Redundant Measurements Research Articles

Tracking of Multiple Closely Spaced Extended Targets Based on Prediction-Driven Measurement Sub-Partitioning Algorithm

For multiple extended target tracking, the accuracy of measurement partitioning directly affects the target tracking performance, so the existing partitioning algorithms tend to use as many partitions as possible to obtain accurate estimates of target number and states. Unfortunately, this may create an intolerable computational burden. What is worse is that the measurement partitioning problem of closely spaced targets is still challenging and difficult to solve well. In view of this, a prediction-driven measurement sub-partitioning (PMS) algorithm is first proposed, in which target predictions are fully utilized to determine the clustering centers for obtaining accurate partitioning results. Due to its concise mathematical forms and favorable properties, redundant measurement partitions can be eliminated so that the computational burden is largely reduced. More importantly, the unreasonable target predictions may be marked and replaced by PMS for solving the so-called cardinality underestimation problem without adding extra measurement partitions. PMS is simple to implement, and based on it, an effective multiple closely spaced extended target tracking approach is easily obtained. Simulation results verify the benefit of what we proposed—it has a much faster tracking speed without degrading the performance compared with other approaches, especially in a closely spaced target tracking scenario.

A Strategic and Significant Method for the Optimal Placement of Phasor Measurement Unit for Power System Network

Currently the new state of power system relies on a precise monitoring of electrical quantities such as voltage and current phasors. Occasionally, its operation gets disturbed because of the flicking in load and generation which may result in the interruption of power supply or may cause catastrophic failure. The advanced technology of phasor measurement unit (PMU) is introduced in the late 1990s to measure the behavior of power system more symmetrically, accurately, and precisely. However, the implementation of this device at every busbar in a grid station is not an easy task because of its expensive installation and manufacturing cost. As a result, an optimum placement of PMU is much needed in this case. Therefore, this paper proposes a new symmetry approach of multiple objectives for the optimum placement of PMU problem (OPPP) in order to minimize the installed number of PMUs and maximize the measurement redundancy of the network. To overcome the drawbacks of traditional techniques in the proposed work a reduction and exclusion of pure transit node technique is used in the placement set. In which only the strategic, significant, and the most desirable buses are selected without considering zero injection buses (ZIBs). The fundamental novelty of the proposed work considers most importantly the reduction technique of ZIBs from the optimum PMU locations, as far as the prior approaches concern almost every algorithm have taken ZIBs as their optimal placement sets. Furthermore, a PMUs channel limits and an alternative symmetry location for the PMUs placement are considered when there is an outage or PMUs failure may occur. The performance of the proposed method is verified on different IEEE-standard such as: IEEE-9, IEEE-14, IEEE-24, IEEE-30, IEEE-57, IEEE-118, and a New England-39 bus system. The success of the proposed work was compared with the existing techniques’ outcomes from the literature.

Vehicle Detection System with Statistical Functions Based on 3-Axis Anisotropic Magnetoresistive Using Wireless Communication Technology

In terms of current issues that the sensor’s output signal drifts along with the surrounding strong magnetic field by using the single or dual-axis analog anisotropic magnetoresistive (AMR) sensor in the traffic flow detection, a traffic flow detection system based on ZigBee wireless sensor network is developed and a novel approach by exercising the new digital three-axis AMR sensor to detect the traffic flow is proposed to solve these issues as mentioned above. Using Single Chip Microcomputer (SCM) control technique and utilizing wireless transmitting, an effective algorithm is designed. The algorithm makes it possible to classify vehicle, calculate vehicle speed and count vehicle, in the meantime it provides a reliable and efficient method to collect intelligent transportation data. Even more important, the algorithm has a statistical functions based on MATLAB. The experimental result shows that the novel method has much better measurement accuracy, reliability and redundancy than single or dual-axis method.

Optimization of Phasor Measurement Units Placement Under Contingency Using Reliability of Network Components

This study reports an optimization technique to reduce the requirement of phasor measurement units (PMUs) for complete observability under contingency with the use of component reliability. The present methodology secures the data measurement of unreliable and weak-load buses of power network considering hybrid observability for multiphase PMUs installation. The (N - 1), (N - 2) contingency present in the power network requires redundancy for data measurement. The network components with high reliability decrease the contingency of power networks. This reduces the requirement of data measurement redundancy for the network component having high reliability. This article utilizes this relationship between network component reliability and power network contingency to propose the concept of hybrid observability for PMU placement. It combines the concept of complete and redundant observabilities. Power network reliability is determined by incorporating the component reliability of the PMU data measurement system. Weak-load buses responsible for voltage collapse are determined by varying reactive power loading. Furthermore, the proposed weak-load bus closeness criteria (WLCC) and reliability observability criterion (ROC) are utilized for performing ranking of PMU locations using an analytical hierarchical process. These criteria identify the most significant location for initial phases of PMU installation. This strengthens the data measurement of weak-load buses through the most reliable network preventing voltage collapse due to data unavailability even during initial phases of PMU placement. The proposed methodology is illustrated on the IEEE 30-bus and NRPG 246-bus system.

Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors.

Inertial motion capture relies on accurate sensor-to-segment calibration. When two segments are connected by a hinge joint, for example in human knee or finger joints as well as in many robotic limbs, then the joint axis vector must be identified in the intrinsic sensor coordinate systems. Methods for estimating the joint axis using accelerations and angular rates of arbitrary motion have been proposed, but the user must perform sufficiently informative motion in a predefined initial time window to accomplish complete identifiability. Another drawback of state of the art methods is that the user has no way of knowing if the calibration was successful or not. To achieve plug-and-play calibration, it is therefore important that 1) sufficiently informative data can be extracted even if large portions of the data set consist of non-informative motions, and 2) the user knows when the calibration has reached a sufficient level of accuracy. In the current paper, we propose a novel method that achieves both of these goals. The method combines acceleration- and angular rate information and finds a globally optimal estimate of the joint axis. Methods for sample selection, that overcome the limitation of a dedicated initial calibration time window, are proposed. The sample selection allows estimation to be performed using only a small subset of samples from a larger data set as it deselects non-informative and redundant measurements. Finally, an uncertainty quantification method that assures validity of the estimated joint axis parameters, is proposed. Experimental validation of the method is provided using a mechanical joint performing a large range of motions. Angular errors in the order of were achieved using 125–1000 selected samples. The proposed method is the first truly plug-and-play method that overcome the need for a specific calibration phase and, regardless of the user’s motions, it provides an accurate estimate of the joint axis as soon as possible.

Compensated Load Flow Solutions for Distribution System State Estimation

State estimation of distribution systems typically relies on measurement sets with very low redundancy levels. In this paper, this fact is exploited by first solving a conventional load flow, using exclusively a critical set of measurements, and then compensating the solution to account for the few redundant measurements available. This leads to a suboptimal but sufficiently accurate estimate. It is shown how the sparse triangular factorization of the load flow Jacobian matrix can be fully exploited throughout the compensation-based procedure, preventing in this way the ill-conditioning associated with the gain matrix arising in the conventional least-squares formulation. Simulation results are provided for measurement configurations customarily found in distribution systems, showing the potential advantages of the proposed methodology.

Precision measurement and evaluation of flatness error for the aero-engine rotor connection surface based on convex hull theory and an improved PSO algorithm

The precision measurement and evaluation of the geometry form error for high-end precision components is one of the most important tasks for metrologists. In this paper, a novel hybrid flatness error evaluation method is proposed to measure and evaluate the flatness error for large-scale point cloud data. This method is based on the convex hull algorithm and the improved particle swarm optimization (IPSO) algorithm. Not only are a large number of redundant measurement data points effectively removed through the convex hull algorithm, but also this operation does not lose any valuable information for flatness error evaluation. Then an accurate mathematical model for flatness error evaluation is established based on the minimum zone criteria recommended by ISO, and then the IPSO algorithm is adopted to solve this complex non-linear optimization problem. In particular, the non-linear dynamic inertia weights w and learning factors c1, c2 are introduced to improve the calculation accuracy. Finally, the proposed method is verified through an experiment measuring and evaluating the flatness error of an aero-engine rotor connection surface. The effective measurement data points are reduced from 18 000 to 856, or 4.76%, which is significant compared to the original data points. The flatness error evaluation result is 369 µm according to the IPSO method that we propose. Last but not least, we compare our method with the traditional least squares method and unmodified particle swarm optimization algorithm. The calculation results indicate that the evaluation accuracy is increased by 21 µm and 44 µm, respectively.

Increasing the reliability of bioenergy parks utilizing agricultural waste feedstock under demand uncertainty

abstract Bioenergy parks are multi-product industrial complexes that convert residual biomass from agro-food systems into valuable products such as biofuel and power. By utilizing waste from food production, such systems can mitigate competition for resources at the food-water-energy nexus. A bioenergy park consists of individual plants that are integrated with each other to improve material and energy efficiency and attain more sustainable operations. However, such advantages can be negated by vulnerability of integrated systems to cascading failures triggered by equipment failure or feedstock supply perturbations. Seasonal variations in the demand may also cause reductions in throughput compared to normal production levels, resulting in similar cascading disruptions. The reliability of bioenergy parks can be enhanced by having redundancy measures. However, as this approach is subject to budget constraints, it is important to identify the most critical plant in the bioenergy park. This key step economizes redundancy allocation and avoids overdesigning system capacities. This work applied this integrated framework to address criticality of process units or plants in bioenergy parks, considering demand uncertainties. The method increases overall reliability of bioenergy parks by allocating standby inventory or supply. Two bioenergy parks are analyzed as case studies to demonstrate the integrated method.

PMU Placement for Measurement Redundancy Distribution Considering Zero Injection Bus and Contingencies

This article proposes a method for optimal placement of phasor measurement units (PMUs) to improve measurement redundancy distribution in a system under normal operating condition or in presence of contingency. The minimum number of PMUs is determined using integer linear programming method, after which the measurement redundancy is optimized using a novel model to represent the redundancy level of each bus. Weight parameters are assigned to auxiliary variables, which enables control of priority of different redundancy level. The impact of zero injection bus is integrated into the model, as well as the channel limitation of PMUs and existing measurements. Besides, contingency is modeled in a generalized form, which enables N-1 or N-2 contingency analysis. The proposed method is tested on various IEEE systems, and the results show that the number of PMUs obtained by the proposed method is further reduced comparing with other methods, while with a highly improved measurement redundancy.

Reduction of time errors of the inertial measuring module by realization of its structural redundancy on the basis of three-axis micromechanical measuring instruments

Redundant inertial measurement units (IMU) are used in security-critical operations since the advent of inertial technology. This approach allows you to create fault-tolerant systems that can detect and isolate defective sensors. Besides, experimental results have shown that redundant IMU is also an effective way to improve the performance of navigation systems. The question is only in the dependence of the accuracy of the unit on the number of sensors used and their mutual orientation.
 This paper analyses the influence of spatial orientation on the accuracy of an IMU with a redundant configuration in the case of using triaxial orthogonal micromechanical measuring modules as atomic structural units.
 The first part of this article concentrates on the geometry of the redundant IMU. Analysis of its metrological model of a redundant showed that, when dealing with orthogonal IMU triads, its resulting accuracy is independent of the relative orientation between them. This fact presents important practical implications since it demonstrates that using complex large-scale installation structures can be avoided. As a result, it is enough to place instead of an array of units, for example, only on one printed circuit board with any orientation. Also, it was found the relationship between the number of sensors employed and the accuracy improvement that enables us to ascertain the exact number of sensors needed to design a navigation system with a certain precision.
 The second part of this article shows the experimental approval of theoretical conclusions during the testing of a prototype block based on three low-cost units (MPU6050) built according to a symmetric tetrahedron scheme.
 The accuracy of the redundant block was experimentally evaluated based on the value of the errors in determining the modulus of gravity acceleration and a given angular velocity of a test rotary platform in a series of positions. The performance of the tested inertial measuring block was better on average in comparison with anyone module from this block that proved the possibility of using these approaches for MEMS sensors in high-accuracy application areas.

Accuracy enhancement of measurand estimate on the base of additive combined measurements

PurposeThe paper aims to develop a method for improving the accuracy of smart sensors (deemed as digital measuring instruments) by organizing combined measurements and processing their results by the parametric adjustment method at heterogeneous dispersion of the random error of the applied regression model.Design/methodology/approachWhen carrying out combined measurements, the problem of joint processing of measurement results of functionally related quantities must be solved. The function type can be known in advance or obtained experimentally. The number of combined measurements exceeds the number of unknown measured quantities. The redundant measurements can improve the accuracy of estimates of measured values but lead to inconsistency of the measurement results. The problem of inconsistency is solved by the parametric adjustment method, which is rather widely used mainly in the field of geodetic measurements, wherein the parametric equations are linear and the measured quantities are additive.FindingsThe proposed method allows to reduce the uncertainty of type B of a measurement result, caused by the maximum permissible error of a digital measuring instrument, by 1.2–4 times in comparison with the direct estimation method.Originality/valueA compact description of the parametric adjustment method in matrix form is given. Recommendations are given on shaping a sensitivity matrix of functions for the proposed method. The geometric interpretation of the proposed method is considered. The results of the proposed method experimental testing are given when evaluating resistance values.

Principal component analysis with autocorrelated data

This paper contributes to the analysis, interpretation and the use of the principal component analysis in a multivariate time-correlated linear process. The effect of ignoring the autocorrelation structure of the vector process is investigated. The results show a spurious impact of the time-correlation on the eigenvalues. To mitigate this impact, a pre-filtering procedure to whiten the data is applied. The methodology is used to identify redundant particulate matter measurements in a region in Brazil. Among the eight considered monitoring stations, it is found that three are needed to characterize the dynamic of the pollutant in the region.

Mobile Sensor Networks and Control: Adaptive Sampling of Spatiotemporal Processes

The control of mobile sensor networks uses sensor measurements to update a model of an unknown or estimated process, which in turn guides the collection of subsequent measurements—a feedback control framework called adaptive sampling. Applications for adaptive sampling exist in a wide range of settings, especially for unmanned or autonomous vehicles that can be deployed cheaply and in cooperative groups. The dynamics of mobile sensor platforms are often simplified to planar self-propelled particles subject to the ambient flow of the surrounding fluid. Sensor measurements are assimilated into continuous or discrete models of the process of interest, which in general can vary in space and time. The variability of the estimated process is one metric to score future candidate sampling trajectories, along with information- and uncertainty-based metrics. Sampling tasks are allocated to the network using centralized or decentralized optimization, in order to avoid redundant measurements and observational gaps.

On-Orbit Calibration Method for Redundant IMU Based on Satellite Navigation & Star Sensor Information Fusion

Aiming at the long term and high-precision performance maintenance requirements of the satellite-borne four-axis redundant inertial measurement unit (IMU) in space, the satellite navigation velocity information, the attitude information provided by the star sensor and the redundant measurement output are used as references to derive the measurement model, including the star sensor installation error. Three rotation sequences of optimal observation, sub-optimal observation, and under-observation are designed and compared. The Kalman filter is used to calibrate the redundant IMU in the orbit. The numerical simulation shows that all three rotation sequences can calibrate more than 91% of the constant error, which meets the requirements of the use, and the calibration accuracy of the optimal rotation sequence is the highest. The ground repeatability calibration experiment is carried out 10 times for the optimal rotation sequence. The calibration results show that the range of all the error terms is less than its threshold, illustrating that the calibration result is stable and reliable. It is a real-time calibration method for all error terms on orbit, providing a reference method for the high-precision performance maintenance of the spacecraft integrated navigation system.

Joint processing of measurements in a rangefinder-doppler multistatic radar system

The article considers the option of organizing joint processing of radar information in a multistatic rangefinder - doppler radar system. The least-squares method is used to obtain analytical expressions for oblique ranges and radial velocities of targets during joint processing of range-finding measurements of various types. The obtained expressions for inclined ranges have some similarities with the secondary processing of radar information, with the only difference being that the weighting coefficients for the evaluated parameters are updated in the case of successive measurements as data are received, and in the case of joint processing, they depend on the number of positions and the number of measurements. It is shown that the joint processing of measurements of the inclined range, the sum of the distances, the radial velocity and the rate of change of the total range allows to increase the accuracy of measuring the location of an air object and the projections of its velocity vector on the axis of a rectangular coordinate system. The physical basis for increasing the accuracy of positioning is to use redundant measurements by processing the total ranges. The considered option of processing redundant measurements in a multistatic radar system does not require time to accumulate data, and the task of increasing accuracy is solved in one measurement cycle. The potential accuracy of determining the location of an air object for different values of the standard errors of the determination of rangefinding parameters in a multistatic radar system at various distances between positions has been calculated. For an arbitrary trajectory of an air object, simulation-statistical modeling was performed, which allows to obtain the mean square errors of determining the location and velocity vector of the air object. A gain is shown in the accuracy of determining the location and velocity vector of an air object in comparison with traditional algorithms for determining coordinates in long-range multistatic radar systems.

Locating ground faults in non-solidly grounded networks via nonlinear least-squares

This paper presents a method for locating ground faults in ungrounded and high resistance grounded networks. In such networks, the contribution of the fault to the line currents is minimal, thus creating a challenge to the popular impedance-based fault locators employed in the industry. A compensation method is introduced to alleviate this impediment to the use of impedance-based fault locators; the compensated signals are consistent with the transmission line wave equations and are formed by the fault signal components thus making the fault information more visible. The proposed approach employs (i) phasor extraction based on forward and backward finite impulse response (FIR) filtering to better capture the fault signal dynamics, (ii) an initialization stage employing redundant measurement equations of the fault voltage, and (iii) an augmented matrix approach for least-squares estimation that models the physical conditions of the fault in symmetrical component coordinates. The proposed method is contrasted with a recent transient analysis technique for locating ground faults in non-solidly grounded systems and a synchronized measurement technology-based fault locator; the numerical results show a significant improvement in accuracy while employing standard data acquisition devices.

GNSS RTK Performance Improvements using Galileo Satellite Signal

Abstract Two factors of the existing GNSS Real-Time Kinematic (RTK) positioning are as follows: distance-dependence and unreliable ambiguity resolution under bad observation conditions in cities or forests. Use of multi-frequency GNSS signals and systems could possibly redefine RTK services in LatPos, regionally and globally, and more redundant measurements from multiple satellite systems, such as NAVSTAR, Galileo, Glonass and BeiDou, can improve the performance of RTK measurement results in terms of accuracy, availability, reliability and time to fix. The benefits of multiple systems of GNSS services are as follows: 1) savings in the reference station infrastructure costs, and 2) improvement on RTK preciseness and reliability for the professional users. The paper aims at studying how the RTK system, using multiple satellite constellations, performs, adding Galileo signal measurements. Galileo measurements are observed using a field receiver and corrections received from LatPos base station network. Numerical analysis is performed using real-time corrections in field receivers, and results from collected RINEX data are compared by various computing schemes, such as L1/L2 and wide lane signals, NAVSTAR and NAVSTAR with Galileo measurements. The results have preliminary demonstrated the significant improvement using both GNSS satellite signals. Further improvements on the LatPos system have been introduced and the planned improvements shown.

Metrological Support for Wireless Sensor Systems

The paper identifies twelve problems of metrological reliability of wireless sensor systems that require their solution. The most urgent is the problem of determining the main parameters of the metrological reliability of wireless sensor systems. It is closely related to solving the problem of self-calibration of optical smart sensors that are part of the smart nodes of the system. The task of self-calibration of smart sensors is solved when three basic conditions are met: 1) when creating and using structurally redundant optical sensors designed to implement methods of redundant measurements; 2) when developing LEDs or other highly stable sources of optical radiation, including those with controlled parameters, for any given spectral range of wavelengths; 3) when creating in micro-performance filters on a priori given range of the spectrum of optical sines. The article describes two methods of metrological support for BSS smart sensors, based on the introduction of information redundancy by conducting three clock cycles of measuring physical quantity with a smart sensor, followed by processing the data using an a priori derived algorithm. The disadvantages and advantages of the methods are shown. It is stated that the use of the theory of excess measurements is the only correct way to solve the problem of self-calibration of sensors and measuring instruments. Given the harsh operating conditions of optical smart sensors, it is preferable to manufacture them using high technology. Today, in Ukraine, no one is engaged in a practical solution to the problems of metrological reliability of wireless sensor systems and the solution of the problems of self-calibration of smart sensors.

Conditions and Tasks : The Effects of Planning and Task Complexity on L2 Speaking

Motivated by different notion of task criteria in the Limited Attention Capacity Hypothesis and the Cognition Hypothesis, the purpose of this study is to investigate how planning and task complexity affect second language (L2) learners’ spoken performance. The study’s motivation is as follows: 1) the effects of online planning (as task condition) and task complexity (as task characteristics) and their interactions were examined; and 2) instead of repeated and redundant measures for complexity, accuracy, and fluency (CAF), multi-layered CAF measure indices were used to capture a careful performance. The study’s subjects were 77 undergraduates divided into four groups: no planning in simple task, no planning in complex task, online planning in simple task, and online planning in complex task. Online planning was modified from Yuan and Ellis’s (2003) and Wang’s (2009) studies, while task complexity was manipulated by [elements] and [reasoning]. The study employed 11 general and specific measure indices for CAF and found that no planning condition significantly enhanced overall accuracy and verb-related specific accuracy or speed and repair fluency regardless of task complexity. Meanwhile, task complexity significantly improved phrasal syntactic complexity regardless of planning. Based on these findings, the study explored two competing theories on L2 speaking.

Less is more: Two automated interventions to increase vitamin B12 measurement when long-term proton pump inhibitor and decrease redundant testing

IntroductionTo test the efficacy of two interventions to promote the measurement of serum vitamin B12 (s- vitamin B12) in patients with long-term proton pump inhibitor (PPI) use and to decrease inadequate s-vitamin B12 measurement by implementing a minimum retest interval. Material and MethodsThe Laboratory Information System (LIS) automatically registered s-vitamin B12 when not ordered by General Practitioners to all requests patients with long term (>3years) PPI treatment, and with no s-vitamin B12 testing in the previous year. Through the second intervention, the LIS reported the previous s-vitamin B12 result through an explanatory comment in the report, when the test had been requested in the previous three months.We calculated the new diagnoses of vitamin deficiency (s-vitamin B12 < 200 pg/mL), examined the number of tests needed to detect a new case and investigated the economic cost of each one identified by the intervention. Additionally, we counted the s-vitamin B12 that were requested but not measured, and calculated the savings in reagent. ResultsThe strategy added 548 s-vitamin B12 and identified 47 new cases of vitamin B12 deficit. The number of added s-vitamin B12 needed to identify a new case by means of the intervention was 12. The total intervention reagent cost was 1446€, with a cost of 30.7€ per new case. The second intervention avoided unnecessary measurement of 611 tests, with 1613€ savings. ConclusionsThrough the two automated interventions we improved the diagnosis of vitamin B12 deficiency, and decreased inadequate redundant s-vitamin B12 measurement, cutting unnecessary laboratory costs.