Real Experimental Data Sets Research Articles

Sustainable machining of Inconel 718 using minimum quantity lubrication: Artificial intelligence-based process modelling

Governments and industries are developing aggressive policies to reduce carbon emissions and shift from fossil fuels to renewable energy. On the other hand, industries struggle to reduce energy consumption and depend on production lot sizes to control energy requirements. In this regard, energy-efficient processing through CNC machine tools can potentially influence energy demand and requires energy-aware power consumption strategies for machining processes. For manufacturing a single product, predicting energy demand can be decisive in determining parametric control and other factors. Previously analytical models have been largely used to model machining requirements and energy demand. However, these models largely depend on parameterization and do not facilitate the integration of external sub-systems. Therefore, in this paper, an artificial intelligence-based power reduction strategy is developed and implemented on single material (Inconel 718), four control parameters (cutting speed, feed rate, depth of cut and flow rate) and two sub-systems (minimum quantity lubrication (MQL) and nanofluids-based minimum quantity lubrication (NF-MQL)). The paper employs four machine learning algorithms,’ K-Nearest Neighbor’, ‘Gaussian Regression’, ‘Decision Tree’, and ‘Logistic Regression’, to evaluate their functionality in predicting power consumption (Pc) of CNC machining systems using a real experimental data set. As per evaluation based on five performance metrics (R-square, MSE, RMSE, MAE, and MedAE), ‘Decision Tree’ has achieved the most accurate power consumption predictions. The comparative results highlight ‘Decision Tree’ as the most better predictor with the optimal max_depth of 2 showing Pc MQL R2 of 0.915 and Pc NF-MQL R2 of 0.931.

Physics-informed deep-learning applications to experimental fluid mechanics

High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy. Deep-learning approaches have been shown suitable for such super-resolution tasks. However, a high number of high-resolution examples is needed, which may not be available for many cases. Moreover, the obtained predictions may lack in complying with the physical principles, e.g. mass and momentum conservation. Physics-informed deep learning provides frameworks for integrating data and physical laws for learning. In this study, we apply physics-informed neural networks (PINNs) for super-resolution of flow-field data both in time and space from a limited set of noisy measurements without having any high-resolution reference data. Our objective is to obtain a continuous solution of the problem, providing a physically-consistent prediction at any point in the solution domain. We demonstrate the applicability of PINNs for the super-resolution of flow-field data in time and space through three canonical cases: Burgers’ equation, two-dimensional vortex shedding behind a circular cylinder and the minimal turbulent channel flow. The robustness of the models is also investigated by adding synthetic Gaussian noise. Furthermore, we show the capabilities of PINNs to improve the resolution and reduce the noise in a real experimental dataset consisting of hot-wire-anemometry measurements. Our results show the adequate capabilities of PINNs in the context of data augmentation for experiments in fluid mechanics.

Smart predictive viscosity mixing of CO2–N2 using optimized dendritic neural networks to implicate for carbon capture utilization and storage

Crucial for carbon capture, utilization, and storage (CCUS) initiatives and diverse industries, heat transfer underscores the need for a precise assessment of carbon dioxide (CO2) and nitrogen (N2) viscosities in gaseous blends across various temperatures. This research pioneers an intelligent model by enhancing the dendritic neural regression (DNR) framework, employing the Seagull Optimization Algorithm with Marine Predator Algorithm (SOAMPA) for optimal predictions. Leveraging recent advancements in metaheuristic optimization techniques, the study reveals the superior performance of the novel SOAMPA approach in predictive accuracy, marking a significant breakthrough in predicting CO2-N2 mixture viscosities with implications for advancing CCUS projects and diverse industries. The optimized DNR model, empowered by the modified SOAMPA optimization technique, contributes to estimating the viscosity of N2-CO2 mixture gases. Utilizing inputs like pressure, temperature, mole fraction of N2, and model fraction of CO2, the models are trained and tested on a dataset comprising over 3030 data samples from public literature. Key contributions encompass proposing an optimized DNR approach, introducing the modified SOAMPA technique, and demonstrating its superiority over established optimization methods in conjunction with the traditional DNR model for predicting viscosity based on real experimental datasets.

Multivariate curve resolution-soft independent modelling of class analogy (MCR-SIMCA)

BackgroundVarious classification, class modeling, and clustering techniques operate within abstract spaces, utilizing Principal Components (e.g., Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA)) or latent variable spaces (e.g., Partial Least Squares Discriminant Analysis (PLS-DA)). It's important to note that PCA, despite being a mathematical tool, defines its Principal Components under certain mathematical constraints, it has a wide range of applications in the analysis of real-world systems. In this research, we assess the viability of employing the Multivariate Curve Resolution (MCR) subspace within class modeling techniques, as an alternative to the PC subspace. (92). ResultsThis study evaluates the use of the MCR subspace in class modeling methods, specifically in tandem with soft independent modeling of class analogy (SIMCA), to investigate the advantages of employing the meaningful physico-chemical subspace of MCR over the mathematical subspace of PCA. In the MCR-SIMCA strategy, the model is constructed by applying MCR to training samples from a target class. The MCR model effectively partitions the data into two smaller sub-matrices: the contribution matrix and the corresponding response matrix. In the next step, the contribution matrix resulting from the decomposition of the training set develops a distance plot (DP). First, the theory of the MCR-SIMCA model is discussed in detail. Next, two real experimental datasets were analyzed, and their performance was compared with the DD-SIMCA model. In most cases, the results were as good as or even more satisfactory than those obtained with the DD-SIMCA model. (146). SignificanceThe suggested class modeling method presents a promising avenue for the analysis of real-world natural systems. The study's results emphasize the practical utility of the MCR approach, underscoring the significance of the MCR subspace advantages over the PCA subspace. (39).

Enhanced PRIM recognition using PRI sound and deep learning techniques.

Pulse repetition interval modulation (PRIM) is integral to radar identification in modern electronic support measure (ESM) and electronic intelligence (ELINT) systems. Various distortions, including missing pulses, spurious pulses, unintended jitters, and noise from radar antenna scans, often hinder the accurate recognition of PRIM. This research introduces a novel three-stage approach for PRIM recognition, emphasizing the innovative use of PRI sound. A transfer learning-aided deep convolutional neural network (DCNN) is initially used for feature extraction. This is followed by an extreme learning machine (ELM) for real-time PRIM classification. Finally, a gray wolf optimizer (GWO) refines the network's robustness. To evaluate the proposed method, we develop a real experimental dataset consisting of sound of six common PRI patterns. We utilized eight pre-trained DCNN architectures for evaluation, with VGG16 and ResNet50V2 notably achieving recognition accuracies of 97.53% and 96.92%. Integrating ELM and GWO further optimized the accuracy rates to 98.80% and 97.58. This research advances radar identification by offering an enhanced method for PRIM recognition, emphasizing the potential of PRI sound to address real-world distortions in ESM and ELINT systems.

A novel intelligent identification approach based on modified hierarchical diversity entropy and extension theory for diagnosis of rotating machinery faults

Aiming at the troubles of difficult extraction of fault features and low fault recognition rate in rotating equipment fault detection approach, a new technique for intelligent diagnosis based on modified hierarchical diversity entropy (MHDE) and extension theory (ET) is proposed in the thesis. Firstly, MHDE employs to comprehensively describe the fault information of the given signals. Secondly, the MHDE feature sets are regarded as the characteristic parameters of the extension matter element model, and the matter element model in various states is established. Finally, the testing datasets are fed into the matter element model for each operating conditions, and the correlation function is used to compute the comprehensive correlation between the testing datasets and the various conditions of the rotating machinery, so as to realize the qualitative and quantitative identification of the testing datasets. The reliability and superiority of the proposed new approach is validated by real experimental datasets on various rotating machinery types. The analysis results show that the proposed novel technology can effectively excavate the feature information and accurately identify various fault conditions of rotating machinery. In addition, compared with other combined model technology in the paper, the proposed intelligent fault diagnosis technology has better classification performance.

Pulse repetition interval modulation recognition using deep CNN evolved by extreme learning machines and IP-based BBO algorithm

Pulse repetition interval modulation (PRIM) recognition is a critical task in electronic intelligence (ELINT) and electronic support measure (ESM) systems for detecting radar threats accurately. However, PRI recognition is a complex issue due to missing and spurious pulses, resulting in noisy PRI pattern changes in real environments. To address this problem, this paper proposes a novel approach that recognizes the five common types of PRIM through a four-phase process. In the first phase, a deep convolutional neural network (DCNN) is used as a feature extractor. Then, extreme learning machines (ELMs) are used for real-time recognition of the PRIM patterns in the second phase. In the third phase, we employ the biogeography-based optimizer (BBO) to enhance the network’s robustness by optimizing the connection weights and biases. To address the increasing complexity of the model, we introduce an optimized variable-length internet protocol-based BBO (VBBO) in the fourth phase. In this approach (i.e., DCNN-VBBO-ELM), each layer of DCNN is encoded by an IP address into a habitat of VBBO in the same sequence as the DCNN layers. To evaluate the proposed method, we develop a real experimental dataset consisting of five common PRI patterns. Our approach achieves a final accuracy of 97.05%, which is better than other ELM-based benchmark models. Moreover, the proposed model requires only 27 s of training time to process 50,000 training images, confirming its real-time capabilities. In conclusion, our proposed approach improves PRI recognition by leveraging DCNN, ELM, and VBBO, resulting in a more accurate and robust real-time radar PRI classifier.

Dual-norm based dynamic graph diffusion network for temporal prediction

Precise prediction of Multivariate Time Series (MTS) has been playing a pivotal role in numerous kinds of applications. Existing works have made significant efforts to capture temporal tendency and periodical patterns, but they always ignore abrupt variations and heterogeneous/spatial associations of sensory data. In this paper, we develop a dual normalization (dual-norm) based dynamic graph diffusion network (DNGDN) to capture hidden intricate correlations of MTS data for temporal prediction. Specifically, we design time series decomposition and dual-norm mechanism to learn the latent dependencies and alleviate the adverse effect of abnormal MTS data. Furthermore, a dynamic graph diffusion network is adopted for adaptively exploring the spatial correlations among variables. Extensive experiments are performed on 3 real world experimental datasets with 8 representative baselines for temporal prediction. The performances of DNGDN outperforms all baselines with at least 4% lower MAPE over all datasets.

Estimating mutation rates in a Markov branching process using approximate Bayesian computation

Estimating microbial mutation rates is an essential task in evolutionary biology, with wide range applications in related fields such as virology, epidemiology, clinic and public health, and antibiotic research. Significant progress has been made on this research since 1943 when Luria–Delbrück fluctuation analysis was first introduced. However, existing estimators of mutation rates are heavily reliant on model assumptions in fluctuation analysis, and become less applicable to real microbial experiments which deviate from the model assumptions. To overcome this difficulty, we propose to model fluctuation experimental data by a two-type Markov branching process (MBP) and use approximate Bayesian computation (ABC) to estimate the mutation probability parameters. Such an ABC-based mutation rate estimator is based on intensive simulations from the mutation process, thereby taking advantage of modern computing power. Most importantly, its likelihood-free feature allows more complex and realistic setups of the mutation process, especially when the distribution of the number of mutants cannot be easily derived. To further improve computation efficiency, we use a Gaussian process surrogate to substitute the simulator in the ABC algorithm, and call the resulting estimator GPS-ABC. Simulation studies show that, when used to estimate constant mutation rate in MBP, ABC-based estimators generally outperform traditional moment or likelihood-based estimators. When mutations occur in two stages, i.e., in MBP with a piece-wise constant mutation rate function, traditional mutation rate estimators become not applicable, yet GPS-ABC still achieves reasonable estimates. Finally, the proposed GPS-ABC estimator is used to analyze real fluctuation experimental datasets for studying drug resistance.

Manifold learning and Lempel-Ziv complexity-based fault severity recognition method for bearing

The health condition of bearings was related to the operation status of mechanical equipment, and there were relatively few methods to recognize the severity of bearing faults. Although the Lempel-Ziv complexity (LZ complexity) has been proven to be effective in recognizing the bearing fault severity, the LZ was affected by noise. And the frequency band selection-based methods were used for noise reduction for LZ, which might cause the loss of frequency band information, and affect the accuracy of LZ. Aiming at this issue, this paper proposed a bearing fault severity recognition method based on manifold learning. Firstly, the original signal was reconstructed to high-dimensional phase space. Then, local tangent space alignment was used to extract effective signals aliased in noise. Finally, the LZ complexity was obtained according to the signal after noise reduction. The proposed method was verified by constructed data and the real experimental dataset of bearing, respectively. The results showed that the proposed method can be effectively used in the fault severity recognition of bearing.

A mechanistic mathematical model for photo fermentative hydrogen and polyhydroxybutyrate production.

An original mathematical model describing the photo fermentation process is proposed. The model represents the first attempt to describe the photo fermentative hydrogen production and polyhydroxybutyrate accumulation, simultaneously. The mathematical model is derived from mass balance principles and consists of a system of ordinary differential equations describing the biomass growth, the nitrogen and the substrate degradation, the hydrogen and other catabolites production, and the polyhydroxybutyrate accumulation in photo fermentation systems. Moreover, the model takes into account important inhibiting phenomena, such as the self-shading and the substrate inhibition, which can occur during the evolution of the process. The calibration was performed using a real experimental data set and it was supported by the results of a sensitivity analysis study. The results showed that the most sensitive parameters for both hydrogen and PHB production were the hydrogen yield on substrate, the catabolites yield on substrate, and the biomass yield. Successively, a different experimental data set was used to validate the model. Performance indicators showed that the model could efficiently be used to simulate the photo fermentative hydrogen and polyhydroxybutyrate production by Rhodopseudomonas palustris. For instance, the index of agreement of 0.95 was observed for the validated hydrogen production trend. Moreover, the model well predicted the maximum PHB accumulation in bacterial cells. Indeed, the predicted and observed accumulated PHB were 4.5 and 4.8%, respectively. Further numerical simulations demonstrated the model consistency in describing process inhibiting phenomena. Numerical simulations showed that the acetate and nitrogen inhibition phenomena take place when concentrations are higher than 12.44 g L-1 and lower than 4.76 mg L-1, respectively. Finally, the potential long term hydrogen production from accumulated polyhydroxybutyrate in bacterial cells was studied via a fast-slow analysis technique.

Low-Complexity Effective Sound Velocity Algorithm for Acoustic Ranging of Small Underwater Mobile Vehicles in Deep-Sea Internet of Underwater Things

Acoustic ranging is required to obtain the location of underwater mobile vehicles in the Internet of Underwater Things (IoUT). As seawater is an inhomogeneous medium, the sound velocity in the ocean is not constant, thereby causing sound waves to deviate from a straight line of propagation and bend. Thus, the travel time of the sound wave from the transmitter to receiver cannot be directly converted to a range value using a linear relationship as is done in the case of wireless radio ranging in the air. Therefore, the concept of effective sound velocity was introduced to account for the differences between the sound velocities at a transmitter and receiver. This enables conversion of the travel time to slant distance via a linear relationship. However, the existing methodologies for computing effective sound velocities are computationally intensive, which hinders the use of acoustic ranging in small underwater mobile vehicles operating in the deep sea. This study aimed to resolve this problem by developing an effective, computationally less demanding algorithm that could improve the precision of acoustic ranging on such platforms. The proposed algorithm converts global integrals into local integrals that correspond to depth variation ranges, thereby reducing the amount of integral calculation. The performance of the proposed algorithm is evaluated using extensive simulations and real deep-sea experimental data sets obtained at a depth of 3000 m. The results verify the efficacy of the algorithm in realizing real-time underwater acoustic ranging in deep sea. The proposed algorithm can improve the accuracy of real-time effective sound velocity measurement by small underwater mobile vehicles, and subsequently realize low-complexity underwater acoustic ranging in the deep-sea IoUT networks.

Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: Application to planar soft tissues

Data-based approaches are promising alternatives to the traditional analytical constitutive models for solid mechanics. Herein, we propose a Gaussian process (GP) based constitutive modeling framework, focusing on planar, hyperelastic and incompressible soft tissues. Specifically, the strain energy density of soft tissues is modeled as a GP, which can be regressed to experimental stress–strain data obtained from biaxial stretching experiments. Moreover, the GP model can be weakly constrained to be convex. A key advantage of a GP-based model is that, in addition to the mean value, it provides a probability density (i.e. associated uncertainty) for the strain energy density. To simulate the effect of this uncertainty, a non-intrusive stochastic finite element analysis (SFEA) framework is proposed. The proposed framework is verified against an artificial dataset based on the Gasser–Ogden–Holzapfel model and applied to a real experimental dataset of a porcine aortic valve leaflet tissue. The results show that the proposed framework can be trained with limited experimental data and fits the data better than several existing models. The SFEA framework provides a straightforward way of using the experimental data and quantifying the resulting uncertainty in simulation-based predictions.

Why it is Unfortunate that Linear Machine Learning "Works" so well in Electromechanical Switching of Ferroelectric Thin Films.

Machine learning (ML)is relied on for materials spectroscopy. It is challenging to make ML models fail because statistical correlations can mimic the physics without causality. Here, using a benchmark band-excitation piezoresponse force microscopy polarization spectroscopy (BEPS) dataset the pitfalls of the so-called "better", "faster", and "less-biased" ML of electromechanical switching are demonstrated and overcome. Using a toy and real experimental dataset, it is demonstrated how linear nontemporal ML methods result in physically reasonable embedding (eigenvalues) while producing nonsensical eigenvectors and generated spectra, promoting misleading interpretations. A new method of unsupervised multimodal hyperspectral analysis of BEPS is demonstrated using long-short-term memory (LSTM) β-variational autoencoders (β-VAEs) . By including LSTM neurons, the ordinal nature of ferroelectric switching is considered. To improve the interpretability of the latent space, a variational Kullback-Leibler-divergency regularization is imposed . Finally, regularization scheduling of β as a disentanglement metric is leveraged to reduce user bias. Combining these experiment-inspired modifications enables the automated detection of ferroelectric switching mechanisms, including a complex two-step, three-state one. Ultimately, this work provides a robust ML method for the rapid discovery of electromechanical switching mechanisms in ferroelectrics and is applicable to other multimodal hyperspectral materials spectroscopies.

Mechanism-Based Structured Deep Neural Network for Cutting Force Forecasting Using CNC Inherent Monitoring Signals

Cutting force can provide highly sensitive and quick response to unpredictable variations of the machining system, which has been considered as the most valuable physical signals for machining state monitoring. As a reliable, practical and cost-effective solution for cutting force monitoring, forecasting cutting force using computer numerical control (CNC) inherent monitoring signals has great potential to be applied in real industry. However, existing mechanism-based methods suffer from inaccurate identification process and underlying modeling errors, while data-driven methods are highly data-dependent due to the lack of physical interpretability and generalization ability. This article proposes a mechanism-based structured deep neural network (MS-DNN) for cutting force forecasting. By taking sub neural networks as approximators for submodels and reserving the connections among the variables of the end-to-end mechanism model without parameter identification, MS-DNN is equivalent to the mechanism model in form, which can naturally inherit the physical interpretability and generalization ability of the mechanism, but is much more powerful in modeling the complex dynamic relationships between cutting force and CNC inherent signals. The proposed MS-DNN is verified on both simulation and real experimental datasets, and the results show MS-DNN can achieve an excellent performance in cutting force forecasting using CNC inherent signals.

Two-sample t α -test for testing hypotheses in small-sample experiments.

It has been reported that about half of biological discoveries are irreproducible. These irreproducible discoveries were partially attributed to poor statistical power. The poor powers are majorly owned to small sample sizes. However, in molecular biology and medicine, due to the limit of biological resources and budget, most molecular biological experiments have been conducted with small samples. Two-sample t-test controls bias by using a degree of freedom. However, this also implicates that t-test has low power in small samples. A discovery found with low statistical power suggests that it has a poor reproducibility. So, promotion of statistical power is not a feasible way to enhance reproducibility in small-sample experiments. An alternative way is to reduce type I error rate. For doing so, a so-called t α -test was developed. Both theoretical analysis and simulation study demonstrate that t α -test much outperforms t-test. However, t α -test is reduced to t-test when sample sizes are over 15. Large-scale simulation studies and real experiment data show that t α -test significantly reduced type I error rate compared to t-test and Wilcoxon test in small-sample experiments. t α -test had almost the same empirical power with t-test. Null p-value density distribution explains why t α -test had so lower type I error rate than t-test. One real experimental dataset provides a typical example to show that t α -test outperforms t-test and a microarray dataset showed that t α -test had the best performance among five statistical methods. In addition, the density distribution and probability cumulative function of t α -statistic were given in mathematics and the theoretical and observed distributions are well matched.

Deep cepstrum-wavelet autoencoder: A novel intelligent sonar classifier

Different marine vessels belonging to the same class may have different and time-varying radiated noise due to different and changing machinery configurations. Further, the time-varying radiated noise, which is propagated underwater, suffers from random fluctuations in frequency, amplitude, and phase. The ambient noise and time-varying multi-path effects further complicate the classification problem. In this study, for the first time, a deep wavelet autoencoder is proposed, evolving by a two-hybrid cepstral concept to reduce the multi-path distortion and time-varying shallow underwater channel effects. In this approach, the autoencoder is an automatic feature extractor, choosing the best feature composition without information loss and human intervention, and wavelet networks extract the periodic frequency signatures of vessels, which are fluctuating by various machinery regimes. Three underwater acoustic datasets, including synthetic, ShipsEar, and real experimental datasets, are exploited to evaluate the performance of the proposed model so that the performance of the designed model is compared with ten benchmark methods. The results show that the designed model with an average accuracy of 96.11% and average giga-multiplier–accumulators equal to 0.019 represents the best performance in terms of accuracy and complexity compared to benchmark methods. Besides, the cepstral liftering procedure and the average cepstral feature can significantly alleviate the shallow underwater channel’s multi-path distortion and time-varying channel effects. Furthermore, the proposed model is less sensitive to SNR level compare to other benchmark methods.

Rank-Based Mixture Models for Temporal Point Processes

Temporal point process, an important area in stochastic process, has been extensively studied in both theory and applications. The classical theory on point process focuses on time-based framework, where a conditional intensity function at each given time can fully describe the process. However, such a framework cannot directly capture important overall features/patterns in the process, for example, characterizing a center-outward rank or identifying outliers in a given sample. In this article, we propose a new, data-driven model for regular point process. Our study provides a probabilistic model using two factors: (1) the number of events in the process, and (2) the conditional distribution of these events given the number. The second factor is the key challenge. Based on the equivalent inter-event representation, we propose two frameworks on the inter-event times (IETs) to capture large variability in a given process—One is to model the IETs directly by a Dirichlet mixture, and the other is to model the isometric logratio transformed IETs by a classical Gaussian mixture. Both mixture models can be properly estimated using a Dirichlet process (for the number of components) and Expectation-Maximization algorithm (for parameters in the models). In particular, we thoroughly examine the new models on the commonly used Poisson processes. We finally demonstrate the effectiveness of the new framework using two simulations and one real experimental dataset.

IMCHGAN: Inductive Matrix Completion With Heterogeneous Graph Attention Networks for Drug-Target Interactions Prediction.

Identification of targets among known drugs plays an important role in drug repurposing and discovery. Computational approaches for prediction of drug-target interactions (DTIs)are highly desired in comparison to traditional biological experiments as its fast and low price. Moreover, recent advances of systems biology approaches have generated large-scale heterogeneous, biological information networks data, which offer opportunities for machine learning-based identification of DTIs. We present a novel Inductive Matrix Completion with Heterogeneous Graph Attention Network approach (IMCHGAN)for predicting DTIs. IMCHGAN first adopts a two-level neural attention mechanism approach to learn drug and target latent feature representations from the DTI heterogeneous network respectively. Then, the learned latent features are fed into the Inductive Matrix Completion (IMC)prediction score model which computes the best projection from drug space onto target space and output DTI score via the inner product of projected drug and target feature representations. IMCHGAN is an end-to-end neural network learning framework where the parameters of both the prediction score model and the feature representation learning model are simultaneously optimized via backpropagation under supervising of the observed known drug-target interactions data. We compare IMCHGAN with other state-of-the-art baselines on two real DTI experimental datasets. The results show that our method is superior to existing methods in term of AUC and AUPR. Moreover, IMCHGAN also shows it has strong predictive power for novel (unknown)DTIs. All datasets and code can be obtained from https://github.com/ljatynu/IMCHGAN/.

Combined angular and energy dispersive diffraction: optimized data acquisition, normalization and reduction

Combined angular and energy dispersive diffraction is particularly well suited to experiments at high pressures in large-volume presses, and to the study of liquid or amorphous systems. This work describes the data acquisition, correction and reduction approach developed at the PSICHE beamline of the SOLEIL synchrotron. The measured data were normalized for both the scattering volume and the effective incident energy spectrum. By optimizing the acquisition strategy, the measurement time and radiation dose are greatly reduced. The correction and reduction protocol outputs normalized scattering profiles that are suitable for pair distribution function or liquid structure analysis. These processes are demostrated with examples from a number of real experimental data sets.