Autocorrelation Function Analysis Research Articles

KIC 6362386: An Eclipsing Binary with γ Doradus–type Pulsations and Starspots

KIC 6362386 is an eclipsing binary system that exhibits both γ Doradus (γ Dor)–type pulsations and starspots. In this study, we investigated this binary system using the Kepler photometry and the spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope. After employing the PHOEBE program for light-curve and radial-velocity-curve synthesis, analyses reveal that the binary is a circle (e ∼ 0.0006), has a small mass ratio (q ∼ 0.311), and is a detached system consisting of an F-type primary star and an M-type secondary star with masses and radii of M 1 = 1.43 ± 0.13 M ⊙, R 1 = 1.68 ± 0.08 R ⊙ and M 2 = 0.44 ± 0.18 M ⊙, R 2 = 0.46 ± 0.06 R ⊙, respectively. Utilizing the Padova isochrone, we estimate the age of the binary system to be 1.58−0.13+0.15 Gyr. By analyzing the out-of-eclipse residuals, we identify variations in the residuals attributed to both starspots and stellar pulsations. The autocorrelation function analysis indicates the decay time of starspots is approximately 37 days with the rotation period aligning with the orbital period. Considering the masses, radii, and positions of the two components on the Hertzsprung–Russell diagram, we deduce that the γ Dor–type g-mode pulsations came from the primary star where the main frequency is 0.1642c/d. Consequently, KIC 6362386 becomes a valuable target for the investigation of γ Dor–type pulsations and asteroseismology in a binary system.

Simple model for the fracture of a polymer chain: Single-bond potential of mean force and tension-based rate constants for chain rupture.

A linear chain of N particles connected by Morse bonds with periodic boundaries in a Brownian bath is considered as a model for polymer fracture. The potential of mean force (pomf) with respect to the length of a bond and its energy and entropic components are computed via Langevin Dynamics simulations at various chain elongations λ > 1. A narrow range of λ values is identified over which equilibrium is established between an intact (i) and a fractured (f) state over a pomf barrier. While the barrier and (f) regions of the pomf are well described by a well-known (N - 1)-bond adiabatic approximation, the shape of the (i) region departs from it, exhibiting a bimodal character. A new, 1-bond adiabatic approximation is proposed to explain this. The lower envelope of the two adiabatic approximations provides an excellent description of the pomf. The relationship between (f) states for individual bonds and for the entire chain is elucidated. A new approach, based on analysis of equilibrium tension autocorrelation functions, is developed to extract rate constants for the fracture and reformation of the chain in dependence of λ. Results from it agree with tension relaxation during nonequilibrium simulations initiated at equispaced configurations of the particles. Rate constants for chain fracture conform to a Boltzmann-Arrhenius-Zhurkov dependence on the tension averaged over the intact state of the chain, the activation length being higher than estimated from pomf extrema. These findings constitute a step toward a predictive multiscale simulation scheme for fracture in polymeric materials.

KIC 8840638: A Newly Discovered Eclipsing Binary with δ Scuti–Type Oscillations

In this paper, we analyze the light variation of KIC 8840638 using high-precision time-series data from the Kepler mission. The analysis reveals that this target is a new detached eclipsing binary system with a δ Scuti component, rather than a single δ Scuti star as previously known. The frequency analysis of short-cadence data reveals 95 significant frequencies, most of which lie in a frequency range of 23−32 day−1. Among them, seven independent frequencies are detected in the typical frequency range of δ Scuti stars, and they are identified as pressure modes. In addition, a possible large separation value of Δν = 36.5 ± 0.1 μHz is also detected with the Fourier transform (FT) and autocorrelation function (AC) analysis. The orbital frequency f orb (= 0.320008 day−1) and its harmonics are also detected directly in the frequency spectrum. The binary modelings derived from PHOEBE indicate that this binary system is in detached configurations with a mass ratio of q=0.33−0.04+0.06 , an inclination angle of 40.19−2.84+3.96 ​​​​​°. The derived parameters and binary evolutionary model suggest that the primary star is an object on the verge of leaving the main sequence with a temperature of ∼7600 K, while the secondary appears to be a cool component entering the giant branch with a temperature ∼3100 K lower than the primary. Moreover, this system may have undergone a mass ratio reversal, where the more massive star is the gainer component and the less massive one is the donor star.

About Air Quality Assessment using Time Series Analysis Techniques on Air Particles Data

This study investigates the dynamics of air pollution caused by particulate matter (PM10 and PM2.5) in Omsk, Russia. Data collected from the Sensor.Community network between 2021 and 2023 were analyzed using time series analysis methods. The findings reveal a subtle weekly seasonality in the data, potentially attributed to fluctuations in industrial activity and traffic patterns throughout the week. Additionally, a concerning trend of deteriorating air quality was identified, characterized by increasing annual average concentrations of PM10 and PM2.5. The study employed various time series analysis techniques, including Empirical Cumulative Distribution Function (ECDF), Seasonal and Trend decomposition using Loess (STL), and autocorrelation function (ACF) analysis. These methods allowed for the visualization and examination of data distribution, trend identification, and seasonality detection. Furthermore, the Mann-Kendall test confirmed a statistically significant trend of increasing pollution levels over the three-year period. To enhance the accuracy of spatial analysis, data interpolation was performed using kriging with a Gaussian model. This method, compared to simple interpolation, provides a more precise assessment of the spatial distribution of pollutants. The study also implemented Long Short-Term Memory (LSTM) for forecasting hourly pollution values. LSTM exhibited promising results, demonstrating its potential for developing early warning systems, particularly when combined with meteorological data such as wind forecasts. The results highlight the need for effective measures to mitigate air pollution in Omsk and emphasize the importance of utilizing data from diverse sources, including official air quality monitoring stations, to ensure accuracy and reliability in air quality assessments.

Timing and Scintillation Studies of Pulsars in Globular Cluster M3 (NGC 5272) with FAST

We present the phase-connected timing solutions of all five pulsars in globular cluster M3 (NGC 5272), namely PSRs M3A to F (PSRs J1342+2822A to F), with the exception of PSR M3C, from FAST archival data. In these timing solutions, those of PSRs M3E and F are obtained for the first time. We find that PSRs M3E and F have low-mass companions and are in circular orbits with periods of 7.1 and 3.0 days, respectively. For PSR M3C, we have not detected its signal in all 41 observations. We found no X-ray counterparts for these pulsars in archival Chandra images in the band of 0.2–20 keV. From the autocorrelation function analysis of M3A and M3B’s dynamic spectra, the scintillation timescale ranges from 7.0 ± 0.3 to 60.0 ± 0.6 minutes, and the scintillation bandwidth ranges from 4.6 ± 0.2 to 57.1 ± 1.1 MHz. The measured scintillation bandwidths from the dynamic spectra indicate strong scintillation, and the scattering medium is anisotropic. From the secondary spectra, we captured a scintillation arc only for PSR M3B with a curvature of 649 ± 23 m−1 mHz−2.

Spectral Analysis of Autocorrelation Functions of Roll and Pitch Angles

Spectral Analysis of Autocorrelation Functions of Roll and Pitch Angles

Photon correlations in the collective emission of hybrid gold-(CdSe/CdS/CdZnS) nanocrystal supraparticles

We investigate the photon statistics of the light emitted by single self-assembled hybrid gold-CdSe/CdS/CdZnS colloidal nanocrystal supraparticles through the detailed analysis of the intensity autocorrelation function g (2)(τ). We first reveal that, despite the large number of nanocrystals involved in the supraparticle emission, antibunching can be observed. We then present a model based on non-coherent Förster energy transfer and Auger recombination that well captures photon antibunching. Finally, we demonstrate that some supraparticles exhibit a bunching effect at short time scales corresponding to coherent collective emission.

Investigating thermal conductivity and mechanical properties of a hybrid material based on cellulose nanofibers and boron nitride nanotubes using molecular dynamics simulations

Cellulose nanofibers (NFCs) have emerged as a preferred choice for fabricating nanomaterials with exceptional mechanical properties. At the same time, boron nitride nanotubes (BNNTs) have long been favored in thermal management devices due to their superior thermal conductivity (k). This study uses reverse non-equilibrium molecular dynamics (MD) simulations to investigate k for a hybrid material based on NFCs and BNNTs. The result is then compared with pure NFC and BNNT-based structures with equivalent total weight content to elucidate how incorporating BNNT fillers enhances k for the hybrid system. Furthermore, the fundamental phonon vibration modes responsible for driving thermal transport in NFC-based materials upon incorporating BNNTS are identified by computing the vibrational density of states from the Fourier transform analysis of the averaged mass-weighted velocity autocorrelation function. Additionally, MD simulations demonstrate how both NFCs and BNNTs synergistically improve the constituting hybrid structure’s mechanical properties (e.g. tensile strength and stiffness). The overarching aim is to contribute towards the engineered design of novel functional materials based on nanocellulose that simultaneously improve crucial physical properties pertaining to thermal transport and mechanics.

Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method.

Diffuse correlation spectroscopy (DCS) is a powerful, noninvasive optical technique for measuring blood flow. Traditionally the blood flow index (BFi) is derived through nonlinear least-square fitting the measured intensity autocorrelation function (ACF). However, the fitting process is computationally intensive, susceptible to measurement noise, and easily influenced by optical properties (absorption coefficient and reduced scattering coefficient ) and scalp and skull thicknesses. We aim to develop a data-driven method that enables rapid and robust analysis of multiple-scattered light's temporal ACFs. Moreover, the proposed method can be applied to a range of source-detector distances instead of being limited to a specific source-detector distance. We present a deep learning architecture with one-dimensional convolution neural networks, called DCS neural network (DCS-NET), for BFi and coherent factor () estimation. This DCS-NET was performed using simulated DCS data based on a three-layer brain model. We quantified the impact from physiologically relevant optical property variations, layer thicknesses, realistic noise levels, and multiple source-detector distances (5, 10, 15, 20, 25, and 30mm) on BFi and estimations among DCS-NET, semi-infinite, and three-layer fitting models. DCS-NET shows a much faster analysis speed, around 17,000-fold and 32-fold faster than the traditional three-layer and semi-infinite models, respectively. It offers higher intrinsic sensitivity to deep tissues compared with fitting methods. DCS-NET shows excellent anti-noise features and is less sensitive to variations of and at a source-detector separation of 30mm. Also, we have demonstrated that relative BFi (rBFi) can be extracted by DCS-NET with a much lower error of 8.35%. By contrast, the semi-infinite and three-layer fitting models result in significant errors in rBFi of 43.76% and 19.66%, respectively. DCS-NET can robustly quantify blood flow measurements at considerable source-detector distances, corresponding to much deeper biological tissues. It has excellent potential for hardware implementation, promising continuous real-time blood flow measurements.

Forecasting carbon price using signal processing technology and extreme gradient boosting optimized by the whale optimization algorithm

AbstractPredicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices.

СКРЫТЫЕ ГАРМОНИЧЕСКИЕ КОЛЕБАНИЯ ГОДОВОГО СТОКА РЕК: ВОЗМОЖНОСТЬ И ЦЕЛЕСООБРАЗНОСТЬ ВЫЯВЛЕНИЯ И ОЦЕНКИ

The article shows the possibility and necessity of revealing hidden harmonic fluctuations of long-term runoff throughthe example of two rivers in Eastern Siberia.Identification of the harmonic component in the series of annual river flow is necessary for a number of reasons:– to determine the potential statistical predictability of the series;– for a correct assessment of the main trend of the long-term flow dynamics;– to reduce the limits of uncertainty during the assessment of future changes in annual runoff in the absence of long-termforecasting techniques.Finally, the accumulation and generalization of data on the patterns of long-term fluctuations in runoff over the entire surfaceof the Earth is, probably, one of the ways to identify global patterns and the development of hydrometeorological phenomena in general.In the case under consideration, the presence of a harmonic component was revealed grapho-analytically, including by meansof visual analysis of graphs of long-term fluctuations, followed by selection of an analytical expression of the function. Then the qualityof approximation by harmonic and linear functions, as well as by the norm, was compared, which showed the advantage of the former. Along with the grapho-analytic method, the calculation procedures of the autocorrelation function and spectral analysis wereapplied to the calculation series. In both cases considered in the article, these calculations confirmed the presence of a harmonic component with similar periodicity values.It is concluded that the applied methods, despite the limitations and disadvantages characteristic of each method, taken together,make it possible to identify annual runoff series with a pronounced harmonic component.

Synchronization of complexity enhanced chaos in semiconductor lasers

In synchronized chaotic lasers based secure key distribution and other encrypted communications, presence of the time delay signature in chaos poses a threat to security. So the transmitter and receiver lasers should preferably be operated in complexity enhanced chaotic regime where the time delay signature is hidden. However, achieving good synchronization in experiments in such regime is challenging. We report experimental demonstration of achieving excellent synchronization between two semiconductor lasers even when both the lasers are operating in complexity enhanced chaotic regime with absolutely no time delay signature present in their output. This chaotic regime is ensured by evaluating the auto correlation function, permutation entropy and spectrum analysis of the time series. As a measure of synchronization, cross-correlation coefficient of 0.923 is achieved between the transmitter and receiver lasers. This results are of immense importance in chaos based secure key distribution and other encrypted communication schemes.

Analysis of Velocity Autocorrelation Functions from Molecular Dynamics Simulations of a Small Peptide by the Generalized Langevin Equation with a Power-Law Kernel.

Internal motions play an essential role in the biological functions of proteins and have been the subject of numerous theoretical and spectroscopic studies. Such complex environments are associated with anomalous diffusion where, in contrast to the classical Brownian motion, the relevant correlation functions have power law decays with time. In this work, we investigate the presence of long memory stochastic processes through the analysis of atomic velocity autocorrelation functions. Analytical expressions of the velocity autocorrelation function spectrum obtained through a Mori-Zwanzig projection approach were shown to be compatible with molecular dynamics simulations of a small helical peptide (8-polyalanine).

Topography measurement methods evaluation for entire bending-fatigued fracture surfaces of specimens obtained by explosive welding

In this paper, the methods of compensation of differences in the results of entire bending-fatigued fracture surface topographies were presented. The roughness evaluation was performed with a focus variation microscope and confocal surface topography measurement techniques. The differences in the ISO 25178 roughness parameters were investigated and procedures for their compensation were studied. It was found that various types of optical measurements can cause differences in the errors occurring in the measurement process, such as outliers, and noise. The reduction of differences in the various optical roughness measurements can be attained when measurement errors are compensated. For this study, the applications of general procedures available in commercial software can be suitable for improvements of the roughness measurement results, such as raw data thresholding technique, digital filtering (S-filter), power spectral density, and autocorrelation function analyses. The validation of measurement techniques was proposed for areal and profile studies, including analysis of differences in the calculation areal ISO 25178 roughness parameters.

Predicting Potential Evapotranspiration for Kalaburagi District using a Seasonal Arima Model

Forecasting potential evapotranspiration (PET) is of great importance in effectively managing irrigation systems. This article centers around models designed to simulate future PET levels for the Kalaburagi district. The study calculates potential evapotranspiration using temperature data in degrees Celsius, employing the Thornthwaite method, and prediction is performed using the Seasonal Autoregressive Moving Average (SARIMA) method. These models are developed based on autocorrelation function (ACF) and partial autocorrelation function (PACF) analysis. Model selection is based on minimizing Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) values. The chosen models for different stations in Kalaburagi, Chincholi, Sedam, Chittapur, Aland, Jewargi, and Afzalpur respectively are SARIMA (1,0,1)(2,1,0)12, SARIMA(1,0,1)(2,1,0)12, SARIMA(1,0,0)(2,1,0)12, SARIMA(1,0,1)(2,1,0)12, SARIMA (1,0,1) (2,1,0) 12, and SARIMA(1,0,1)(2,1,0)12. The results indicate that the models developed for Jewargi and Chincholi stations show particular promise compared to the other two stations, with all four models performing well. These models have the potential to significantly enhance decision-making in irrigation planning and command area management practices, contributing to improved water resource management.

Desertification simulation using wavelet and box-jenkins time series analysis based on TGSI and albedo remote sensing indices

Desertification has been listed as one of the most critical global environmental issues, posing a significant threat to life, particularly in arid and semiarid regions. Therefore, gaining a comprehensive understanding of the present and future desertification trends becomes imperative. This study employs a feature space model, which effectively captures land surface changes related to desertification, enabling the extraction of pertinent information. Subsequently, time series models are used to determine the most accurate desertification simulation. Twenty-one ETM + sensor images were utilized to calculate the Topsoil Grain Size (TGSI) and Albedo remotely sensed indexes. Constructing the Albedo-TGSI feature space, the Desertification Degree Index (DDI) was extracted for each year. Different levels of desertification were identified by applying a natural break classification on the DDI values, and corresponding break values were obtained. The representative desertification degree for each year was determined by calculating the average of the minimum and maximum break values, resulting in the generation of five distinct time series for five desertification degrees. Different ARIMA models and wavelet transforms were selected to simulate the various desertification degrees based on the analysis of autocorrelation and partial autocorrelation functions and trial and error, respectively. The most suitable ARIMA models with the lowest errors were identified as follows: ARIMA (1,0,7) for severe desertification, ARIMA (0,1,6) for high desertification, ARIMA (0,0,7) for moderate desertification, and ARIMA (3,0,6) for non-desertification degrees. Among the various wavelet transform families tested, the Symlet family proved to be the most effective, except for the low desertification degree. The following wavelet transforms yielded the best results for each degree of desertification: Symlet3 for severe desertification, Symlet7 for high desertification, Symlet7 for moderate desertification, Daubechies 5 (db5) for low desertification, and Symlet7 for non-desertification degree simulations, all exhibiting the minimum error rates.

Resonance Light-Scattering Correlation Spectroscopy and Its Application in Analytical Chemistry for Life Science.

Resonance light-scattering correlation spectroscopy (RLSCS) is a new single-particle detection method with its working principle being like fluorescence correlation spectroscopy (FCS). RLSCS is obtained by autocorrelation function analysis on the measured fluctuation of the resonance light scattering (RLS) intensity occurring within a subfemtoliter volume when a single nanoparticle (such as gold nanoparticles (NPs) or silver (SNPs)) freely diffuses through the volume. The RLSCS technique can detect such parameters as concentration, diffusion coefficient (translation and rotation), etc. Compared with the FCS technique, the correlated fluorescence intensity signal in RLSCS is replaced with the RLS signal of the nanoparticles, overcoming some limits of the fluorescent probes such as photobleaching under high-intensity or long-term illumination. In this Account, we showcase RLSCS methods, theoretical models at different optical configurations, and some key applications. First, the RLSCS optical detection system was constructed based on the confocal optics, its theoretical model was proposed, and the diffusion behaviors of the nanoparticles in the solution were studied including the rotational and translational diffusion. And, methods were developed to measure the concentration, size, aspect ratio, and size distribution of the NPs. Second, based on the RLSCS methods, some detection strategies were developed for homogeneous DNA detection, immunoassay, apoptosis assay, self-thermophoresis of the nanomotor, and quantitative assay in single living cells. Meanwhile, a new fluorescence/scattering cross-correlation spectroscopy (FSCCS) method was proposed for monitoring the molecule-particle interaction. This method enriched the conventional fluorescence/fluorescence cross-correlation spectroscopy (FCCS) method. Third, using the EMCCD with high sensitivity and rapid response as an optical detector, two temporospatially resolved scattering correlation spectroscopy methods and their theoretical models were developed: total internal reflection (TIR) configuration-based spatially resolved scattering correlation spectroscopy (SRSCS) and dark-field illumination-based scattering correlation spectroscopy (DFSCS). These methods extended single-spot confocal RLSCS to imaging RLSCS, which makes RLSCS have the ability for multiple channel detection with temporospatial resolution. The method was successfully used for investigating the dynamic behaviors of gold NPs in live cells and obtained its temporospatial concentration distribution and diffusion behaviors. The final section of this Account outlines future directions in the development of RLSCS.

Sedimentary P- and S-Wave Velocity Structures in Shimousa Region of Kanto Basin Determined from Joint Autocorrelation and Receiver Function Analysis

ABSTRACT To determine the P- and S-wave velocities (VP and VS, respectively) of the sedimentary layers in the Shimousa region of the Kanto basin, Japan, the autocorrelations of P and S waves and the receiver function (RF) for the local earthquakes at the strong-motion stations in the region were jointly analyzed. Because the autocorrelations and RF were insensitive to the event location, the stacked functions for various events captured clear P and S reflections and the Ps phase at the bedrock of the basin. The PpPs phase was also clearly observed at some stations in the region. The arrival times of the P and S reflections and the Ps phase depended on the station. These phases arrived earlier at the northern and eastern parts of the region, and later at the southern and western parts. This trend shows good agreement with that expected from the 3D velocity structure model developed by the Japan Seismic Hazard Information Station. The VP, VS, and thickness (H) values were then estimated from the histogram of the H–V stack for the P and S autocorrelations and the Ps and PpPs phases in the RF. VP, which is less sensitive than VS and H, was not always well determined at some stations, whereas VS and H were well estimated with high resolution. It was found that the resolution depends on the station location in the region. This dependence could be caused by the appearance of clear phases in the functions.

Examination of one-dimensional S-wave velocity structure using strong-motion data for high-seismic-intensity area during the 2018 Hokkaido Eastern Iburi earthquake

The Yufutsu Plain, a sedimentary basin surrounded by mountains, is located in the southern part of the Ishikari–Yufutsu Lowlands, Hokkaido, Japan. The Hidaka arc–arc collision zone, located in the eastern part of the Yufutsu Plain, forms the Hidaka Mountain range in central Hokkaido, with the Ishikari–Teichi–Toen Fault Zone of the Ishikari–Yufutsu Lowlands on the west side, which forms part of a major geological boundary that extends in the north–south direction. The 2018 Hokkaido Eastern Iburi earthquake (Mw 6.6) occurred at 03:08 JST on September 6, 2018, in this arc–arc collision zone. The K-NET HKD126 station in Mukawa Town, which is close to the severely damaged basin margin area, recorded strong-motion data with strong power for a predominant frequency of 0.5–1.0 Hz during the main shock. The peak ground acceleration was 661 cm/s2 in the east–west direction. The site amplification characteristics of the shallow S-wave velocity structure, which was estimated from microtremor array observations and surface wave explorations, were one of the causes of this strong ground motion. It is essential to accurately estimate the depth of the seismic bedrock and basin margin to evaluate the long period of large earthquakes. In this study, we used strong-motion data recorded at HKD126 and three temporary strong-motion stations near the basin margin area to tune the deep S-wave velocity structure. First, we performed microtremor array observations and surface wave explorations to estimate the S-wave velocity structure to a depth of 1 km beneath the station at the hill site because a detailed shallow structure is not available for this site. Then, with a combination of the estimated S-wave velocity structure and the existing structure, we tuned the deep structures based on an autocorrelation function analysis using strong-motion data. The validity of the estimated structures from the shallowest depth to the seismic bedrock was verified based on the differences between the observed arrival time difference and theoretical travel time difference for the S-wave initial motion. We estimated the seismic bedrock of the four stations to be at a depth of 7–10 km. In addition, an autocorrelation function analysis suggests topological bedrock undulations.Graphical

A theoretical and experimental investigation into surface generation in ultra-precision diamond milling

Ultra-precision diamond milling (UPDM) provides a promising generation of high-quality surface up to sub-micrometric form accuracy and nanometric surface roughness. Prior to its machining process, the investigation on surface generation would be beneficial to achieve desired quality with reduced cost and boosted productivity. However, few studies discussed this issue so far. In this study, a theoretical and experimental investigation into surface generation was carried out in UPDM. Firstly, a geometric model was built up considering the material removal process between the milling tool and the workpiece. Secondly, surface generation process was elaborated according to the developed model. Finally, UPDM experiments were performed on a typical brass material at different cutting conditions and surface texture parameters were characterized according to ISO 25178-2. Autocorrelation function (ACF) analysis was employed for a detailed evaluation of surface generation. The results showed that the static factors (cutting parameters) as well as the dynamic factors (material effects and spindle vibration), which were associated with the cutting conditions, yielded a great influence on surface generation. Surface texture characterization indicated that there was a good consistence between simulation and experiment, demonstrating the feasibility of the proposed method. ACF analysis indicated that, with the increased feedrate/step ratio, surface generation approximately achieved a transition from the periodical surface (dominated by cutting residuals) to the random surface (determined by multiple components). Significantly, this study draws up a fundamental understanding toward surface generation in UPDM.