scholarly journals Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7202
Author(s):  
Jianfei Huang ◽  
Xinchun Cheng ◽  
Yuying Shen ◽  
Dewen Kong ◽  
Jixin Wang
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Short Term Memory ◽  
Prediction Method ◽  
Good Prediction ◽  
Construction Machinery ◽  
Autonomous Operation ◽  
Good Prediction Accuracy ◽  
Wheel Loaders ◽  
The Cost

Accurate prediction of the throttle value and state for wheel loaders can help to achieve autonomous operation, thereby reducing the cost and accident rate. However, existing methods based on a physical model cannot accurately reflect the operator’s driving habits and the interaction between wheel loaders and the environment. In this paper, a deep-learning-based prediction model is developed to predict the throttle value and state for wheel loaders by learning from driving data. Multiple long–short-term memory (LSTM) networks are used to extract the temporal features of different stages during the operation of the wheel loader. Two backward-propagation neural networks (BPNNs), which use the temporal feature extracted by LSTM as the input, are designed to output the final prediction results of throttle value and state, respectively. The proposed prediction model is trained and tested using the data from two different conditions. The end-to-end LSTM prediction model and BPNNs are used as benchmark models. The results indicate that the proposed prediction model has good prediction accuracy and adaptability. Furthermore, the relationship between the prediction performance and signal sampling frequency is also studied. The proposed prediction method that combines driving data and deep learning can make the throttle action conform to the decisions of an experienced operator, providing technical support for the autonomous operation of construction machinery.

scholarly journals Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6756
Author(s):  
DongHyun Ko ◽  
Seok-Hwan Choi ◽  
Sungyong Ahn ◽  
Yoon-Ho Choi
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Indoor Localization ◽  
Artificial Noise ◽  
Good Prediction ◽  
Localization Method ◽  
Good Prediction Accuracy ◽  
Attack Scenario ◽  
Natural Noise

With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural noise, some studies were conducted to remove it. So far, two representative methods, i.e., the filtering-based method and deep learning-based method, have shown a significant effect in removing natural noise. However, the prediction accuracy of these methods severely decreased under artificial noise caused by adversaries. In this paper, we introduce a new media access control (MAC) spoofing attack scenario injecting artificial noise, where the prediction accuracy of Wi-Fi-based indoor localization system significantly decreases. We also propose a new deep learning-based indoor localization method using random forest(RF)-filter to provide the good prediction accuracy under the new MAC spoofing attack scenario. From the experimental results, we show that the proposed indoor localization method provides much higher prediction accuracy than the previous methods in environments with artificial noise.

scholarly journals Research on the Estimate of Gas Hydrate Saturation Based on LSTM Recurrent Neural Network

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6536
Author(s):  
Chuanhui Li ◽  
Xuewei Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Gas Hydrate ◽  
Short Term Memory ◽  
Chloride Concentration ◽  
Well Logs ◽  
Good Prediction ◽  
Learning Technology ◽  
Hydrate Saturation

Gas hydrate saturation is an important index for evaluating gas hydrate reservoirs, and well logs are an effective method for estimating gas hydrate saturation. To use well logs better to estimate gas hydrate saturation, and to establish the deep internal connections and laws of the data, we propose a method of using deep learning technology to estimate gas hydrate saturation from well logs. Considering that well logs have sequential characteristics, we used the long short-term memory (LSTM) recurrent neural network to predict the gas hydrate saturation from the well logs of two sites in the Shenhu area, South China Sea. By constructing an LSTM recurrent layer and two fully connected layers at one site, we used resistivity and acoustic velocity logs that were sensitive to gas hydrate as input. We used the gas hydrate saturation calculated by the chloride concentration of the pore water as output to train the LSTM network. We achieved a good training result. Applying the trained LSTM recurrent neural network to another site in the same area achieved good prediction of gas hydrate saturation, showing the unique advantages of deep learning technology in gas hydrate saturation estimation.

scholarly journals Application of Data Mining Technology Based on Wireless Sensor Networks in Oceanographic Forecasting

2018 ◽  
Vol 14 (04) ◽  
pp. 137 ◽  
Author(s):  
Wei Zhai
Keyword(s):  
Support Vector Regression ◽  
Prediction Method ◽  
Advanced Technology ◽  
Wireless Sensor ◽  
Utilization Rate ◽  
Observation System ◽  
Support Vector ◽  
Good Prediction ◽  
Hydrological Data ◽  
Good Prediction Accuracy

This paper aims to present a desirable prediction method for oceanographic trends. Therefore, an online monitoring scheme was prepared to collect the accurate oceanographic hydrological data based on wireless sensor network (WSN) and computer technology. Then, the data collected by the WSN were processed by support vector regression algorithm. To obtain the most important parameters of the algorithm, the particle swarm optimization was introduced to search for the global optimal solution through the coopetition between the particles. After that, an oceanographic hydrological data collection and observation system was created based on the hydrological situation of New York harbour. Then, the traditional support vector regression and the proposed method were applied to predict the oceanographic trends based on water temperature, salinity and other indices. The results show that the proposed algorithm enhanced the data utilization rate of the WSN, and achieved good prediction accuracy. The research provides important insights into the application of advanced technology in oceanographic forecast.

Bayesian Network for Freeway Traffic State Prediction

2018 ◽  
Vol 2672 (45) ◽  
pp. 124-135 ◽  
Author(s):  
Ho-Chul Park ◽  
Dong-Kyu Kim ◽  
Seung-Young Kho
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Bayesian Network ◽  
Traffic Flow ◽  
Sensitivity Analyses ◽  
Good Prediction ◽  
Traffic Operations ◽  
Traffic State ◽  
State Prediction ◽  
Good Prediction Accuracy

Traffic state prediction is an important issue in traffic operations. One of the main purposes of traffic operations is to prevent a flow breakdown. Therefore, it is necessary to predict the traffic state in such a way as to reflect the stochastic process of traffic flow. To predict accurately the traffic state, machine learning-based models have been widely adopted, but they have difficulty in obtaining insights for traffic state prediction due to black-box procedures of the models. A Bayesian network (BN) is a methodology that is suitable for dealing with problems that involve uncertainty, and it can also improve the understanding of such problems. In this study, we develop a traffic state prediction model using a BN to reflect the dynamic and stochastic characteristics of traffic flow. To improve the BN, which has been used with a simple structure in transportation problems, we propose a modeling procedure using a mixture of Gaussians (MoGs). In the performance evaluation, the BN has better performance than a logistic regression, and it has the same level of performance as an artificial neural network based on machine learning. Also, by performing sensitivity analyses, we provide the understanding of traffic state prediction and the guidelines for improving the model. The BN developed in this study can be considered as a traffic state prediction model with good prediction accuracy and interpretability.

scholarly journals Development and validation of prediction model to estimate 10-year risk of all-cause mortality using modern statistical learning methods: a large population-based cohort study and external validation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Olesya Ajnakina ◽  
Deborah Agbedjro ◽  
Ryan McCammon ◽  
Jessica Faul ◽  
Robin M. Murray ◽  
...  
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Absolute Risk ◽  
Large Population ◽  
External Validation ◽  
The United States ◽  
Good Prediction ◽  
Good Prediction Accuracy ◽  
All Cause Mortality ◽  
Development And Validation

Abstract Background In increasingly ageing populations, there is an emergent need to develop a robust prediction model for estimating an individual absolute risk for all-cause mortality, so that relevant assessments and interventions can be targeted appropriately. The objective of the study was to derive, evaluate and validate (internally and externally) a risk prediction model allowing rapid estimations of an absolute risk of all-cause mortality in the following 10 years. Methods For the model development, data came from English Longitudinal Study of Ageing study, which comprised 9154 population-representative individuals aged 50–75 years, 1240 (13.5%) of whom died during the 10-year follow-up. Internal validation was carried out using Harrell’s optimism-correction procedure; external validation was carried out using Health and Retirement Study (HRS), which is a nationally representative longitudinal survey of adults aged ≥50 years residing in the United States. Cox proportional hazards model with regularisation by the least absolute shrinkage and selection operator, where optimisation parameters were chosen based on repeated cross-validation, was employed for variable selection and model fitting. Measures of calibration, discrimination, sensitivity and specificity were determined in the development and validation cohorts. Results The model selected 13 prognostic factors of all-cause mortality encompassing information on demographic characteristics, health comorbidity, lifestyle and cognitive functioning. The internally validated model had good discriminatory ability (c-index=0.74), specificity (72.5%) and sensitivity (73.0%). Following external validation, the model’s prediction accuracy remained within a clinically acceptable range (c-index=0.69, calibration slope β=0.80, specificity=71.5% and sensitivity=70.6%). The main limitation of our model is twofold: 1) it may not be applicable to nursing home and other institutional populations, and 2) it was developed and validated in the cohorts with predominately white ethnicity. Conclusions A new prediction model that quantifies absolute risk of all-cause mortality in the following 10-years in the general population has been developed and externally validated. It has good prediction accuracy and is based on variables that are available in a variety of care and research settings. This model can facilitate identification of high risk for all-cause mortality older adults for further assessment or interventions.

scholarly journals Intelligent Prediction Method of Building Energy Consumption Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bingqian Fan ◽  
Xuanxuan Xing
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Simulation Experiment ◽  
Short Term Memory ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Building energy consumption prediction plays an important role in realizing building energy conservation control. Limited by some external factors such as temperature, there are some problems in practical applications, such as complex operation and low prediction accuracy. Aiming at the problem of low prediction accuracy caused by poor timing of existing building energy consumption prediction methods, a building energy consumption prediction and analysis method based on the deep learning network is proposed in this paper. Before establishing the energy consumption prediction model, the building energy consumption data source is preprocessed and analyzed. Then, based on the Keras deep learning framework, an improved long short-term memory (ILSTM) prediction model is built to support the accurate analysis of the whole cycle of the prediction network. At the same time, the adaptive moment (Adam) estimation algorithm is used to update and optimize the weight parameters of the model to realize the adaptive and rapid update and matching of network parameters. The simulation experiment is based on the actual dataset collected by a university in Southwest China. The experimental results show that the evaluation indexes MAE and RMSE of the proposed method are 0.015 and 0.109, respectively, which are better than the comparison method. The simulation experiment proves that the proposed method is feasible.

scholarly journals Study of the impact of the COVID-19 pandemic on international air transportation

2021 ◽  
Vol 29 (1) ◽  
pp. 22-35
Author(s):  
Eugeny Yu. Shchetinin
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Stock Prices ◽  
Short Term Memory ◽  
Air Transportation ◽  
Time Series Forecasting ◽  
The Cost ◽  
The Impact

Time Series Forecasting has always been a very important area of research in many domains because many different types of data are stored as time series. Given the growing availability of data and computing power in the recent years, Deep Learning has become a fundamental part of the new generation of Time Series Forecasting models, obtaining excellent results.As different time series problems are studied in many different fields, a large number of new architectures have been developed in recent years. This has also been simplified by the growing availability of open source frameworks, which make the development of new custom network components easier and faster.In this paper three different Deep Learning Architecture for Time Series Forecasting are presented: Recurrent Neural Networks (RNNs), that are the most classical and used architecture for Time Series Forecasting problems; Long Short-Term Memory (LSTM), that are an evolution of RNNs developed in order to overcome the vanishing gradient problem; Gated Recurrent Unit (GRU), that are another evolution of RNNs, similar to LSTM.The article is devoted to modeling and forecasting the cost of international air transportation in a pandemic using deep learning methods. The author builds time series models of the American Airlines (AAL) stock prices for a selected period using LSTM, GRU, RNN recurrent neural networks models and compare the accuracy forecast results.

scholarly journals Easily created prediction model using deep learning software (Prediction One, Sony Network Communications Inc.) for subarachnoid hemorrhage outcomes from small dataset at admission

2020 ◽  
Vol 11 ◽  
pp. 374
Author(s):  
Masahito Katsuki ◽  
Yukinari Kakizawa ◽  
Akihiro Nishikawa ◽  
Yasunaga Yamamoto ◽  
Toshiya Uchiyama
Keyword(s):  
Deep Learning ◽  
Subarachnoid Hemorrhage ◽  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Training Dataset ◽  
Validation Dataset ◽  
Good Prediction ◽  
Network Communications ◽  
Learning Software

Background: Reliable prediction models of subarachnoid hemorrhage (SAH) outcomes are needed for decision-making of the treatment. SAFIRE score using only four variables is a good prediction scoring system. However, making such prediction models needs a large number of samples and time-consuming statistical analysis. Deep learning (DL), one of the artificial intelligence, is attractive, but there were no reports on prediction models for SAH outcomes using DL. We herein made a prediction model using DL software, Prediction One (Sony Network Communications Inc., Tokyo, Japan) and compared it to SAFIRE score. Methods: We used 153 consecutive aneurysmal SAH patients data in our hospital between 2012 and 2019. Modified Rankin Scale (mRS) 0–3 at 6 months was defined as a favorable outcome. We randomly divided them into 102 patients training dataset and 51 patients external validation dataset. Prediction one made the prediction model using the training dataset with internal cross-validation. We used both the created model and SAFIRE score to predict the outcomes using the external validation set. The areas under the curve (AUCs) were compared. Results: The model made by Prediction One using 28 variables had AUC of 0.848, and its AUC for the validation dataset was 0.953 (95%CI 0.900–1.000). AUCs calculated using SAFIRE score were 0.875 for the training dataset and 0.960 for the validation dataset, respectively. Conclusion: We easily and quickly made prediction models using Prediction One, even with a small single-center dataset. The accuracy of the model was not so inferior to those of previous statistically calculated prediction models.

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