Loop Optimization using Hierarchical Compilation and Kernel Decomposition

Despite their seemingly impressive claims, current products for Condition Monitoring, Diagnostic and Decision Support Systems (CMD&D) do not provide the reliable bottom line information that end users and operators need. Instead they confuse the issue with gigabytes of logged trends, complex cause-effect matrices, fault signatures etc. The term “Intelligent Health Control” here refers to the next generation of such systems which provide usable information on: • the existence and severity of faults; • how their severity will progress with utilization; • how this progress can be influenced or controlled. In this paper the fundamental shortcomings of current approaches are discussed prior to introducing the basics of Intelligent Health Control in terms of fault models and how they can be used to close the diagnostic, prognostic and intelligent control triangle. The industry will unavoidably shift towards an “information centric” view from the currently predominant “data centric” view. Gigabytes of performance trends will no longer be relevant. Instead, reliable bottom line information will be required on how to minimize or control the costs associated with machinery health degradation or faults. In order to keep the discussion real, the current state of the art of enabling technologies are discussed, including: • Open Information Buses; • Adding real time data server functionality to the control system; • Computational Steering, Human-in-the-Loop Optimization (or semi-automatic problem solving); • Fault Models; • Faster than real time simulation; • Neural Nets.

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Source-level loop optimization for DSP code generation

1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258) ◽

10.1109/icassp.1999.758361 ◽

1999 ◽

Cited By ~ 2

Author(s):

Bogong Su ◽

Jian Wang ◽

A. Esguerra

Keyword(s):

Code Generation ◽

Loop Optimization ◽

Source Level

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Gait-symmetry-based human-in-the-loop optimization for unilateral transtibial amputees with robotic prostheses

10.1101/2020.10.04.325720 ◽

2020 ◽

Author(s):

Yanggan Feng ◽

Chengqiang Mao ◽

Qining Wang

Keyword(s):

Metabolic Cost ◽

Optimization Method ◽

Metabolic Energy ◽

Joint Degeneration ◽

Joint Injury ◽

Loop Optimization ◽

Gait Symmetry ◽

Human In The Loop ◽

Risk Of Injury ◽

Transtibial Amputees

AbstractGait asymmetry due to the loss of unilateral limb increases the risk of injury or progressive joint degeneration. The development of wearable robotic devices paves a way to improve gait symmetry of unilateral amputees. Moreover, the state-of-the-art studies on human-in-the-loop optimization strategies through decreasing the metabolic cost as the optimization task, have met several challenges, e.g. too long period of optimization and the optimization feasibility for unilateral amputees who have the deficit of gait symmetry. Here, in this paper, we proposed gait-symmetry-based human-in-the-loop optimization method to decrease the risk of injury or progressive joint degeneration for unilateral transtibial amputees. The experimental results (N = 3 unilateral transtibial subjects) demonstrate that only average 9.0±4.1min of convergence was taken. Compared to gait symmetry while wearing prosthetics, after optimization, the gait symmetry indicator value of the subjects wearing the robotic prostheses was improved by 21.0% and meanwhile the net metabolic energy consumption value was reduced by 9.2%. Also, this paper explores the rationality of gait indicators and what kind of gait indicators are the optimization target. These results suggest that gait-symmetry-based human-in-the-loop strategy could pave a practical way to improve gait symmetry by accompanying the reduction of metabolic cost, and thus to decrease the risk of joint injury for the unilateral amputees.

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