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About 10,000 papers for “optimization model”

Model optimization.

Rosanna Migliore (323323), Carmen A. Lupascu (5753753), Luca L. Bologna (5753756) et al. · Figshare · 2018

<p>Typical optimization results for <i>cAC</i> pyramidal cells (top) and interneurons (bottom). The top left graph of each panel shows a few examples of model traces from three individuals during a current injection of -0.4, 0.4, and 0.8 nA (black, red, and blue traces, respectively). The right graph of each panel reports the objective scores for the best individual. The bottom left graph in each panel shows a typical evolution of the total score during an optimization run.</p>

Cited by 0Open access

Multireservoir Optimization Model

James S. Windsor, Ven Te Chow · Journal of the Hydraulics Division · 1972

The analysis of river basin developments is viewed as a multilevel optimization problem. In this particular study, mixed integer programming is coupled with historical, or stochastically generated, streamflow sequences to derive the optimal design for a complex river basin development. In formulating the model, emphasis is placed on the interrelationships which exist between the various components of the system and the coordination and integration of these components into a single economic unit. The model is designed to determine simultaneously the optimal set and sizes of reservoirs in the system, the optimal target outputs for the tangible water uses, power and irrigation, and the optimal operating procedure for attaining these outputs subject to the technological constraints. Intangible water users, such as recreation and water quality control, are treated as optional constraints and their imputed values are obtained by a multiple solution technique. Part of the input to this model is provided by the irrigation submodel developed in a previous study.

Cited by 5

Model performance optimization.

Eui-Jae Kim (18334267), Hyun-Wook Kang (3168468), Seong-Man Park (18334270) · Figshare · 2024

<div><p>Leisure plays a key role in the happiness of youth. Studies have shown that various factors of leisure, such as the type, the time, the cost, and the space, have an influence on the adolescents’ happiness. However, little is known about which of these factors is a major factor in youth’s happiness. The purpose of this study is to explore the leisure factors that determine happiness in adolescents by examining the relationship between happiness and various leisure factors. The study used the method of machine learning to analyze national statistical data, National Leisure Activity Survey. The data used in this study were from the National Leisure Activity Survey 2019, which is a national statistic produced by the Ministry of Culture, Sports and Tourism in the Republic of Korea. The analysis found that leisure perceptions, academic and leisure balance, and public leisure space have a very important impact on the adolescents’ well-being. The findings of this research may contribute to a better understanding of leisure and happiness in adolescents, and will also help adolescents make better use of their leisure time, leading to better leisure lives, and ultimately contribute to raising their level of happiness.</p></div>

Cited by 0Open access

GA2CNN optimization model.

Cun Lin (18880471), XiaoChen Hu (18880474), TianYi Cheng (18880477) et al. · Figshare · 2024

<div><p>This study aims to develop a digital retrieval system for art museums to solve the problems of inaccurate information and low retrieval efficiency in the digital management of cultural heritage. By introducing an improved Genetic Algorithm (GA), digital management and access efficiency are enhanced, to bring substantial optimization and innovation to the digital management of cultural heritage. Based on the collection of art museums, this study first integrates the collection’s images, texts, and metadata with multi-source intelligent information to achieve a more accurate and comprehensive description of digital content. Second, a GA is introduced, and a GA 2 Convolutional Neural Network (GA2CNN) optimization model combining domain knowledge is proposed. Moreover, the convergence speed of traditional GA is improved to adapt to the characteristics of cultural heritage data. Lastly, the Convolutional Neural Network (CNN), GA, and GA2CNN are compared to verify the proposed system’s superiority. The results show that in all models, the sample output results’ actual value is 2.62, which represents the real data observation results. For sample number 5, compared with the actual value of 2.62, the predicted values of the GA2CNN and GA models are 2.6177 and 2.6313, and their errors are 0.0023 and 0.0113. The CNN model’s predicted value is 2.6237, with an error of 0.0037. It can be found that the network fitting accuracy after optimization of the GA2CNN model is high, and the predicted value is very close to the actual value. The digital retrieval system integrated with the GA2CNN model has a good performance in enhancing retrieval efficiency and accuracy. This study provides technical support for the digital organization and display of cultural heritage and offers valuable references for innovative exploration of museum information management in the digital era.</p></div>

Cited by 0Open access

LCA model optimization.

Alexander P. Landry (6725738), Zsolt Zador (3157953), Rashida Haq (6725741) et al. · Figshare · 2019

Cited by 0Open access

Optimization model solutions.

Dung Nguyen (1503286), Yu Wei (365236), Erin J. Belval (19659634) et al. · Figshare · 2024

<div><p>We present a mixed integer programming model for prioritizing fuel treatments within a landscape fuel break network to maximize protection against wildfires, measured by the total fire size reduction or the sum of Wildland Urban Interface areas avoided from burning. This model uses a large dataset of simulated wildfires in a large landscape to inform fuel break treatment decisions. Its mathematical formulation is concise and computationally efficient, allowing for customization and expansion to address more complex and challenging fuel break management problems in diverse landscapes. We constructed test cases for Southern California of the United States to understand model outcomes across a wide range of fire and fuel management scenarios. Results suggest optimal fuel treatment layouts within the Southern California’s fuel break network responding to various model assumptions, which offer insights for regional fuel break planning. Comparative tests between the proposed optimization model and a rule-based simulation approach indicate that the optimization model can provide significantly better solutions within reasonable solving times, highlighting its potential to support fuel break management and planning decisions.</p></div>

Cited by 0Open access

3D Model Optimization

Zatun · Bulletin of Miscellaneous Information (Royal Gardens Kew) · 2021

At Zatun, we offer a full range of 3D modelling services for various applications. As a leading 3D model service provider in the Industry, our team has the latest knowledge and hands-on experience to fulfill any requirements in 3D modelling, from games to e-commerce visualizations .

Cited by 0

Model-free Optimization

Gio Wiederhold, Darpa Sisto · 1992

We consider how programming-in-the-large differs from programming-in-the-small. We then focus on resolving a critical conflict that arises when building large software systems. Large systems have both the requirement to be adequately efficient in execution and sufficiently flexible to be maintainable in a changing world. For our purpose their scale is assumed to be such that multiple processors, multiple applications, and multiple types of software modules will be simultaneously active. After a general background discussion, two critical points are elucidated: 1 The high level application process must obtain performance estimates as well as results from lower modules to create efficient schedules for each application type. 2 There will be no single optimization model or overall schedule, but this task will be as distributed and parallelized as the computations themselves. The general conclusion is that higher level abstractions are needed in all aspects of software technology to overco...

Cited by 0

Blasting Optimization Model

Anna Neeshsalo, A.V. Chernay, S.I. Kostrytska · Institutional Repository National Mining University of Ukraine (National Mining University of Ukraine) · 2017

Cited by 0Open access

Model optimization scheme.

Shasha Yu (407307), Peng Zhou (116747) · Figshare · 2025

<div> This study presents the development and application of an optimized Detection Transformer (DETR) model, known as CD-DETR, for the detection of thoracic diseases from chest X-ray (CXR) images. The CD-DETR model addresses the challenges of detecting minor pathologies in CXRs, particularly in regions with uneven medical resource distribution. In the central and western regions of China, due to a shortage of radiologists, CXRs from township hospitals are concentrated in central hospitals for diagnosis. This requires processing a large number of CXRs in a short period of time to obtain results. The model integrates a multi-scale feature fusion approach, leveraging Efficient Channel Attention (ECA-Net) and Spatial Attention Upsampling (SAU) to enhance feature representation and improve detection accuracy. It also introduces a dedicated Chest Diseases Intersection over Union (CDIoU) loss function to optimize the detection of small targets and reduce class imbalance. Experimental results on the NIH Chest X-ray dataset demonstrate that CD-DETR achieves a precision of 88.3% and recall of 86.6%, outperforming other DETR variants by an average of 5% and CNN-based models like YOLOv7 by 6–8% in these metrics, showing its potential for practical application in medical imaging diagnostics. </div>

Cited by 0Open access