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Journal articles on the topic 'Computational load reduction'
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1
Curtis, Mathew, and Andrew Lewis. "Reduction of Computational Load for MOPSO." Procedia Computer Science 51 (2015): 2789–93. http://dx.doi.org/10.1016/j.procs.2015.05.435.
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Jeon, Nam-Ryul, Han-Byul Lee, Chan Gook Park, Seong Yun Cho, and Seong-Cheol Kim. "Superresolution TOA Estimation With Computational Load Reduction." IEEE Transactions on Vehicular Technology 59, no. 8 (2010): 4139–44. http://dx.doi.org/10.1109/tvt.2010.2063044.
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Et. al., Venkatesh E. ,. "Current Harmonics Reduction In Microgrids Using Dual Interfacing Converters." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 1647–54. http://dx.doi.org/10.17762/turcomat.v12i2.1450.
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The Growing Installation of Distributed Generation (DG) units in low voltage conveyance frameworks has promoted the idea of nonlinear load symphonious current remuneration utilizing multi-utilitarian DG interfacing converters. In appropriation frameworks, the load has been an unexpected increment or diminishes and it resembles as nonlinear loads so the load draw non-sinusoidal flows from the AC mains and causes the load sounds and responsive power, and over the top impartial flows that give contamination in power frameworks. It is investigated in this paper that the pay of neighborhood load symphonious current utilizing a solitary DG interfacing converter may make the intensification of supply voltage sounds touchy loads, especially when the primary framework voltage is profoundly twisted. To address this impediment, in contrast to the activity of customary unified power quality conditioners (UPQC) with arrangement converter, another concurrent stock voltage and matrix current symphonious pay technique is proposed utilizing composed control of two shunt interfacing converters. Through the presentation of staggered control destinations, it is delineated that the proposed framework could ride through voltage unsettling influences and proceed with the power move between the nearby generation and the matrix, while an excellent voltage is kept up for the neighborhood loads. By utilizing this proposed regulator, the lattice voltage stage bolted circle and the identification of the load current and the stockpile voltage music are superfluous for both interfacing converters. In this manner, the computational load of interfacing converters can be fundamentally decreased. Mimicked and test results are caught to approve the exhibition of the proposed geography and the control technique.
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Candela Esclapez, Alfredo, Miguel López García, Sergio Valero Verdú, and Carolina Senabre Blanes. "Reduction of Computational Burden and Accuracy Maximization in Short-Term Load Forecasting." Energies 15, no. 10 (2022): 3670. http://dx.doi.org/10.3390/en15103670.
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Electrical energy is consumed at the same time as it is generated, since its storage is unfeasible. Therefore, short-term load forecasting is needed to manage energy operations. Due to better energy management, precise load forecasting indirectly saves money and CO2 emissions. In Europe, owing to directives and new technologies, prediction systems will be on a quarter-hour basis, which will reduce computation time and increase the computational burden. Therefore, a predictive system may not dispose of sufficient time to compute all future forecasts. Prediction systems perform calculations throughout the day, calculating the same forecasts repeatedly as the predicted time approaches. However, there are forecasts that are no more accurate than others that have already been made. If previous forecasts are used preferentially over these, then computational burden will be saved while accuracy increases. In this way, it will be possible to optimize the schedule of future quarter-hour systems and fulfill the execution time limits. This paper offers an algorithm to estimate which forecasts provide greater accuracy than previous ones, and then make a forecasting schedule. The algorithm has been applied to the forecasting system of the Spanish electricity operator, obtaining a calculation schedule that achieves better accuracy and involves less computational burden. This new algorithm could be applied to other forecasting systems in order to speed up computation times and to reduce forecasting errors.
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Cerone, Vito, Dario Piga, and Diego Regruto. "Computational Load Reduction in Bounded Error Identification of Hammerstein Systems." IEEE Transactions on Automatic Control 58, no. 5 (2013): 1317–22. http://dx.doi.org/10.1109/tac.2012.2223334.
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Acevedo-Rodríguez, J., S. Maldonado-Bascón, S. Lafuente-Arroyo, P. Siegmann, and F. López-Ferreras. "Computational load reduction in decision functions using support vector machines." Signal Processing 89, no. 10 (2009): 2066–71. http://dx.doi.org/10.1016/j.sigpro.2009.03.032.
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Yeh, Cheng-Yu, and Shaw-Hwa Hwang. "Efficient Detection Approach for DTMF Signal Detection." Applied Sciences 9, no. 3 (2019): 422. http://dx.doi.org/10.3390/app9030422.
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A novel tone detection approach, designated as the multi-frequency detecting (MFD) algorithm, is presented in this work as an alternative to conventional single point detection approaches but it is an efficient way to achieve the aim of further computational load reduction for a dual-tone multi-frequency (DTMF) signal detection. The idea is that an optimal phase search is performed over the frequency band of interest in each tone detection, and then the optimal frequency response of a detector is built accordingly. In this manner, a DTMF detection task is done following one-time detection computation. This proposal demonstrates an overall computational load reduction of 80.49% and 74.06% in comparison with a discrete Fourier transform (DFT) approach and the Goertzel algorithm, respectively. This detection complexity reduction is an advantage and an important issue for applying DTMF detection technique to embedded devices.
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Markousi, Michalina, Dimitrios K. Fytanidis, and Johannes V. Soulis. "Wind Load Reduction in Hollow Panel Arrayed Set." Modelling and Simulation in Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/1034539.
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Reducing the wind loading of photovoltaic structures is crucial for their structural stability. In this study, two solar panel arrayed sets were numerically tested for load reduction purposes. All panel surface areas of the arrayed set are exposed to the wind similarly. The first set was comprised of conventional panels. The second one was fitted with square holes located right at the gravity center of each panel. Wind flow analysis on standalone arrayed set of panels at fixed inclination was carried out to calculate the wind loads at various flow velocities and directions. The panels which included holes reduced the velocity in the downwind flow region and extended the low velocity flow region when compared to the nonhole panels. The loading reduction, in the arrayed set of panels with holes ranged from 0.8% to 12.53%. The maximum load reduction occurred at 6.0 m/s upwind velocity and 120.0° approach angle. At 30.00 approach angle, wind load increased but marginally. Current research work findings suggest that the panel holes greatly affect the flow pattern and subsequently the wind load reduction. The computational analysis indicates that it is possible to considerably reduce the wind loading using panels with holes.
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Wang, Haojing, Yingjie Tian, An Li, Jihai Wu, and Gaiping Sun. "Resident user load classification method based on improved Gaussian mixture model clustering." MATEC Web of Conferences 355 (2022): 02024. http://dx.doi.org/10.1051/matecconf/202235502024.
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In view of the limitation of “hard assignment” of clusters in traditional clustering methods and the difficulty of meeting the requirements of clustering efficiency and clustering accuracy simultaneously in regard to massive data sets, a load classification method based on a Gaussian mixture model combining clustering and principal component analysis is proposed. The load data are fed into a Gaussian mixture model clustering algorithm after principal component analysis and dimensionality reduction to achieve classification of large-scale load datasets. The method in this paper is used to classify loads in the Canadian AMPds2 public dataset and is compared with K-Means, Gaussian mixed model clustering and other methods. The results show that the proposed method can not only achieve load classification more effectively and finely, but also save computational cost and improve computational efficiency.
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Chen, Yuanchang, Bangji Zhang, and Shengzhao Chen. "Model Reduction Technique Tailored to the Dynamic Analysis of a Beam Structure under a Moving Load." Shock and Vibration 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/406093.
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This study presents a technique that uses a model reduction method for the dynamic response analysis of a beam structure to a moving load, which can be modeled either as a moving point force or as a moving body. The nature of the dedicated condensation method tailored to address the moving load case is that the master degrees of freedom are reselected, and the coefficient matrices of the condensed model are recalculated as the load travels from one element to another. Although this process increases computational burden, the overall computational time is still greatly reduced because of the small scale of motion equations. To illustrate and validate the methodology, the technique is initially applied to a simply supported beam subjected to a single-point load moving along the beam. Subsequently, the technique is applied to a practical model for wheel-rail interaction dynamic analysis in railway engineering. Numerical examples show that the condensation model can solve the moving load problem faster than an analytical model or its full finite element model. The proposed model also exhibits high computational accuracy.
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Vijesh, Kumar Valiyappurakkal, and Ahmed Chundeli Faiz. "A Computational Analysis of Trees' Effects on Tropical Climate Thermal Load Reduction." Research & Reviews: Journal of Architectural Designing 6, no. 2 (2024): 1–13. https://doi.org/10.5281/zenodo.11196060.
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<em>This paper numerically looks into trees' most preferred position for maximum thermal load reduction for one and two-floor buildings using 1 to 5 tree configurations in Vijayawada. The study was conducted during summertime, i.e., limiting solar access from June 21 to September 22. Results show that cooling energy consumption decreased from 3.45% to 11.22% and 3.22% to 9.73% for one- and two-floor models. The 2-tree configuration showed up to 20% savings. A sharp energy consumption reduction was generally observed between 1 to 3 tree configurations. Interestingly, for the 2-floors model, there is an increase in the rate of change of energy reduction potential in the last quadrant, which is more than 10% of the total energy reduction potential.</em>
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Huang, Nantian, Hao Wang, Xinran Wang, Chenhan Hu, and Dongxu Wang. "A Non-Invasive Load Identification Method Considering Feature Dimensionality Reduction and DB-LSTM." Electronics 13, no. 2 (2024): 343. http://dx.doi.org/10.3390/electronics13020343.
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As the demand for detailed load data descriptions in modern power systems continues to increase, challenges such as high computational complexity in load identification tasks and high hardware requirements for devices have significantly hindered progress. Therefore, this paper proposes a non-intrusive load identification method using Densely-connected Bi-directional Long Short-Term Memory (DB-LSTM) with Kernel Principal Component Analysis. Firstly, a bilateral sliding window algorithm is employed for event detection in the data collected by load identification devices, checking for the switching on and off of electrical appliances. Secondly, after detecting the switching of load devices and extracting features, Kernel Principal Component Analysis is used to reduce data dimensions due to the complexity of existing features, selecting more relevant characteristics. Finally, a densely connected Bi-directional Long Short-Term Memory (LSTM) network is utilized. This enhances global and dynamic local features by stacking LSTM units and combining them with dense skip connections, providing additional channels for signal transmission, thereby strengthening feature propagation and reducing the number of parameters. This approach lowers computational complexity and improves the efficiency of the model’s load identification. The proposed model is compared and validated against mainstream non-intrusive load identification models through experiments, demonstrating its higher efficiency in load identification.
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Boorsma, Koen, Florian Wenz, Koert Lindenburg, Mansoor Aman, and Menno Kloosterman. "Validation and accommodation of vortex wake codes for wind turbine design load calculations." Wind Energy Science 5, no. 2 (2020): 699–719. http://dx.doi.org/10.5194/wes-5-699-2020.
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Abstract. The computational effort for wind turbine design load calculations is more extreme than it is for other applications (e.g., aerospace), which necessitates the use of efficient but low-fidelity models. Traditionally the blade element momentum (BEM) method is used to resolve the rotor aerodynamic loads for this purpose, as this method is fast and robust. With the current trend of increasing rotor size, and consequently large and flexible blades, a need has risen for a more accurate prediction of rotor aerodynamics. Previous work has demonstrated large improvement potential in terms of fatigue load predictions using vortex wake models together with a manageable penalty in computational effort. The present publication has contributed towards making vortex wake models ready for application to certification load calculations. The observed reduction in flapwise blade root moment fatigue loading using vortex wake models instead of the blade element momentum (BEM) method from previous publications has been verified using computational fluid dynamics (CFD) simulations. A validation effort against a long-term field measurement campaign featuring 2.5 MW turbines has also confirmed the improved prediction of unsteady load characteristics by vortex wake models against BEM-based models in terms of fatigue loading. New light has been shed on the cause for the observed differences and several model improvements have been developed, both to reduce the computational effort of vortex wake simulations and to make BEM models more accurate. Scoping analyses for an entire fatigue load set have revealed the overall fatigue reduction may be up to 5 % for the AVATAR 10 MW rotor using a vortex wake rather than a BEM-based code.
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Miler, Daniel, Matija Hoić, Rudolf Tomić, Andrej Jokić, and Robert Mašović. "Simultaneous Multi-Objective and Topology Optimization: Effect of Mesh Refinement and Number of Iterations on Computational Cost." Computation 13, no. 7 (2025): 168. https://doi.org/10.3390/computation13070168.
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In this study, a multi-objective optimization procedure with embedded topology optimization was presented. The procedure simultaneously optimizes the spatial arrangement and topology of bodies in a multi-body system. The multi-objective algorithm determines the locations of supports, joints, active loads, reactions, and load magnitudes, which serve as inputs for the topology optimization of each body. The multi-objective algorithm dynamically adjusts domain size, support locations, and load magnitudes during optimization. Due to repeated topology optimization calls within the genetic algorithm, the computational cost is significant. To address this, two reduction strategies are proposed: (I) using a coarser mesh and (II) reducing the number of iterations during the initial generations. As optimization progresses, Strategy I gradually refines the mesh, while Strategy II increases the maximum allowable iteration count. The effectiveness of both strategies is evaluated against a baseline (Reference) without reductions. By the 25th generation, all approaches achieve similar hypervolume values (Reference: 2.181; I: 2.112; II: 2.133). The computation time is substantially reduced (Reference: 42,226 s; I: 16,814 s; II: 21,674 s), demonstrating that both strategies effectively accelerate optimization without compromising solution quality.
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Battagello, Vinícius Antonio, Nei Yoshihiro Soma, and Rubens Junqueira Magalhães Afonso. "Computational load reduction of the agent guidance problem using Mixed Integer Programming." PLOS ONE 15, no. 6 (2020): e0233441. http://dx.doi.org/10.1371/journal.pone.0233441.
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Bouchard, Martin, and Scott Norcross. "Computational load reduction of fast convergence algorithms for multichannel active noise control." Signal Processing 83, no. 1 (2003): 121–34. http://dx.doi.org/10.1016/s0165-1684(02)00382-1.
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Sharapova, E. "Computational load reduction of fuzzy duplicate detection in large amounts of information." IOP Conference Series: Materials Science and Engineering 734 (January 29, 2020): 012119. http://dx.doi.org/10.1088/1757-899x/734/1/012119.
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Chen, Qizhan, Ruifeng Zhao, Bin Li, Zewei Liu, Huijun Zhuang, and Chunqiang Hu. "Regional Load Forecasting Scheme for Security Outsourcing Computation." Electronics 13, no. 18 (2024): 3712. http://dx.doi.org/10.3390/electronics13183712.
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Smart grids generate an immense volume of load data. When analyzed using intelligent technologies, these data can significantly improve power load management, optimize energy distribution, and support green energy conservation and emissions reduction goals. However, in the process of data utilization, a pertinent issue arises regarding potential privacy leakage concerning both regional and individual user power load data. This paper addresses the scenario of outsourcing computational tasks for regional power load forecasting in smart grids, proposing a regional-level load forecasting solution based on secure outsourcing computation. Initially, the scheme designs a secure outsourcing training protocol to carry out model training tasks while ensuring data security. This protocol guarantees that sensitive information, including but not limited to individual power consumption data, remains comprehensively safeguarded throughout the entirety of the training process, effectively mitigating any potential risks of privacy infringements. Subsequently, a secure outsourcing online prediction protocol is devised, enabling efficient execution of prediction tasks while safeguarding data privacy. This protocol ensures that predictions can be made without compromising the privacy of individual or regional power load data. Ultimately, experimental analysis demonstrates that the proposed scheme meets the requirements of privacy, accuracy, and timeliness for outsourcing computational tasks of load forecasting in smart grids.
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Zheng, Chaoran, Mohsen Eskandari, Ming Li, and Zeyue Sun. "GA−Reinforced Deep Neural Network for Net Electric Load Forecasting in Microgrids with Renewable Energy Resources for Scheduling Battery Energy Storage Systems." Algorithms 15, no. 10 (2022): 338. http://dx.doi.org/10.3390/a15100338.
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The large−scale integration of wind power and PV cells into electric grids alleviates the problem of an energy crisis. However, this is also responsible for technical and management problems in the power grid, such as power fluctuation, scheduling difficulties, and reliability reduction. The microgrid concept has been proposed to locally control and manage a cluster of local distributed energy resources (DERs) and loads. If the net load power can be accurately predicted, it is possible to schedule/optimize the operation of battery energy storage systems (BESSs) through economic dispatch to cover intermittent renewables. However, the load curve of the microgrid is highly affected by various external factors, resulting in large fluctuations, which makes the prediction problematic. This paper predicts the net electric load of the microgrid using a deep neural network to realize a reliable power supply as well as reduce the cost of power generation. Considering that the backpropagation (BP) neural network has a good approximation effect as well as a strong adaptation ability, the load prediction model of the BP deep neural network is established. However, there are some defects in the BP neural network, such as the prediction effect, which is not precise enough and easily falls into a locally optimal solution. Hence, a genetic algorithm (GA)−reinforced deep neural network is introduced. By optimizing the weight and threshold of the BP network, the deficiency of the BP neural network algorithm is improved so that the prediction effect is realized and optimized. The results reveal that the error reduction in the mean square error (MSE) of the GA–BP neural network prediction is 2.0221, which is significantly smaller than the 30.3493 of the BP neural network prediction. Additionally, the error reduction is 93.3%. The error reductions of the root mean square error (RMSE) and mean absolute error (MAE) are 74.18% and 51.2%, respectively.
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Hansen, R. O., and R. S. Pawlowski. "Reduction to the pole at low latitudes by Wiener filtering." GEOPHYSICS 54, no. 12 (1989): 1607–13. http://dx.doi.org/10.1190/1.1442628.
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Using simple estimates of the signal and noise power from gridded magnetic data, we design regulated frequency‐domain operators for reduction to the pole at low magnetic latitudes. These operators suppress the artifacts along the direction of the magnetic declination associated with the conventional reduction‐to‐the‐pole procedure, with negligible increase in computational load. The new procedure is applied to produce high‐quality reductions to the pole for noisy low‐latitude synthetic data and for magnetic data from the Dixon Seamount.
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Yang, Haofan, Haowen Pan, Chenfeng Zong, Ziyi Wang, and Gang Jiang. "An Improvement of the Load Transfer Method for Energy Piles Under Thermo-Mechanical Loads." Applied Sciences 15, no. 11 (2025): 6046. https://doi.org/10.3390/app15116046.
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The energy pile integrates shallow geothermal energy extraction with underground structural engineering, thereby expanding the functional applications and scope of pile foundations. Due to its widespread adoption, research on energy pile analysis theory has advanced significantly. Among existing analytical methods, the load transfer method is widely employed owing to its computational simplicity and readily obtainable parameters. However, current load transfer models for energy piles remain imperfect, primarily because their results often fail to accurately reflect real-world loading conditions. This study investigates the underlying causes of this discrepancy and proposes an iterative method to eliminate unbalanced forces at the pile head, based on the displacement coordination algorithm for energy pile load transfer. The calculated results at the pile head show an 18% reduction in error compared to previous studies. The average error compared with field test results is within 20%, with consistent trend patterns, confirming the feasibility of the proposed method. Computational results demonstrate that the proposed method effectively captures the combined effects of mechanical load and temperature variations on the bearing behavior of energy piles. It should be noted that this paper focuses specifically on improving the temperature-dependent load transfer method for energy piles. Consequently, the conventional load transfer method and results under purely mechanical loading are not discussed herein.
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Rahmani, Rasoul, Irene Moser, and Antonio L. Cricenti. "Inter-continental Data Centre Power Load Balancing for Renewable Energy Maximisation." Electronics 11, no. 10 (2022): 1564. http://dx.doi.org/10.3390/electronics11101564.
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The ever increasing popularity of Cloud and similar services pushes the demand for data centres, which have a high power consumption. In an attempt to increase the sustainability of the power generation, data centres have been fed by microgrids which include renewable generation—so-called `green data centres’. However, the peak load of data centres often does not coincide with solar generation, because demand mostly peaks in the evening. Shifting power to data centres incurs transmission losses; shifting the data transmission has no such drawback. We demonstrate the effectivity of computational load shifting between data centres located in different time zones using a case study that balances demands between three data centres on three continents. This study contributes a method that exploits the opportunities provided by the varied timing of peak solar generation across the globe, transferring computation load to data centres that have sufficient renewable energy whenever possible. Our study shows that balancing computation loads between three green data centres on three continents can improve the use of renewables by up to 22%. Assuming the grid energy does not include renewables, this amounts to a 13% reduction in CO2 emissions.
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Trąbka, Arkadiusz. "The Influence of Clearances in a Drive System on Dynamics and Kinematics of a Telescopic Crane." Acta Mechanica et Automatica 9, no. 1 (2015): 9–13. http://dx.doi.org/10.1515/ama-2015-0002.
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Abstract The paper presents the results of numerical analyses that were carried out in order to evaluate how a change in a size of a clearance in a slewing motion drive system of a telescopic crane influences the movement of a load and the dynamic loads of a structure. A computational model was developed based on a real structure of an experimental crane by using the ADAMS software. The analyses showed that a circumferential clearance at the output of a reduction gear, which is less than 1º, does not adversely affect the precision of the load movement. An excessive clearance leads to losing fluidity of a body slewing motion and to changes in the trajectory of the load.
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Zhang, Yikun, Jing Zhang, Gang Yao, Xiao Xu, and Kewen Wei. "Method for Clustering Daily Load Curve Based on SVD-KICIC." Energies 13, no. 17 (2020): 4476. http://dx.doi.org/10.3390/en13174476.
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Clustering electric load curves is an important part of the load data mining process. In this paper, we propose a clustering algorithm by combining singular value decomposition and KICIC clustering algorithm (SVD-KICIC) for analyzing the characteristics of daily load curves to mitigate some of the traditional clustering algorithm problems, such as only considering intra-class distance and low computational efficiency when dealing with massive load data. Our method identifies effective daily load curve characteristics using the singular value decomposition technique to improve dimensionality reduction, which improves low computational efficiency by reducing the number of dimensions inherent in big data. Additionally, the method performs SVD on the load data to obtain singular values for determination of weight of the KICIC algorithm, which leverages intra-class and inter-class distances of the load data and further improves the computational efficiency of the algorithm. Finally, we perform a series of simulations of actual load curves from a certain city to validate that the algorithm proposed in this paper has a short operation time, high clustering quality, and solid robustness that improves the clustering performance of the load curves.
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Cheema, Ibrahim A., Mudassar Ahmad, Fahad Jan, and Dr Asri Bin Ngadi. "Prognostic Load Balancing Strategy for Latency reduction in Mobile Cloud Computing." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 9, no. 2 (2013): 1080–90. http://dx.doi.org/10.24297/ijct.v9i2.4172.
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In Mobile Cloud Computing (MCC), load balancing is essential to distribute the local workload evenly across all the nodes either statically or dynamically. A high level of user satisfaction and resource utilization ratio can be achieved by ensuring an efficient and fair allocation of all computing resources. In the absence of proper load balancing strategy/technique the growth of MCC will never go as per predictions. The appropriate load balancing helps in minimizing resource consumption, implementing fail-over, enabling scalability, avoiding bottlenecks. In this paper, a prognostic load balancing strategy is proposed and implemented for computational latency reduction in MCC. Also the results of proposed technique is compared with existing techniques. Finally this study concludes that the proposed predictive technique reduces associated overheads, service response time and improves performance. There are also Various parameters that are identified and used to compare the existing techniques. Â
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Sarkar, Falguni, and Sajal K. Das. "Design and Implementation of Dynamic Load Balancing Algorithms for Rollback Reduction in Optimistic PDES." VLSI Design 9, no. 3 (1999): 271–90. http://dx.doi.org/10.1155/1999/64750.
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In an optimistic parallel simulation, logical processes (Ips) proceed with their computation without any constraints. However, if the computing requirements of different lps are not balanced or if the processors are not homogeneous, some lps may lag behind in simulation time while others surge forward. In other words, if the simulation clocks of different lps are not progressing at the same rate, cascading rollbacks may occur nullifying the potential benefit of an optimistic parallel discrete event simulation (PDES). Hence it is necessary to balance the computational load on different lps in such a way that their local simulation clocks advance almost at the same rate. In this paper, we propose two algorithms for dynamic load balancing which reduce the number of rollbacks in an optimistic PDES system. Our first algorithm is based on the load transfer mechanism between lps; while the second algorithm, based on the principle of evolutionary strategy, migrates logical processes between several pairs of physical processors. We have implemented both of these algorithms on a cluster of heterogeneous workstations and studied their performance. The experimental results show that the algorithm based on the load transfer is effective when the grain size is greater than 10 milliseconds. The algorithm based on the process migration yields good performance only for grain sizes of 20 milliseconds or larger. In both of these cases the speed up ranges mostly between and 2 using four processors.
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Vojgani, Elaheh, Armin C. Hölker, Manfred Mayer, Chris-Carolin Schön, Henner Simianer, and Torsten Pook. "Genomic prediction using information across years with epistatic models and dimension reduction via haplotype blocks." PLOS ONE 18, no. 3 (2023): e0282288. http://dx.doi.org/10.1371/journal.pone.0282288.
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The importance of accurate genomic prediction of phenotypes in plant breeding is undeniable, as higher prediction accuracy can increase selection responses. In this regard, epistasis models have shown to be capable of increasing the prediction accuracy while their high computational load is challenging. In this study, we investigated the predictive ability obtained in additive and epistasis models when utilizing haplotype blocks versus pruned sets of SNPs by including phenotypic information from the last growing season. This was done by considering a single biological trait in two growing seasons (2017 and 2018) as separate traits in a multi-trait model. Thus, bivariate variants of the Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) and selective Epistatic Random Regression BLUP (sERRBLUP) as epistasis models were compared with respect to their prediction accuracies for the second year. The prediction accuracies of bivariate GBLUP, ERRBLUP and sERRBLUP were assessed with eight phenotypic traits for 471/402 doubled haploid lines in the European maize landrace Kemater Landmais Gelb/Petkuser Ferdinand Rot. The results indicate that the obtained prediction accuracies are similar when utilizing a pruned set of SNPs or haplotype blocks, while utilizing haplotype blocks reduces the computational load significantly compared to the pruned sets of SNPs. The number of interactions considered in the model was reduced from 323.5/456.4 million for the pruned SNP panel to 4.4/5.5 million in the haplotype block dataset for Kemater and Petkuser landraces, respectively. Since the computational load scales linearly with the number of parameters in the model, this leads to a reduction in computational time of 98.9% from 13.5 hours for the pruned set of markers to 9 minutes for the haplotype block dataset. We further investigated the impact of genomic correlation, phenotypic correlation and trait heritability as factors affecting the bivariate models’ prediction accuracy, identifying the genomic correlation between years as the most influential one. As computational load is substantially reduced, while the accuracy of genomic prediction is unchanged, the here proposed framework to use haplotype blocks in sERRBLUP provided a solution for the practical implementation of sERRBLUP in real breeding programs. Furthermore, our results indicate that sERRBLUP is not only suitable for prediction across different locations, but also for the prediction across growing seasons.
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Jia, Jia, Jhensi Chen, and Daping Chu. "Computational load reduction by avoiding the recalculation of angular redundancy in computer‐generated holograms." ETRI Journal 41, no. 1 (2019): 52–60. http://dx.doi.org/10.4218/etrij.2018-0454.
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Maleki, A., and A. Ahmadi. "Buckling Analysis of Orthotropic Thick Cylindrical Shells Considering Geometrical Imperfection Using Differential Quadrature Method (DQM)." Journal of Theoretical and Applied Mechanics 48, no. 4 (2018): 45–60. http://dx.doi.org/10.2478/jtam-2018-0022.
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Abstract This paper presented a three dimensional analysis for the buckling behavior of an imperfect orthotropic thick cylindrical shells under pure axial or external pressure loading. Critical loads are computed for different imperfection parameter. Both ends of the shell have simply supported conditions. Governing differential equations are driven based on the second Piola–Kirchhoff stress tensor and are reduced to a homogenous linear system of equations using differential quadrature method. Buckling loads reduction factor is computed for different imperfection parameters and geometrical properties of orthotropic shells. The sensitivity is established through tables of buckling load reduction factors versus imperfection amplitude. It is shown that imperfections have higher effects on the buckling load of thin shells than thick ones. Results show that the presented method is very accurate and can capture the various geometrical imperfections observed during the manufacturing process or transportation.
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Tuninetti, Víctor, Geovanni Fuentes, Angelo Oñate, et al. "Computational Shape Design Optimization of Femoral Implants: Towards Efficient Forging Manufacturing." Applied Sciences 14, no. 18 (2024): 8289. http://dx.doi.org/10.3390/app14188289.
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Total hip replacement is one of the most successful orthopedic operations in modern times. Osteolysis of the femur bone results in implant loosening and failure due to improper loading. To reduce induced stress, enhance load transfer, and minimize stress, the use of Ti-6Al-4V alloy in bone implants was investigated. The objective of this study was to perform a three-dimensional finite element analysis (FEA) of the femoral stem to optimize its shape and analyze the developed deformations and stresses under operational loads. In addition, the challenges associated with the manufacturing optimization of the femoral stem using large strain-based finite element modeling were addressed. The numerical findings showed that the optimized femoral stem using Ti-6Al-4V alloy under the normal daily activities of a person presented a strains distribution that promote uniform load transfer from the proximal to the distal area, and provided a mass reduction of 26%. The stress distribution was found to range from 700 to 0.2 MPa in the critical neck area of the implant. The developed computational tool allows for improved customized designs that lower the risk of prosthesis loss due to stress shielding.
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Lin, Liangliang, Junjie Wu, Ran An, Song Ma, Kun Zhao, and Han Ding. "LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches." Applied Sciences 14, no. 22 (2024): 10515. http://dx.doi.org/10.3390/app142210515.
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The rise of mobile communication, low-power chips, and the Internet of Things has made smartwatches increasingly popular. Equipped with inertial measurement units (IMUs), these devices can recognize user activities through artificial intelligence (AI) analysis of sensor data. However, most existing AI-based activity recognition algorithms require significant computational power and storage, making them unsuitable for low-power devices like smartwatches. Additionally, discrepancies between training data and real-world data often hinder model generalization and performance. To address these challenges, we propose LIMUNet and its smaller variant LIMUNet-Tiny—lightweight neural networks designed for human activity recognition on smartwatches. LIMUNet utilizes depthwise separable convolutions and residual blocks to reduce computational complexity and parameter count. It also incorporates a dual attention mechanism specifically tailored to smartwatch sensor data, improving feature extraction without sacrificing efficiency. Experiments on the PAMAP2 and LIMU datasets show that the LIMUNet improves recognition accuracy by 2.9% over leading lightweight models while reducing parameters by 88.3% and computational load by 58.4%. Compared to other state-of-the-art models, LIMUNet achieves a 9.6% increase in accuracy, with a 60% reduction in parameters and a 57.8% reduction in computational cost. LIMUNet-Tiny further reduces parameters by 75% and computational load by 80%, making it even more suitable for resource-constrained devices.
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Rishi Kumar Singh, Mohd Navaid Ansari,. "Application of D-STATCOM for Harmonic Reduction using Power Balance Theory." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 2496–503. http://dx.doi.org/10.17762/turcomat.v12i6.5694.
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In modern power systems due to prolific use of non-linear devices harmonics are present in the distribution system. To reduce the harmonics and source neutral current D-FACT along with a star-delta transformer and Power Filter is proposed in shunt. In this article, the three phase load has been taken from NTS Industries, which deals with FMCG products. This system includes 3 phase 3 wire system having industrial load such as mixer grinder, pulverizer, packaging machine, etc. A star-delta transformer along with three leg voltage source inverter and a capacitor used to filter out harmonics present in supply current. Power balance theory is used to control the D-FACT. A MATLAB Simulink model was created and the results were discussed.
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Jyothi, Anantula, and Baddam Indira. "A Two Way Validation Framework for Cloud Storage Security." International Journal of Engineering & Technology 7, no. 2.20 (2018): 236. http://dx.doi.org/10.14419/ijet.v7i2.20.14769.
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High Performance Computing (HPC) has become one of the predominant techniques for processing the large scale applications. Cloud environment has been chosen to provide the required services and to process these high demand applications. Management of such applications challenges us on three major things i.e. network feasibility, computational feasibility and data security. Several research endeavours are focused on network load and computing cloud date and provided better outcomes. Still those approaches are not able to provide standard mechanisms in view of data security. On the other side, research towards enabling the auditing features on the cloud based data by various researchers has been addressed but their performance is poor. However, the complexity of the audit process proven to be the bottleneck in improving performance of the application as it consumes the computational resources of the same application. Henceforth, this work proposes a novel framework for cloud data auditing at multiple levels to audit the access requests and upon validating the conditions of one level, the connection request will be moved to the further complex levels in order to reduce the computational loads. The proposed framework determines a substantial reduction in the computational load on the cloud server, thus improves the application performance leveraging the infrastructure use.
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Mao, Chengfang, Jiewen Wei, Wangsheng Lan, and Ananchai Ukaew. "The Effect of Bioalcohol Additives on Biofuel Diesel Engines." Fire 7, no. 11 (2024): 404. http://dx.doi.org/10.3390/fire7110404.
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This study experimentally investigated a water-cooled four-cylinder turbocharged diesel engine (DE) under different loads and fuel blend ratios. The integration of Computational Fluid Dynamics (CFD) simulations enables a deeper analysis of the combustion process. Through an in-depth analysis of the combustion process, the focus was placed on investigating the specific impacts of ethanol and n-butanol additives on diesel engine performance. Research shows that a fuel mixture consisting of 70% diesel, 10% biodiesel, and 20% ethanol reduced NOx emissions by 5.56% compared to pure diesel at 75% load. Furthermore, this study explores the combustion performance of diesel/biodiesel blended with butanol/ethanol. The findings indicate that n-butanol improves thermal efficiency, particularly at 100% load, with the D70B10E20 and D70B10BU20 blends demonstrating thermal efficiencies of 9.94%and 8.72% higher than that of diesel alone, respectively. All mixed fuels exhibited reduced hydrocarbon and CO emissions under different loads, with a notable reduction in hydrocarbon emissions of 34.4% to 46.1% at 75% load.
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García, V., L. Vargas, A. Acuña, et al. "Evaluation of Basalt Fibers on Wind Turbine Blades through Finite Element Analysis." Advances in Materials Science and Engineering 2019 (April 18, 2019): 1–12. http://dx.doi.org/10.1155/2019/1536925.
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Here we use finite element analysis to determine the suitability of basalt fiber as a substitute for E-glass in structural applications, which would improve the cost effectiveness of small wind turbine blades. Five NACA (National Advisory Committee for Aeronautics) profiles were evaluated to select the optimum shape for the wind operation conditions. To obtain the wind load pressure distribution over the blade, a computational aerodynamic analysis by CFD (computational fluid dynamics) was performed based on the blade’s design and operating conditions. Material properties and mechanical tests were carried out to obtain the fiber volume fraction, density, Young’s modulus, shear modulus, and Poisson relation of polymeric matrix composites made using basalt and fiberglass. The obtained wind loads and material properties were used on a FEM (finite element model) analysis to evaluate the structural behavior of the blade under normal and critical operating conditions. Both fibers meet the structural requirements under normal operating conditions. We detected a reduction of 4% in the blade stress when basalt fibers are used instead of glass fibers, and a reduction of 68% in the total deformation for a critical load case of 40 m/s was obtained when using basalt fibers, which met the structural requirements and maximum power generation required for this wind turbine design.
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Ramos, Leandro Ito, Douglas Jhon Ramos, and Gregory Bregion Daniel. "Evaluation of textured journal bearings under dynamic operating conditions in rotating machinery." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 234, no. 6 (2019): 842–57. http://dx.doi.org/10.1177/1350650119887568.
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In order to obtain rotating machinery with improved energy efficiency, the approach of surface texturing journal bearings has been adopted to reduce the viscous dissipation in the lubricant. A possible reduction in shear viscous forces in the bearings of rotating machines could reduce the amount of heat released along its operation, resulting in a lower operating temperature that tends to improve journal bearings performance and the machine’s energetic efficiency. Thus, this work aims to investigate the texturing of journal bearings under dynamic loading conditions, considering the application in rotating systems. For this, computational simulations are performed through a rotating system model constructed by means of the finite element method in which the hydrodynamic journal bearings that support the rotor are modeled by Reynolds’ equation using the finite volume method and the full multigrid technique. The numerical results show that textured journal bearings can be applied to rotating machines, providing reductions in shear viscous forces. However, the magnitude of this reduction should be carefully evaluated, as the Reynolds cavitation model was applied to ensure a lower computational spent time and thus enable the simulations involved in this study. The novelty of this study is related to determining the appropriate distributions and geometric parameters of the textures for the journal bearing under dynamic load condition considering its equivalent static load condition what tends to drastically reduce computational time to perform this procedure, representing an important alternative for industrial application.
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Schlosser, Michail, Axel Schumacher, and Klaus Bellendir. "Effective Modeling of Load Applications in Composite Structures - Accuracy, Complexity, Computational Time." Key Engineering Materials 809 (June 2019): 461–66. http://dx.doi.org/10.4028/www.scientific.net/kem.809.461.
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The simulation of load application elements requires the modeling of the contact point and a nonlinear analysis. This contact analysis is still time-consuming despite of powerful computers. A reduction of this contact by a simple load model would result in enormous time savings. The Hertzian contact theory provides an analytical approach to the contact problem. However, an isotropic material behavior is assumed, which is problematic especially with fiber reinforced structures. Nevertheless, a suitable load model can be developed for a simplified model of a bolt joint. The edge effects occurring at the edge of the hole are determined using an approximation function (parameterized polynomial approach). The anisotropic material behavior is represented by alternative models or it can also be integrated into the calculation by an extension of Hertzian theory. The different approaches are compared in respect of accuracy, complexity and computing time. For reference and verification of the results, a contact model is created using the FEM software HyperMesh and Optistruct from Altair. Besides the contact model can be used as an aid for creating the load model. Finally, a method is presented, which reduces a contact analysis to a purely linear static structural analysis and thus enables a significantly reduced computing time. The corresponding load model also gives a good representation of reality.
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Xue, Yuqun, Yongsen Wang, Jianhua Jiang, et al. "An Efficient Codebook Search Algorithm for Line Spectrum Frequency (LSF) Vector Quantization in Speech Codec." Electronics 10, no. 4 (2021): 380. http://dx.doi.org/10.3390/electronics10040380.
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A high-performance vector quantization (VQ) codebook search algorithm is proposed in this paper. VQ is an important data compression technique that has been widely applied to speech, image, and video compression. However, the process of the codebook search demands a high computational load. To solve this issue, a novel algorithm that consists of training and encoding procedures is proposed. In the training procedure, a training speech dataset was used to build the squared-error distortion look-up table for each subspace. In the encoding procedure, firstly, an input vector was quickly assigned to a search subspace. Secondly, the candidate code word group was obtained by employing the triangular inequality elimination (TIE) equation. Finally, a partial distortion elimination technique was employed to reduce the number of multiplications. The proposed method reduced the number of searches and computation load significantly, especially when the input vectors were uncorrelated. The experimental results show that the proposed algorithm provides a computational saving (CS) of up to 85% in the full search algorithm, up to 76% in the TIE algorithm, and up to 63% in the iterative TIE algorithm. Further, the proposed method provides CS and load reduction of up to 29–33% and 67–69%, respectively, over the BSS-ITIE algorithm.
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Bekemeyer, P., R. Thormann, and S. Timme. "Rapid gust response simulation of large civil aircraft using computational fluid dynamics." Aeronautical Journal 121, no. 1246 (2017): 1795–807. http://dx.doi.org/10.1017/aer.2017.104.
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ABSTRACTSeveral critical load cases during the aircraft design process result from atmospheric turbulence. Thus, rapidly performable and highly accurate dynamic response simulations are required to analyse a wide range of parameters. A method is proposed to predict dynamic loads on an elastically trimmed, large civil aircraft using computational fluid dynamics in conjunction with model reduction. A small-sized modal basis is computed by sampling the aerodynamic response at discrete frequencies and applying proper orthogonal decomposition. The linear operator of the Reynolds-averaged Navier-Stokes equations plus turbulence model is then projected onto the subspace spanned by this basis. The resulting reduced system is solved at an arbitrary number of frequencies to analyse responses to 1-cos gusts very efficiently. Lift coefficient and surface pressure distribution are compared with full-order, non-linear, unsteady time-marching simulations to verify the method. Overall, the reduced-order model predicts highly accurate global coefficients and surface loads at a fraction of the computational cost, which is an important step towards the aircraft loads process relying on computational fluid dynamics.
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Yeh, Cheng-Yu, and Hung-Hsun Huang. "An Upgraded Version of the Binary Search Space-Structured VQ Search Algorithm for AMR-WB Codec." Symmetry 11, no. 2 (2019): 283. http://dx.doi.org/10.3390/sym11020283.
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Adaptive multi-rate wideband (AMR-WB) speech codecs have been widely used for high speech quality in modern mobile communication systems, e.g., handheld mobile devices. Nevertheless, a major handicap is that a remarkable computational load is required in the vector quantization (VQ) of immittance spectral frequency (ISF) coefficients of an AMR-WB coding. In view of this, a two-stage search algorithm is presented in this paper as an efficient way to reduce the computational complexity of ISF quantization in AMR-WB coding. At stage 1, an input vector is assigned to a search subspace in an efficient manner using the binary search space-structured VQ (BSS-VQ) algorithm, and a codebook search is performed over the subspace at stage 2 using the iterative triangular inequality elimination (ITIE) approach. Through the use of the codeword rejection mechanisms equipped in both stages, the computational load can be remarkably reduced. As compared with the original version of the BSS-VQ algorithm, the upgraded version provides a computational load reduction of up to 51%. Furthermore, this work is expected to satisfy the energy saving requirement when implemented on an AMR-WB codec of mobile devices.
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Saad, Mootez, José Antonio Hernández López, Boqi Chen, Dániel Varró, and Tushar Sharma. "An Adaptive Language-Agnostic Pruning Method for Greener Language Models for Code." Proceedings of the ACM on Software Engineering 2, FSE (2025): 1183–204. https://doi.org/10.1145/3715773.
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Language models of code have demonstrated remarkable performance across various software engineering and source code analysis tasks. However, their demanding computational resource requirements and consequential environmental footprint remain as significant challenges. This work introduces ALPINE, an adaptive programming language-agnostic pruning technique designed to substantially reduce the computational overhead of these models. The proposed method offers a pluggable layer that can be integrated with all Transformer-based models. With ALPINE, input sequences undergo adaptive compression throughout the pipeline, reaching a size that is up to x3 less their initial size, resulting in significantly reduced computational load. Our experiments on two software engineering tasks, defect prediction and code clone detection across three language models CodeBERT, GraphCodeBERT and UniXCoder show that ALPINE achieves up to a 50% reduction in FLOPs, a 58.1% decrease in memory footprint, and a 28.1% improvement in throughput on average. This led to a reduction in CO2 emissions by up to 44.85%. Importantly, it achieves a reduction in computation resources while maintaining up to 98.1% of the original predictive performance. These findings highlight the potential of ALPINE in making language models of code more resource-efficient and accessible while preserving their performance, contributing to the overall sustainability of their adoption in software development. Also, it sheds light on redundant and noisy information in source code analysis corpora, as shown by the substantial sequence compression achieved by ALPINE.
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Harati, Ehsan, and Hossein Ahmadi Noubari. "Long Time Prediction of Uncertain Systems Using Singular Perturbation." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, no. 05 (2017): 707–21. http://dx.doi.org/10.1142/s0218488517500301.
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This paper considers the problem of long time prediction of uncertain dynamic systems. Spectral methods such as polynomial chaos expansion (PCE) provides a suitable alternative for classical Monte Carlo method with lower computational load. However, polynomial chaos expansion has a major drawback of long time integration error. In this paper, we will apply singular perturbation (SP) method for reducing long time integration error. Using SP the accuracy of long time predictions are improved with comparable computational load. We will apply SP to illustrative exemplify problems to show effectiveness of our approach. Moreover, we will illustrate application of SP together with a model order reduction tool to reduce long time integration error as applied to distributed parameter.
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Elsayed, Hassan, Ahmed El-Mowafy, Amir Allahvirdi-Zadeh, Kan Wang, and Xiaolong Mi. "A Combination of Classification Robust Adaptive Kalman Filter with PPP-RTK to Improve Fault Detection for Integrity Monitoring of Autonomous Vehicles." Remote Sensing 17, no. 2 (2025): 284. https://doi.org/10.3390/rs17020284.
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Real-time integrity monitoring (IM) is essential for autonomous vehicle positioning, requiring high availability and manageable computational load. This research proposes using precise point positioning real-time kinematic (PPP-RTK) as the positioning method, combined with an improved classification adaptive Kalman filter (CAKF) for processing. PPP-RTK enhances IM availability by allowing undifferenced and uncombined observations, enabling individual observation exclusion during fault detection and exclusion (FDE). The CAKF reduces FDE computational load by using a robustness test instead of traditional FDE methods, improving precision and availability in protection level estimation. Epoch-wise weighting adjustments in the robustness test create a more accurate stochastic model, aided by an adaptive unit weight variance (UWV) calculated with a sliding window, achieving a 7–28% UWV reduction. Three test scenarios with up to four simultaneous faults in code and phase observations, ranging from 1 to 200 m and 0.4 to 20 m, respectively, demonstrated successful identification and de-weighting of faults, resulting in maximum positioning errors of 6 mm (horizontal) and 11 mm (vertical). The method reduced FDE computational load by 50–99.999% compared to other approaches.
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Kim, Kwangsoo. "A Study on Stator Design for Electromagnetic Noise Reduction in Washing Machine Motor." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 585–89. http://dx.doi.org/10.17762/turcomat.v12i6.1999.
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Received:11 november 2020; Accepted: 27 December 2020; Published online: 05 April 2021
 Abstract : For low cost, spoke type ferrite magnet motors with small air gap are using to washing machine. At that time, a noise problem occurs, so a design to reduce the noise is required. In the washing machine, set noise increased due to the reduction of air gap of motor at high speed spin mode. Not only the overall noise, but also the harmonics noises are greatly increased at set. As a result of set noise test and noise analysis through finite elements method, a stator tooth shoe shape in which the magnetic flux changes rapidly is cause of noise. Its shape creates many harmonics of load torque. The harmonics component of the load torque increases the overall noise of the set. The harmonics noise of the set has a large correlation with radial force component as well as the torque ripple. Especially, it is found that the harmonics noise of the set is related to harmonics component of load torque and the harmonics component of the radial force at stator tooth. The noise is reduced by applying the new design of stator tooth shoe that can minimize the change in magnetic flux through the noise cause analysis. Through this research, the harmonics noise as well as overall noise are reduced at small airgap motor. Therefore, it is possible to develop cost reduction motor for washing machine.
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Chirkov, D., V. Lapin, D. Esipov, V. Skorospelov, P. Turuk, and S. Cherny. "Realizable velocity profiles downstream Francis runner for reduction of part load pressure pulsations." IOP Conference Series: Earth and Environmental Science 1483, no. 1 (2025): 012038. https://doi.org/10.1088/1755-1315/1483/1/012038.
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Abstract The range of stable operation of a Francis turbine is restricted by unsteady phenomena observed in the draft tube at high load and part load operating conditions. At part load, the pressure pulsations are caused by the rotation of the helical vortex rope in the draft tube. The aim of this investigation is to develop the methodology for minimizing part load pressure pulsations through the optimization of the runner shape. The main challenge here is the high computational cost of unsteady CFD analysis required to evaluate the amplitude of pressure pulsations. The obvious idea to overcome this problem suggests performing CFD analysis in steady state and estimating pulsation characteristics indirectly based on the computed velocity profile downstream of the runner. However this approach raises the second problem: finding a correlation between the velocity profile and the corresponding amplitude of draft tube pressure pulsations. To avoid trivial but unacceptable solutions such as “the less swirl, the smaller the pulsations”, we need to focus only on those velocity profiles, that can be realized at part load by high efficiency runners, meeting power and efficiency requirements at other operating points (BEP, full load, etc.). To generate such a set of admissible part load velocity profiles, we propose solving an auxiliary multi-point optimization problem. Once the set of admissible profiles is found, the desired correlation can be determined using unsteady CFD analysis for several dozen selected geometry variants. This paper discusses the preliminary results obtained during the practical implementation of the above approach.
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Stamatiadou, Marianna E., Dimitrios I. Katsourinis, and Maria A. Founti. "Computational assessment of a full-scale Mediterranean building incorporating wallboards with phase change materials." Indoor and Built Environment 26, no. 10 (2016): 1429–43. http://dx.doi.org/10.1177/1420326x16645384.
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In this study, a lightweight residential building in Greece was investigated, focusing on the summer comfort when wallboards with phase change materials (PCM) were installed in the external and internal walls. The effectiveness of the PCM wallboards installed was numerically assessed, while the energy performance of the building was examined, in order to quantify the effect of PCM in the annual cooling load needs, as a way of saving energy. Potential bigger energy savings were evaluated by defining the appropriate PCM melting temperature range and the ‘energy-conscious’ occupant behaviour (passive vs. active). Results were expressed in terms of percentage savings of cooling loads and with comparison to wall elements incorporated with plain gypsumboards instead of the PCM wallboards. The optimum phase change temperature change for the specific location was investigated by examining two-phase change transition temperatures of the PCM wallboards (PCM24 and PCM26 respectively). The use of PCM24 produced a 29% reduction of annual cooling loads, compared to 16% reduction produced by PCM26. Five scenarios were also examined, showing the behaviour of the PCM which was enhanced when a cooling system was installed. The cooling needs were lowered by an average of 25.7%, compared to the respective no-PCM scenarios.
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Chen, Hanshu, Zeng Meng, and Huanlin Zhou. "A Hybrid Framework of Efficient Multi-Objective Optimization of Stiffened Shells with Imperfection." International Journal of Computational Methods 17, no. 04 (2019): 1850145. http://dx.doi.org/10.1142/s0219876218501451.
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In the practical engineering applications of stiffened shell, the initial imperfection is inevitable and it could cause significant reduction in the load-carrying capacity of stiffened shell. The light-weight optimization of stiffened shell is generally performed under the constraint of fixed maximum load-carrying capacity. However, the load-carrying capacity of stiffened shell has been improved continuously as the promotion of manufacturing technology, which causes the previous strategies of light-weight optimization become conservative and outdated. Therefore, an improved hybrid framework of multi-objective optimization of stiffened shell with imperfection is necessary and presented in this paper, which focus on developing a general posterior design method to determine the optimal weight according to the different collapse loads. A new adaptive update criterion based on the Kriging model is developed to improve the efficiency and accuracy of the hybrid framework. The present optimal results provide a set of the Pareto optimal points and form a Pareto front, from which new posterior design can be achieved.
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B, Yuvaraj, Karanam Ramesh Rao, Anbarasu R, and Kadirvelu G. "ENHANCING AI-DRIVEN SENSORS WITH DECISION TREE ALGORITHMS FOR ADVANCED DATA SCIENCE APPLICATIONS." ICTACT Journal on Microelectronics 10, no. 2 (2024): 1812–16. https://doi.org/10.21917/ijme.2024.0313.
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Microelectromechanical Systems (MEMS) sensors play a pivotal role in collecting data for various applications, yet their computational load often poses a challenge, leading to increased power consumption and reduced efficiency. This study addresses this issue by integrating Decision Tree algorithms to enhance AI-driven MEMS sensors. The primary problem is the high computational burden faced by MEMS sensors when processing large volumes of data, which can impair performance and battery life. The proposed method involves applying Decision Tree algorithms to preprocess and filter data, thereby reducing the volume of information processed directly by the MEMS sensors. Experimental results show a significant reduction in computational load, with a 35% decrease in processing time and a 28% improvement in battery efficiency. Additionally, the accuracy of data classification improved by 20% compared to traditional methods. These improvements demonstrate the effectiveness of Decision Trees in optimizing MEMS sensor performance for advanced data science applications.
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Yang, Lufeng, Bo Yu, and Yongping Qiao. "Elastic modulus reduction method for limit load evaluation of frame structures." Acta Mechanica Solida Sinica 22, no. 2 (2009): 109–15. http://dx.doi.org/10.1016/s0894-9166(09)60095-1.
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Yan, Jian, Tefang Chen, E. Deng, Weichao Yang, Shu Cheng, and Biming Zhang. "Aerodynamic Response and Running Posture Analysis When the Train Passes a Crosswind Region on a Bridge." Applied Sciences 11, no. 9 (2021): 4126. http://dx.doi.org/10.3390/app11094126.
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Trains running on a bridge face more significant safety risks. Based on the Unsteady Reynolds-Averaged Navier–Stokes turbulence model, a three-dimensional Computational Fluid Dynamics computational model of the train–bridge–wind barrier was proposed in this study to measure the transient aerodynamic load of the train. The transient aerodynamic load was input into the wind–train–bridge coupling dynamic system to perform dynamic analysis of running safety. Significant fluctuations in the aerodynamic coefficients were found when the train entered and exited the wind barrier due to the dramatic change in flow pattern. The maximum value of the derailment coefficient decreased with the height of wind barriers, which hardly affected the wheel load reduction rate. The 2 m high wind barrier had no evident influence on the running posture of a general high-speed train, while the 4 m high wind barrier was proven to have better protection. Over-protection was found with an even higher wind barrier.