Signal Correction Research Articles

Removal of external influences from on-line vis-NIR spectra for predicting soil organic carbon using machine learning

External factors including moisture content negatively affect the prediction accuracy of soil organic carbon (SOC) using on-line visible and near-infrared (vis-NIR) spectroscopy. This study compared the performances of four algorithms to remove the moisture content effect [direct standardization (DS), piecewise direct standardization (PDS), external parameter orthogonalization (EPO), and orthogonal signal correction (OSC)] against non-corrected (NC) spectral models developed with partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and M5Rules regression. An on-line soil sensing platform coupled with a vis-NIR spectrophotometer (305–1700 nm) was used to scan twelve agricultural fields in Belgium and France. A total of 372 soil samples collected during the on-line measurement were divided into a calibration (260) and a prediction (112) dataset, using the Kennard-Stone algorithm. The latter set together with identical laboratory-measured 112 dry soil spectra formed a transfer dataset to develop EPO, DS and PDS correction matrices. Results showed that models after EPO, PDS and OSC corrections resulted in improved accuracy [coefficient of determination (R2) = 0.60–0.82, root mean square error (RMSE) = 16.1–5.7 g kg−1)], compared to the NC models (R2 = 0.58–0.73, RMSE = 16.5–6.8 g kg−1), whereas the DS (R2 = −0.10 to 0.26, RMSE = 26.8–21.9 g kg−1) provided deteriorated prediction accuracy. The EPO and OSC models provided better prediction accuracy than that of the PDS corrected models. The OSC-M5Rules (R2 = 0.82, RMSE = 5.7 g kg−1) obtained the highest accuracy followed by EPO-M5Rules (R2 = 0.74, RMSE = 6.7 g kg−1) and NC-M5Rules (R2 = 0.73, RMSE = 6.8 g kg−1), which outperformed all PLSR, RF and SVM models. Therefore, on-line vis-NIR spectra should be corrected with the OSC algorithm before calibrating a machine learning model for accurate prediction of SOC.

Fault Diagnosis and Fault-Tolerant Compensation Strategy for Wheel Angle Sensor of Steer-by-Wire Vehicle via Extended Kalman Filter

In order to improve the fault-tolerant performance of wheel angle sensor of steer-by-wire (SBW) vehicle, a fault diagnosis and fault-tolerant compensation (FDFTC) strategy for wheel angle sensor of SBW vehicle via extended Kalman filter (EKF) is proposed in this paper. Firstly, the steering motor model, rack and pinion model, vehicle dynamics model and variable transmission ratio model are established. Then, based on the analytical redundancy method, the EKF algorithm is adopted to obtain the wheel angle estimation signal. On this basis, a sensor FDFTC strategy with improved majority voting scheme is proposed, which is composed of fault diagnosis module and fault isolation compensation module. Finally, combined with Matlab/Simulink and Carsim, the estimation results are analyzed under sinusoidal condition and double lane change condition, and the performance of the proposed FDFTC strategy is further verified under the sensor stuck fault condition, deviation fault condition and noise interference condition. The results indicate that the proposed FDFTC strategy can not only accurately diagnose the fault signal, but also reconstruct the correct wheel angle signal, which is of great significance to improve the safety and reliability of SBW vehicle.

Rapid and accurate determination of prohibited components in pesticides based on near infrared spectroscopy

Highly toxic pesticides are forbidden because of threatening to human lives, property, and the environment. However, some of them are still used illegally by added into normal pesticides to enhance the effectiveness and lower the cost. Therefore, it is very important to detect the illegally added ingredients in pesticides, such as Isofenphos-methyl in Tolfenpyrad. In this research, a rapid and accurate method for determining the concentration of Isofenphos-methyl in Tolfenpyrad is studied by near infrared spectroscopy with only nine characteristic wavelengths. Different pre-treating methods of direct orthogonal signal correction and first-order derivative on raw spectra and analysis methods of back propagation neural network and partial least squares are investigated to establish quantitative analysis models for comparison. Among them, the back propagation neural network model pre-treated with the direct orthogonal signal correction method has good prediction accuracy and prevents the data from being over-corrected. The correlation coefficients of the model calibration set and prediction set are as high as 0.999 and 0.989, respectively. Root Mean Square Error of Prediction and Ratio of Prediction to Deviation are 0.27% and 9.17, respectively. The prediction concentration limit can be as low as 0.5%. The results show that the direct orthogonal signal correction pre-treating method combined with back propagation neural network can be used for precisely quantitative determination of pesticide doping concentration. It provides an effective approach for rapid and accurate determination of prohibited components in pesticides.

Continuous background correction of refractive index signal to improve monoclonal antibody concentration monitoring during UF/DF and SPTFF operations.

Inline refractive index (RI)has the potential for monitoring protein concentration during final bulk concentration. While useful for monitoring and controlling product concentration, RI is sensitive to the respective background buffer being used for processing. This raises concerns around variations in buffer preparations, and during diafiltration where the buffer background is a mixture of different buffers during exchange. This study evaluated whether the use of a RI probe in the permeate line could facilitate continuous background subtraction (dual RI) and improve concentration monitoring during ultrafiltration/diafiltration and single pass TFF concentration for IgG1 and IgG4 antibodies. The proposed dual RI strategy yielded reductions in % error compared to the use of a single refractive index estimate from the retentate line (6.18% vs 8.63% for IgG4 and 2.65% vs 8.85% for IgG1) during traditional ultrafiltration/diafiltration. The improvement in IgG estimates were best during diafiltration where the continuous background subtraction of the permeate RI-enabled continuous monitoring of antibody material without knowledge of what the background buffer was compared to the use of a single RI estimate (6.47% vs 10.79% for IgG4 and 3.29% vs 19.59% for IgG1). In contrast minimal improvement to accuracy was obtained when using SPTFF as a concentration step. The ability to monitor product concentration changes via the proposed dual RI approach removes the need for complex calibrations, minimal worry about changing buffer backgrounds during diafiltration, and could enable better process control during product concentration in the cGMP manufacture of biologics.

Influence of data pre-processing techniques for plsr model to predict blood glucose by nir spectroscopy-=SUP=-*-=/SUP=-

NIR diffuse reflectance spectroscopic spectra can be mathematically modelled to extract quantitative information by suitable multivariate calibration models. The analysis of spectral data becomes complex as the data is more prone to noise due to light scattering and baseline effects. These errors reduces the robustness and reliability of the developed calibration model. Hence data pre-processing becomes the most important aspect in data analysis. Different mathematical transformations are applied to remove the noise present in the data. This work focuses on the various empirical data pre-processing techniques like baseline correction, multiplicative scatter correction (MSC), robust MSC, extended multiplicative signal correction (EMSC), orthogonal signal correction (OSC) and (-log R) followed by standard normal variate (SNV) techniques for Partial Least Square Regression (PLSR) model in the prediction of blood glucose non-invasively. The performance of the PLSR model for the acquired (raw) spectral data and the same data subjected to different pre-processing techniques is analyzed. The model complexity and robustness is evaluated in terms of the number of latent variables (LVs) required to build the calibration model and obtained mean square prediction error after cross validation. This study utilizes the spectral data collected from 207 subjects from a diabetic center using Diffuse Reflectance Spectrometer (DRS). The analyzed results show that pre-processing based on (-log R) followed by SNV is found to perform well with reduced model complexity and minimum estimated mean square prediction error of 0.23 mg/dl among the other empirical pre-processing techniques. Keywords:

Influence of data pre-processing techniques for PLSR model to predict blood glucose by NIR spectroscopy

NIR diffuse reflectance spectroscopic spectra can be mathematically modelled to extract quantitative information by suitable multivariate calibration models. The analysis of spectral data becomes complex as the data is more prone to noise due to light scattering and baseline effects. These errors reduces the robustness and reliability of the developed calibration model. Hence data pre-processing becomes the most important aspect in data analysis. Different mathematical transformations are applied to remove the noise present in the data. This work focuses on the various empirical data pre-processing techniques like baseline correction, multiplicative scatter correction (MSC), robust MSC, extended multiplicative signal correction (EMSC), orthogonal signal correction (OSC) and (-log R) followed by standard normal variate (SNV) techniques for Partial Least Square Regression (PLSR) model in the prediction of blood glucose non-invasively. The performance of the PLSR model for the acquired (raw) spectral data and the same data subjected to different pre-processing techniques is analyzed. The model complexity and robustness is evaluated in terms of the number of latent variables (LVs) required to build the calibration model and obtained mean square prediction error after cross validation. This study utilizes the spectral data collected from 207 subjects from a diabetic center using Diffuse Reflectance Spectrometer (DRS). The analyzed results show that pre-processing based on (-log R) followed by SNV is found to perform well with reduced model complexity and minimum estimated mean square prediction error of 0.23 mg/dl among the other empirical pre-processing techniques. Keywords: multiplicative scatter correction (MSC), orthogonal signal correction (OSC), standard normal variate (SNV), Diffuse Reflectance Spectrometer (DRS).

Estimation of Locational Marginal Pricing Using Hybrid Optimization Algorithms

At present, the restructured electricity market has been a prominent research area and attracted attention. The motivation of the restructuring in the power system is to introduce the competition at various levels and to generate a correct economic signal to reduce the generation cost. As a result, it is required to have an effective price scheme to deliver useful information about the power. The pricing mechanism is dependent on the demand at the load level, the generator bids, and the limits of the transmission network. To address the congestion charges, Locational Marginal Pricing (LMP) is utilized in restructured electricity markets. To improve the system efficiency, in this paper, a Hybrid Backtracking Search Optimization Algorithm with Grey Wolf Optimization (GWO-HBOA) is proposed, based on the Security Constrained Economic Dispatch (SCED). The objective of the proposed scheme is to minimize fuel cost in the transmission system under the loss and lossless conditions, considering both the normal and congestion conditions. BOA is conducted first, and Grasshopper Optimization Algorithm (GOA) is subsequently combined with the BOA, which results in the GWO-HBOA. To predict the demand for generator power, the demand response is estimated exactly. Moreover, the load LMP can be segregated at service security levels with respect to various load entities. In this way, the overcharging and underpayment issues can be solved under the security constrained market optimization. Furthermore, to determine the LMP loss, DC optimal power flow is analyzed. The proposed GWO with constrained security is estimated on IEEE 30-bus test system. Compared with the existing techniques, the proposed algorithm achieves better performance, in terms of fuel cost, voltage stability, voltage deviations, real power loss, and reactive power loss.

Detection of adulterated coffee by fourier-transform infrared (FTIR) spectroscopy associated with sensory analysis

Because of its huge economic value, coffee has been the target of adulteration worldwide. Given the successful application of spectroscopic methods in detecting adulterants, this study aimed to employ attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) to detect adulterants in roasted coffee samples and compare the results with that of sensory analysis. In this study, twelve coffee samples were intentionally adulterated with varying concentrations, i.e., 10%, 30%, and 50%, of corn, beans, sawdust, and coffee husk. These adulterated samples were compared with one unadulterated coffee sample and four roasted and ground commercially available coffee samples; spectral readings of caffeine and chlorogenic acid (CGA) standards were performed for reference. The sensory analysis was performed by 17 tasters who were trained by a Q-grader. The infrared (IR) spectra (FTIR) data were processed by multiplicative signal correction (MSC) and subjected to a principal component analysis (PCA), along with the results of the sensory analysis. The combination of sensory analysis and IR spectrum allowed to differentiate samples of adulterated coffee and unadulterated coffee by PCA, with an explanation of 79% variance. The results demonstrated that the wavenumbers associated with CGA and caffeine contribute significantly in distinguishing adulterated coffee samples.Key words: Adulterants; coffee quality; chemometric; sensory analysis.

The quantitative detection of botanical trashes contained in seed cotton with near infrared spectroscopy method

This study is performed to investigate the potential of near infrared (NIR) spectroscopy for the detection of botanical trashes content of seed cotton harvested by cotton-picker (SCHCP). Large quantity of trashes become comingled with cotton fiber in the harvesting process, especially when the cotton is harvested with cotton-picker. In China, trashes content of seed cotton (SC) has to be detected when farmers sell the SC to ginneries because trashes reduce the prices of SC and it should be deducted from the whole weight. The conventional instrumental method used to detect the trashes content of SC, ginning and trashes analysis, is complex and time consuming. In this study, 353 SC samples were collected from three ginneries, the NIR spectra bands from 12,000 to 4000 cm−1 were collected with the FT-NIR spectrometer Nexus. Models between NIR spectra and the trashes contents of these SC samples have been developed with the method of partial least square regression (PLSR), bands of 12,000–4000 cm−1, multiplicative signal correction (MSC) was used to eliminate the negative effects caused by sample shapes, second derivative spectra were used to eliminate the translation and the rotation in the spectral baseline. And the parameters of optimized model: R2 is up to 0.985 (calibration set) and 0.973 (prediction set), RMSEC is as low as 0.072 g and RMSEP is 0.158 g. Results of ANOVA also certified the trashes contents calculated with the models are consistent with the actual trashes contents.

A survey on QoT prediction using machine learning in optical networks

In optical networks, a connection (e.g., light-path and light-tree) is set up to carry data from its source to destination(s). When the optical signal transmits through the fiber links and optical devices, the quality of transmission (QoT) degrades due to various physical layer impairments (PLIs), including linear and nonlinear impairments. QoT is an important metric that determines the availability of a connection. Therefore, the QoT guarantee is the premise of successful connection establishment in optical networks. QoT prediction before connections establishment can provide guidance for the routing and resources allocation of connections. In order to receive the correct signal at the receiving end, during network planning design margins are introduced to compensate the inaccuracy of the QoT prediction model itself and its inputs. Improving the accuracy of prediction can make better use of network resources and reduce margins. With the help of strong computing power and data acquisition based on software defined optical network (SDON), machine learning (ML) based models are more suitable for QoT prediction than analytical models that are difficult to derive and computationally heavy. This paper provides an overview on the applications of ML technologies in QoT prediction. Firstly, we elaborate the QoT problem in optical networks, including main QoT influence factors, QoT metrics, and QoT prediction strategies. Then, suitable ML algorithms, the generation of sample data, ML frameworks and the construction of QoT prediction model, are briefly introduced. Next, three solutions of QoT prediction using various ML technologies in recent studies and their practical feasibility are reviewed and discussed in detail. Finally, based on the existing researches, we present some future research directions about the improvement of QoT prediction.

ДВУХСТУПЕНЧАТЫЙ АЛГОРИТМ СОГЛАСОВАННОЙ ФИЛЬТРАЦИИ СИГНАЛОВ

Introduction. Introduction. To combat inter-symbol interference resulting from multipath signal propagation, spreading of the signal frequency spectrum, channel equalizing, OFDM-multiplexing with orthogonal frequency division of channels is used. To reduce the influence of non-linear signal distortions, methods of pre-distortion, diversity reception, as well as special algorithms for digital processing of the demodulator output signal are used. The effect of additive noise is reduced by using a filter matched to the signal. Objective. It is advisable to develop a matched filtering algorithm that allows one to simultaneously reduce the influence of additive noise, non-linear signal distortions and its multipath propagation on the correct message reception. The idea of selecting the orthogonality weight can also be used to achieve the goal. Result. To reduce the influence of non-linear distortions on the correct reception of a message under conditions of multipath signal propagation and additive interference, it is proposed to use two stages of signal processing. The first step is to minimize the noise dispersion caused by intersymbol interference and non-linear signal distortions. The specified dispersion is minimized by determining the orthogonality weight of the basis-functions that make up the signal. The second step is to use a classic matched filter. The proposed two-stage matched filtering algorithm makes it possible to simultaneously reduce the influence of non-linear distortions, intersymbol interference resulting from multipath propagation, and additive interference on the correct signal reception. Signals distorted and delayed in time relative to the main signal are proposed to be considered as additive interference.

BERTFOREX: Cascading Model for Forex Market Forecasting Using Fundamental and Technical Indicator Data Based on BERT

Foreign exchange (Forex) rate forecasting is presently pursued by many researchers as it plays an important role in financial technology and business. The challenge of Forex research lies in its characteristics, fluctuation, non-linearity, and random walk phenomena. Several related studies generate forecasting signals using fundamental data (FD) and technical indicator data (TI) to support Forex. FD is an indicator of country economic conditions, while TI shows the price pattern-based signal. However, these two indicators pose two major limitations on their deployment. First, modeling a sequential neural network causes gradient vanishing and information loss. Second, FD exerts a significant impact on currency price upon its quarterly update and release. The second limitation is known as FD releasing problem. Moreover, Forex forecasting using FD and TI is typically conducted in an equal aggregation manner, resulting in inaccurate prediction due to unequal data changing frequency. In this work, we propose a cascading model for forex market forecasting using FD and TI based on BERT (BERTFOREX). The processing step of BERTFOREX are as follows: 1) BERT is applied to FD for extracting the hidden patterns over the designated period. 2) These extracted hidden patterns of FD are aggregated as additional weights to TI since FD frequency changes slower than that of TI. 3) BERT is again applied to extract the aggregated pattern within TI and FD over other influencing days. 4) To demonstrate the efficiency of BERTFOREX, the aggregated pattern is fed into a simple neural network for forecasting. The proposed method outperforms other methods in terms of percentage of correct signals, sensitivity, specificity, precision, and negative predictive value.

Anti-windup-like Compensator Synthesis for Discrete-Time Quantized Control Systems

This paper addresses the problem of designing an anti-windup like compensator for discrete-time linear control systems with quantized input. The proposed compensator provides a correction signal proportional to the quantization error that is fed to the controller. The compensator is designed to ensure that solutions to the closed-loop systems converge in finite time into a compact set containing the origin that can be tuned by the designer. A numerically tractable algorithm with feasibility guarantees is provided for the design of the compensator. The proposed results are illustrated on an academic example and an open-loop unstable aircraft system.

A Vision-based Navigation System for an Agricultural Autonomous Tractor

RTK-GNSS (Real-time Kinematic-Global Navigation Satellite System) signal is necessary for the safe navigation of an autonomous tractor. However, the main problem associated with this type of precise GNSS is that the RTK correction signal might not be available at all geographical locations. Tree canopies present close to the tractors’ vicinity obstruct satellite signals and degrade the position accuracy. This can lead the autonomous tractor to travel outside of its predetermined path, which is unsafe. To avoid such situations, this study aims to develop a vision-based road detection system, which does not rely on RTK-GNSS. This system aims to detect both paved and unpaved roads in rural farm areas. It takes the RGB images obtained from an HD onboard camera and segments the road surface and road edges. The segmentation is performed by using several image filters, the sliding window method, and a set of rules that are determined manually. Finally, the lateral error obtained from the machine vision system is transmitted to the tractor's automatic navigation system. Experimental runs performed at the experimental farm of Hokkaido University showed that the lateral error calculated by this system is less than 0.2 m for unpaved roads and 0.4 m for paved roads. This shows that the system can detect both paved and unpaved roads. Future work addresses considerations to make the system more robust.

Cancer-Homing CAR-T Cells and Endogenous Immune Population Dynamics.

Chimeric antigen receptor (CAR) therapy is based on patient blood-derived T cells and natural killer cells, which are engineered in vitro to recognize a target antigen in cancer cells. Most CAR-T recognize target antigens through immunoglobulin antigen-binding regions. Hence, CAR-T cells do not require the major histocompatibility complex presentation of a target peptide. CAR-T therapy has been tremendously successful in the treatment of leukemias. On the other hand, the clinical efficacy of CAR-T cells is rarely detected against solid tumors. CAR-T-cell therapy of cancer faces many hurdles, starting from the administration of engineered cells, wherein CAR-T cells must encounter the correct chemotactic signals to traffic to the tumor in sufficient numbers. Additional obstacles arise from the hostile environment that cancers provide to CAR-T cells. Intense efforts have gone into tackling these pitfalls. However, we argue that some CAR-engineering strategies may risk missing the bigger picture, i.e., that a successful CAR-T-cell therapy must efficiently intertwine with the complex and heterogeneous responses that the body has already mounted against the tumor. Recent findings lend support to this model.

Handheld Spectral Sensing Devices Should Not Mislead Consumers as Far as Non-Authentic Food Is Concerned: A Case Study with Adulteration of Milk Powder.

With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5–10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications.

Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications

Functional Near-Infrared Spectroscopy (fNIRS) captures activations and inhibitions of cortical areas and implements a viable approach to neuromonitoring in clinical research. Compared to more advanced methods, continuous wave fNIRS (CW-fNIRS) is currently used in clinics for its simplicity in mapping the whole sub-cranial cortex. Conversely, it often lacks hardware reduction of confounding factors, stressing the importance of a correct signal processing. The proposed pipeline includes movement artifact reduction (MAR), bandpass filtering (BPF), and principal component analysis (PCA). Eight MAR algorithms were compared among 23 young adult volunteers under motor-grasping task. Single-subject examples are shown followed by the percentage in energy reduction (ERD%) statistics by single steps and cumulative values. The block average of the hemodynamic response function was compared with generalized linear model fitting. Maps of significant activation/inhibition were illustrated. The mean ERD% of pre-processed signals concerning the initial raw signal energy reached 4%. A tested multichannel MAR variant showed overcorrection on 4-fold more expansive windows. All of the MAR algorithms found similar activations in the contralateral motor area. In conclusion, single channel MAR algorithms are suggested followed by BPF and PCA. The importance of whole cortex mapping for fNIRS integration in clinical applications was also confirmed by our results.

Prenatal choline supplementation improves child sustained attention: A 7-year follow-up of a randomized controlled feeding trial.

Numerous rodent studies demonstrate developmental programming of offspring cognition by maternal choline intake, with prenatal choline deprivation causing lasting adverse effects and supplemental choline producing lasting benefits. Few human studies have evaluated the effect of maternal choline supplementation on offspring cognition, with none following children to school age. Here, we report results from a controlled feeding study in which pregnant women were randomized to consume 480 mg choline/d (approximately the Adequate Intake [AI]) or 930 mg choline/d during the 3rd trimester. Sustained attention was assessed in the offspring at age 7 years (n = 20) using a signal detection task that showed benefits of maternal choline supplementation in a murine model. Children in the 930 mg/d group showed superior performance (vs. 480 mg/d group) on the primary endpoint (SAT score, p = .02) and a superior ability to maintain correct signal detections (hits) across the 12‐min session (p = .02), indicative of improved sustained attention. This group difference in vigilance decrement varied by signal duration (p = .04). For the briefest (17 ms) signals, the 480 mg/d group showed a 22.9% decline in hits across the session compared to a 1.5% increase in hits for the 930 mg/d group (p = .04). The groups did not differ in vigilance decrement for 29 or 50 ms signals. This pattern suggests an enhanced ability to sustain perceptual amplification of a brief low‐contrast visual signal by children in the 930 mg/d group. This inference of improved sustained attention by the 930 mg/d group is strengthened by the absence of group differences for false alarms, omissions, and off‐task behaviors. This pattern of results indicates that maternal 3rd trimester consumption of the choline AI for pregnancy (vs. double the AI) produces offspring with a poorer ability to sustain attention—reinforcing concerns that, on average, choline consumption by pregnant women is approximately 70% of the AI.

A new algorithm for planetary boundary layer height calculation based on multilayer recognition

Aerosol lidar is widely used in the planetary boundary layer (PBL) height calculation due to its high spatiotemporal resolution. Most of the PBL height (PBLH) algorithms for aerosol lidar are valid for single aerosol layer structure, but overestimate the PBLH under multilayer aerosol/cloud structures. To fill the gap, a new algorithm of PBLH calculation based on multilayer recognition and idealized-profile (MR-IP) is developed. In this algorithm, residual layer and/or suspended aerosol/cloud layer are first recognized based on the signal to noise ratio (SNR) of lidar, the range squared correction signal (RCS) and its gradient (∇RCS). In residual and/or suspended aerosol/cloud layer, positive and negative ∇RCS exist simultaneously, while inside the PBL only a single negative ∇RCS exists. These characteristics are used to discern residual layer and/or suspended aerosol/cloud layers. Aerosol and cloud layers are further distinguished by the ratio of RCS in the objective layer (RCS(rS)) to the mean RCS in the background layer. After multilayer recognition, the PBLH is calculated based on idealized-profile (IP) method. A yearlong (642 samples) comparison of the PBLH calculated by lidar and radiosonde verified the new algorithm in PBLH calculation under complicated structures (R = 0.81). The mean PBLH calculated by the MR-IP method is 635.4 m, consistent with the PBLH defined by radiosonde (665.3 m). Furthermore, the residual layer, suspended aerosol layer and cloud layer can also be discerned by the new algorithm.

Thermocouple response time estimation and temperature signal correction for an accurate heat flux calculation in inverse heat conduction problems

Many heat transfer processes do not allow temperature measurements with advanced instruments like IR cameras, and thus require the use of thermocouples – often inserted sheathed thermocouples – in order to measure the temperature in some key-regions. Nevertheless, thermocouples have an intrinsic response time that can dampen substantially temperature measurements and, when applicable, affect heat flux estimations by inverse methods. In this study, a thermocouple measurement correction method is proposed, especially for fast thermal transients like quenching, to avoid underestimation of the boundary heat fluxes due to delayed temperature responses. A simplified energy balance at the hot junction allowed modeling the temperature measurement by a thermocouple, in which heat losses and the response time were taken into account so they can be estimated using the least squares method on experimental data. A series of tests was performed to validate the present method with a heated jet impinging on a copper body instrumented with two thermocouples: one ungrounded and sheathed, then with high response time (”slow”); and another with exposed welded wires, hence with low response time (”fast”). After having estimated the slow thermocouple response time and heat loss parameters, the model allowed a good reconstruction of the fast thermocouple signal using the slow thermocouple measurements. Also, the heat flux estimation by the inverse method using the reconstructed signal resulted in practically the same results obtained using the fast thermocouple data. A parametric sensitivity analysis showed the heat loss parameters can be neglected for heat flux estimations, while simulations showed that temperature noises degrade the response time estimation but data filtering can mitigate this noise effect. Finally, the present method was applied to a large scale jet-cooling of a hot plate – near industrial conditions – and showed a substantially higher dissipated heat flux than the initially estimated. In fact, the results using reconstructed signals demonstrated that the heat flux dissipated by the jet has a higher dependence on the jet Reynolds number than the observed with the original thermocouple measurements, showing how important the temperature measurement correction is to evaluate fast thermal processes.