scholarly journals Evidence-Theory-Based Robust Optimization and Its Application in Micro-Electromechanical Systems

2019 ◽  
Vol 9 (7) ◽  
pp. 1457 ◽  
Author(s):  
Zhiliang Huang ◽  
Jiaqi Xu ◽  
Tongguang Yang ◽  
Fangyi Li ◽  
Shuguang Deng
Keyword(s):  
Probabilistic Model ◽  
Epistemic Uncertainty ◽  
Evidence Theory ◽  
Robustness Analysis ◽  
Force Sensor ◽  
Image Sensor ◽  
Optimization Approach ◽  
Electromechanical Systems ◽  
Capacitive Accelerometer ◽  
Robustness Optimization

The conventional engineering robustness optimization approach considering uncertainties is generally based on a probabilistic model. However, a probabilistic model faces obstacles when handling problems with epistemic uncertainty. This paper presents an evidence-theory-based robustness optimization (EBRO) model and a corresponding algorithm, which provide a potential computational tool for engineering problems with multi-source uncertainty. An EBRO model with the twin objectives of performance and robustness is formulated by introducing the performance threshold. After providing multiple target belief measures (Bel), the original model is transformed into a series of sub-problems, which are solved by the proposed iterative strategy driving the robustness analysis and the deterministic optimization alternately. The proposed method is applied to three problems of micro-electromechanical systems (MEMS), including a micro-force sensor, an image sensor, and a capacitive accelerometer. In the applications, finite element simulation models and surrogate models are both given. Numerical results show that the proposed method has good engineering practicality due to comprehensive performance in terms of efficiency, accuracy, and convergence.

scholarly journals Towards an Efficient CNN Inference Architecture Enabling In-Sensor Processing

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1955
Author(s):  
Md Jubaer Hossain Pantho ◽  
Pankaj Bhowmik ◽  
Christophe Bobda
Keyword(s):  
Power Consumption ◽  
High Speed ◽  
Image Sensor ◽  
Machine Learning Algorithms ◽  
Hierarchical Optimization ◽  
Computation Method ◽  
Optimization Approach ◽  
Efficient Computation ◽  
Feature Maps ◽  
Dynamic Power

The astounding development of optical sensing imaging technology, coupled with the impressive improvements in machine learning algorithms, has increased our ability to understand and extract information from scenic events. In most cases, Convolution neural networks (CNNs) are largely adopted to infer knowledge due to their surprising success in automation, surveillance, and many other application domains. However, the convolution operations’ overwhelming computation demand has somewhat limited their use in remote sensing edge devices. In these platforms, real-time processing remains a challenging task due to the tight constraints on resources and power. Here, the transfer and processing of non-relevant image pixels act as a bottleneck on the entire system. It is possible to overcome this bottleneck by exploiting the high bandwidth available at the sensor interface by designing a CNN inference architecture near the sensor. This paper presents an attention-based pixel processing architecture to facilitate the CNN inference near the image sensor. We propose an efficient computation method to reduce the dynamic power by decreasing the overall computation of the convolution operations. The proposed method reduces redundancies by using a hierarchical optimization approach. The approach minimizes power consumption for convolution operations by exploiting the Spatio-temporal redundancies found in the incoming feature maps and performs computations only on selected regions based on their relevance score. The proposed design addresses problems related to the mapping of computations onto an array of processing elements (PEs) and introduces a suitable network structure for communication. The PEs are highly optimized to provide low latency and power for CNN applications. While designing the model, we exploit the concepts of biological vision systems to reduce computation and energy. We prototype the model in a Virtex UltraScale+ FPGA and implement it in Application Specific Integrated Circuit (ASIC) using the TSMC 90nm technology library. The results suggest that the proposed architecture significantly reduces dynamic power consumption and achieves high-speed up surpassing existing embedded processors’ computational capabilities.

An Objective Evaluation of Displacement-Amplifying Compliant Mechanisms for Sensor Applications

2006 ◽  
Author(s):  
Girish Krishnan ◽  
G. K. Ananthasuresh
Keyword(s):  
Natural Frequency ◽  
Compliant Mechanisms ◽  
Selection Procedure ◽  
Force Sensor ◽  
Objective Evaluation ◽  
Performance Criteria ◽  
Sensor Applications ◽  
Capacitive Accelerometer ◽  
General Evaluation

Displacement-amplifying compliant mechanisms (DaCMs) reported in literature are mostly used for actuator applications. This paper considers them for sensor applications that rely on displacement measurement, and evaluates them objectively. The main goal is to increase the sensitivity under constraints imposed by several secondary requirements and practical constraints. A spring-mass-lever model that effectively captures the addition of a DaCM to a sensor is used in comparing eight DaCMs. We observe that they significantly differ in performance criteria such as geometric advantage, stiffness, natural frequency, mode amplification, factor of safety against failure, cross-axis stiffness, etc., but none excel in all. Thus, a combined figure of merit is proposed using which the most suitable DaCM could be selected for a sensor application. A case-study of a micro machined capacitive accelerometer and another case-study of a vision-based force sensor are included to illustrate the general evaluation and selection procedure of DaCMs with specific applications. Some other insights gained with the analysis presented here were the optimum size-scale for a DaCM, the effect on its natural frequency, limits on its stiffness, and working range of the sensor.

An Efficient Reliability Analysis Method for Structures With Epistemic Uncertainty Using Evidence Theory

2014 ◽  
Author(s):  
Zhe Zhang ◽  
Chao Jiang ◽  
G. Gary Wang ◽  
Xu Han
Keyword(s):  
Reliability Analysis ◽  
Structural Reliability ◽  
Epistemic Uncertainty ◽  
Limit State ◽  
Computational Cost ◽  
Evidence Theory ◽  
State Function ◽  
Limited Information ◽  
Design Point ◽  
Engineering Problems

Evidence theory has a strong ability to deal with the epistemic uncertainty, based on which the uncertain parameters existing in many complex engineering problems with limited information can be conveniently treated. However, the heavy computational cost caused by its discrete property severely influences the practicability of evidence theory, which has become a main difficulty in structural reliability analysis using evidence theory. This paper aims to develop an efficient method to evaluate the reliability for structures with evidence variables, and hence improves the applicability of evidence theory for engineering problems. A non-probabilistic reliability index approach is introduced to obtain a design point on the limit-state surface. An assistant area is then constructed through the obtained design point, based on which a small number of focal elements can be picked out for extreme analysis instead of using all the elements. The vertex method is used for extreme analysis to obtain the minimum and maximum values of the limit-state function over a focal element. A reliability interval composed of the belief measure and the plausibility measure is finally obtained for the structure. Two numerical examples are investigated to demonstrate the effectiveness of the proposed method.

Unified Uncertainty Analysis by the First Order Reliability Method

2008 ◽  
Vol 130 (9) ◽  
Author(s):  
Xiaoping Du
Keyword(s):  
Probability Theory ◽  
Interval Analysis ◽  
Probabilistic Analysis ◽  
Epistemic Uncertainty ◽  
Evidence Theory ◽  
Black Box ◽  
Aleatory Uncertainty ◽  
First Order Reliability Method ◽  
First Order ◽  
Reliability Method

Two types of uncertainty exist in engineering. Aleatory uncertainty comes from inherent variations while epistemic uncertainty derives from ignorance or incomplete information. The former is usually modeled by the probability theory and has been widely researched. The latter can be modeled by the probability theory or nonprobability theories and is much more difficult to deal with. In this work, the effects of both types of uncertainty are quantified with belief and plausibility measures (lower and upper probabilities) in the context of the evidence theory. Input parameters with aleatory uncertainty are modeled with probability distributions by the probability theory. Input parameters with epistemic uncertainty are modeled with basic probability assignments by the evidence theory. A computational method is developed to compute belief and plausibility measures for black-box performance functions. The proposed method involves the nested probabilistic analysis and interval analysis. To handle black-box functions, we employ the first order reliability method for probabilistic analysis and nonlinear optimization for interval analysis. Two example problems are presented to demonstrate the proposed method.

Evidence-theory-based kinematic uncertainty analysis of a dual crane system with epistemic uncertainty

2021 ◽  
pp. 1-29
Author(s):  
Bin Zhou ◽  
Bin Zi ◽  
Yishang Zeng ◽  
Weidong Zhu
Keyword(s):  
Uncertainty Analysis ◽  
Perturbation Method ◽  
Epistemic Uncertainty ◽  
Evidence Theory ◽  
Taylor Series Expansion ◽  
Computational Time ◽  
Evidence Based ◽  
Interval Equations ◽  
Basic Probability ◽  
Interval Perturbation Method

Abstract An evidence-theory-based interval perturbation method (ETIPM) and an evidence-theory-based subinterval perturbation method (ETSPM) are presented for the kinematic uncertainty analysis of a dual cranes system (DCS) with epistemic uncertainty. A multiple evidence variable (MEV) model that consists of evidence variables with focal elements (FEs) and basic probability assignments (BPAs) is constructed. Based on the evidence theory, an evidence-based kinematic equilibrium equation with the MEV model is equivalently transformed to several interval equations. In the ETIPM, the bounds of the luffing angular vector (LAV) with respect to every joint FE are calculated by integrating the first-order Taylor series expansion and interval algorithm. The bounds of the expectation and variance of the LAV and corresponding BPAs are calculated by using the evidence-based uncertainty quantification method. In the ETSPM, the subinterval perturbation method is introduced to decompose original FE into several small subintervals. By comparing results yielded by the ETIPM and ETSPM with those by the evidence theory-based Monte Carlo method, numerical examples show that the accuracy and computational time of the ETSPM are higher than those of the ETIPM, and the accuracy of the ETIPM and ETSPM can be significantly improved with the increase of the number of FEs and subintervals.

Evidence-Theory-Based Analysis for Structural-Acoustic Field with Epistemic Uncertainties

2017 ◽  
Vol 14 (02) ◽  
pp. 1750012 ◽  
Author(s):  
Longxiang Xie ◽  
Jian Liu ◽  
Jinan Zhang ◽  
Xianfeng Man
Keyword(s):  
Interval Analysis ◽  
Interpolation Method ◽  
Epistemic Uncertainty ◽  
Computational Cost ◽  
Evidence Theory ◽  
System Response ◽  
Response Analysis ◽  
Acoustic System ◽  
Response Characteristics ◽  
Focal Element

Evidence theory has a strong capacity to deal with epistemic uncertainty, in view of the overestimation in interval analysis, the responses of structural-acoustic problem with epistemic uncertainty could be untreated. In this paper, a numerical method is proposed for structural-acoustic system response analysis under epistemic uncertainties based on evidence theory. To improve the calculation accuracy and reduce the computational cost, the interval analysis technique and radial point interpolation method are adopted to obtain the approximate frequency response characteristics for each focal element, and the corresponding formulations of structural-acoustic system for interval response analysis are deduced. Numerical examples are introduced to illustrate the efficiency of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document