Relevant bibliographies by topics / Predicting filter

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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 'Predicting filter'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Predicting filter"

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Aprilia, Mellyani, and Nayla Desviona. "The Implementation of a Filter Kalman Method Forecasting Rainfall Obtained Through Model ARIMA in Kota Jambi." NUCLEUS 2, no. 2 (November 30, 2021): 69–77. http://dx.doi.org/10.37010/nuc.v2i2.607.

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In the last three years the climatic conditions in Jambi City have experienced erratic weather conditions. One way to predict rainfall is using the Kalman Filter approach. However, in this case, the Kalman Filter method is implemented on the forecasting results from ARIMA (Autoregressive Integrated Moving Average) because there has been rainfall measurement data from 2008 to 2017 at the BMKG Muaro Jambi Climatology Station which is also a function of time and the existing pattern will be described with using Time Series Analysis. Time series data is data that has a time series of more than one year on one object or data collected from time to time on one object. ARIMA model will be used to predict the next data. Kalman filter is a model part of state space that can be applied in forecasting models. The Kalman filter consists of a prediction stage and a correction stage. This method uses a recursive technique to integrate the latest observational data into the model to correct previous predictions and make further predictions. This study aims to determine the implementation of the kalman filter method in predicting rainfall obtained through the ARIMA model in Jambi City. The results of the 2018 Jambi City rainfall prediction research show that the best ARIMA model formed is the ARIMA model (1,0,1). In the Kalman Filter model, a MAPE value of 24.92% is obtained, which indicates that the Kalman Filter has a fairly good predictive ability.
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Du, Jin Xin, Xiang Rong Cao, and Xiao Lin Zhang. "A Low Power Way-Predicting D-Cache with Partial Tag Comparison Filter." Advanced Materials Research 986-987 (July 2014): 1350–55. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1350.

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This paper proposes a way-predicting algorithm which is specially equipped with a Partial Tag Comparison Filter (PTCF) to reduce the energy consumption in high associative D-caches. Conventional way-predicting algorithm achieves good performance and energy efficiency on I-cache which usually can guarantee the high prediction accuracy. However, the D-cache usually cannot reach such high prediction accuracy; therefore it suffers unavoidably from severe prediction inaccuracy penalties. The introduced PTCF aims at reducing energy penalties in case of prediction-miss and thus brings improvement in energy efficiency. The experiments show that the new D-cache reduces energy consumption by about 20%~60% without any latency degradation compared to conventional way-predicting D-cache.
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Raghunath, Bala, Michel Pailhes, and Thomas Mistretta. "Predicting Filter Size Using Vmax Testing." BioProcessing Journal 5, no. 3 (September 30, 2006): 38–40. http://dx.doi.org/10.12665/j53.raghunath.

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Dwyer, RW. "Predicting the Pressure Drops across Cellulose Acetate Filters." Beiträge zur Tabakforschung International/Contributions to Tobacco Research 13, no. 4 (August 1, 1986): 157–68. http://dx.doi.org/10.2478/cttr-2013-0565.

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AbstractA theoretical model of the pressure drop across a fibrous cigarette filter is derived. The pressure drop is expressed as a function of the filter dimensions, the fiber tow characteristics, the filter weight, the fluid flow rate, and a filter fiber factor. The fiber factor is affected by the distribution of the fibers within the filter, the relative orientations of the fibers, and their cross-sectional shapes. The model allows one to accurately calculate the influences of these variables on the filter pressure drop. Additionally, it can be used to predict capability curves and select an optimum cellulose acetate tow for a given filter pressure drop.
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WIRA, PATRICE, and JEAN-PHILIPPE URBAN. "PREDICTING UNKNOWN MOTION FOR MODEL INDEPENDENT VISUAL SERVOING." International Journal of Computational Intelligence and Applications 01, no. 03 (September 2001): 287–302. http://dx.doi.org/10.1142/s1469026801000135.

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Prediction in real-time image sequences is a key-feature for visual servoing applications. It is used to compensate for the time-delay introduced by the image feature extraction process in the visual feedback loop. In order to track targets in a three-dimensional space in real-time with a robot arm, the target's movement and the robot end-effector's next position are predicted from the previous movements. A modular prediction architecture is presented, which is based on the Kalman filtering principle. The Kalman filter is an optimal stochastic estimation technique which needs an accurate system model and which is particularly sensitive to noise. The performances of this filter diminish with nonlinear systems and with time-varying environments. Therefore, we propose an adaptive Kalman filter using the modular framework of mixture of experts regulated by a gating network. The proposed filter has an adaptive state model to represent the system around its current state as close as possible. Different realizations of these state model adaptive Kalman filters are organized according to the divide-and-conquer principle: they all participate to the global estimation and a neural network mediates their different outputs in an unsupervised manner and tunes their parameters. The performances of the proposed approach are evaluated in terms of precision, capability to estimate and compensate abrupt changes in targets trajectories, as well as to adapt to time-variant parameters. The experiments prove that, without the use of models (e.g. the camera model, kinematic robot model, and system parameters) and without any prior knowledge about the targets movements, the predictions allow to compensate for the time-delay and to reduce the tracking error.
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Dastgerdi, Amin Karimi, and Paolo Mercorelli. "Investigating the Effect of Noise Elimination on LSTM Models for Financial Markets Prediction Using Kalman Filter and Wavelet Transform." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 19 (January 18, 2022): 432–41. http://dx.doi.org/10.37394/23207.2022.19.39.

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Predicting financial markets is of particular importance for investors who intend to make the most profit. Analysing reasonable and precise strategies for predicting financial markets has a long history. Deep learning techniques include analyses and predictions that can assist scientists in discovering unknown patterns of data. In this project, application of noise elimination techniques such as Wavelet transform and Kalman filter in combination of deep learning methods were discussed for predicting financial time series. The results show employing noise elimination techniques such as Wavelet transform and Kalman filter, have considerable effect on performance of LSTM neural network in extracting hidden patterns in the financial time series and can precisely predict future actions in these markets.
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Levêque, Jonas G., and Robert C. Burns. "Predicting water filter and bottled water use in Appalachia: a community-scale case study." Journal of Water and Health 15, no. 3 (February 24, 2017): 451–61. http://dx.doi.org/10.2166/wh.2017.219.

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A questionnaire survey was conducted in order to assess residents’ perceptions of water quality for drinking and recreational purposes in a mid-sized city in northcentral West Virginia. Two logistic regression analyses were conducted in order to investigate the factors that influence bottle use and filter use. Results show that 37% of respondents primarily use bottled water and that 58% use a household filter when drinking from the tap. Respondents with lower levels of environmental concern, education levels, and lower organoleptic perceptions were most likely to perceive health risks from tap water consumption, and were most likely to use bottled water. Income, age, and organoleptic perceptions were predictors of water filter use among respondents. Clean water for recreational purposes was not found to be significant with either of these models. Our results demonstrate that bottle use and filter use are explained differently. We argue that more education and better communication about local tap water quality would decrease the use of bottled water. We demonstrate that household filters could be used as an alternative to bottled water.
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Li, Chengliang, Zhongsheng Wang, Shuhui Bu, Hongkai Jiang, and Zhenbao Liu. "A novel method based on least squares support vector regression combing with strong tracking particle filter for machinery condition prognosis." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 6 (August 1, 2013): 1048–62. http://dx.doi.org/10.1177/0954406213494158.

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A reliable prediction method is very important to avoid a catastrophic failure. This paper presents a novel method for machinery condition prognosis, named least squares support vector regression strong tracking particle filter which is based on least squares support vector regression combing with strong tracking particle filter. There are two main contributions in our work: first, the regression function of least squares support vector regression is extended, which constructs a bridge for the application of combining data-driven method with a recursive filter based on extend Kalman filter; second, an extend Kalman filter-based particle filter is studied by introducing a strong tracking filter into a particle filter. The strong tracking filter is used to update particles and produce importance densities which can improve the performance of the particle filter in tracking saltatory states, and finally strong tracking particle filter improves the prediction performance of least squares support vector regression in predicting saltatory states. In the experiment, it can be concluded that the proposed method is better than classical condition predictors in machinery condition prognosis.
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Tare, Vinod, and C. Venkobachar. "New conceptual formulation for predicting filter performance." Environmental Science & Technology 19, no. 6 (June 1985): 497–99. http://dx.doi.org/10.1021/es00136a003.

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Dharmarajah, A. H., and John L. Cleasby. "Predicting the Expansion Behavior of Filter Media." Journal - American Water Works Association 78, no. 12 (December 1986): 66–76. http://dx.doi.org/10.1002/j.1551-8833.1986.tb02768.x.

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Dissertations / Theses on the topic "Predicting filter"

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Murthy, Sudhir N. "Predicting dewatering equipment performance from laboratory tests." Thesis, Virginia Tech, 1992. http://hdl.handle.net/10919/43976.

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Mathema, Najma. "Predicting Plans and Actions in Two-Player Repeated Games." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8683.

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Artificial intelligence (AI) agents will need to interact with both other AI agents and humans. One way to enable effective interaction is to create models of associates to help to predict the modeled agents' actions, plans, and intentions. If AI agents are able to predict what other agents in their environment will be doing in the future and can understand the intentions of these other agents, the AI agents can use these predictions in their planning, decision-making and assessing their own potential. Prior work [13, 14] introduced the S# algorithm, which is designed as a robust algorithm for many two-player repeated games (RGs) to enable cooperation among players. Because S# generates actions, has (internal) experts that seek to accomplish an internal intent, and associates plans with each expert, it is a useful algorithm for exploring intent, plan, and action in RGs. This thesis presents a graphical Bayesian model for predicting actions, plans, and intents of an S# agent. The same model is also used to predict human action. The actions, plans and intentions associated with each S# expert are (a) identified from the literature and (b) grouped by expert type. The Bayesian model then uses its transition probabilities to predict the action and expert type from observing human or S# play. Two techniques were explored for translating probability distributions into specific predictions: Maximum A Posteriori (MAP) and Aggregation approach. The Bayesian model was evaluated for three RGs (Prisoners Dilemma, Chicken and Alternator) as follows. Prediction accuracy of the model was compared to predictions from machine learning models (J48, Multi layer perceptron and Random Forest) as well as from the fixed strategies presented in [20]. Prediction accuracy was obtained by comparing the model's predictions against the actual player's actions. Accuracy for plan and intent prediction was measured by comparing predictions to the actual plans and intents followed by the S# agent. Since the plans and the intents of human players were not recorded in the dataset, this thesis does not measure the accuracy of the Bayesian model against actual human plans and intents. Results show that the Bayesian model effectively models the actions, plans, and intents of the S# algorithm across the various games. Additionally, the Bayesian model outperforms other methods for predicting human actions. When the games do not allow players to communicate using so-called “cheap talk”, the MAP-based predictions are significantly better than Aggregation-based predictions. There is no significant difference in the performance of MAP-based and Aggregation-based predictions for modeling human behavior when cheaptalk is allowed, except in the game of Chicken.
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Mild, Andreas, and Thomas Reutterer. "An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2002. http://epub.wu.ac.at/414/1/document.pdf.

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Retail managers have been interested in learning about cross-category purchase behavior of their customers for a fairly long time. More recently, the task of inferring cross-category relationship patterns among retail assortments is gaining attraction due to its promotional potential within recommender systems used in online environments. Collaborative filtering algorithms are frequently used in such settings for the prediction of choices, preferences and/or ratings of online users. This paper investigates the suitability of such methods for situations when only binary pick-any customer information (i.e., choice/nonchoice of items, such as shopping basket data) is available. We present an extension of collaborative filtering algorithms for such data situations and apply it to a real-world retail transaction dataset. The new method is benchmarked against more conventional algorithms and can be shown to deliver superior results in terms of predictive accuracy. (author's abstract)
Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Wells, James Z. "Application of Path Prediction Techniques for Unmanned Aerial System Operations in the National Airspace." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin161710909594714.

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Kalapati, Raga S. "Analysis of Ozone Data Trends as an Effect of Meteorology and Development of Forecasting Models for Predicting Hourly Ozone Concentrations and Exceedances for Dayton, OH, Using MM5 Real-Time Forecasts." University of Toledo / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1091216133.

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Евсеенко, Олег Николаевич, and Сергей Михайлович Савицкий. "Описание метода управления тепловым объектом с распределёнными параметрами с помощью широтно-импульсной модуляции и предсказывающего фильтра." Thesis, Національний технічний університет "Харківський політехнічний інститут", 2013. http://repository.kpi.kharkov.ua/handle/KhPI-Press/48405.

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Предложен метод управления для тепловых объектов, получены разгонные кривые теплового объекта, рассчитан максимально допустимый период дискретизации переходного процесса по теореме Котельникова, выбрана частота дискретизации, проведён эксперимент по управлению инерционным тепловым объектом.
Proposed a method of control thermal objects, obtained the acceleration curves of an object, calculated the maximum allowable sampling period of transition with the theorem of Kotelnikov, chosen the sampling frequency, conducted experiments for control the temperature of inertial object.
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Wang, Zijian. "Conducted EMI Noise Prediction and Filter Design Optimization." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/73166.

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Power factor correction (PFC) converter is a species of switching mode power supply (SMPS) which is widely used in offline frond-end converter for the distributed power systems to reduce the grid harmonic distortion. With the fast development of information technology and multi-media systems, high frequency PFC power supplies for servers, desktops, laptops and flat-panel TVs, etc. are required for more efficient power delivery within limited spaces. Therefore the critical conduction mode (CRM) PFC converter has been becoming more and more popular for these information technology applications due to its advantages in inherent zero-voltage soft switching (ZVS) and negligible diode reverse recovery. With the emerging of the high voltage GaN devices, the goal of achieving soft switching for high frequency PFC converters is the top priority and the trend of adopting the CRM PFC converter is becoming clearer. However, there is the stringent electromagnetic interference (EMI) regulation worldwide. For the CRM PFC converter, there are several challenges on meeting the EMI standards. First, for the CRM PFC converter, the switching frequency is variable during the half line cycle and has very wide range dependent on the AC line RMS voltage and the load, which makes it unlike the traditional constant-frequency PFC converter and therefore the knowledge and experience of the EMI characteristics for the traditional constant-frequency PFC converter cannot be directly applied to the CRM PFC converter. Second, for the CRM PFC converter, the switching frequency is also dependent on the inductance of the boost inductor. It means the EMI spectrum of the CRM PFC converter is tightly related the boost inductor selection during the design of the PFC power stage. Therefore, unlike the traditional constant-frequency PFC converter, the selection of the boost inductor is also part of the EMI filter design process and EMI filter optimization should begin at the same time when the power stage design starts. Third, since the EMI filter optimization needs to begin before the proto-type of the CRM PFC converter is completed, the traditional EMI-measurement based EMI filter design will become much more complex and time-consuming if it is applied to the CRM PFC converter. Therefore, a new methodology must be developed to evaluate the EMI performance of the CRM PFC converter, help to simplify the process of the EMI filter design and achieve the EMI filter optimization. To overcome these challenges, a novel mathematical analysis method for variable frequency PFC converter is thus proposed in this dissertation. Based on the mathematical analysis, the quasi-peak EMI noise, which is specifically required in most EMI regulation standards, is investigated and accurately predicted for the first time. A complete approximate model is derived to predict the quasi-peak DM EMI noise for the CRM PFC converter. Experiments are carried out to verify the validity of the prediction. Based on the DM EMI noise prediction, worst case analysis is carried out and the worst DM EMI noise case for all the input line and load conditions can be found to avoid the overdesign of the EMI filter. Based on the discovered worst case, criteria to ease the DM EMI filter design procedure of the CRM boost PFC are given for different boost inductor selection. Optimized design procedure of the EMI filter for the front-end converter is then discussed. Experiments are carried out to verify the validity of the whole methodology.
Ph. D.
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Park, Keun Joo. "GPS receiver self survey and attitude determination using pseudolite signals." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/1250.

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This dissertation explores both the estimation of various parameters from a multiple antenna GPS receiver, which is used as an attitude sensor, and attitude determination using GPS-like Pseudolite signals. To use a multiple antenna GPS receiver as an attitude sensor, parameters such as baselines, integer ambiguities, line biases, and attitude, should be resolved beforehand. Also, due to a cycle slip problem a subsystem to correct this problem should be implemented. All of these tasks are called a self survey. A new algorithm to estimate these parameters from a GPS receiver is developed usingnonlinear batch filteringmethods.For convergence issues, both the nolinear least squares (NLS) and Levenberg-Marquardt (LM) methods are applied in the estimation.Acomparison ofthe NLSand LMmethods shows that the convergence of the LM method for the large initial errors is more robust than that of the NLS. In the proximity of the International Space Station (ISS), Pseudolite signals replace the GPSsignals since almostallsignals are blocked.Since the Pseudolite signals have spherical wavefronts, a new observation model should be applied. A nonlinear predictive filter, an extended Kalman filter (EKF), and an unscented filter (UF) are developed and compared using Pseudolite signals. A nonlinear predictive filter can provide a deterministic solution; however, it cannot be used for the moving case. Instead, the EKF or the UF can be used with the angular rate measurements. A comparison of EKF and UF shows that the convergence of the UF for the large initial errors is more robust than that of the EKF. Also, an alternative global navigation constellation is presented by using the Flower Constellation (FC) scheme. A comparison of FC global navigation constellation and other GPS constellations, U.S. GPS, Galileo, and GLONASS, shows that position and attitude errors of the FC constellation are smaller that those of the others.
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Vatis, Yuri. "Non-symmetric adaptive interpolation filter for motion compensated prediction /." Düsseldorf : VDI-Verl, 2009. http://d-nb.info/998470724/04.

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Aldokhayel, Abdulaziz. "A Kalman Filter-based Dynamic Model for Bus Travel Time Prediction." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38060.

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Urban areas are currently facing challenges in terms of traffic congestion due to city expansion and population increase. In some cases, physical solutions are limited. For example, in certain areas it is not possible to expand roads or build a new bridge. Therefore, making public transpiration (PT) affordable, more attractive and intelligent could be a potential solution for these challenges. Accuracy in bus running time and bus arrival time is a key component of making PT attractive to ridership. In this thesis, a dynamic model based on Kalman filter (KF) has been developed to predict bus running time and dwell time while taking into account real-time road incidents. The model uses historical data collected by Automatic Vehicle Location system (AVL) and Automatic Passenger Counters (APC) system. To predict the bus travel time, the model has two components of running time prediction (long and short distance prediction) and dwell time prediction. When the bus closes its doors before leaving a bus stop, the model predicts the travel time to all downstream bus stops. This is long distance prediction. The model will then update the prediction between the bus’s current position and the upcoming bus stop based on real-time data from AVL. This is short distance prediction. Also, the model predicts the dwell time at each coming bus stop. As a result, the model reduces the difference between the predicted arrival time and the actual arrival time and provides a better understanding for the transit network which allows lead to have a good traffic management.
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Books on the topic "Predicting filter"

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Luhmann, Hans-Jochen. Die Blindheit der Gesellschaft: Filter der Risikowahrnehmung. München: Gerling Akademie Verlag, 2001.

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Jones, J. C. Peyton. Testing for nonlinearity using a prediction error filter. Sheffield: University of Sheffield, Dept. of Control Engineering, 1988.

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Mutaf, Asim. A Kalman filter with smoothing for hurricane tracking and prediction. Monterey, Calif: Naval Postgraduate School, 1989.

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Nonlinear prediction ladder-filters for higher-order stochastic sequences. Berlin: Springer-Verlag, 1985.

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Zarzycki, Jan, ed. Nonlinear Prediction Ladder-Filters for Higher-Order Stochastic Sequences. Berlin/Heidelberg: Springer-Verlag, 1985. http://dx.doi.org/10.1007/bfb0007210.

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Kiruluta, Andrew M. Predictive head movement tracking using innovations generated by Kalman filters. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1993.

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1952-, Sin Kwai Sang, ed. Adaptive filtering prediction and control. Mineola, N.Y: Dover, 2009.

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Chuah, Teong Guan. Prediction of the pressure and velocity distributions in rigid ceramic filters. Birmingham: University of Birmingham, 2001.

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Gaver, Donald Paul. A Kalman filter for a Poisson series with covariates and Laplace approximation integration. Monterey, Calif: Naval Postgraduate School, 1991.

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Levan, N. On-line range prediction system (II). [Los Angeles, Calif: UCLA School of Engineering and Applied Science, 1988.

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Book chapters on the topic "Predicting filter"

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Saha, Manika, Ratna Ghosh, and Bhaswati Goswami. "Representing the Extended H ∞ Filter (EHF) as a Modified Extended Kalman Filter (EKF) for Predicting the Range of the Robustness Bound." In Intelligent Computing and Applications, 453–67. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2268-2_47.

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Chang, Li, and Jiang Xiaoluo. "Kalman Filter Based on SVM Innovation Update for Predicting State-of Health of VRLA Batteries." In Communications in Computer and Information Science, 455–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23220-6_58.

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Tan, Wai-Yuan, and Zhihua Xiang. "Estimating and Predicting the Number of Free HIV and T Cells by Nonlinear Kalman Filter." In Artificial Immune Systems and Their Applications, 115–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-59901-9_7.

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Tanizaki, Hisashi. "Prediction and Smoothing." In Nonlinear Filters, 205–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03223-7_7.

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Funk, James E., and Dennis R. Dinger. "Filter Press." In Predictive Process Control of Crowded Particulate Suspensions, 711–21. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-3118-0_46.

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Schuller, Gerald. "Predictive Coding." In Filter Banks and Audio Coding, 109–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51249-1_3.

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Schuller, Gerald. "Predictive Lossless Audio Coding." In Filter Banks and Audio Coding, 161–66. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51249-1_7.

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Leiner, Bernd. "Prediction with Arima Filters." In Contributions to Econometrics and Statistics Today, 177–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-70189-4_16.

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Funk, James E., and Dennis R. Dinger. "Filter Pressing Effects Due to HID." In Predictive Process Control of Crowded Particulate Suspensions, 481–506. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-3118-0_31.

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Marafioti, Giancarlo, Sorin Olaru, and Morten Hovd. "State Estimation in Nonlinear Model Predictive Control, Unscented Kalman Filter Advantages." In Nonlinear Model Predictive Control, 305–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_25.

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Conference papers on the topic "Predicting filter"

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Frončková, Kateřina, and Pavel Pražák. "Predicting Exchange Rates Using the Kalman Filter." In Hradec Economic Days 2020, edited by Petra Maresova, Pavel Jedlicka, Krzysztof Firlej, and Ivan Soukal. University of Hradec Kralove, 2020. http://dx.doi.org/10.36689/uhk/hed/2020-01-018.

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Youssef, Alilou, Bourrous Soleiman, Thomas Dominique, Bardin-Monnier Nathalie, Nérisson Philippe, and Gélain Thomas. "Experimental Characterization of Airflow Within a Clean HEPA Filter Used for the Containment of Radioactive Contamination in Nuclear Facilities." In 2016 24th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/icone24-60901.

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In hazardous industrial activities such as in nuclear facilities, High Efficiency Particulate Air filters (HEPA filters) are essential to ensure the containment of airborne contamination. Most of the filters used in ventilation networks are pleated, in order to offer a larger surface of filtration. For industrial risks likely to lead to an important release of particles (e.g. fire), predicting the evolution of the pressure drop of pleated filters is very important, in order to anticipate any dysfunction, failure or breaking of these devices. Pressure drop variations are linked to airflow rate variations and to clogging process of the medium by airborne particles. Thus, the airflow pattern in a pleat channel is essential for optimizing the filter design and enhancing its lifetime. Particles are transported by the airflow and deposited at the filter surface; hence, the geometry of the dust cake (shape and location) is partially determined knowing the velocity streamlines. The present paper focuses on the characterization of airflows in a clean HEPA filter. The difficulty to perform fine measurement on a real scale filter led us to develop an experimental device, consisting in the reproduction of a single pleat, identical to a real pleat constituting industrial filters. The small dimension of the pleat makes the velocity measurement difficult to establish. That is why μ-PIV method has been adapted to measure the velocity field inside the filter for different filtration velocities at the first moments of the experiment, in order to avoid the impact of clogging by particles used to seed the flow. These particles are DEHS droplets 0.01 < St < 0.05. In the future, these well-characterized airflows will be the basis for CFD computation of particle transport and deposition inside the pleats. Ultimately, the aim is to develop or upgrade physical models predicting the pressure drop evolution of pleated filters, during clogging process in accidental situations.
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Jones, M. H. "Information driven optimization search filter: predicting tabu regions." In 2004 IEEE Systems and Information Engineering Design Symposium. IEEE, 2004. http://dx.doi.org/10.1109/sieds.2004.239767.

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Daria, Lavrova, Zegzhda Dmitry, and Yarmak Anastasiia. "Predicting cyber attacks on industrial systems using the Kalman filter." In 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4). IEEE, 2019. http://dx.doi.org/10.1109/worlds4.2019.8904038.

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Jin, Zhigang, and Nelson L. Passos. "Predicting conditional branch outcomes on a Sobel edge detecting filter." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5745328.

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Zhigang Jin and Passos. "Predicting conditional branch outcomes on a Sobel edge detecting filter." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005366.

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Wang, Xiaoqin, and Xiaolong Wang. "The comparison of particle filter and extended Kalman filter in predicting building envelope heat transfer coefficient." In 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2012. http://dx.doi.org/10.1109/ccis.2012.6664639.

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Baek, Sungchul, Robert A. Taylor, and Tracie J. Barber. "Development of a Dynamic Testing Device for Predicting the Enhanced Permeation and Retention (EPR) Effect of Different Nanoparticles in Tumor Vessels." In ASME 2013 2nd Global Congress on NanoEngineering for Medicine and Biology. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/nemb2013-93075.

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A microfluidic device was developed to simulate the dynamic conditions of the transvascular transport of nanoparticles. The device utilizes a microfluidic channel, filter paper, collagen gel—which represent the blood vessel, porous vessel wall, and interstitial matrix of the tumor, respectively. By controlling these components, the fluid-dynamic conditions of the tumor blood vessels can be simulated. For the initial study, Durapore® filters with the nominal diameter of 0.22 μm and 5 mg/ml type 1 collagen gel were used. The transvascular transport parameters of the membrane for a model particle, 20 nm gold spheres, were similar to those of rabbit VX2 carcinoma model. Overall, this design allows for fundamental research into the fluid dynamic transport of particles inside different organs, cancer types and stages. To investigate the physiological conditions of cancer, future studies will include modification of the filter membranes with proteins as well as subsequent culturing of endothelial cells on the filter and tumor cells in the gel matrix. Through this device, we will be able to prescribe nanoparticle fluids for to obtain enhanced permeation and retention.
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Wu, M., A. V. Kuznetsov, and W. Jasper. "Modeling Mass Transfer and Nanoparticle Capture in Electrostatically Charged Monolith Filters." In 2010 14th International Heat Transfer Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ihtc14-22205.

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Analyzing trajectories of particles in monolith filters is important for predicting the capture efficiency and improving the design of this class of filters. Modeling and simulations of the particle trajectories are carried out to evaluate the probability of capture by the filter’s front surface and filter channel’s inner wall. Due to Brownian motion and electrostatic attraction, the particles exhibit a random walk and their trajectories deviate from the streamlines of the fluid flow. Particle trajectories are computed by the integration of Newton’s second law, where the electrostatic force, the Brownian motion force resulting from random collisions of the particle with air molecules, and the drag force from the surrounding fluid are all taken into account. A computer simulation for computing the particle trajectories and evaluating the probability of particle capture by the filter was developed. For this model, both flow field and electric field must be provided. The electric charge was assumed to be uniformly distributed along the edge of the channels of the filter and calculated numerically. The flow field is difficult to obtain due to the complex geometry of the model. The commercial CFD package ANSYS CFX [1] is used to compute the flow field. The resulting velocity flow field is then used to evaluate the drag force on the particles. We assume a one-way coupling between the fluid flow and the particle motion. Although there can be over one million uniformly distributed channels per square centimeter in the monolith filter, for simulation purposes, a single unit cell which models only one channel is used. The single unit model effectively describes the behavior of particles outside and inside the channels of monolith filter. The effects of different forces and different particle sizes were analyzed to investigate which factors affect the capture efficiency.
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Wang, Zhijun, Zongqiang Nie, and Guanaming Sheng. "Dynamic Position Predicting of Underactuated Surface Vessel with Unscented Kalman Filter." In 2018 Chinese Automation Congress (CAC). IEEE, 2018. http://dx.doi.org/10.1109/cac.2018.8623748.

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Reports on the topic "Predicting filter"

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Canavan, G. H. Comparison of filter predictions with satellite observations. Office of Scientific and Technical Information (OSTI), October 1997. http://dx.doi.org/10.2172/569118.

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Mercier, Jeffrey Alan, Mark William Smith, Jeffery P. Hunt, and Aaron M. Ison. Modeling, sensor design, and performance predictions for gas filter correlation radiometers. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1055639.

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Liu, C. T., Max Yen, and H. K. Ching. Predicting the Crack Growth Behavior in a Filled Elastomer. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada439995.

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Hart, Roland J., and Stephen C. Bradshaw. Interactivity Theory: Analyzing Human Environments Using Linear Prediction Filters. Fort Belvoir, VA: Defense Technical Information Center, August 1985. http://dx.doi.org/10.21236/ada172066.

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Cain, S. M. Predicting the performance of ceramic filters by the use of silt density index. Office of Scientific and Technical Information (OSTI), January 1989. http://dx.doi.org/10.2172/5379129.

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Mizrach, Amos, Michal Mazor, Amots Hetzroni, Joseph Grinshpun, Richard Mankin, Dennis Shuman, Nancy Epsky, and Robert Heath. Male Song as a Tool for Trapping Female Medflies. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7586535.bard.

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This interdisciplinaray work combines expertise in engineering and entomology in Israel and the US, to develop an acoustic trap for mate-seeking female medflies. Medflies are among the world's most economically harmful pests, and monitoring and control efforts cost about $800 million each year in Israel and the US. Efficient traps are vitally important tools for medfly quarantine and pest management activities; they are needed for early detection, for predicting dispersal patterns and for estimating medfly abundance within infested regions. Early detection facilitates rapid response to invasions, in order to contain them. Prediction of dispersal patterns facilitates preemptive action, and estimates of the pests' abundance lead to quantification of medfly infestations and control efforts. Although olfactory attractants and traps exist for capturing male and mated female medflies, there are still no satisfactorily efficient means to attract and trap virgin and remating females (a significant and dangerous segment of the population). We proposed to explore the largely ignored mechanism of female attraction to male song that the flies use in courtship. The potential of such an approach is indicated by studies under this project. Our research involved the identification, isolation, and augmentation of the most attractive components of male medfly songs and the use of these components in the design and testing of traps incorporating acoustic lures. The project combined expertise in acoustic engineering and instrumentation, fruit fly behavior, and integrated pest management. The BARD support was provided for 1 year to enable proof-of-concept studies, aimed to determine: 1) whether mate-seeking female medflies are attracted to male songs; and 2) over what distance such attraction works. Male medfly calling song was recorded during courtship. Multiple acoustic components of male song were examined and tested for synergism with substrate vibrations produced by various surfaces, plates and loudspeakers, with natural and artificial sound playbacks. A speaker-funnel system was developed that focused the playback signal to reproduce as closely as possible the near-field spatial characteristics of the sounds produced by individual males. In initial studies, the system was tasted by observing the behavior of females while the speaker system played songs at various intensities. Through morning and early afternoon periods of peak sexual activity, virgin female medflies landed on a sheet of filter paper at the funnel outlet and stayed longer during broadcasting than during the silent part of the cycle. In later studies, females were captured on sticky paper at the funnel outlet. The mean capture rates were 67 and 44%, respectively, during sound emission and silent control periods. The findings confirmed that female trapping was improved if a male calling song was played. The second stage of the research focused on estimating the trapping range. Initial results indicated that the range possibly extended to 70 cm, but additional, verification tests remain to be conducted. Further studies are planned also to consider effects of combining acoustic and pheromonal cues.
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Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li, and A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, November 2001. http://dx.doi.org/10.32747/2001.7580664.bard.

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Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential nonpoint source pollution of ground and surface waters. This has resulted in increased interest in site specific fertilizer management. One way to solve pollution problems would be to determine crop nutrient needs in real time, using remote detection, and regulating fertilizer dispensed by an applicator. By detecting actual plant needs, only the additional nitrogen necessary to optimize production would be supplied. This research aimed to develop techniques for real time assessment of nitrogen status of corn using a mobile sensor with the potential to regulate nitrogen application based on data from that sensor. Specifically, the research first attempted to determine the system parameters necessary to optimize reflectance spectra of corn plants as a function of growth stage, chlorophyll and nitrogen status. In addition to that, an adaptable, multispectral sensor and the signal processing algorithm to provide real time, in-field assessment of corn nitrogen status was developed. Spectral characteristics of corn leaves reflectance were investigated in order to estimate the nitrogen status of the plants, using a commercial laboratory spectrometer. Statistical models relating leaf N and reflectance spectra were developed for both greenhouse and field plots. A basis was established for assessing nitrogen status using spectral reflectance from plant canopies. The combined effect of variety and N treatment was studied by measuring the reflectance of three varieties of different leaf characteristic color and five different N treatments. The variety effect on the reflectance at 552 nm was not significant (a = 0.01), while canonical discriminant analysis showed promising results for distinguishing different variety and N treatment, using spectral reflectance. Ambient illumination was found inappropriate for reliable, one-beam spectral reflectance measurement of the plants canopy due to the strong spectral lines of sunlight. Therefore, artificial light was consequently used. For in-field N status measurement, a dark chamber was constructed, to include the sensor, along with artificial illumination. Two different approaches were tested (i) use of spatially scattered artificial light, and (ii) use of collimated artificial light beam. It was found that the collimated beam along with a proper design of the sensor-beam geometry yielded the best results in terms of reducing the noise due to variable background, and maintaining the same distance from the sensor to the sample point of the canopy. A multispectral sensor assembly, based on a linear variable filter was designed, constructed and tested. The sensor assembly combined two sensors to cover the range of 400 to 1100 nm, a mounting frame, and a field data acquisition system. Using the mobile dark chamber and the developed sensor, as well as an off-the-shelf sensor, in- field nitrogen status of the plants canopy was measured. Statistical analysis of the acquired in-field data showed that the nitrogen status of the com leaves can be predicted with a SEP (Standard Error of Prediction) of 0.27%. The stage of maturity of the crop affected the relationship between the reflectance spectrum and the nitrogen status of the leaves. Specifically, the best prediction results were obtained when a separate model was used for each maturity stage. In-field assessment of the nitrogen status of corn leaves was successfully carried out by non contact measurement of the reflectance spectrum. This technology is now mature to be incorporated in field implements for on-line control of fertilizer application.
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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.
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Dickens, J. K. Comparisons of experimental beta-ray spectra important to decay heat predictions with ENSDF (Evaluated Nuclear Structure Data File) evaluations. Office of Scientific and Technical Information (OSTI), March 1990. http://dx.doi.org/10.2172/7044536.

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Apiyo, Eric, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Improving Pharmacovigilliance Quality Management System in the Pharmacy and Poisions Board of Kenya. Purdue University, December 2021. http://dx.doi.org/10.5703/1288284317444.

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The purpose of this study was to explore ways of improving the pharmacovigilance quality system employed by the Pharmacy and Poisons Board of Kenya. The Pharmacy and Poisons Board of Kenya employs a hybrid system of pharmacovigilance that utilizes an online system of reporting pharmacovigilance incidences and a physical system, where a yellow book is physically filled by the healthcare worker and sent to the Pharmacy and Poisons Board for onward processing. This system, even though it has been relatively effective compared to other systems employed in Africa, has one major flaw. It is a slow and delayed system that captures the data much later after the fact and the agency will always be behind the curve in controlling the adverse incidents and events. This means that the incidences might continue to arise or go out of control. This project attempts to develop a system that would be more proactive in the collection of pharmacovigilance data and more predictive of pharmacovigilance incidences. The pharmacovigilance system should have the capacity to detect and analyze subtle changes in reporting frequencies and in patterns of clinical symptoms and signs that are reported as suspected adverse drug reactions. The method involved carrying out a thorough literature review of the latest trends in pharmacovigilance employed by different regulatory agencies across the world, especially the more stringent regulatory authorities. A review of the system employed by the Pharmacy and Poisons Board of Kenya was also done. Pharmacovigilance data, both primary and secondary, were collected and reviewed. Media reports on adverse drug reactions and poor-quality medicines over the period were also collected and reviewed. An appropriate predictive pharmacovigilance tool was also researched and identified. It was found that the Pharmacy and Poisons Board had a robust system of collecting historical pharmacovigilance data both from the healthcare workers and the general public. However, a more responsive data collection and evaluation system is proposed that will help the agency achieve its pharmacovigilance objectives. On analysis of the data it was found that just above half of all the product complaints, about 55%, involved poor quality medicines; 15% poor performance, 13% presentation, 8% adverse drug reactions, 7% market authorization, 2% expired drugs and 1% adulteration complaints. A regulatory pharmacovigilance prioritization tool was identified, employing a risk impact analysis was proposed for regulatory action.
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