Accurate Temperature Estimation Research Articles

Development of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST)

Abstract An accurate estimation of the sea surface temperature (SST) is of great importance. Therefore, the objective of this work was to develop an adaptive neuro-fuzzy inference system (ANFIS) model to predict SST in the Çanakkale Strait. The observed monthly air temperature, evaporation and precipitation data from the Çanakkale meteorological observation station were used as input data. The Takagi–Sugeno fuzzy inference system was applied. The grid partition method (ANFIS-GP) and the subtractive clustering partitioning method (ANFIS-SC) were used with Gaussian membership functions to generate the fuzzy inference system. Six performance evaluation criteria were used to evaluate the developed SST prediction models, including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE) and correlation of determination (R2). The dataset was randomly divided into training and testing datasets for the machine learning process. Training data accounted for 75% of the dataset, while 25% of the dataset was allocated for testing in ANFIS. The hybrid algorithm was selected as a training algorithm for the ANFIS. Simulation results revealed that the ANFIS-SC4 model provided a higher correlation coefficient of 0.96 between the observed and predicted SST values. The results of this study suggest that the developed ANFIS model can be applied for predicting sea surface temperature around the world.

Spectrophotometric Color Measurement to Assess Temperature of Exposure in Cortical and Medullar Heated Human Bones: A Preliminary Study.

Heated-bone color changes may provide information about temperature of exposure, with interest for anthropologists and forensic experts. The aim of this study was to assess heat-induced color changes by spectrophotometry in cortical and medullar human bones heated at different temperatures and times. CIELAB (International Commission on Illumination-LAB) color parameters (L*, a*, and b*) and whiteness (WI) and yellowness (YI) indexes were obtained by spectrophotometry in the cortical and medullar zones of 36 bone sections exposed at 200, 400, 600, and 800 °C for 30 and 60 min. The accuracy of color-based temperature estimations was evaluated by Receiver Operating Characteristics (ROC) analysis. Chromaticity a* showed the best significant discrimination power with the area under the ROC curve (AUC) values ranged from 0.9 to 1.0 in cortical zones and 0.7 to 1.0 in medullar zones for all temperatures of exposures and both time of exposures. Chromaticity b*, and WI and YI indexes showed an AUC of 1.0 at 400, 600, and 800 °C for 30 and 60 min in the cortical and medullar zones. The spectrophotometric color parameters provided a highly accurate estimation of the temperature of exposure to discriminate between temperatures and exposure times in the cortical and medullar zones. Spectrophotometric bone color measurement in cortical and medullar zones can be an objective and reproducible method to estimate the temperature of exposition, and it can be considered useful for forensic and anthropological purposes.

Retrieval of Land Surface Temperature With Topographic Effect Correction From Landsat 8 Thermal Infrared Data in Mountainous Areas

Accurate estimation of land surface temperature (LST) is crucial for ecological environment monitoring and climate change studies in mountainous areas. The current LST retrieval algorithms were developed without accounting for the topographic effect, which can only be used to retrieve LST over relatively flat surfaces. Due to the impact of 3-D structure of mountainous surfaces, rugged terrain makes the processes of thermal radiation more complex. In this study, a radiative transfer equation (RTE)-based single-channel algorithm was proposed to retrieve LST with topographic effect correction from the Landsat 8 thermal infrared (TIR) data in mountainous areas. This algorithm accounts for the changes in the thermal radiation components in the TIR RTE caused by the topographic effect. According to the analysis of simulation data, sky-view factor (SVF), atmospheric water vapor content, surface emissivity of target pixel, and average LST of the surrounding terrain have significant influence on the magnitude of the topographic effect. The differences between the LST retrieved without/with topographic effect correction from the Landsat 8 TIR data are related to SVF. The topographic effect should be taken into account in the LST retrieval algorithm when SVF is smaller than 0.7. The largest LST difference of approximately 1 K occurs in the deep valley. The results indicate that LST without topographic effect correction could be overestimated to be as high as 1 K. Due to a lack of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> LST measurements, the performance of the LST retrieval algorithm in mountainous areas was only evaluated by comparing the brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the DART+MODTRAN model and the TIR RTE over mountainous surfaces at three subregions. There is a good consistency between BT at the TOA simulated by the DART+MODTRAN model and the TIR RTE over mountainous surfaces at the three subregions, with a root-mean-squared error (RMSE) of less than 0.23 K.

City-Scale Building Anthropogenic Heating during Heat Waves

More frequent and longer duration heat waves have been observed worldwide and are recognized as a serious threat to human health and the stability of electrical grids. Past studies have identified a positive feedback between heat waves and urban heat island effects. Anthropogenic heat emissions from buildings have a crucial impact on the urban environment, and hence it is critical to understand the interactive effects of urban microclimate and building heat emissions in terms of the urban energy balance. Here we developed a coupled-simulation approach to quantify these effects, mapping urban environmental data generated by the mesoscale Weather Research and Forecasting (WRF) coupled to Urban Canopy Model (UCM) to urban building energy models (UBEM). We conducted a case study in the city of Los Angeles, California, during a five-day heat wave event in September 2009. We analyzed the surge in city-scale building heat emission and energy use during the extreme heat event. We first simulated the urban microclimate at a high resolution (500 m by 500 m) using WRF-UCM. We then generated grid-level building heat emission profiles and aggregated them using prototype building energy models informed by spatially disaggregated urban land use and urban building density data. The spatial patterns of anthropogenic heat discharge from the building sector were analyzed, and the quantitative relationship with weather conditions and urban land-use dynamics were assessed at the grid level. The simulation results indicate that the dispersion of anthropogenic heat from urban buildings to the urban environment increases by up to 20% on average and varies significantly, both in time and space, during the heat wave event. The heat dispersion from the air-conditioning heat rejection contributes most (86.5%) of the total waste heat from the buildings to the urban environment. We also found that the waste heat discharge in inland, dense urban districts is more sensitive to extreme events than it is in coastal or suburban areas. The generated anthropogenic heat profiles can be used in urban microclimate models to provide a more accurate estimation of urban air temperature rises during heat waves.

A Monitoring Method for Average Winding and Hot-Spot Temperatures of Single-Phase, Oil-Immersed Transformers

Transformers are the most expensive element of a power system, and so transformer monitoring systems are becoming more important as loads increase. Under heavy loading conditions, the hot-spot temperature of a transformer is the most critical parameter to determine the limit of loading. Therefore, to make an accurate estimation of hot-spot temperature and maintain the reliability of a power system, dynamic thermal models or methods must be employed. Due to the complexity of thermal phenomena, the methods presented so far needs to be improved to reduce computational errors. This is particularly the case for variable loading conditions and power utilities are still in need of good techniques. This paper describes two methods to determine temperature in single-phase, oil-immersed transformers. The first method calculates average windings temperature (AWT) using the voltage and current from primary and secondary windings. The second method, which uses data from the first method, determines the hot-spot temperature of the transformer. The methods have been investigated by experiments on a 30 kVA single-phase transformer and verified the results using COMSOL ™ software. Our methods have proven more accurate than the IEEE Annex G method.

Global mean surface temperature and climate sensitivity of the early Eocene Climatic Optimum (EECO), Paleocene–Eocene Thermal Maximum (PETM), and latest Paleocene

Abstract. Accurate estimates of past global mean surface temperature (GMST) help to contextualise future climate change and are required to estimate the sensitivity of the climate system to CO2 forcing through Earth's history. Previous GMST estimates for the latest Paleocene and early Eocene (∼57 to 48 million years ago) span a wide range (∼9 to 23 °C higher than pre-industrial) and prevent an accurate assessment of climate sensitivity during this extreme greenhouse climate interval. Using the most recent data compilations, we employ a multi-method experimental framework to calculate GMST during the three DeepMIP target intervals: (1) the latest Paleocene (∼57 Ma), (2) the Paleocene–Eocene Thermal Maximum (PETM; 56 Ma), and (3) the early Eocene Climatic Optimum (EECO; 53.3 to 49.1 Ma). Using six different methodologies, we find that the average GMST estimate (66 % confidence) during the latest Paleocene, PETM, and EECO was 26.3 °C (22.3 to 28.3 °C), 31.6 °C (27.2 to 34.5 °C), and 27.0 °C (23.2 to 29.7 °C), respectively. GMST estimates from the EECO are ∼10 to 16 °C warmer than pre-industrial, higher than the estimate given by the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (9 to 14 °C higher than pre-industrial). Leveraging the large “signal” associated with these extreme warm climates, we combine estimates of GMST and CO2 from the latest Paleocene, PETM, and EECO to calculate gross estimates of the average climate sensitivity between the early Paleogene and today. We demonstrate that “bulk” equilibrium climate sensitivity (ECS; 66 % confidence) during the latest Paleocene, PETM, and EECO is 4.5 °C (2.4 to 6.8 °C), 3.6 °C (2.3 to 4.7 °C), and 3.1 °C (1.8 to 4.4 °C) per doubling of CO2. These values are generally similar to those assessed by the IPCC (1.5 to 4.5 °C per doubling CO2) but appear incompatible with low ECS values (&lt;1.5 per doubling CO2).

Temperature simulation of three-point bending geometry in a dynamic mechanical analyzer

Dynamic mechanical analysis (DMA) is a thermo-analytical technique that is widely used as a part of polymer characterization. One of the most common tests consists of measuring viscoelastic properties as a function of temperature while subjecting the sample to controlled heating rates. In that tests, due to sample and instrument geometry and sample size, it is not possible to measure the temperature in all parts of the sample. As a result, the gradient of temperatures between different parts of the sample is unknown. Thus, an accurate estimation of the sample temperature in all its parts and of the temperature gradients between different parts are crucial for setting up experimental conditions and establishing confidence temperature ranges to better interpret the test results. In the present work, a simulation study is performed through the Comsol ™ software, to estimate the temperature distribution of samples of different density and heat capacity that are located inside a typical DMA furnace, which is subjected to different heating rates. The furnace has two gas inlets and three outlets and the sample is attached through standard 3-point bending fixtures. The results show that some of the temperature gradients produced in the sample high enough to significantly affect the viscoelastic response.

A data-driven approach to estimate battery cell temperature using a nonlinear autoregressive exogenous neural network model

Abstract Battery cell temperature is a key parameter in battery life degradation, safety, and dynamic performance. Intense charging-discharging operations and high-ambient temperatures escalate battery cell temperature, which in turn accelerates its degradation. Therefore, accurate battery cell temperature estimation can play a significant role in ensuring the optimal operation of a battery energy storage system (BESS). In order to estimate battery cell temperature as accurate as possible, use of non-linear models is imperative due to the non-linear nature of the battery operation. This paper proposes a data-driven model based on a Non-linear Autoregressive Exogenous (NARX) neural network to estimate battery cell temperatures in a utility-scale BESS, considering strongly-correlated independent variables, e.g., charging-discharging current and ambient temperature. Due to different temperature and weather characteristics in each season, seasonal NARX models have also been derived and compared with the universal one. The proposed models’ performance has been verified using the field data collected from a grid-connected BESS within a PV plant. The simulation results show high accuracy of the proposed model compared to the measured data for both seasonal and universal models without considering the complexity of the large-scale battery and container thermal dynamics. In particular, in more than 95% of the time, the estimated values yield root mean squared errors (RMSE) below 1 ∘C in different conditions, which confirms the validity and accuracy of the proposed model. Moreover, seasonal models show better performance with 18% to 50% less RMSE on average (for 1 h to 24 h forward estimation) compared to the universal model.

Kernel Extreme Learning Machine: An Efficient Model for Estimating Daily Dew Point Temperature Using Weather Data

Accurate estimation of dew point temperature (Tdew) has a crucial role in sustainable water resource management. This study investigates kernel extreme learning machine (KELM), boosted regression tree (BRT), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), and multivariate adaptive regression spline (MARS) models for daily dew point temperature estimation at Durham and UC Riverside stations in the United States. Daily time scale measured hydrometeorological data, including wind speed (WS), maximum air temperature (TMAX), minimum air temperature (TMIN), maximum relative humidity (RHMAX), minimum relative humidity (RHMIN), vapor pressure (VP), soil temperature (ST), solar radiation (SR), and dew point temperature (Tdew) were utilized to investigate the applied predictive models. Results of the KELM model were compared with other models using eight different input combinations with respect to root mean square error (RMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE) statistical indices. Results showed that the KELM models, using three input parameters, VP, TMAX, and RHMIN, with RMSE = 0.419 °C, NSE = 0.995, and R2 = 0.995 at Durham station, and seven input parameters, VP, ST, RHMAX, TMIN, RHMIN, TMAX, and WS, with RMSE = 0.485 °C, NSE = 0.994, and R2 = 0.994 at UC Riverside station, exhibited better performance in the modeling of daily Tdew. Finally, it was concluded from a comparison of the results that out of the five models applied, the KELM model was found to be the most robust by improving the performance of BRT, RBFNN, MLPNN, and MARS models in the testing phase at both stations.

Temperature dynamics and its effects on gas influx handling during managed pressure drilling operations

With Managed Pressure Drilling (MPD) becoming increasingly important to oil and gas well construction, there has been a growing demand for comprehensive models that can accurately describe the well hydraulic dynamics, especially for complex well geometries, High-Pressure High-Temperature (HPHT) conditions, during well control events, etc. To meet this demand, new multi-phase hydraulic models have been developed. However, despite their improvement over the traditional lumped parameter models, these models typically do not consider detailed temperature effects, which markedly influence well hydraulics and need to be explicitly considered for accurate MPD control.The development of a new integrated multi-phase thermal and hydraulic modeling framework is presented in this paper. This integrated model can estimate the mud temperature in the drillstring and the annulus, as well as the temperature variations of the formation during complex well control situations. The model uses a semi-implicit finite volume approach and solves the mixture energy equation for the wellbore fluids, assuming that all the phases are in thermal equilibrium. Heat transfer between the drillstring and wellbore fluids, and between the wellbore fluids and the formation is calculated using thermal resistance networks.The steady-state and transient temperature behaviors during mud circulation are compared to the steady-state analytical model by Hasan and Kabir and commercial software. These comparisons show good agreement for both steady-state and transient cases. To demonstrate the importance of accurate temperature estimation of the drillstring and annulus fluids in HPHT conditions, offshore kick scenarios for non-aqueous drilling fluids and dynamic kick control methods are simulated. Simulation results indicate that the model enables real-time estimation of crucial parameters during well control, such as the wellbore pressure and temperature profiles, increased outflow and pit gain during kicks, gas thermodynamic behavior including solubility and unloading behavior at low pressure conditions, gas rising velocity, and temperature-dependent formation strength.

Robust Artificial Neural Network-Based Models for Accurate Surface Temperature Estimation of Batteries

The temperature of a battery cell is a major parameter that must be continuously monitored to ensure a safe operation. Most battery failures are linked to thermal runaway due to temperature rise in the battery, which if not detected early can result in battery destruction or fire hazard. This article proposes robust artificial neural network models with reduced complexity to estimate the surface temperature of different battery chemistries. The proposed models are accurate, reliable, and use no temperature sensor. Different neural network architectures are evaluated and optimized. Derivation of the models followed by experimental verification using commercial battery cells of different chemistries, specifications, and aging conditions is presented.

Accurate Temperature Estimation for Each Gate of GaN HEMT With n-Gate Fingers

An analytical approach based on the thermoelectrical analogy to calculate the maximum channel temperature of multifinger AlGaN/GaN HEMT transistor is presented. The model is capable of predicting the maximum channel temperature, including the impact of the nonlinearity of thermal conductivity for every single gate with excellent accuracy. The model remains invariant under device scaling up and down and gives an accurate estimation for any number of gate fingers. Furthermore, the temperature field fluctuation due to the interaction between gates and variation in the number of gate fingers can be easily identified by the model. The validity of the proposed approach has been testified by comparing the results with those from infrared spectroscopy and numerical simulations for different AlGaN/GaN HEMT transistors with a different number of gates. The significance of models is justified by its ability to evaluate the temperature of any gate finger with high accuracy. Such an ability is particularly highly important while optimizing the structure layout and cooling system for high-power AlGaN/GaN HEMT transistors. The model can serve as a powerful tool for power devices and microwave IC designers and can be easily incorporated into SPICE empirical or physical-based models.

Nuclear Magnetic Relaxation Mapping of Spin Relaxation in Electrically Stressed Glycerol.

This work discusses nuclear magnetic relaxation effects in glycerol subject to a strong electric field. The methods used are 1.5 T magnetic resonance imaging (MRI), referenced by 9.4 T nuclear magnetic resonance (NMR). While MRI allows a glycerol probe to be sampled with a high voltage (HV) of 16 kV applied to the probe, NMR provides precise molecular data from the sample, but the sample cannot be tested under HV. Using MRI, the recording of magnetic relaxation times was possible while HV was applied to the glycerol. NMR spectroscopy was used to confirm that MRI provides a reasonably accurate estimation of temperature. The applied HV was observed to have a negligible effect on the spin–lattice relaxation time T1, which represents the energy release to the thermal bath or system enthalpy. In contrast to that, the spin–spin relaxation time T2, which does represent the local entropy of the system, shows a lower response to temperature while the liquid is electrically stressed. These observations point toward a proton population in electrically stressed glycerol that is more mobile than that found in the bulk, an observation that is in agreement with previously published results for water.

High temperature stress does not distort the geochemical thermometers based on biogenic calcium carbonate: Stable oxygen isotope values and Sr/Ca ratios of gastropod shells in response to rearing temperature

Quantitative profiles of stable isotopes and trace elements in biogenic carbonates have received considerable attention for the purpose of reconstructing past temperatures. The relationships between temperature and the geochemical parameters of carbonates are often subject to both inter- and intraspecific variations, which critically impair the accuracy of temperature estimation. One of the issues that must be addressed to improve temperature estimation is enhancing our understanding of the influence of the kinetic effect. This effect matters because the purely thermodynamic effect to geochemical profiles cannot be effectively separated from the effect of temperature-driven calcification rate. Cowries (Mollusca, Gastropoda, Cypraeidae) may provide a good model system for quantitatively investigating these two effects, because they undergo a callus-building stage in which aragonitic shell formation exclusively occurs to monotonically increase the thickness outward. We reared a total of 230 juvenile individuals of Monetaria annulus under six constant thermal regimes, ranging from the minimum growing temperature (21 °C) to the maximum tolerated long-term temperature (34 °C). The portions of the shells built in the callus-building stage were analyzed to quantify isotopic and elemental compositions. Shell growth rate positively depended on temperature from 21 °C to 33 °C, but dramatically decreased at 34 °C, presumably because of high temperature stress. Nevertheless, the heat stress did not make any impact on the thermal dependences (and among-individual variabilities within the same temperature) of either δ18Oc or Sr/Ca. A linear relationship exists between temperature and the stable oxygen isotope value of the shells (δ18Oc) across the entire thermal range (n = 103), whereas the shell Sr/Ca ratio increased exponentially with increasing temperature (n = 227). These results strongly suggest that any chemical processes responsible for quantitative incorporation of 18O and Sr into aragonite crystals were not impacted by heat stress at temperatures up to 34 °C. Within-temperature residual correlations between shell growth rates and δ18Oc were weakly positive, which is compatible with a widely accepted hypothesis that carbonic anhydrase suppresses the kinetic effect in molluscs. Likewise, we found no association between shell growth rates and Sr/Ca, which rejected the possibility that faster CaCO3 precipitation caused the positive relationship between temperature and Sr/Ca. A large proportion of among-individual variability in shell Sr/Ca was caused by unspecified factors other than temperature. This makes it difficult to accurately estimate the past temperature by measuring the Sr/Ca value of a single shell specimen.

Computational Insight into Diatomic Molecules as Probes to Measure the Variation of the Proton-to-electron Mass Ratio

Abstract Astrophysical molecular spectroscopy is an important means of searching for new physics through probing the variation of the proton-to-electron mass ratio, μ. New molecular probes could provide tighter constraints on the variation of μ and better direction for theories of new physics. Here we summarize our previous paper for astronomers, highlighting the importance of accurate estimates of peak molecular abundance and temperature as well as spectral resolution and sensitivity of telescopes in different regions of the electromagnetic spectrum. While none of the 11 astrophysical diatomic molecules we investigated showed enhanced sensitive rovibronic transitions at observable intensities for astrophysical environments, we have gained a better understanding of the factors that contribute to high sensitivities. From our results, CN, CP, SiN and SiC have shown the most promise of all astrophysical diatomic molecules for further investigation, with further work currently being done on CN.

Estimation and Control of Motor Core Temperature With Online Learning of Thermal Model Parameters: Application to Musculoskeletal Humanoids

The estimation and management of motor temperature are important for the continuous movements of robots. In this study, we propose an online learning method of thermal model parameters of motors for an accurate estimation of motor core temperature. Also, we propose a management method of motor core temperature using the updated model and anomaly detection method of motors. Finally, we apply this method to the muscles of the musculoskeletal humanoid and verify the ability of continuous movements.

Introducing a Hybrid Model SAE-BP for Regression Analysis of Soil Temperature With Hyperspectral Data

Soil temperature, as one of the critical meteorological parameters, plays a key role in physical, chemical and biological processes in terrestrial ecosystems. Accurate estimation of dynamic soil temperature is crucial for underground soil ecological research. In this work, a hybrid model SAE-BP is proposed by combining stacked auto-encoders (SAE) and back propagation (BP) algorithm to estimate soil temperature using hyperspectral remote sensing data. Experimental results show that the proposed SAE-BP model achieves a more stable and effective performance than the existing logistic regression (LR), support vector regression (SVR) and BP neural network with an average value of mean square error (MSE) = 1.926, mean absolute error (MAE) = 0.962 and coefficient of determination (R2) = 0.910. In addition, the effect of hidden structures and labeled training data ratios in SAE-BP is further explored. The SAE-BP model demonstrates the potential in high-dimensional and small hyperspectral datasets, representing a significant contribution to soil remote sensing.

Wearable Technologies For Real-time Monitoring Of Body Core Temperature Under Heat Stress Conditions

Athletes and workers are at risk of heat illness whenever they work for a prolonged duration in outdoor extreme temperatures due to their metabolic demands. Safe performance limits under heat stress conditions are currently determined using predictive models for ambient temperature and exercise intensity. Wearable technology is now being adopted, but a system that accurately measures core temperature using wearable devices has yet to be reported. PURPOSE: To develop a new wearable patch-type sensor system that predicts core temperature based on heat-flux data from the chest. METHODS: We performed experiments that compared our predicted temperatures (Tpre, using a revised algorithm from the dual-heat-flux method), with the actual temperatures in both esophageal (Teso) and rectal (Trec) sites during exercise in three heat conditions. Thirty-two volunteers walked for 60 min at 4-5 km/h at 30°C, 35°C, or 40°C. Tpre was monitored using a smartphone application receiving wirelessly transmitted data from the patch-type sensors (65 × 45 × 8 mm) on the chest. RESULTS: In the 40°C condition, Teso, Trec, and Tpre increased from 37.2 ± 0.2°C, 36.9 ± 0.2°C, and 37.3 ± 0.2°C to 38.2 ± 0.3°C, 37.9 ± 0.3°C, and 38.0 ± 0.2°C (mean ± standard deviation), respectively, during exercise. The difference between Tpre and Teso was -0.10 ± 0.15°C and that between Tpre and Trec was 0.02 ± 0.19°C, using data sampled at 5-min intervals during exercise. In the 35°C condition, Teso, Trec, and Tpre increased to 37.9 ± 0.3°C, 37.8 ± 0.3°C, and 37.9 ± 0.2°C after exercise. In this case, the difference between Tpre and Teso was -0.06 ± 0.17°C and that between Tpre and Trec was 0.04 ± 0.14°C. In the 30°C condition, the differences were -0.13 ± 0.24°C (Tpre-Teso) and 0.06 ± 0.25°C (Tpre-Trec). Body mass, fat percentage, and sex did not affect the Tpre algorithm, but skin temperature changes during exercise yielded errors. CONCLUSIONS: The error ranges for our system are slightly superior to those in previous studies involving noninvasive core temperature measurements. Our system uses simple wearable devices and can provide real-time, subject-specific, and accurate body core temperature estimates under heat stress conditions. In combination with other physiological and environmental parameters, this early warning system will reduce the risk of heat illness.

Iron oxide magnetic nanoparticles based low-field MR thermometry

This paper reports on a highly accurate approach of magnetic resonance (MR) thermometry using iron oxide magnetic nanoparticles (MNPs) as temperature sensors. An empirical model for the description of the temperature dependent R2 relaxation rate is proposed by taking into account the temperature sensitivity of the MNP magnetization. The temperature sensitivity of the MNP magnetization (η) and the temperature sensitivity of the R2 relaxation rate (κ) are simulated with the proposed empirical models to investigate their dependence on the magnetic field and the particle size. Simulation results show the existence of optimal magnetic fields Hoη and Hoκ that maximize the temperature sensitivities η and κ. Furthermore, simulations and experiments demonstrate that the optimal magnetic field Hoη (Hoκ) decreases with increasing the particle size. Experiments on temperature dependent R2 relaxation rate are performed at different magnetic fields for MNP samples with different iron concentrations. Experimental results show that the proposed MR thermometry using MNPs as temperature sensors allows a temperature estimation accuracy of about 0.05 °C. We believe that the achieved approach of highly accurate MR thermometry is of great interest and significance to biomedicine and biology.

Characterization of Background Temperature Dynamics of a Multitemporal Satellite Scene through Data Assimilation for Wildfire Detection

Detection of an active fire in an image scene relies on an accurate estimation of the background temperature of the scene, which must be compared to the observed temperature, to decide on the presence of fire. The expected background temperature of a pixel is commonly derived based on spatial-contextual information that can overestimate the background temperature of a fire pixel and therefore results in the omission of a fire event. This paper proposes a method that assimilates brightness temperatures acquired from the Geostationary Earth Orbit (GEO) sensor MSG-SEVIRI into a Diurnal Temperature Cycle (DTC) model. The expected brightness temperatures are observational forecasts derived using the ensemble forecasting approach. The threshold on the difference between the observed and expected temperatures is derived under a Constant False Alarm Rate (CFAR) framework. The detection results are assessed against a reference dataset comprised of MODIS MOD14/MYD14 and EUMETSAT FIR products, and the performance is presented in terms of user’s and producer’s accuracies, and Precision-Recall and Receiver Operating Characteristic (ROC) graphs. The method has a high detection rate when the data assimilation is implemented with an Ensemble Kalman Filter (EnKF) and a Sampling Importance Resampling (SIR) particle filter, while the weak-constraint Four-Dimensional Variational Assimilation (4D-Var) has comparatively lower detection and false alarm rates according to the reference dataset. Consideration of the diurnal variation in the background temperature enables the proposed method to detect even low-power fires.