Unified Measurement Model Research Articles

Design and performance of a triaxial load cell for conical picks

Measuring the load on picks in mining operations poses a significant challenge, which hinders the optimization of cutting mechanism design. To address this issue, a unified load measurement model was initially developed using angular transformation. Subsequently, the measuring-point positions were determined based on the principles of large strain and ease of measurement, using Finite Element Analysis. By analyzing the strain law of the measuring-point positions, a sensor layout scheme was designed, which led to the development of a triaxial load cell. Utilizing the piecewise least squares method and BP neural network, the calibration and decoupling were performed. The findings indicated that the BP neural network effectively reduced the error. Finally, cutting experiments were conducted. The error was less than 8 %, effectively identifying the load variation characteristics when cutting materials with different hardness. The results demonstrate that the designed triaxial load cell can accurately and reliably capture cutting loads.

BIGOWL4DQ: Ontology-driven approach for Big Data quality meta-modelling, selection and reasoning

Context:Data quality should be at the core of many Artificial Intelligence initiatives from the very first moment in which data is required for a successful analysis. Measurement and evaluation of the level of quality are crucial to determining whether data can be used for the tasks at hand. Conscientious of this importance, industry and academia have proposed several data quality measurements and assessment frameworks over the last two decades. Unfortunately, there is no common and shared vocabulary for data quality terms. Thus, it is difficult and time-consuming to integrate data quality analysis within a (Big) Data workflow for performing Artificial Intelligence tasks. One of the main reasons is that, except for a reduced number of proposals, the presented vocabularies are neither machine-readable nor processable, needing human processing to be incorporated. Objective:This paper proposes a unified data quality measurement and assessment information model. This model can be used in different environments and contexts to describe data quality measurement and evaluation concerns. Method:The model has been developed as an ontology to make it interoperable and machine-readable. For better interoperability and applicability, this ontology, BIGOWL4DQ, has been developed as an extension of a previously developed ontology for describing knowledge management in Big Data analytics. Conclusions:This extended ontology provides a data quality measurement and assessment framework required when designing Artificial Intelligence workflows and integrated reasoning capacities. Thus, BIGOWL4DQ can be used to describe Big Data analysis and assess the data quality before the analysis. Result:Our proposal has been validated with two use cases. First, the semantic proposal has been assessed using an academic use case. And second, a real-world case study within an Artificial Intelligence workflow has been conducted to endorse our work.

A decentralized and reliable trust measurement for edge computing enabled Internet of Things

SummaryThe combination of edge computing (EC) and the Internet of Things is a hot research topic. And security is one of the most important problems to be solved. The trust measurement of devices is an effective way to solve the security problem, and the lack of a unified trust measurement model makes the untrusted devices destroy the quality of service. Establishing a reliable trust relationship between devices can effectively improve the security of the system. A decentralized trust measurement model for devices is proposed. First, a decentralized trust measurement framework is proposed, which combines EC with blockchain technology to establish a decentralized hierarchical structure; second, the credibility of devices is measured from multilevel and multi‐attribute; finally, a feedback trust filtering mechanism is designed to filter reliable feedback information, and then the trust between devices and the comprehensive trust of devices are calculated. Experiments and analysis show that the proposed decentralized trust measurement model can effectively measure the trust degree of devices and resist various malicious feedback attacks.

Resource Allocation Algorithm Based on Modeling of Ubiquitous Network Node Capability

With the continuous development and progress of the Internet, computing power is gradually moving from the center to the edge. In order to perceive, measure, schedule and manage the distributed computing resources, it is necessary to model and evaluate the node capability. Based on the development trend of network computing convergence and the demand of network computing service scheduling, this paper studies the unified measurement model and resource allocation algorithm for the integration of network computing and heterogeneous resources. The node resources are divided into four dimensions: computing, communication, memory and storage, and on this basis, the comprehensive ability evaluation index is proposed. Through simulation analysis, the effectiveness of the resource allocation algorithm based on node capability modeling is verified.

Adaptive event‐triggered fault detection for nonlinear networked systems in finite‐frequency domain under stochastic cyber‐attacks

SummaryThis article investigates the fault detection problem in finite‐frequency domain for a class of nonlinear networked systems under stochastic cyber‐attacks. A novel adaptive event‐triggered scheme is introduced to mitigate the transmission burden of the network. A unified measurement model is proposed to take the randomly occurring cyber‐attacks and the transmission delays into account simultaneously. Under the consideration of fault sensitivity and disturbance robustness, the addressed fault detection problem is converted into an auxiliary filtering problem by properly augmenting the states of the original system and the fault detection filter. Intensive stochastic analysis is carried out to obtain sufficient conditions for the existence of the desired fault detection filter, and the corresponding optimal filter parameters can be easily derived by solving a convex optimization problem. Finally, an illustrative example is presented to show the effectiveness and applicability of the proposed method.

Robust integrated covariance intersection fusion Kalman estimators for networked systems with a unified measurement model including five uncertainties

For networked multisensor systems (NMSs), a soft measurement model with five uncertainties is presented, which can be viewed as a “soft sensor” such that the measurements received by estimators can be obtained via the computations of this model. A novel “soft sensor” concept is presented. Three new approaches are presented, which include a fictitious white noises approach with compensating random uncertainties, an extended Lyapunov equation approach with three kinds of the Lyapunov equations, and a universal integrated covariance intersection (ICI) fusion approach with integrating local estimators and their cross-covariances. Applying them, the universal ICI and two fast ICI (FICI) fusion time-varying and steady-state minimax robust Kalman estimators (predictor, filter and smoother) are presented in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds. They improve the robust accuracies of the original covariance intersection (CI) and fast CI(FCI) fusers, and overcome their drawback to require known local estimators and their conservative variances. Their robustness, accuracy relations, stability, steady-state property, and three modes of convergence are proved. The proposed new concept, approaches and results constitute a new methodology, a universal robust fusion Kalman filtering theory and a new filtering stability theory. A simulation example applied to the vehicle suspension system shows their effectiveness and applicability.

Sustainability of Regional Agroecological Economic System Based on Emergy Theory: A Case Study of Anhui Province, China

The agroecological economic system is the basic system on which human beings depend for survival. In order to better evaluate the operation status of a regional agroecological economic system and deepen the cognition of the input and output of the regional agroecological economic system from the angle of emergy, the evaluation method of sustainable development of the regional agroecological economic system with comprehensive consideration of resources, economy, and environment was proposed by constructing a unified dimensional measurement model. This paper analyzed and evaluated the data of the agroecological economic system in Anhui Province from 2010 to 2019. The results showed that the agroecological economic system in Anhui Province bore less environmental pressure and gradually decreased, and had a good system efficiency and economic benefits. The average emergy sustainability index (ESI) was 3.12, indicating that the agroecological economic system in Anhui Province had certain vitality of sustainable development. Based on this, the paper puts forward some suggestions on sustainable and high-quality agricultural development in Anhui Province, which provides theoretical and methodical support for sustainable development of a regional agricultural economy.

Measurement system and model for simultaneously measuring 6DOF geometric errors.

A measurement system to simultaneously measure six degree-of-freedom (6DOF) geometric errors is proposed. The measurement method is based on a combination of mono-frequency laser interferometry and laser fiber collimation. A simpler and more integrated optical configuration is designed. To compensate for the measurement errors introduced by error crosstalk, element fabrication error, laser beam drift, and nonparallelism of two measurement beam, a unified measurement model, which can improve the measurement accuracy, is deduced and established using the ray-tracing method. A numerical simulation using the optical design software Zemax is conducted, and the results verify the correctness of the model. Several experiments are performed to demonstrate the feasibility and effectiveness of the proposed system and measurement model.

Robust estimator design for networked uncertain systems with imperfect measurements and uncertain-covariance noises

The robust Kalman filter design problem is investigated for networked uncertain systems subject to uncertain-covariance noises and incomplete measurements. The norm-bounded parameter uncertainties exist simultaneously in the state, output and white noise covariance matrices. A unified measurement model is adopted to describe multi-step random delays and packet dropouts. Utilizing the measurement reorganization technique, the addressed system is transformed into uncertain stochastic system without delay, for which a robust Kalman filter is proposed. With resort to state augmentation and Riccati difference equations, an upper bound on the filtering error covariance is obtained for all admissible uncertainties. Subsequently, filter parameters are determined by minimizing the trace of the derived upper bound. As the main difference from other existing results, the robust filter developed in this paper considers the effect from uncertain-covariance white noises, multi-step random delays and packet dropouts by using measurement reorganization technique. Finally, an example is provided to demonstrate the effectiveness of the proposed filter.

Exponential mean-square [formula omitted] filtering for arbitrarily switched neural networks with missing measurements

In this paper, the H∞ filtering problem is investigated for a class of discrete-time arbitrary switched neural networks with missing measurements, stochastic perturbations, and communication delays. Based on the average dwell time approach and a set of Kronecker delta functions, a unified measurement model is established to represent the phenomena of missing measurements, time delays and nonlinearities. The aim of this paper is to design an H∞ filter such that the filter error dynamics is exponentially mean-square stable and the H∞ performance requirement is satisfied simultaneously. By using the Lyapunov stability theory and the matrix technology, the design method of the desired filter is given in terms of a matrix inequality which can be solved by using the available software. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.

Thoughts on high-performance computing

Parallel computing is the main technical approach for achieving very high performance computing. In the history of parallel computing, there have been threephases, i.e.moderateparallelismdescribed by Amdahl’s law [1], large-scale parallelism described by Gustafson’s law [2], andhigh-productivity parallelismdescribed by the productivity evaluation model [3]. In April 2010, IBM Inc. in their report ‘Some Challenges on Road fromPetascale to Exascale’ presented five challenges in an exascale system; these stem from power consumption, memory access, communication, reliability, and programming [4], respectively referred to as the energy wall, memory wall, communication wall, reliability wall, and programming wall. Faced with the challenges of ‘walls’, we investigate wall measurement models at the scientific level. For example, existing reliability theories, such as probability theory, do not consider the effect of reliability on performance, while the classic speedup model does not reflect the relation between performance and reliability. To incorporate reliability and performance into a unified measurement model, we measure reliability based on fault-tolerant overhead. As current faulttolerant techniques include a certain time overhead,we created a reliability speedup model with fault tolerance to measure the effect of fault-tolerant overhead on speedup:

Least-Squares Fault Detection and Diagnosis for Networked Sensing Systems Using A Direct State Estimation Approach

In this paper, the problems of fault detection, isolation, and estimation are considered for a class of discrete time-varying networked sensing systems with incomplete measurements. A unified measurement model is utilized to simultaneously characterize both the phenomena of multiple communication delays and data missing. A least-squares filter that minimizes the estimation variance is first designed for the addressed time-varying networked sensing systems, and then a novel residual matching (RM) approach is developed to isolate and estimate the fault once it is detected. The RM strategy is implemented via a series of Kalman filters, where each filter is designed to estimate the augmented signal composed of the system state and a specific fault signal. The design scheme for each filter is proposed in a recursive way. The main idea for the fault detection and estimation is that the Kalman filter with least residual value is regarded as corresponding to the right fault signal, and its estimation is utilized to represent the actual occurred fault. The effectiveness of our proposed method is demonstrated via simulation experiments on a real Internet-based three-tank system.

Probability-dependent H ∞ filtering for nonlinear stochastic systems with missing measurements and randomly occurring communication delays

In this article, the H ∞ filtering problem is investigated for a class of nonlinear stochastic systems with incomplete measurements. The considered incomplete measurements include both the missing measurements and the randomly occurring communication delays. By using a set of Kronecker delta functions, a unified measurement model is employed to describe the phenomena of random communication delays and missing measurements. The purpose of the problem addressed is to design an H ∞ filter such that, for all nonlinearities, incomplete measurements and external disturbances, the filtering error dynamics is exponentially mean-square stable and the H ∞-norm requirement is satisfied. A sufficient condition for the existence of the desired filter is established in terms of certain linear matrix inequalities. A numerical example is given to illustrate the effectiveness of the proposed filter scheme.

Fault-Tolerant Control for Networked Control Systems with Imperfect Measurements

This paper studies the problem of active fault-tolerant control for networked control systems with imperfect measurements. A unified measurement model is utilized to take time-varying delay and random packet dropout into consideration simultaneously. The fault is asymptotically estimated via a fault estimation filter. Based on the fault estimation information, an output feedback fault-tolerant controller is constructed to guarantee the stability and a certain performance constraint of the closed-loop systems. In terms of linear matrix inequality, a sufficient condition is derived for the existence of fault estimation filter and fault-tolerant controller. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.

Fault detection for a class of discrete-time nonlinear systems subject to network-induced uncertainties

This paper addresses the problem of fault detection (FD) for discrete-time systems with global Lipschitz conditions and network-induced uncertainties. By utilizing Bernoulli stochastic variables and a switching signal, a unified measurement model is proposed to describe three kinds of network-induced uncertainties, that is, access constraints, time delays, and packet dropouts. We aim to design a mode-dependent fault detection filter (FDF) such that, for all external disturbances and the above uncertainties, the error between the residual and fault is made as small as possible. The addressed FD problem is then converted into an auxiliary H∞ filtering problem for discrete-time stochastic system with multiple time-varying delays. By applying the Lyapunov-Krasovskii approach, a sufficient condition for the existence of the FDF is derived in terms of certain linear matrix inequalities (LMI). When these LMIs are feasible, the explicit expression of the desired FDF can also be characterized. A numerical example is exploited to show the effectiveness of the results obtained. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

Network‐based robust fault detection with incomplete measurements

AbstractIn this paper, we deal with the robust fault detection problem for a class of discrete‐time networked systems with unknown inputs. Three types of incomplete measurements frequently occurred in a network environment are simultaneously considered, which include (1) measurements with communication delays, (2) measurements with packet dropouts, and (3) measurements with signal quantization. A unified measurement model utilizing a set of Kronecker delta functions is proposed to describe the delay and missing phenomenon, and the quantization is assumed to be of the logarithmic type. Attention is focused on the analysis and design of a robust full‐order fault detection filter such that, for all admissible unknown inputs and incomplete measurements, the error between residual and fault is kept as small as possible. By augmenting the states of the original system and the fault detection filter, the addressed robust fault detection problem is converted into an auxiliary robust H∞ filtering problem. Parameter‐dependent and delay‐probability‐dependent approaches are developed in the design process in order to have a less conservative result. Sufficient conditions for the existence of the desired robust fault detection filters are established in terms of certain linear matrix inequalities (LMIs), and explicit parameters of the desired filter are then characterized if these LMIs are feasible. A numerical example is provided to illustrate the applicability of the proposed technique. Copyright © 2008 John Wiley & Sons, Ltd.