Fiber Bragg Grating Sensor for Strain Sensing in Low Temperature Superconducting Magnet

Fiber Bragg grating (FBG) sensor for monitoring the electromagnetic strain in a low temperature superconducting (LTS) magnet was studied. Before used to LTS magnet strain sensing, the strain response of the sensor with 1.54-mu m wavelength at liquid helium was experimentally studied. It was found that the wavelength shift showed good linearity with longitudinal applied loads and the strain sensitivity is constant at 4.2 K. And then, the hoop strain measurement of a LTS magnet was carried out on the basis of measured results. Furthermore, the finite element method (FEM) was used to simulate the magnet strain. The difference between the experimental and numerical analysis results is very small.

Fiber Bragg Grating Sensor for Strain Sensing in Low Temperature Superconducting Magnet

Fiber Bragg grating (FBG) sensor for monitoring the electromagnetic strain in a low temperature superconducting (LTS) magnet was studied. Before used to LTS magnet strain sensing, the strain response of the sensor with 1.54-mu m wavelength at liquid helium was experimentally studied. It was found that the wavelength shift showed good linearity with longitudinal applied loads and the strain sensitivity is constant at 4.2 K. And then, the hoop strain measurement of a LTS magnet was carried out on the basis of measured results. Furthermore, the finite element method (FEM) was used to simulate the magnet strain. The difference between the experimental and numerical analysis results is very small.

Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing

The aim of this paper is to show that Dempster-Shafer evidence theory may be successfully applied to unsupervised classification in multisource remote sensing. Dempster-Shafer formulation allows for consideration of unions of classes, and to represent both imprecision and uncertainty, through the definition of belief and plausibility functions. These two functions, derived from mass function, are generally chosen in a supervised way. In this paper, the authors describe an unsupervised method, based on the comparison of monosource classification results, to select the classes necessary for Dempster-Shafer evidence combination and to define their mass functions. Data fusion is then performed, discarding invalid clusters (e.g. corresponding to conflicting information) thank to an iterative process. Unsupervised multisource classification algorithm is applied to MAC-Europe'91 multisensor airborne campaign data collected over the Orgeval French site. Classification results using different combinations of sensors (TMS and AirSAR) or wavelengths (L- and C-bands) are compared. Performance of data fusion is evaluated in terms of identification of land cover types. The best results are obtained when all three data sets are used. Furthermore, some other combinations of data are tried, and their ability to discriminate between the different land cover types is quantified

Surface pigments,algal biomass profiles,and potential production of the euphotic layer:Relationships reinvestigated in view of remote-sensing applications

Maps of surface chlorophyllous pigment (Chl a + Pheo a) are currently produced from ocean color sensors. Transforming such maps into maps of primary production can be reliably done only by using light-production models in conjuction with additional information about the column-integrated pigment content and its vertical distribution. As a preliminary effort in this direction. $\ticksim 4,000$ vertical profiles pigment (Chl a + Pheo a) determined only in oceanic Case 1 waters have been statistically analyzed. They were scaled according to dimensionless depths (actual depth divided by the depth of the euphotic layer, $Z_e$) and expressed as dimensionless concentrations (actual concentration divided by the mean concentration within the euphotic layer). The depth $Z_e$ generally unknown, was computed with a previously develop bio-optical model. Highly sifnificant relationships were found allowing $\langle C \rangle_tot$, the pigment content of the euphotic layer, to be inferred from the surface concentration, $\bar C_pd$, observed within the layer of one penetration depth. According to their $\bar C_pd$ values (ranging from $0.01 to > 10 mg m^-3$), we categorized the profiles into seven trophic situations and computed a mean vertical profile for each. Between a quasi-uniform profile in eutrophic waters and a profile with a strong deep maximum in oligotrophic waters, the shape evolves rather regularly. The wellmixed cold waters, essentially in the Antarctic zone, have been separately examined. On average, their profiles are featureless, without deep maxima, whatever their trophic state. Averaged values their profiles are featureless, without deep maxima, whatever their trophic state. Averaged values their profiles are featureless, without deep maxima, whatever their trophic state. Averaged values of $ρ$, the ratio of Chl a tp (Chl a + Pheo a), have also been obtained for each trophic category. The energy stored by photosynthesizing algae, once normalized with respect to the integrated chlorophyll biomass $\langle C \rangle _tot $ is proportional to the available photosythetic energy at the surface via a parameter $ψ∗$ which is the cross-section for photosynthesis per unit of areal chlorophyll. By tanking advantage of the relative stability of $ψ∗.$ we can compute primary production from ocean color data acquired from space. For such a computation, inputs are the irradiance field at the ocean surface, the "surface" pigment from which $\langle C \rangle _tot$ can be derived, the mean $ρ value pertinent to the trophic situation as depicted by the $\bar C_pd or $\langle C \rangle _tot$ values, and the cross-section $ψ∗$. Instead of a contant $ψ∗.$ value, the mean profiles can be used; they allow the climatological field of the $ψ∗.$ parameter to be adjusted through the parallel use of a spectral light-production model.

Mercury(II) ion sensing with PVC-matrix based membrane sensors

Thiosemicarbazone derivatives have been used as ion carriers for the preparation of PVC-matrix based mercury(II)-selective membrane sensors. The electrodes give near-Nernstian responses in the linear concentration range of 1.0×10-1-5.0×10-6 M with detection limits of the order of 10-6 M. The stable potentiometric signals are obtained within a short time period of 20-25s. The effect of different plasticizers has been studied and dioctylsebacate (DOS) found to give a better response in comparison to other plasticizers. Selectivity coefficient values (log KPotHg,M) have been evaluated using fixed interference method. Better selectivity for mercury(II) ions is observed over many of the monovalent (Na+, K+ and NH4+) and divalent ions (Mg2+, Ca2+, Zn2+, Pb2+, Ni2+, Co2+, etc.). The sensors have also been used as indicator electrodes in potentiometric titration of mercury(II) ions with EDTA and its determination in synthetic water samples.

Microstructure and NO2 sensing properties of Fe2O3 thin films

W-doped Fe2O3 films have been fabricated on polycrystalline alumina substrates by the RF magnetron sputtering method, and effects of annealing temperature on the NO2 sensing properties have been examined. The crystal structure of the obtained film changed from Fe3O4 to α-Fe2O3 after annealing at temperatures of 500 to 500°C in air. An increase in the annealing temperature increased the particle size, resulting in a decrease in the NO2-gas sensitivity. These results suggest that the NO2 sensitivity of W-doped Fe2O3 film depends on the particle size.

A data-model fusion approach for upscaling gross ecosystem productivity to the landscape scale based on remote sensing and flux footprint modelling

Chinese Academy of Sciences ; National Science Foundation of China [41071059]; National Key Technology R&D Program of China [2008BAK50B06-02]; National Basic Research Program of China [2010CB950900, 2010CB950704]; Natural Sciences and Engineering Research Council of Canada

Research on extracting method of micro-scale remote sensing information combination and application in coastal zone

973 Project of China [2006CB701305]; "863" Project of China [2009AA12Z148]; National Natural Science Foundation of China [40971224]

Spatial-temporal characteristics of siping urban expansion based on remote sensing and GIS

Based on the RS and GIS methods, Siping city is selected as a study case with four remote sensing images in 25 years. Indices of urban morphology such as fractal dimension and compactness are employed to research the characteristics of urban expansion. Through digital processing and interpreting of the images, the process and characteristics of urban expansion are analysed using urban area change, fractal dimension and compactness. The results showed that there are three terms in this period. It expended fastest in the period of 1979~1991, and in the period of 1992~2001, the emphases on urban redevelopment made it expended slower. And this is in agreement with the Siping Statistical Yearbook. This indicates that the united of metrics of urban morphology and statistical data can be used to satisfactorily describe the process and characteristics of urban expansion. © 2008 IEEE.

Study of remote sensing based parameter uncertainty in Production Efficiency Models

The remote sensing based Production Efficiency Models (PEMs), springs from the concept of "Light Use Efficiency" and has been applied more and more in estimating terrestrial Net Primary Productivity (NPP) regionally and globally. However, global NPP estimates vary greatly among different models in different data sources and handling methods. Because direct observation or measurement of NPP is unavailable at global scale, the precision and reliability of the models cannot be guaranteed. Though, there are ways to improve the accuracy of the models from input parameters. In this study, five remote sensing based PEMs have been compared: CASA, GLO-PEM, TURC, SDBM and VPM. We divided input parameters into three categories, and analyzed the uncertainty of (1) vegetation distribution, (2) fraction of photosynthetically active radiation absorbed by the canopy (fPAR) and (3) light use efficiency (e). Ground measurements of Hulunbeier typical grassland and meteorology measurements were introduced for accuracy evaluation. Results show that a real-time, more accurate vegetation distribution could significantly affect the accuracy of the models, since it's applied directly or indirectly in all models and affects other parameters simultaneously. Higher spatial and spectral resolution remote sensing data may reduce uncertainty of fPAR up to 51.3%, which is essential to improve model accuracy.

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.

Gold Nanoparticle-based Colorimetric Sensor for pH Sensing

We present a newly designed colormetric sensor sensitive to pH value based on a gold nanocomposite composed of gold nanoparticles and pH-sensitive polymer{dodecylthioether end functionalized poly[2-(diethlamino) ethyl methacrylate], poly(DEAEMA)-DDT}. We have shown that this design can produce stable GNP precipitate under weakly basic condition(pH=7.5) and this precipitate can be dispersed in acidic solution(pH=4.0), due to the 2-(diethylamino) ethyl methacrylate protonated by H+.