Dynamic responses and control of geared transmission system based on multibody modeling methodology

In this study, an innovative modeling approach is put forward to research the effect of eccentricity on the nonlinear dynamical behaviors of geared-bearing system. This refined model contains the rigid body of the rotor-bearing system and separated gear teeth which are considered as individual bodies elastically attached to the gear hub with revolute joints. The internal and external excitations of the proposed model include torsional joint stiffness, roll bearing forces, friction between gear pair, gear eccentricity, and so on. The systematic procedure for the calculation of torsional joint stiffness, bearing forces and friction coefficient considering elastohydrodynamic is also conducted. After that, the influence of eccentricity on nonlinear dynamic characteristics of the geared transmission system is analyzed. To avoid the system moving in the unstable motion state, a dry friction damper controller is designed to control the nonlinear behaviors simulated on the basis of above model. The linear feedback and periodic excitation non-feedback control strategies are, respectively, selected to design the actuator. It is indicated that undesirable behaviors of the geared transmission system can be avoided effectively by applying the proposed control method.

Oligomerization of peripheral membrane proteins provides tunable control of cell surface polarity

Asymmetric distributions of peripheral membrane proteins define cell polarity across all kingdoms of life. These asymmetries are shaped by membrane binding, diffusion and transport. Theoretical studies have revealed a general requirement for non-linear positive feedback to spontaneously amplify and/or stabilize asymmetries against dispersion by diffusion and dissociation. But how specific molecular sources of non-linearity shape polarization dynamics remains poorly understood. Here we study how oligomerization of peripheral membrane proteins shapes polarization dynamics in simple feedback circuits. We show that size dependent binding avidity and mobility of membrane bound oligomers endow polarity circuits generically with several key properties. Size-dependent binding avidity confers a form of positive feedback in which the effective rate constant for subunit dissociation decreases with increasing subunit density. This combined with additional weak linear positive feedback is sufficient for spontaneous emergence of stably polarized states. Size-dependent oligomer mobility makes symmetry-breaking and stable polarity more robust with respect to variation in subunit diffusivities and cell sizes, and slows the approach to a final stable spatial distribution, allowing cells to "remember" polarity boundaries imposed by transient external cues. Together, these findings reveal how oligomerization of peripheral membrane proteins can provide powerful and highly tunable sources of non-linear feedback in biochemical circuits that govern cell-surface polarity. Given its prevalence and widespread involvement in cell polarity, we speculate that self-oligomerization may have provided an accessible path to evolving simple polarity circuits.

Identification of COM Controller of a Human in Stance Based on Motion Measurement and Phase-Space Analysis

This article proposes a process to identify the standing stabilizer, namely, the controller in humans to keep upright posture stable against perturbations. We model the controller as a piecewise-linear feedback system, where the state of the center of mass (COM) is regulated by coordinating the whole body so as to locate the zero-moment point (ZMP) at the desired position. This was developed for humanoid robots and is possibly able to elaborate the fundamental control scheme used by humans to stabilize themselves. Difficulties lie on how to collect motion trajectories in a wide area of the state space for reliable identification and how to identify the piecewise-affine dynamical system. For the former problem, a motion measurement protocol is devised based on the theoretical phase portrait of the system. Regarding the latter problem, some clustering techniques including K-means method and EM (Expectation-and-Maximization) algorithm were examined. We found that a modified K-means method produced the most accurate result in this study. The method was applied to the identification of a lateral standing controller of a human subject. The result of the identification quantitatively supported a hypothesis that the COM-ZMP regulator reasonably models the human’s controller when deviations of the angular momentum about the COM are limited.

Scrambled Linear Pseudorandom Number Generators

F 2 -linear pseudorandom number generators are very popular due to their high speed, to the ease with which generators with a sizable state space can be created, and to their provable theoretical properties. However, they suffer from linear artifacts that show as failures in linearity-related statistical tests such as the binary-rank and the linear-complexity test. In this article, we give two new contributions. First, we introduce two new F 2 -linear transformations that have been handcrafted to have good statistical properties and at the same time to be programmable very efficiently on superscalar processors, or even directly in hardware. Then, we describe some scramblers , that is, nonlinear functions applied to the state array that reduce or delete the linear artifacts, and propose combinations of linear transformations and scramblers that give extremely fast pseudorandom number generators of high quality. A novelty in our approach is that we use ideas from the theory of filtered linear-feedback shift registers to prove some properties of our scramblers, rather than relying purely on heuristics. In the end, we provide simple, extremely fast generators that use a few hundred bits of memory, have provable properties, and pass strong statistical tests.

Biorealistic Control of Hand Prosthesis Augments Functional Performance of Individuals With Amputation

The human hand has compliant properties arising from muscle biomechanics and neural reflexes, which are absent in conventional prosthetic hands. We recently proved the feasibility to restore neuromuscular reflex control (NRC) to prosthetic hands using real-time computing neuromorphic chips. Here we show that restored NRC augments the ability of individuals with forearm amputation to complete grasping tasks, including standard Box and Blocks Test (BBT), Golf Balls Test (GBT), and Potato Chips Test (PCT). The latter two were more challenging, but novel to prosthesis tests. Performance of a biorealistic controller (BC) with restored NRC was compared to that of a proportional linear feedback (PLF) controller. Eleven individuals with forearm amputation were divided into two groups: one with experience of myocontrol of a prosthetic hand and another without any. Controller performances were evaluated by success rate, failure (drop/break) rate in each grasping task. In controller property tests, biorealistic control achieved a better compliant property with a 23.2% wider range of stiffness adjustment than that of PLF control. In functional grasping tests, participants could control prosthetic hands more rapidly and steadily with neuromuscular reflex. For participants with myocontrol experience, biorealistic control yielded 20.4, 39.4, and 195.2% improvements in BBT, GBT, and PCT, respectively, compared to PLF control. Interestingly, greater improvements were achieved by participants without any myocontrol experience for BBT, GBT, and PCT at 27.4, 48.9, and 344.3%, respectively. The functional gain of biorealistic control over conventional control was more dramatic in more difficult grasp tasks of GBT and PCT, demonstrating the advantage of NRC. Results support the hypothesis that restoring neuromuscular reflex in hand prosthesis can improve neural motor compatibility to human sensorimotor system, hence enabling individuals with amputation to perform delicate grasps that are not tested with conventional prosthetic hands.

Generalized Galois-Fibonacci Matrix Generators Pseudo-Random Sequences

The article discusses various options for constructing binary generators of pseudo-random numbers (PRN) based on the so-called generalized Galois and Fibonacci matrices. The terms "Galois matrix" and "Fibonacci matrix" are borrowed from the theory of cryptography, in which the linear feedback shift registers (LFSR) generators of the PRN according to the Galois and Fibonacci schemes are widely used. The matrix generators generate identical PRN sequences as the LFSR generators. The transition from classical to generalized matrix PRN generators (PRNG) is accompanied by expanding the variety of generators, leading to a significant increase in their cryptographic resistance. This effect is achieved both due to the rise in the number of elements forming matrices and because generalized matrices are synthesized based on primitive generating polynomials and polynomials that are not necessarily primitive. Classical LFSR generators of PRN (and their matrix equivalents) have a significant drawback: they are susceptible to Berlekamp-Messi (BM) attacks. Generalized matrix PRNG is free from BM attack. The last property is a consequence of such a feature of the BM algorithm. This algorithm for cracking classical LFSR generators of PRN solves the problem of calculating the only unknown – a primitive polynomial generating the generator. For variants of generalized matrix PRNG, it becomes necessary to determine two unknown parameters: both an irreducible polynomial and a forming element that produces a generalized matrix. This problem turns out to be unsolvable for the BM algorithm since it is designed to calculate only one unknown parameter. The research results are generalized for solving PRNG problems over a Galois field of odd characteristics.

Optimal sensor and actuator placement for feedback control of vortex shedding

We consider linear feedback control of the two-dimensional flow past a cylinder at low Reynolds numbers, with a particular focus on the optimal placement of a single sensor and a single actuator. To accommodate the high dimensionality of the flow, we compute its leading resolvent forcing and response modes to enable the design of $\mathcal {H}_2$ -optimal estimators and controllers. We then investigate three control problems: (i) optimal estimation (OE) in which we measure the flow at a single location and estimate the entire flow; (ii) full-state information control (FIC) in which we measure the entire flow but actuate at only one location; and (iii) the overall feedback control problem in which a single sensor is available for measurement and a single actuator is available for control. We characterize the performance of these control arrangements over a range of sensor and actuator placements and discuss implications for effective feedback control when using a single sensor and a single actuator. The optimal sensor and actuator placements found for the OE and FIC problems are also compared with those found for the overall feedback control problem over a range of Reynolds numbers. This comparison reveals the key factors and conflicting trade-offs that limit feedback control performance.

Optimal Stochastic Computing Randomization

Stochastic computing (SC) is a probabilistic-based processing methodology that has emerged as an energy-efficient solution for implementing image processing and deep learning in hardware. The core of these systems relies on the selection of appropriate Random Number Generators (RNGs) to guarantee an acceptable accuracy. In this work, we demonstrate that classical Linear Feedback Shift Registers (LFSR) can be efficiently used for correlation-sensitive circuits if an appropriate seed selection is followed. For this purpose, we implement some basic SC operations along with a real image processing application, an edge detection circuit. Compared with the literature, the results show that the use of a single LFSR architecture with an appropriate seeding has the best accuracy. Compared to the second best method (Sobol) for 8-bit precision, our work performs 7.3 times better for the quadratic function; a 1.5 improvement factor is observed for the scaled addition; a 1.1 improvement for the multiplication; and a 1.3 factor for edge detection. Finally, we supply the polynomials and seeds that must be employed for different use cases, allowing the SC circuit designer to have a solid base for generating reliable bit-streams.