Data Noise Reduction Research Articles

Nondestructive intelligent detection of total viable count in pork based on miniaturized spectral sensor

Changes in the freshness of pork due to microbial action during complex transportation and storage indicate an urgent need for in-situ, real-time monitoring techniques for chemical spoilage of meat. This study reported the use of a portable detection device based on a miniaturized visible/near-infrared spectrometer, combined with data noise reduction and machine learning methods, to predict the total viable count (TVC) in pork samples. A rapid detection device for pork TVC was designed based on the miniaturized spectrometer; a spectral preprocessing method based on the resolution of the spectrometer was proposed; the effects of different preprocessing methods on the full-wavelength modeling effect were compared; and four different feature wavelength extraction algorithms were utilized for the feature wavelengths of pork TVC. The modeling effects of different simplified models were compared. The results showed that resolution interval correction combined with standard normal variation was the most effective in full-wavelength modeling, with correlation coefficients of prediction set (RP), root mean square errors in prediction set (RMSEP), and relative percent deviation (RPD) of 0.918, 0.464 (lg CFU/g), and 2.508, respectively; interval random frog − partial least squares regression (iRF-PLSR) had the best predictive ability among all simplified models, the number of wavelengths used in the simplified model was reduced by 85.45% compared with the full-wavelength model. In contrast, the model performance was improved with RP, RMSEP, and RPD of 0.948, 0.392 (lg CFU/g) and 2.970, respectively. The combination of a rational spectral acquisition setup and a data processing methodology, the miniaturized spectrometer showed competitive results with the complex detection system in predicting meat TVC.

Research on differentiated lightning protection application of power lines based on laser point cloud data

Abstract The collection and processing of data in lightning protection assessment of transmission lines takes a long time and accuracy is difficult to ensure. Therefore, a laser point cloud data registration method based on pole and tower material coordinate transformation is proposed for differential lightning protection evaluation of transmission lines. Using DBSCAN algorithm, combined with point cloud data voxel filtering and noise reduction methods, to achieve data segmentation of transmission line tower body. Based on the characteristics of the symmetrical structure of the tower body, the coordinate transformation method is adopted to achieve the registration of multiple point cloud data. The parameters in the tower structure are obtained by concatenating and fusing elements such as the wire point cloud, lightning protection line point cloud, and tower point cloud. After verification by lightning protection assessment, the proposed method simplifies the processing flow of data compliance detection during the assessment process, reduces the time cost of lightning protection assessment, and greatly improves the accuracy and effectiveness of lightning risk assessment results.

Research on LSTM-Based Maneuvering Motion Prediction for USVs

Maneuvering motion prediction is central to the control and operation of ships, and the application of machine learning algorithms in this field is increasingly prevalent. However, challenges such as extensive training time, complex parameter tuning processes, and heavy reliance on mathematical models pose substantial obstacles to their application. To address these challenges, this paper proposes an LSTM-based modeling algorithm. First, a maneuvering motion model based on a real USV model was constructed, and typical operating conditions were simulated to obtain data. The Ornstein–Uhlenbeck process and the Hidden Markov Model were applied to the simulation data to generate noise and random data loss, respectively, thereby constructing a sample set that reflects real experiment characteristics. The sample data were then pre-processed for training, employing the MaxAbsScaler strategy for data normalization, Kalman filtering and RRF for data smoothing and noise reduction, and Lagrange interpolation for data resampling to enhance the robustness of the training data. Subsequently, based on the USV maneuvering motion model, an LSTM-based black-box motion prediction model was established. An in-depth comparative analysis and discussion of the model’s network structure and parameters were conducted, followed by the training of the ship maneuvering motion model using the optimized LSTM model. Generalization tests were then performed on a generalization set under Zigzag and turning conditions to validate the accuracy and generalization performance of the prediction model.

A novel CatractNetDetect deep learning model for effective cataract classification through data fusion of fundus images

Cataracts are common eye disorders characterized by the clouding of the lens, preventing light from passing through and impairing vision. Various factors, including changes in the lens’s hydration or alterations in its proteins, may contribute to their development. Regular eye examinations conducted by an ophthalmologist or optometrist are imperative for detecting cataracts and other ocular conditions early on. Manual checks by caregivers pose several problems, including subjectivity, human error, and a lack of expertise. Biomedical fusion involves combining or linking various characteristics specific to certain diseases from different medical imaging resources. The primary objectives of this approach in disease classification are to reduce the error rate and increase the number of retrieved features. The aim of this study is to evaluate the outcomes associated with fusing visual features related to left and right eye cataract characteristics. Additionally, we investigate the impact of limited variability in deep learning models, specifically in the classification of cataract fundus versus normal fundus images. To address this issue, this study introduces CataractNetDetect, an innovative multi-label deep learning classification system that fuses feature representations from pairs of fundus images (e.g., left and right eyes) for the automatic diagnosis of various ocular disorders. Our focus is on achieving improved performance by stacking discriminative deep feature representations to combine two fundus images into a unified feature representation. Several deep learning architectures are utilized as feature descriptors, including ResNet-50, DenseNet-121, and Inception-V3, enhancing the resilience and quality of representations. Fine-tuning of these DL architectures is conducted using the ImageNet dataset, followed by an integrated stacking approach combining ResNet-50, DenseNet-121, and Inception-V3 models. The model is trained on the publicly available ODIR-5k dataset, which includes 5000 left/right eye images depicting eight different ocular conditions, ranging from healthy states to uncommon ailments such as cataracts, glaucoma, age-related macular degeneration (AMD), diabetes, hypertension, and myopia abnormalities. Moreover, extensive preprocessing of the images is performed, including data augmentation, noise reduction, contrast enhancement, scaling, and circular border cropping. The CataractNetDetect system demonstrates F1-scores, AUC, and maximum validation scores of 98.0%, 97.9%, and 100%, respectively. This ensemble-based model distinguishes itself by surpassing the performance of conventional established methodologies, including ResNet-50, DenseNet-121, and Inception-V3, thereby underscoring its efficacy in diagnostic applications.

A novel interpretability machine learning model for wind speed forecasting based on feature and sub-model selection

Accurate wind speed forecasting holds the potential to optimize wind farm design and enhance the utilization of wind energy resources. However, the majority of current research has encountered three issues: (1) Feature selection is independent of the sub-models and does not consider the sensitivity of the model to features, resulting in all sub-models using the same combination of features for training and prediction. (2) Sub-models are chosen only on the basis of their fitting ability, neglecting the distinctions between them. (3) There is a lack of interpretability analysis regarding the model’s forecasting. It is particularly important to make interpretable analyses for predictive models, as it not only helps to increase the trustworthiness of the model, but also assist in fine-tuning the prediction model and identifying wind speed-related datasets. To address these issues, this paper proposes a novel method for combining wind speed forecasts. The approach comprises five steps: data noise reduction, feature selection, sub-model selection, sub-model combination, and interpretability analysis. (1) In the data noise reduction module, noise reduction is carried out on the raw data based on a combination of singular spectral analysis (SSA) and intrinsic computing expressive empirical mode decomposition with adaptive noise (ICEEMDAN). (2) In the feature selection module, a quantitative feature importance calculation approach is proposed to select the optimal feature input for eight different sub-models, back propagation neural network (BPNN), extreme learning machine (ELM), Elman, wavelet neural network (WNN), generalized regression neural network (GRNN), long short-term memory (LSTM), gate recurrent unit (GRU) and recurrent neural network (RNN). The method calculates the average marginal effect of each feature output in each sub-model and its fluctuation as the feature importance. (3) In the sub-model selection module, we propose an optimal comprehensive sub-model selection metric (CSMS), it combines the fitting ability and the variability of the sub-model. (4) In the combination module, a multi-objective coati optimization algorithm (MOCOA) is used to combine the selected optimal sub-models. (5) In the interpretability analysis, we leverage the deletion diagnosis method to explain and analyze the forecasting behavior of sub-models from both global and local sample perspectives. The experimental results show that compared with the other models, the proposed model is optimal, the MAPE is 8.4571 %, the R2 can reach 0.9755, and it can be concluded at a pr=99% confidence level that the proposed model and the sub-models have a significant difference in performance.

Artificial intelligence multiprocessing scheme for pathology images based on transformer for nuclei segmentation

Malignant tumors are a common cytopathologic disease. Pathological tissue examination is a key tool for diagnosing malignant tumors. Doctors need to manually analyze the images of pathological tissue sections, which is not only time-consuming but also highly subjective, easily leading to misdiagnosis. Most of the existing computer-aided diagnostic techniques focus too much on accuracy when processing pathological tissue images, and do not take into account the problems of insufficient resources in developing countries to meet the training of large models and the difficulty of obtaining medical annotation data. Based on this, this study proposes an artificial intelligence multiprocessing scheme (MSPInet) for digital pathology images of malignant tumors. We use techniques such as data expansion and noise reduction to enhance the dataset. Then we design a coarse segmentation method for cell nuclei of pathology images based on Transformer for Semantic Segmentation and further optimize the segmentation of tumor edges using conditional random fields. Finally, we improve the training strategy for knowledge distillation. As a medical assistive system, the method can quantify and convert complex pathology images into analyzable image information. Experimental results show that our method performs well in terms of segmentation accuracy and also has advantages in terms of time and space efficiency. This makes our technology available to developing countries that are not as well resourced, and equipped in terms of medical care. The teacher model and lightweight student model included in our method achieve 71.6% and 66.1% Intersection over Union (IoU) in cell segmentation respectively, outperforming Swin-unet and CSWin Transformer.

Real-time Abnormal Detection of GWAC Light Curve based on Wavelet Transform Combined with GRU-Attention

Nowadays, astronomy has entered the era of Time-Domain Astronomy, and the study of the time-varying light curves of various types of objects is of great significance in revealing the physical properties and evolutionary history of celestial bodies. The Ground-based Wide Angle Cameras telescope, on which this paper is based, has observed more than 10 million light curves, and the detection of anomalies in the light curves can be used to rapidly detect transient rare phenomena such as microgravity lensing events from the massive data. However, the traditional statistically based anomaly detection methods cannot realize the fast processing of massive data. In this paper, we propose a Discrete Wavelet (DW)-Gate Recurrent Unit-Attention (GRU-Attention) light curve warning model. Wavelet transform has good effect on data noise reduction processing and feature extraction, which can provide richer and more stable input features for a neural network, and the neural network can provide more flexible and powerful output model for wavelet transform. Comparison experiments show an average improvement of 61% compared to the previous pure long-short-term memory unit (LSTM) model, and an average improvement of 53.5% compared to the previous GRU model. The efficiency and accuracy of anomaly detection in previous paper work are not good enough, the method proposed in this paper possesses higher efficiency and accuracy, which incorporates the Attention mechanism to find out the key parts of the light curve that determine the anomalies. These parts are assigned higher weights, and in the actual anomaly detection, the star is detected with 83.35% anomalies on average, and the DW-GRU-Attention model is compared with the DW-LSTM model, and the detection result f1 is improved by 5.75% on average, while having less training time, thus providing valuable information and guidance for astronomical observation and research.

LSTM Short-Term Wind Power Prediction Method Based on Data Preprocessing and Variational Modal Decomposition for Soft Sensors.

Soft sensors have been extensively utilized to approximate real-time power prediction in wind power generation, which is challenging to measure instantaneously. The short-term forecast of wind power aims at providing a reference for the dispatch of the intraday power grid. This study proposes a soft sensor model based on the Long Short-Term Memory (LSTM) network by combining data preprocessing with Variational Modal Decomposition (VMD) to improve wind power prediction accuracy. It does so by adopting the isolation forest algorithm for anomaly detection of the original wind power series and processing the missing data by multiple imputation. Based on the process data samples, VMD technology is used to achieve power data decomposition and noise reduction. The LSTM network is introduced to predict each modal component separately, and further sum reconstructs the prediction results of each component to complete the wind power prediction. From the experimental results, it can be seen that the LSTM network which uses an Adam optimizing algorithm has better convergence accuracy. The VMD method exhibited superior decomposition outcomes due to its inherent Wiener filter capabilities, which effectively mitigate noise and forestall modal aliasing. The Mean Absolute Percentage Error (MAPE) was reduced by 9.3508%, which indicates that the LSTM network combined with the VMD method has better prediction accuracy.

Protecting the weak signals in distributed acoustic sensing data processing using local orthogonalization: The FORGE data example

The development of the distributed acoustic sensing (DAS) technique enables us to record seismic data at a significantly improved spatial sampling rate at meter scales, which offers new opportunities for high-resolution subsurface imaging. However, DAS recordings are often characterized by a low signal-to-noise ratio (S/N) due to the presence of data noise, significantly degrading the reliability of imaging and interpretation. Current DAS data noise reduction methods remain insufficient in simultaneously preserving weak signals and eliminating various types of noise. Particularly when dealing with DAS data that are contaminated by four types of noise (i.e., high-frequency noise, high-amplitude erratic noise, horizontal noise, and random background noise), it becomes challenging to attenuate the strong noise while maintaining fine-scale features. To address these issues, we develop an integrated local orthogonalization (LO) method that can remove a mixture of different types of noise while protecting the useful signal. Our LO method effectively eliminates the aforementioned noise by concatenating multiple denoising operators including a band-pass filter, a structure-oriented, spatially varying median filter, a dip filter in the frequency-wavenumber domain, and a curvelet filter. Next, the local orthogonalization weighting operator is applied to extract signal energy from the removed noise section. We demonstrate the robustness of our LO method on various challenging DAS data sets from the Frontier Observatory for Research in Geothermal Energy geothermal field. The denoising results demonstrate that our LO method can successfully minimize the levels of different types of noise while preserving the energy of weak signals.

The multi-strategy hybrid forecasting base on SSA-VMD-WST for complex system.

In view of the strong randomness and non-stationarity of complex system, this study suggests a hybrid multi-strategy prediction technique based on optimized hybrid denoising and deep learning. Firstly, the Sparrow search algorithm (SSA) is used to optimize Variational mode decomposition (VMD) which can decompose the original signal into several Intrinsic mode functions (IMF). Secondly, calculating the Pearson correlation coefficient (PCC) between each IMF component and the original signal, the subsequences with low correlation are eliminated, and the remaining subsequence are denoised by Wavelet soft threshold (WST) method to obtain effective signals. Thirdly, on the basis of the above data noise reduction and reconstruction, our proposal combines Convolutional neural network (CNN) and Bidirectional short-term memory (BiLSTM) model, which is used to analyze the evolution trend of real time sequence data. Finally, we applied the CNN-BiLSTM-SSA-VMD-WST to predict the real time sequence data together with the other methods in order to prove it's effectiveness. The results show that SNR and CC of the SSA-VMD-WST are the largest (the values are 20.2383 and 0.9342). The performance of the CNN-BiLSTM-SSA-VMD-WST are the best, MAE and RMSE are the smallest (which are 0.150 and 0.188), the goodness of fit R2 is the highest(its value is 0.9364). In contrast with other methods, CNN-BiLSTM-SSA-VMD-WST method is more suitable for denoising and prediction of real time series data than the traditional and singular deep learning methods. The proposed method may provide a reliable way for related prediction in various industries.

Enhancing Traffic Flow Forecasting with Deep‐Learning and Noise Reduction Techniques

Traditional short‐term traffic volume forecasting approaches make it difficult to predict the highly spatiotemporally coupled short‐time traffic. To tackle the problem, this paper first proposes a variational modal algorithm (GWO‐VMD) based on the optimization of the gray wolf search algorithm. It aims to decompose and reduce the noise of short‐time traffic flows. Meanwhile, it reduces the intricacy of data sequences and enhances the regularity pattern. To address the insufficient utilization of spatiotemporal features, this paper presents an innovative deep‐learning traffic prediction framework based on the stacking of multiple temporal trend‐aware graph attention (TGA) layers and gated temporal convolution (GTC) layers, which are called trend‐aware temporal graph neural network (TTGAN). TGA dynamically models the space‐time relationships of traffic data, and GTC models the temporal characteristics of traffic data. The experimental findings demonstrate that the MAPE model, as presented, achieves a reduction of 9% and 2% compared to the AGCRN and GWNET models, respectively, in the domain of deep spatiotemporal graph modeling. Data decomposition and noise reduction are necessary to achieve accurate results. This model has superior performance in terms of mean absolute error (MAE), coefficient of determination (R2), and explained variance score (EVAR).

Automated Detection of Floating Wood Logs on River Surfaces using YOLOv5 Model for Flood Warning System

In Sabah and Sarawak, Malaysia, log transportation by rivers poses risks due to log fragmentation. This can obstruct water flow, causing navigation problems and flood risks during heavy rainfall. Current monitoring methods involve personnel at checkpoints but are slow. This project proposes an AI-based system using YOLO-v5 to detect intact logs and fragments on river surfaces. A dataset will be created by scraping websites and using Google Colab commands to download relevant keywords. Preprocessing includes data augmentation, contrast adjustment, noise reduction, and resolution standardization. The model is trained in Google Colab and integrated into a warning system using Thonny IDE. Performance metrics like precision, recall, F1 score, and confusion matrix are generated. By automating monitoring through AI, this project aims to improve safety and sustainability in Malaysian river log transportation.

Modelling Small Artefact for Preservation – A Case Study of Perlis Heritage

Heritage preservation is essential for preserving historical sites and cultural artefact for future generations. However, they are prone to damages and destructions due to weather conditions and other factors. 3D models and reconstructions can aid in the conservation of historical sites and artefact. LiDAR (light detection and ranging) technology can be utilized to obtain accurate 3D representation of object or area of interest. This project aims to preserve one of the historical artefact in Perlis, Malaysia which is known as ‘Batu Nisan Acheh’ or the Acheh Gravestone by using 3D model and reconstruction. iPhone 13 Pro Max LiDAR scanner is used to collect the raw dataset of the artefact with Scaniverse application. MATLAB is employed for data processing which includes data filtering, noise reduction, downsampling and 3D surface reconstruction. In addition, a GUI application is also developed in enabling users to upload their desired point cloud files and produce its 3D model for future usage. Results show that the accuracy, effectiveness, and usability of heritage preservation initiatives are improved by combining iPhone 13 Pro Max LiDAR scanning with MATLAB processing, which is useful for virtual displays, restoration, and future study.

Hydrogen leakage location prediction at hydrogen refueling stations based on deep learning

Accurate and efficient localization of hydrogen leakage is crucial for ensuring the safe and stable operation of hydrogen refueling stations. In this paper, a hybrid model (CEEMDAN–CNN–LSTM) based on data noise reduction and deep learning is proposed to predict the location of hydrogen leakage in hydrogen refueling stations. The model employs the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm to effectively reduce noise and process 720 hydrogen leakage condition datasets. The CNN-LSTM model is constructed by synergistically combining the spatiotemporal feature extraction capabilities of the convolutional neural network (CNN) with the temporal processing capabilities of the long-short-term memory neural network (LSTM) while optimizing hyperparameter combinations through GridSearch. Compared with other models, the proposed CEEMDAN–CNN–LSTM model demonstrates superior prediction performance, achieving an accuracy of 99.54 %, precision of 99.42 %, recall of 99.54 %, and F1-score of 99.46 %. Moreover, when utilizing a 5-s duration of leakage data as input, the model attains an impressive accuracy of 97.64 %. These results underscore the favorable application prospects of the CEEMDAN–CNN–LSTM model in the field of gas leak localization.

Blockchain based trusted execution environment architecture analysis for multi - source data fusion scenario

Multi-source data fusion techniques are widely applied in dynamic target detection scenarios, such as target situational awareness, radar signal resolution, and feature fusion labeling. Currently, techniques including clustering, neural networks, Bayesian analysis, and machine learning have been applied to improve the success rate of multi-source data fusion in terms of interference data noise reduction. The research on data tampering prevention of multiple data sources is mainly based on the data distributed authentication technology. The research on performing data fusion process in a trusted execution environment is mainly based on cryptography and codec technology. This paper focuses on the technical application architecture that can effectively improve the comprehensive efficiency of multi-source data fusion processing under the constraints of business scenarios. Accordingly, this paper proposes a trusted execution environment architecture based on blockchain technology for multi-source data fusion scenarios. It integrates the strategy of trusted data source data verification in blockchain smart contracts into the typical multi-source data fusion application architecture. After comparison tests in a simulation environment, the trusted execution environment architecture based on blockchain technology has shown considerable improvements in fusion success rate with limited performance cost.

Data Compression and Noise Reduction in Smart Grid Using Discrete Wavelet Transform

This paper proposed wavelet-based design using Discrete Wavelet Transform to compress smart grid electrical signals and to reduce noise. For the Smart Grid’s smooth functioning, the power signal must be monitored, and proper actions must be taken quickly for any abnormality. The compressed data takes less time to communicate the disturbances. The proposed design is tested for the phasor measurement unit, which monitors and records the status of the smart grid hence circulating extensive data to utilities, control centers, etc. It is also tested for load voltage data. Effective data compression can reduce the cost of data storage and transmission. Noise dramatically affects the effectiveness of the techniques detecting the disturbances. Hence data compression and denoising the data with minimum distortion is essential. The proposed design is simpler as it uses fewer filters and less number of decomposition level as compared to the existing design. Simulation results show that compression ratio and signal-to-noise ratios are increased as compared to existing design.

A hybrid forecasting method for quantitative investments based on GA-VMD and SSA-DELM optimization

In the modern financial market, quantitative investment is a more advanced investment method, which has been widely used in the capital market with the characteristics of simplicity, efficiency, and plasticity. Compared with other countries, China started late in the quantitative field, and the strategies are not perfect enough to be studied in depth. To study quantitative investment in-depth, this paper proposes a GA-VMD-SSA-DELM quantitative model. By building an extreme learning machine (DELM) model optimized by the sparrow algorithm (SSA), and simultaneously using a variational modal decomposition model (VMD) optimized by the genetic algorithm (GA) for data noise reduction, a quantitative investment model is constructed, and a portfolio strategy is formed. The method first performs correlation tests on the data sources, uses the daily frequency trading indicators of Guizhou Maotai, Zhifei Bio, and Yangtze River Power for the years 2018-2022 as the database, and uses SPSS to conduct Pearson correlation coefficient analysis to establish the correlation between the data. Then the GA-VMD algorithm was used for data noise reduction. Finally, the SSA-DELM algorithm is used to establish a quantitative investment model, construct a portfolio, and draw relevant conclusions.

A Semi-Self-Supervised Intrusion Detection System for Multilevel Industrial Cyber Protection.

Industry 4.0 affects all components of the modern industry value chain. The accelerating use of the Internet and the convergence of industrial and operational networks constantly increase the need for secure industrial communication solutions. Therefore, “multilevel industrial cyber protection” is critical to Industry 4.0. In general, industrial protection refers to safeguarding information and data and the intellectual property rights of production processes related to the overall industry environment. The availability, integrity, and confidentiality of systems must be maintained. The goal challenge is the best possible protection from attacks and threats which create immediate financial damage and other risks in the industry (reputation, etc.). Based on the Defense-in-Depth strategy, a holistic, multilayered, and in-depth protection of industrial systems is developed in this paper. Specifically, a Semi-Self-Supervised Intrusion Detection System (S3IDS) is proposed, which combines advanced machine learning techniques for industrial data noise reduction to automate the discovery and separation of classes, which are essentially equivalent to cyber-related anomalies. As demonstrated by a mathematical simulation based on computational number theory and specifically on the concept of the single object, the proposed S3IDS learns to accurately reconstruct samples to predict the nature of an anomaly created directly by the industrial ecosystem.

Prediction of Carbon Financial Transaction Price Based on CEEMD Denoising and PSO-LSSVM——Take Guangdong Province as an example

Climate change has an unprecedented impact on the world, and it is also an important era proposition to be solved in China's development. Carbon emissions trading occupies an important position, which aims to adapt to the global carbon neutrality context and implement sustainable development requirements. The market mechanism of China is to deal with climate change, and carbon emissions trading occupies an important position. Based on the carbon emission trading price data of Guangdong Province from 2015 to 2021, this paper uses the contribution analysis model of complete ensemble empirical mode decomposition (CEEMD), and particle swarm optimization algorithm-least squares support vector machine (PSO-LSSVM) to predict the carbon price of Guangdong Province. Firstly, the carbon emission trading data of Guangdong Province are obtained. Based on CEEMD decomposition data, the data filtering and noise reduction are completed. The PSO-LSSVM model is used for iterative particles and obtains the optimal solution, and each component is predicted respectively. Finally, the results are integrated. The results are shown that: (1) CEEMD data decomposition noise reduction technology is used to improve the prediction accuracy of IMFs ; (2) The optimization of parameters in LSSVM modeling by PSO algorithm helps to select parameters more reasonably and avoids the randomness of artificial selection to some extent. The research results show that the decomposition ensemble prediction model applies to the carbon emission prediction in Guangdong Province. The good generalization ability of the model provides a more accurate prediction scheme for carbon price prediction in China.

Equilibrium and non-equilibrium molecular dynamics approaches for the linear viscoelasticity of polymer melts

Viscoelastic properties of polymer melts are particularly challenging to compute due to the intrinsic stress fluctuations in molecular dynamics (MD). We compared equilibrium and non-equilibrium MD approaches for extracting the storage (G′) and loss moduli (G″) over a wide frequency range from a bead-spring chain model in both unentangled and entangled regimes. We found that, with properly chosen data processing and noise reduction procedures, different methods render quantitatively equivalent results. In equilibrium MD (EMD), applying the Green−Kubo relation with a multi-tau correlator method for noise filtering generates smooth stress relaxation modulus profiles from which accurate G′ and G″ can be obtained. For unentangled chains, combining the Rouse model with a short-time correction provides a convenient option that circumvents the stress fluctuation challenge altogether. For non-equilibrium MD (NEMD), we found that combining a stress pre-averaging treatment with discrete Fourier transform analysis reliably computes G′ and G″ with a much shorter simulation length than previously reported. Comparing the efficiency and statistical accuracy of these methods, we concluded that EMD is both reliable and efficient, and is suitable when the whole spectrum of linear viscoelastic properties is desired, whereas NEMD offers flexibility only when some frequency ranges are of interest.