Measuring the Resilience of Supply Chain Systems Using a Survival Model

Disruptions at any stage of a supply chain system can cause mammoth operational and financial losses to a firm. When there is a disruption with a supply chain system, it is highly desired that the system quickly recover. The ability of recovery is, in short, called resilience. This paper proposes a new measure of the resilience of a supply chain system based on the concept of survival and, subsequently, a survival model [Cox proportional hazard (Cox-PH) model]. The survival model represents a time interval or period from the time the system failed to function to the time the system gets back with its function (i.e., recovery). The input to the model is, thus, a failure event; the output from the model is the recovery time. This model has been implemented. There is a case study to illustrate how the model is used to give a quantitative measurement of resilience, in terms of recovery time. © 2014 IEEE.published_or_final_versio

Kinematic calibration of Delta robot using distance measurements

This paper deals with kinematic calibration of the Delta robot using distance measurements. The work is mainly placed upon: (1) the error modeling with a goal to classify the source errors affecting both the compensatable and uncompensatable pose accuracy; (2) the full/partial source error identification using a set of distance measurements acquired by a laser tracker; and (3) design of a linearized compensator for real-time error compensation. Experimental results on a prototype show that positioning accuracy of the robot can significantly be improved by the proposed approach

From Software-Defined Vehicles to Self-Driving Vehicles: A Report on CPSS-Based Parallel Driving

On June 11th, 2017, the 28th IEEE Intelligent Vehicles Symposium (IV'2017) was held in Redondo Beach, California, USA. As one of the 8 workshops at IV'2017, the cyber-physical-social systems (CPSS)-based parallel driving (WS'08), organized by the State Key Laboratory for Management and Control of Complex Systems (SKL-MCCS), Institute of Automation, Chinese Academy of Sciences, China, Xi'an Jiaotong University, China, Tsinghua University, China, Indiana University-Purdue University Indianapolis, USA, and Cranfield University, U.K, has attracted both researchers and practitioners in intelligent vehicles. About 60-70 participants from various countries had extensive and deep discussions on definition, challenges and alternative solutions for CPSS-based parallel driving, and widely agreed that it is a novel paradigm of cloud-based automated driving technologies. Six speakers shared their ideas, studies, field applications, and vision for future along these emerging directions from software-defined vehicles to self-driving vehicles

Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning

The increased complexity and intelligence of automation systems require the development of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected space, this study develops explainable data-driven IFD approaches for nonlinear dynamic systems. More specifically, we parameterize nonlinear systems through a generalized kernel representation for system modeling and the associated fault diagnosis. An important result obtained is a unified form of kernel representations, applicable to both unsupervised and supervised learning. More importantly, through a rigorous theoretical analysis, we discover the existence of a bridge (i.e., a bijective mapping) between some supervised and unsupervised learning-based entities. Notably, the designed IFD approaches achieve the same performance with the use of this bridge. In order to have a better understanding of the results obtained, both unsupervised and supervised neural networks are chosen as the learning tools to identify the generalized kernel representations and design the IFD schemes; an invertible neural network is then employed to build the bridge between them. This article is a perspective article, whose contribution lies in proposing and formalizing the fundamental concepts for explainable intelligent learning methods, contributing to system modeling and data-driven IFD designs for nonlinear dynamic systems

Filter-Based Fading Channel Modeling

A channel simulator is an essential component in the development and accurate performance evaluation of wireless systems. A key technique for producing statistically accurate fading variates is to shape the flat spectrum of Gaussian variates using digital filters. This paper addresses various challenges when designing real and complex spectrum shaping filters with quantized coefficients for efficient realization of both isotropic and nonisotropic fading channels. An iterative algorithm for designing stable complex infinite impulse response (IIR) filters with fixed-point coefficients is presented. The performance of the proposed filter design algorithm is verified with 16-bit fixed-point simulations of two example fading filters