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1
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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Ahmed, Osman, Shermeen Sheikh, Patrick Tran, Brian Funaki, Alexandria M. Shadid, Rakesh Navuluri, and Thuong Van Ha. "Inferior Vena Cava Filter Evaluation and Management for the Diagnostic Radiologist: A Comprehensive Review Including Inferior Vena Cava Filter-Related Complications and PRESERVE Trial Filters." Canadian Association of Radiologists Journal 70, no. 4 (November 2019): 367–82. http://dx.doi.org/10.1016/j.carj.2019.06.003.
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Inferior vena cava filters are commonly encountered devices on diagnostic imaging that were highlighted in a 2010 Food and Drug Administration safety advisory regarding their complications from long-term implantation. The Predicting the Safety and Effectiveness of Inferior Vena Cava Filters (PRESERVE) trial is an ongoing after-market study investigating the safety and utility of commonly utilized filters in practice today. While most of these filters are safe, prompt recognition and management of any filter-associated complication is imperative to prevent or reduce the morbidity and mortality associated with them. This review is aimed at discussing the appropriate utilization and placement of inferior vena cava filters in addition to the recognition of filter-associated complications on cross-sectional imaging. An overview of the PRESRVE trial filters is also provided to understand each filter's propensity for specific complications.
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Miljanović, Dejan, Milka Potrebić, and Dejan V. Tošić. "Design of Microwave Multibandpass Filters with Quasilumped Resonators." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/647302.
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Design of RF and microwave filters has always been the challenging engineering field. Modern filter design techniques involve the use of the three-dimensional electromagnetic (3D EM) solvers for predicting filter behavior, yielding the most accurate filter characteristics. However, the 3D EM simulations are time consuming. In this paper, we propose electric-circuit models, instead of 3D EM models, suitable for design of RF and microwave filters with quasilumped coupled resonators. Using the diakoptic approach, the 3D filter structure is decomposed into domains that are modeled by electric networks. The coupling between these domains is modeled by capacitors and coupled inductors. Furthermore, we relate the circuit-element values to the physical dimensions of the 3D filter structure. We propose the filter design procedure that is based on the circuit models and fast circuit-level simulations, yielding the element values from which the physical dimensions can be obtained. The obtained dimensions should be slightly refined for achieving the desired filter characteristics. The mathematical problems encountered in the procedure are solved by numerical and symbolic computations. The procedure is exemplified by designing a triple-bandpass filter and validated by measurements on the fabricated filter. The simulation and experimental results are in good agreement.
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Dou, Yu. "An Improved Prediction Model of IGBT Junction Temperature Based on Backpropagation Neural Network and Kalman Filter." Complexity 2021 (February 26, 2021): 1–10. http://dx.doi.org/10.1155/2021/5542889.
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With the rapid development of emerging technologies such as electric vehicles and high-speed railways, the insulated gate bipolar transistor (IGBT) is becoming increasingly important as the core of the power electronic devices. Therefore, it is imperative to maintain the stability and reliability of IGBT under different circumstances. By predicting the junction temperature of IGBT, the operating condition and aging degree can be roughly evaluated. However, the current predicting approaches such as optical, physical, and electrical methods have various shortcomings. Hence, the backpropagation (BP) neural network can be applied to avoid the difficulties encountered by conventional approaches. In this article, an advanced prediction model is proposed to obtain accurate IGBT junction temperature. This method can be divided into three phases, BP neural network estimation, interpolation, and Kalman filter prediction. First, the validities of the BP neural network and Kalman filter are verified, respectively. Then, the performances of them are compared, and the superiority of the Kalman filter is proved. In the future, the application of neural networks or deep learning in power electronics will create more possibilities.
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Song, Jin Bao, Jun Yu Li, and Qin Zhang. "The Model Based Behavior Driven Arithmetic Research." Advanced Engineering Forum 6-7 (September 2012): 1066–71. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.1066.
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This paper is based on the particle filter for discrete particle track prediction theory, analyses the motion of animation with the methods of picking key points and predicting motion trace by utilizing particle filter. The behavior model has been built for the already existing animation character. During the research, the thesis realized using existed animation motion trace model to drive a similar figure and create a new animation.
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Pratt, C., and A. Shilton. "Suitability of adsorption isotherms for predicting the retention capacity of active slag filters removing phosphorus from wastewater." Water Science and Technology 59, no. 8 (April 1, 2009): 1673–78. http://dx.doi.org/10.2166/wst.2009.163.
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Active slag filters are an emerging technology for removing phosphorus (P) from wastewater. A number of researchers have suggested that adsorption isotherms are a useful tool for predicting P retention capacity. However, to date the appropriateness of using isotherms for slag filter design remains unverified due to the absence of benchmark data from a full-scale, field filter operated to exhaustion. This investigation compared the isotherm-predicted P retention capacity of a melter slag with the P adsorption capacity determined from a full-scale, melter slag filter which had reached exhaustion after five years of successfully removing P from waste stabilization pond effluent. Results from the standard laboratory batch test showed that P adsorption correlated more strongly with the Freundlich Isotherm (R2=0.97, P<0.01) than the Langmuir Isotherm, a similar finding to previous studies. However, at a P concentration of 10 mg/L, typical of domestic effluent, the Freundlich equation predicted a retention capacity of 0.014 gP/kg slag; markedly lower than the 1.23 gP/kg slag adsorbed by the field filter. Clearly, the result generated by the isotherm bears no resemblance to actual field capacity. Scanning electron microscopy analysis revealed porous, reactive secondary minerals on the slag granule surfaces from the field filter which were likely created by weathering. This slow weathering effect, which generates substantial new adsorption sites, is not accounted for by adsorption isotherms rendering them ineffective in slag filter design.
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Sia, Erwin Ruslim, and Seng Hansun. "RANCANG BANGUN APLIKASI PERAMALAN NILAI SAHAM MENGGUNAKAN ALGORITMA KALMAN FILTER." Komputa : Jurnal Ilmiah Komputer dan Informatika 3, no. 2 (October 20, 2014): 74–79. http://dx.doi.org/10.34010/komputa.v3i2.2393.
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Every prediction have different probability, including prediction in stock market. In order to give the best prediction with the highest probability, we try to determine how Kalman Filter, an algorithm that uses recursive function to predict future value, produce high probability in predicting stock price. There are two set of data companies that are used in this application, namely XL Axiata Tbk. with success percentage at 95,83%, and Astra Agro Lestari Tbk. with success percentage at 95,07%. This application is developed using C# programming language and SQL SERVER.
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Cao, Lu, Wei Wei Yang, Xiao Qian Chen, and Yi Yong Huang. "The Research of Micro-Satellite Attitude Determination Based on Predictive Filter." Applied Mechanics and Materials 110-116 (October 2011): 5413–19. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.5413.
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— An extended predictive filter is presented for attitude determination of Micro-satellite based on the basic theory of predictive filter. This algorithm enhances the precision of kinematics equation, accurately estimates satellite attitude angle and angular velocity using measurement vectors from magnetometer and sun sensors by predicting model errors and angular velocity errors one step ahead. In addition, a new measurement model is derived about angular velocity, which improved the efficiency of the use of measurement vectors. Simulation results show both the high reliability and precision of this method.
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Ikaouassen, Halima, Abderraouf Raddaoui, Miloud Rezkallah, and Hussein Ibrahim. "Improved predictive current model control based on adaptive PR controller for standalone system based DG set." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (April 1, 2020): 1905. http://dx.doi.org/10.11591/ijece.v10i2.pp1905-1914.
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This paper investigates an improved current predictive model control (PCMC) strategy with a prediction horizon of one sampling time for voltage regulation in standalone system based on diesel engine driven fixed speed of a synchronous generator. An adaptive PR controller with anti-windup scheme is employed to achieve high performance regulation without saturation issues. In addition, new method to obtain the optimal parameters of the adaptive PR controller to achieve high performance during the transition and in steady state is provided. To balance the power at the point of common coupling (PCC) as well as to feed a clean power to the connected loads, a three-phase voltage source inverter (VSI) with LRC filter is controlled using the developed improved PCMC strategy, where the output filter current is controlled using the predicting of the system behaviour model in the future step, at each sampling prediction time. The performances of the proposed configuration and the improved control strategy are verified using Matlab/Simulink interface.
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Novak, John, William Knocke, William Burgos, and Paul Schuler. "Predicting the Dewatering Performance of Belt Filter Presses." Water Science and Technology 28, no. 1 (July 1, 1993): 11–19. http://dx.doi.org/10.2166/wst.1993.0006.
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Two sewage sludges were used to compare full-scale belt press performance to two types of laboratory devices to determine if either could be used to predict the performance. The wedge zone simulator (WZS) could accurately predict polymer demand, filtrate quality and cake solids. The shear tester used with a CST device was no better (ban a free drainage test at predicting polymer requirements.
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Simionescu, Mihaela. "Kalman Filter or VAR Models to Predict Unemployment Rate in Romania?" Naše gospodarstvo/Our economy 61, no. 3 (June 1, 2015): 3–21. http://dx.doi.org/10.1515/ngoe-2015-0009.
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Abstract This paper brings to light an economic problem that frequently appears in practice: For the same variable, more alternative forecasts are proposed, yet the decision-making process requires the use of a single prediction. Therefore, a forecast assessment is necessary to select the best prediction. The aim of this research is to propose some strategies for improving the unemployment rate forecast in Romania by conducting a comparative accuracy analysis of unemployment rate forecasts based on two quantitative methods: Kalman filter and vector-auto-regressive (VAR) models. The first method considers the evolution of unemployment components, while the VAR model takes into account the interdependencies between the unemployment rate and the inflation rate. According to the Granger causality test, the inflation rate in the first difference is a cause of the unemployment rate in the first difference, these data sets being stationary. For the unemployment rate forecasts for 2010-2012 in Romania, the VAR models (in all variants of VAR simulations) determined more accurate predictions than Kalman filter based on two state space models for all accuracy measures. According to mean absolute scaled error, the dynamic-stochastic simulations used in predicting unemployment based on the VAR model are the most accurate. Another strategy for improving the initial forecasts based on the Kalman filter used the adjusted unemployment data transformed by the application of the Hodrick-Prescott filter. However, the use of VAR models rather than different variants of the Kalman filter methods remains the best strategy in improving the quality of the unemployment rate forecast in Romania. The explanation of these results is related to the fact that the interaction of unemployment with inflation provides useful information for predictions of the evolution of unemployment related to its components (i.e., natural unemployment and cyclical component).
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Al-Hajeri, M. H., A. Aroussl, K. Simmons, and S. J. Pickering. "A parametric study of filtration through a ceramic candle filter." Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 219, no. 1 (February 1, 2005): 77–90. http://dx.doi.org/10.1243/095765005x6908.
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Ceramic candle filters have been developed for cleaning high-temperature high-pressure (HTHP) gas streams. They meet environmental and economical considerations in combined cycle power plant, where gas turbine blades can be protected from the erosion resulting from the use of HTHP exhaust from the fluidized bed. Ceramic candle filters are the most promising hot gas filtration technology and have demonstrated high collection efficiencies at high-temperature high-pressure conditions. This paper reports a computational fluid dynamics (CFD) investigation of a candle filter in cross-flow arrangement. The aim is to increase understanding of the deposition process and the factors that affect the build-up of the filter cake. A parametric investigation is undertaken, with particular emphasis on the effects of the ratio of the approach cross-flow velocity to filter face velocity on the deposition pattern as a function of the particle size (1–300 μm). Velocity fields and particle tracks are presented, in addition to the radius of convergence which is a parameter that characterizes the deposition process for each flow regime. Furthermore, a method has been developed for predicting filter cake growth using CFD and particle deposits distributed around the filter element surface uniformly for particle sizes below 50 μm. The paper contains a potential flow solution for the flow around a single porous filter element in cross-flow.
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Mukherjee, Sudhanshu. "Predicting Malignant Cancer Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1668–72. http://dx.doi.org/10.22214/ijraset.2021.39078.
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Abstract: One of the primary concerns that is also a demanding issue within the realm of medical specialism is the detection and removal of tumours. Because visualisation approaches had the drawback of being adversarial, doctors relied heavily on MRI images to provide a superior result. Pre-processing, tumour segmentation, and tumour operations are the three stages in which tumour image processing takes place. Following the acquisition of the source image, the original image is converted to grayscale. Additionally, a noise removal filter and a median filter for quality development are provided, followed by an exploration stage that yields hits orgasmic identical images. Finally, the watershed algorithm is used to complete the segmentation. This proposed methodology is useful in automatically organising reports in a short amount of time, and exploration has resulted in the removal of many less tumour parameters. Keywords: MRI Imaging, Segmentation, Watershed Algorithm.
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Yi, Sang-ri, and Junho Song. "Particle Filter Based Monitoring and Prediction of Spatiotemporal Corrosion Using Successive Measurements of Structural Responses." Sensors 18, no. 11 (November 13, 2018): 3909. http://dx.doi.org/10.3390/s18113909.
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Prediction of structural deterioration is a challenging task due to various uncertainties and temporal changes in the environmental conditions, measurement noises as well as errors of mathematical models used for predicting the deterioration progress. Monitoring of deterioration progress is also challenging even with successive measurements, especially when only indirect measurements such as structural responses are available. Recent developments of Bayesian filters and Bayesian inversion methods make it possible to address these challenges through probabilistic assimilation of successive measurement data and deterioration progress models. To this end, this paper proposes a new framework to monitor and predict the spatiotemporal progress of structural deterioration using successive, indirect and noisy measurements. The framework adopts particle filter for the purpose of real-time monitoring and prediction of corrosion states and probabilistic inference of uncertain and/or time-varying parameters in the corrosion progress model. In order to infer deterioration states from sparse indirect inspection data, for example structural responses at sensor locations, a Bayesian inversion method is integrated with the particle filter. The dimension of a continuous domain is reduced by the use of basis functions of truncated Karhunen-Loève expansion. The proposed framework is demonstrated and successfully tested by numerical experiments of reinforcement bar and steel plates subject to corrosion.
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Jamil, Faisal, and DoHyeun Kim. "Enhanced Kalman filter algorithm using fuzzy inference for improving position estimation in indoor navigation." Journal of Intelligent & Fuzzy Systems 40, no. 5 (April 22, 2021): 8991–9005. http://dx.doi.org/10.3233/jifs-201352.
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In recent few years, the widespread applications of indoor navigation have compelled the research community to propose novel solutions for detecting objects position in the Indoor environment. Various approaches have been proposed and implemented concerning the indoor positioning systems. This study propose an fuzzy inference based Kalman filter to improve the position estimation in indoor navigation. The presented system is based on FIS based Kalman filter aiming at predicting the actual sensor readings from the available noisy sensor measurements. The proposed approach has two main components, i.e., multi sensor fusion algorithm for positioning estimation and FIS based Kalman filter algorithm. The position estimation module is used to determine the object location in an indoor environment in an accurate way. Similarly, the FIS based Kalman filter is used to control and tune the Kalman filter by considering the previous output as a feedback. The Kalman filter predicts the actual sensor readings from the available noisy readings. To evaluate the proposed approach, the next-generation inertial measurement unit is used to acquire a three-axis gyroscope and accelerometer sensory data. Lastly, the proposed approach’s performance has been investigated considering the MAD, RMSE, and MSE metrics. The obtained results illustrate that the FIS based Kalman filter improve the prediction accuracy against the traditional Kalman filter approach.
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Kicha, Gennadiy Petrovich, Sergey Petrovich Boyko, and Paul Petrovich Kicha. "Modeling working processes of self-regenerating filters functioning in lubricating systems of ship diesels." Vestnik of Astrakhan State Technical University. Series: Marine engineering and technologies 2020, no. 2 (May 22, 2020): 69–80. http://dx.doi.org/10.24143/2073-1574-2020-2-69-80.
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The article highlights the urgency of the research of working processes of a self-cleaning filter designed to purify the engine oil in the internal combustion engines. There was carried out experimental modeling of countercurrent regeneration of self-cleaning filters used in fuel preparation and diesel lubrication systems on ships. The layout of the unit for modeling the working processes of self-regenerating filters includes a service tank, a hydraulic motor with a stirrer, a pump, a hydraulic cylinder, a receiver of compressed air, a waste tank, a filtering element, shut-off and pneumatic control equipment, a pollution gauge, a temperature sensor, and other elements. A planning matrix is formed, and the experiment results on evaluating the efficiency of the self-regenerating filters are illustrated. The main factors of the regeneration coefficient are considered. There are presented the dependencies of evaluating the process effectiveness and predicting the life of filters between dry cleanings. The methods of calculating the regeneration parameters of automated filters based on the SRF-60 and SRFD-120 modules have been presented, which allows choosing the hydrodynamic modes and backwash time of filter elements taking into account the operating conditions of oil filters. The influence of the filtering process regeneration efficiency identified by the specific intensity of the removal of the dispersed phase from the oil, hydrodynamics (Reynolds number) and the relative backwash time, the adhesive properties of sludge, the filter design, and dispersion of the pollution are analyzed. The evidence of the validation of the calculated experimental dependences obtained from the laboratory tests of the filter model and their compliance with the results of field tests of self-cleaning filters on ships are presented. The possibility of calculating and adjusting the off-line operation of the self-cleaning filter in the lubrication systems of marine diesel engines is presented subject to diesel forcing, fuel quality and lubricants used, oil aging, additives wear and contamination by coarse-grained mechanical impurities.
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Bomba, Andrii, Yurii Klymyuk, and Igor Prysіazhnіuk. "Computer Prediction of Adsorption Water Purification Process in Rapid Cone-Shaped Filters." Modeling, Control and Information Technologies, no. 3 (November 6, 2019): 13–16. http://dx.doi.org/10.31713/mcit.2019.62.
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In the paper a mathematical model for computer predicting the process of adsorption purification of water from impurities in rapid filters taking into account changes in the temperature of the filtration flow along the height of the filter while observing the constant filtration rate is formulated. Analgorithm for numerically-asymptotic approximation of solution of the corresponding nonlinear singularly perturbed boundary value problem for a model region of a conical shape, bounded two equipotential surfaces and a surface flow, is developed. The proposed model allows through computer experiments to investigate changes in the characteristics of porous loads (filtration coefficients, active porosity), to predict the optimal variants for using adsorbents, and increasing the duration of the filters operation due to the choice of their shape, taking into account the effect on the process of adsorption purification ofwater not only changes in the filtration rate flow along the height of the filter, but also the temperature.
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Song, Jin Bao, Long Ye, and Qin Zhang. "A Behavior Retargeting Algorithm Based on the Model." Applied Mechanics and Materials 668-669 (October 2014): 1021–24. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1021.
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This paper analyses the motion of animation with the methods of picking key points and predicting motion trace based on the particle filter for discrete particle track prediction theory. The behavior model has been built for the already existing animation character. During the research, the thesis realized using existed animation motion trace model to drive a similar figure and create a new animation.
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Lin, Wan-Ju, Shih-Hsuan Lo, Hong-Tsu Young, and Che-Lun Hung. "Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis." Applied Sciences 9, no. 7 (April 8, 2019): 1462. http://dx.doi.org/10.3390/app9071462.
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The use of surface roughness (Ra) to indicate product quality in the milling process in an intelligent monitoring system applied in-process has been developing. From the considerations of convenient installation and cost-effectiveness, accelerator vibration signals combined with deep learning predictive models for predicting surface roughness is a potential tool. In this paper, three models, namely, Fast Fourier Transform-Deep Neural Networks (FFT-DNN), Fast Fourier Transform Long Short Term Memory Network (FFT-LSTM), and one-dimensional convolutional neural network (1-D CNN), are used to explore the training and prediction performances. Feature extraction plays an important role in the training and predicting results. FFT and the one-dimensional convolution filter, known as 1-D CNN, are employed to extract vibration signals’ raw data. The results show the following: (1) the LSTM model presents the temporal modeling ability to achieve a good performance at higher Ra value and (2) 1-D CNN, which is better at extracting features, exhibits highly accurate prediction performance at lower Ra ranges. Based on the results, vibration signals combined with a deep learning predictive model could be applied to predict the surface roughness in the milling process. Based on this experimental study, the use of prediction of the surface roughness via vibration signals using FFT-LSTM or 1-D CNN is recommended to develop an intelligent system.
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Dillner, A. M., and S. Takahama. "Predicting ambient aerosol thermal–optical reflectance measurements from infrared spectra: elemental carbon." Atmospheric Measurement Techniques 8, no. 10 (October 2, 2015): 4013–23. http://dx.doi.org/10.5194/amt-8-4013-2015.
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Abstract. Elemental carbon (EC) is an important constituent of atmospheric particulate matter because it absorbs solar radiation influencing climate and visibility and it adversely affects human health. The EC measured by thermal methods such as thermal–optical reflectance (TOR) is operationally defined as the carbon that volatilizes from quartz filter samples at elevated temperatures in the presence of oxygen. Here, methods are presented to accurately predict TOR EC using Fourier transform infrared (FT-IR) absorbance spectra from atmospheric particulate matter collected on polytetrafluoroethylene (PTFE or Teflon) filters. This method is similar to the procedure developed for OC in prior work (Dillner and Takahama, 2015). Transmittance FT-IR analysis is rapid, inexpensive and nondestructive to the PTFE filter samples which are routinely collected for mass and elemental analysis in monitoring networks. FT-IR absorbance spectra are obtained from 794 filter samples from seven Interagency Monitoring of PROtected Visual Environment (IMPROVE) sites collected during 2011. Partial least squares regression is used to calibrate sample FT-IR absorbance spectra to collocated TOR EC measurements. The FT-IR spectra are divided into calibration and test sets. Two calibrations are developed: one developed from uniform distribution of samples across the EC mass range (Uniform EC) and one developed from a uniform distribution of Low EC mass samples (EC < 2.4 μg, Low Uniform EC). A hybrid approach which applies the Low EC calibration to Low EC samples and the Uniform EC calibration to all other samples is used to produce predictions for Low EC samples that have mean error on par with parallel TOR EC samples in the same mass range and an estimate of the minimum detection limit (MDL) that is on par with TOR EC MDL. For all samples, this hybrid approach leads to precise and accurate TOR EC predictions by FT-IR as indicated by high coefficient of determination (R2; 0.96), no bias (0.00 μg m−3, a concentration value based on the nominal IMPROVE sample volume of 32.8 m3), low error (0.03 μg m−3) and reasonable normalized error (21 %). These performance metrics can be achieved with various degrees of spectral pretreatment (e.g., including or excluding substrate contributions to the absorbances) and are comparable in precision and accuracy to collocated TOR measurements. Only the normalized error is higher for the FT-IR EC measurements than for collocated TOR. FT-IR spectra are also divided into calibration and test sets by the ratios OC/EC and ammonium/EC to determine the impact of OC and ammonium on EC prediction. We conclude that FT-IR analysis with partial least squares regression is a robust method for accurately predicting TOR EC in IMPROVE network samples, providing complementary information to TOR OC predictions (Dillner and Takahama, 2015) and the organic functional group composition and organic matter estimated previously from the same set of sample spectra (Ruthenburg et al., 2014).
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Jöhl, Alexander, Yannick Berdou, Matthias Guckenberger, Stephan Klöck, Mirko Meboldt, Melanie Zeilinger, Stephanie Tanadini-Lang, and Marianne Schmid Daners. "Performance behavior of prediction filters for respiratory motion compensation in radiotherapy." Current Directions in Biomedical Engineering 3, no. 2 (September 7, 2017): 429–32. http://dx.doi.org/10.1515/cdbme-2017-0090.
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AbstractIntroduction: In radiotherapy, tumors may move due to the patient’s respiration, which decreases treatment accuracy. Some motion mitigation methods require measuring the tumor position during treatment. Current available sensors often suffer from time delays, which degrade the motion mitigation performance. However, the tumor motion is often periodic and continuous, which allows predicting the motion ahead. Method and Materials: A couch tracking system was simulated in MATLAB and five prediction filters selected from literature were implemented and tested on 51 respiration signals (median length: 103 s). The five filters were the linear filter (LF), the local regression (LOESS), the neural network (NN), the support vector regression (SVR), and the wavelet least mean squares (wLMS). The time delay to compensate was 320 ms. The normalized root mean square error (nRMSE) was calculated for all prediction filters and respiration signals. The correlation coefficients between the nRMSE of the prediction filters were computed. Results: The prediction filters were grouped into a low and a high nRMSE group. The low nRMSE group consisted of the LF, the NN, and the wLMS with a median nRMSE of 0.14, 0.15, and 0.14, respectively. The high nRMSE group consisted of the LOESS and the SVR with both a median nRMSE of 0.34. The correlations between the low nRMSE filters were above 0.87 and between the high nRMSE filters it was 0.64. Conclusion: The low nRMSE prediction filters not only have similar median nRMSEs but also similar nRMSEs for the same respiration signals as the high correlation shows. Therefore, good prediction filters perform similarly for identical respiration patterns, which might indicate a minimally achievable nRMSE for a given respiration pattern.
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Zhu, Difeng, Guojiang Shen, Duanyang Liu, Jingjing Chen, and Yijiang Zhang. "FCG-ASpredictor: An Approach for the Prediction of Average Speed of Road Segments with Floating Car GPS Data." Sensors 19, no. 22 (November 14, 2019): 4967. http://dx.doi.org/10.3390/s19224967.
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The average speed (AS) of a road segment is an important factor for predicting traffic congestion, because the accuracy of AS can directly affect the implementation of traffic management. The traffic environment, spatiotemporal information, and the dynamic interaction between these two factors impact the predictive accuracy of AS in the existing literature, and floating car data comprehensively reflect the operation of urban road vehicles. In this paper, we proposed a novel road segment AS predictive model, which is based on floating car data. First, the impact of historical AS, weather, and date attributes on AS prediction has been analyzed. Then, through spatiotemporal correlations calculation based on the data from Global Positioning System (GPS), the predictive method utilizes the recursive least squares method to fuse the historical AS with other factors (such as weather, date attributes, etc.) and adopts an extended Kalman filter algorithm to accurately predict the AS of the target segment. Finally, we applied our approach on the traffic congestion prediction on four road segments in Chengdu, China. The results showed that the proposed predictive model is highly feasible and accurate.
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Yeom, Hong Gi, Wonjun Hong, Da-Yoon Kang, Chun Kee Chung, June Sic Kim, and Sung-Phil Kim. "A Study on Decoding Models for the Reconstruction of Hand Trajectories from the Human Magnetoencephalography." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/176857.
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Decoding neural signals into control outputs has been a key to the development of brain-computer interfaces (BCIs). While many studies have identified neural correlates of kinematics or applied advanced machine learning algorithms to improve decoding performance, relatively less attention has been paid to optimal design of decoding models. For generating continuous movements from neural activity, design of decoding models should address how to incorporate movement dynamics into models and how to select a model given specific BCI objectives. Considering nonlinear and independent speed characteristics, we propose a hybrid Kalman filter to decode the hand direction and speed independently. We also investigate changes in performance of different decoding models (the linear and Kalman filters) when they predict reaching movements only or predict both reach and rest. Our offline study on human magnetoencephalography (MEG) during point-to-point arm movements shows that the performance of the linear filter or the Kalman filter is affected by including resting states for training and predicting movements. However, the hybrid Kalman filter consistently outperforms others regardless of movement states. The results demonstrate that better design of decoding models is achieved by incorporating movement dynamics into modeling or selecting a model according to decoding objectives.
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Ani R, Anand P S, Sreenath B, and Deepa O S. "In Silico Prediction Tool for Drug-likeness of Compounds based on Ligand Based Screening." International Journal of Research in Pharmaceutical Sciences 11, no. 4 (October 6, 2020): 6273–81. http://dx.doi.org/10.26452/ijrps.v11i4.3310.
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Drug Likeness prediction is a time-consuming and tedious process. An in-vitro method the drug development takes a long time to come to market. The failure rate is also another one to think about in this method. There are many in-silico methods currently available and developing to help the drug discovery and development process. Many online tools are available for predicting and classifying a drug after analyzing the drug-likeness properties of compounds. But most tools have their advantages and disadvantages. In this study, a tool is developed to predict the drug-likeness of compounds given as input to this software. This may help the chemists in analyzing a compound before actually preparing a compound for the drug discovery process. The tool includes both descriptor-based calculation and fingerprint-based calculation of the particular compounds. The descriptor-calculation also includes a set of rules and filters like Lipinski’s rule, Ghose filter, Veber filter and BBB likeness. The previous studies proved that the fingerprint-based prediction is more accurate than descriptor-based prediction. So, in the current study, the drug-likeness prediction tool incorporated the molecular descriptors and fingerprint-based calculations based on five different fingerprint types. The current study incorporated five different machine learning algorithms for prediction of drug-likeness and selected the algorithm, which has a high accuracy rate. When a chemist inputs a particular compound in SMILES format, the drug-likeness prediction tool predicts whether the given candidate compound is drug or non-drug.
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Gong, Yu-Seok, Dowan Kim, and Sungho Mun. "Estimation of the Dynamic Moduli of Viscoelastic Asphalt Mixtures Using the Extended Kalman Filter Algorithm." Advances in Civil Engineering 2018 (December 10, 2018): 1–8. http://dx.doi.org/10.1155/2018/3089085.
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Here, we develop a model predicting the dynamic moduli of hot-mix asphalt/concrete using the extended Kalman filter (EKF) algorithm and draw frequency-domain master curves. Discrete dynamic moduli were obtained via impact resonance tests (IRTs) on linear viscoelastic (LVE) asphalt at 20, 30, 35, 40, and 50°C. Typically, viscoelastic characteristics have been used to derive asphalt dynamic moduli; compressive frequency sweep tests at different frequencies (Hz) and temperatures are employed to this end. We compared IRT-derived viscoelastic master curves obtained via compressive frequency sweep testing to those derived using the EKF algorithm, which employs a nonlinear sigmoidal curve and a Taylor series to explore the viscoelastic function. The model reduced errors at both low and high frequencies by correcting the coefficients of the master curve. Furthermore, the predictive model effectively estimated dynamic moduli at various frequencies, and also root-mean-square errors (RMSEs) which, together with the mean percentage errors (MPEs), were used to compare predictions.
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Dillner, A. M., and S. Takahama. "Predicting ambient aerosol Thermal Optical Reflectance (TOR) measurements from infrared spectra: elemental carbon." Atmospheric Measurement Techniques Discussions 8, no. 6 (June 23, 2015): 6325–54. http://dx.doi.org/10.5194/amtd-8-6325-2015.
Full textAbstract:
Abstract. Elemental carbon (EC) is an important constituent of atmospheric particulate matter because it absorbs solar radiation influencing climate and visibility and it adversely affects human health. The EC measured by thermal methods such as Thermal-Optical Reflectance (TOR) is operationally defined as the carbon that volatilizes from quartz filter samples at elevated temperatures in the presence of oxygen. Here, methods are presented to accurately predict TOR EC using Fourier Transform Infrared (FT-IR) absorbance spectra from atmospheric particulate matter collected on polytetrafluoroethylene (PTFE or Teflon) filters. This method is similar to the procedure tested and developed for OC in prior work (Dillner and Takahama, 2015). Transmittance FT-IR analysis is rapid, inexpensive, and non-destructive to the PTFE filter samples which are routinely collected for mass and elemental analysis in monitoring networks. FT-IR absorbance spectra are obtained from 794 filter samples from seven Interagency Monitoring of PROtected Visual Environment (IMPROVE) sites collected during 2011. Partial least squares regression is used to calibrate sample FT-IR absorbance spectra to collocated TOR EC measurements. The FTIR spectra are divided into calibration and test sets. Two calibrations are developed, one which is developed from uniform distribution of samples across the EC mass range (Uniform EC) and one developed from a~uniform distribution of low EC mass samples (EC < 2.4 μg, Low Uniform EC). A hybrid approach which applies the low EC calibration to low EC samples and the Uniform EC calibration to all other samples is used to produces predictions for low EC samples that have mean error on par with parallel TOR EC samples in the same mass range and an estimate of the minimum detection limit (MDL) that is on par with TOR EC MDL. For all samples, this hybrid approach leads to precise and accurate TOR EC predictions by FT-IR as indicated by high coefficient of variation (R2; 0.96), no bias (0.00 μg m−3, concentration value based on the nominal IMPROVE sample volume of 32.8 m−3), low error (0.03 μg m−3) and reasonable normalized error (21 %). These performance metrics can be achieved with various degrees of spectral pretreatment (e.g., including or excluding substrate contributions to the absorbances) and are comparable in precision and accuracy to collocated TOR measurements. Only the normalized error is higher for the FT-IR EC measurements than for collocated TOR. FT-IR spectra are also divided into calibration and test sets by the ratios OC/EC and ammonium/EC to determine the impact of OC and ammonium on EC prediction. We conclude that FT-IR analysis with partial least squares regression is a robust method for accurately predicting TOR EC in IMPROVE network samples; providing complementary information to TOR OC predictions (Dillner and Takahama, 2015) and the organic functional group composition and organic matter (OM) estimated previously from the same set of sample spectra (Ruthenburg et al., 2014).
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Landman, Kerry A., and Lee R. White. "Predicting filtration time and maximizing throughput in a pressure filter." AIChE Journal 43, no. 12 (December 1997): 3147–60. http://dx.doi.org/10.1002/aic.690431204.
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Chang, You-Im, and Hsun-Chih Chan. "Correlation equation for predicting filter coefficient under unfavorable deposition conditions." AIChE Journal 54, no. 5 (2008): 1235–53. http://dx.doi.org/10.1002/aic.11466.
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Shehata, Ali I., Mohamed Shokry, Khalid M. Saqr, and Mohamed Shehadeh. "Validation of a CFD Non-Newtonian Eulerian-Eulerian Model for Predicting Wellbore Filter Cake Formation." Applied Mechanics and Materials 819 (January 2016): 376–81. http://dx.doi.org/10.4028/www.scientific.net/amm.819.376.
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During oil wellbore drilling processes, filter cake is formed on the sidewalls of the well hole due to filtration of drilling fluid particles. The filter cake is crucial to the drilling process, since it helps to maintain the wellbore hole, protects the drilling bit from jamming and facilitates the subsequent phases of the well development. The most important parameter for filter cake formation is its thickness and its variation due to drilling conditions. In this paper, the drilling fluid particles filtration process was simulated at conditions mimicking deep wellbore drilling. The drilling fluid was simulated as a non-Newtonian two-phase fluid of liquid and particles, utilizing an Eulerian-Eulerian approach. The model successfully predicted a filter cake thickness which agrees well with measurements and previous CFD work.
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Firdaus, Fathania Firwan, Hanung Adi Nugroho, and Indah Soesanti. "A Review of Feature Selection and Classification Approaches for Heart Disease Prediction." IJITEE (International Journal of Information Technology and Electrical Engineering) 4, no. 3 (June 18, 2021): 75. http://dx.doi.org/10.22146/ijitee.59193.
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Cardiovascular disease has been the number one illness to cause death in the world for years. As information technology develops, many researchers have conducted studies on a computer-assisted diagnosis for heart disease. Predicting heart disease using a computer-assisted system can reduce time and costs. Feature selection can be used to choose the most relevant variables for heart disease. It includes filter, wrapper, embedded, and hybrid. The filter method excels in computation speed. The wrapper and embedded methods consider feature dependencies and interact with classifiers. The hybrid method takes advantage of several methods. Classification is a data mining technique to predict heart disease. It includes traditional machine learning, ensemble learning, hybrid, and deep learning. Traditional machine learning uses a specific algorithm. The ensemble learning combines the predictions of multiple classifiers to improve the performance of a single classifier. The hybrid approach combines some techniques and takes advantage of each method. Deep learning does not require a predetermined feature engineering. This research provides an overview of feature selection and classification methods for the prediction of heart disease in the last ten years. Thus, it can be used as a reference in choosing a method for heart disease prediction for future research.
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Zhou, Yan. "Prediction and Analysis of Conduction Electromagnetic Interference in Communication Power." Journal of Nanoelectronics and Optoelectronics 16, no. 12 (December 1, 2021): 1892–96. http://dx.doi.org/10.1166/jno.2021.3155.
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Conducted electromagnetic interference (EMI) in power electronic equipment is an important factor restricting the development of power electronics technology, and the research is of great hot and difficulty. The prediction of conducted EMI is to effectively guide the design of EMI filters. On the one hand, the selection of commonmode capacitance and inductance values can be guided by the analysis of propagation path impedance; on the other hand, various passive devices are analyzed by simulation the influence of parasitic parameters on conducted EMI can guide the selection of filter capacitors and inductors. In this paper, we take the widely used switching power supply as an example, a novel EMI conduction modelling is proposed, the impedance analysis method is used to model the high-frequency parasitic parameters, the model was built for predicting the conducted EMI accurately of switching power supply.
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Xiong, Xi, Kaan Ozbay, Li Jin, and Chen Feng. "Dynamic Origin–Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filter." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 8 (May 31, 2020): 491–503. http://dx.doi.org/10.1177/0361198120919399.
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Modern intelligent transportation systems provide data that allow real-time dynamic demand prediction, which is essential for planning and operations. The main challenge of prediction of dynamic origin–destination (O-D) demand matrices is that demand cannot be directly measured by traffic sensors; instead, it has to be inferred from aggregate traffic flow data on traffic links. Specifically, spatial correlation, congestion and time dependent factors need to be considered in general transportation networks. This paper proposes a novel O-D prediction framework combining heterogeneous prediction in graph neural networks and Kalman filter to recognize spatial and temporal patterns simultaneously. The underlying road network topology is converted into a corresponding line graph in the newly designed fusion line graph convolutional networks (FL-GCNs), which provide a general framework of predicting spatial-temporal O-D flows from link information. Data from the New Jersey Turnpike network are used to evaluate the proposed model. The results show that the proposed approach yields the best performance under various prediction scenarios. In addition, the advantage of combining deep neural networks and Kalman filter is demonstrated.
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Tien, Chuen-Lin, Kuan-Po Chen, and Hong-Yi Lin. "Internal Stress Prediction and Measurement of Mid-Infrared Multilayer Thin Films." Materials 14, no. 5 (February 26, 2021): 1101. http://dx.doi.org/10.3390/ma14051101.
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We present an experimental method for evaluating interfacial force per width and predicting internal stress in mid-infrared band-pass filters (MIR-BPF). The interfacial force per width between the two kinds of thin-film materials was obtained by experimental measurement values, and the residual stress of the multilayer thin films was predicted by the modified Ennos formula. A dual electron beam evaporation system combined with ion-assisted deposition was used to fabricate mid-infrared band-pass filters. The interfacial forces per width for Ge/SiO2 and SiO2/Ge were 124.9 N/m and 127.6 N/m, respectively. The difference between the measured stress and predicted stress in the 23-layer MIR-BPF was below 0.059 GPa. The residual stresses of the four-layer film, as well as the 20-layer and 23-layer mid-infrared band-pass filter, were predicted by adding the interface stress to the modified Ennos formula. In the four-layer film, the difference between the predicted value and the measured stress of the HL (high–low refractive index) and LH (low–high refractive index) stacks were −0.384 GPa for (HL)2 and −0.436 GPa for (LH)2, respectively. The predicted stress and the measured stress of the 20-layer mid-infrared filter were −0.316 GPa and −0.250 GPa. The predicted stress and the measured stress of the 23-layer mid-infrared filter were −0.257 GPa and −0.198 GPa, respectively.
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Malko, Mihail, Sergey Vasilevich, Andrei Mitrofanov, and Vadim Mizonov. "Development of the Method for Predicting and Calculating the Operation of Sorption Systems for Cleaning the Generator Gas based on Dolomite Use. Part II." Problems of the Regional Energetics, no. 4(52) (November 2021): 31–42. http://dx.doi.org/10.52254/1857-0070.2021.4-52.04.
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At present, instead of a direct combustion of solid fuel, its thermochemical conversion is exten-sively used to produce a generator gas. The use of this technology is connected with the need for gas purification. One of the promising and widely spread sorbents for the purification of the generator gas is dolomite, whose particles compose the active component of the bed filters. Forecasting the technological characteristics of the functioning of the bed filters of a various de-sign is an extremely urgent task. The objective of the study is to develop a method for forecast-ing and calculating the operation of sorption systems for purification of the generator gas based on dolomite. It is achieved by constructing and verifying a mathematical model of the function-ing of the bed sorption filter with a radial-axial flow pattern of the generator gas through the do-lomite filling. The Markov chains theory of a mathematical apparatus is used to design the one-dimensional mathematical model of the process with discrete space and time. The main recurrent balance ratio is formed at each calculation step taking into account the current characteristics of the process, which makes the model nonlinear. The significance of the research is that an approach to the problem of increasing the reliability of the description and reliability of forecasting technological processes in a bed filter was proposed based on the construction of mathematical models of these processes, in which the filter is considered as a system with distributed characteristics, and the calculation was based on local exchange potentials between particles and gas.
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Akgiray, Ömer, and Ahmet M. Saatçı. "A new look at filter backwash hydraulics." Water Supply 1, no. 2 (March 1, 2001): 65–72. http://dx.doi.org/10.2166/ws.2001.0022.
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A new approach to model media expansion during filter backwash is presented. The proposed approach is based on the assumption that the Ergun equation remains valid after fluidization. Mathematical formulas are derived for predicting expanded porosity for a given backwash velocity or backwash velocity for a given expanded porosity. These formulas can be easily used by the engineer. Values predicted using the proposed approach are in good agreement with experimental measurements.
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Gotra, A., C. Doucet, P. Delli Fraine, A. Bessissow, C. Dey, B. Gallix, L. M. Boucher, and D. Valenti. "Predicting inferior vena cava (IVC) filter retrievability using positional parameters: A comparative study of various filter types." Diagnostic and Interventional Imaging 99, no. 10 (October 2018): 615–24. http://dx.doi.org/10.1016/j.diii.2018.04.003.
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Abreu, José Alano Peres de, Roberto Célio Limão de Oliveira, and João Viana da Fonseca Neto. "Rocket tracking impact point prediction using α-β, standard Kalman, extended, Kalman, and unscented Kalman filters: a comparative analysis." Research, Society and Development 9, no. 3 (January 1, 2020): e42932022. http://dx.doi.org/10.33448/rsd-v9i3.2022.
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Accurate information about the impact point (IP) of a suborbital rocket on Earth’s surface during a launch is an important requirement for range safety operations. Four different estimators, i.e., the α-β filter, standard Kalman filter (SKF), extended Kalman filter (EKF), and unscented Kalman filter (UKF), are considered for the suborbital rocket tracking problem, whose data are used specifically for improving the accuracy of the IP prediction (IPP) of these vehicles. This paper presents a comparative analysis between the results of the estimators. Rocket flight data are discussed to demonstrate the advantages and disadvantages of the estimators and to determine the inherent limitations in predicting the aerodynamic effects found in certain flight situations. We discuss the appropriate mathematical model of a filter capable of running the real-time algorithm for the estimation of target position and velocity. This work uses actual data from a radar sensor to evaluate the tracking algorithms. We insert the filter result into the mathematical model developed to predict the rocket IP on Earth's surface. The main goal of this study is to evaluate the performance of four different estimators when specifically applied for the improvement of the IPP of suborbital rockets. It is demonstrated that the UKF outperforms all other tracking algorithms in terms of the accuracy and robustness of IP estimation.
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Shao, Qing Bo, Hsin Guan, and Xin Jia. "Vehicle Trajectory Prediction Based on Road Recognition." Applied Mechanics and Materials 599-601 (August 2014): 760–66. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.760.
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Predicting vehicle trajectory accurately is a crucial task for an autonomous vehicle. It is also necessary for many Advanced Driver Assistance System to predict trajectory of the ego-vehicle’s. In recent years, some vehicles trajectory prediction algorithm is mainly based on a simple Motion Model. This paper puts forward a method which combines road recognition and the hypothesis of steady preview and dynamic correction for trajectory prediction. In the road recognition algorithm, both methods of Kalman Filter (KF) and Recursive Least-Square (RLS) work well to estimate the road slope and road friction coefficient.
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Wang, Juxing, and Linyong Shen. "Semi-Adaptable Human Hand Motion Prediction Based on Neural Networks and Kalman Filter." Journal of Physics: Conference Series 2029, no. 1 (September 1, 2021): 012091. http://dx.doi.org/10.1088/1742-6596/2029/1/012091.
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Abstract This paper focuses on predicting trajectories of the human hand in order to improve the safety for human-robot interactions. In this work, the position and orientation are represented by two curves in the operation space such that the same algorithm can be used for both position and orientation prediction. The motion prediction is achieved in two steps. Firstly, the neural network (NN) model is applied for offline training to model the human hand motion. Secondly, the Kalman filter is added to adjust the weight coefficients of the NN model’s output layer online when a set of new data is measured, such that the NN model is adaptive to new data. An experiment study has been conducted to validate the effectiveness of the proposed algorithm. The result shows that the proposed algorithm achieves a higher prediction accuracy and requires a smaller amount of data to achieve optimal performance compared with the advanced method.
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MARTIN, ALBERTO J. M., DAVIDE BAÙ, ALESSANDRO VULLO, IAN WALSH, and GIANLUCA POLLASTRI. "LONG-RANGE INFORMATION AND PHYSICALITY CONSTRAINTS IMPROVE PREDICTED PROTEIN CONTACT MAPS." Journal of Bioinformatics and Computational Biology 06, no. 05 (October 2008): 1001–20. http://dx.doi.org/10.1142/s0219720008003783.
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Protein topology representations such as residue contact maps are an important intermediate step towards ab initio prediction of protein structure, but the problem of predicting reliable contact maps is far from solved. One of the main pitfalls of existing contact map predictors is that they generally predict unphysical maps, i.e. maps that cannot be embedded into three-dimensional structures or, at best, violate a number of basic constraints observed in real protein structures, such as the maximum number of contacts for a residue. Here, we focus on the problem of learning to predict more "physical" contact maps. We do so by first predicting contact maps through a traditional system (XXStout), and then filtering these maps by an ensemble of artificial neural networks. The filter is provided as input not only the bare predicted map, but also a number of global or long-range features extracted from it. In a rigorous cross-validation test, we show that the filter greatly improves the predicted maps it is input. CASP7 results, on which we report here, corroborate this finding. Importantly, since the approach we present here is fully modular, it may be beneficial to any other ab initio contact map predictor.
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Dhaniyala, Suresh, and Benjamin Liu. "Investigations of Particle Penetration in Fibrous Filters: Part II. Theoretical." Journal of the IEST 42, no. 2 (March 14, 1999): 40–46. http://dx.doi.org/10.17764/jiet.42.2.p8881880466431u6.
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This article is the second part of a two-part series. The first part, "Investigation of Particle Penetration in Fibrous Filters: Experimental," appeared in the January/February, 1999, issue of the Journal of the IEST. The performance of seven state-of-the-art air cleaning filter media having a wide range of filter efficiencies is described. The figure of merit values have been obtained to compare the performance of different media. The experimentally obtained pressure drops and the theoretically calculated fiber drags are used in deriving an expression to calculate an equivalent fiber diameter for different media. The theoretical values of fiber diameter compare well with the experimental observations. Use of the equivalent fiber diameter values in calculating the parameters of the dimensionless correlation model results in better agreement between the experiments and theory. The calculated equivalent fiber diameter is useful in predicting the most penetrating particle size of different media.