, e.g. the aerospace industry, industrial X-ray inspection is an essential as well as a resource intense part of quality control. Within such industries the X-ray image interpretation is typically still done by humans, therefore, increasing the interpretation automatization would be of great value. We claim, that safe automatic interpretation of industrial X-ray images, requires a robust confidence estimation with respect to out-of-distribution (OOD) data. In this work we have explored if such a confidence estimation can be achieved by comparing input images with a model of the accepted images. For the image model we derived an autoencoder which we trained unsupervised on a public dataset with X-ray images of metal fusion-welds. We achieved a true positive rate at 80–90% at a 4% false positive rate, as well as correctly detected an OOD data example as an anomaly.

Smart Communication Assistant for Deaf and Dumb People

Communication is the medium by which we can share our thoughts or convey the messages with other person. Nowadays we can give commands using voice recognition. But what if one absolutely cannot hear anything and eventually cannot speak. So the Sign Language is the main communicating tool for hearing impaired and mute people, and also to ensure an independent life for them, the automatic interpretation of sign language is an extensive research area. Sign language recognition (SLR) aims to interpret sign languages automatically by an application in order to help the deaf people to communicate with hearing society conveniently. Our aim is to design a system to help the Deaf and Dumb person to communicate with the rest of the world using sign language. With the use of image processing and artificial intelligence, many techniques and algorithms have been developed in this area. Every sign language recognition system is trained for recognizing the signs and converting them into required pattern. The proposed system aim to provide speech to speechless, in this paper we have introduced Sign Language Recognition using CNN for dynamic gestures to achieve faster results with high accuracy.

Transformation Approach of Open Web Data to Linked Open Data

A large amount of data available over the Web and, in particular, the open data have, generally, heterogeneous formats and are not machine-readable. One promising solution to overcome the problems of heterogeneity and automatic interpretation is the Linked Data initiative, which aims to provide unified practices for publishing and contextually to link data on the Web, by using World Wide Web Consortium standards and the Semantic Web technologies. LinkedIn data promote the Web’s transformation from a web of documents to a web of data, ensuring that machines and software agents can interpret the semantics of data correctly and therefore infer new facts and return relevant web data search results. This paper presents an automatic generic transformation approach that manipulates several input formats of open web data to linked open data. This work aims to participate actively in the movement of publishing data compliant with linked data principles.

The FMM Approach to Analyze Biomedical Signals: Theory, Software, Applications and Future

Oscillatory systems arise in the different biological and medical fields. Mathematical and statistical approaches are fundamental to deal with these processes. The Frequency Modulated Mobiüs approach (FMM), reviewed in this paper, is one of these approaches. Little known as it has been recently developed, it solves a variety of exciting questions with real data; some of them, such as the decomposition of the signal into components and their multiple uses, are of general application, others are specific. Among the exciting specific applications is the automatic interpretation of the electrocardiogram signal. In this paper, a summary of the theoretical, statistical and computational properties of the FMM approach are revised. Additionally, as a novelty, the FMM approach’s usefulness for the analysis of blood pressure signals is shown. For the latter, a new robust estimation algorithm is proposed using FMM models with restrictions. The paper ends with a view about challenges for the future.