Optimization of Function-Generating Mechanisms Using Input-Output Curve Planning: Part II — Formulation and Optimization Strategies

1992 ◽  
Author(s):  
Zheng Liu ◽  
Jorge Angeles
Keyword(s):  
Optimization Procedure ◽  
Second Step ◽  
Output Curve ◽  
Data Preparation ◽  
Input Output ◽  
Optimization Scheme ◽  
Design Data ◽  
Transmission Quality ◽  
Design Requirements ◽  
Defect Elimination

Abstract As a sequel to Part I, in which design-data preparation based on Input-output curve planning is discussed, Part II focuses on the second step of the optimization scheme. This step includes the basic formulation and some strategies, in the optimization procedure, for: i) transmission-quality evaluation; and ii) branch-defect elimination. Since design requirements on mobility conditions are already considered in the curve-planning phase, discussed in Part I, there is no need to introduce constraints pertaining to mobility type in the formulation, the procedure thus becoming remarkably simple.

Optimization of Function-Generating Mechanisms Using Input-Output Curve Planning: Part I — Curve Planning and Data Preparation

1992 ◽  
Author(s):  
Zheng Liu ◽  
Jorge Angeles
Keyword(s):  
General Scheme ◽  
Optimization Procedure ◽  
Output Curve ◽  
Data Preparation ◽  
Input Output ◽  
Design Strategies ◽  
Design Data ◽  
Design Error ◽  
Structural Error ◽  
Design Requirements

Abstract A general scheme for the optimization of one-degree-of-freedom mechanisms for function generation is proposed in this and an accompanying paper. The problem is solved here following two basic steps: i) planning input-output curves based on design requirements (input-output pairs, mobility, time ratio, etc.) and selecting data from the planned curve; and ii) setting up an optimization procedure to minimize a performance index (design error or structural error). Discussed in Part I is the first step of the optimization scheme, i.e., design-data preparation based on input-output curve planning, while, in Part II, the ensuing formulation and design strategies.

Optimization of Planar, Spherical and Spatial Function Generators Using Input-Output Curve Planning

1994 ◽  
Vol 116 (3) ◽  
pp. 915-919 ◽  
Author(s):  
Zheng Liu ◽  
J. Angeles
Keyword(s):  
Performance Index ◽  
General Scheme ◽  
Optimization Procedure ◽  
Output Curve ◽  
Input Output ◽  
Function Generation ◽  
Spatial Function ◽  
Function Generators ◽  
Design Requirements ◽  
A Performance

A general scheme for the optimization of planar, spherical and spatial bimodal linkages for function generation is proposed. The problem is solved here following two basic steps: (i) planning input-output ((I/O) curves based on design requirements and selecting data from the planned curve; and (ii) setting up an optimization procedure to minimize a performance index.

scholarly journals Artificial neural networks for inverse design of resonant nanophotonic components with oscillatory loss landscapes

Nanophotonics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 385-392
Author(s):  
Joeri Lenaerts ◽  
Hannah Pinson ◽  
Vincent Ginis
Keyword(s):  
Machine Learning ◽  
Optimization Procedure ◽  
Linear Mapping ◽  
Inverse Design ◽  
Design Parameters ◽  
Second Step ◽  
Input Parameters ◽  
Novel Variant ◽  
Resonant Systems ◽  
The Fourier Transform

AbstractMachine learning offers the potential to revolutionize the inverse design of complex nanophotonic components. Here, we propose a novel variant of this formalism specifically suited for the design of resonant nanophotonic components. Typically, the first step of an inverse design process based on machine learning is training a neural network to approximate the non-linear mapping from a set of input parameters to a given optical system’s features. The second step starts from the desired features, e.g. a transmission spectrum, and propagates back through the trained network to find the optimal input parameters. For resonant systems, this second step corresponds to a gradient descent in a highly oscillatory loss landscape. As a result, the algorithm often converges into a local minimum. We significantly improve this method’s efficiency by adding the Fourier transform of the desired spectrum to the optimization procedure. We demonstrate our method by retrieving the optimal design parameters for desired transmission and reflection spectra of Fabry–Pérot resonators and Bragg reflectors, two canonical optical components whose functionality is based on wave interference. Our results can be extended to the optimization of more complex nanophotonic components interacting with structured incident fields.

Structural Design of the Vermicelli Strapping Machine

2012 ◽  
Vol 215-216 ◽  
pp. 217-220 ◽  
Author(s):  
Shou Jun Wang ◽  
Ren Zhe Wei
Keyword(s):  
Structural Design ◽  
Second Step ◽  
Cotton Thread ◽  
Working Principle ◽  
Design Requirements

This paper introduced the design requirements, structure, as well as the working principle of vermicelli strapping machine. The process of strapping mainly divided into two steps. The first step is to make the cotton thread wrapped around the vermicelli, during which, the speed of wrapping is greatly improved because the distance of the winding is shorten by the innovative teardrop-shaped winding track. The second step is to knot the cotton thread that wrapped the vermicelli. The type of knot used in this strapping machine can be implemented on machine; besides, the knot is so tight that it’s not easy to be loosed. By adjusting the position of knotting structure, the gap of bundled vermicelli is reduced. As a result, the vermicelli will be strapped tight.

Important Components Analysis of Ceramic Rapid Molding Machine

2014 ◽  
Vol 633-634 ◽  
pp. 360-363
Author(s):  
Xiu Duan Gong ◽  
Zhou Wen ◽  
Jun Ling Zhang
Keyword(s):  
Element Model ◽  
Digital Design ◽  
Molding Machine ◽  
Original Design ◽  
Design Data ◽  
Mechanical Products ◽  
Design Requirements ◽  
Components Analysis ◽  
Set Up ◽  
The Finite Element Model

The digital design of mechanical products is usually first reference existing parts, drawings, and experience to make original design data and design requirements, according to the design of digital model is set up, after an analysis of the finite element model according to the analysis results contrast design goal to design or structure parameter changes, to the expected design purpose. Analysis of the product is an essential part of product design.

scholarly journals Eight weeks of local vibration training increases dorsiflexor muscle cortical voluntary activation

2017 ◽  
Vol 122 (6) ◽  
pp. 1504-1515 ◽  
Author(s):  
Robin Souron ◽  
Adrien Farabet ◽  
Léonard Féasson ◽  
Alain Belli ◽  
Guillaume Y. Millet ◽  
...  
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Magnetic Stimulation ◽  
Motor Evoked Potential ◽  
Silent Period ◽  
Voluntary Contraction ◽  
Voluntary Activation ◽  
Output Curve ◽  
Input Output ◽  
Local Vibration ◽  
Vibration Training

The aim of this study was to evaluate the effects of an 8-wk local vibration training (LVT) program on functional and corticospinal properties of dorsiflexor muscles. Forty-four young subjects were allocated to a training (VIB, n = 22) or control (CON, n = 22) group. The VIB group performed twenty-four 1-h sessions (3 sessions/wk) of 100-Hz vibration applied to the right tibialis anterior. Both legs were tested in each group before training (PRE), after 4 (MID) and 8 (POST) wk of training, and 2 wk after training (POST2W). Maximal voluntary contraction (MVC) torque was assessed, and transcranial magnetic stimulation (TMS) was used to evaluate cortical voluntary activation (VATMS), motor evoked potential (MEP), cortical silent period (CSP), and input-output curve parameters. MVC was significantly increased for VIB at MID for right and left legs [+7.4% ( P = 0.001) and +6.2% ( P < 0.01), respectively] and remained significantly greater than PRE at POST [+12.0% ( P < 0.001) and +10.1% ( P < 0.001), respectively]. VATMS was significantly increased for right and left legs at MID [+4.4% ( P < 0.01) and +4.7% ( P < 0.01), respectively] and at POST [+4.9% ( P = 0.001) and +6.2% ( P = 0.001), respectively]. These parameters remained enhanced in both legs at POST2W. MEP and CSP recorded during MVC and input-output curve parameters did not change at any time point for either leg. Despite no changes in excitability or inhibition being observed, LVT seems to be a promising method to improve strength through an increase of maximal voluntary activation, i.e., neural adaptations. Local vibration may thus be further considered for clinical or aging populations. NEW & NOTEWORTHY The effects of a local vibration training program on cortical voluntary activation measured with transcranial magnetic stimulation were assessed for the first time in dorsiflexors, a functionally important muscle group. We observed that training increased maximal voluntary strength likely because of the strong and repeated activation of Ia spindle afferents during vibration training that led to changes in the cortico-motoneuronal pathway, as demonstrated by the increase in cortical voluntary activation.

Transient and long-lasting electrical responses to direct hippocampal stimulation

1960 ◽  
Vol 198 (4) ◽  
pp. 687-692 ◽  
Author(s):  
E. R. Kandel ◽  
W. A. Spencer ◽  
F. J. Brinley
Keyword(s):  
Output Curve ◽  
Input Output ◽  
Negative Wave ◽  
Typical Response ◽  
Test Response ◽  
Hippocampal Stimulation ◽  
Direct Cortical Response ◽  
Electrical Stimuli ◽  
Apical Dendrites

Widely accepted use of the direct cortical response (DCR) for the study of neocortical apical dendrites prompted this study of the response of the surface of hippocampal pallium to direct electrical stimuli in rabbits anesthetized with Dial or Evipal. The hippocampus was directly exposed by radical decortication. The most typical response to direct hippocampal stimulation (DHR) is a monophasic 20–25 msec. negative wave. The DHR is linearly graded throughout the early part of its input-output curve, shows no refractoriness, exhibits long lasting (400 msec.) potentiation of a previously conditioned test response, is rapidly (3–5 sec.) inverted by GABA and is associated with two types of d.c. shifts: a) d.c. shift without concomitant loss of the DHR and b) a variant of spreading hippocampal depression. From these properties the DHR would appear to be quite similar to the DCR. However, different bioelectric generators must be postulated since the hippocampal neural geometry is different from neocortex with respect to the orientation of its predominant neurons.

scholarly journals Lognormal firing rate distribution reveals prominent fluctuation–driven regime in spinal motor networks

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Peter C Petersen ◽  
Rune W Berg
Keyword(s):  
Neuronal Population ◽  
Output Curve ◽  
Input Output ◽  
Rhythmic Movements ◽  
Trade Off ◽  
Firing Rates ◽  
Spinal Motor ◽  
Rich Diversity ◽  
The Mean ◽  
Lumbar Spinal

When spinal circuits generate rhythmic movements it is important that the neuronal activity remains within stable bounds to avoid saturation and to preserve responsiveness. Here, we simultaneously record from hundreds of neurons in lumbar spinal circuits of turtles and establish the neuronal fraction that operates within either a ‘mean-driven’ or a ‘fluctuation–driven’ regime. Fluctuation-driven neurons have a ‘supralinear’ input-output curve, which enhances sensitivity, whereas the mean-driven regime reduces sensitivity. We find a rich diversity of firing rates across the neuronal population as reflected in a lognormal distribution and demonstrate that half of the neurons spend at least 50 % of the time in the ‘fluctuation–driven’ regime regardless of behavior. Because of the disparity in input–output properties for these two regimes, this fraction may reflect a fine trade–off between stability and sensitivity in order to maintain flexibility across behaviors.

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