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Journal articles on the topic 'Energy-Accuracy Scaling'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

Raquibuzzaman, Md, Aleksandar Milenkovic, and Biswajit Ray. "EXPRESS: Exploiting Energy–Accuracy Tradeoffs in 3D NAND Flash Memory for Energy-Efficient Storage." Electronics 11, no. 3 (2022): 424. http://dx.doi.org/10.3390/electronics11030424.

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The density and cost-effectiveness of flash memory chips continue to increase, driven by: (a) The continuous physical scaling of memory cells in a single layer; (b) The vertical stacking of multiple layers; and (c) Logical scaling through storing multiple bits of information in a single memory cell. The physical properties of flash memories impose disproportionate latency and energy expenditures to ensure the high integrity of the data during flash memory writes. This paper experimentally explores this disproportionality on state-of-the-art commercial 3D NAND flash memories and introduces EXPR

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Jeong, Jonghyun, and Youngmin Kim. "ASAD-RD: Accuracy Scalable Approximate Divider Based on Restoring Division for Energy Efficiency." Electronics 10, no. 1 (2020): 31. http://dx.doi.org/10.3390/electronics10010031.

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Approximate computing can considerably improve energy efficiency by mitigating the accuracy requirements of calculations in error resilient application programming, such as machine learning, audio–video signal processing, data mining, and search engines. In this study, we propose an approximate divider for dynamic energy-quality scaling, which involves a trade-off between accuracy and latency. Previous approximate dividers for dynamic energy-quality scaling are well-configured, but lack energy-quality scalability. The key is to create a more accurate dynamic approximate divider while extending

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Ghedini, Giulia, Martino E. Malerba, and Dustin J. Marshall. "How to estimate community energy flux? A comparison of approaches reveals that size-abundance trade-offs alter the scaling of community energy flux." Proceedings of the Royal Society B: Biological Sciences 287, no. 1933 (2020): 20200995. http://dx.doi.org/10.1098/rspb.2020.0995.

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Size and metabolism are highly correlated, so that community energy flux might be predicted from size distributions alone. However, the accuracy of predictions based on interspecific energy–size relationships relative to approaches not based on size distributions is unknown. We compare six approaches to predict energy flux in phytoplankton communities across succession: assuming a constant energy use among species (per cell or unit biomass), using energy–size interspecific scaling relationships and species-specific rates (both with or without accounting for density effects). Except for the per

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Lastovetsky, Alexey, and Ravi Reddy Manumachu. "Energy-Efficient Parallel Computing: Challenges to Scaling." Information 14, no. 4 (2023): 248. http://dx.doi.org/10.3390/info14040248.

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The energy consumption of Information and Communications Technology (ICT) presents a new grand technological challenge. The two main approaches to tackle the challenge include the development of energy-efficient hardware and software. The development of energy-efficient software employing application-level energy optimization techniques has become an important category owing to the paradigm shift in the composition of digital platforms from single-core processors to heterogeneous platforms integrating multicore CPUs and graphics processing units (GPUs). In this work, we present an overview of

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Zhang, Junzhu, Yating Hou, and Liming Wang. "Scaling-basis chirplet extracting transform and its application in bearing fault diagnosis." PLOS ONE 20, no. 4 (2025): e0319497. https://doi.org/10.1371/journal.pone.0319497.

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In this paper, we propose a new time-frequency analysis (TFA) method, namely scaling-basis chirplet extracting transform (SBCET). Based on the time-frequency representation (TFR) results obtained by scaling-basis chirplet transform (SBCT), the method introduces a new “extraction operator” to extract the time-frequency (TF) energy associated with the signal to portray the TF energy distribution information of the signal with high accuracy. SBCET can also obtain a TFR with concentrated energy and high resolution for non-stationary signals with close frequency intervals and intense background noi

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Blažek, Mikuláš. "Multifractality in High Energy Collisions." Fractals 05, no. 02 (1997): 309–20. http://dx.doi.org/10.1142/s0218348x97000292.

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With increasing energy of nuclear collisions, several statistical distributions of produced particles show changes in shape. This also concerns the scaling indices which characterize multifractality in the observed particle density distributions. In the present contribution, the self-similar processes governing that multifractality are described in more detail. It is shown especially that the corresponding extended fundamental equation reproduces, with very good accuracy, the data resulting from the oxygen beam at 60 and 200 A GeV colliding with the emulsion nuclei. The approximate description

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Cozzolino, Ersilia, Gabriela Del Risco, Natalia von Windheim, Cameron Gygi, Antonello Astarita, and Nathan Ames. "Scaling up the 3D printing of surgical guides: repeatability and energy efficiency." Rapid Prototyping Journal 31, no. 11 (2025): 148–59. https://doi.org/10.1108/rpj-05-2024-0226.

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Purpose This paper aims to investigate the repeatability of the Stereolithography (SLA) process and to provide reliable guidelines to minimize energy consumption without sacrificing part quality. To tackle these aims, an extensive experimental campaign has been conceived: two different machines (of the same model) have been operated in different production environment, specimens and parts with different orientations have been manufactured. Design/methodology/approach A two-step experimental campaign has been carried out. In the first stage, surgical guides were printed with different positions

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Gebauer, Ralph, Morrel H. Cohen, and Roberto Car. "A well-scaling natural orbital theory." Proceedings of the National Academy of Sciences 113, no. 46 (2016): 12913–18. http://dx.doi.org/10.1073/pnas.1615729113.

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We introduce an energy functional for ground-state electronic structure calculations. Its variables are the natural spin-orbitals of singlet many-body wave functions and their joint occupation probabilities deriving from controlled approximations to the two-particle density matrix that yield algebraic scaling in general, and Hartree–Fock scaling in its seniority-zero version. Results from the latter version for small molecular systems are compared with those of highly accurate quantum-chemical computations. The energies lie above full configuration interaction calculations, close to doubly occ

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DOLGOPOLOV, M. V., M. V. ELISOV, S. A. RADZHAPOV, V. I. CHEPURNOV, and A. S. CHIPURA. "EFFICIENCY OF ACTIVATED NANO-HETEROJUNCTIONS ON SILICON AND SILICON CARBIDE SUBSTRATES." Computational nanotechnology 10, no. 4 (2023): 91–102. http://dx.doi.org/10.33693/2313-223x-2023-10-4-91-102.

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The concept, model, and examples of activated nanoscale heterojunctions on special silicon carbide and silicon substrates to ensure maximum power with combined geometric and quantitative scaling of semiconductor energy converter chips are considered. The issue of maximum efficiency of energy conversion and efficiency of separation of electron-hole pairs is investigated. A variant of optimizing the scaling solution is implemented by heterojunctions with variations in the sequences of layers with an increase in the concentration and direction of movement of nonequilibrium carriers for further in

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Mikhalev, A., M. Nottoli, and B. Stamm. "Linearly scaling computation of ddPCM solvation energy and forces using the fast multipole method." Journal of Chemical Physics 157, no. 11 (2022): 114103. http://dx.doi.org/10.1063/5.0104536.

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This paper proposes the first linear scaling implementation for the domain decomposition approach of the polarizable continuum model (ddPCM) for the computation of the solvation energy and forces. The ddPCM-equation consists of a (non-local) integral equation on the van der Waals or solvent accessible surface of the solute’s cavity resulting in a dense solution matrix, and, in turn, one matrix–vector multiplication has a quadratic arithmetic complexity with respect to the number of atoms of the solute molecule. The use of spherical harmonics as basis functions makes it natural to employ the fa

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Gebrelibanos, Kalekirstos G., Kaspar Vereide, Sirak A. Weldemariam, Asli Bor, Asfafaw H. Tesfay, and Leif Lia. "Hydraulic Scale Modeling of Pressurized Sediment Laden Flow." Water 17, no. 13 (2025): 1970. https://doi.org/10.3390/w17131970.

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In hydropower tunnel systems, unlined pressurized tunnels in competent rock are commonly used for cost-effective construction. Incorporating pressurized sand traps at the downstream end of these tunnels can increase plant capacity and improve energy efficiency. The present work focuses on optimizing the performance of existing pressurized sand traps. Hydraulic scale models were developed and tested at the Hydraulic Laboratory of NTNU, Within the 960 MW Tonstad Hydropower Plant in southern Norway as a case study. This study compares 1:1 velocity/sediment scaling with Froude scaling through phys

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Zhang, Shuhao, Huimin Zhang, Ying Zhan, Xiaokai Wei, and Yang Liu. "Cluster Networking and Cooperative Localization Based on Biogeography Optimization and Improved Super-Multidimensional Scaling for Multi-Unmanned Aerial Vehicles." Sensors 25, no. 9 (2025): 2887. https://doi.org/10.3390/s25092887.

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The cooperative localization of Unmanned Aerial Vehicles (UAVs) has emerged as a pivotal application in Internet of Things (IoT) tasks. However, the frequent exchange of localization data among UAVs leads to significant energy consumption and escalates the computational complexity involved in multi-UAV cooperative localization tasks. To address these challenges, this paper proposes a cooperative localization algorithm that integrates a biogeography optimization-based cluster networking and adaptive sampling-improved Nystrom super-multidimensional scaling (BOCN-ASNSMS). The proposed method leve

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Zhang, Dengyong, Xiao Chen, Feng Li, Arun Kumar Sangaiah, and Xiangling Ding. "Seam-Carved Image Tampering Detection Based on the Cooccurrence of Adjacent LBPs." Security and Communication Networks 2020 (December 21, 2020): 1–12. http://dx.doi.org/10.1155/2020/8830310.

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Seam carving has been widely used in image resizing due to its superior performance in avoiding image distortion and deformation, which can maliciously be used on purpose, such as tampering contents of an image. As a result, seam-carving detection is becoming crucially important to recognize the image authenticity. However, existing methods do not perform well in the accuracy of seam-carving detection especially when the scaling ratio is low. In this paper, we propose an image forensic approach based on the cooccurrence of adjacent local binary patterns (LBPs), which employs LBP to better disp

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Suijs, Ward, and Sebastian Verhelst. "Scaling Performance Parameters of Reciprocating Engines for Sustainable Energy System Optimization Modelling." Energies 16, no. 22 (2023): 7497. http://dx.doi.org/10.3390/en16227497.

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The increased share of variable renewable energy sources such as wind and solar power poses constraints on the stability of the grid and the security of supply due to the imbalance between electricity production and demand. Chemical storage or power-to-X technologies can provide the flexibility that is needed to overcome this issue. To quantify the needs of such storage systems, energy system optimization models (ESOMs) are used, guiding policy makers in nationwide energy planning. The key input parameters for such models are the capacity and efficiency values of the conversion devices. Gas tu

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Hassan, Salma, Sameh Attia, Khaled Nabil Salama, and Hassan Mostafa. "EANN: Energy Adaptive Neural Networks." Electronics 9, no. 5 (2020): 746. http://dx.doi.org/10.3390/electronics9050746.

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This paper proposes an Energy Adaptive Feedforward Neural Network (EANN). It uses multiple approximation techniques in the hardware implementation of the neuron unit. The used techniques are precision scaling, approximate multiplier, computation skipping, neuron skipping, activation function approximation and truncated accumulation. The proposed EANN system applies the partial dynamic reconfiguration (PDR) feature supported by the FPGA platform to reconfigure the hardware elements of the neural network based on the energy budget. The PDR technique enables the EANN system to remain functioning

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Chafid, Much, Abdul Syukur, Moch Arief Soeleman, and Affandy Affandy. "Seam Cerving and Salient Detection for Thumbnail Photos." Journal of Development Research 6, no. 1 (2022): 16–21. http://dx.doi.org/10.28926/jdr.v6i1.204.

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Image resizing is a process of processing images or images with the aim of changing the size of the image. The most commonly used methods are cropping or scaling. Scaling is changing the size of the image based on the scale. Contents in the image are not considered in scaling. Seam carving often uses energy functionality that is useful as a determinant of the pixel level contained in an image. Seam is a connecting path of image pixels both vertically and horizontally that is passed by a low energy function. Changing the image size using seam carving is considered better than cropping and scali

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Wright, Michael Dennis, Francesco Gambioli, and Arnaud George Malan. "CFD Based Non-Dimensional Characterization of Energy Dissipation Due to Verticle Slosh." Applied Sciences 11, no. 21 (2021): 10401. http://dx.doi.org/10.3390/app112110401.

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We present the CFD based non-dimensional characterization of violent slosh induced energy dissipation due a tank under vertical excitation. Experimentally validated CFD is used for this purpose as an ideally suited and versatile tool. It is thus first demonstrated that a weakly compressible VoF based CFD scheme is capable of computing violent slosh induced energy dissipation with high accuracy. The resulting CFD based energy analysis further informs that the main source of energy dissipation during violent slosh is due liquid impact. Next, a functional relationship characterising slosh induced

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Brissaud, Quentin, and Victor C. Tsai. "Validation of a fast semi-analytic method for surface-wave propagation in layered media." Geophysical Journal International 219, no. 2 (2019): 1405–20. http://dx.doi.org/10.1093/gji/ggz351.

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SUMMARY Green’s functions provide an efficient way to model surface-wave propagation and estimate physical quantities for near-surface processes. Several surface-wave Green’s function approximations (far-field, no mode conversions and no higher mode surface waves) have been employed for numerous applications such as estimating sediment flux in rivers, determining the properties of landslides, identifying the seismic signature of debris flows or to study seismic noise through cross-correlations. Based on those approximations, simple empirical scalings exist to derive phase velocities and amplit

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Geng, Chenhua, Hong-Ye Hu, and Yijian Zou. "Differentiable programming of isometric tensor networks." Machine Learning: Science and Technology 3, no. 1 (2022): 015020. http://dx.doi.org/10.1088/2632-2153/ac48a2.

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Abstract Differentiable programming is a new programming paradigm which enables large scale optimization through automatic calculation of gradients also known as auto-differentiation. This concept emerges from deep learning, and has also been generalized to tensor network optimizations. Here, we extend the differentiable programming to tensor networks with isometric constraints with applications to multiscale entanglement renormalization ansatz (MERA) and tensor network renormalization (TNR). By introducing several gradient-based optimization methods for the isometric tensor network and compar

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CASTRO, FRANCISCO JOSÉ BRITO. "DISPERSION ANALYSIS OF AN ENERGY-ORTHOGONAL THIN PLATE FINITE ELEMENT." International Journal of Computational Methods 08, no. 03 (2011): 425–62. http://dx.doi.org/10.1142/s0219876211002642.

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This paper studies the propagation of plane harmonic waves in thin plates discretized by using a finite element where the stiffness matrix and the elastic energy are split into basic and higher-order components. These ones are obtained from constant and deviatoric curvature fields, respectively. Based on the properties of the higher-order energy, four reference frequencies are defined. Depending on the desired precision, one of those values is selected as optimum cutoff frequency to properly capture a wave field. A scaling coefficient multiplying the higher-order stiffness is updated in order

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Shen, Zichao, Neil Howard, and Jose Nunez-Yanez. "Big–Little Adaptive Neural Networks on Low-Power Near-Subthreshold Processors." Journal of Low Power Electronics and Applications 12, no. 2 (2022): 28. http://dx.doi.org/10.3390/jlpea12020028.

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This paper investigates the energy savings that near-subthreshold processors can obtain in edge AI applications and proposes strategies to improve them while maintaining the accuracy of the application. The selected processors deploy adaptive voltage scaling techniques in which the frequency and voltage levels of the processor core are determined at the run-time. In these systems, embedded RAM and flash memory size is typically limited to less than 1 megabyte to save power. This limited memory imposes restrictions on the complexity of the neural networks model that can be mapped to these devic

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Hu, Cheng, and Yuhui Deng. "Extracting a Credible Hint of Response Time to Scale Resources in Elastic Clusters." Journal of Circuits, Systems and Computers 29, no. 13 (2020): 2050211. http://dx.doi.org/10.1142/s0218126620502114.

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In elastic clusters, the service resources (or called “resources” for short) can be dynamically scaled, thus providing opportunities to cut down the energy cost of redundant resources. Generally, taking into account the Quality of Service (QoS) of clusters, resources are carefully scaled according to specific hints which are some features of system status. However, when the Service Quality Requirement (SQR) is referred to the response time of requests, some widely used features cannot well reflect the status of the QoS. Consequently, the QoS cannot be well maintained, and the energy-saving eff

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Brockmann, Susanne, and Tim Schlippe. "Optimizing Convolutional Neural Networks for Image Classification on Resource-Constrained Microcontroller Units." Computers 13, no. 7 (2024): 173. http://dx.doi.org/10.3390/computers13070173.

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Running machine learning algorithms for image classification locally on small, cheap, and low-power microcontroller units (MCUs) has advantages in terms of bandwidth, inference time, energy, reliability, and privacy for different applications. Therefore, TinyML focuses on deploying neural networks on MCUs with random access memory sizes between 2 KB and 512 KB and read-only memory storage capacities between 32 KB and 2 MB. Models designed for high-end devices are usually ported to MCUs using model scaling factors provided by the model architecture’s designers. However, our analysis shows that

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Träbert, Elmar. "Atomic Lifetime Data and Databases." Atoms 10, no. 2 (2022): 46. http://dx.doi.org/10.3390/atoms10020046.

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Atomic-level lifetimes span a wide range, from attoseconds to years, relating to transition energy, multipole order, atomic core charge, relativistic effects, perturbation of atomic symmetries by external fields, and so on. Some parameters permit the application of simple scaling rules, others are sensitive to the environment. Which results deserve to be tabulated or stored in atomic databases? Which results require high accuracy to give insight into details of the atomic structure? Which data may be useful for the interpretation of plasma experiments or astrophysical observations without any

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Bahman, Shafie, and Hamidreza Zareipour. "Long-Term Multi-Resolution Probabilistic Load Forecasting Using Temporal Hierarchies." Energies 18, no. 11 (2025): 2908. https://doi.org/10.3390/en18112908.

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Accurate long-term electricity load forecasting is critical for energy planning, infrastructure development, and risk management, especially under increasing uncertainty from climate and economic shifts. This study proposes a multi-resolution probabilistic load forecasting framework that leverages temporal hierarchies to generate coherent forecasts at hourly, daily, monthly, and yearly levels. The model integrates climate and economic indicators and employs tailored forecasting techniques at each resolution, including XGBoost and ARIMAX. Initially incoherent forecasts across time scales are re

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Skalidis, R., J. Sternberg, J. R. Beattie, V. Pavlidou, and K. Tassis. "Why take the square root? An assessment of interstellar magnetic field strength estimation methods." Astronomy & Astrophysics 656 (December 2021): A118. http://dx.doi.org/10.1051/0004-6361/202142045.

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Context. The magnetic field strength in interstellar clouds can be estimated indirectly from measurements of dust polarization by assuming that turbulent kinetic energy is comparable to the fluctuating magnetic energy, and using the spread of polarization angles to estimate the latter. The method developed by Davis (1951, Phys. Rev., 81, 890) and by Chandrasekhar and Fermi (1953, ApJ, 118, 1137) (DCF) assumes that incompressible magnetohydrodynamic (MHD) fluctuations induce the observed dispersion of polarization angles, deriving B ∝ 1∕δθ (or, equivalently, δθ ∝ MA, in terms of the Alfvénic Ma

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Paul, Ankita, Md Abu Saleh Tajin, Anup Das, William M. Mongan, and Kapil R. Dandekar. "Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants." Electronics 11, no. 5 (2022): 682. http://dx.doi.org/10.3390/electronics11050682.

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Precise monitoring of respiratory rate in premature newborn infants is essential to initiating medical interventions as required. Wired technologies can be invasive and obtrusive to the patients. We propose a deep-learning-enabled wearable monitoring system for premature newborn infants, where respiratory cessation is predicted using signals that are collected wirelessly from a non-invasive wearable Bellypatch put on the infant’s body. We propose a five-stage design pipeline involving data collection and labeling, feature scaling, deep learning model selection with hyperparameter tuning, model

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Belleflamme, Fabian, Anna-Sophia Hehn, Marcella Iannuzzi, and Jürg Hutter. "A variational formulation of the Harris functional as a correction to approximate Kohn–Sham density functional theory." Journal of Chemical Physics 158, no. 5 (2023): 054111. http://dx.doi.org/10.1063/5.0122671.

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Accurate descriptions of intermolecular interactions are of great importance in simulations of molecular liquids. We present an electronic structure method that combines the accuracy of the Harris functional approach with the computational efficiency of approximately linear-scaling density functional theory (DFT). The non-variational nature of the Harris functional has been addressed by constructing a Lagrangian energy functional, which restores the variational condition by imposing stationarity with respect to the reference density. The associated linear response equations may be treated with

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Green, Sheldon, David L. Cochrane, and Donald G. Truhlar. "Accuracy of the energy‐corrected sudden (ECS) scaling procedure for rotational excitation of CO by collisions with Ar." Journal of Chemical Physics 84, no. 7 (1986): 3865–69. http://dx.doi.org/10.1063/1.450097.

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Onizawa, Naoya, Daisaku Katagiri, Kazumichi Matsumiya, Warren J. Gross, and Takahiro Hanyu. "An Accuracy/Energy-Flexible Configurable Gabor-Filter Chip Based on Stochastic Computation With Dynamic Voltage–Frequency–Length Scaling." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 8, no. 3 (2018): 444–53. http://dx.doi.org/10.1109/jetcas.2018.2844329.

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Maheshkumar, Kiruthick Sundakaraipudur. "IoT-Integrated Smart Energy Meter for Industrial Energy Monitoring." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41416.

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- Electric energy monitoring in industrial loads like pressure die casting is extremely vital especially due to their energy handling requirements. This work details the conception and implementation of an Internet of Things based Smart Energy Meter designed specifically for Industrial purposes. The system can measure three-phase voltage, current (up to 100A) and furnace temperatures with the aid of Resistance Temperature detectors. Using the ESP32 microcontroller and the Blynk Internet of things platform, the meter enables the collection, storage, and displaying of the data on the Cloud in re

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Jia, Yuru, Ruiliang Zhang, Xue Lv, Tao Zhang, and Zhengwu Fan. "Research on Temperature Control of Fuel-Cell Cooling System Based on Variable Domain Fuzzy PID." Processes 10, no. 3 (2022): 534. http://dx.doi.org/10.3390/pr10030534.

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To ensure the energy conversion efficiency of a proton-exchange membrane fuel cell (PEMFC), it is necessary to establish a water-cooled cooling system to keep the inlet temperature of fuel-cell coolant and the temperature difference between the inlet and outlet temperature within the set range. First, a semi-empirical and semi-mechanism model was built in Simulink. Then, a variable-universe fuzzy PID controller was designed to adjust the quantization factor and scaling factor by scaling factor α1, α2 and β to improve the accuracy of the control results. Finally, the cooling system model with c

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Salvati, Laura, Marian Douspis, and Nabila Aghanim. "Impact of systematics on cosmological parameters from future galaxy cluster surveys." Astronomy & Astrophysics 643 (October 27, 2020): A20. http://dx.doi.org/10.1051/0004-6361/202038465.

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Galaxy clusters are a recent cosmological probe. The precision and accuracy of the cosmological parameters inferred from these objects are affected by the knowledge of cluster physics, entering the analysis through the mass-observable scaling relations, and the theoretical description of their mass and redshift distribution, modelled by the mass function. In this work we forecast the impact of different modelling of these ingredients for clusters detected by future optical and near-IR surveys. We consider the standard cosmological scenario and the case with a time-dependent equation of state f

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Chuang, Man-Tsai, Yi-Hua Liu, and Song-Pei Ye. "A Novel Variable Step Size Incremental Conductance Method with an Adaptive Scaling Factor." Applied Sciences 10, no. 15 (2020): 5214. http://dx.doi.org/10.3390/app10155214.

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In this paper, a novel variable step size (VSS) incremental conductance (INC) method with an adaptive scaling factor is proposed. The proposed technique utilizes the model-based state estimation method to calculate the irradiance level and then determine an appropriate scaling factor accordingly to enhance the capability of maximum power point tracking (MPPT). The fast and accurate tracking can be achieved by the presented method without the need for extra irradiance and temperature sensors. Only the voltage-and-current sets of any two operating points on the characteristic curve are needed to

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Parts, Egert-Ronald, Ergo Pikas, Tuule Mall Parts, Endrik Arumägi, Innar Liiv, and Targo Kalamees. "Quality and accuracy of digital twin models for the neighbourhood level building energy performance calculations." E3S Web of Conferences 396 (2023): 04021. http://dx.doi.org/10.1051/e3sconf/202339604021.

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For scaling renovation, there is a need for assessing the energy performance of buildings at the neighborhood level. Traditional methods for assessing individual buildings are manual and time-consuming – not sufficient to enable the neighborhood level assessment of the energy performance of buildings. Instead, new methods based on existing data in national registries and building typologies are required. The aim in this article is to develop for obtaining necessary initial building geometry information for energy performance calculations from the Estonian national Building Registry (EBR), incl

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Vahidi, Dara, and Fernando Porté-Agel. "Potential of Wake Scaling Techniques for Vertical-Axis Wind Turbine Wake Prediction." Energies 17, no. 17 (2024): 4527. http://dx.doi.org/10.3390/en17174527.

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Analytical wake models are widely used to predict wind turbine wakes. While these models are well-established for horizontal-axis wind turbines (HAWTs), the analytical wake models for vertical-axis wind turbines (VAWTs) remain under-explored in the wind energy community. In this study, the accuracy of two wake scaling techniques is evaluated to predict the change in the normalized maximum wake velocity deficit behind VAWTs by re-scaling the maximum wake velocity deficit behind an actuator disk with the same thrust coefficient. The wake scaling is defined in terms of equivalent diameter, consid

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Leiria, D., M. Schaffer, H. Johra, A. Marzsal-Pomianowska, and M. Z. Pomianowski. "Estimating residential space heating and domestic hot water from truncated smart heat data." Journal of Physics: Conference Series 2600, no. 2 (2023): 022017. http://dx.doi.org/10.1088/1742-6596/2600/2/022017.

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Abstract The EU aims to digitize the building stock across all member states to better understand energy use and achieve energy efficiency goals to address climate change. Smart heat meters are currently used for billing purposes in district heating (DH) grids. Their data is recorded as integer kWh values, which restricts usability for the modeling and analysis of DH networks. Previous research devised a methodology to estimate space heating (SH) and domestic hot water (DHW) energy from total heating data, but the data truncation process reduced accuracy. This study integrates the SPMS (Smooth

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ariyo, Bashiru, mutalub lambe, olalekan ogunbiyi, musa abdulwaheed, bilkisu ojuolape, and monsurat balogun. "AI-driven demand forecasting for enhanced energy management in renewable microgrids: A hybrid LSTM-CNN approach." Journal of Engineering Research and Reviews 2, no. 1 (2025): 1. https://doi.org/10.5455/jerr.20241007042800.

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Aim/Background: The increasing integration of renewable energy sources into microgrids underscores the need for accurate demand forecasting to ensure stable and efficient microgrid operation. However, the inherent unpredictability of renewable energy sources presents significant challenges in energy management. This study aims to develop and validate an AI-driven demand forecasting model that improves prediction accuracy compared to traditional methods, thereby enhancing energy management in renewable microgrids. Methods: A hybrid forecasting model that combines long short-term memory (LSTM) n

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Aroua, Ayoub, Walter Lhomme, Florian Verbelen, et al. "Impact of scaling laws of permanent magnet synchronous machines on the accuracy of energy consumption computation of electric vehicles." eTransportation 18 (October 2023): 100269. http://dx.doi.org/10.1016/j.etran.2023.100269.

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Chahal, Ayushi, Preeti Gulia, Nasib Singh Gill, and Jyotir Moy Chatterjee. "Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid." Complexity 2022 (August 3, 2022): 1–13. http://dx.doi.org/10.1155/2022/7319010.

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The stability of the power grid is concernment due to the high demand and supply to smart cities, homes, factories, and so on. Different machine learning (ML) and deep learning (DL) models can be used to tackle the problem of stability prediction for the energy grid. This study elaborates on the necessity of IoT technology to make energy grid networks smart. Different prediction models, namely, logistic regression, naïve Bayes, decision tree, support vector machine, random forest, XGBoost, k-nearest neighbor, and optimized artificial neural network (ANN), have been applied on openly available

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Li, Anran, Wenfeng Gao, and Tao Liu. "Bulk Scaling Model of Entrainment Zone Thickness in a Convective Boundary Layer, with a Shear Effect Promoted by Velocity Difference." Atmosphere 11, no. 1 (2020): 63. http://dx.doi.org/10.3390/atmos11010063.

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Studying the thickness of the convective boundary layer (CBL) is helpful for understanding atmospheric structure and the diffusion of air pollutants. When there is velocity shear in CBL, the flow field structure is very different from that of shear-free CBL, which makes the thickness model of the entrainment zone deviate. A large-eddy simulation (LES) approach is carried out for a horizontally homogeneous, atmospheric CBL, with a shear effect promoted by velocity difference to explore the bulk scaling model of the entrainment zone thickness. The post-processed data indicate that the existing b

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Basu, Sukanta, Jean-François Vinuesa, and Andrew Swift. "Dynamic LES Modeling of a Diurnal Cycle." Journal of Applied Meteorology and Climatology 47, no. 4 (2008): 1156–74. http://dx.doi.org/10.1175/2007jamc1677.1.

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Abstract The diurnally varying atmospheric boundary layer observed during the Wangara (Australia) case study is simulated using the recently proposed locally averaged scale-dependent dynamic subgrid-scale (SGS) model. This tuning-free SGS model enables one to dynamically compute the Smagorinsky coefficient and the subgrid-scale Prandtl number based on the local dynamics of the resolved velocity and temperature fields. It is shown that this SGS-model-based large-eddy simulation (LES) has the ability to faithfully reproduce the characteristics of observed atmospheric boundary layers even with re

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JAIMUNGAL, S., M. H. S. AMIN, and G. ROSE. "PERSISTENT CURRENTS IN MESOSCOPIC RINGS AND BOUNDARY CONFORMAL FIELD THEORY." International Journal of Modern Physics B 13, no. 27 (1999): 3171–81. http://dx.doi.org/10.1142/s0217979299002939.

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A tight-binding model of electron dynamics in mesoscopic normal rings is studied using boundary conformal field theory. The partition function is calculated in the low energy limit and the persistent current generated as a function of an external magnetic flux threading the ring is found. We study the cases where there are defects and electron–electron interactions separately. The same temperature scaling for the persistent current is found in each case and the functional form can be fitted, with a high degree of accuracy, to experimental data.

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Kitsios, Vassili, Jorgen S. Frederiksen, and Meelis J. Zidikheri. "Theoretical comparison of subgrid turbulence in atmospheric and oceanic quasi-geostrophic models." Nonlinear Processes in Geophysics 23, no. 2 (2016): 95–105. http://dx.doi.org/10.5194/npg-23-95-2016.

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Abstract. Due to the massive disparity between the largest and smallest eddies in the atmosphere and ocean, it is not possible to simulate these flows by explicitly resolving all scales on a computational grid. Instead the large scales are explicitly resolved, and the interactions between the unresolved subgrid turbulence and large resolved scales are parameterised. If these interactions are not properly represented then an increase in resolution will not necessarily improve the accuracy of the large scales. This has been a significant and long-standing problem since the earliest climate simul

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Kitsios, V., J. S. Frederiksen, and M. J. Zidikheri. "Theoretical comparison of subgrid turbulence in the atmosphere and ocean." Nonlinear Processes in Geophysics Discussions 2, no. 6 (2015): 1675–704. http://dx.doi.org/10.5194/npgd-2-1675-2015.

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Abstract. Due to the massive disparity between the largest and smallest eddies in the atmosphere and ocean, it is not possible to simulate these flows by explicitly resolving all scales on a computational grid. Instead the large scales are explicitly resolved, and the interactions between the unresolved subgrid turbulence and large resolved scales are parameterised. If these interactions are not properly represented then an increase in resolution will not necessarily improve the accuracy of the large scales. This has been a significant and long standing problem since the earliest climate simul

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Иванова, Е. П., та В. А. Панфилов. "Энергии ионизации Cu-подобных ионов с Z≤92". Оптика и спектроскопия 131, № 3 (2023): 323. http://dx.doi.org/10.21883/os.2023.03.55381.3518-22.

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The overview of the experimental and theoretical data on the ionization energies (IE) of the Cu -like ions is presented. In various works data are obtained by interpolation and extrapolation the parameters of model potentials for Dirac equations. In other approaches, the function of the dependence of the ionization energy on Z (Z is nuclear charge) is approximated by a polynomial with power-law dependence on Z in order to achieve a minimum difference between theoretical and experimental data. The compilation of ionization energies is in database of the National Institute of Standards and Techn

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Haussmann, Marc, Stephan Simonis, Hermann Nirschl, and Mathias J. Krause. "Direct numerical simulation of decaying homogeneous isotropic turbulence — numerical experiments on stability, consistency and accuracy of distinct lattice Boltzmann methods." International Journal of Modern Physics C 30, no. 09 (2019): 1950074. http://dx.doi.org/10.1142/s0129183119500748.

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Stability, consistency and accuracy of various lattice Boltzmann schemes are investigated by means of numerical experiments on decaying homogeneous isotropic turbulence (DHIT). Therefore, the Bhatnagar–Gross–Krook (BGK), the entropic lattice Boltzmann (ELB), the two-relaxation-time (TRT), the regularized lattice Boltzann (RLB) and the multiple-relaxation-time (MRT) collision schemes are applied to the three-dimensional Taylor–Green vortex, which represents a benchmark case for DHIT. The obtained turbulent kinetic energy, the energy dissipation rate and the energy spectrum are compared to refer

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Zhang, Ziyang, Yang Zhao, Ming-Ching Chang, Changyao Lin, and Jie Liu. "E4: Energy-Efficient DNN Inference for Edge Video Analytics via Early Exiting and DVFS." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 1165–73. https://doi.org/10.1609/aaai.v39i1.32104.

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Deep neural network (DNN) models are increasingly popular in edge video analytic applications. However, the computeintensive nature of DNN models pose challenges for energyefficient inference on resource-constrained edge devices. Most existing solutions focus on optimizing DNN inference latency and accuracy, often overlooking energy efficiency. They also fail to account for the varying complexity of video frames, leading to sub-optimal performance in edge video analytics. In this paper, we propose an EnergyEfficient Early-Exit (E4) framework that enhances DNN inference efficiency for edge vide

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Vijayakumar, Shanmugam, Palanisamy Shanmugapriya, Pasoubady Saravanane, Thanakkan Ramesh, Varunseelan Murugaiyan, and Selvaraj Ilakkiya. "Precision Weed Control Using Unmanned Aerial Vehicles and Robots: Assessing Feasibility, Bottlenecks, and Recommendations for Scaling." NDT 3, no. 2 (2025): 10. https://doi.org/10.3390/ndt3020010.

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Weeds cause significant yield and economic losses by competing with crops and increasing production costs. Compounding these challenges are labor shortages, herbicide resistance, and environmental pollution, making weed management increasingly difficult. In response, precision weed control (PWC) technologies, such as robots and unmanned aerial vehicles (UAVs), have emerged as innovative solutions. These tools offer farmers high precision (±1 cm spatial accuracy), enabling efficient and sustainable weed management. Herbicide spraying robots, mechanical weeding robots, and laser-based weeders ar

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Gao, Siyu, Yuchen Wang, Nan Feng, Zhongcheng Wei, and Jijun Zhao. "Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks." Future Internet 15, no. 5 (2023): 184. http://dx.doi.org/10.3390/fi15050184.

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With the proliferation of video surveillance system deployment and related applications, real-time video analysis is very critical to achieving intelligent monitoring, autonomous driving, etc. Analyzing video stream with high accuracy and low latency through the traditional cloud computing represents a non-trivial problem. In this paper, we propose a non-orthogonal multiple access (NOMA)-based edge real-time video analysis framework with one edge server (ES) and multiple user equipments (UEs). A cost minimization problem composed of delay, energy and accuracy is formulated to improve the quali

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