Introduction to Real Time Prediction

To improve the quality of experience (QoE) of a video playback, the real-time prediction of the video stuck can be a help.

Through computer simulation study of an aquaponic system, the proposed approach is effective to provide promising real-time prediction performance.

Meanwhile, the real-time prediction speed of 0.

This research provides an effective method for real-time prediction of the effluent turbidity of sedimentation tank according to the influent water quality data.

Accurate, reliable, and real-time prediction of ship fuel consumption is the basis and premise of the development of fuel optimization; however, ship fuel consumption data mainly come from noon reports, and many current modeling methods have been based on a single model; therefore they have low accuracy and robustness.

Accurate and real-time prediction of network traffic can not only help system operators allocate resources rationally according to their actual business needs but also help them assess the performance of a network and analyze its health status.

We have also applied Spark’s built-in support of the machine learning library in order to make real-time predictions.

In this work, we develop a two-stream dense feature fusion convolutional network model based on RGB-D data for the real-time prediction of the fresh weight of weeds.

Finally, two independent and stochastic pore pressure prediction methods have been developed that could be used in the pre-drill well planning phase and the real-time prediction during drilling.

Most studies focused on capsule endoscopy readings in CD (n = 5) and on the AI-assisted assessment of mucosal activity in UC (n = 10) for automated endoscopic scoring or real-time prediction of histological disease.

Moreover, their performance depends on large-scale computing resources, so they cannot meet the requirement of near real-time prediction of seismic ground motion.

Using a mock real-time prediction experiment and simulation study, based on this methodology of monitoring accelerated rates of physical anomalies measured at the surface, we show the potential for an early warning system with a lead time of a few days.

The results of this study provide real-time predictions of groundwater quality and quantity parameters, including water levels, nitrate concentrations.

To improve the adaptability of a TBM in complex geological conditions, this study proposes an integrated data mining framework to perform near real-time prediction of two operational parameters including thrust and torque.

Thus, the proposed CNN architecture accompanied by spectral analysis of EEG signals provides a viable method for reliable and real-time prediction of epileptic seizures.

Furthermore, PLS regression was used for real-time prediction of the Melt Flow Index and percentage of bounded acrylonitrile (%AN).

Our real-time prediction models can help reduce highway traffic accidents and congestion as well.

At the same time, this paper also realizes rolling real-time prediction of vertical water wall temperature.

The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.

The proposed intention estimation pipeline is computationally efficient and was successfully tested and used for real-time prediction of wheelchair user’s intentions.

Machine learning methods are used to train a model for real-time prediction of force, torque, and contact using the tactile data collected.

In this paper, we propose a real-time prediction model that can respond to particulate matters (PM) in the air, which are an indication of poor air quality.

In this study, we describe the results of empirical research using a statistical model constructed based on rainfall and water level observation data as a highly accurate water level prediction method suitable for real-time prediction.

Real-time prediction of icing on power transmission lines is of great significance for grid disaster warning.

Fast diesel engine models for real-time prediction in dynamic conditions are required to predict engine performance parameters, to identify emerging failures early on and to establish trends in per.

The real-time prediction efficiency of the proposed solution is determined by implementing and calculating the outcomes on both the dew and cloud platforms.

In this study, we aimed to present deep learning algorithms for real-time predictions 5, 10, and 15 min before a hypotensive event.

Based on these results, the employment of drilling data and machine learning is a strong tool for real-time prediction of geomechanical properties without additional cost.

Finally, this model is applied in the quantitative investigation of the influence of working conditions on the thermal characteristics of the spindle and the real-time prediction of spindle’s thermal deformation with 5.

Research in any field of study requires analysis and comparisons or real-time predictions to extract useful information To prove that the results have practical potential, various filtering techniques and methodologies should be designed and implemented Filters being a class of signal processing helps innovate new technologies with various kinds of outcomes, using filters there are always various methods to solve a problem Considering the current COVID-19 situation, researchers are working on sequencing the novel coronavirus and the genomes of people afflicted with COVID-19 using CPUs and GPUs along with various filtering techniques In this paper we are using a method of filter implementation to collect raw heart rate data samples from fingertip and ear lobe and process those results on CPUs and GPUs Our method of implementation to collect raw heart rate data is using a photoplethysmography method We all know that the moving average filtering technique is the most commonly usedfor averaging an array of sampled data but in this paper we reconstructed the entire moving average filter with a slightly different averaging method where we will prove how our filter technique is better than the traditional moving average filter This filtering technique is implemented and compared on both GPUs and CPUs However, the filters on GPUs are slightly altered as per the GPU framework and CUDA programming techniques to optimize and output challenging results We will also conduct Human trials for this concept and talk about how the heart rate changes while driving and also considering environmental conditions and scenarios The findings of this work are also compared with apple watch heart rate data as it is the most accurate heart rate sensing device in the market with less than 2% error rate © 2021 SAE International All rights reserved.

There is broad interest in developing methods for real-time prediction of ABH events to inform interventions that prevent or reduce their incidence and severity.

The research results provide not only a new method for the real-time prediction of lithology in horizontal well bits but also a theoretical basis for the geosteering of oilfield development and valuable information for future research.

Finally, the sound ray tracing simulations confirm that the model meets the accuracy requirements of acoustic sounding and verify the model’s feasibility for the real-time prediction of the vertical sound speed in saltwater bodies.

As an alternative, we propose to exploit biomechanical factors of human gait, in particular to combine the sampled position and orientation of the subject with her hip orientation to correct these fluctuations thus permitting the models to be applied in a real-time prediction context.

This provides an accurate and real-time prediction model for model predictive control (MPC).

The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions.

These results show the convenient features of electromechanical coupling in rubbers for the non-contact, real-time prediction of the rubber fatigue failure, adding to the possibility of environmental energy harvesting.

Based on GIS technology and DEM data, a real-time prediction method of forest fire was proposed.

This paper targets to achieve a real-time prediction (within 1 s) of the spatial-temporal temperature distribution inside the numerical tunnel model by using artificial intelligence (AI) methods.

However, the existing relevant approaches fail to predict the usage amount of cloud resources on the basis of the requested workloads of users’ applications, and the characteristics of changing workload data are rarely considered for the real-time prediction.

On this basis, we propose a special difference equation model, especially the use of nonlinear diffusion equations to characterize the temporal and spatial dynamic characteristics of propagation between and within clusters for real-time prediction.

Its basic idea was to automatically recognize precursory microseismic sequences for the real-time prediction of rockburst intensity.

In this paper, the parking volume data of public parking garages of Hongqiao Railway Station in Shanghai of China is used to verify the model, and the proposed model makes it possible for the accurate and real-time prediction of parking volumes which are divided into different parking duration categories.

In this article, we develop a hierarchical model predictive control (H-MPC) strategy for eco-cooling of CAVs, which reduces energy consumption through real-time prediction and multi-timescale and multi-layer optimization.

To enable an effective blacklisting, especially in highly varying networks, an accurate real-time prediction of the quality of all available channels is of paramount importance.

Due to the rise of digital twin (DT), which has the characteristics of virtual reality interaction and real-time mapping, a DT-based real-time prediction method of the remaining useful life (RUL) and preventive maintenance scheme is proposed in this study.

Additionally, the processing time for predicting the transient performance from 0 to 300 s can be reduced to 13 s, suggesting a super-real-time prediction.

OBJECTIVE  To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions.

For real-time prediction, a basic web-application is built using a python web framework, Flask and the trained model is called to predict the yield.

We propose a novel dual-memory network (DMNet) to wisely relate both global and local spatio-temporal knowledge to augment the current features, boosting the segmentation performance and retaining the real-time prediction capability.

This identification of risk zones provides a strong basis for the real-time prediction of future catastrophic hazards in the coal mines.

The Since the parameters used for early prediction can be easily acquired with the help of sensors, the proposed approach proves its potential for development of mobile and website applications for patient monitoring, real-time prediction of sepsis and generation of appropriate alert system.

This result will be useful for the real-time prediction system of meteotsunamis in the Korea Strait.

Specifically, a two-stage neural network is trained offline in an unsupervised manner, which is then deployed online for real-time prediction.

The real-time prediction of business processes using historical event data is an important capability of modern business process monitoring systems.

Nowadays, trajectory data and other data are available for real-time prediction for return demand.

In this paper, we propose a fusion method that can improve the real-time prediction accuracy of the EMG system by merging a probability matrix that represents the usage of the six grasping patterns for the targeted object.

Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines (TBMs).

In an endeavor to design better forecasting tools for real-time prediction, the present work highlights the strength of the multi-model multi-physics ensemble over its operational predecessor version.

Good real-time predictions help government decision makers make informed emergency rescue decision in a timely manner.

This paper presents the new method that enables quick real-time predictions of transient instability and the number of generators that must be tripped.

This presents evidence that accurate, near real-time predictions of the wave-generated forces and moments on an ROV can be produced, laying the foundation for developing model-based predictive control strategies that better suit operation in harsh environments.

As such, the 1-step ahead with retraining method is suitable for efficient real-time prediction of AD performance in real-world operations.

The assessment method provides real-time prediction of indoor infection risk and good assist in spread control of COVID-19.

This paper proposes an integrated CNN-Adam-BO algorithm based on microseismic monitoring data to achieve real-time prediction of rockburst intensity.

First, in Chapter 4, an autoregressive with exogenous inputs (ARX) model was developed for real-time prediction of the inflow into a network with a prediction horizon of a few hours.

The experimental results confirmed that the BI-SNN resulted in strongly correlated population activity and demonstrated the feasibility for real-time prediction.

The information generated from the geo-referenced locations are routed via GPS to the central analytics server in the cloud and appropriate algorithms are used to carry out data analysis for real-time prediction and messages to farmers through the Internet and Short Message Services (SMS).

In this work, the real-time prediction models are developed for the output power prediction of the STPV system.

A real-time prediction system was developed for draw resistance of the cigarette based on a recurrent neural network.

Many methods have been proposed to predict ground deformation caused by shield tunneling, but few of them provide real-time prediction because the required input parameters are difficult to determine.

The real-time prediction and UQ performances of the suggested model are validated using both synthetic and experimental data.

Real-time prediction of change provides an understanding related to the land cover, environmental changes, habitat fragmentation, coastal alteration, urban sprawl, etc.

However, they need a large number of calculations based on real data and depend on large-scale computation resources, limiting practice of real-time prediction.

0 system has been used for real-time predictions since June 2017 with 35 ensemble members, and has been operationally used in the two operational prediction centers of China.

This paper presents the development of predictive optimization models for autonomous rotary drilling systems where emphasis is placed on the shift from human (manual) operation as the driving force for drill rate performance to Quantitative Real-time Prediction (QRP) using machine learning.

This research also provides real-time prediction that can be automatically updated when any new data is inputted.

Queue behavioural patterns are captured by Machine Learning models, which can be used to offer improved passenger services (such as real-time predictions for expected waiting time at queues), or can be considered in a dynamic approach for terminal services design (as the entire progress of terminal handling depends on the stochastic behaviour of passengers).

5% by using less than half the parameters for real-time predictions.

Real-time prediction of the dynamics of energetic electrons in Earth’s radiation belts incorporating incomplete observation data is important to protect valuable artificial satellites and to understand their physical processes.

The real-time prediction analysis shows that the prediction algorithm can improve the matching accuracy of short-time traffic flow.

To provide a model for real-time prediction of cutting forces, vibrations and optimal energy consumption (EC) model, a new hybrid algorithm based on Back-Propagation Neural Network and Multi-Objective Particle Swarm Optimization (BPNN-MOPSO) was developed to determine the optimal cutting parameters with minimal total energy consumption.

A web-based real-time prediction application was created capable of integrating patient specific data to predict TKA size, which will require external validation prior to clinical use.

To balance the real-time prediction errors economically, this paper proposes a model predictive control (MPC) based real-time scheduling strategy to optimize the real-time operation of the integrated energy system, which makes real-time operational decisions based on the measured state of the system and future information of uncertainties.

As a proof of principle, we implemented such an interface in ProteomicsDB thereby allowing the retrieval of spectra acquired within the ProteomeTools project or real-time prediction of tandem mass spectra from the deep learning framework Prosit.

Urban flood inundation is worsening as the number of short-duration rainstorms increases, and it is difficult to accurately predict urban flood inundation over a long lead time; however, the traditional hydrodynamic-based urban flood models still have difficulty realizing real-time prediction.

The data validation shows that the real-time prediction accuracy of this model has been greatly improved compared with the original model, which meets the research expectations.

For real-time prediction of TCs in 2020 (July to November), FGOALS-f2 demonstrates a skillful probabilistic prediction of TC genesis and movement.

In this study, we present a comprehensive literature review of the recent research conducted in bunching and real-time prediction models.

Objective We aimed to develop models that can be applied for real-time prediction of COVID-19 activity in all individual countries and territories worldwide.

This allows the readership to upload TM images for real-time predictions using the developed algorithms.

Regional physics-based forecast models provide valid predictions but are too computationally expensive for real-time prediction necessary for AUV navigation.

Once a suitable model is developed, it is deployed as part of a particular software to generate real-time predictions.

The model constructs a tool for real-time prediction of the TJ resistance, and the resistances for 22,254 different TJ structures were predicted.

Moreover, we built a web app based on random forest model to achieve real-time predictions for guiding surgeons’ intraoperative decision making.

In this perspective, in this study, a philosophical industrial and technological direction involving Deep Insight-as-a-Service (DINSaaS) on Forged Cloud Platforms (FCP) along with Advanced Insight Analytics (AIA), primarily motivated by the global benefit is systematically analysed within sophisticated theoretical knowledge, and consequently, a conceptual geo-distributed framework is proposed to (1) guide the national/international leading organizations, governments, cloud service providers and leading companies in order to establish a scalable framework within the hyperscale geo-distributed infrastructure in which exponentially increasing voluminous Big Data (BD) can be harvested effectively and efficiently, (2) inspire the transformation of BD into wiser abstract formats in Specialized Insight Domains (SID), (3) provide fusion and networking of insights rather than BD in order to obtain globally generated distributed intelligence and help make better decisions and near-real-time predictions, in particular for time-critical latency-sensitive applications, and (4) direct all the stakeholders to rivet the high-quality products and services within Automation of Everything (AoE) by exploiting continuously created and updated insights in dedicated taxonomic SID within large-scale geo-distributed datacenters.

Model parameters are estimated using the observations in a short time interval in temporal dimension before the prediction time, providing an accurate and real-time prediction which is critical in cognitive radars deployed in marine environments.

Using such methods allows for rapid fit and prediction times to readily incorporate new data and make real-time predictions.