We consider the problem of identifying a dissipative linear model of an unknown nonlinear system that is known to be dissipative, from time-domain input-output data. We first learn an approximate ...linear model of the nonlinear system using standard system identification techniques and then perturb the system matrices of the linear model to enforce dissipativity, while closely approximating the dynamical behavior of the nonlinear system. Further, we provide an analytical relationship between the size of the perturbation and the radius in which the dissipativity of the linear model guarantees local dissipativity of the unknown nonlinear system. We demonstrate the application of this identification technique through two examples.
Soil carbon dioxide emission is a major component of ecosystem respiration, responsible for organic carbon losses from the ecosystem. In
Pinus roxburghii
Sarg. plantations, higher CO
2
emission ...coincided with maximum soil moisture and soil temperature during the rainy season (4.23 µmol CO
2
m
-2
s
-1
) followed by summer season (1.69 µmol CO
2
m
-2
s
-1
) and winter season (1.35 µmol CO
2
m
-2
s
-1
). The soil CO
2
emission rates recorded during the rainy season differed significantly from other seasons (
p
< 0.05). Multiple linear regression revealed that rainfall was the main dominant factor affecting the soil CO
2
emission. A significant positive correlation with minimum air temperature and average air temperature during the lag period, i.e., preceding 15 days of data, was recorded. A significant positive correlation was also observed between annual soil CO
2
emission rates with soil temperature, soil moisture, air temperature, and rainfall (
p
< 0.05). Vapor pressure and relative humidity at 14.19 h also emerged as additional scientific variables affecting soil CO
2
emission with significant positive correlations. Annual soil CO
2
emission rates and soil properties were not significantly correlated but were positively correlated with organic carbon, exchangeable potassium and negatively correlated with available nitrogen and phosphorous (
p
> 0.05). Higher annual average carbon stock, 95.05 t ha
-1
in
P. roxburghii
plantations than the yearly soil CO
2
emission, 33.23 t ha
-1
indicates that plantations sequester more carbon than the emissions.
We consider the problem of designing distributed controllers to guarantee dissipativity of a networked system comprised of dynamically coupled subsystems. We require that the control synthesis is ...carried out locally at the subsystem-level, without explicit knowledge of the dynamics of other subsystems in the network. We solve this problem in two steps. First, we provide distributed subsystem-level dissipativity analysis conditions whose feasibility is sufficient to guarantee dissipativity of the networked system. We then use these conditions to synthesize controllers locally at the subsystem-level, using only the knowledge of the dynamics of that subsystem, and limited information about the dissipativity of the subsystems to which it is dynamically coupled. We show that the subsystem-level controllers synthesized in this manner are sufficient to guarantee dissipativity of the networked dynamical system. We also provide an approach to make this synthesis compositional, that is, when a new subsystem is added to an existing network, only the dynamics of the new subsystem, and information about the dissipativity of the subsystems in the existing network to which it is coupled are used to design a controller for the new subsystem, while guaranteeing dissipativity of the networked system including the new subsystem. Finally, we demonstrate the application of this synthesis in enabling plug-and-play operations of generators in a microgrid by extending our results to networked switched systems.
Two Indian rice cultivars, namely Sonasakthi (SS) and Gobindbhog (GB), were hydrated for 6, 12 and 18 h of soaking time and temperature of 30, 40 and 50 °C to analyse the physicochemical and ...structural properties of rice flour. The moisture content of flour ranged between 14.18 and 20.71% among various soaking treatments and increased significantly with soaking time at all temperatures. The amylose content was found to be decreasing at a higher soaking temperature of 50 °C. Swelling power of rice flour was found to be increasing with increased soaking temperature and time varied from 1.821 to 1.911% for SS and from 1.743 to 1.924% for GB. Since the gel consistency values are directly correlated with amylose content, for SS it was highest (133.79 mm) at 40 °C. Response surface methodology (RSM) was used to optimise soaking conditions, resulting in an intermediate soaking temperature (40 °C) and the shortest soaking time (6 h) being preferred for SS with desirability of 0.696. Morphological changes show typical A type crystalline structure and deformed fractions under optimised soaking condition. Correlation matrix displays the correlation coefficient of rice flour under various hydration conditions in which flow properties were mutually linked. Multivariate analysis in terms of principal component analysis (PCA) and Hierarchical cluster analysis (HCA) also illustrates the association among variables and hydration conditions. Thus, the soaking condition of rice provides desired properties that can contribute to the development of rice flour based deep fat fried products.
Restructured foods are a blend of various ingredients that are dried or fried to obtain a ready-to-eat product. Several frying techniques have been employed viz., deep fat, microwave, vacuum, air, ...and spray frying. Deep-fat frying is the most common technique used for products that have improved texture and sensory characteristics. It facilitates various transformations that include starch gelatinization, protein denaturation, nutrient loss, non-enzymatic browning, lipid oxidation, etc. This physicochemical change alters both the product and the fried oil quality. The frying conditions will also influence the product characteristics and affect the properties of the fried product. This review focuses on the mechanisms and transformations during deep fat frying. The properties, namely physical, chemical, sensory, thermal, rheological, and microstructural changes of restructured foods were discussed. Thus, a better understanding of mechanisms and properties at optimum frying conditions would yield the desired product quality.
•Restructured foods are a blend of ingredients that may be molded into desired shapes.•Bubble end point indicates frying completion by decreasing moisture loss.•Deep-frying develops a porous structure and crust formation after frying.•Optimizing deep-fat frying parameters can enhance restructured food characteristics.
The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the US becoming the epicenter of COVID-19 cases since late March. As the US begins to gradually resume ...economic activity, it is imperative for policymakers and power system operators to take a scientific approach to understanding and predicting the impact on the electricity sector. Here, we release a first-of-its-kind cross-domain open-access data hub, integrating data from across all existing US wholesale electricity markets with COVID-19 case, weather, mobile device location, and satellite imaging data. Leveraging cross-domain insights from public health and mobility data, we rigorously uncover a significant reduction in electricity consumption that is strongly correlated with the number of COVID-19 cases, degree of social distancing, and level of commercial activity.
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•Open-access data hub to track the impact of COVID-19 on the electricity sector•Cross-domain analysis of electricity sector using mobility and public health data•Mobility in the retail sector is the key factor influencing electricity consumption•Visualization of the impact of COVID-19 through satellite night-time-light data
As the lifeblood of civil society, the electricity sector is undergoing highly volatile changes due to COVID-19. The dramatic reduction in electricity consumption due to shutdown policies during COVID-19 can in fact be seen from space, as visualized by night-light satellite imagery in our study. In this work, we release a first-of-its-kind open-access data hub and cross-domain analysis integrating traditional electricity data with cross-domain sources such as public health and mobility data to quantify the impact of these social distancing and public health policies on electricity consumption. In the short term, this cross-domain analysis can immediately aid power system operators and policy makers in their response to the ongoing crisis. From a broader perspective, this research departs from the traditional landscape of forecasting, reliability, and risk assessment in the electricity sector by introducing new domains of data that will inform planning, policy, and operation in the future.
We present a cross-domain, data-driven approach to tracking and quantifying the impact of COVID-19 on the US electricity sector, including (1) a first-of-its-kind open-access data hub integrating electricity data with public health, mobility, weather, and satellite data and (2) a cross-domain analysis quantifying the sensitivity of electricity consumption to social distancing and public health policies. Population mobility, particularly in the retail sector, which is indicative of social distancing policy measures, emerges as the key factor driving changes in electricity consumption.
In the article entitled "Distributed synthesis of local controllers for networked systems with arbitrary interconnection topologies" published in the IEEE Transactions on Automatic Control , vol. 66, ...pp. 683-698, 2021, there were some missing terms in Lemma 1 and the definition of the messenger matrices. Here, we include these terms and suitably modify the corresponding proofs. Note that all the main claims of the original article, including distributed analysis, distributed synthesis, and compositionality hold with the modified results.
Spectrum sensing will be an essential component in developing cognitive radio networks, which will be an essential component of the subsequent generation of wireless communication systems. Over the ...course of several decades, a great deal of different strategies, including cyclo-stationary, energy detectors, and matching filters, have been put up as potential solutions. Obviously, each of these methods comes with a few of negatives that you have to take into consideration. When the Signal-to-Noise Ratio (SNR) changes, energy detectors work poorly; cyclo-stationary detectors are technically sophisticated; and employing matching filters needs experience with Primary User (PU) signals. Researchers have recently been devoting a great deal of attention to Machine Learning (ML) and Deep Learning (DL) algorithms as a result of the potential uses that these algorithms may have in the development of exceptionally accurate spectrum sensing models. The capacity to learn from data in a way that traditional learning algorithms are unable to has led to the rise in prominence of these types of algorithms. The Hybrid Model of Improved Long Short Term Memory with Improved Extreme Learning Models (HILSTM-IELM), to be more specific, is what is being suggested since it reduces the amount of energy that is used during data transmission as well as the range and the duty cycle. Because of this, the disadvantage in existing methodology, proposed technique reduced to a certain level in energy consumption. In the last step of this analysis, the performance of the HILSTM-IELM-based spectrum sensing is compared to that of a variety of different methods that are currently in use. According to the findings of recent studies, the spectrum sensing method that was created provides superior performance to that of technologies in terms of the accuracy, sensitivity, and specificity of data transmission systems.
The study has outlined the performance evaluation of bidirectional wavelength division multiple access broadband optical passive elastic networks operation efficiency. Signal power level,
coefficient ...parameter, and minimum data error rates are measured numerically in both downstream and upstream directions based on broadband passive optical networks (BPONs). The transmission data rate available in the downstream direction is 10 Gb/s, while its value in the upstream direction is 2 Gb/s. The suitable operating wavelength is selected in the near-infrared region around 1550 nm for minimum losses. The bits sequence length of the optical transmitter is taken in to account for testing the optical network performance efficiency. The proposed model has presented better performance than the previous model by 25.76 % in downstream direction and by 12.28 % in upstream direction. As well as the bidirectional wavelength division multiple access broadband passive optical elastic networks performance efficiency is measured.
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