scholarly journals An LSTM Approach for SAG Mill Operational Relative-Hardness Prediction

Minerals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 734
Author(s):  
Sebastian Avalos ◽  
Willy Kracht ◽  
Julian Ortiz
Keyword(s):  
Network Architecture ◽  
Short Term Memory ◽  
Critical Role ◽  
Neural Network Architecture ◽  
Sag Mill ◽  
Categorical Attributes ◽  
One Year ◽  
Relative Hardness ◽  
Downstream Processes ◽  
Block Models

Ore hardness plays a critical role in comminution circuits. Ore hardness is usually characterized at sample support in order to populate geometallurgical block models. However, the required attributes are not always available and suffer for lack of temporal resolution. We propose an operational relative-hardness definition and the use of real-time operational data to train a Long Short-Term Memory, a deep neural network architecture, to forecast the upcoming operational relative-hardness. We applied the proposed methodology on two SAG mill datasets, of one year period each. Results show accuracies above 80% on both SAG mills at a short upcoming period of times and around 1% of misclassifications between soft and hard characterization. The proposed application can be extended to any crushing and grinding equipment to forecast categorical attributes that are relevant to downstream processes.

scholarly journals EARTHQUAKE FORECASTING USING ARTIFICIAL NEURAL NETWORKS

2018 ◽  
Vol XLII-5 ◽  
pp. 823-827 ◽  
Author(s):  
A. Bhatia ◽  
S. Pasari ◽  
A. Mehta
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Short Term Memory ◽  
Economic Loss ◽  
Earthquake Forecasting ◽  
Accuracy Evaluation ◽  
Neural Network Architecture ◽  
Forecasting System ◽  
Artificial Neural

<p><strong>Abstract.</strong> Earthquake is one of the most devastating natural calamities that takes thousands of lives and leaves millions more homeless and deprives them of the basic necessities. Earthquake forecasting can minimize the death count and economic loss encountered by the affected region to a great extent. This study presents an earthquake forecasting system by using Artificial Neural Networks (ANN). Two different techniques are used with the first focusing on the accuracy evaluation of multilayer perceptron using different inputs and different set of hyper-parameters. The limitation of earthquake data in the first experiment led us to explore another technique, known as nowcasting of earthquakes. The nowcasting technique determines the current progression of earthquake cycle of higher magnitude earthquakes by taking into account the number of smaller earthquake events in the same region. To implement the nowcasting method, a Long Short Term Memory (LSTM) neural network architecture is considered because such networks are one of the most recent and promising developments in the time-series analysis. Results of different experiments are discussed along with their consequences.</p>

scholarly journals A Novel Deep Learning Approach Using Contextual Embeddings for Toponym Resolution

2021 ◽  
Vol 11 (1) ◽  
pp. 28
Author(s):  
Ana Bárbara Cardoso ◽  
Bruno Martins ◽  
Jacinto Estima
Keyword(s):  
Network Architecture ◽  
Additional Data ◽  
Short Term Memory ◽  
External Information ◽  
Neural Network Architecture ◽  
Novel Approach ◽  
The Neural Network ◽  
Proposed Model ◽  
Textual Data ◽  
Spatial Coordinates

This article describes a novel approach for toponym resolution with deep neural networks. The proposed approach does not involve matching references in the text against entries in a gazetteer, instead directly predicting geo-spatial coordinates. Multiple inputs are considered in the neural network architecture (e.g., the surrounding words are considered in combination with the toponym to disambiguate), using pre-trained contextual word embeddings (i.e., ELMo or BERT) as well as bi-directional Long Short-Term Memory units, which are both regularly used for modeling textual data. The intermediate representations are then used to predict a probability distribution over possible geo-spatial regions, and finally to predict the coordinates for the input toponym. The proposed model was tested on three datasets used on previous toponym resolution studies, specifically the (i) War of the Rebellion, (ii) Local–Global Lexicon, and (iii) SpatialML corpora. Moreover, we evaluated the effect of using (i) geophysical terrain properties as external information, including information on elevation or terrain development, among others, and (ii) additional data collected from Wikipedia articles, to further help with the training of the model. The obtained results show improvements using the proposed method, when compared to previous approaches, and specifically when BERT embeddings and additional data are involved.

scholarly journals Application of deep learning methods to predict ionosphere parameters in real time

2020 ◽  
Vol 196 ◽  
pp. 02007
Author(s):  
Vladimir Mochalov ◽  
Anastasia Mochalova
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Short Term Memory ◽  
Neural Network Architecture ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.

scholarly journals Evaluation of Sequence Learning Models for Large Commercial Building Load Forecasting

2019 ◽  
Author(s):  
Cristina Nichiforov ◽  
Grigore Stamatescu ◽  
Iulia Stamatescu ◽  
Ioana Fagarasan
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Energy Management ◽  
Smart Grids ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Critical Role ◽  
Load Forecasting ◽  
Quality Data

Buildings have started to play a critical role in the stability and resilience of modern smart grids, leading to a refocusing of large scale energy management strategies from the supply side to the consumer side. When the buildings integrate local renewable energy generation in the form of renewable energy resources they become prosumers and this reflects into additional complexity into the operation of the interconnected complex energy systems. A class of methods of modelling the energy consumption patterns of the building have recently emerged as black-box input-output approaches with the ability to capture underlying consumption trends. These make use and require large quantities of quality data produces by non-deterministic processes underlying the energy consumption. We present an application of a class of neural networks, namely deep learning techniques for time series sequence modelling with the goal of accurate and reliable building energy load forecasting. The Recurrent Neural Network implementation uses Long Short-Term Memory layers in increasing density of nodes to quantify prediction accuracy. The case study is illustrated on four university buildings from temperate climates over one year of operation using a reference benchmarking dataset that allows replicable results. The obtained results are discussed in terms of accuracy metrics and computational and network architecture aspects and are considered suitable for further used in future in situ energy management at the building and neighbourhood levels.

scholarly journals Galaxy morphology prediction using Capsule Networks

2019 ◽  
Vol 486 (2) ◽  
pp. 1539-1547 ◽  
Author(s):  
Reza Katebi ◽  
Yadi Zhou ◽  
Ryan Chornock ◽  
Razvan Bunescu
Keyword(s):  
Network Architecture ◽  
Critical Role ◽  
Neural Network Architecture ◽  
Sky Surveys ◽  
The Galaxy ◽  
Formation And Evolution ◽  
The One ◽  
Rotationally Invariant ◽  
Key Parameter ◽  
Galaxy Morphology

Abstract Understanding morphological types of galaxies is a key parameter for studying their formation and evolution. Neural networks that have been used previously for galaxy morphology classification have some disadvantages, such as not being inherently invariant under rotation. In this work, we studied the performance of Capsule Network (CapsNet), a recently introduced neural network architecture that is rotationally invariant and spatially aware, on the task of galaxy morphology classification. We designed two evaluation scenarios based on the answers from the question tree in the Galaxy Zoo project. In the first scenario, we used CapsNet for regression and predicted probabilities for all of the questions. In the second scenario, we chose the answer to the first morphology question that had the highest user agreement as the class of the object and trained a CapsNet classifier, where we also reconstructed galaxy images. We achieved promising results in both of these scenarios. Automated approaches such as the one introduced here will play a critical role in the upcoming large sky surveys.

scholarly journals Dynamic Malware Analysis with Feature Engineering and Feature Learning

2020 ◽  
Vol 34 (01) ◽  
pp. 1210-1217
Author(s):  
Zhaoqi Zhang ◽  
Panpan Qi ◽  
Wei Wang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Expert Knowledge ◽  
Malware Detection ◽  
Feature Representation ◽  
Feature Engineering ◽  
Malware Analysis ◽  
Neural Network Architecture

Dynamic malware analysis executes the program in an isolated environment and monitors its run-time behaviour (e.g. system API calls) for malware detection. This technique has been proven to be effective against various code obfuscation techniques and newly released (“zero-day”) malware. However, existing works typically only consider the API name while ignoring the arguments, or require complex feature engineering operations and expert knowledge to process the arguments. In this paper, we propose a novel and low-cost feature extraction approach, and an effective deep neural network architecture for accurate and fast malware detection. Specifically, the feature representation approach utilizes a feature hashing trick to encode the API call arguments associated with the API name. The deep neural network architecture applies multiple Gated-CNNs (convolutional neural networks) to transform the extracted features of each API call. The outputs are further processed through bidirectional LSTM (long-short term memory networks) to learn the sequential correlation among API calls. Experiments show that our solution outperforms baselines significantly on a large real dataset. Valuable insights about feature engineering and architecture design are derived from the ablation study.

scholarly journals Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1086
Author(s):  
Raoul Hoffmann ◽  
Hanna Brodowski ◽  
Axel Steinhage ◽  
Marcin Grzegorzek
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Capacitive Sensor ◽  
Legged Locomotion ◽  
Sensor Data ◽  
Neural Network Architecture ◽  
Gait Patterns ◽  
Before And After

Gait patterns are a result of the complex kinematics that enable human two-legged locomotion, and they can reveal a lot about a person’s state and health. Analysing them is useful for researchers to get new insights into the course of diseases, and for physicians to track the progress after healing from injuries. When a person walks and is interfered with in any way, the resulting disturbance can show up and be found in the gait patterns. This paper describes an experimental setup for capturing gait patterns with a capacitive sensor floor, which can detect the time and position of foot contacts on the floor. With this setup, a dataset was recorded where 42 participants walked over a sensor floor in different modes, inter alia, normal pace, closed eyes, and dual-task. A recurrent neural network based on Long Short-Term Memory units was trained and evaluated for the classification task of recognising the walking mode solely from the floor sensor data. Furthermore, participants were asked to do the Unilateral Heel-Rise Test, and their gait was recorded before and after doing the test. Another neural network instance was trained to predict the number of repetitions participants were able to do on the test. As the results of the classification tasks turned out to be promising, the combination of this sensor floor and the recurrent neural network architecture seems like a good system for further investigation leading to applications in health and care.

scholarly journals Artificial Recurrent Neural Network Architecture in Customer Consumption Prediction for Business Development

2020 ◽  
Vol 2 (2) ◽  
pp. 111-120
Author(s):  
Dr. Karrupusamy P.
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Association Rule ◽  
Network Architecture ◽  
Short Term Memory ◽  
Real Life ◽  
Consumption Pattern ◽  
Neural Network Architecture ◽  
Competitive Edge ◽  
Frame Work

The customer consumption pattern prediction has become one of a significant role in developing the business and taking it to a competitive edge. For forecasting the behaviors of the consumers the paper engages an artificial recurrent neural network architecture the long short-term memory an improvement of recurrent neural network. The mechanism laid out to predict the pattern of the consumption, uses the information’s about the consumption of products based on the age and the gender. The information essential are extracted and described with the prefix-span procedure based association rule. Utilizing the information about the day to day products purchase pattern as input a frame work to predict the customer daily essentials was designed, the designed frame was capable enough to learn the dissimilarities across the predicted and the original miscalculation rates. The frame work devised was tested using real life applications and the results observed demonstrated that the proposed LSTM based prediction with the prefix span association rule to acquire the day today consumption details is compatible for forecasting the customer consumption over time accurately.

scholarly journals System for Neural Network Determination of Atrial Fibrillation on ECG Signals with Wavelet-Based Preprocessing

2021 ◽  
Vol 11 (16) ◽  
pp. 7213
Author(s):  
Pavel Lyakhov ◽  
Mariya Kiladze ◽  
Ulyana Lyakhova
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Network Architecture ◽  
Short Term Memory ◽  
Developed Countries ◽  
Neural Network Architecture ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Entropy Functions

Today, cardiovascular disease is the leading cause of death in developed countries. The most common arrhythmia is atrial fibrillation, which increases the risk of ischemic stroke. An electrocardiogram is one of the best methods for diagnosing cardiac arrhythmias. Often, the signals of the electrocardiogram are distorted by noises of varying nature. In this paper, we propose a neural network classification system for electrocardiogram signals based on the Long Short-Term Memory neural network architecture with a preprocessing stage. Signal preprocessing was carried out using a symlet wavelet filter with further application of the instantaneous frequency and spectral entropy functions. For the experimental part of the article, electrocardiogram signals were selected from the open database PhysioNet Computing in Cardiology Challenge 2017 (CinC Challenge). The simulation was carried out using the MatLab 2020b software package for solving technical calculations. The best simulation result was obtained using a symlet with five coefficients and made it possible to achieve an accuracy of 87.5% in recognizing electrocardiogram signals.

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