1,720,993 research outputs found
PSO adaptive fading memory Kalman filter based state estimation of indoor thermal model with unknown inputs
No full textAn adaptive filtering approach is proposed in this paper to address the thermal state estimation methodology along with the model parameters jointly for an indoor thermodynamic resistance capacitance model with uncertain stochastic heating inputs. The adaptive dynamics of the state of the model is combined with a particle swarm optimization (PSO) based metaheuristic approach to feed the knowledge of measurement noise statistics and the initial estimation error covariance along with forgetting factor for implementation of fading memory Kalman filter (FMKF). This study has been carried out with the variation of uncertain influential input information to enhance the estimation efficiency with the proposed PSO adaptive FMKF (PSO-AdFMKF) strategy for a real life the test thermodynamic environment scenario inside the building space. Potential observations demonstrate that the proposed estimation algorithm performs encouragingly, with a satisfactory improvement of estimation performance in terms of evaluating error metrics.</p
Joint state estimation of indoor thermal dynamics with unknown inputs using augmented fading memory Kalman filter
No full textAn intelligent and efficient utilization of a heating, ventilation, and air conditioning system can be instrumental to reduce the building energy consumption, which in turn, is expected to reduce the green-house gases. The energy profiling requires modelling and estimation of the building environment with uncertainties. This paper proposes a strategy to estimate indoor thermal dynamics at multiple walls using a forgetting factor-based fading memory Kalman filter (FMKF) in presence of unknown inputs. This work also proposes a joint state estimation scheme based on FMKF which considers augmentation of the unknown heating energy inputs along with the thermal parameters of the thermodynamic model developed for indoor environment. The contribution of unknown inputs in the process of state estimation have been studied in the context of measuring node distribution. The proposed scheme has been implemented for multiple real-life thermal scenarios and results outperformed the conventional Kalman filter-based estimation scheme.</p
Dynamic nonlinear indoor environment thermal state estimation with unknown inputs using PSO guided regularizer based adaptive EKF
No full textThe present paper shows how the dynamic indoor temperature profile of an HVAC (Heating, Ventilation, and Air Conditioning) system in a building can be developed using Kalman filters, in presence of unknown inputs. An RC network based dynamic, nonlinear thermal model is first developed for the indoor environment with a novel consideration of relative humidity factor. Then an extended Kalman Filter based algorithm in presence of unknown inputs (called EKF-UI) and an adaptive variation of this EKF-UI algorithm (called AdEKF-UI) are developed for the real indoor environment under consideration. Next, a particle swarm optimization (PSO) guided adaptive extended Kalman filter with unknown inputs (PSOgAdEKF-UI) algorithm is proposed to overcome limitations of the EKF-UI and AdEKF-UI algorithms, especially under bad initialization situations. This PSOgAdEKF-UI algorithm proposes an effective utilization of regularizer based initializations for the initial state estimation error covariance matrix and the measurement noise covariance matrix. Extensive experiments showed that, overall, PSOgAdEKF-UI algorithm could outperform EKF-UI and AdEKF-UI algorithms by 46.59% and 20.66%, respectively, in terms of mean square error, while estimating an unknown state. Note to Practitioners - This paper was motivated to estimate the nonlinear dynamics of indoor HVAC thermal profile in presence of unknown inputs. The study explores a proposed Kalman filter-based heuristic regularizer-assisted adaptive filtering methodology for nonlinear state estimation that can circumvent the constraints imposed by current approaches. The proposed method demonstrates its applicability in actual nonlinear physical systems since many matrices needed for such state estimation algorithms do not have accurate initialization information. The nature of inferential stochastic inputs in practical HVAC system can be evaluated utilizing our novel state estimation method of the altering relative humidity coupled nonlinear dynamic thermal model. The thermal profile of a practical HVAC system, in presence of varying unknown inputs, can be more accurately modeled when temporal variations in relative humidity are included in the nonlinear dynamic model, as an additional influencing factor.</p
Time-varying unknown input constrained UKF with unbiased minimum variance estimator for nonlinear dynamic indoor thermal profile estimation
No full textEstimating unknown inputs in indoor heating, ventilation, and air conditioning (HVac) systems, particularly under the influence of diverse environmental constraints and time-varying relative humidity, presents a significant challenge. A viable solution is to use a weighted least-squares (WLS) approach for estimating unknown inputs, which uses an unbiased minimum variance (UMV) estimator in conjunction with an unscented Kalman filter (UKF)-based nonlinear filtering technique. This allows for the simultaneous estimation of the system’s state and the unknown inputs. To accurately represent the real-life nonlinear thermal profile influenced by these uncertain inputs, it is essential to adopt an RC network-based mathematical modeling approach that captures the system’s dynamic behavior over time. The integration of the UMV-based optimal estimator with the UKF culminates in the proposed UKF with UMV for unknown inputs (UKF-UMV-UI) estimation algorithm. Extensive experimentation with the proposed UKF-UMV-UI algorithm has been conducted in a laboratory-scale realistic environment, dealing with uncertain and challenging unknown inputs. The results of the investigation indicate that the proposed method outperforms the UKF with unknown input (UKF-UI) by 41.64% and 35.85% in cumulative mean squared error (CuMSE) for two distinct measurement conditions, respectively.</p
PSO-guided optimal estimator enabled regularized adaptive extended Kalman filter with unknown inputs for dynamic nonlinear indoor thermal state estimation
No full textThis paper presents a particle swarm optimization-guided maximum likelihood estimation enabled (MLE) adaptive extended Kalman filter (EKF) with unknown inputs algorithm for estimating the dynamic nonlinear thermal states for an indoor heating ventilation and air conditioning system. The concept of MLE has been introduced to enhance the speed of convergence of the filtering parameters in adaptive EKF. The nonlinear indoor environment has been modelled employing equivalent RC network taking relative humidity into account. At the outset, an EKF-based method accommodating the unknown inputs and an adaptive estimator-based variant of it are developed for estimating the temperature of the walls of a laboratory-scale realistic environment. Subsequently the proposed scheme comes into play to deal with the scenarios associated with undesirable divergence and poor initialization utilizing the metaheuristically adapted optimal regularizer. The proposed technique outperforms the other contemporary state-of-the-art counterparts in terms of mean squared error.</p
Nitrogen Dynamics in Soils from the Red River Valley of the North
No full textVideo summarizing Ph.D. dissertation for a non-specialist audience
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Soil CO2 and N2O fluxes from simple and diversified crop rotations in the Central Corn Belt
Full text linkAgriculture in the United States has become highly productive but environmental consequences remain. Agriculture makes up a disproportionate share of net US greenhouse gas emissions compared to its contribution to the economy; the issue may be related to the decrease in crop diversity and reliance on synthetic fertilizer. In the US Corn Belt, crop rotational diversity is mostly limited to maize (Zea mays) and soybean (Glycine max). We compared soil carbon dioxide (CO₂) and nitrous oxide (N₂O) fluxes from a long-term experiment comparing the 2-year maize–soybean rotation to two other more diversified rotations: a 3-year maize–soybean–oat (Avena sativa)/red clover (Trifolium pratense) and a 4-year maize–soybean–oat/alfalfa (Med- icago sativa]–alfalfa rotations. Both 3- and 4-year rotations also received composted cattle (Bos taurus) manure. We tested whether these more diversified rotations that replace a portion of the synthetic fertilizer with organic sources could decrease CO₂ and N₂O losses. Soil CO₂ fluxes in the 3- or 4-year rotations were 36% and 54% greater than in the 2-year rotation, driven by the maize phase, which might be due to the prior years’ leguminous crops (red cover or alfalfa), tillage, and manure. The crop phases within a rotation had significant effect on soil CO₂ (alfalfa > oat > maize = soybean) and N₂O (maize = alfalfa > soybean = oat) fluxes. Soil temperature–crop phase interactions had more control over soil CO₂ fluxes than soil moisture. In the Central Corn Belt of the United States, replacing fertilizer-N supported maize–soybean rotation with diversified rotation and replacing inorganic N with an organic N source increased soil CO₂ flux but did not affect N₂O flux.This article is published as Chatterjee, Amitava, Bryan Emmett, Peter O'Brien, Marshall D. McDaniel, Thomas Sauer, and Matt Liebman. "Soil CO2 and N2O fluxes from simple and diversified crop rotations in the Central Corn Belt." Agrosystems, Geosciences & Environment 8, no. 3 (2025): e70171. doi:10.1002/agg2.7017
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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