Set Of Generators Research Articles

Are There Different Shades of Green? The “Greenium” in Municipal Power Bonds

Using data on all municipal “green” bonds issued from 2010 to 2021, the authors examine whether municipal bonds issued to finance green power projects command a premium (lower yield to maturity) relative to other similar-risk municipal green bonds. Consistent with the idea that green projects are easier to identify and certify in the setting of power generation, the authors find that green power bonds command a premium of 11 basis points relative to other similar-risk municipal green bonds. Those results support a nonpecuniary utility for such easily identifiable green investments. The authors illustrate the incremental value added from this reduction in the cost of capital and a gradual move from fossil fuel to green power generation for one of the largest municipal utilities in the United States.

Carrollian and Galilean conformal higher-spin algebras in any dimensions

We present higher-spin algebras containing a Poincaré subalgebra and with the same set of generators as the Lie algebras that are relevant to Vasiliev’s equations in any space-time dimension D ≥ 3. Given these properties, they can be considered either as candidate rigid symmetries for higher-spin gauge theories in Minkowski space or as Carrollian conformal higher-spin symmetries in one less dimension. We build these Lie algebras as quotients of the universal enveloping algebra of mathfrak{iso}left(1,D-1right) and we show how to recover them as Inönü-Wigner contractions of the rigid symmetries of higher-spin gauge theories in Anti de Sitter space or, equivalently, of relativistic conformal higher-spin symmetries. We use the same techniques to also define higher-spin algebras with the same set of generators and containing a Galilean conformal subalgebra, to be interpreted as non-relativistic limits of the conformal symmetries of a free scalar field. We begin by showing that the known flat-space higher-spin algebras in three dimensions can be obtained as quotients of the universal enveloping algebra of mathfrak{iso}left(1,2right) and then we extend the analysis along the same lines to a generic number of space-time dimensions. We also discuss the peculiarities that emerge for D = 5.

A Visualizable Test Problem Generator for Many-Objective Optimization

Visualizing the search behavior of a series of points or populations in their native domain is critical in understanding biases and attractors in an optimization process. Distance-based many-objective optimization test problems have been developed to facilitate visualization of search behavior in a 2-D design space with arbitrarily many objective functions. Previous works have proposed a few commonly seen problem characteristics into this problem framework, such as the definition of disconnected Pareto sets and dominance resistant regions of the design space. The authors’ previous work has advanced this research further by providing a problem generator to automatically create user-defined problem instances featuring any combination of these problem features as well as newly introduced ones, such as landscape discontinuities, varying objective ranges, and neutrality. This work makes a number of additional contributions including the proposal of an enhanced, open-source feature-rich problem generator that can create user-defined problem instances exhibiting a range of problem features—some of which are newly introduced here or form extensions of existing features. A comprehensive validation of the problem generator is also provided using popular multiobjective optimization algorithms, and some problem generator settings to create instances exhibiting different challenges for an optimizer are identified.

Free Covers and Minimal Sets of Generators

We continue our study of the relation between existence of projective covers and existence of minimal sets of generators for right modules over an arbitrary ring.

Evaluation on Distribution Characteristics of Pore Water Pressure within Saturated Pavement Structure Based on the Proposed Tire-Fluid-Pavement Coupling Model

To investigate the flow characteristics of pore water in asphalt pavement and the variation law of the pore water pressure under vehicle loading, a novel method based on BA network and quartet structure generation set method was proposed to reconstruct the three-dimensional (3D) pavement model with pores. The permeability coefficient and the gradation curve were adopted to evaluate the reliability and stability of the random growth pavement model. Then, the tire-fluid-pavement coupling model was established with FLUENT 3D based on the fluid Mie–Gruneisen state equation. According to the built fluid-solid coupling model, the pressure-velocity coupled finite volume algorithm was applied to study the distribution of the pore water pressure in asphalt pavement. Results show that the pore water pressure in asphalt pavement decays periodically with time under vehicle loading. For different types of asphalt pavement, the pore water pressure in open-graded friction course (OGFC) pavement is the smallest during the whole process. Moreover, the peak values of the pore water pressure decrease in the order of asphalt concrete (AC) pavement, stone mastic asphalt (SMA) pavement, and OGFC pavement. The maximum negative value of the pore water pressure is generally less than 0.3 times the maximum positive values. As for saturated pavement pores, the pore water pressure is hardly affected by the water film thickness. The positive peak value of the pore water pressure increases on an approximate parabolic curve as the vehicle speed improves gradually, while the negative one remains largely unchanged. The results are expected to help reduce tire hydroplaning risk and provide guidance for the selection of asphalt mixtures of drainage asphalt pavement.

The lack of polynomial stability to mixtures with memory

We consider the system modeling a mixture of n materials with memory. We show that the corresponding semigroup is exponentially stable if and only if the imaginary axis is contained in the resolvent set of the innitesimal generator. In particular this implies the lack of polynomial stability to the corresponding semigroup.

First report on the persist time of the free radical produced by shock wave pulses employed in clinical ESWL

The shock wave used in extracorporeal shock wave lithotripsy (ESWL) induces strong cavitation and generates a large amount of free radicals (FR). In order to evaluate the harmfulness of FR in the ESWL, information on the incidence and persist time of FR caused by shock waves is required. FR markers can estimate the amount of FR generated, but not how long the FRs will survive. The OH* FR generated by the ESWL shock wave reacts with luminol and emits blue light, which is called sonochemical luminescence (SCL) phenomenon. In this study, FR generation and persist time were measured by recording SCL phenomenon with a sensitive photomultiplier tube (PMT) that responds in nanoseconds. As a result of measurement with the PMT, when the electromagnetic shock wave used in clinical practice was irradiated to the luminol solution, the amount of light emitted per unit time reached its maximum value within a very short time (< ∼600us) and then exponentially decreased for a long time (∼several hundred ms). The measured FR persist time reaches a maximum of 1000 ms. As the output setting of the shock wave generator increases, the minimum or average FR persist time increases, but the maximum value does not show a high correlation with the output setting. The amount of generated FR shows a very high correlation with the shock wave setting, and when the setting is changed from low to high, it increases very sensitively, rapidly and non-linearly. In order to reduce the risk of FR in patient treatment using lithotripsy, the output setting of the shock wave should be minimized, and the interval between the shock wave pulses should be sufficiently larger than the FR persist time. Therefore, it is recommended to avoid increasing the output setting and setting the shock wave irradiation frequency below 1 Hz to shorten the treatment time in clinical practice. For the purpose of formulating these recommendations, additional studies on the generation and persist time of FR depending on the shock wave generation method and set conditions in living tissue or similar environment are required in the future.

A decomposition method by interaction prediction for the optimization of maintenance scheduling

Optimizing maintenance scheduling is a major issue to improve the performance of hydropower plants. We study a system of several physical components of the same family: either a set of turbines, a set of transformers or a set of generators. The components share a common stock of spare parts and experience random failures that occur according to known failure distributions. We seek a deterministic preventive maintenance strategy that minimizes an expected cost depending on maintenance and forced outages of the system. The Auxiliary Problem Principle is used to decompose the original large-scale optimization problem into a sequence of independent subproblems of smaller dimension while ensuring their coordination. Each subproblem consists in optimizing the maintenance on a single component. Decomposition-coordination techniques are based on variational techniques but the maintenance optimization problem is a mixed-integer problem. Therefore, we relax the dynamics and the cost functions of the system. The resulting algorithm iteratively solves the subproblems on the relaxed system with a blackbox method and coordinates the components. Relaxation parameters have an important influence on the optimization and must be appropriately chosen. An admissible maintenance strategy is then derived from the resolution of the relaxed problem. We apply the decomposition algorithm on a system with 80 components. It outperforms the reference blackbox method applied directly on the original problem.

Confronting Deep-Learning and Biodiversity Challenges for Automatic Video-Monitoring of Marine Ecosystems.

With the availability of low-cost and efficient digital cameras, ecologists can now survey the world’s biodiversity through image sensors, especially in the previously rather inaccessible marine realm. However, the data rapidly accumulates, and ecologists face a data processing bottleneck. While computer vision has long been used as a tool to speed up image processing, it is only since the breakthrough of deep learning (DL) algorithms that the revolution in the automatic assessment of biodiversity by video recording can be considered. However, current applications of DL models to biodiversity monitoring do not consider some universal rules of biodiversity, especially rules on the distribution of species abundance, species rarity and ecosystem openness. Yet, these rules imply three issues for deep learning applications: the imbalance of long-tail datasets biases the training of DL models; scarce data greatly lessens the performances of DL models for classes with few data. Finally, the open-world issue implies that objects that are absent from the training dataset are incorrectly classified in the application dataset. Promising solutions to these issues are discussed, including data augmentation, data generation, cross-entropy modification, few-shot learning and open set recognition. At a time when biodiversity faces the immense challenges of climate change and the Anthropocene defaunation, stronger collaboration between computer scientists and ecologists is urgently needed to unlock the automatic monitoring of biodiversity.

Optimizing design and dispatch of a renewable energy system with combined heat and power

We embellish a mixed-integer program that prescribes a set of renewable energy, conventional generation, and storage technologies to procure, along with a corresponding dispatch strategy. Specifically, we add combined heat and power to this set. The model minimizes fixed and operational costs less incentives for the use of various technologies, subject to a series of component interoperability and system-wide constraints. The resulting mixed-integer linear program contains hundreds of thousands of variables and constraints. We demonstrate how to efficiently formulate and solve the corresponding instances such that we produce near-optimal solutions in minutes. A previous rendition of the model required hours of solution time for the same instances.

RCE-GAN: A Rebar Clutter Elimination Network to Improve Tunnel Lining Void Detection from GPR Images

Ground penetrating radar (GPR) is one of the most recommended tools for routine inspection of tunnel linings. However, the rebars in the reinforced concrete produce a strong shielding effect on the electromagnetic waves, which may hinder the interpretation of GPR data. In this work, we proposed a method to improve the identification of tunnel lining voids by designing a generative adversarial network-based rebar clutter elimination network (RCE-GAN). The designed network has two sets of generators and discriminators, and by introducing the cycle-consistency loss, the network is capable of learning high-level features between unpaired GPR images. In addition, an attention module and a dilation center part were designed in the network to improve the network performance. Validation of the proposed method was conducted on both synthetic and real-world GPR images, collected from the implementation of finite-difference time-domain (FDTD) simulations and a controlled physical model experiment, respectively. The results demonstrate that the proposed method is promising for its lower demand on the training dataset and the improvement in the identification of tunnel lining voids.

Random walks on finite nilpotent groups driven by long-jump measures

We consider a variant of random walks on finite groups. At each step, we choose an element from a set of generators ("directions") uniformly, and an integer from a power law ("speed") distribution associated with the chosen direction. We show that if the finite group is nilpotent, the mixing time of this walk is of the same order of magnitude as the diameter of a suitable pseudo-metric, D(S, a), on the group, which depends only on the generators and speeds. Additionally, we give sharp bounds on the $\ell^2$-distance between the distribution of the position of the walker and the stationary distribution, and compute D(S,a) for some examples.

Dynamic Virtual Power Plant: A New Concept for Grid Integration of Renewable Energy Sources

The concept of Virtual Power Plant (VPP) has arisen over a decade ago from the relatively low competitiveness of the back then emerging non-dispatchable RES. A set of smaller generators imitates the behavior of large synchronous generators. So far, static aspects such as generation or slow dynamics have been of interest, as it is the case for the zonal secondary frequency control scheme in Spain, which can be viewed as a VPP. However, considering dynamic aspects is of high importance, especially to further increase the current penetration level of Renewable Energy Sources (RES). Indeed, one should deal with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">full</i> participation of RES in grid ancillary services. This means not only to get some positive impact on grid voltage and frequency dynamics but to bring concepts which allow integrating RES to existing secondary regulation schemes on the same level as classic synchronous generators. For that, we propose here a new concept called Dynamic VPP (DVPP) which fully integrates the dynamic aspects at all levels: locally (for each RES generator), globally (for grid ancillary services and interaction with other close-by elements of the grid) and economically (for internal optimal dispatch and participation in electricity markets). A DVPP is a set of dispatchable and non-dispatchable RES along with a set of common control and operation procedures. The latter procedures include the choice of dispatchable and non-dispatchable RES constituting the DVPP, the control of DVPP generators for local objectives and participation of the DVPP as a single unit in ancillary services (especially in case of loss of natural resources - e.g., wind, sun - on a part of the DVPP), the limitation of the risk of adversely interaction with close-by elements and the feasibility in both current power systems scenarios and future ones with large share of RES. This new DVPP framework and approaches developed for its implementation allows ensuring optimal operation of a mixed portfolio of dispatchable and non-dispatchable RES generators for planning, participation to the markets and real-time control. For the control, all time-scale dynamics are considered to improve RES management (internal re-dispatch inside the DVPP to take advantage of dispatchable/non-dispatchable nature of each RES and to optimally manage the lack of natural resources in some regions of the DVPP) and their participation to grid ancillary services. Concrete structures of DVPP as well as ways to address the other control and economical aspects will be shown. This new DVPP concept is now under development in the H2020 POSYTYF project (https://posytyf-h2020.eu/).

Fault Diagnosis of Exhaust Gas Treatment System Combining Physical Insights and Neural Networks

Fault diagnosis is important for automotive systems, e.g., to reduce emissions and improve system reliability. Developing diagnosis systems is complicated by model inaccuracies and limited training data from relevant operating conditions, especially for new products and models. One solution is the use of hybrid fault diagnosis techniques combining model-based and data-driven methods. In this work, data-driven residual generation for fault detection and isolation is investigated for a system injecting urea into the aftertreatment system of a heavy-duty truck. A set of recurrent neural network-based residual generators is designed using a structural model of the system. The performance of this approach is compared to a baseline model-based approach using data collected from a heavy-duty truck during different fault scenarions with promising results.

Distributed Koopman-Based Control of Improved Swing Equation

This paper presents a data-driven control for a set of synchronous generators based on the improved swing equation model by using the Koopman operator. First, the nonlinear dynamic of the generators is represented by a linear model in lifted space using extended dynamic mode decomposition (EDMD). Then, a linear predictor is built and used for the design of a model predictive control following three strategies: decentralized, non-cooperative, and cooperative. The Koopman representation of the generator is used to carry out an analysis, determining its eigenvalues. Several interconnected generators are controlled showing the performance of the interconnected system including voltage faults.

Research on the Bipolar Microsecond Pulse Generator Using the Multistage Resonant Charging

In this article, a bipolar pulse generator based on multistage resonant charging has been proposed, which can deliver microsecond-range pulses for medical and industrial applications. The main circuit consists of two sets of identical Marx generators connected back to back with the load between them. It can flexibly output bipolar pulses with adjustable amplitude, pulse width, and frequency. In this research, six eight-stage Marx generators have been employed to verify the performance of the proposed bipolar pulse generator. From the experimental results, the proposed generator can deliver pulses with the amplitude of ±10 kV to no-load and a resistive load of 50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{k}\Omega $ </tex-math></inline-formula> . The repetitive frequency can be adjusted from 1 Hz to 1 kHz, and pulse width can be adjusted from 0.2 to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$400~\mu \text{s}$ </tex-math></inline-formula> with 50 ns rising and falling time. The relaxation time between the positive and negative pulses can be determined from 0.2 to 400 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> . In the high-voltage field, the proposed Marx generator can deliver higher output voltage pulses and has smaller voltage differences between stages than the traditional solid-state Marx generator.

Factorizations of $k$-nonnegative matrices

A matrix is $k$-nonnegative if all its minors of size $k$ or less are nonnegative. We give a parametrized set of generators and relations for the semigroup of $k$-nonnegative $n\times n$ invertible matrices in two special cases: when $k = n-1$ and when $k = n-2$, restricted to unitriangular matrices. For these two cases, we prove that the set of $k$-nonnegative matrices can be partitioned into cells based on their factorizations into generators, generalizing the notion of Bruhat cells from totally nonnegative matrices. Like Bruhat cells, these cells are homeomorphic to open balls and have a topological structure that neatly relates closure of cells to subwords of factorizations. In the case of $(n-2)$-nonnegative unitriangular matrices, we show the cells form a Bruhat-like CW-complex.

Minimal generators of annihilators of even neat elements in the exterior algebra footnotesize

This paper deals with an exterior algebra of a vector space whose base field is of positive characteristic. In this work, a minimal set of generators forming the annihilator of even neat elements of such an exterior algebra is exhibited. The annihilator of some special type of even neat elements is determined to prove the conjecture established in [3]. Moreover, a vector space basis for the annihilators under consideration is calculated.

Annealing Genetic GAN for Imbalanced Web Data Learning

Class imbalance is one of the most basic and important problems of web data. The key to overcoming the class imbalance problems is to increase the effective instances of the minority, that is, data augmentation. Generative Adversarial Networks (GANs), which have recently been successfully applied in the field of image generation, can be used for data augmentation because they can learn the data distribution given ample training data instances and generate more data. However, learning the distributions from the imbalanced data can make GANs easily get stuck in a local optimum. In this work, we propose a new training strategy called Annealing Genetic GAN (AGGAN), which incorporates simulated annealing genetic algorithm into the training process of GANs. And this can help GANs avoid the local optimum trapping problem, which easily occurs when the training set is imbalanced. Unlike existing GANs, which use a fixed adversarial learning objective alternately training a generator, we use multiple adversarial learning objectives to train a set of generators and use the Metropolis criterion in simulated annealing to decide whether the generator should update. More specifically, the Metropolis criterion accepts worse solutions with a certain probability, so it can make our AGGAN escape from the local optimum and find a better solution. Theory and mathematical analysis provide strong theoretical support for the proposed training strategy. And experiments on several datasets demonstrate that AGGAN achieves convincing ability to solve the class imbalanced problem and reduces the training problems inherent in existing GANs.

Pore-scale study of the anisotropic effect on immiscible displacement in porous media under different wetting conditions and capillary numbers

This article investigates the effect of anisotropy on two-phase flows in porous media (both drainage and imbibition) under various wetting angles and capillary numbers. The focus is on the applied results useful to petroleum engineering. Mathematical modeling based on lattice Boltzmann equations in combination with multi-relaxation time and color-gradient model is used as a research tool. This paper proposes a new method for creating digital models of porous media, based on a combination of a quartet structure generation set and Monte-Carlo algorithms. The advantage is the ability to control both heterogeneity and anisotropy. This paper presents visual phase diagrams of the invasion patterns in anisotropic samples as a function of wetting angle and capillary number. The results show that the ratio between capillary and viscous forces is highest for the sample with favorable anisotropy, which has the biggest permeability, and decreases on going to unfavorable anisotropy (lowest permeability). This effect leads to the finding that boundary capillary numbers, which determine the transition between crossover mode and invasion patterns (viscous and capillary fingering, and compact displacement), are shifted towards lower values as anisotropy increases. The images of fluid distributions are numerically described using fractal dimension and displacement efficiency. It was found that the anisotropic effect is most pronounced at capillary fingering and does not significantly affect the invasion characteristics in the imbibition mode. The sensitivity of the fractal dimension and displacement efficiency to a change in the wetting angle is maximal at favorable anisotropy and decreases on going to unfavorable anisotropy. During drainage, the fractal dimensions, estimated for favorable and unfavorable anisotropy, show opposite trends depending on the capillary number. In the imbibition mode, there is an increase in the fractal dimension with an increase in the capillary number for all porous structures. In addition, this paper investigates the anisotropy effect on the «injected fluid - skeleton » interfacial length under different wetting and dynamic conditions. It was shown that the anisotropy has no significant effect on this parameter, and the best interaction efficiency was found with a compact displacement, and the least with a viscous fingering. Also, the influence of the no-flow and periodic boundary conditions on the invasion characteristics is examined. The results show that boundary conditions do not significantly affect the invasion in the sample with favorable anisotropy for any wetting angles. Periodic boundary conditions negatively influence the displacement efficiency for samples with isotropic and unfavorable structures at drainage, but no noticeable effect was found during imbibition. • The anisotropic effect is most pronounced with capillary fingering. • Anisotropy does not significantly affect invasion during imbibition. • Anisotropy has no significant effect on the «injected fluid - skeleton » interface. • Boundary conditions do not affect invasion with favorable anisotropy for any wettability.