Number Of Available Sensors Research Articles

Cokriging for cross-attribute fusion in sensor networks

Optimisation of the number of required measurement points and their location is an important research topic in sensor networks. Finding the optimal positions increases spatial coverage and reduces deployment costs. This paper presents an approach for the case that two attributes have to be measured with a different number of available sensors. The proposed cokriging method performs cross-attribute fusion in sensor networks by being based on the analysis of multi-variable spatial correlations. To the best of our knowledge, this scientific work is the first one considering kriging and cokriging interpolations as IF methods. The single-variable ordinary kriging and bi-variable methods were applied to experimental data. The combination of humidity and temperature data in a refrigerated container is used as exemplary case, humidity measurements are considered to be the expensive attribute to measure. The average estimation error for intermediate points was estimated as a function of the number of humidity sensors. When variability is high, data fusion using the bi-variable method produced results as accurate as the single-variable one, without the necessity of deploying a large number of humidity measuring points, by complementing the estimation with temperature measurements.

Assessing partial observability in network sensor location problems

The quality of information on a network is crucial for different transportation planning and management applications. Problems focusing on where to strategically extract this information can be broadly subdivided into observability problems, which rely on the topological properties of the network, and flow-estimation problems, where (prior) information on observed flows is needed to identify optimal sensor locations.This paper contributes mainly to the first category: more specifically, it presents a new methodology and an intuitive metric able to quantify the quality of a solution in case of partial observability, i.e. when not all flow variables are observed or can be uniquely determined from the observed flows. This methodology is based on existing approaches that can efficiently find solutions for full observability (i.e., the set of sensors needed to make the system fully determined), and exploits only the algebraic relations between link, route and origin–destination flow variables to quantify the information contained in any arbitrary subset of these variables.The new metric allows, through its adoption within simple search algorithms, to efficiently select sensor locations when the number of available sensors is limited by, for example, budget constraints and is less than the number needed to guarantee full observability. The chosen positions aim at selecting those locations that contain the largest information content on the whole network. This is an important contribution in this field, since even in small sized networks the solution for full observability requires an exceedingly large amount of sensors. The assessment of partial observability solutions, based on explicit route enumeration, allows one to categorize families of full observability solutions, and shows that these contain different information potential. This way, it is possible to rank solutions requiring a lower number of sensors while containing the same information content.We tested this new methodology both on toy networks, in order to analyse the properties of the metric and illustrate its logic, and to explain and test heuristic search algorithms for optimal sensor positioning on a real-sized network. Analysis of partial observability solutions shows that the basic search algorithms succeed in finding the links that contain the largest deal of information in a network.

The identification of a dam's modal parameters under random support excitation based on the Hankel matrix joint approximate diagonalization technique

Modal analysis is an effective method for monitoring a dam's health. Modal parameters can be identified from the measured vibration response of a dam under ambient excitation, such as that from an earthquake. In this paper, we first use the state space model to analyze the vibration of a dam under ambient support excitation and conclude that the nature excitation technique (NExT) can be used to the measured absolute acceleration response of the dam under band-limited stochastic support excitation to obtain its impulse response. To overcome some of the limitations of the traditional modal identification method for a structure under ambient excitation, we propose a modal parameter identification method based on the Hankel matrix joint approximate diagonalization (HJAD) technique. In this method, the Hankel matrix is defined as the covariance matrix of a vector, which is composed of measured acceleration responses, and their time-lagged data. This modal parameter identification method can be regarded as an improvement to the traditional time domain method because it introduces the joint approximate diagonalization (JAD) technique into the original method. On the other hand, the method can be regarded as an improvement to the SOBI-based modal identification method because it uses the Hankel matrix instead of the covariance matrix of response to perform the JAD. Therefore, this method combines the advantages of the two existing modal identification methods and overcomes some of their limitations. Compared with the SOBI-based modal parameter identification method, the implementation of the method presented in this paper is very convenient because we need only to add time-lagged response data to the analysis. The numerical analysis results show that the proposed modal parameter identification method based on HJAD technology not only has the advantage of a traditional blind modal parameter identification algorithm, but can also overcome the limitation of not being able to estimate more active modes than the number of available sensors. According to the satisfactory performance of this method in the analysis of strong-motion earthquake observation data for a gravity dam, the modal parameter identification method proposed in this paper has the potential for application in water conservancy engineering.

A study and extension of second-order blind source separation to operational modal analysis

Second-order blind source separation (SOBSS) has gained recent interest in operational modal analysis (OMA), since it is able to separate a set of system responses into modal coordinates from which the system poles can be extracted by single-degree-of-freedom techniques. In addition, SOBSS returns a mixing matrix whose columns are the estimates of the system mode shapes. The objective of this paper is threefold. First, a theoretical analysis of current SOBSS methods is conducted within the OMA framework and its precise conditions of applicability are established. Second, a new separation method is proposed that fixes current limitations of SOBSS: It returns estimate of complex mode shapes, it can deal with more active modes than the number of available sensors, and it shows superior performance in the case of heavily damped and/or strongly coupled modes. Third, a theoretical connection is drawn between SOBSS and stochastic subspace identification (SSI), which stands as one of the points of reference in OMA. All approaches are finally compared by means of numerical simulations.

Maintaining Quality of Sensing with Actors in Wireless Sensor Networks

In this paper, we consider using actors to maintain the quality of sensing in the wireless sensor networks. Due to factors such as battery drainage or physical malfunctions, the number of available sensors normally decreases over time after initial deployment, resulting in performance degradation. To maintain the quality of sensing in the network, actors can be used to allocate spare sensors to sensor-deficient regions (sensor allocation) or to relocate sensors from sensor-abundant regions to sensor-deficient regions (sensor relocation). We first focus on the sensor allocation problem. We introduce a baseline centralized greedy algorithm (GA) for sensor allocation, where global sensor information is communicated to obtain the optimal solution. As GA is only efficient for small networks, we proceed to design a distributed patrolling algorithm for achieving global optimization (DPAG) by using only local information. We then extend our work to the application scenario of sensor relocation by proposing a modified GA and DPAG (M-GA and M-DPAG), respectively. Extensive simulation results are provided to demonstrate the performance of the proposed algorithms.

A Study on Engine Health Monitoring in the Frequency Domain

Most of the techniques developed to date for module performance analysis rely on steady-state measurements from a single operating point to evaluate the level of deterioration of an engine. One of the major difficulties associated with this estimation problem comes from its underdetermined nature. It results from the fact that the number of health parameters exceeds the number of available sensors. Among the panel of remedies to this issue, a few authors have investigated the potential of using data collected during a transient operation of the engine. A major outcome of these studies is an improvement in the assessed health condition. The present study proposes a framework that formalizes this observation for a given class of input signals. The analysis is performed in the frequency domain, following the lines of system identification theory. More specifically, the mean-squared estimation error is shown to drastically decrease when using transient input signals. This study is conducted with an engine model representative of a commercial turbofan.

Single pulse laser-induced breakdown spectroscopy of bulk aqueous solutions at oceanic pressures: interrelationship of gate delay and pulse energy

The ability of oceanographers to make sustained measurements of ocean processes is limited by the number of available sensors for long-term in situ analysis. In recent years, laser-induced breakdown spectroscopy (LIBS) has been identified as a viable technique to develop into an oceanic chemical sensor. We performed single pulse laser-induced breakdown spectroscopy of high pressure bulk aqueous solutions to detect three analytes (sodium, manganese, and calcium) that are of key importance in hydrothermal vent fluids, an ocean environment that would greatly benefit from the development of an oceanic LIBS sensor. The interrelationship of the key experimental parameters, pulse energy and gate delay, for a range of pressures up to 2.76x10(7) Pa, is studied. A minimal effect of pressure on the peak intensity is observed. A short gate delay (less than 200 ns) must be used at all pressures. The ability to use a relatively low laser pulse energy (less than approximately 60 mJ) for detection of analytes at high pressure is also established. Na, Mn, and Ca are detectable at pressures up to 2.76x10(7) Pa at 50, 500, and 50 ppm, respectively, using an Echelle spectrometer.

A General Data-Based Approach for Developing Reduced-Order Models of Nonlinear MDOF Systems

A general procedure is presented for analyzing dynamic response measurements from complex multi-degree-of-freedom nonlinear systems incorporating arbitrary types of nonlinear elements. The analysis procedure develops a reduced-order, nonlinear model whose format is convenient for numerical simulation studies. No information about the system’s mass properties is needed, and only the applied excitations and corresponding response are needed to develop the model whose dimension is compatible with the number of available sensors. The utility of the approach is demonstrated by means of a three-degree-of-freedom system incorporating polynomial-type nonlinear features with hardening as well as softening characteristics.

Application of a Web-enabled real-time structural health monitoring system for civilinfrastructure systems

The system architecture of a novel structural health monitoring system that isoptimized for the continuous real-time monitoring of dispersed civil infrastructures ispresented. The monitoring system is based on a highly efficient multithreaded softwaredesign that allows the system to acquire data from a large number of channels,monitor and condition this data, and distribute it, in real time, over the Internet tomultiple remote locations. Bandwidth and latency issues that impact the operationof monitoring systems are discussed. The application of the monitoring systemunder discussion to a long span, flexible bridge in the metropolitan Los Angelesregion is described. The bridge had previously been instrumented with 26 strongmotion accelerometers. Sample ‘quick analysis’ results continuously provided by themonitoring system are presented and interpreted. System identification results,obtained through off-line batch processing, are presented for a data set from arecent earthquake that automatically triggered the recording capability of thesystem. It is shown that, using a time domain system identification approach,the bridge stiffness and damping matrices can be identified from the earthquakedata set and subsequently used to determine the bridge modal properties, suchas frequencies and damping ratios. In this approach the bridge is modeled as amulti-input/multi-output system with order compatible with the number of availablesensors. Implementation issues requiring further investigation are presented and discussed.

Cyberbotics Ltd. Webots™: Professional Mobile Robot Simulation

Cyberbotics Ltd. develops Webots™, a mobile robotics simulation software that provides you with a rapid prototyping environment for modelling, programming and simulating mobile robots. The provided robot libraries enable you to transfer your control programs to several commercially available real mobile robots. Webots™ lets you define and modify a complete mobile robotics setup, even several different robots sharing the same environment. For each object, you can define a number of properties, such as shape, color, texture, mass, friction, etc. You can equip each robot with a large number of available sensors and actuators. You can program these robots using your favorite development environment, simulate them and optionally transfer the resulting programs onto your real robots. Webots™ has been developed in collaboration with the Swiss Federal Institute of Technology in Lausanne, thoroughly tested, well documented and continuously maintained for over 7 years. It is now the main commercial product available from Cyberbotics Ltd.

Optimization of gas-sensitive polymer arrays using combinations of heterogeneous and homogeneous subarrays

Results for optimizing an array of conducting polymer gas sensors for sensing one of five analytes in the presence of up to four interferents are presented. The optimized array consists of subarrays of homogeneous (like) sensors contributing to a larger heterogeneous array of up to ten points (unlike sensors) in multidimensional sensor space. The optimization techniques presented here are linear, since the polymer sensors in their useful (low concentration) operating range exhibit linear and additive response characteristics. The optimization of these arrays produces maximum separability between analytes, demonstrating the trade-off between the addition of both information and variability induced by increasing the size of the heterogeneous array. Optimization results for sensing acetone, hexane, THF, toluene, and ethanol in the presence of interferents result in array sizes that are significantly less than the maximum available number of sensors (ten in the heterogeneous partition of the array). This result adds fuel to the argument that fewer sensors are better; the argument for more sensors, however, is also made in the context of the electronic nose systems where significant chemical diversity is required. Homogeneous subarrays of up to four elements each improve the separability of analytes in these optimized heterogeneous arrays by over 10% and also effectively flag broken or unhealthy sensors in a manner that is independent of analyte and concentration.

Surveillance of Mechanical Systems on the Basis of Vibration Signature Analysis

A comprehensive experimental study is presented to assess the utility of a proposed structural health monitoring and damage detection methodology based on vibration signature analysis of the test article. The approach uses a time-domain least-squares-based method to identify the reduced-order system matrices of an equivalent linear model whose order matches the number of available sensors. A quantification of the level of the system nonlinearity is obtained by determining the residual nonlinear forces involved in the system dynamics. The approach is applied to an intricate mechanical system about which virtually no information was available; i.e., the system was essentially a “black box.” By using similar measurements from a reference version of the test article and two subsequently modified versions, it is shown that through the use of higher-order statistics involving the probability density functions of key system parameters, a reliable measure of the extent of variation of the system influence functions may be obtained. The use of measures of the identified quantities’ dispersion offers a practical method for quantifying the reliability of the estimated changes involved in dealing with real-world (i.e., not noise-free) measurements that result in uncertain estimates of the physical changes in the article being monitored. [S0021-8936(00)03303-1]

Technical note: Use of AVHRR imagery in arctic marine mammal research

Abstract Remote sensing has traditionally been afforded only limited use in Arctic marine mammal research. This is primarily because the summers are short and often cloud covered; winters are long and solar illumination is limited to a few hours of twilight. Orbital sensors operating on a repeat cycle of 16 days simply do not offer the biologist a sufficiently reliable tool for habitat assessment studies. Compounding the data availability problem was the substantial entry cost to acquire and interpret digital remote sensing imagery. With the increase in computational power of microcomputers and the increasing availability of orbital remote sensing imagery (both the decreasing costs and increase in the number of available sensors) the utility of orbital platforms for Arctic marine mammal research should be re-examined. This review describes the activities of the Marine Mammal Management section of the Department of Fisheries and Oceans (DFO), Central and Arctic Region, specifically relating to the use of t...

Enhanced modal control of flexible structures via innovations feedthrough

Abstract Active control of large flexible structures in space requires the control of a very large dimensional system with one of substantially lower dimension. Modern modal control (MMC) has been developed previously to deal with this active structure problem ; however, control and observation spillover (as well as modelling error) can seriously degrade the controller performance by causing pole-shifts from the desired closed-loop locations. In this paper, the original MMC is enhanced by the addition of an innovations feedthrough term to the control law and a residual mode aggregation term to the state estimator. Under conditions presented the enhanced MMC can eliminate the observation spillover from critical residual modes ; hence, the spillover-induced pole shifting in these modes can be mitigated without changing the design of the original MMC. As shown, the trade-off is that the number of residual modes that can bo so enhanced is directly related to the number of available sensors