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Relevant bibliographies by topics / Predictive parameters

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Academic literature on the topic 'Predictive parameters'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Predictive parameters"

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Schoettler, Jochen J., Kathrin Brohm, Sonani Mindt, et al. "Mortality Prediction by Kinetic Parameters of Lactate and S-Adenosylhomocysteine in a Cohort of Critically Ill Patients." International Journal of Molecular Sciences 25, no. 12 (2024): 6391. http://dx.doi.org/10.3390/ijms25126391.

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Tissue hypoxia is associated with the development of organ dysfunction and death in critically ill patients commonly captured using blood lactate. The kinetic parameters of serial lactate evaluations are superior at predicting mortality compared with single values. S-adenosylhomocysteine (SAH), which is also associated with hypoxia, was recently established as a useful predictor of septic organ dysfunction and death. We evaluated the performance of kinetic SAH parameters for mortality prediction compared with lactate parameters in a cohort of critically ill patients. For lactate and SAH, maxim

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Lin, Chia-Ying, Yi-Ting Yen, Li-Ting Huang, et al. "An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors." Diagnostics 12, no. 4 (2022): 889. http://dx.doi.org/10.3390/diagnostics12040889.

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This study aimed to build machine learning prediction models for predicting pathological subtypes of prevascular mediastinal tumors (PMTs). The candidate predictors were clinical variables and dynamic contrast–enhanced MRI (DCE-MRI)–derived perfusion parameters. The clinical data and preoperative DCE–MRI images of 62 PMT patients, including 17 patients with lymphoma, 31 with thymoma, and 14 with thymic carcinoma, were retrospectively analyzed. Six perfusion parameters were calculated as candidate predictors. Univariate receiver-operating-characteristic curve analysis was performed to evaluate

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Chien, Wen T., and W. C. Hung. "Investigation on the Predictive Model for Burr in Laser Cutting Titanium Alloy." Materials Science Forum 526 (October 2006): 133–38. http://dx.doi.org/10.4028/www.scientific.net/msf.526.133.

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The purpose of this study is to develop two predictive models for burr height in cutting titanium alloy plates by using Nd:YAG laser. Firstly, Taguchi method has been used to arrange the experimental scheme and analyze the results via analysis of mean . The important laser cutting parameters affecting burr height can be found. It shows that the pressure of assistant gas, the focusing position and the pulsed frequency are the most important cutting parameters in order. Then they have been chosen as the input variables for response surface methodology and used to construct a mathematical equatio

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Hu, Yawei, Ran Wei, Yang Yang, et al. "Performance Degradation Prediction Using LSTM with Optimized Parameters." Sensors 22, no. 6 (2022): 2407. http://dx.doi.org/10.3390/s22062407.

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Predicting the degradation of mechanical components, such as rolling bearings is critical to the proper monitoring of the condition of mechanical equipment. A new method, based on a long short-term memory network (LSTM) algorithm, has been developed to improve the accuracy of degradation prediction. The model parameters are optimized via improved particle swarm optimization (IPSO). Regarding how this applies to the rolling bearings, firstly, multi-dimension feature parameters are extracted from the bearing’s vibration signals and fused into responsive features by using the kernel joint approxi

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G, Ottos C., and Isaac E. O. "Modeling of Predictive interaction of Water Parameters in Groundwater." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 1091–96. http://dx.doi.org/10.31142/ijtsrd11292.

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Chien, Wen T., and S. W. Chang. "Study on the Predictive Model for Shear Strength in Laser Welding Stainless Steel." Materials Science Forum 505-507 (January 2006): 205–10. http://dx.doi.org/10.4028/www.scientific.net/msf.505-507.205.

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A predictive model is presented for the prediction of shear strength in laser welding AISI304 stainless steel. Welding experiments conducted using a pulsed Nd:YAG laser machine while the laser welding parameters and their levels have been arranged according to design of experiments of Taguchi method. The tensile tests are performed after welding and the measurements of tensile strength are further calculated for shear strength. The data can be analyzed using the principles of Taguchi method for determining the optimal laser welding parameters and for investigating the most significant laser we

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Nagler, Harris M., and Michael Rotman. "Predictive parameters for microsurgical reconstruction." Urologic Clinics of North America 29, no. 4 (2002): 913–19. http://dx.doi.org/10.1016/s0094-0143(02)00094-0.

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Zakuskin, Aleksandr S., and Timur A. Labutin. "StarkML: application of machine learning to overcome lack of data on electron-impact broadening parameters." Monthly Notices of the Royal Astronomical Society 527, no. 2 (2023): 3139–45. http://dx.doi.org/10.1093/mnras/stad3387.

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ABSTRACT Parameters of electron-impact (Stark) broadening and shift of spectral lines are of key importance in various studies of plasma spectroscopy and astrophysics. To overcome the lack of accurately known Stark parameters, we developed a machine learning approach for predicting Stark parameters of neutral atoms’ lines. By implementing a data pre-processing routine and explicitly testing models’ predictive ability and generalizability, we achieve a high level of accuracy in parameters prediction as well as physically meaningful temperature dependence. The applicability of the results is dem

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Yatmanov, Alexey N., Vasiliy Ya Apchel, Dmitrii V. Ovchinnikov, et al. "Use of value-based and motivational parameters with artificial intelligence technology to predict cadet maladjustment." Bulletin of the Russian Military Medical Academy 26, no. 4 (2024): 587–96. https://doi.org/10.17816/brmma635764.

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The paper demonstrates the potential for using value-based and motivational parameters with artificial intelligence technology to predict cadet maladjustment. A retrospective cohort study was conducted. For 2013–2021, 734 cadets of the Navy Military Training and Research Center “Soviet Union Fleet Admiral N.G. Kuznetsov Naval Academy” were examined, 48 of them were diagnosed with maladjustment. Neural networks were used for mathematical modeling of maladjustment prediction. The study included 8 cycles of neural network training and 7 cycles of neural network model testing. As the actual materi

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Zhao, Yunxiang, Ning Gao, Jian Cheng, et al. "Genetic Parameter Estimation and Genomic Prediction of Duroc Boars’ Sperm Morphology Abnormalities." Animals 9, no. 10 (2019): 710. http://dx.doi.org/10.3390/ani9100710.

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Artificial insemination (AI) has been used globally as a routine technology in the swine production industry. However, genetic parameters and genomic prediction accuracy of semen traits have seldom been reported. In this study, we estimated genetic parameters and conducted genomic prediction for five types of sperm morphology abnormalities in a large Duroc boar population. The estimated heritability of the studied traits ranged from 0.029 to 0.295. In the random cross-validation scenario, the predictive ability ranged from 0.212 to 0.417 for genomic best linear unbiased prediction (GBLUP) and

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Dissertations / Theses on the topic "Predictive parameters"

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Naeeni, Mojgan. "Predictive values of semen parameters for fertility." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245535.

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Yevseienko, Oleg, Anatoliy Gapon, and Dmytro Salnikov. "Searching for Optimal Control Parameters of Thermal Object Using Pulse-Width Modulation (PWM) Control with Predictive Filter." Thesis, Lviv Polytechnic Publishing House, 2015. http://repository.kpi.kharkov.ua/handle/KhPI-Press/41116.

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The thesis is devote to the temperature control of objects with lumped or distributed parameters. The problems of choosing the right value of regulator’s heater power and prediction period are discussed. The major attention is paid to the process of searching the minimum value of control quantities. It is shown that the approximated second-order plane has an exact accordance with the original data. It is concluded that algorithm of PWM-control with prediction filter provides good quality control.

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Palma, André Manuel Moreira. "Predictive methods for the association parameters of multifunctional molecules with the CPA EoS." Doctoral thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/22848.

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Doutoramento em Engenharia Química<br>O projeto e otimização de processos envolvendo moléculas associativas multifuncionais é de elevada importância para as indústrias química, petroquímica, farmacêutica, alimentar, energética e de cosméticos. A equação de estado (EoS) Cubic –Plus-Association (CPA) tem demonstrado ser um modelo termodinâmico adequado para a descrição de diversas moléculas associativas. Este modelo é utilizado frequentemente na indústria de gás e petróleo para a descrição, entre outros, de sistemas de água com hidrocarbonetos e de formação e inibição de hidratos de gás. Os seu

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Wiberg, Viktor. "Terrain machine learning : A predictive method for estimating terrain model parameters using simulated sensors, vehicle and terrain." Thesis, Umeå universitet, Institutionen för fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149815.

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Predicting terrain trafficability of deformable terrain is a difficult task with applications in e.g, forestry, agriculture, exploratory missions. The currently used techniques are neither practical, efficient, nor sufficiently accurate and inadequate for certain soil types. An online method which predicts terrain trafficability is of interest for any vehicle with purpose to reduce ground damage, improve steering and increase mobility. This thesis presents a novel approach for predicting the model parameters used in modelling a virtual terrain. The model parameters include particle stiffness,

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Maganini, Natalia Diniz. "FGAMP: um novo método para previsão de séries temporais financeiras usando parâmetros multifractais." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/96/96133/tde-17072017-161414/.

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Este trabalho fornece informações sobre uma questão importante que é a previsão de preços e apresenta um novo método para fazer previsão para o comportamento de séries temporais financeiras, que foi nomeado de FGAMP (Forecasting with a general average of the multifractal parameters). O novo método utiliza os parâmetros extraídos do momento 5 do método MFDFA (Multifractal Detrended Fluctuation Analysis) como marcador para reconhecer um padrão e realizar uma previsão. As séries temporais utilizadas para verificar a viabilidade do método consistiram de 6 cotações de preços de ativos de alta frequ

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Sadeghkhani, Abdolnasser. "Estimation d'une densité prédictive avec information additionnelle." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/11238.

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Dans le contexte de la théorie bayésienne et de théorie de la décision, l'estimation d'une densité prédictive d'une variable aléatoire occupe une place importante. Typiquement, dans un cadre paramétrique, il y a présence d’information additionnelle pouvant être interprétée sous forme d’une contrainte. Cette thèse porte sur des stratégies et des améliorations, tenant compte de l’information additionnelle, pour obtenir des densités prédictives efficaces et parfois plus performantes que d’autres données dans la littérature. Les résultats s’appliquent pour des modèles avec données gaussie

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Phillips, Roger. "The predictive value of in vitro chemosensitivity tests of anticancer drugs : in vitro chemosensitivity of a panel of murine colon tumours determined by a colony forming assay at drug exposure parameters measured in vivo." Thesis, University of Bradford, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329305.

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Vega-Brown, Will (William Robert). "Predictive parameter estimation for Bayesian filtering." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81715.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 113-117).<br>In this thesis, I develop CELLO, an algorithm for predicting the covariances of any Gaussian model used to account for uncertainty in a complex system. The primary motivation for this work is state estimation; often, complex raw sensor measurements are processed into low dimensional observations of a vehicle state. I argue that the covariance of these observations can be well-modelled as a function of the r

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Shakibi, Babak. "Predicting parameters in deep learning." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50999.

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The recent success of large and deep neural network models has motivated the training of even larger and deeper networks with millions of parameters. Training these models usually requires parallel training methods where communicating large number of parameters becomes one of the main bottlenecks. We show that many deep learning models are over-parameterized and their learned features can be predicted given only a small fraction of their parameters. We then propose a method which exploits this fact during the training to reduce the number of parameters that need to be learned. Our method is or

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Khan, Bilal. "Efficient parameterise solutions of predictive control." Thesis, University of Sheffield, 2013. http://etheses.whiterose.ac.uk/4097/.

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Model based predictive control (MPC) is well established and has gained widespread acceptance in the industry and the academic community. The success of earlier industrial heuristic MPC algorithms motivated the research community to develop several algorithms with improved performance and enlarge the region of attraction. All proposed algorithms to some extent form a trade off between a region of attraction, performance and inexpensive optimisation. This thesis makes contributions in the area of MPC algorithm design and in particular examines to what extent different methods for parameterising

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Books on the topic "Predictive parameters"

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Evernden, J. F. Predictive model for important ground motion parameters associated with large and great earthquakes. U.S. Geological Survey, 1988.

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Evernden, J. F. Predictive model for important ground motion parameters associated with large and great earthquakes. U.S. G.P.O., 1988.

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Gross, Daniel. Data sources for parameters used in predictive modeling of fire growth and smoke spread. U.S. Dept. of Commerce, National Bureau of Standards, 1985.

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Farmer, Stephen. Evaluation of some blood chemistry parameters as predictive indices of reproduction in captive Atlantic salmon (Salmo salar) brookstock. University of Prince Edward Island, 1988.

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Quintiere, James G. Significant parameters for predicting flame spread. U.S. Dept. of Commerce, National Bureau of Standards, 1985.

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Quintiere, James G. Significant parameters for predicting flame spread. U.S. Dept. of Commerce, National Bureau of Standards, 1985.

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Lundholm, Steven E. Predicting antenna parameters from antenna physical dimensions. Naval Postgraduate School, 1993.

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Vladisavljevic, Tomislav. Predicting the T2K Neutrino Flux and Measuring Oscillation Parameters. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51174-6.

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Kaller, K. P. Skeletal and dentoalveolar parameters in the prediction of treatment outcome. University of Toronto, Faculty of Dentistry], 1998.

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Chen, S. A recursive prediction error parameter estimator for nonlinear models. University of Sheffield, Dept. of Control Engineering, 1988.

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Book chapters on the topic "Predictive parameters"

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Ma, Ziwei, Xiaonan Zhu, Tonghui Wang, and Kittawit Autchariyapanitkul. "Joint Plausibility Regions for Parameters of Skew Normal Family." In Predictive Econometrics and Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70942-0_16.

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Seuser, A., P. Böhm, A. Kurme, and K. Kurnik. "Predictive Parameters of Fitness in Hemophiliac Children." In 37th Hemophilia Symposium. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-73535-9_6.

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Zhu, Xiaonan, Baokun Li, Mixia Wu, and Tonghui Wang. "Plausibility Regions on Parameters of the Skew Normal Distribution Based on Inferential Models." In Predictive Econometrics and Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70942-0_21.

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Han, Yaofei, Chao Gong, and Jinqiu Gao. "Observer-Based Robustness Improvement for FCS-MPCC Used in IMs." In Model Predictive Control for AC Motors. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8066-3_2.

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AbstractThis Chapter proposes a sliding mode (SM) disturbance observer based finite control set model predictive current control (FCS-MPCC) strategy to improve the control performance of induction motors. FCS-MPCC method is achieved based on the machine model, leading to the fact that the parameters have great impacts on the control performance, especially the steady-state characteristics.

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Qian, Shenghua. "Vehicle Collision Prediction Model on the Internet of Vehicles." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_53.

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AbstractAn active collision prediction model on the Internet of Vehicles is proposed. Through big data calculation on the cloud computing platform, the model predicts whether the vehicles may collide and the time of the collision, so the server actively sends warning signals to the vehicles that may collide. Firstly, the vehicle collision prediction model preprocesses the data set, and then constructs a new feature set through feature engineering. For the imbalance of the data set, which affects predictive results, SMOTE algorithm is proposed to generate new samples. Then, the LightGBM algorit

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Tyagi, Kratika, and Sanjeev Thakur. "Predictive Classification of ECG Parameters Using Association Rule Mining." In Advances in Computer and Computational Sciences. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3773-3_60.

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Dawson, John, Anna Gams, Ivan Rajen, Andrew M. Soltisz, and Andrew G. Edwards. "Computational Prediction of Cardiac Electropharmacology - How Much Does the Model Matter?" In Computational Physiology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05164-7_5.

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AbstractAnimal data describing drug interactions in cardiac tissue are abundant, however, nuanced inter-species differences hamper the use of these data to predict drug responses in humans. There are many computational models of cardiomyocyte electrophysiology that facilitate this translation, yet it is unclear whether fundamental differences in their mathematical formalisms significantly impact their predictive power. A common solution to this problem is to perform inter-species translations within a collection of models with internally consistent formalisms, termed a “lineage”, but there has

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Dhanya, J., Dwijesh Sagar, and S. T. G. Raghukanth. "Predictive Models for Ground Motion Parameters Using Artificial Neural Network." In Lecture Notes in Civil Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0365-4_8.

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Jia, Laiqiang, Lei Shan, Xiaolong Wang, et al. "Predictive Modeling of Transmission Line Parameters for Different Operating Conditions." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-7047-2_35.

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Gupta, N., and R. S. Walia. "Predictive Soft Modeling of Turning Parameters Using Artificial Neural Network." In Lecture Notes in Mechanical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3033-0_17.

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Conference papers on the topic "Predictive parameters"

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Yi, Juncheng, Jialin Shi, and Tao Jiang. "Research on Parameters Predictive Methods for Sea Clutter Model." In 2024 Photonics & Electromagnetics Research Symposium (PIERS). IEEE, 2024. http://dx.doi.org/10.1109/piers62282.2024.10617954.

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Ponselvakumar, A. P., Mirthula R E, BarathKumar M, and Chindrella S. "Predictive Modeling of Environmental Parameters Using Ensemble Machine Learning Techniques." In 2024 International Conference on Communication, Control, and Intelligent Systems (CCIS). IEEE, 2024. https://doi.org/10.1109/ccis63231.2024.10932016.

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Pande, Prashant, Samruddhi Mahakalkar, Sneha Kendre, et al. "Predictive Modelling of Concrete Compressive Strength Based on Drilling Parameters." In 2024 2nd International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS). IEEE, 2024. https://doi.org/10.1109/icetems64039.2024.10964959.

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A, Janagiraman, Nivedha G, Gunapriya S, Vinisha Laxmi G, and Kalaiyarassi M. "SQP based Predictive Controller for Grid Parameters Optimization in Distributed Generation System." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10985524.

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Kumar Sharma, Manoj, Somnath Dey, Pradipta Kumar Saha, and Debasis Samanta. "Parameters effecting the predictive virtual keyboard." In 2010 IEEE Students Technology Symposium (TechSym). IEEE, 2010. http://dx.doi.org/10.1109/techsym.2010.5469160.

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Martinez, Gerardo Santillan, Tuomas Miettinen, Antti Aikala, et al. "Parameters selection in predictive online simulation." In 2016 IEEE 14th International Conference on Industrial Informatics (INDIN). IEEE, 2016. http://dx.doi.org/10.1109/indin.2016.7819254.

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Ivan, Cosmin, and Mihai Catalin Arva. "Optimizing process parameters using predictive control." In 2022 14th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2022. http://dx.doi.org/10.1109/ecai54874.2022.9847424.

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Giacobello, Daniele, Manohar N. Murthi, Mads Graesboll Christensen, Soren Holdt Jensen, and Marc Moonen. "Re-estimation of linear predictive parameters in sparse linear prediction." In 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers. IEEE, 2009. http://dx.doi.org/10.1109/acssc.2009.5470202.

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Loktionov, O. A. "DETERMINATION OF INITIAL PARAMETERS LIST FOR PREDICTIVE ASSESSMENT OF OCCUPATIONAL INJURIES." In The 16th «OCCUPATION and HEALTH» Russian National Congress with International Participation (OHRNC-2021). FSBSI “IRIOH”, 2021. http://dx.doi.org/10.31089/978-5-6042929-2-1-2021-1-321-325.

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Abstract: Introduction. The development and improvement of mechanisms for assessing and predicting occupational injuries, both in the short term and for a long period of time, is a key task in the field of occupational safety for each branch of economic activity. Aim is to determine the list of the most characteristic initial data and parameters for predictive assessment of occupational injuries and accidents of various severity. Research methods. Comparative analysis of various models for assessing and predicting injuries is carried out. Based on the methods of expert analysis, the advantages

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Wang, Yizhe, Lingxiang Huang, Sheng Huang, Shoudao Huang, and Feifan Sheng. "Parameters Identification of IPMSM Based on Deadbeat Predictive Current Control." In 2021 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE). IEEE, 2021. http://dx.doi.org/10.1109/precede51386.2021.9681031.

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Reports on the topic "Predictive parameters"

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Wood, Sheila J. Predictive Binding Parameters for DNA-DNA Association within a Fluid Stream. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada328050.

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Gross, Daniel. Data sources for parameters used in predictive modeling of fire growth and smoke spread. National Bureau of Standards, 1985. http://dx.doi.org/10.6028/nbs.ir.85-3223.

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Boven, Van, Jack, and King. L51965 Environmental Factors-Effect of SCC Growth. Pipeline Research Council International, Inc. (PRCI), 2002. http://dx.doi.org/10.55274/r0011264.

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Stress corrosion cracking (SCC) is an established threat to the integrity of gas transmission pipelines and is a significant concern to pipeline operators. In line inspection (ILI) tools are actively being developed to detect SCC. This project combined the implementation of new ILI tools with the environmental characterization of identified SCC sites. Characterization of sites included site and soil parameters traditionally derived from surface inspection as well as the measurement of pipe depth soil parameters (electrical resistance, oxidation reduction potential, temperature, pipe to soil po

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Daniel. L52353 Materials Selection, Welding and Weld Monitoring - Optimized Welding Solutions for X100 Line Pipe. Pipeline Research Council International, Inc. (PRCI), 2012. http://dx.doi.org/10.55274/r0010650.

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Two rounds of pipe welding were completed to understand the influence of the welding parameters on the weld metal and HAZ properties and microstructure. Thermal data was also obtained from these welds. This information was used to refine the thermal microstructural model with predictive capabilities. Essential welding variables were validated on flat plate experiments and recommendations for welding process control established. Ultimately, these recommendations were evaluated by pipeline welding contractors to assess its viability for field application.

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Psaila-Dombrowski, M. J., W. A. Van Der Sluys, and B. P. Miglin. GRI-97-0001 Investigation of Pipeline Stress Corrosion Cracking Under Controlled Chemistry Conditions. Pipeline Research Council International, Inc. (PRCI), 1997. http://dx.doi.org/10.55274/r0012043.

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Stress corrosion cracking failures of high-pressure gas transmission pipelines have occurred. Although such failures are infrequent, there is a concern about their potentially catastrophic nature. The susceptibility of a material to this failure is controlled by crack growth kinetics which is governed by the composition of the water at the crack tip, the material composition, temperature, and stress/strain conditions. Hence, there is a need to investigate these parameters to begin to understand and develop predictive capabilities to avoid this phenomenon.

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Luthi, Samuel. Statistically Quantifying the Efficacy of MCS Predictive Parameters in Pinpointing the Location of Initiation of an MCS in the Great Plains Region. Iowa State University, 2018. http://dx.doi.org/10.31274/cc-20240624-1331.

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Ladd, Neuner, and Olsen. PR-179-13207-R01 Variable Fuel Composition Air Fuel Ratio Control of Lean Burn Engines. Pipeline Research Council International, Inc. (PRCI), 2016. http://dx.doi.org/10.55274/r0010864.

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This research evaluates the effects of variable fuel quality on a large bore 2 stroke natural gas engine by varying ethane in the fuel gas from 5 to 25%. Four control strategies were evaluated at ~2 g/bhp-hr NOx, constant boost control, trapped gas ratio (TGR) control, trapped equivalence ratio (TER) control and a novel NOx sensor feedback control methodology. These control approaches were evaluated during variations in intake manifold temperature, relative humidity, ethane volume percentage, and engine speed. Emissions, combustion parameters, controller performance, and engine performance wer

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Kokpol, Sirirat. Relationships between biological activity and molecular properties of artemisinin compounds. Chulalongkorn University, 2001. https://doi.org/10.58837/chula.res.2001.38.

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Relationships between biological activity and molecular properties of 68 antimalarial artemisinin compounds were investigated in the quantitative manner. All compounds were geometrically optimized at the HF/3-21G level. Totally 102 molecular properties covering hydrophobicity, polarizability, electronic, and steric parameters were calculated from the optimized structures. The activities against 2 different strains of malarial parasites, D-6 and W-2, were taken from the literatures. Statistical analyses were performed to find the relationships between the activities and the calculated molecular

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Russo, David, Daniel M. Tartakovsky, and Shlomo P. Neuman. Development of Predictive Tools for Contaminant Transport through Variably-Saturated Heterogeneous Composite Porous Formations. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7592658.bard.

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The vadose (unsaturated) zone forms a major hydrologic link between the ground surface and underlying aquifers. To understand properly its role in protecting groundwater from near surface sources of contamination, one must be able to analyze quantitatively water flow and contaminant transport in variably saturated subsurface environments that are highly heterogeneous, often consisting of multiple geologic units and/or high and/or low permeability inclusions. The specific objectives of this research were: (i) to develop efficient and accurate tools for probabilistic delineation of dominant geol

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Magdalinos, Tassos, and Katerina Petrova. Uniform Inference with General Autoregressive Processes. Federal Reserve Bank of New York, 2025. https://doi.org/10.59576/sr.1151.

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A unified theory of estimation and inference is developed for an autoregressive process with root in (-∞, ∞) that includes the stationary, local-to-unity, explosive and all intermediate regions. The discontinuity of the limit distribution of the t-statistic outside the stationary region and its dependence on the distribution of the innovations in the explosive regions (-∞, -1) ∪ (1, ∞) are addressed simultaneously. A novel estimation procedure, based on a data-driven combination of a near-stationary and a mildly explosive artificially constructed instrument, delivers mixed-Gaussian limit theor

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