Relevant bibliographies by topics / Realistic Accuracy Model (RAM)

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

Academic literature on the topic 'Realistic Accuracy Model (RAM)'

Author: Grafiati
Published: 19 February 2023
Last updated: 31 July 2025

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Journal articles on the topic "Realistic Accuracy Model (RAM)"

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Kalsum, Tehmina, and Zahid Mehmood. "A Novel Lightweight Deep Convolutional Neural Network Model for Human Emotions Recognition in Diverse Environments." Journal of Sensors 2023 (August 5, 2023): 1–20. http://dx.doi.org/10.1155/2023/6987708.

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A picture is worth a thousand words to convey information during daily life communication. Recognizing a person’s emotions from facial expressions (FEs) has become a prevalent research field in the past few decades. Deep learning (DL) models, mainly deep convolutional neural networks (DCNNs), are trending in the last few years to perform recognition/classification tasks. Several prior DCNNs have resulted in good recognition accuracy for facial emotion recognition (FER) systems. Still, there is a need for an effective as well as efficient FER system that can recognize the FEs irrespective of th
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Kalsum, Tehmina, Zahid Mehmood, Farzana Kulsoom, et al. "Localization and classification of human facial emotions using local intensity order pattern and shape-based texture features." Journal of Intelligent & Fuzzy Systems 40, no. 5 (2021): 9311–31. http://dx.doi.org/10.3233/jifs-201799.

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Facial emotion recognition system (FERS) recognize the person’s emotions based on various image processing stages including feature extraction as one of the major processing steps. In this study, we presented a hybrid approach for recognizing facial expressions by performing the feature level fusion of a local and a global feature descriptor that is classified by a support vector machine (SVM) classifier. Histogram of oriented gradients (HoG) is selected for the extraction of global facial features and local intensity order pattern (LIOP) to extract the local features. As HoG is a shape-based
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Delfaure, Colin, Jean-Paul Mazellier, Nicolas Tranchant, Philippe Bergonzo, Pascal Ponard, and Samuel Saada. "Nanofocus diamond X-ray windows: Thermal modeling of nano-sized heat source systems." Diamond And Related Materials 59 (September 28, 2015): 104–15. https://doi.org/10.5281/zenodo.49966.

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An analytical thermal model is proposed to study heat transfers occurring at high power density in X-ray tubes with micron to submicron sized source. The use of a simple analytical approach instead of a complex numerical simulation allows readily modeling of more andmore challenging systems such as multi-source X-ray tubes. By significantly reducing the computing time, it enables a wider parameter range evaluation for engineering phase.We focused our work on tubes integrating a transmission window that is mainly edge-cooled. Our approach can be generally used in cylindrical lateral heat spread
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Middelburg, Cornelis. "Program Algebra for Random Access Machine Programs." Scientific Annals of Computer Science XXXII, no. 2 (2022): 285–319. http://dx.doi.org/10.7561/sacs.2022.2.285.

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This paper presents an algebraic theory of instruction sequences with instructions for a random access machine (RAM) as basic instructions, the behaviours produced by the instruction sequences concerned under execution, and the interaction between such behaviours and RAM memories. This theory provides a setting for the development of theory in areas such as computational complexity and analysis of algorithms that distinguishes itself by offering the possibility of equational reasoning to establish whether an instruction sequence computes a given function and being more general than the setting
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Middelburg, Cornelis. "Program Algebra for Random Access Machine Programs." Scientific Annals of Computer Science XXXII, no. 2 (2022): 285–319. https://doi.org/10.7561/SACS.2022.2.285.

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This paper presents an algebraic theory of instruction sequences with instructions for a random access machine (RAM) as basic instructions, the behaviours produced by the instruction sequences concerned under execution, and the interaction between such behaviours and RAM memories. This theory provides a setting for the development of theory in areas such as computational complexity and analysis of algorithms that distinguishes itself by offering the possibility of equational reasoning to establish whether an instruction sequence computes a given function and being more general than the setting
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HASAN, NADEEM, and SANJEEV SANGHI. "Proper orthogonal decomposition and low-dimensional modelling of thermally driven two-dimensional flow in a horizontal rotating cylinder." Journal of Fluid Mechanics 573 (February 2007): 265–95. http://dx.doi.org/10.1017/s0022112006003806.

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A proper orthogonal decomposition (POD) analysis and low-dimensional modelling of thermally driven two-dimensional flow of air in a horizontal rotating cylinder, subject to the Boussinesq approximation, is considered. The problem is unsteady due to the harmonic nature of the gravitational buoyancy force with respect to the rotating observer and is characterized by four dimensionless numbers: gravitational Rayleigh number (Rag), the rotational Rayleigh number (RaΩ), the Taylor number (Ta) and Prandtl number (Pr). The data for the POD analysis are obtained by numerical integration of the governi
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Bshouty, Nader H. "Lower Bounds for the Complexity of Functions in a Realistic RAM Model." Journal of Algorithms 32, no. 1 (1999): 1–20. http://dx.doi.org/10.1006/jagm.1998.0994.

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Börjesson Axén, Jenny, Henrik Ekström, Erika Widenkvist Zetterström, and Göran Lindbergh. "Building a P2D Model for the NiMH Battery." ECS Meeting Abstracts MA2023-02, no. 1 (2023): 52. http://dx.doi.org/10.1149/ma2023-02152mtgabs.

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For understanding a battery when working under realistic conditions, a dynamic battery model is a useful tool. One of the most known electrochemical model types for batteries is the Newman P2D model.[1] This model has a good balance between complexity and computational efficiency. Due to the level of complexity, using a P2D model allows us to make simulations to optimize battery design by adjusting parameters such as electrode porosity and tortuosity, electrode thickness, and overall cell thickness. But it can also be used to optimize battery management system (BMS) functionality. There are ma
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Cronin, MaryAnne, Nancy Dengler, Eugene S. Krauss, et al. "Completion of the Updated Caprini Risk Assessment Model (2013 Version)." Clinical and Applied Thrombosis/Hemostasis 25 (January 1, 2019): 107602961983805. http://dx.doi.org/10.1177/1076029619838052.

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The Caprini risk assessment model (RAM) has been validated in over 250 000 patients in more than 100 clinical trials worldwide. Ultimately, appropriate treatment options are dependent on precise completion of the Caprini RAM. As the numerical score increases, the clinical venous thromboembolism rate rises exponentially in every patient group where it has been properly tested. The 2013 Caprini RAM was completed by specially trained medical students via review of the presurgical assessment history, medical clearances, and medical consults. The Caprini RAM was completed for every participant both
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Zhang, Hengjian. "Sound-based animal recognition model." Applied and Computational Engineering 4, no. 1 (2023): 760–67. http://dx.doi.org/10.54254/2755-2721/4/2023420.

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Conservation of biological and biological diversity is the goal of joint efforts worldwide, and protecting wild creatures requires various monitoring equipment with identification capabilities. In this case, this article will introduce a model with animal call recognition ability designed using edge impulse. A two-layers convolutional neural network (CNN) is selected and trained with the input features generated by Mel Frequency Cepstral Coefficient, Spectrogram and Mel-Spectrogram. The model hoped not only to focus on the recognition accuracy rate of the model but also on the inferencing time
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Dissertations / Theses on the topic "Realistic Accuracy Model (RAM)"

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Wiemers, Melissa. "Making It Up: Exploring How Personality Judges Construct Ratings." Thesis, Griffith University, 2014. http://hdl.handle.net/10072/365927.

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The aim of this thesis was to investigate the processes involved in constructing a personality rating: specifically, what judges attend to and use when rating personality. In doing so, this thesis addressed the call for research on factors that contribute to personality ratings, which was originally posed by Webb (1915). A key model for addressing this question and which is consequently integrated throughout this research is Funder’s (1995) realistic accuracy model (RAM). According to RAM, the accuracy of personality judgements is affected by the relevance, availability, detection, and utilisa
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Book chapters on the topic "Realistic Accuracy Model (RAM)"

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Grandjean, Etienne, and J. M. Robson. "RAM with compact memory: a realistic and robust model of computation." In Computer Science Logic. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-54487-9_60.

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Bshouty, Nader H. "Lower bounds for the complexity of functions in a realistic RAM model." In Theory of Computing and Systems. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/bfb0035162.

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Bertrand, Ambre, Carolyna Yamamoto, Giulia Monopoli, et al. "Augmentation of Cardiac Ischemic Geometry for Improving Machine Learning Performance in Arrhythmic Risk Stratification." In Computational Physiology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53145-3_3.

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AbstractVentricular arrhythmias frequently occur as a complication of myocardial infarction (MI), due to significant changes in the heart’s structure and electrophysiology. If left untreated, these alterations may lead to sudden cardiac death (SCD). It is therefore critical to evaluate risk prediction accurately in post-infarction patients to enable early intervention and improve patient outcomes. This work introduces a novel approach to improve arrhythmia risk assessment in post-infarction patients. We propose a new pipeline to build physiologically realistic image-based models of patient hea
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Lestingi, Livia. "Model-Driven Development of Formally Verified Human-Robot Interactions." In Special Topics in Information Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51500-2_4.

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AbstractIntroducing service robots into everyday settings entails a significant technological shift for the robotics community. Service settings are characterized by critical sources of uncertainty (mainly due to human behavior) that current software engineering techniques do not handle. This chapter introduces a model-driven framework for developing interactive service robotic scenarios, relying on formal verification to guarantee robustness with respect to unexpected runtime contingencies. Target users specify the characteristics of the scenario under analysis through a custom textual Domain
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Keser, Ahmet Esat, and Onur Behzat Tokdemir. "Machine Learning-Based Construction Planning and Forecasting Model." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.71.

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Construction planning and scheduling are crucial aspects of project management that require a lot of time and resources to manage effectively. Machine learning and artificial intelligence techniques have shown great potential in improving construction planning and scheduling by providing more accurate insights into project progress and forecasting. This paper proposed a machine learning model that utilizes regularly updated site data to generate predictions of quantity variances from the plan and enable a more accurate forecast of future progress based on historical data on concrete activities
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Keser, Ahmet Esat, and Onur Behzat Tokdemir. "Machine Learning-Based Construction Planning and Forecasting Model." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.71.

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Construction planning and scheduling are crucial aspects of project management that require a lot of time and resources to manage effectively. Machine learning and artificial intelligence techniques have shown great potential in improving construction planning and scheduling by providing more accurate insights into project progress and forecasting. This paper proposed a machine learning model that utilizes regularly updated site data to generate predictions of quantity variances from the plan and enable a more accurate forecast of future progress based on historical data on concrete activities
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Särestöniemi, Mariella, Attaphongse Taparugssanagorn, Jari Iinatti, and Teemu Myllylä. "Detection of Intestinal Tumors Outside the Visibility of Capsule Endoscopy Camera Utilizing Radio Signal Recognition." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_28.

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AbstractEarly cancer detection is crucial, especially for intestinal cancer with subtle early symptoms. While camera-based Wireless Capsule Endoscopy (WCE) systems are efficient, patient-friendly, and safe investigating gastrointestinal (GI) track thoroughly, some limitations persist in visualizing only the inner part of the GI regions. Our study introduces a radio channel analysis -based approach to detect intestinal/abdominal tumors which are not visible for the WCE camera, i.e., the tumors which have started to grow on the outer parts of the intestinal track. Focused on S-parameter patterns
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Feng, Y., Q. Wang, D. Wu, and W. Gao. "Machine Learning-Aided Nonlinear Dynamic Analysis of Engineering Structures." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3330-3_36.

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AbstractA machine learning (ML) technique was used to assist in the dynamic analysis of mixed geometric and material nonlinearities of real-life engineering structures. Various types of inputs of system properties were considered in the 3D dynamic geometric elastoplastic analysis, giving a series of realistic nonlinear descriptions of complex, large deformation structural behaviors. To resolve the numerical challenges of solving the mixed nonlinear problems, a newly established ML technique using a new cluster-based extended support vector regression (X-SVR) algorithm was applied. With this te
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Cao, Qun, and Lin Qiu. "Research on Atmospheric Dispersion Factor Used in the Calculation of Emergency Planning Zone." In Springer Proceedings in Physics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1023-6_24.

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AbstractReasonable division of emergency planning zones is an important technical basis for emergency preparedness and responses. In order to evaluate the size of the emergency planning zone, it’s important to determine the appropriate atmospheric dispersion factor based on the realistic site conditions to calculate the dose consequences caused by the accident through the radioactive plume. This paper introduces the current progress in the calculation of emergency planning zoning for different reactor nuclear power plant sites, the general method of determining the emergency planning zone in C
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Hager, Lukas, and Tobias Kuen. "Optimization of Underground Train Systems." In Unlocking Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_16.

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AbstractThis chapter presents two approaches for enhancing the sustainability and efficiency of underground train systems. The first approach focuses on the optimization of DC railway power systems, employing a novel Mixed-Integer Quadratically Constrained Quadratic Program (MIQCQP) to control substation feed-in voltages effectively. By minimizing energy losses, this optimization approach demonstrates substantial potential for cost and emission reduction, contributing to a more energy-efficient underground train network. Validation results confirm the accuracy of the proposed model, and realis
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Conference papers on the topic "Realistic Accuracy Model (RAM)"

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Lopera, Maria J., Jorge Gracía-Sucerquia, Yunfeng Nie, Heidi Ottevaere, and Carlos Trujillo. "Simulating digital lensless holographic microscopy holograms through a realistic model." In Digital Holography and Three-Dimensional Imaging. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/dh.2024.tu2b.3.

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This work introduces a realistic modeling approach for Digital Lensless Holographic Microscopy (DLHM) by decomposing the complex light propagation with several stratightfoward steps. Experimental results show that this realistic model outperforms other methods with higher reconstruction accuracy and low computational cost.
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Jia, Qinlong, and Dong Xu. "The role evaluation of the biaxial tension stiffening of rebars and crack behaviors on the performance of low-reinforced concrete panels." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.2672.

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<p>The study of RC panels underpins numerous standards regarding shear theory for beams, including AASHTO and the Fib Model Code 2010. However, existing models rely on empirical calibration of panel tests with complex loading conditions, which are often not universally applicable. In particular, the computational accuracy for low-reinforced panels is typically inadequate. This study proposes a modified cracked membrane model (MCMM) that incorporates a more realistic bond-slip law with fewer simplifications and introduces a biaxial tension stiffening model for rebars. Additionally, the cr
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Stevenson, A. "An Overview of the Requirements for Seal Life Prediction." In CORROSION 1992. NACE International, 1992. https://doi.org/10.5006/c1992-92065.

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Abstract Accurate prediction of the life of elastomeric seals is a difficult but essential goal for their use in critical applications where replacement may be prohibitively expensive and premature failure may be catastrophic. An international joint industry project has been underway since 1989 supported by a consortium of companies involved in end use, equipment manufacture, seal manufacture and raw materials manufacture. Work on the project has been carried out by a project team formed jointly by MERL and BHRG. This project takes a new look at the whole problem of elastomer seal life predict
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Mousavi, Mahdi, and Ville Alopaeus. "Redefining Stage Efficiency in Liquid-Liquid Extraction: Development and Application of a Modified Murphree Efficiency." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.174222.

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Liquid-liquid extraction stages often deviate from equilibrium due to factors like insufficient mixing, making accurate efficiency modeling essential for process simulation. This study addresses the limitations of Aspen Plus (AP), which distorts equilibrium calculations by directly multiplying efficiency with the distribution coefficient. A modified Murphree efficiency definition, more suitable for liquid-liquid systems but absent in AP's Extraction Column module, was implemented using Aspen Custom Modeler (ACM). The custom multi-stage extraction column model replaces mole fractions with mole
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Tran, Phuc M., Eric G. O'Neill, and Christos T. Maravelias. "Methods for Efficient Solutions of Spatially Explicit Biofuels Supply Chain Models." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.133228.

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The growing size and complexity of energy system optimization models, driven by high-resolution spatial data, pose significant computational challenges. This study introduces methods to reduce model�s size and improve computational efficiency while preserving solution accuracy. First, a composite-curve-based approach is proposed to aggregate granular data into larger resolutions without averaging out specific properties. Second, a general clustering method groups geographically proximate fields, replacing multiple transportation arcs with a single arc to reduce transportation-related variables
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Lin, Zhihui, Chun Yuan, and Maomao Li. "HAF-SVG: Hierarchical Stochastic Video Generation with Aligned Features." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/138.

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Stochastic video generation methods predict diverse videos based on observed frames, where the main challenge lies in modeling the complex future uncertainty and generating realistic frames. Numerous of Recurrent-VAE-based methods have achieved state-of-the-art results. However, on the one hand, the independence assumption of the variables of approximate posterior limits the inference performance. On the other hand, although these methods adopt skip connections between encoder and decoder to utilize multi-level features, they still produce blurry generation due to the spatial misalignment betw
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Chabot, Samuel, Wendy Lee, Rebecca Elder, and Jonas Braasch. "Using a Multimodal Immersive Environment to Investigate Perceptions in Augmented Virtual Reality Systems." In The 24th International Conference on Auditory Display. The International Community for Auditory Display, 2018. http://dx.doi.org/10.21785/icad2018.014.

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The Collaborative-Research Augmented Immersive Virtual Environment Laboratory at Rensselaer is a state-of-the-art space that offers users the capabilities of multimodality and immersion. Realistic and abstract sets of data can be explored in a variety of ways, even in large group settings. This paper discusses the motivations of the immersive experience and the advantages over smaller scale and single-modality expressions of data. One experiment focuss on the influence of immersion on perceptions of architectural renderings. Its findings suggest disparities between participants’ judgment when
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Kang, Rongjie, Zongxia Jiao, Shuai Wu, Yaoxing Shang, and Jean-Charles Mare. "The Nonlinear Accuracy Model of Electro-Hydrostatic Actuator." In 2008 IEEE Conference on Robotics, Automation and Mechatronics (RAM). IEEE, 2008. http://dx.doi.org/10.1109/ramech.2008.4681367.

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Hauke, Guillermo, Daniel Fuster, and Cesar Dopazo. "Coupled Heat Transfer Phenomena in Cavitating Bubble Dynamics." In ASME 2005 Summer Heat Transfer Conference collocated with the ASME 2005 Pacific Rim Technical Conference and Exhibition on Integration and Packaging of MEMS, NEMS, and Electronic Systems. ASMEDC, 2005. http://dx.doi.org/10.1115/ht2005-72803.

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Cavitation is an incipient technique that finds application in advanced medicine, engineering and chemistry, where the high temperature and pressure of the cavitating bubbles are used as micro-reactors. However, present models to study the dynamics of cavitating bubbles have ignored important aspects of the transport phenomena, such as the actual gas and liquid thermal boundary layers across the bubble interphase. This leads to gross inaccuracies in estimating the heat transfer between the liquid and the gas, and to wrong interphase temperatures. In order to depth the understanding of this com
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Omeke, James. "Advances in Virtual Flow Metering Using Deep Composite Lstm-Autoencoder Network for Gas-Condensate Wells." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22524-ms.

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Abstract In terms of cost and execution time, data-driven Virtual Flow Meters (VFM) are alternative solutions to traditional well testing (WT) and physical multiphase flow meters (MPFM) for production rate determination which is needed for critical decisions by operators but faced with the challenge of low accuracy due to the transient and dynamic state of multiphase flow systems. Recently, some progress has been recorded by training steady state feed-forward neural networks to learn to approximate production rate based on certain number of input features (e.g., choke opening, pressure and tem
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Reports on the topic "Realistic Accuracy Model (RAM)"

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Chen, Weixing. PR378-173601-Z01 Effect of Pressure Fluctuations on the Growth Rate of Near-Neutral pH SCC. Pipeline Research Council International, Inc. (PRCI), 2021. http://dx.doi.org/10.55274/r0012112.

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This report summarizes the work completed in PRCI SCC-2-12A project: The Effect of Pressure Fluctuations on the Growth Rate of Near-Neutral pH SCC, which is Phase 3 of the work on the same subject of investigation. The following insights from the current phase of the PRCI SCC-2-12A project are thought to be the most important: - Near neutral pH crack initiation is pressure-fluctuation dependent. Severe pressure fluctuations accelerate the fracture and spallation of mill scale on the pipeline steel surfaces, making it harder to initiate SCC cracks from the bottom of pits that are developed at f
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