Real Time Speed Trend Analysis and Hours of Service Forecasting Using LSTM Network

2019 ◽  
Author(s):  
Arunabha Choudhury ◽  
Srimathi Shanmugavadivelu ◽  
Bhargav Velpuri
Keyword(s):  
Trend Analysis ◽  
Real Time ◽  
Lstm Network

scholarly journals Real-Time Detection of Dictionary DGA Network Traffic Using Deep Learning

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Kate Highnam ◽  
Domenic Puzio ◽  
Song Luo ◽  
Nicholas R. Jennings
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Traffic ◽  
Short Term Memory ◽  
Domain Names ◽  
Control Networks ◽  
Detection Techniques ◽  
Lstm Network ◽  
And Control

AbstractBotnets and malware continue to avoid detection by static rule engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses. Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closely mirror legitimate domains. To combat this, we created a novel hybrid neural network, Bilbo the “bagging” model, that analyses domains and scores the likelihood they are generated by such algorithms and therefore are potentially malicious. Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. Our unique architecture is found to be the most consistent in performance in terms of AUC, $$F_1$$ F 1 score, and accuracy when generalising across different dictionary DGA classification tasks compared to current state-of-the-art deep learning architectures. We validate using reverse-engineered dictionary DGA domains and detail our real-time implementation strategy for scoring real-world network logs within a large enterprise. In 4 h of actual network traffic, the model discovered at least five potential command-and-control networks that commercial vendor tools did not flag.

scholarly journals Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction

2020 ◽  
Vol 12 (13) ◽  
pp. 5374 ◽  
Author(s):  
Stephen Stajkowski ◽  
Deepak Kumar ◽  
Pijush Samui ◽  
Hossein Bonakdari ◽  
Bahram Gharabaghi
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Water Temperature ◽  
Real Time ◽  
River Water ◽  
Time Window ◽  
Water Quality Management ◽  
Water Quality Monitoring ◽  
Lstm Network ◽  
River Water Temperature

Advances in establishing real-time river water quality monitoring networks combined with novel artificial intelligence techniques for more accurate forecasting is at the forefront of urban water management. The preservation and improvement of the quality of our impaired urban streams are at the core of the global challenge of ensuring water sustainability. This work adopted a genetic-algorithm (GA)-optimized long short-term memory (LSTM) technique to predict river water temperature (WT) as a key indicator of the health state of the aquatic habitat, where its modeling is crucial for effective urban water quality management. To our knowledge, this is the first attempt to adopt a GA-LSTM to predict the WT in urban rivers. In recent research trends, large volumes of real-time water quality data, including water temperature, conductivity, pH, and turbidity, are constantly being collected. Specifically, in the field of water quality management, this provides countless opportunities for understanding water quality impairment and forecasting, and to develop models for aquatic habitat assessment purposes. The main objective of this research was to develop a reliable and simple urban river water temperature forecasting tool using advanced machine learning methods that can be used in conjunction with a real-time network of water quality monitoring stations for proactive water quality management. We proposed a hybrid time series regression model for WT forecasting. This hybrid approach was applied to solve problems regarding the time window size and architectural factors (number of units) of the LSTM network. We have chosen an hourly water temperature record collected over 5 years as the input. Furthermore, to check its robustness, a recurrent neural network (RNN) was also tested as a benchmark model and the performances were compared. The experimental results revealed that the hybrid model of the GA-LSTM network outperformed the RNN and the basic problem of determining the optimal time window and number of units of the memory cell was solved. This research concluded that the GA-LSTM can be used as an advanced deep learning technique for time series analysis.

Combining Live Drilling Data Stream with a Cloud Data Analytics Pipeline to Perform Real-Time Automated Projections to the Bit

2021 ◽  
Author(s):  
Daniel Cardoso Braga ◽  
Mohammadreza Kamyab ◽  
Brian Harclerode ◽  
Deep Joshi
Keyword(s):  
Trend Analysis ◽  
Real Time ◽  
Data Stream ◽  
Data Analytics ◽  
Data Sources ◽  
Rotary Drilling ◽  
Cloud Data ◽  
West Texas ◽  
Multiple Data ◽  
Drilling Data

Abstract During drilling, surveys to determine the wellbore trajectory are performed at every drilling connection. However, due to the offset between the survey instrument and the bit (typically between 30-100 ft), this survey represents the sensor's position which is lagged compared to the bit. This paper describes a method to automatically calculate projections to the bit in real-time utilizing multiple data sources: WITSML stream, BHA components and rotary trend analysis while rotary drilling. The projection to the bit calculation routine is performed in real time every 30 seconds. This paper presents results of projections for four horizontal unconventional wells drilled in West Texas. Nearly 75,000 projections were generated on the four wells, validated with 839 survey stations, with median divergence of the projections from the nearest survey stations being less than one foot.

scholarly journals A Real-Time Patient Monitoring Framework for Fall Detection

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Dharmitha Ajerla ◽  
Sazia Mahfuz ◽  
Farhana Zulkernine
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Short Term Memory ◽  
Detection System ◽  
Fall Detection ◽  
Edge Computing ◽  
Streaming Data ◽  
Real Time Analysis ◽  
Long Short Term Memory ◽  
Lstm Network

Fall detection is a major problem in the healthcare department. Elderly people are more prone to fall than others. There are more than 50% of injury-related hospitalizations in people aged over 65. Commercial fall detection devices are expensive and charge a monthly fee for their services. A more affordable and adaptable system is necessary for retirement homes and clinics to build a smart city powered by IoT and artificial intelligence. An effective fall detection system would detect a fall and send an alarm to the appropriate authorities. We propose a framework that uses edge computing where instead of sending data to the cloud, wearable devices send data to a nearby edge device like a laptop or mobile device for real-time analysis. We use cheap wearable sensor devices from MbientLab, an open source streaming engine called Apache Flink for streaming data analytics, and a long short-term memory (LSTM) network model for fall classification. The model is trained using a published dataset called “MobiAct.” Using the trained model, we analyse optimal sampling rates, sensor placement, and multistream data correction. Our edge computing framework can perform real-time streaming data analytics to detect falls with an accuracy of 95.8%.

scholarly journals Hybrid CNN-LSTM Network for Real-Time Apnea-Hypopnea Event Detection Based on IR-UWB Radar

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hyun Bin Kwon ◽  
Dongyeon Son ◽  
Dongseok Lee ◽  
Heenam Yoon ◽  
Mi Hyun Lee ◽  
...  
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Uwb Radar ◽  
Lstm Network

scholarly journals Real-Time PPG Signal Conditioning with Long Short-Term Memory (LSTM) Network for Wearable Devices

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 164
Author(s):  
Marek Wójcikowski
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Time Domain ◽  
Short Term Memory ◽  
Computational Cost ◽  
Computational Effort ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network

This paper presents an algorithm for real-time detection of the heart rate measured on a person’s wrist using a wearable device with a photoplethysmographic (PPG) sensor and accelerometer. The proposed algorithm consists of an appropriately trained LSTM network and the Time-Domain Heart Rate (TDHR) algorithm for peak detection in the PPG waveform. The Long Short-Term Memory (LSTM) network uses the signals from the accelerometer to improve the shape of the PPG input signal in a time domain that is distorted by body movements. Multiple variants of the LSTM network have been evaluated, including taking their complexity and computational cost into consideration. Adding the LSTM network caused additional computational effort, but the performance results of the whole algorithm are much better, outperforming the other algorithms from the literature.

Real-time Human Activity Classification From Radar With CNN-LSTM Network

2021 ◽  
Author(s):  
Zhengtao Yang ◽  
Haili Wang ◽  
Peiyuan Ni ◽  
Pengfei Wang ◽  
Qixin Cao ◽  
...  
Keyword(s):  
Real Time ◽  
Human Activity ◽  
Activity Classification ◽  
Lstm Network
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