scholarly journals Modeling of Merging Decision during Execution Period Based on Random Forest

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Gen Li ◽  
Jianxiao Ma ◽  
Qiangru Shen
Keyword(s):  
Random Forest ◽  
Decision Model ◽  
State Of The Art ◽  
Selection Process ◽  
Data Driven ◽  
Relative Speed ◽  
Traffic Condition ◽  
Vehicle Trajectories ◽  
Merging Behavior ◽  
And Control

This study aims to investigate the key feature variables and build an accurate decision model for merging behavior during the execution period by using a data-driven method called random forest (RF). To comprehensively explore the feature variables during merging execution period, nineteen candidate variables including speeds, relative speeds, gaps, time-to-collisions (TTCs), and locations are extracted from a dataset including 375 noise-filtered vehicle trajectories. After the variable selection process, an RF model with 9 key feature variables is finally built. Results show that the gap between the merging vehicle and its putative following vehicle and the ration of this gap to the total accepted gap are the two most important feature variables. It is because merging vehicle drivers can easily observe the putative leading vehicles and control the relative speeds and positions to the putative leading vehicles and they tend to leave more space for their putative following vehicles. Relative speed between the merging vehicle and its following vehicle in the auxiliary lane is the only variable related to the vehicles in the auxiliary lane, which means merging vehicles mainly focus on the traffic condition in the adjacent main lane. Evaluation of the performance in comparison with the state-of-the-art method reveals that the proposed method can obtain much more accurate results in both training and testing datasets, which means RF is practical for predicting the merging decision behavior during execution period and has better transferability.

Designing Algorithms for Data‐Driven Network Management and Control: State‐of‐the‐Art and Challenges 1

2021 ◽  
pp. 175-198
Author(s):  
Andreas Blenk ◽  
Patrick Kalmbach ◽  
Johannes Zerwas ◽  
Stefan Schmid
Keyword(s):  
Network Management ◽  
State Of The Art ◽  
Data Driven ◽  
And Control ◽  
Control State

scholarly journals DESCRIPTION OF A NOVEL SUPPLIER SELECTION METHOD FOR COMPANIES MANUFACTURING FOOD SUPPLEMENTS

2021 ◽  
Vol 8 (3) ◽  
pp. 297-308
Author(s):  
Szabolcs Szentesi ◽  
Béla Illés ◽  
Ákos Cservenák ◽  
Róbert Skapinyecz ◽  
Péter Tamás
Keyword(s):  
Supplier Selection ◽  
Literature Search ◽  
State Of The Art ◽  
Selection Process ◽  
Food Supplement ◽  
Selection Method ◽  
Data Driven ◽  
Systematic Literature Search ◽  
Selection Methods ◽  
Logistics System

The publication presents a novel supplier selection method that can be of significant help in the optimal design of the supply chains of so-called commissioned food supplement companies. As a prelude to this, the article also explores the literature on existing supplier selection methods using the method of systematic literature search. A key characteristic of the newly developed method is that it incorporates such aspects into the supplier selection process as the environmental impact of logistics processes or the modernity of the supplier’s logistics system, both becoming highly important criteria in recent years. The method also supports one and multi actor decision making following state of the art principles in modern data driven supply chain management. As a result, it can be stated that the newly introduced method provides a state of the art approach to supplier selection, while it also takes into account all the traditional aspects related to the field.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

scholarly journals A Multi-Timescale Bilinear Model for Optimization and Control of HVAC Systems with Consistency

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 400 ◽  
Author(s):  
Zelin Nie ◽  
Feng Gao ◽  
Chao-Bo Yan
Keyword(s):  
Optimization Model ◽  
Air Conditioning ◽  
State Of The Art ◽  
Hvac Systems ◽  
Practical Significance ◽  
Hvac System ◽  
Bilinear Model ◽  
Optimization And Control ◽  
And Control ◽  
Slow Timescale

Reducing the energy consumption of the heating, ventilation, and air conditioning (HVAC) systems while ensuring users’ comfort is of both academic and practical significance. However, the-state-of-the-art of the optimization model of the HVAC system is that either the thermal dynamic model is simplified as a linear model, or the optimization model of the HVAC system is single-timescale, which leads to heavy computation burden. To balance the practicality and the overhead of computation, in this paper, a multi-timescale bilinear model of HVAC systems is proposed. To guarantee the consistency of models in different timescales, the fast timescale model is built first with a bilinear form, and then the slow timescale model is induced from the fast one, specifically, with a bilinear-like form. After a simplified replacement made for the bilinear-like part, this problem can be solved by a convexification method. Extensive numerical experiments have been conducted to validate the effectiveness of this model.

Railway crossing vertical vibration response prediction using a data-driven neuro-fuzzy model – Influence of train factors

2021 ◽  
pp. 095440972098666
Author(s):  
Kaveh Mehrzad ◽  
Shervan Ataei
Keyword(s):  
Model Comparison ◽  
Selection Process ◽  
Fuzzy Model ◽  
Response Prediction ◽  
Vibration Response ◽  
Vibration Monitoring ◽  
Data Driven ◽  
Steady Increase ◽  
Neuro Fuzzy ◽  
Railway Crossing

This paper provides a data-driven model of the vibration response of a railway crossing during vehicle passages. Many of the features of trains passing through instrumented crossing are extracted from measured data. Based on the feature selection process, speed, dynamic axle load and the number of wagons are found proper inputs in the prediction model. Train-crossing interaction response at a crossing due to passing trains is modeled from a data-driven Neuro-Fuzzy soft computing approach. Locally Linear Model Tree (LOLIMOT) is applied to predict the crossing nose acceleration. The model comparison against measurements shows that the ability to predict the extrapolation cases at off-range speeds has satisfactory compatibility. The monitored passing trains are ranked based on the LOLIMOT input space dimension cuts and extrapolation of the model up to higher train speeds. The influence of train factors (i.e. speed, dynamic axle load, number of wagons) on crossing response is demonstrated. Also, based on the analysis results, it is concluded that with a steady increase in train speeds, some trains show a greater amplification in vibration response than others. The results can be applied in data processing in the crossing vibration monitoring and detection of trains with crossing impact sensitive to speed increasing that can lead to proper operation policies to reduce damages and maintenance costs.

scholarly journals Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2371
Author(s):  
Matthieu Dubarry ◽  
David Beck
Keyword(s):  
Cobalt Oxide ◽  
State Of The Art ◽  
Data Driven ◽  
Nickel Aluminum ◽  
Li Ion Battery ◽  
Diagnosis And Prognosis ◽  
Detailed Statistical Analysis ◽  
Li Ion ◽  
Synthetic Datasets ◽  
Nickel Manganese

The development of data driven methods for Li-ion battery diagnosis and prognosis is a growing field of research for the battery community. A big limitation is usually the size of the training datasets which are typically not fully representative of the real usage of the cells. Synthetic datasets were proposed to circumvent this issue. This publication provides improved datasets for three major battery chemistries, LiFePO4, Nickel Aluminum Cobalt Oxide, and Nickel Manganese Cobalt Oxide 811. These datasets can be used for statistical or deep learning methods. This work also provides a detailed statistical analysis of the datasets. Accurate diagnosis as well as early prognosis comparable with state of the art, while providing physical interpretability, were demonstrated by using the combined information of three learnable parameters.

scholarly journals Drone Deep Reinforcement Learning: A Review

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 999
Author(s):  
Ahmad Taher Azar ◽  
Anis Koubaa ◽  
Nada Ali Mohamed ◽  
Habiba A. Ibrahim ◽  
Zahra Fathy Ibrahim ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Real Life ◽  
Environment Monitoring ◽  
Simulated Environments ◽  
Infrastructure Inspection ◽  
Remote Sensing Mapping ◽  
And Control ◽  
The Military ◽  
Uav Navigation

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios.

A Data-Driven Electric Water Heater Scheduling and Control System

2021 ◽  
pp. 110924
Author(s):  
Gulai Shen ◽  
Zachary E. Lee ◽  
Ali Amadeh ◽  
K. Max Zhang
Keyword(s):  
Control System ◽  
Data Driven ◽  
Water Heater ◽  
And Control

scholarly journals Data-Driven Wildfire Risk Prediction in Northern California

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 109
Author(s):  
Ashima Malik ◽  
Megha Rajam Rao ◽  
Nandini Puppala ◽  
Prathusha Koouri ◽  
Venkata Anil Kumar Thota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Curves ◽  
Data Driven ◽  
Northern California ◽  
Combined Model ◽  
Wildfire Risk ◽  
Study Results ◽  
Forest Models ◽  
Random Forest Models

Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the wildfire risk at areas near Monticello and Winters, California. This study demonstrated how the models were developed and applied with comprehensive data parameters such as powerlines, terrain, and vegetation in different perspectives that improved the spatial and temporal accuracy in predicting the risk of wildfire including fire ignition. The combined model uses the spatial and the temporal parameters as a single combined dataset to train and predict the fire risk, whereas the ensemble model was fed separate parameters that were later stacked to work as a single model. Our experiment shows that the combined model produced better results compared to the ensemble of random forest models on separate spatial data in terms of accuracy. The models were validated with Receiver Operating Characteristic (ROC) curves, learning curves, and evaluation metrics such as: accuracy, confusion matrices, and classification report. The study results showed and achieved cutting-edge accuracy of 92% in predicting the wildfire risks, including ignition by utilizing the regional spatial and temporal data along with standard data parameters in Northern California.

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