Relevant bibliographies by topics / Linear generative model

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  2. Dissertations / Theses
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Academic literature on the topic 'Linear generative model'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Linear generative model"

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Liao, Huadong, Jiawei He, and Kunxian Shu. "Generative Model With Dynamic Linear Flow." IEEE Access 7 (2019): 150175–83. http://dx.doi.org/10.1109/access.2019.2947567.

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Roweis, Sam, and Zoubin Ghahramani. "A Unifying Review of Linear Gaussian Models." Neural Computation 11, no. 2 (February 1, 1999): 305–45. http://dx.doi.org/10.1162/089976699300016674.

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Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate observations and derivations made by many previous authors and introducing a new way of linking discrete and continuous state models using a simple nonlinearity. Through the use of other nonlinearities, we show how independent component analysis is also a variation of the same basic generative model. We show that factor analysis and mixtures of gaussians can be implemented in autoencoder neural networks and learned using squared error plus the same regularization term. We introduce a new model for static data, known as sensible principal component analysis, as well as a novel concept of spatially adaptive observation noise. We also review some of the literature involving global and local mixtures of the basic models and provide pseudocode for inference and learning for all the basic models.
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Kuo, Ping-Huan, Ssu-Ting Lin, and Jun Hu. "DNAE-GAN: Noise-free acoustic signal generator by integrating autoencoder and generative adversarial network." International Journal of Distributed Sensor Networks 16, no. 5 (May 2020): 155014772092352. http://dx.doi.org/10.1177/1550147720923529.

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Linear predictive coding is an extremely effective voice generation method that operates through simple process. However, linear predictive coding–generated voices have limited variations and exhibit excessive noise. To resolve these problems, this article proposes an artificial intelligence model that combines a denoise autoencoder with generative adversarial networks. This model generates voices with similar semantics through the random input from the latent space of generator. The experimental results indicate that voices generated exclusively by generative adversarial networks exhibit excessive noise. To solve this problem, a denoise autoencoder was connected to the generator for denoising. The experimental results prove the feasibility of the proposed voice generation method. In the future, this method can be applied in robots and voice generation applications to increase the humanistic language expression ability of robots and enable robots to demonstrate more humanistic and natural speaking performance.
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Li, Guangyu, Bo Jiang, Hao Zhu, Zhengping Che, and Yan Liu. "Generative Attention Networks for Multi-Agent Behavioral Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7195–202. http://dx.doi.org/10.1609/aaai.v34i05.6209.

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Understanding and modeling behavior of multi-agent systems is a central step for artificial intelligence. Here we present a deep generative model which captures behavior generating process of multi-agent systems, supports accurate predictions and inference, infers how agents interact in a complex system, as well as identifies agent groups and interaction types. Built upon advances in deep generative models and a novel attention mechanism, our model can learn interactions in highly heterogeneous systems with linear complexity in the number of agents. We apply this model to three multi-agent systems in different domains and evaluate performance on a diverse set of tasks including behavior prediction, interaction analysis and system identification. Experimental results demonstrate its ability to model multi-agent systems, yielding improved performance over competitive baselines. We also show the model can successfully identify agent groups and interaction types in these systems. Our model offers new opportunities to predict complex multi-agent behaviors and takes a step forward in understanding interactions in multi-agent systems.
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Bishop, Christopher M., Markus Svensén, and Christopher K. I. Williams. "GTM: The Generative Topographic Mapping." Neural Computation 10, no. 1 (January 1, 1998): 215–34. http://dx.doi.org/10.1162/089976698300017953.

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Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis, which is based on a linear transformation between the latent space and the data space. In this article, we introduce a form of nonlinear latent variable model called the generative topographic mapping, for which the parameters of the model can be determined using the expectation-maximization algorithm. GTM provides a principled alternative to the widely used self-organizing map (SOM) of Kohonen (1982) and overcomes most of the significant limitations of the SOM. We demonstrate the performance of the GTM algorithm on a toy problem and on simulated data from flow diagnostics for a multiphase oil pipeline.
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Bikmukhamedo, Radion F., and Adel F. Nadeev. "Generative transformer framework for network traffic generation and classification." T-Comm 14, no. 11 (2020): 64–71. http://dx.doi.org/10.36724/2072-8735-2020-14-11-64-71.

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for generating and classification tasks. Only packet size and inter-packet time sequences are used as flow features to unify the inputs for the two tasks. The source feature space is scaled and clustered with K-Means to form discrete sequences as model inputs. The model can be trained in two modes: (i) autoregressively, for network traffic generating, where the first token of training sequence represents a flow class, (ii) as a network flow classifier. The evaluation of generated traffic by means of Kolmogorov-Smirnov statistic demonstrated that its quality is on par with the first-order Markov chain, which was trained on each traffic class independently. The metric measured distances between source and generated empirical cumulative distributions of such parameters as packet size, inter-arrival time, throughput and number of packets per flow in directions to and from traffic origin. It was shown that enriching the dataset with external traffic from different domain improves quality of the generated traffic on target classes. The experiment results showed positive influence of generative pre-training on quality of the traffic classification task. In case of using the pre-trained model as a feature extractor for a linear algorithm, the quality was close to Random Forest trained on the raw sequences. When all model parameters are trained, the classifier outperforms the ensemble on average by 4% according to the F1-macro metric.
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Arora, Sanjeev, Yuanzhi Li, Yingyu Liang, Tengyu Ma, and Andrej Risteski. "A Latent Variable Model Approach to PMI-based Word Embeddings." Transactions of the Association for Computational Linguistics 4 (December 2016): 385–99. http://dx.doi.org/10.1162/tacl_a_00106.

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Semantic word embeddings represent the meaning of a word via a vector, and are created by diverse methods. Many use nonlinear operations on co-occurrence statistics, and have hand-tuned hyperparameters and reweighting methods. This paper proposes a new generative model, a dynamic version of the log-linear topic model of Mnih and Hinton (2007). The methodological novelty is to use the prior to compute closed form expressions for word statistics. This provides a theoretical justification for nonlinear models like PMI, word2vec, and GloVe, as well as some hyperparameter choices. It also helps explain why low-dimensional semantic embeddings contain linear algebraic structure that allows solution of word analogies, as shown by Mikolov et al. (2013a) and many subsequent papers. Experimental support is provided for the generative model assumptions, the most important of which is that latent word vectors are fairly uniformly dispersed in space.
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Neal, Radford M., and Peter Dayan. "Factor Analysis Using Delta-Rule Wake-Sleep Learning." Neural Computation 9, no. 8 (November 1, 1997): 1781–803. http://dx.doi.org/10.1162/neco.1997.9.8.1781.

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We describe a linear network that models correlations between real-valued visible variables using one or more real-valued hidden variables—a factor analysis model. This model can be seen as a linear version of the Helmholtz machine, and its parameters can be learned using the wake sleep method, in which learning of the primary generative model is as sisted by a recognition model, whose role is to fill in the values of hidden variables based on the values of visible variables. The generative and recognition models are jointly learned in wake and sleep phases, using just the delta rule. This learning procedure is comparable in simplicity to Hebbian learning, which produces a somewhat different representation of correlations in terms of principal components. We argue that the simplicity of wake-sleep learning makes factor analysis a plausible alternative to Hebbian learning as a model of activity-dependent cortical plasticity.
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Sinaga, Regina Sabariah. "PENGARUH MODEL PEMBELAJARAN GENERATIF TERHADAP PEMECAHAN MASALAH MATEMATIKA PADA MATERI PERSAMAAN LINIER DUA VARIABEL KELAS X SMK SWASTA ASAHAN KISARAN TAHUN PELAJARAN 2018/2019." Jurnal Serunai Matematika 12, no. 1 (March 29, 2020): 26–31. http://dx.doi.org/10.37755/jsm.v12i1.264.

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Penelitian ini merupakan penelitian eksperimen yaitu pengaruh model pembelajaran generative terhadap pemecahan masalah matematika pada materi persamaan linear dua variabel. Tujuan penelitian ini untuk mengetahui pengaruh model pembelajaran generative terhadap pemecahan masalah matematika pada materi persamaan linear dua variabel di kelas X SMK Swasta Asahan Kisaran. Populasi dalam penelitian ini adalah siswa kelas X SMK Swasta Asahan Kisaran yang berakreditasi dengan sampel kelas X AK (kelas eksperimen) dan XAP (kelas kontrol), masing-masing terdiri dari 30 siswa. Instrumen dalam penelitian ini berupa uraian. Tes yang diberikan telah memenuhi kreteria validitas, reabelitas, daya pembeda, tingkat kesukaran, setelah data dikumpul kemudian dianalisis. Pengujian persyaratan anlisis data meliputi uji normalitas data dengan menggunakan uji Lilliefors dan uji homogenitas data dengan menggunakan uji F. Pengujian hipotesis menggunakan uji t. Berdasarkan hasil data post tes diperoleh rata- rata pemecahan masalah matematika siswa yang di ajarkan dengan menggunakan model pembelajaran generative adalah 88,43 dan kelas control 65,43. Hasil perhitungan uji hipotesis diperoleh harga thitung = 3,74, dengan melihat tabel nilai t dengan taraf nyata = 0,05 diperoleh ttabel 1,67. Harga thitung =3,74 > ttabel 1,67 dapat disimpulakan bahwa kemampuan pemecahan masalah matematika siswa yang diajarkan dengan menggunakan model pembelajaran generative lebih baik dari pada kemampuan pemecahan masalah matematika siswa yang diajarkan dengan menggunakan model konvensional dalam materi persamaan linear dua variabel.
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Liu, Yang, Qun Liu, and Shouxun Lin. "Discriminative Word Alignment by Linear Modeling." Computational Linguistics 36, no. 3 (September 2010): 303–39. http://dx.doi.org/10.1162/coli_a_00001.

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Word alignment plays an important role in many NLP tasks as it indicates the correspondence between words in a parallel text. Although widely used to align large bilingual corpora, generative models are hard to extend to incorporate arbitrary useful linguistic information. This article presents a discriminative framework for word alignment based on a linear model. Within this framework, all knowledge sources are treated as feature functions, which depend on a source language sentence, a target language sentence, and the alignment between them. We describe a number of features that could produce symmetric alignments. Our model is easy to extend and can be optimized with respect to evaluation metrics directly. The model achieves state-of-the-art alignment quality on three word alignment shared tasks for five language pairs with varying divergence and richness of resources. We further show that our approach improves translation performance for various statistical machine translation systems.
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Dissertations / Theses on the topic "Linear generative model"

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Poire, Xavier Corvera. "Model generation and sampling algorithms for dynamic stochastic programming." Thesis, University of Essex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.294674.

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Henriksson, Johan. "Generating Solutions in General Relativity using a Non-Linear Sigma Model." Thesis, Uppsala universitet, Teoretisk fysik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-226272.

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This report studies the generation of new solutions to Einstein's field equations in general relativity by the method of sigma models. If, when projected from four to three dimensions, the relativistic action decouples into a gravity term and a non-linear sigma model term, target space isometries of the sigma model can be found that correspond to generating new solutions. We give a self-contained description of the method and relate it to the early articles through which the method was introduced. We discuss the virtues of the method and how it is used today. We find that it is a powerful technique of finding new solutions and can also give insight to the general features of the theory. We also identify some possible further developments of the method.
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Zhang, Li Zhang Li. "Automatic digital surface model (DSM) generation from linear array images /." [S.l.] : [s.n.], 2005. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=16078.

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De, Oliveira Steven. "Finding constancy in linear routines." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS207/document.

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La criticité des programmes dépasse constamment de nouvelles frontières car ils sont de plus en plus utilisés dans la prise de décision (voitures autonomes, robots chirurgiens, etc.). Le besoin de développer des programmes sûrs et de vérifier les programmes existants émerge donc naturellement.Pour prouver formellement la correction d'un programme, il faut faire face aux défis de la mise à l'échelle et de la décidabilité. Programmes composés de millions de lignes de code, complexité de l'algorithme, concurrence, et même de simples expressions polynomiales font partis des problèmes que la vérification formelle doit savoir gérer. Pour y arriver, les méthodes formelles travaillent sur des abstractions des états des programmes étudiés afin d'analyser des approximations de leur comportement. L'analyse des boucles est un axe entier de la vérification formelle car elles sont encore aujourd'hui peu comprises. Bien que certaines d'entre elles peuvent facilement être traitées, il existe des exemples apparemment très simples mais dont le comportement n'a encore aujourd'hui pas été résolu (par exemple, on ne sait toujours pas pourquoi la suite de Syracuse, simple boucle linéaire, converge toujours vers 1).L'approche la plus commune pour gérer les boucles est l'utilisation d'invariants de boucle, c'est à dire de relations sur les variables manipulées par une boucle qui sont vraies à chaque fois que la boucle recommence. En général, les invariants utilisent les mêmes expressions que celles utilisées dans la boucle : si elle manipule explicitement la mémoire par exemple, on s'attend à utiliser des invariants portant sur la mémoire. Cependant, il existe des boucles contenant uniquement des affectations linéaires qui n'admettent pas d'invariants linéaires, mais polynomiaux.Les boucles linéaires sont elles plus expressives que ce qu'il paraîtrait ?Cette thèse présente de nouvelles propriétés sur les boucles linéaires et polynomiales. Il est déjà connu que les boucles linéaires sont polynomialement expressives, au sens ou si plusieurs variables évoluent linéairement dans une boucle, alors n'importe quel monôme de ces variables évolue linéairement. La première contribution de cette thèse est la caractérisation d'une sous classe de boucles polynomiales exactement aussi expressives que des boucles linéaires, au sens où il existe une boucle linéaire avec le même comportement. Ensuite, deux nouvelles méthodes de génération d'invariants sont présentées.La première méthode est basée sur l'interprétation abstraite et s'intéresse aux filtres linéaires convergents. Ces filtres jouent un rôle important dans de nombreux systèmes embarqués (dans l'avionique par exemple) et requièrent l'utilisation de flottants, un type de valeurs qui peut mener à des erreurs d'imprécision s'ils sont mal utilisés. Aussi, la présence d'affectations aléatoires dans ces filtres rend leur analyse encore plus complexe.La seconde méthode traite d'une approche différente basée sur la génération d'invariants pour n'importe quel type de boucles linéaires. Elle part d'un nouveau théorème présenté dans cette thèse qui caractérise les invariants comme étant les vecteurs propres de la transformation linéaire traitée. Cette méthode est généralisée pour prendre en compte les conditions, les boucles imbriquées et le non déterminisme dans les affectations.La génération d'invariants n'est pas un but en soi, mais un moyen. Cette thèse s'intéresse au genre de problèmes que peut résoudre les invariants générés par la seconde méthode. Le premier problème traité est problème de l'orbite (Kannan-Lipton Orbit problem), dont il est possible de générer des certificats de non accessibilité en utilisant les vecteurs propres de la transformation considerée. En outre, les vecteurs propres sont mis à l'épreuve en pratique par leur utilisation dans le model-checker CaFE basé sur la verification de propriétés temporelles sur des programmes C
The criticality of programs constantly reaches new boundaries as they are relied on to take decisions in place of the user (autonomous cars, robot surgeon, etc.). This raised the need to develop safe programs and to verify the already existing ones.Anyone willing to formally prove the soundness of a program faces the two challenges of scalability and undecidability. Million of lines of code, complexity of the algorithm, concurrency, and even simple polynomial expressions are part of the issues formal verification have to deal with. In order to succeed, formal methods rely on state abstraction to analyze approximations of the behavior of the analyzed program.The analysis of loops is a full axis of formal verification, as this construction is still today not well understood. Though some of them can be easily handled when they perform simple operations, there still exist some seemingly basic loops whose behavior has not been solved yet (the Syracuse sequence for example is suspected to be undecidable).The most common approach for the treatment of loops is the use of loop invariants, i.e. relations on variables that are true at the beginning of the loop and after every step. In general, invariants are expected to use the same set of expressions used in the loop: if a loop manipulates the memory on a structure for example, invariants will naturally use expressions involving memory operations. However, there exist loops containing only linear instructions that admit only polynomial invariants (for example, the sum on integers $sumlimits_{i=0}^n i$ can be computed by a linear loop and is a degree 2 polynomial in n), hence using expressions that are syntacticallyabsent of the loop. Is the previous remark wrong then ?This thesis presents new insights on loops containing linear and polynomial instructions. It is already known that linear loops are polynomially expressive, in the sense that if a variable evolves linearly, then any monomial of this variable evolves linearly. The first contribution of this thesis is the extraction of a class of polynomial loops that is exactly as expressive as linear loops, in the sense that there exist a linear loop with the exact same behavior. Then, two new methods for generating invariants are presented.The first method is based on abstract interpretation and is focused on a specific kind of linear loops called linear filters. Linear filters play a role in many embedded systems (plane sensors for example) and require the use of floating point operations, that may be imprecise and lead to errors if they are badly handled. Also, the presence of non deterministic assignments makes their analysis even more complex.The second method treats of a more generic subject by finding a complete set of linear invariants of linear loops that is easily computable. This technique is based on the linear algebra concept of eigenspace. It is extended to deal with conditions, nested loops and non determinism in assignments.Generating invariants is an interesting topic, but it is not an end in itself, it must serve a purpose. This thesis investigates the expressivity of invariantsgenerated by the second method by generating counter examples for the Kannan-Lipton Orbit problem.It also presents the tool PILAT implementing this technique and compares its efficiency technique with other state-of-the-art invariant synthesizers. The effective usefulness of the invariants generated by PILAT is demonstrated by using the tool in concert with CaFE, a model-checker for C programs based on temporal logics
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DUCA, VICTOR EDUARDO LEITE DE A. "A NEW APPROACH TO GENERATE TIME SERIES PERIODICAL SCENARIOS VIA NON-LINEAR MODELS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33375@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
PROGRAMA DE EXCELENCIA ACADEMICA
Os modelos autorregressivos são comumente encontrados dentro do contexto de séries hidrológicas, especificamente em séries de vazões e/ou ENA (Energia Natural Afluente). Muitos destes modelos são de ordem 1, possuem parâmetros constantes ou periódicos e necessitam do requisito de normalidade. Segundo a literatura, séries de vazões anuais podem ser aproximadas para distribuições normais, porém em períodos de tempo curtos como diário, semanal e mensal esta característica não é observada, especialmente pelo problema de assimetria. Devido a isto, uma nova classe de modelo de ordem 1 foi estudada na tentativa de suprir tal problema. O novo modelo mantém estrutura autorregressiva, pode ser aditivo, multiplicativo ou híbrido, onde incorpora propriedades aditivas e multiplicativas conjunta- mente, porém suas marginais assumirão distribuição gama. Além disso, a modelagem parte do pressuposto que os Métodos de Momentos são eficientes para estimação de seus parâmetros. Recentemente esta abordagem, sob a forma híbrida, não demonstrou sucesso para o contexto do despacho hidrotérmico brasileiro. O presente trabalho foca na análise completa do modelo híbrido para as séries do Setor Elétrico Brasileiro, trazendo como novidade a estimação via máxima verossimilhança além dos estudos isolados de modelos aditivos e multiplicativos. Os resultados revelaram uma linha de pesquisa promissora, abrindo um campo de possibilidades para que novas ordens superiores a primeira ou distribuições assimétricas possam ser estudadas partindo deste princípio.
Autoregressive models are commonly found in the context of hydrological series, specifically in streamflow and/or ANE series (Affluent Natural Energy). Most of them are models of order 1, which have constant or periodic parameters and need the requirement of normality. According to the literature, annual streamflow series can be approximated for normal distributions, however, in short periods of time, like daily, weekly and monthly, this feature is not observed, especially because of the asymmetry issue. Due to this reason, a new class of model of order 1 was studied for attempting to solve such problem. The new model keeps autoregressive structure and can be additive, multiplicative or hybrid, in which embodies additive and multiplicative properties together, but its marginals will assume gamma distribution. Moreover, this modeling departs from the presupposition that Methods of Moments are efficient to the estimation of its parameters. Recently, this approach, under the hybrid way, has not proved to be successful to the context of Brazilian hydrothermal dispatch. This work focuses on the complete analysis of hybrid model to the series of the Brazilian Electric Sector, bringing, as novelty, Maximum Likelihood Estimation, besides isolated studies of additive and multiplicative models. The results revealed a prosperous line of research, opening a field of possibilities for new orders or asymmetric distributions to be studied starting from this point.
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Alqattan, Nael Abdulhameed. "A Multi-Period Mixed Integer Linear Programming Model for Desalination and Electricity Co-generation in Kuwait." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5167.

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Water is the root of life and the engine that drives agriculture, industry, economy and services. The demand for water often necessitates desalination, particularly in arid coastal environments where there are several desalination technologies in use today such as Multi-Effect Distillation (MED) and Reverse Osmosis (RO). The key utility requirement for technologies such as desalination and population in general include energy in one form or another. Therefore, desalination and co-generation are often integrated. Another key utility is electricity which is generated from either renewable or non-renewable sources. The demands for water and electricity change over time and are subject to uncertainty. In this dissertation, a country-wide large-scale energy and water cogeneration planning model for Kuwait was proposed and solved. Five different plant technologies where the planning horizon used was set to 37 years starting in year 2014 and until 2050. A Mixed Integer Mathematical programming model was proposed and formulated using General Algebraic Modeling System (GAMS), the resulting model was solved using the CPLEX solver engine. In this research obtained detailed data on the consumption on water and energy in Kuwait and performed time series analysis of the population growth and individual behavior of water and energy consumption and novel method to represent cogeneration plants was implemented in the proposed mathematical programming model. ix A modeling framework that involves a data spreadsheet and a proprietary model was implemented. The data spreadsheet and the model were formulated as a template that can receive data from different applications. In addition, automation using Visual Basic for Application (VBA) was made to the data spreadsheets such that the data is sent to the model template, Gams-Cylix, and are written back to the spreadsheet. An analysis was made between oil-based plants, natural gas (NG) plants, and solar-based plants for co-generation. It was found that for water production solar-based plants can supply 50 percent or more of the demand during after period 2020 and after implementation and for electric power generation solar plants are limited. The results indicate the preferred technology for energy generation was NG-RO. With the implementation of solar based plants the electric power load is distributed among the technologies. NG-RO plants are more scalable and therefore were expanded to cope with the future demand. The percentage of the electric power supplied by solar plant was below 35 percent across the planning horizon. By the end of the planning horizon the percentage of electric power supplied by solar base plants was nearly 20 percent. Near 70 percent of the electric power was supplied by NG RO by period 2050. Other technologies had a representation of less than 10 percent by the end of the planning horizon.
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Freitas, Patrícia Fernanda da Silva. "Planejamento da expansão de sistemas de transmissão considerando múltiplos cenários de geração /." Ilha Solteira, 2018. http://hdl.handle.net/11449/157393.

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Orientador: Rubén Augusto Romero Lázaro
Resumo: Tradicionalmente, o problema de Planejamento da Expansão de Sistemas de Transmissão (PEST) é solucionado considerando apenas um único cenário de geração, embora sistemas elétricos reais operem em diferentes cenários de geração. Nessa pesquisa são propostos modelos matemáticos para resolver o problema de PEST, considerando múltiplos cenários de geração de forma que o plano de expansão obtido permita uma operação adequada do sistema. No modelo proposto, o custo de investimento é maior em relação aos planos de expansão encontrados pelo planejamento tradicional, que considera apenas um cenário de geração. Para reduzir o correspondente custo de investimento são apresentadas estratégias eficientes para encontrar planos de expansão para o problema de PEST considerando múltiplos cenários. As estratégias utilizadas foram: permitir pequenos cortes de carga; permitir o deslocamento do nível de geração em uma pequena faixa de geração mínima e máxima em relação à geração ideal e permitir pequenas sobrecargas nas linhas de transmissão. Adicionalmente, uma combinação entre essas estratégias é apresentada e o problema PEST também foi resolvido para o planejamento multiestágio, considerando múltiplos cenários de geração. O método proposto foi implementado com o uso da linguagem de modelagem algébrica AMPL e resolvido com o uso do solver comercial CPLEX. Os resultados encontrados correspondem à propostas de solução que são válidas para diferentes cenários de geração e apresentam diferentes alt... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: Traditionally, the Transmission Network Expansion Problem is solved considering only a single generation scenario. However, a real power system operates in different generation scenarios. This work presents the disjunctive linear model for the Transmission Network Expansion Problem considering multiple generation scenarios to provide a single expansion plan, which must operate in a appropriate way in each one of the different scenarios. The investment cost of the proposed model is greater in relation to the traditional expansion plans, that consider single generation scenario. In order to reduce the investment costs, efficient strategies are presented to find the expansion plans for multiples scenarios. Therefore those strategies are: allow small load cuts; allow generation level displacement in a narrow generation range in relation to the ideal one; and allow small overload in the transmission lines. Moreover, a combination between those strategies is shown and the Transmission Network Expansion Problem was also solved for multistage planning for multiple generation scenarios. The proposed method was implemented using A Mathematical Programming Language (AMPL) and the commercial solver CPLEX. The results were of optimal quality, considering the characteristics of the used solver, and they were compared with methods found in the specialized literature.
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Krisztin, Tamás. "Semi-parametric spatial autoregressive models in freight generation modeling." Elsevier, 2018. https://publish.fid-move.qucosa.de/id/qucosa%3A72336.

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This paper proposes for the purposes of freight generation a spatial autoregressive model framework, combined with non-linear semi-parametric techniques. We demonstrate the capabilities of the model in a series of Monte Carlo studies. Moreover, evidence is provided for non-linearities in freight generation, through an applied analysis of European NUTS-2 regions. We provide evidence for significant spatial dependence and for significant non-linearities related to employment rates in manufacturing and infrastructure capabilities in regions. The non-linear impacts are the most significant in the agricultural freight generation sector.
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Song, Ge. "Sound generation by coherent structures in mixing layers." Phd thesis, Paris, ENSAM, 2012. http://pastel.archives-ouvertes.fr/pastel-00835374.

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A mixing layer is generally considered as a prototype of free shear flows whichoccur in a very broad spectrum of applications from natural phenomena to the engineeringscience. The increasing development of the aeroacoustics, more particularly thanks to theprogress in numerical simulations, has allowed to identify the coherent structures which evolvein the flow as the main sound sources. Furthermore, the emergence of the instability wavestaking the form of the coherent structures can be characterized by the stability analysis. In thiscontext, we propose through this work to perform a global stability analysis, in order tounderstand the emergence of fundamental frequencies, as well as a modal decompositionwithin both a linear and nonlinear framework, to characterize the coherent structures primarilyresponsible for the sound generation. In particular, we propose to develop the methods of theglobal stability with respect to the compressible flows as well as a technique of calculation ofcoherent structures applied to the nonlinear regimes based on the DMD method. Such analysistools are validated and illustrated on an academic configuration of a co-flowing mixing layer, atypical noise amplifier, from a simple case where a single source is implicated in the acousticradiation to a more complex case where two sources are present. Both the temporal and spatialDMD are shown capable of describing the characteristics of the coherent structures in the nearfield and the behaviour of the acoustic waves in the far field. Finally, the methods of the modaldecomposition have proven themselves as a relevant model reduction aiming at designing anefficient control strategy.
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Loe, Bao Sheng. "The effectiveness of automatic item generation for the development of cognitive ability tests." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289990.

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Research has shown that the increased use of computer-based testing has brought about new challenges. With the ease of online test administration, a large number of items are necessary to maintain the item bank and minimise the exposure rate. However, the traditional item development process is time-consuming and costly. Thus, alternative ways of creating items are necessary to improve the item development process. Automatic Item Generation (AIG) is an effective method in generating items rapidly and efficiently. AIG uses algorithms to create questions for testing purposes. However, many of these generators are in the closed form, available only to the selected few. There is a lack of open source, publicly available generators that researchers can utilise to study AIG in greater depth and to generate items for their research. Furthermore, research has indicated that AIG is far from being understood, and more research into its methodology and the psychometric properties of the items created by the generators are needed for it to be used effectively. The studies conducted in this thesis have achieved the following: 1) Five open source item generators were created, and the items were evaluated and validated. 2) Empirical evidence showed that using a weak theory approach to develop item generators was just as credible as using a strong theory approach, even though they are theoretically distinct. 3) The psychometric properties of the generated items were estimated using various IRT models to assess the impact of the template features used to create the items. 4) Joint responses and response time modelling was employed to provide new insights into cognitive processes that go beyond those obtained by typical IRT models. This thesis suggests that AIG provides a tangible solution for improving the item development process for content generation and reducing the procedural cost of generating a large number of items, with the possibility of a unified approach towards test administration (i.e. adaptive item generation). Nonetheless, this thesis focused on rule-based algorithms. The application of other forms of item generation methods and the potential for measuring the intelligence of artificial general intelligence (AGI) is discussed in the final chapter, proposing that the use of AIG techniques create new opportunities as well as challenges for researchers that will redefine the assessment of intelligence.
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Books on the topic "Linear generative model"

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Glovackaya, Alevtina. Computational model. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1013723.

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The textbook covers the basics of classical numerical methods of computational mathematics used for solving linear and nonlinear equations and systems; interpolation and approximation of functions; numerical integration and differentiation; solutions of ordinary differential equations by methods of one-dimensional and multidimensional optimization. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is intended for students of higher educational institutions studying in the discipline "Numerical methods".
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Seslavin, Andrey. Theory of automatic control. Linear, continuous systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1014654.

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The textbook presents the basics of the classical theory of automatic control, based on mathematical models of real systems, given in the form of systems of linear differential equations with constant coefficients. Methods based on Laplace and Fourier transforms, stability, controllability, and observability theory, as well as directed graph theory and linear algebra are used. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students of higher educational institutions studying in the areas of training and specialties 15.00.00 "Mechanical Engineering", 27.00.00 "Management in technical systems".
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Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
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Linear and non-linear video and TV applications using IPv6 and IPv6 multicast: Deploying the infrastructure to deliver evolving next-generation TV and video services. Hoboken, N.J: Wiley, 2012.

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Chemodurov, Vladimir, and Ella Litvinova. Physical and mathematical modeling of building systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1014191.

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Physical and mathematical modeling is widely used in scientific research. This is due to the fact that field experiments on real construction sites are often impossible to organize for various reasons. The material included in the textbook is a summary of the authors ' experience in the field of system analysis. In the first section, the regularities of physical modeling of the functioning of objects based on the similarity and dimension theorems are considered. The second section presents modern models and methods for choosing optimal solutions: linear, nonlinear, stochastic, and statistical. The third section deals with experimental methods of system optimization based on the theory of experimental planning. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students of higher educational institutions studying in the direction of training 08.04.01 "Construction", and graduate students of higher educational institutions. It will be useful for specialists in the field of mathematical methods for the study of complex systems and their applications.
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1964-, Hartley T. T., and Lewis Research Center, eds. A method for generating reduced-order linear models of multidimensional supersonic inlets. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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Walsh, Bruce, and Michael Lynch. Short-term Changes in the Mean: 2. Truncation and Threshold Selection. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0014.

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The selection intensity, the mean change in a trait within a generation expressed in phenotypic standard deviations, provides an important metric for comparing the strength of selection over designs. Further, under truncation selection (only individuals above some threshold leave offspring), the selection intensity is a function of the fraction saved, and hence the breeder's equation is often expressed in terms of the selection intensity. An important special case of truncation selection is a threshold trait, wherein an individual only expresses a particular phenotype when its underlying liability value exceeds some threshold. This chapter examines selection on such traits, and generalizes this binary-trait setting (with binomial residuals) to other classes of discrete traits, wherein some underling linear model (generating the threshold) is this transformed via a generalized linear mixed model into an observed trait value.
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Jappelli, Tullio, and Luigi Pistaferri. The Buffer Stock Model. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199383146.003.0007.

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We analyze models that combine precautionary saving and liquidity constraints to provide a unified, more realistic treatment of intertemporal decisions. We start off with a simple three-period model to illustrate how the expectation of future borrowing constraints can induce precautionary saving even in scenarios in which marginal utility is linear. A more general model that allows liquidity constraints and precautionary saving to interact fully is the buffer stock model, of which there are two versions. One, developed by Deaton (1991), emphasizes the possibility that a prudent and impatient consumer may face credit constraints. The other, by Carroll (1997), features the same type of consumer but allows for the possibility of income falling to zero and so generating a natural borrowing constraint.
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1964-, Hartley T. T., and United States. National Aeronautics and Space Administration., eds. A method for generating reduced order linear models of supersonic inlets: Under grant NAG3-1450. [Washington, DC: National Aeronautics and Space Administration, 1997.

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10

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Soft constraints: exponential random graph models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0004.

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Exponential random graph models (ERGMs) provide conceptually elegant recipes for generating soft-constrained random graphs. This chapter begins by explaining the theory and describing how to properly specify an ERGM, including demonstrating Lagrange’s method to derive the values of the model parameters that correspond to the desired constraints. Three ERGMs, all with constraints depending linearly on the adjacency matrix, are solved exactly: the targeted total number of links, targeted individual node degrees and targeted number of two-way links in a directed graph. However, when the controlled features become more complicated, ERGMs have a tendency to produce graphs in extreme phases (very dense or very sparse). The two-star model and the Strauss model are worked through in detail using advanced techniques from statistical mechanics in order to analyze the phase transitions. The chapter closes with a discussion of the strengths and weaknesses of ERGMs as null models.
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Book chapters on the topic "Linear generative model"

1

Luo, Bin, Richard C. Wilson, and Edwin R. Hancock. "A Linear Generative Model for Graph Structure." In Graph-Based Representations in Pattern Recognition, 54–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31988-7_6.

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Rundo, Francesco, Giuseppe Luigi Banna, Francesca Trenta, Concetto Spampinato, Luc Bidaut, Xujiong Ye, Stefanos Kollias, and Sebastiano Battiato. "Advanced Non-linear Generative Model with a Deep Classifier for Immunotherapy Outcome Prediction: A Bladder Cancer Case Study." In Pattern Recognition. ICPR International Workshops and Challenges, 227–42. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68763-2_17.

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Jaminet, Jean, Gabriel Esquivel, and Shane Bugni. "Serlio and Artificial Intelligence: Problematizing the Image-to-Object Workflow." In Proceedings of the 2021 DigitalFUTURES, 3–12. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5983-6_1.

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AbstractVirtual design production demands that information be increasingly encoded and decoded with image compression technologies. Since the Renaissance, the discourses of language and drawing and their actuation by the classical disciplinary treatise have been fundamental to the production of knowledge within the building arts. These early forms of data compression provoke reflection on theory and technology as critical counterparts to perception and imagination unique to the discipline of architecture. This research examines the illustrated expositions of Sebastiano Serlio through the lens of artificial intelligence (AI). The mimetic powers of technological data storage and retrieval and Serlio’s coded operations of orthographic projection drawing disclose other aesthetic and formal logics for architecture and its image that exist outside human perception. Examination of aesthetic communication theory provides a conceptual dimension of how architecture and artificial intelligent systems integrate both analog and digital modes of information processing. Tools and methods are reconsidered to propose alternative AI workflows that complicate normative and predictable linear design processes. The operative model presented demonstrates how augmenting and interpreting layered generative adversarial networks drive an integrated parametric process of three-dimensionalization. Concluding remarks contemplate the role of human design agency within these emerging modes of creative digital production.
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López, Toni, Reinhold Elferich, and Eduard Alarcón. "Model Level 1: Piecewise Linear Analytical Switching Model." In Voltage Regulators for Next Generation Microprocessors, 133–95. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7560-7_3.

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Perrie, Will. "The Third Generation WAM Models for Wind-Generated Ocean Waves." In Non-Linear Variability in Geophysics, 257–59. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-009-2147-4_19.

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Ciardo, Gianfranco, Alex Blakemore, Philip F. Chimento, Jogesh K. Muppala, and Kishor S. Trivedi. "Automated Generation and Analysis of Markov Reward Models Using Stochastic Reward Nets." In Linear Algebra, Markov Chains, and Queueing Models, 145–91. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4613-8351-2_11.

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Węsowski, Andrzej. "Automatic Generation of Program Families by Model Restrictions." In Software Product Lines, 73–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28630-1_5.

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Hughes, Jack, and Dominic Orchard. "Resourceful Program Synthesis from Graded Linear Types." In Logic-Based Program Synthesis and Transformation, 151–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68446-4_8.

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AbstractLinear types provide a way to constrain programs by specifying that some values must be used exactly once. Recent work on graded modal types augments and refines this notion, enabling fine-grained, quantitative specification of data use in programs. The information provided by graded modal types appears to be useful for type-directed program synthesis, where these additional constraints can be used to prune the search space of candidate programs. We explore one of the major implementation challenges of a synthesis algorithm in this setting: how does the synthesis algorithm efficiently ensure that resource constraints are satisfied throughout program generation? We provide two solutions to this resource management problem, adapting Hodas and Miller’s input-output model of linear context management to a graded modal linear type theory. We evaluate the performance of both approaches via their implementation as a program synthesis tool for the programming language Granule, which provides linear and graded modal typing.
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Aleksandrowicz, Gadi, Alexander Ivrii, Oded Margalit, and Dan Rasin. "Generating Modulo-2 Linear Invariants for Hardware Model Checking." In Hardware and Software: Verification and Testing, 54–67. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13338-6_5.

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Appel, Simon, and Jaap Wijker. "Generation of Linear Conductors for Lumped Parameter Thermal Models." In Simulation of Thermoelastic Behaviour of Spacecraft Structures, 225–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78999-2_9.

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Conference papers on the topic "Linear generative model"

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Han, Tian, Jiawen Wu, and Ying Nian Wu. "Replicating Active Appearance Model by Generator Network." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/305.

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A recent Cell paper [Chang and Tsao, 2017] reports an interesting discovery. For the face stimuli generated by a pre-trained active appearance model (AAM), the responses of neurons in the areas of the primate brain that are responsible for face recognition exhibit strong linear relationship with the shape variables and appearance variables of the AAM that generates the face stimuli. In this paper, we show that this behavior can be replicated by a deep generative model called the generator network, which assumes that the observed signals are generated by latent random variables via a top-down convolutional neural network. Specifically, we learn the generator network from the face images generated by a pre-trained AAM model using variational auto-encoder, and we show that the inferred latent variables of the learned generator network have strong linear relationship with the shape and appearance variables of the AAM model that generates the face images. Unlike the AAM model that has an explicit shape model where the shape variables generate the control points or landmarks, the generator network has no such shape model and shape variables. Yet the generator network can learn the shape knowledge in the sense that some of the latent variables of the learned generator network capture the shape variations in the face images generated by AAM.
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Santhanam, Anand P., Brad Stiehl, Michael Lauria, Igor Barjaktarevic, Jonathan Goldin, Jane Yanagawa, and Daniel Low. "A quantitative prediction of the post-operative lobectomy lung physiology using a GPU-based linear elastic lung biomechanics model and a constrained generative adversarial learning approach." In Image-Guided Procedures, Robotic Interventions, and Modeling, edited by Cristian A. Linte and Jeffrey H. Siewerdsen. SPIE, 2021. http://dx.doi.org/10.1117/12.2582271.

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Villanueva Llerena, Julissa. "Predictive Uncertainty Estimation for Tractable Deep Probabilistic Models." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/745.

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Tractable Deep Probabilistic Models (TPMs) are generative models based on arithmetic circuits that allow for exact marginal inference in linear time. These models have obtained promising results in several machine learning tasks. Like many other models, TPMs can produce over-confident incorrect inferences, especially on regions with small statistical support. In this work, we will develop efficient estimators of the predictive uncertainty that are robust to data scarcity and outliers. We investigate two approaches. The first approach measures the variability of the output to perturbations of the model weights. The second approach captures the variability of the prediction to changes in the model architecture. We will evaluate the approaches on challenging tasks such as image completion and multilabel classification.
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Zhang Xiaojin and Xie Xiaorong. "A multimass model with non-linear modal damping for SSR analysis of turbine generators." In 2009 International Conference on Sustainable Power Generation and Supply. SUPERGEN 2009. IEEE, 2009. http://dx.doi.org/10.1109/supergen.2009.5347928.

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Jalali, Shirin, and Xin Yuan. "Solving linear inverse problems using generative models." In 2019 IEEE International Symposium on Information Theory (ISIT). IEEE, 2019. http://dx.doi.org/10.1109/isit.2019.8849788.

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Laksari, Kaveh, and Kurosh Darvish. "Brain Deformation in Linear Head Impact." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-11697.

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In this study, a 2D model of the head underwent linear impact and the experiments were simulated by finite element models. A cylinder with a diameter of 100mm and height of 20mm was filled with 5% gelatin, which was used as the brain surrogate material. The physical model was mounted onto a High Speed Computer Controlled Impact System to generate inertial loading of approximately 50 G average deceleration. The deformation of the samples was studied through image processing. Finite element (FE) analysis was used to simulate the experiments. The impact tests were modeled with two methods: a Lagrangian formulation with single point integration and an Arbitrary Lagrangian Eulerian (ALE) formulation with single point integration and void using LS-Dyna FE code. In the model with slip contact, the normal and shear strains reached more than 20% in some regions, which confirmed the risk of axonal injury in the linear impacts applied in this study. It was seen that in the Lagrangian models, in order to stabilize the simulation, high bulk moduli needed to be used; however, this resulted in much smaller void generation in the posterior region of the model. It was shown that the void generation reaches the experimental values by introducing 1–2 mm initial gaps between brain and skull. The ALE model was more stable and less sensitive to the bulk modulus, but showed smaller deformations.
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Okada, Takashi, Satoshi Teramoto, and Nobuo Furuta. "New Generation Model of Linear Oxygen Sensor." In JSAE/SAE 2015 International Powertrains, Fuels & Lubricants Meeting. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2015. http://dx.doi.org/10.4271/2015-01-2020.

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Kanev, Valeriy. "Solution Modeling in System Coordination of Objectives and Resourcing Linear Business Processes." In Contemporary Issues in Business, Management and Education. Vilnius Gediminas Technical University, 2017. http://dx.doi.org/10.3846/cbme.2017.104.

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The paper deals with the analytical approach to reaching compromises in coordinating objectives and resourcing business processes, algorithms and software for generating strategies – compromises for situational analysis. Unlike traditional schemes it is suggested using a vector-mode display to analytically manage both resources in a risk-tolerant way and signaling event occurrence dates in a technologically practicable schedule of a net model. Model-methodical support of linear business processes realization programs can be used for the algorithmic control of objectives coordination and resourcing when controlling program realization of linear engineering, e.g. highway and oil-and-gas pipeline engineering, railway and power lines engineering, etc., i.e. engineering of technologically one-type objects linearly allocated in space. The experimental calculations based on actual construction programs confirmed the model construction adequacy, practical usefulness of the modeling results and modeling solutions in the system coordination of objectives and resourcing linear business processes.
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Hor, S., A. Tabesh, and A. Zamani. "Analytical model of an improved linear generator for seawave energy harvesting." In IET Conference on Renewable Power Generation (RPG 2011). IET, 2011. http://dx.doi.org/10.1049/cp.2011.0230.

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Yang, Junggi, Youngho Lee, and Un Gu Kang. "Cardiovascular disease prediction models on Linear Discriminant Analysis of depression." In Next Generation Computer and Information Technology 2014. Science & Engineering Research Support soCiety, 2014. http://dx.doi.org/10.14257/astl.2014.63.30.

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Reports on the topic "Linear generative model"

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Noble, Michael. How to implement sub-national poverty lines in a SOUTHMOD country model using conditional constants: The case of UGAMOD. UNU-WIDER, March 2021. http://dx.doi.org/10.35188/unu-wider/wtn/2021-3.

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This note describes how to incorporate sub-national poverty lines into a SOUTHMOD country model using conditional constants within the constants function in such a way that the Statistics Presenter can generate national-level poverty statistics. The Uganda tax-benefit microsimulation model UGAMOD is used as an example.
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Allen, Luke, Joon Lim, Robert Haehnel, and Ian Detwiller. Rotor blade design framework for airfoil shape optimization with performance considerations. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41037.

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A framework for optimizing rotor blade airfoil shape is presented. The framework uses two digital workflows created within the Galaxy Simulation Builder (GSB) software package. The first is a workflow enabling the automated creation of a surrogate model for predicting airfoil performance coefficients. An accurate surrogate model for the rapid generation of airfoil coefficient tables has been developed using linear interpolation techniques that is based on C81Gen and ARC2D CFD codes. The second workflow defines the rotor blade optimization problem using GSB and the Dakota numerical optimization library. The presented example uses a quasi-Newton optimization algorithm to optimize the tip region of the UH-60A main rotor blade with respect to vehicle performance. This is accomplished by morphing the blade tip airfoil shape for optimum power, subject to a constraint on the maximum pitch link load.
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Blonigan, Patrick Joseph, Gianluca Geraci, Francesco Rizzi, Michael S. Eldred, and Kevin Carlberg. On-line Generation and Error Handling for Surrogate Models within Multifidelity Uncertainty Quantification. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1567834.

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Wissink, Andrew, Jude Dylan, Buvana Jayaraman, Beatrice Roget, Vinod Lakshminarayan, Jayanarayanan Sitaraman, Andrew Bauer, James Forsythe, Robert Trigg, and Nicholas Peters. New capabilities in CREATE™-AV Helios Version 11. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/40883.

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CREATE™-AV Helios is a high-fidelity coupled CFD/CSD infrastructure developed by the U.S. Dept. of Defense for aeromechanics predictions of rotorcraft. This paper discusses new capabilities added to Helios version 11.0. A new fast-running reduced order aerodynamics option called ROAM has been added to enable faster-turnaround analysis. ROAM is Cartesian-based, employing an actuator line model for the rotor and an immersed boundary model for the fuselage. No near-body grid generation is required and simulations are significantly faster through a combination of larger timesteps and reduced cost per step. ROAM calculations of the JVX tiltrotor configuration give a comparably accurate download prediction to traditional body-fitted calculations with Helios, at 50X less computational cost. The unsteady wake in ROAM is not as well resolved, but wake interactions may be a less critical issue for many design considerations. The second capability discussed is the addition of six-degree-of-freedom capability to model store separation. Helios calculations of a generic wing/store/pylon case with the new 6-DOF capability are found to match identically to calculations with CREATE™-AV Kestrel, a code which has been extensively validated for store separation calculations over the past decade.
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Guidati, Gianfranco, and Domenico Giardini. Joint synthesis “Geothermal Energy” of the NRP “Energy”. Swiss National Science Foundation (SNSF), February 2020. http://dx.doi.org/10.46446/publication_nrp70_nrp71.2020.4.en.

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Near-to-surface geothermal energy with heat pumps is state of the art and is already widespread in Switzerland. In the future energy system, medium-deep to deep geothermal energy (1 to 6 kilometres) will, in addition, play an important role. To the forefront is the supply of heat for buildings and industrial processes. This form of geothermal energy utilisation requires a highly permeable underground area that allows a fluid – usually water – to absorb the naturally existing rock heat and then transport it to the surface. Sedimentary rocks are usually permeable by nature, whereas for granites and gneisses permeability must be artificially induced by injecting water. The heat gained in this way increases in line with the drilling depth: at a depth of 1 kilometre, the underground temperature is approximately 40°C, while at a depth of 3 kilometres it is around 100°C. To drive a steam turbine for the production of electricity, temperatures of over 100°C are required. As this requires greater depths of 3 to 6 kilometres, the risk of seismicity induced by the drilling also increases. Underground zones are also suitable for storing heat and gases, such as hydrogen or methane, and for the definitive storage of CO2. For this purpose, such zones need to fulfil similar requirements to those applicable to heat generation. In addition, however, a dense top layer is required above the reservoir so that the gas cannot escape. The joint project “Hydropower and geo-energy” of the NRP “Energy” focused on the question of where suitable ground layers can be found in Switzerland that optimally meet the requirements for the various uses. A second research priority concerned measures to reduce seismicity induced by deep drilling and the resulting damage to buildings. Models and simulations were also developed which contribute to a better understanding of the underground processes involved in the development and use of geothermal resources. In summary, the research results show that there are good conditions in Switzerland for the use of medium-deep geothermal energy (1 to 3 kilometres) – both for the building stock and for industrial processes. There are also grounds for optimism concerning the seasonal storage of heat and gases. In contrast, the potential for the definitive storage of CO2 in relevant quantities is rather limited. With respect to electricity production using deep geothermal energy (> 3 kilometres), the extent to which there is potential to exploit the underground economically is still not absolutely certain. In this regard, industrially operated demonstration plants are urgently needed in order to boost acceptance among the population and investors.
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