Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response

Data telemetry is a critical element of successful unconventional well drilling operations, involving the transmission of information about the well-surrounding geology to the surface in real-time to serve as the basis for geosteering and well planning. However, the data extraction and code recovery (demodulation) process can be a complicated system due to the non-linear and time-varying characteristics of high amplitude surface noise. In this work, a novel model fuzzy wavelet neural network (FWNN) that combines the advantages of the sigmoidal logistic function, fuzzy logic, a neural network, and wavelet transform was established for the prediction of the transmitted signal code from borehole to surface with effluent quality. Moreover, the complete workflow involved the pre-processing of the dataset via an adaptive processing technique before training the network and a logistic response algorithm for acquiring the optimal parameters for the prediction of signal codes. A data reduction and subtractive scheme are employed as a pre-processing technique to better characterize the signals as eight attributes and, ultimately, reduce the computation cost. Furthermore, the frequency-time characteristics of the predicted signal are controlled by selecting an appropriate number of wavelet bases “N” and the pre-selected range for pij3 to be used prior to the training of the FWNN system. The results, leading to the prediction of the BPSK characteristics, indicate that the pre-selection of the N value and pij3 range provides a significantly accurate prediction. We validate its prediction on both synthetic and pseudo-synthetic datasets. The results indicated that the fuzzy wavelet neural network with logistic response had a high operation speed and good quality prediction, and the correspondingly trained model was more advantageous than the traditional backward propagation network in prediction accuracy. The proposed model can be used for analyzing signals with a signal-to-noise ratio lower than 1 dB effectively, which plays an important role in the electromagnetic telemetry system.

Feed-Forward Back Propagation Network for the prediction of diabetic retinopathy disorder

Some retina disorders mainly involve some blocked blood clots so that, the retinal vessels change their structure, being unable to completely nourish the retina. For an accurate investigation of retina disorders, the extraction of the retinal vessel anatomical structures or lesions is the main task. This paper reports a combination of various features extracted from retinal images, that are further used to train a Feed-Forward Back Propagation Network (FFBPN) as a decision system. The main goal is determining the combination of the appropriate features for more accurate classification of healthy and diseased patients. To achieve this goal, 120 binary images covering both categories of patients that belong to the STARE (Structured Analysis of the Retina) database were analyzed. The input data are the number of ridges, bifurcation, and bridges for retinal vessel pattern recognition. The FFBPNs with 4, 8, 12, 16, and 20 neurons in the hidden layer are trained. The FFBNP architecture with 12 neurons in the hidden layer, using the tansig transfer function in the hidden layer and linear transfer function in the output layer provides the most appropriate model for retinopathy disease classification. The correlation between the number of ridges and bridges computed for healthy patients (as actual values) and the number of ridges and bridges for diabetic patients (as predicted values) provides the best result, a regression coefficient (R) of 0. 8575 and a mean-square error (MSE) of 0.00163.

Exploration and Research on the Propagation Law of Seepage Risk Network in Tailings Storage Facility

The seepage accident of a tailings pond poses a serious threat to the stability of tailings dams and the surrounding environment. To reduce the occurrence of seepage accidents, this paper studies the identification of seepage hazards, the propagation law of seepage risk, the importance of hazards, and the priority of hazard treatment. To overcome the subjectivity and omission of hazard identification, according to the complexity and dynamics of tailings seepage, this paper proposes the evidence-based identification method of three-dimensional seepage hazards (EIMTSH) to identify the hazards of the tailings seepage system and the relationship between hazards. Then, on the basis of identifying the hazards of the tailings seepage system, the propagation network of seepage risk in tailing ponds (PNSRTP) is constructed based on the complex network theory. By analyzing the characteristics of the PNSRTP, it can be found that the propagation of seepage risk is scale-free and small-world. Through the node deletion method, this paper finds that the nodes with a higher degree value can reduce the network efficiency more quickly and should be governed first. By giving priority to the treatment of hazards with higher degree, the propagation of seepage risk can be reduced more quickly and the risk management level of tailings ponds can be improved, which is helpful to realize the sustainable development of mining production.

A Novel Emerging Topic Identification and Evolution Discovery Method on Time-Evolving and Heterogeneous Online Social Networks

With the fast development of web 2.0, information generation and propagation among online users become deeply interweaved. How to effectively and immediately discover the new emerging topic and further how to uncover its evolution law are still wide open and urgently needed by both research and practical fields. This paper proposed a novel early emerging topic detection and its evolution law identification framework based on dynamic community detection method on time-evolving and scalable heterogeneous social networks. The framework is composed of three major steps. Firstly, a time-evolving and scalable complex network denoted as KeyGraph is built up by deeply analyzing the text features of all kinds of data crawled from heterogeneous online social network platforms; secondly, a novel dynamic community detection method is proposed by which the new emerging topic is detected on the modeled time-evolving and scalable KeyGraph network; thirdly, a unified directional topic propagation network modeled by a great number of short texts including microblogs and news titles is set up, and the topic evolution law of the previously detected early emerging topic is identified by fully utilizing local network variations and modularity optimization of the “time-evolving” and directional topic propagation network. Our method is proved to yield preferable results on both a huge amount of computer-generated test data and a great amount of real online network data crawled from mainstream heterogeneous social networks.

Propagation Network of Tailings Dam Failure Risk: An Empirical Research and The Identification of Key Hazard Node

The tailings dam system is complex, and the dam structure changes continuously over time, which makes it difficult to identify hazards and analyze the causes of failure accidents. This paper uses hazards to represent the nodes, and the relationship between hazards to represent the edges. Based on the complex network theory, the propagation network of tailings dam failure risk is constructed. The traditional identification methods usually focus on one aspect of the information of the network, while it cannot take into account to absorb the advantages of different methods, resulting in the lack of information, which will lead to a certain difference between identified key hazards and real key hazards. In order to solve this problem, by absorbing the advantages of different methods under different hazard remediation (deleted) ratios, combined with the characteristics of multi-stage propagation of tailings dam failure risk, this paper proposes a multi-stage collaborative hazard remediation method (MCHRM) to determine the importance of hazard nodes. When the important nodes of this network that affect the network efficiency are found, by consulting the monitoring data, daily inspection results and safety evaluation information of each hazard before the dam failure, we can determine the real cause of the accident from the above important nodes according to the grading standards of hazard indicators. In the application example of Feijão Dam I, this article compares the key hazards obtained by the above methods with the conclusions of the accident investigation team. It can be found that the above method has a very good effect on finding the key causes of tailings dam failure.

Assessing the combination of DistilBERT news representations and difusion topological features to classify fake news

Fake news (FN) have affected people’s lives in unimaginable ways. The automatic classification of FN is a vital tool to prevent their dissemination and support fact-checking. Related work has shown that FN spread faster, deeper, and more broadly than truthful news on social media. Deep learning has produced state-of-the-art solutions in this field, mainly based on textual attributes. In this paper, we propose to combine compact representations of the textual news properties generated using DistilBERT, with topological metrics extracted from their propagation network in social media. Using a dataset related to politics and distinct learning algorithms, we extensively assessed the components of the proposed solution. Regarding the textual attributes, we reached results comparable to stateof-the-art solutions using only the news title and contents, which is useful for FN early detection. We assessed the influential topological metrics, and the effect of their combination with the news textual features. We also explored the use of ensembles. Our results were very promising, revealing the potential of the features proposed and the adoption of ensembles.