Relevant bibliographies by topics / FOV PREDICTION

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'FOV PREDICTION'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "FOV PREDICTION"

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Batchuluun, Ganbayar, Ja Hyung Koo, Yu Hwan Kim, and Kang Ryoung Park. "Image Region Prediction from Thermal Videos Based on Image Prediction Generative Adversarial Network." Mathematics 9, no. 9 (May 7, 2021): 1053. http://dx.doi.org/10.3390/math9091053.

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Various studies have been conducted on object detection, tracking, and action recognition based on thermal images. However, errors occur during object detection, tracking, and action recognition when a moving object leaves the field of view (FOV) of a camera and part of the object becomes invisible. However, no studies have examined this issue so far. Therefore, this article proposes a method for widening the FOV of the current image by predicting images outside the FOV of the camera using the current image and previous sequential images. In the proposed method, the original one-channel thermal image is converted into a three-channel thermal image to perform image prediction using an image prediction generative adversarial network. When image prediction and object detection experiments were conducted using the marathon sub-dataset of the Boston University-thermal infrared video (BU-TIV) benchmark open dataset, we confirmed that the proposed method showed the higher accuracies of image prediction (structural similarity index measure (SSIM) of 0.9839) and object detection (F1 score (F1) of 0.882, accuracy (ACC) of 0.983, and intersection over union (IoU) of 0.791) than the state-of-the-art methods.
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Batchuluun, Ganbayar, Na Rae Baek, and Kang Ryoung Park. "Enlargement of the Field of View Based on Image Region Prediction Using Thermal Videos." Mathematics 9, no. 19 (September 25, 2021): 2379. http://dx.doi.org/10.3390/math9192379.

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Various studies have been conducted for detecting humans in images. However, there are the cases where a part of human body disappears in the input image and leaves the camera field of view (FOV). Moreover, there are the cases where a pedestrian comes into the FOV as a part of the body slowly appears. In these cases, human detection and tracking fail by existing methods. Therefore, we propose the method for predicting a wider region than the FOV of a thermal camera based on the image prediction generative adversarial network version 2 (IPGAN-2). When an experiment was conducted using the marathon subdataset of the Boston University-thermal infrared video benchmark open dataset, the proposed method showed higher image prediction (structural similarity index measure (SSIM) of 0.9437) and object detection (F1 score of 0.866, accuracy of 0.914, and intersection over union (IoU) of 0.730) accuracies than state-of-the-art methods.
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Lei, Ke, Ali B. Syed, Xucheng Zhu, John M. Pauly, and Shreyas V. Vasanawala. "Automated MRI Field of View Prescription from Region of Interest Prediction by Intra-Stack Attention Neural Network." Bioengineering 10, no. 1 (January 10, 2023): 92. http://dx.doi.org/10.3390/bioengineering10010092.

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Manual prescription of the field of view (FOV) by MRI technologists is variable and prolongs the scanning process. Often, the FOV is too large or crops critical anatomy. We propose a deep learning framework, trained by radiologists’ supervision, for automating FOV prescription. An intra-stack shared feature extraction network and an attention network are used to process a stack of 2D image inputs to generate scalars defining the location of a rectangular region of interest (ROI). The attention mechanism is used to make the model focus on a small number of informative slices in a stack. Then, the smallest FOV that makes the neural network predicted ROI free of aliasing is calculated by an algebraic operation derived from MR sampling theory. The framework’s performance is examined quantitatively with intersection over union (IoU) and pixel error on position and qualitatively with a reader study. The proposed model achieves an average IoU of 0.867 and an average ROI position error of 9.06 out of 512 pixels on 80 test cases, significantly better than two baseline models and not significantly different from a radiologist. Finally, the FOV given by the proposed framework achieves an acceptance rate of 92% from an experienced radiologist.
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Øygard, Sigrid H., Mélanie Audoin, Andreas Austeng, Erik V. Thomsen, Matthias B. Stuart, and Jørgen A. Jensen. "Accurate prediction of transmission through a lensed row-column addressed array." Journal of the Acoustical Society of America 151, no. 5 (May 2022): 3207–18. http://dx.doi.org/10.1121/10.0010528.

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Using a diverging lens on a row-column array (RCA) can increase the size of its volumetric image and thus significantly improve its clinical value. Here, a ray tracing method is presented to predict the position of the transmitted wave so that it can be used to make beamformed images. The usable transmitted field-of-view (FOV) is evaluated for a lensed 128 + 128 element RCA by comparing the theoretic prediction of the emitted wavefront position with three-dimensional (3D) finite element simulation of the emitted field. The FOV of the array is found to be [Formula: see text] in the direction orthogonal to the emitting elements and 28.5°–51.2°, depending on depth and element position, for the direction lying along the element. Moreover, the proposed ray tracing method is compared with a simpler thin lens model, and it is shown that the improved accuracy of the proposed method can increase the usable transmitted FOV up to 25.1°.
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Fang, Yuan, Zhang Xiaoyong, Huang Zhiwu, Wentao Yu, and Yabo Wang. "A Switched Extend Kalman-Filter for Visual Servoing Applied in Nonholonomic Robot with the FOV Constraint." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 2 (March 20, 2015): 185–90. http://dx.doi.org/10.20965/jaciii.2015.p0185.

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In this paper, a switched Kalmanfilter (KF) is used to predict the status of feature points leaving the field of view (FOV), which is one of the most common constraints in FOV. By using the prediction of status to compensate for the real state of feature points, nonholonomic robots conduct visual servoing tasks efficiently. Results of simulation and experiments verify the effectiveness of the proposed approach.
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Li, Jie, Ling Han, Cong Zhang, Qiyue Li, and Weitao Li. "Adaptive Panoramic Video Multicast Streaming with Limited FoV Feedback." Complexity 2020 (December 18, 2020): 1–14. http://dx.doi.org/10.1155/2020/8832715.

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Virtual reality (VR) provides an immersive 360-degree viewing experience and has been widely used in many areas. However, the transmission of panoramic video usually places a large demand on bandwidth; thus, it is difficult to ensure a reliable quality of experience (QoE) under a limited bandwidth. In this paper, we propose a field-of-view (FoV) prediction methodology based on limited FoV feedback that can fuse the heat map and FoV information to generate a user view. The former is obtained through saliency detection, while the latter is extracted from some user perspectives randomly, and it contains the FoV information of all users. Then, we design a QoE-driven panoramic video streaming system with a client/server (C/S) architecture, in which the server performs rate adaptation based on the bandwidth and the predicted FoV. We then formulate it as a nonlinear integer programming (NLP) problem and propose an optimal algorithm that combines the Karush–Kuhn–Tucker (KKT) conditions with the branch-and-bound method to solve this problem. Finally, we evaluate our system in a simulation environment, and the results show that the system performs better than the baseline.
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Huang, Po-Chia, Ho-Hui Hsieh, Ching-Han Hsu, and Ing-Tsung Hsiao. "AN EFFICIENT SENSITIVITY CALCULATION OF TILTED APERTURES FOR PRECLINICAL MULTI-PINHOLE SPECT." Biomedical Engineering: Applications, Basis and Communications 27, no. 01 (February 2015): 1550006. http://dx.doi.org/10.4015/s1016237215500064.

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Sensitivity performance is one of the key issues in multi-pinhole collimator design for small animal pinhole Single photon emission computed tomography (SPECT). Currently, there are two approaches in predicting sensitivity performance: analytical formula and Monte Carlo (MC) simulation. Analytical formula offers fast computation, while MC simulation provides better modeling of various physical effects and generates more accurate sensitivity prediction. Tilted pinhole apertures become popular in modern system design, because they can avoid projection overlapping and increase field-of-view (FOV) compared to traditional multiple pinholes. However, conventional analytical formula for sensitivity prediction cannot be directly applied to tilted apertures. In this research, we present a modified analytical formula to predict the sensitivity performance by considering tilted and translated pinhole apertures in a multi-pinhole collimation design. The modification is based on a construction of a virtual object plane which is parallel to the aperture plane in the tilted pinhole. Since the new formula is derived in the vector domain, it can be readily integrated to computer-aided-design software to greatly simplify the collimator design optimization. The results show that the modified formula generates sensitivity prediction similar to that from the MC simulation for a multi-pinhole system with tilted pinholes. When larger tilted pinholes are used to increase FOV, the formula can also accurately generate sensitivity prediction with a slightly reduced peak value. For a tilted pinhole aperture up to 40°, the simulation results indicate that the conventional analytical formula may overestimate as much as 25% sensitivity.
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Chuang, Shu-Min, Chia-Sheng Chen, and Eric Hsiao-Kuang Wu. "The Implementation of Interactive VR Application and Caching Strategy Design on Mobile Edge Computing (MEC)." Electronics 12, no. 12 (June 16, 2023): 2700. http://dx.doi.org/10.3390/electronics12122700.

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Virtual reality (VR) and augmented reality (AR) have been proposed as revolutionary applications for the next generation, especially in education. Many VR applications have been designed to promote learning via virtual environments and 360° video. However, due to the strict requirements of end-to-end latency and network bandwidth, numerous VR applications using 360° video streaming may not achieve a high-quality experience. To address this issue, we propose relying on tile-based 360° video streaming and the caching capacity in Mobile Edge Computing (MEC) to predict the field of view (FoV) in the head-mounted device, then deliver the required tiles. Prefetching tiles in MEC can save the bandwidth of the backend link and support multiple users. Smart caching decisions may reduce the memory at the edge and compensate for the FoV prediction error. For instance, caching whole tiles at each small cell has a higher storage cost compared to caching one small cell that covers multiple users. In this paper, we define a tile selection, caching, and FoV coverage model as the Tile Selection and Caching Problem and propose a heuristic algorithm to solve it. Using a dataset of real users’ head movements, we compare our algorithm to the Least Recently Used (LRU) and Least Frequently Used (LFU) caching policies. The results show that our proposed approach improves FoV coverage by 30% and reduces caching costs by 25% compared to LFU and LRU.
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Liu, Tailong, Teng Pan, Shuijie Qin, Hui Zhao, and Huikai Xie. "Dynamic Response Analysis of an Immersed Electrothermally Actuated MEMS Mirror." Actuators 12, no. 2 (February 15, 2023): 83. http://dx.doi.org/10.3390/act12020083.

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MEMS mirrors have a wide range of applications, most of which require large field-of-view (FOV). Immersing MEMS mirrors in liquid is an effective way to improve the FOV. However, the increased viscosity, convective heat transfer and thermal conductivity in liquid greatly affect the dynamic behaviors of electrothermally actuated micromirrors. In this paper, the complex interactions among the multiple energy domains, including electrical, thermal, mechanical and fluidic, are studied in an immersed electrothermally actuated MEMS mirror. A damping model of the immersed MEMS mirror is built and dimensional analysis is applied to reduce the number of variables and thus significantly simplify the model. The solution of the fluid damping model is solved by using regression analysis. The dynamic response of the MEMS mirror can be calculated easily by using the damping model. The experimental results verify the effectiveness and accuracy of these models. The difference between the model prediction and the measurement is within 4%. The FOV scanned in a liquid is also increased by a factor of 1.6. The model developed in this work can be applied to study the dynamic behaviors of various immersed MEMS actuators.
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Whang, Allen Jong-Woei, Yi-Yung Chen, Wei-Chieh Tseng, Chih-Hsien Tsai, Yi-Ping Chao, Chieh-Hung Yen, Chun-Hsiu Liu, and Xin Zhang. "Pupil Size Prediction Techniques Based on Convolution Neural Network." Sensors 21, no. 15 (July 21, 2021): 4965. http://dx.doi.org/10.3390/s21154965.

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The size of one’s pupil can indicate one’s physical condition and mental state. When we search related papers about AI and the pupil, most studies focused on eye-tracking. This paper proposes an algorithm that can calculate pupil size based on a convolution neural network (CNN). Usually, the shape of the pupil is not round, and 50% of pupils can be calculated using ellipses as the best fitting shapes. This paper uses the major and minor axes of an ellipse to represent the size of pupils and uses the two parameters as the output of the network. Regarding the input of the network, the dataset is in video format (continuous frames). Taking each frame from the videos and using these to train the CNN model may cause overfitting since the images are too similar. This study used data augmentation and calculated the structural similarity to ensure that the images had a certain degree of difference to avoid this problem. For optimizing the network structure, this study compared the mean error with changes in the depth of the network and the field of view (FOV) of the convolution filter. The result shows that both deepening the network and widening the FOV of the convolution filter can reduce the mean error. According to the results, the mean error of the pupil length is 5.437% and the pupil area is 10.57%. It can operate in low-cost mobile embedded systems at 35 frames per second, demonstrating that low-cost designs can be used for pupil size prediction.
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Dissertations / Theses on the topic "FOV PREDICTION"

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Björsell, Joachim. "Long Range Channel Predictions for Broadband Systems : Predictor antenna experiments and interpolation of Kalman predictions." Thesis, Uppsala universitet, Signaler och System, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-281058.

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The field of wireless communication is under massive development and the demands on the cellular system, especially, are constantly increasing as the utilizing devices are increasing in number and diversity. A key component of wireless communication is the knowledge of the channel, i.e, how the signal is affected when sent over the wireless medium. Channel prediction is one concept which can improve current techniques or enable new ones in order to increase the performance of the cellular system. Firstly, this report will investigate the concept of a predictor antenna on new, extensive measurements which represent many different environments and scenarios. A predictor antenna is a separate antenna that is placed in front of the main antenna on the roof of a vehicle. The predictor antenna could enable good channel prediction for high velocity vehicles. The measurements show to be too noisy to be used directly in the predictor antenna concept but show potential if the measurements can be noise-filtered without distorting the signal. The use of low-pass filter and Kalman filter to do this, did not give the desired results but the technique to do this should be further investigated. Secondly, a interpolation technique will be presented which utilizes predictions with different prediction horizon by estimating intermediate channel components using interpolation. This could save channel feedback resources as well as give a better robustness to bad channel predictions by letting fresh, local, channel predictions be used as quality reference of the interpolated channel estimates. For a linear interpolation between 8-step and 18-step Kalman predictions with Normalized Mean Square Error (NMSE) of -15.02 dB and -10.88 dB, the interpolated estimates had an average NMSE of -13.14 dB, while lowering the required feedback data by about 80 %. The use of a warning algorithm reduced the NMSE by a further 0.2 dB. It mainly eliminated the largest prediction error which otherwise could lead to retransmission, which is not desired.
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Kock, Peter. "Prediction and predictive control for economic optimisation of vehicle operation." Thesis, Kingston University, 2013. http://eprints.kingston.ac.uk/35861/.

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Truck manufacturers are currently under pressure to reduce pollution and cost of transportation. The cost efficient way to reduce CO[sub]2 and cost is to reduce fuel consumption by adaptation of the vehicle speed to the driving conditions - by heuristic knowledge or mathematical optimisation. Due to their experience, professional drivers are capable of driving with great efficiency in terms of fuel consumption. The key research question addressed in this work is the comparison of the fuel efficiency for an unassisted drive by an experienced professional driver versus an enhanced drive using driver assistance system. The motivation for this is based on the advantage of such a system in terms of price (lower than driver's training) but potentially it can be challenging to obtain drivers' acceptance of the system. There is a range of fundamental issued that have to be addressed prior to the design and implementation of the driver assistance system. The first issue is related to the evaluation of the correctness of the prediction model under development, due to a range of inaccuracies introduced by slope errors in digital maps, imprecise modelling of combustion engine, vehicle physics etc. The second issue is related to the challenge in selecting a suitable method for optimisation of mixed integer non-linear systems. Dynamic Programming proved to be very suitable for this work and some methods of search space reduction are presented here. Also an analytical solution of the Bernoulli differential equation of the vehicle dynamics is presented and used here in order to reduce computing effort. Extensive simulation and driving tests were performed using different driving approaches to compare well trained human experts with a range of different driving assistance systems based on standard cruise control, heuristic and mathematical optimisation. Finally the acceptance of the systems by drivers been evaluated.
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Schön, Tomas. "Identification for Predictive Control : A Multiple Model Approach." Thesis, Linköping University, Department of Electrical Engineering, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1050.

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Predictive control relies on predictions of the future behaviour of the system to be controlled. These predictions are calculated from a model of this system, thus making the model the cornerstone of the predictive controller. Furthermore predictive control is the only advanced control methodology that has managed to become widely used in the industry. The necessity of good models in the predictive control context can thus be motivated both from the very nature of predictive control and from its widespread use in industry.

This thesis is concerned with examining the use of multiple models in the predictive controller. In order to do this the standard predictive control formulation has been extended to incorporate the use of multiple models. The most general case of this new formulation allows the use of an individual model for each prediction horizon.

The models are estimated using measurements of the input and output sequences from the true system. When using this data to find a good model of the system it is important to remember the intended purpose of the model. In this case the model is going to be used in a predictive controller and the most important feature of the models is to deliver good k-step ahead predictions. The identification algorithms used to estimate the models thus strives for estimating models good at calculating these predictions.

Finally this thesis presents some complete simulations of these ideas showing the potential of using multiple models in the predictive control framework.

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Shrestha, Rakshya. "Deep soil mixing and predictive neural network models for strength prediction." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.607735.

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Bangalore, Narendranath Rao Amith Kaushal. "Online Message Delay Prediction for Model Predictive Control over Controller Area Network." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78626.

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Today's Cyber-Physical Systems (CPS) are typically distributed over several computing nodes communicating by way of shared buses such as Controller Area Network (CAN). Their control performance gets degraded due to variable delays (jitters) incurred by messages on the shared CAN bus due to contention and network overhead. This work presents a novel online delay prediction approach that predicts the message delay at runtime based on real-time traffic information on CAN. It leverages the proposed method to improve control quality, by compensating for the message delay using the Model Predictive Control (MPC) algorithm in designing the controller. By simulating an automotive Cruise Control system and a DC Motor plant in a CAN environment, it goes on to demonstrate that the delay prediction is accurate, and that the MPC design which takes the message delay into consideration, performs considerably better. It also implements the proposed method on an 8-bit 16MHz ATmega328P microcontroller and measures the execution time overhead. The results clearly indicate that the method is computationally feasible for online usage.
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Chen, Yutao. "Algorithms and Applications for Nonlinear Model Predictive Control with Long Prediction Horizon." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3421957.

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Fast implementations of NMPC are important when addressing real-time control of systems exhibiting features like fast dynamics, large dimension, and long prediction horizon, as in such situations the computational burden of the NMPC may limit the achievable control bandwidth. For that purpose, this thesis addresses both algorithms and applications. First, fast NMPC algorithms for controlling continuous-time dynamic systems using a long prediction horizon have been developed. A bridge between linear and nonlinear MPC is built using partial linearizations or sensitivity update. In order to update the sensitivities only when necessary, a Curvature-like measure of nonlinearity (CMoN) for dynamic systems has been introduced and applied to existing NMPC algorithms. Based on CMoN, intuitive and advanced updating logic have been developed for different numerical and control performance. Thus, the CMoN, together with the updating logic, formulates a partial sensitivity updating scheme for fast NMPC, named CMoN-RTI. Simulation examples are used to demonstrate the effectiveness and efficiency of CMoN-RTI. In addition, a rigorous analysis on the optimality and local convergence of CMoN-RTI is given and illustrated using numerical examples. Partial condensing algorithms have been developed when using the proposed partial sensitivity update scheme. The computational complexity has been reduced since part of the condensing information are exploited from previous sampling instants. A sensitivity updating logic together with partial condensing is proposed with a complexity linear in prediction length, leading to a speed up by a factor of ten. Partial matrix factorization algorithms are also proposed to exploit partial sensitivity update. By applying splitting methods to multi-stage problems, only part of the resulting KKT system need to be updated, which is computationally dominant in on-line optimization. Significant improvement has been proved by giving floating point operations (flops). Second, efficient implementations of NMPC have been achieved by developing a Matlab based package named MATMPC. MATMPC has two working modes: the one completely relies on Matlab and the other employs the MATLAB C language API. The advantages of MATMPC are that algorithms are easy to develop and debug thanks to Matlab, and libraries and toolboxes from Matlab can be directly used. When working in the second mode, the computational efficiency of MATMPC is comparable with those software using optimized code generation. Real-time implementations are achieved for a nine degree of freedom dynamic driving simulator and for multi-sensory motion cueing with active seat.
Implementazioni rapide di NMPC sono importanti quando si affronta il controllo in tempo reale di sistemi che presentano caratteristiche come dinamica veloce, ampie dimensioni e orizzonte di predizione lungo, poiché in tali situazioni il carico di calcolo dell'MNPC può limitare la larghezza di banda di controllo ottenibile. A tale scopo, questa tesi riguarda sia gli algoritmi che le applicazioni. In primo luogo, sono stati sviluppati algoritmi veloci NMPC per il controllo di sistemi dinamici a tempo continuo che utilizzano un orizzonte di previsione lungo. Un ponte tra MPC lineare e non lineare viene costruito utilizzando linearizzazioni parziali o aggiornamento della sensibilità. Al fine di aggiornare la sensibilità solo quando necessario, è stata introdotta una misura simile alla curva di non linearità (CMoN) per i sistemi dinamici e applicata agli algoritmi NMPC esistenti. Basato su CMoN, sono state sviluppate logiche di aggiornamento intuitive e avanzate per diverse prestazioni numeriche e di controllo. Pertanto, il CMoN, insieme alla logica di aggiornamento, formula uno schema di aggiornamento della sensibilità parziale per NMPC veloce, denominato CMoN-RTI. Gli esempi di simulazione sono utilizzati per dimostrare l'efficacia e l'efficienza di CMoN-RTI. Inoltre, un'analisi rigorosa sull'ottimalità e sulla convergenza locale di CMoN-RTI viene fornita ed illustrata utilizzando esempi numerici. Algoritmi di condensazione parziale sono stati sviluppati quando si utilizza lo schema di aggiornamento della sensibilità parziale proposto. La complessità computazionale è stata ridotta poiché parte delle informazioni di condensazione sono sfruttate da precedenti istanti di campionamento. Una logica di aggiornamento della sensibilità insieme alla condensazione parziale viene proposta con una complessità lineare nella lunghezza della previsione, che porta a una velocità di un fattore dieci. Sono anche proposti algoritmi di fattorizzazione parziale della matrice per sfruttare l'aggiornamento della sensibilità parziale. Applicando metodi di suddivisione a problemi a più stadi, è necessario aggiornare solo parte del sistema KKT risultante, che è computazionalmente dominante nell'ottimizzazione online. Un miglioramento significativo è stato dimostrato dando operazioni in virgola mobile (flop). In secondo luogo, sono state realizzate implementazioni efficienti di NMPC sviluppando un pacchetto basato su Matlab chiamato MATMPC. MATMPC ha due modalità operative: quella si basa completamente su Matlab e l'altra utilizza l'API del linguaggio MATLAB C. I vantaggi di MATMPC sono che gli algoritmi sono facili da sviluppare e eseguire il debug grazie a Matlab e le librerie e le toolbox di Matlab possono essere utilizzate direttamente. Quando si lavora nella seconda modalità, l'efficienza computazionale di MATMPC è paragonabile a quella del software che utilizza la generazione di codice ottimizzata. Le realizzazioni in tempo reale sono ottenute per un simulatore di guida dinamica di nove gradi di libertà e per il movimento multisensoriale con sedile attivo.
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Ge, Esther. "The query based learning system for lifetime prediction of metallic components." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/18345/4/Esther_Ting_Ge_Thesis.pdf.

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This research project was a step forward in developing an efficient data mining method for estimating the service life of metallic components in Queensland school buildings. The developed method links together the different data sources of service life information and builds the model for a real situation when the users have information on limited inputs only. A practical lifetime prediction system was developed for the industry partners of this project including Queensland Department of Public Works and Queensland Department of Main Roads. The system provides high accuracy in practice where not all inputs are available for querying to the system.
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Ge, Esther. "The query based learning system for lifetime prediction of metallic components." Queensland University of Technology, 2008. http://eprints.qut.edu.au/18345/.

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This research project was a step forward in developing an efficient data mining method for estimating the service life of metallic components in Queensland school buildings. The developed method links together the different data sources of service life information and builds the model for a real situation when the users have information on limited inputs only. A practical lifetime prediction system was developed for the industry partners of this project including Queensland Department of Public Works and Queensland Department of Main Roads. The system provides high accuracy in practice where not all inputs are available for querying to the system.
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Zhu, Zheng. "A Unified Exposure Prediction Approach for Multivariate Spatial Data: From Predictions to Health Analysis." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin155437434818942.

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Aldars, García Laila. "Predictive mycology as a tool for controlling and preventing the aflatoxin risk in postharvest." Doctoral thesis, Universitat de Lleida, 2017. http://hdl.handle.net/10803/418806.

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Les aflatoxines són potents carcinògens que representen una amenaça significativa per a la salut humana. La incidència d'aquestes micotoxines en els aliments és alta, de manera que el seu control i prevenció són necessaris en la indústria alimentària. El desenvolupament de models predictius apropiats que ens permetin predir el creixement fúngic i la producció de micotoxines és de gran utilitat com a eina per controlar, predir i prevenir el risc de micotoxines en aliments. És important que els models predictius siguin capaços d'explicar les condicions ambientals que es troben al llarg de la cadena alimentària. Entre aquestes condicions trobem: condicions subòptimes per al creixement i producció de micotoxines, distribució aleatòria d'espores en l'aliment, presència de diferents soques de la mateixa espècie o condicions ambientals canviants. El present treball proporciona una base per al desenvolupament de models científicament provats, que poden ser aplicats per la indústria alimentària per millorar el control en postcollita.
Las aflatoxinas son potentes carcinógenos que representan una amenaza significativa para la salud humana. La incidencia de estas micotoxinas en los alimentos es alta, por lo que su control y prevención es obligatoria en la industria alimentaria. El desarrollo de modelos predictivos apropiados que nos permitan predecir el crecimiento fúngico y la producción de micotoxinas es de gran utilidad como herramienta para controlar, predecir y prevenir el riesgo de micotoxinas en alimentos. Es importante que los modelos predictivos sean capaces de explicar las condiciones ambientales que se encuentran a lo largo de la cadena alimentaria. Entre tales condiciones encontramos: condiciones subóptimas para el crecimiento y producción de micotoxinas, distribución aleatoria de esporas fúngicas en el alimento, presencia de diferentes cepas de la misma especie o condiciones ambientales dinámicas. El presente trabajo proporciona una base para el desarrollo de modelos científicamente probados, que pueden ser aplicados por la industria alimentaria para mejorar el control de micotoxinas en postcosecha.
Aflatoxins are potent carcinogens that pose a significant threat to human health. Incidence of these mycotoxins in foodstuffs is high, thus their control and prevention is mandatory in the food industry. The development of appropriate predictive models that allow us to predict fungal growth and mycotoxin production will be a valuable tool to monitor, predict and prevent the mycotoxin risk. To develop accurate predictive models it is important to account for the real conditions that we will encounter through the food chain. Such conditions include: suboptimal conditions for growth and mycotoxin production, even distribution of spores across the food matrix, presence of different strains of the same species or dynamic environmental conditions. Given the scope and complexity of the problem the present work provides the basis for scientifically proven models, which can be applied in the food industry in order to improve postharvest control of commodities.
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Books on the topic "FOV PREDICTION"

1

Houston, Walter. Central prediction systems for predicting specific course grades. Iowa City: American College Testing Program, 1988.

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Predicting Prehistory: Predictive models and field research methods for detecting prehistoric contexts. Firenze: Museo e istituto fiorentino di preistoria "Paolo Graziosi,", 2015.

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Cherdanceva, Tat'yana, Vladimir Klimechev, and Igor' Bobrov. Pathological and molecular biological analysis of renal cell carcinoma. Diagnosis and prognosis. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1020785.

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The monograph is devoted to the study of pathomorphological and molecular-biological characteristics of renal cell carcinoma and peritumoral zone depending on the degree of malignancy, and determine prognostic significance of criteria for predicting the postoperative survival of patients. Of interest to urologists, oncologists, pathologists, researchers, graduate students, dealing with the diagnosis of renal cell carcinoma and subsequent prediction of postoperative survival of patients.
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Altmisdort, F. Nadir. Development of a new prediction algorithm and a simulator for the Predictive Read Cache (PRC). Monterey, Calif: Naval Postgraduate School, 1996.

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Casey, Douglas R. Predictions for 1988. 2nd ed. Alexandria, VA: KCI Communications, 1988.

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United States. National Weather Service, ed. National Centers for Environmental Prediction. [Silver Spring, Md.?]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, 1996.

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Vasil'eva, Natal'ya. Mathematical models in the management of copper production: ideas, methods, examples. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014071.

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Presents the current status in modelling of metallurgical processes considered by the model the mathematical model used in the description of the processes of copper production and their classification. Set out a system of methods and models in the field of mathematical modeling of technological processes, including balance sheet, statistics, optimization models, forecasting models and predictive models. For specific technological processes are developed: the model of the balance of the cycle of pyrometallurgical production of copper, polynomial model for prediction of matte composition on the basis of the passive experiment, predictive model of quantitative estimation of the copper content in the matte based on fuzzy logic. Of interest to students, postgraduates, teachers of technical universities, engineers and research workers who use mathematical methods for processing of data of laboratory and industrial experiments.
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Rathore, Santosh Singh, and Sandeep Kumar. Fault Prediction Modeling for the Prediction of Number of Software Faults. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7131-8.

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Hanson, R. Karl. Prediction statistics for psychological assessment. Washington: American Psychological Association, 2022. http://dx.doi.org/10.1037/0000275-000.

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Bolfarine, Heleno, and Shelemyahu Zacks. Prediction Theory for Finite Populations. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2904-9.

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Book chapters on the topic "FOV PREDICTION"

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Li, Yunqiao, Yiling Xu, Shaowei Xie, Liangji Ma, and Jun Sun. "Two-Layer FoV Prediction Model for Viewport Dependent Streaming of 360-Degree Videos." In Communications and Networking, 501–9. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06161-6_49.

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Pourbafrani, Mahsa, Shreya Kar, Sebastian Kaiser, and Wil M. P. van der Aalst. "Remaining Time Prediction for Processes with Inter-case Dynamics." In Lecture Notes in Business Information Processing, 140–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_11.

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AbstractProcess mining techniques use event data to describe business processes, where the provided insights are used for predicting processes’ future states (Predictive Process Monitoring). Remaining Time Prediction of process instances is an important task in the field of Predictive Process Monitoring (PPM). Existing approaches have two key limitations in developing Remaining Time Prediction Models (RTM): (1) The features used for predictions lack process context, and the created models are black-boxes. (2) The process instances are considered to be in isolation, despite the fact that process states, e.g., the number of running instances, influence the remaining time of a single process instance. Recent approaches improve the quality of RTMs by utilizing process context related to batching-at-end inter-case dynamics in the process, e.g., using the time to batching as a feature. We propose an approach that decreases the previous approaches’ reliance on user knowledge for discovering fine-grained process behavior. Furthermore, we enrich our RTMs with the extracted features for multiple performance patterns (caused by inter-case dynamics), which increases the interpretability of models. We assess our proposed remaining time prediction method using two real-world event logs. Incorporating the created inter-case features into RTMs results in more accurate and interpretable predictions.
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Liu, Wendi, Léan E. Garland, Jesus Ochoa, and Michael J. Pyrcz. "A Geostatistical Heterogeneity Metric for Spatial Feature Engineering." In Springer Proceedings in Earth and Environmental Sciences, 3–19. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_1.

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AbstractHeterogeneity is a vital spatial feature for subsurface resource recovery predictions, such as mining grade tonnage functions, hydrocarbon recovery factor, and water aquifer draw-down predictions. Feature engineering presents the opportunity to integrate heterogeneity information, but traditional heterogeneity engineered features like Dykstra-Parsons and Lorenz coefficients ignore the spatial context; therefore, are not sufficient to quantify the heterogeneity over multiple scales of spatial intervals to inform predictive machine learning models. We propose a novel use of dispersion variance as a spatial-engineered feature that accounts for heterogeneity within the spatial context, including spatial continuity and sample data and model volume support size to improve predictive machine-learning-based models, e.g., for pre-drill prediction and uncertainty quantification. Dispersion variance is a generalized form of variance that accounts for volume support size and can be calculated from the semivariogram-based spatial continuity model. We demonstrate dispersion variance as a useful predictor feature for the case of hydrocarbon recovery prediction, with the ability to quantify the spatial variation over the support size of the production well drainage radius, given the spatial continuity from the variogram and trajectory of the well. We include a synthetic example based on geostatistical models and flow simulation to show the sensitivity of dispersion variance to production. Then we demonstrate the dispersion variance as an informative predictor feature for production forecasting with a field case study in the Duvernay formation.
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Fani Sani, Mohammadreza, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, and Wil M. P. van der Aalst. "Event Log Sampling for Predictive Monitoring." In Lecture Notes in Business Information Processing, 154–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_12.

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AbstractPredictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. This paper proposes an instance selection procedure that allows sampling training process instances for prediction models. We show that our sampling method allows for a significant increase of training speed for next activity prediction methods while maintaining reliable levels of prediction accuracy.
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Lee, Suhwan, Marco Comuzzi, and Xixi Lu. "Continuous Performance Evaluation for Business Process Outcome Monitoring." In Lecture Notes in Business Information Processing, 237–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_18.

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AbstractWhile a few approaches to online predictive monitoring have focused on concept drift model adaptation, none have considered in depth the issue of performance evaluation for online process outcome prediction. Without such a continuous evaluation, users may be unaware of the performance of predictive models, resulting in inaccurate and misleading predictions. This paper fills this gap by proposing a framework for evaluating online process outcome predictions, comprising two different evaluation methods. These methods are partly inspired by the literature on streaming classification with delayed labels and complement each other to provide a comprehensive evaluation of process monitoring techniques: one focuses on real-time performance evaluation, i.e., evaluating the performance of the most recent predictions, whereas the other focuses on progress-based evaluation, i.e., evaluating the ability of a model to output correct predictions at different prefix lengths. We present an evaluation involving three publicly available event logs, including a log characterised by concept drift.
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Warmuth, Christian, and Henrik Leopold. "On the Potential of Textual Data for Explainable Predictive Process Monitoring." In Lecture Notes in Business Information Processing, 190–202. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0_14.

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AbstractPredictive process monitoring techniques leverage machine learning (ML) to predict future characteristics of a case, such as the process outcome or the remaining run time. Available techniques employ various models and different types of input data to produce accurate predictions. However, from a practical perspective, explainability is another important requirement besides accuracy since predictive process monitoring techniques frequently support decision-making in critical domains. Techniques from the area of explainable artificial intelligence (XAI) aim to provide this capability and create transparency and interpretability for black-box ML models. While several explainable predictive process monitoring techniques exist, none of them leverages textual data. This is surprising since textual data can provide a rich context to a process that numerical features cannot capture. Recognizing this, we use this paper to investigate how the combination of textual and non-textual data can be used for explainable predictive process monitoring and analyze how the incorporation of textual data affects both the predictions and the explainability. Our experiments show that using textual data requires more computation time but can lead to a notable improvement in prediction quality with comparable results for explainability.
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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Linear Mixed Models." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 141–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_5.

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AbstractThe linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and illustrate how genomic-enabled predictions are performed under this framework. We illustrate the use of linear mixed models by using the predictor several components such as environments, genotypes, and genotype × environment interaction. Also, the linear mixed model is illustrated under a multi-trait framework that is important in the prediction performance when the degree of correlation between traits is moderate or large. We illustrate the use of single-trait and multi-trait linear mixed models and provide the R codes for performing the analyses.
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Bautista-Hernández, Jorge, and María Ángeles Martín-Prats. "Monte Carlo Simulation Applicable for Predictive Algorithm Analysis in Aerospace." In Technological Innovation for Connected Cyber Physical Spaces, 243–56. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36007-7_18.

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AbstractSafety investigations about electrical wiring harness caused by failures in electrical systems establish that origin of these accidents are related to electrical installation. Predictive techniques which mitigate and reduce risk of the occurrence of errors to enhance safety shall be considered. The development of machine learning has evolved towards the creation of innovative predictive algorithms which show high performance in data analysis and making predictions in the context of artificial intelligence. The Monte Carlo approach is used to validate the model performance. In this paper, Monte Carlo simulation was used to evaluate the level of the uncertainty of the selected parameters over 1000 runs. This study analyzes the reliability of the predictive algorithm in order to be implemented as an automatic error predictor in aerospace. The results obtained are within the expected range suggesting that the model used is accurate and reliable.
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Onishi, Ryo, Joe Hirai, Dmitry Kolomenskiy, and Yuki Yasuda. "Real-Time High-Resolution Prediction of Orographic Rainfall for Early Warning of Landslides." In Progress in Landslide Research and Technology, Volume 1 Issue 1, 2022, 237–48. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16898-7_17.

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AbstractHeavy rainfall often causes devastating landslides. Early warning based on reliable rainfall prediction can help reduce human and economic damages. This paper describes a recent development of reliable high-resolution prediction of orographic (topographic) rainfall using our next-generation numerical weather prediction model, the Multi-Scale Simulator for the Geoenvironment (MSSG). High-resolution computing is required for reliable rainfall prediction, and the MSSG can run with very high resolutions. Robust cloud microphysics is another key to realizing reliable predictions of orographic clouds, where the atmospheric boundary turbulence can affect. This paper clarifies that in-cloud turbulence can enhance cloud development. The recent cloud microphysics model that can consider turbulence enhancement is newly implemented in the MSSG. The emerging machine-learning technology is also coupled with the MSSG for reliable operational predictions. We show the recent development towards reliable predictions of orographic rainfall for realizing early warning of landslides.
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Spenrath, Yorick, Marwan Hassani, and Boudewijn F. van Dongen. "Online Prediction of Aggregated Retailer Consumer Behaviour." In Lecture Notes in Business Information Processing, 211–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_16.

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AbstractPredicting the behaviour of consumers provides valuable information for retailers, such as the expected spend of a consumer or the total turnover of the retailer. The ability to make predictions on an individual level is useful, as it allows retailers to accurately perform targeted marketing. However, with the expected large number of consumers and their diverse behaviour, making accurate predictions on an individual consumer level is difficult. In this paper we present a framework that focuses on this trade-off in an online setting. By making predictions on a larger number of consumers at a time, we improve the predictive accuracy but at the cost of usefulness, as we can say less about the individual consumers. The framework is developed in an online setting, where we update the prediction model and make new predictions over time. We show the existence of the trade-off in an experimental evaluation on a real-world dataset consisting of 39 weeks of transaction data.
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Conference papers on the topic "FOV PREDICTION"

1

Zhang, Zhihao, Haipeng Du, Shouqin Huang, Weizhan Zhang, and Qinghua Zheng. "VRFormer: 360-Degree Video Streaming with FoV Combined Prediction and Super resolution." In 2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2022. http://dx.doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom57177.2022.00074.

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Deshwal, Aryan, Janardhan Rao Doppa, and Dan Roth. "Learning and Inference for Structured Prediction: A Unifying Perspective." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/878.

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In a structured prediction problem, one needs to learn a predictor that, given a structured input, produces a structured object, such as a sequence, tree, or clustering output. Prototypical structured prediction tasks include part-of-speech tagging (predicting POS tag sequence for an input sentence) and semantic segmentation of images (predicting semantic labels for pixels of an input image). Unlike simple classification problems, here there is a need to assign values to multiple output variables accounting for the dependencies between them. Consequently, the prediction step itself (aka ``inference" or ``decoding") is computationally-expensive, and so is the learning process, that typically requires making predictions as part of it. The key learning and inference challenge is due to the exponential size of the structured output space and depend on its complexity. In this paper, we present a unifying perspective of the different frameworks that address structured prediction problems and compare them in terms of their strengths and weaknesses. We also discuss important research directions including integration of deep learning advances into structured prediction, and learning from weakly supervised signals and active querying to overcome the challenges of building structured predictors from small amount of labeled data.
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Rohani, Muhammad Joehan Bin, Azam Bin A. Rahman, M. Syazwan Kamil Bin Abdullah, M. Nazmi Bin Ali, I. Wayan Eka Putra, Hazwani Binti Hidzir, and Ehsan Amirian. "IMGESA (Integrated Meteorological and Geohazard System Advisory) as Predictive Analytics Tool for Managing Geohazard Impacts to Pipeline." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211292-ms.

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Abstract There is an industry need to have a fit for purpose whilst accurate method of predicting geohazard impact to pipelines. Geohazard events are influenced by rain occurrences. The effect of rain intensity and duration has been much researched in the context of understanding slope failures in Malaysia's context, however timely prediction of slope failures triggered by rain events that can cause pipeline damage remains challenging. The major challenge arises from the fact that numerous variables influence the prediction of the target parameters called PR (Risk Index for geohazard impact to pipeline) and pipeline strain predictions. Uncertainties that makes prediction challenging includes but are not limited to soil strength parameters, subterrain geological conditions, occurrences of external disturbances that are outside zone of concerns and also numerous pipeline related parameters that renders understanding influences complex. To further enhance timely and improved accuracy of predicting geohazard impact to pipeline, new Machine Learning capabilities were used to develop a tool called IMGESA (Integrated Meteorological and Geohazard System Advisory) leveraging on probabilities method to study the features of terrain degradation that are impacted by rain intensity and its duration. New probabilistic algorithms can be used to manage uncertainties. Machine Learning methods can provide the basis for continuous improvement to predictions. Two main parts in establishing the impact of geohazards to terrain degradation are discussed in this paper: the first is associated with availability of data, namely which data can be considered as main influencer to terrain degradation; the second is associated with development of methodology in establishing the predictive model of PR (Risk Index) and Strain Prediction that can be acceptable by industry. This paper will explore the issues from these two important parts and will present salient Machine Learning related experiences to provide the much-needed technology enhancement to push the needle in predicting terrain degradation and its impact to onshore assets.
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Assaf, Roy, and Anika Schumann. "Explainable Deep Neural Networks for Multivariate Time Series Predictions." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/932.

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We demonstrate that CNN deep neural networks can not only be used for making predictions based on multivariate time series data, but also for explaining these predictions. This is important for a number of applications where predictions are the basis for decisions and actions. Hence, confidence in the prediction result is crucial. We design a two stage convolutional neural network architecture which uses particular kernel sizes. This allows us to utilise gradient based techniques for generating saliency maps for both the time dimension and the features. These are then used for explaining which features during which time interval are responsible for a given prediction, as well as explaining during which time intervals was the joint contribution of all features most important for that prediction. We demonstrate our approach for predicting the average energy production of photovoltaic power plants and for explaining these predictions.
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Colban, Will F., Karen A. Thole, and David Bogard. "A Film-Cooling Correlation for Shaped Holes on a Flat-Plate Surface." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-50121.

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A common method of optimizing coolant performance in gas turbine engines is through the use of shaped film-cooling holes. Despite widespread use of shaped holes, existing correlations for predicting performance are limited to narrow ranges of parameters. This study extends the prediction capability for shaped holes through the development of a physics-based empirical correlation for predicting laterally-averaged film-cooling effectiveness on a flat plate downstream of a row of shaped film-cooling holes. Existing data was used to determine the physical relationship between film-cooling effectiveness and several parameters, including; blowing ratio, hole coverage ratio, area ratio, and hole spacing. Those relationships were then incorporated into the skeleton form of an empirical correlation, using results from the literature to determine coefficients for the correlation. Predictions from the current correlation, as well as existing shaped hole correlations and a cylindrical hole correlation were compared to the existing experimental data. Results show that the current physics-based correlation yields a significant improvement in predictive capability, by expanding the valid parameter range and improving agreement with experimental data. Particularly significant is the inclusion of higher blowing ratio conditions (up to M = 2.5) into the current correlation, whereas the existing correlations worked adequately only at lower blowing ratios (M ≈ 0.5).
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Jakša, Rudolf, Martina Zeleňáková, Juraj Koščák, and Helena Hlavatá. "Local Prediction of Precipitation Based on Neural Network." In Environmental Engineering. VGTU Technika, 2017. http://dx.doi.org/10.3846/enviro.2017.079.

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The paper is focused on analysis of local neural network model of precipitation. We use basic multilayer perceptron neural network with the time-window on input data to predict the precipitation. We predict the precipitation in the next day from the local meteorological data from past days. Data from the past 60 years were used to train the predictor. Obtained prediction model is specific for given area of Košice City in Slovakia, as the prediction is based on the statistics of the weather in given area. This precipitation predictor is multiple-input-single-output architecture with a single value per day resolution on output. Obtained results show that good local temperature prediction accuracy is possible with chosen setup, but it is worse for the precipitation prediction. Also the training requirements of precipitation predictor seem to be significantly higher then for the temperature predictor. Obtained prediction results can be used for applications based on local meteorological station data, although they are not as accurate as the state of art agency predictions based on satellite data. In the paper we will analyze design of the precipitation predictor based on existing design of the temperature predictor and provide the reader with recommended setup of such predictor for application with his/her local precipitation data.
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Lu, Ziqi, Shixiao Fu, Mengmeng Zhang, Haojie Ren, and Leijian Song. "A Non-Iterative Method for Vortex Induced Vibration Prediction of Marine Risers." In ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/omae2017-61216.

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A non-iterative method in frequency domain for vortex induced vibration (VIV) predictions of marine risers is proposed in this paper. A solving model is established in modal space for predicting risers’ VIV responses, which consists of a hydrodynamic force equation and a dynamic response equation. By utilizing a non-iterative solving process in the modal space, the equations are solved without power-balance iterations. And through comparisons between this method and conventional prediction methods, the validity and applicability of this non-iterative method is verified. This method completely gets rid of the difficulty of getting convergent predictions, which is conducive to practical engineering applications and provides fresh ideas for understanding conventional empirical model methods for VIV prediction.
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Lauerova, Dana, Vladislav Pistora, Milan Brumovsky, and Milos Kytka. "Warm Pre-Stressing Tests for WWER 440 Reactor Pressure Vessel Material." In ASME 2009 Pressure Vessels and Piping Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/pvp2009-77287.

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During years 2006 – 2008, warm pre-stressing tests on small (Charpy size) and 1T CT specimens were performed at NRI Rez. The specimens were made from WWER 440 reactor pressure vessel material in as-received, thermally treated (artificially aged) and irradiated conditions, the last two conditions simulating the end of life state of the RPV. In this paper, only results of tests performed for this material in as-received and irradiated conditions are presented. Evaluation of WPS tests was performed with using Chell and Wallin predictive models. The attention was paid to 5% probability level fracture predictions, since this level of probability is important for WPS application in pressurized thermal shock evaluation performed within the RPV integrity assessment. From point of view of this 5% probability fracture prediction, both Chell and Wallin models appeared not to be sufficiently conservative for LCF regime (prediction of “Case 2”); for other regimes (LUCF, LPUCF, LTUF and LPTUF) they appeared to be sufficiently conservative (in almost all cases). Based on the results of the tests, Wallin model was selected for implementation into the RPV integrity evaluation procedure, but simultaneously a decision was adopted to decrease its predictions when the “Case 2” is predicted: instead of predicting some surplus (15% of virgin KIC) above the value of KWPS, only value of KWPS (without any surplus) is predicted. This measure enhances conservativeness of the Wallin model to a sufficient level: the performed WPS experiments then well confirm the Wallin model predictions decreased in this manner. Taking 90% of the value of KWPS represents an additional margin implemented currently in the WPS methodology.
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Dhakksinesh, A., Olivia R. Katherine, and V. S. Pooja. "Crime Analysis and Prediction Based on Machine Learning Algorithm." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-y21866.

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Crime prediction is a unique approach to identify and to find pattern trends of crime. Prediction means, using analysis and learning techniques, to find predictive actions of a specific activity and this is found to be effective in doing predictive analysis for various tasks such as crime prediction. The aim of this paper is to implement an approach for the problem in predicting the number of cases of crime happening in different parts of India. During the research we considered the machine learning model Random Forest and used the same for the prediction for crime. The prediction metrics used in this model are taken from feature selection technique. This technique increases the efficiency and accuracy of the prediction and also to avoid the model from over fitting. This model was tested on the crime data of India.
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Tong, Michael T. "A Machine-Learning Approach to Assess Aircraft Engine System Performance." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14661.

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Abstract Machine learning and big data have become the most disruptive technologies for organizations to improve workplace efficiency and productivity. This work explored the application of machine learning-based predictive analytics that would enable aircraft engine designers to estimate engine system performance quickly during the conceptual design stage. Supervised machine-learning algorithm was employed to study patterns in an open-source database of one-hundred-eighty-three production and research turbofan engines, and built predictive analytics for use in predicting system performance of new turbofan designs. Specifically, the author developed deep-learning analytics to predict turbofan system weight, using turbofan design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks API (application program interface) written in Python, with TensorFlow (an open-source Google machine learning library) serving as the backend engine. The current engine-weight prediction results, together with those for the TSFC (thrust specific fuel consumption) and core-size predictions that were studied previously by the author, show that machine learning-based predictive analytics can be an effective, time-saving tool for assessing aircraft engine system performance (TSFC, weight, and core size) during the conceptual design stage. It would enable expeditious identification of the best engine design amongst several candidates.
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Reports on the topic "FOV PREDICTION"

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Roberson, Madeleine, Kathleen Inman, Ashley Carey, Isaac Howard, and Jameson Shannon. Probabilistic neural networks that predict compressive strength of high strength concrete in mass placements using thermal history. Engineer Research and Development Center (U.S.), June 2022. http://dx.doi.org/10.21079/11681/44483.

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This study explored the use of artificial neural networks to predict UHPC compressive strengths given thermal history and key mix components. The model developed herein employs Bayesian variational inference using Monte Carlo dropout to convey prediction uncertainty using 735 datapoints on seven UHPC mixtures collected using a variety of techniques. Datapoints contained a measured compressive strength along with three curing inputs (specimen maturity, maximum temperature experienced during curing, time of maximum temperature) and five mixture inputs to distinguish each UHPC mixture (cement type, silicon dioxide content, mix type, water to cementitious material ratio, and admixture dosage rate). Input analysis concluded that predictions were more sensitive to curing inputs than mixture inputs. On average, 8.2% of experimental results in the final model fell outside of the predicted range with 67.9%of these cases conservatively underpredicting. The results support that this model methodology is able to make sufficient probabilistic predictions within the scope of the provided dataset but is not for extrapolating beyond the training data. In addition, the model was vetted using various datasets obtained from literature to assess its versatility. Overall this model is a promising advancement towards predicting mechanical properties of high strength concrete with known uncertainties.
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Kumar, Kaushal, and Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, March 2023. http://dx.doi.org/10.31979/mti.2023.2211.

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Traffic congestion causes Americans to lose millions of hours and dollars each year. In fact, 1.9 billion gallons of fuel are wasted each year due to traffic congestion, and each hour stuck in traffic costs about $21 in wasted time and fuel. The traffic congestion can be caused by various factors, such as bottlenecks, traffic incidents, bad weather, work zones, poor traffic signal timing, and special events. One key step to addressing traffic congestion and identifying its root cause is an accurate prediction of traffic flow. Accurate traffic flow prediction is also important for the successful deployment of smart transportation systems. It can help road users make better travel decisions to avoid traffic congestion areas so that passenger and freight movements can be optimized to improve the mobility of people and goods. Moreover, it can also help reduce carbon emissions and the risks of traffic incidents. Although numerous methods have been developed for traffic flow predictions, current methods have limitations in utilizing the most relevant part of traffic flow data and considering the correlation among the collected high-dimensional features. To address this issue, this project developed attention-based methodologies for traffic flow predictions. We propose the use of an attention-based deep learning model that incorporates the attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This attention mechanism can calculate the importance level of traffic flow data and enable the model to consider the most relevant part of the data while making predictions, thus improving accuracy and reducing prediction duration.
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Zhu, Xian-Kui, Brian Leis, and Tom McGaughy. PR-185-173600-R01 Reference Stress for Metal-loss Assessment of Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 2018. http://dx.doi.org/10.55274/r0011516.

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This project focused on quantifying the reference stress to be used in predictive models for assessing the effects of metal loss on pipeline integrity. The results of this project will work in concert with the outcomes of project EC-2-7 that examined sources of scatter in metal-loss predictions with respect to the metal-loss defect geometry. The methodology for developing a new reference stress included empirical and finite element analyses along with comparison of full-scale experimental results that indicate the failure behavior of defect-free pipe has dependence on the strain hardening rate, n, of the pipe steel. Since the strain hardening rate is often unreported in qualification test records and mill certification reports, the development of a new reference stress will seek to include the utilization of the ratio of yield-to-tensile strength (Y/T) as a surrogate for n. This approach ideally would be insensitive to pipe grade, and thus, allow broad application of the reference stress without increasing scatter or bias across grade levels. This work also compared the resulting metal-loss criterion with the new reference stress relative to the B31G and Modified B31G models using a dataset of approximately 75 full-scale burst test results for test vessels containing isolated defects. This comparison was performed by C-FER Technologies under sub-contract to EWI and quantified the prediction bias and prediction variability of the new criterion relative to those widely in use.
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Vecherin, Sergey, Stephen Ketcham, Aaron Meyer, Kyle Dunn, Jacob Desmond, and Michael Parker. Short-range near-surface seismic ensemble predictions and uncertainty quantification for layered medium. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45300.

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To make a prediction for seismic signal propagation, one needs to specify physical properties and subsurface ground structure of the site. This information is frequently unknown or estimated with significant uncertainty. This paper describes a methodology for probabilistic seismic ensemble prediction for vertically stratified soils and short ranges with no in situ site characterization. Instead of specifying viscoelastic site properties, the methodology operates with probability distribution functions of these properties taking into account analytical and empirical relationships among viscoelastic variables. This yields ensemble realizations of signal arrivals at specified locations where statistical properties of the signals can be estimated. Such ensemble predictions can be useful for preliminary site characterization, for military applications, and risk analysis for remote or inaccessible locations for which no data can be acquired. Comparison with experiments revealed that measured signals are not always within the predicted ranges of variability. Variance-based global sensitivity analysis has shown that the most significant parameters for signal amplitude predictions in the developed stochastic model are the uncertainty in the shear quality factor and the Poisson ratio above the water table depth.
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Peterson, Warren. PR-663-19600-Z01 Develop Guidance for Calculation of HCDP in Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 2020. http://dx.doi.org/10.55274/r0011659.

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To maintain the integrity and reliability of natural gas transportation systems, system operators ensure that products in transit remain in the gas phase under foreseeable operating conditions. Compliance with pipeline hydrocarbon dew point (HCDP) specifications are demonstrated though in-situ testing or predictive models based on Equations of State (EOS) calculations. Numerical prediction of HCDP is a product of contributing elements, including gas chromatography, calibration gas quality, thermophysical science and the experimental data that underpins equations of state. Some hydrocarbon mixtures, such as those from non-traditional gas supplies, are more difficult to sample and assess than others. The methods described in this paper and accompanying spreadsheet examples are designed to assist persons in making technically defendable decisions with respect to predictive methods and the operational impacts of liquid dropout. The primary focus of this work is to connect the over-all performance of HCDP prediction to its operational implications. The secondary objective of the work is to provide tools for assessing the potential benefit from using C9+ versus C6+ gas chromatographs.
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Martin, Marcus G., Edward J. Maginn, Robin D. Rogers, Greg Voth, and Mark S. Gordon. Technologies for Developing Predictive Atomistic and Coarse-Grained Force Fields for Ionic Liquid Property Prediction. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada485626.

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Oliver, Amanda, Catherine Murphy, Edmund Howe, and John Vest. Comparing methods for estimating water surface elevation between gages in the Lower Mississippi River. Engineer Research and Development Center (U.S.), April 2023. http://dx.doi.org/10.21079/11681/46915.

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Predicting a water surface elevation (WSElev) at a particular location has a wide range of applications like determining if a levee will overtop or how much a dike notch will increase water flow into a secondary channel. Five existing methods for predicting the water’s surface, (1) daily slope, (2) average slope, (3) River Analysis System (RAS) 1D, (4) RAS 2D, and (5) Adaptive Hydraulics modeling system (AdH), were used to predict the Mississippi River’s daily water surface from 10 October 2014 to 31 May 2016 at Friar’s Point, Greenville, and Natchez gages. The error, calculated as the model-predicted water surface minus the gage-observed water surface, was compared among the methods. The average slope method, using Helena and Fair Landing gages, and the daily slope method, using either Memphis and Helena or Helena and Arkansas City gages, most closely estimated the observed WSElev. The RAS 1D predictions for Friar Point and Greenville produced more accurate estimates than the RAS 2D model and were the only estimates that did not show a pattern of over- or underestimation. When the daily slope method was applied to gages that were farther apart (Memphis and Arkansas City, Arkansas City and Vicksburg, or Vicksburg and Knoxville), the error became greater than most RAS 1D and 2D predictions. The low error and simple calculations of the daily slope and average slope methods using gages <110 river miles apart make these methods useful for calculating current and historic conditions. The lack of over- or underestimation in the RAS 1D predictions (for locations away from the edges of the model area) make this method a better choice for predicting average WSElevs and a good choice for forecasting future WSElevs.
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Buchanan, Randy, Christina Rinaudo, George Gallarno, and M. Lagarde. Early life-cycle prediction of reliability. Engineer Research and Development Center (U.S.), April 2023. http://dx.doi.org/10.21079/11681/46919.

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The intent of this project is to investigate a variety of approaches for the development of a basic model for the early life-cycle prediction of reliability (pre-Milestone A). The United States Department of Defense (DoD) currently utilizes an acquisition framework in which system development advances through a series of checkpoints known as milestones. Each milestone represents a decision point, with Milestone A being the earliest in the life cycle. At Milestone A, also known as the risk-reduction decision, the DoD evaluates design concepts while also committing funds to the maturation of technologies in an effort to mitigate future risks. Typically, little is known about the particular system to be developed at this point in the acquisition life cycle, but DoD regulations require program man-agers to submit system reliability information (OUSD[A&S] 2015). Traditional reliability predictions, however, require extensive knowledge of the system of interest to produce accurate results. This level of knowledge is unavailable at or before Milestone A, there-fore, there is a need to create models and methodologies for the prediction of system reliability. This report provides an overview of a variety of methods investigated to improve the prediction of early life cycle reliability.
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Panek, Krol, and Huth. PR-312-12208-R03 USEPA AERMOD Plume Rise and Volume Formulations and Implications for Existing RICE. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), February 2016. http://dx.doi.org/10.55274/r0010858.

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AERMOD is the EPA recommended dispersion modeling tool for evaluating impacts from typical compressor station engine sources. This is a companion document to two previous PRCI reports that addressed AERMOD Fortran compiler issues and a subsequent report that examined AERMOD Plume Volume Molar ratio Method (PVMRM) issues that lead to conservative model over-predictions. This report further explores AERMOD plume rise and volume estimates as a possible cause or contributor of model over-prediction and resulting plume chemistry concerns. AERMOD over-prediction bias has significant negative implications for permitting new sources, permit renewal for existing sources, and NAAQS compliance analyses, where modeled impacts are compared to the NO2 NAAQS at or beyond the facility fenceline. AERMOD conservatism also impacts state agency State Implementation Plans and resulting control strategies. Permitting requirements associated with the new 1-hour standard could impose unnecessary controls, overly stringent controls, and a significant compliance burden. Where mitigation may be warranted, costs will escalate due to �over-control� in response to model conservatism and deficiencies in model performance.
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Wei, Dongmei, Yang Sun, and Rongtao Chen. Risk prediction model for ISR after coronary stenting-a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, April 2023. http://dx.doi.org/10.37766/inplasy2023.4.0014.

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Review question / Objective: The efficacy of risk prediction model for ISR. Condition being studied: Coronary heart disease (CHD), with high morbidity and high mortality rate, is still a serious public health concern around the world. PCI is fast becoming a key instrument in revascularization for patients with CHD, as well as an important technology in the management of CHD patients.1 Although the clinical application of coronary stents brought about a dramatic improvement in patients’ clinical and procedural outcomes, the mid-and long-term outcome of stent implantation remains significantly hampered by the risk of developing ISR with a prevalence rate of 3–20% over time. Predictive models have the advantage of formally combining risk factors to allow more accurate risk estimation. And it is essential to establish a model to predict ISR in patients with CAD and drug-eluting stents (DESs) implantation.However, predictive model performance needs further evaluation.
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