Comparative Analysis of Actuator Dimensioning Methods in Pneumatics

In the modern automation technology, there are often two ways to complete a drive task: pneumatically or electrically. In order to remain competitive with the electromechanics and to contribute to the achievement of climate protection goals, manufacturers and users of pneumatic systems are required to increase the energy efficiency of pneumatics. One of the primal and simplest methods to reduce the energy consumption of existing and to-be-developed pneumatic systems is the correct sizing of actuators. However, even in the most modern machines drives are often overdimensioned thus creating a higher energy consumption than necessary. To counteract this, different dimensioning methods have been developed in the last few years, which could contribute to a significant reduction of energy consumption. Design tools based on dynamic simulations are highly reliable, but their calculation methods can be complex and non-transparent. Therefore, more pragmatic and simple dimensioning methods have been developed, based on algebraic approaches like force equilibrium, exergy equilibrium and pneumatic frequency ratio. In this paper these methods are evaluated using mathematical analysis and practical drive examples. Their possible application fields and limitations are shown and compared.

Static Attitude Determination Using Convolutional Neural Networks

The need to estimate the orientation between frames of reference is crucial in spacecraft navigation. Robust algorithms for this type of problem have been built by following algebraic approaches, but data-driven solutions are becoming more appealing due to their stochastic nature. Hence, an approach based on convolutional neural networks in order to deal with measurement uncertainty in static attitude determination problems is proposed in this paper. PointNet models were trained with different datasets containing different numbers of observation vectors that were used to build attitude profile matrices, which were the inputs of the system. The uncertainty of measurements in the test scenarios was taken into consideration when choosing the best model. The proposed model, which used convolutional neural networks, proved to be less sensitive to higher noise than traditional algorithms, such as singular value decomposition (SVD), the q-method, the quaternion estimator (QUEST), and the second estimator of the optimal quaternion (ESOQ2).

On the Group-Theoretical Approach to Relativistic Wave Equations for Arbitrary Spin

Formulating a relativistic equation for particles with arbitrary spin remains an open challenge in theoretical physics. In this study, the main algebraic approaches used to generalize the Dirac and Kemmer–Duffin equations for particles of arbitrary spin are investigated. It is proved that an irreducible relativistic equation formulated using spin matrices satisfying the commutation relations of the de Sitter group leads to inconsistent results, mainly as a consequence of violation of unitarity and the appearance of a mass spectrum that does not reflect the physical reality of elementary particles. However, the introduction of subsidiary conditions resolves the problem of unitarity and restores the physical meaning of the mass spectrum. The equations obtained by these approaches are solved and the physical nature of the solutions is discussed.

Chapter 1. A History of Satisfiability

This chapter traces the links between the notion of Satisfiability and the attempts by mathematicians, philosophers, engineers, and scientists over the last 2300 years to develop effective processes for emulating human reasoning and scientific discovery, and for assisting in the development of electronic computers and other electronic components. Satisfiability was present implicitly in the development of ancient logics such as Aristotle’s syllogistic logic, its extentions by the Stoics, and Lull’s diagrammatic logic of the medieval period. From the renaissance to Boole algebraic approaches to effective process replaced the logics of the ancients and all but enunciated the meaning of Satisfiability for propositional logic. Clarification of the concept is credited to Tarski in working out necessary and sufficient conditions for “p is true” for any formula p in first-order syntax. At about the same time, the study of effective process increased in importance with the resulting development of lambda calculus, recursive function theory, and Turing machines, all of which became the foundations of computer science and are linked to the notion of Satisfiability. Shannon provided the link to the computer age and Davis and Putnam directly linked Satisfiability to automated reasoning via an algorithm which is the backbone of most modern SAT solvers. These events propelled the study of Satisfiability for the next several decades, reaching “epidemic proportions” in the 1990s and 2000s, and the chapter concludes with a brief history of each of the major Satisfiability-related research tracks that developed during that period.

Chapter 7. Proof Complexity and SAT Solving

This chapter gives an overview of proof complexity and connections to SAT solving, focusing on proof systems such as resolution, Nullstellensatz, polynomial calculus, and cutting planes (corresponding to conflict-driven clause learning, algebraic approaches using linear algebra or Gröbner bases, and pseudo-Boolean solving, respectively). There is also a discussion of extended resolution (which is closely related to DRAT proof logging) and Frege and extended Frege systems more generally. An ample supply of references for further reading is provided, including for some topics omitted in this chapter.

Machine learning and algebraic approaches towards complete matter spectra in 4d F-theory

Motivated by engineering vector-like (Higgs) pairs in the spectrum of 4d F-theory compactifications, we combine machine learning and algebraic geometry techniques to analyze line bundle cohomologies on families of holomorphic curves. To quantify jumps of these cohomologies, we first generate 1.8 million pairs of line bundles and curves embedded in dP3, for which we compute the cohomologies. A white-box machine learning approach trained on this data provides intuition for jumps due to curve splittings, which we use to construct additional vector-like Higgs-pairs in an F-Theory toy model. We also find that, in order to explain quantitatively the full dataset, further tools from algebraic geometry, in particular Brill-Noether theory, are required. Using these ingredients, we introduce a diagrammatic way to express cohomology jumps across the parameter space of each family of matter curves, which reflects a stratification of the F-theory complex structure moduli space in terms of the vector-like spectrum. Furthermore, these insights provide an algorithmically efficient way to estimate the possible cohomology dimensions across the entire parameter space.

Algebraic approaches for solving isogeny problems of prime power degrees

Recently, supersingular isogeny cryptosystems have received attention as a candidate of post-quantum cryptography (PQC). Their security relies on the hardness of solving isogeny problems over supersingular elliptic curves. The meet-in-the-middle approach seems the most practical to solve isogeny problems with classical computers. In this paper, we propose two algebraic approaches for isogeny problems of prime power degrees. Our strategy is to reduce isogeny problems to a system of algebraic equations, and to solve it by Gröbner basis computation. The first one uses modular polynomials, and the second one uses kernel polynomials of isogenies. We report running times for solving isogeny problems of 3-power degrees on supersingular elliptic curves over 𝔽p2 with 503-bit prime p, extracted from the NIST PQC candidate SIKE. Our experiments show that our first approach is faster than the meet-in-the-middle approach for isogeny degrees up to 310.