Rolling Time Research Articles

Identification of recurring patterns and repetitions in time-series, with application to pseudo-stochastic ship rolling

A scheme for capturing repeating portions in time-series is proposed. Such repetitions may be temporal or permanent. The scheme is applied for investigating repetitions in ship roll time simulation data, incurred by the use of discretized wave spectra. The presence of repetitions in the forcing can slant the ship motion response data collected via Monte Carlo simulations unless certain precautions are applied. The conventional check for repetitions in data series is based on the observation of high peaks in the autocorrelation function. However, as the autocorrelation reflects averaged resemblance between sets of successive data separated by a time interval, these peaks may not imply repetition in exact sense. By the proposed computational scheme can be elicited whether any given portion of the time-series reappears. The basic idea is the construction of low-dimensional vectorial representatives of short-time portions of the series and the check for similarity (to a level of tolerance) through application of a Euclidian distance metric to these vectors. The scheme is also useful for locating the end of the transient part in simulated ship roll motion data deriving from repeating pseudo-stochastic excitation.

A Dynamic Lane-Changing Driving Strategy for CAV in Diverging Areas Based on MPC System.

Freeway-diverging areas are prone to low traffic efficiency, congestion, and frequent accidents. Because of the fluctuation of the surrounding traffic flow distribution, the individual decision-making of vehicles in diverging areas is typically unable to plan a departure trajectory that balances safety and efficiency well. Consequently, it is critical that vehicles in freeway-diverging regions develop a lane-changing driving strategy that strives to improve both the safety and efficiency of divergence areas. For CAV leaving the diverging area, this study suggested a full-time horizon optimum solution. Since it is a dynamic strategy, an MPC system based on rolling time horizon optimization was constructed as the primary algorithm of the strategy. A simulation experiment was created to verify the viability of the proposed methodology based on a mixed-flow environment. The results show that, in comparison with the feasible strategies exiting to off-ramp, the proposed strategy can take over 60% reduction in lost time traveling through a diverging area under the premise of safety and comfort without playing a negative impact on the surrounding traffic flow. Thus, the MPC system designed for the subject vehicle is capable of performing an optimal driving strategy in diverging areas within the full-time and space horizon.

Mathematical modeling of the rolling speed of a car on the first profile site of the slide

The article shows the consideration of the rolling friction moments (pure rolling) of the wheels on the rail threads and in the bearings of the front axlebox units. and rear bogies of the car with their subsequent replacement by conditional sliding friction. It is noted that if the active force in the form of a projection of the force of gravity and the aerodynamic drag force on the direction of rolling of the car (or release), acting on the car, is greater than the limiting friction force, then simultaneously with rolling, the wheels may also slip on the rail threads. An analytical formula was obtained for finding the difference between the driving forces and all resistance forces when rolling wheels with sliding, which, based on the theorem on the change in the momentum of a material point, made it possible to create a mathematical model for finding the speed of a single car (or cut) on the first profile section of the hump. An analysis of the results of solving the problem of determining the rolling speed of a car has been carried out. It is noted that the theorem on the change in the momentum of a material point for solving the problem of finding the time of rolling down a hill is applicable only when the final speed of the car is known, otherwise solving the problem in such a formulation will be meaningless.

Analysis of carbon nanotube for low power nano electronics applications

In the area of nanotechnology, carbon nanotubes are a notable and remarkable invention. Its structure is much similar to the structure of graphite. CNTs are small in size, light in weight, have good strength, and have good conductivity, making them the building blocks of futuristic new technologies. CNTs have promised to be the catalyst for the next revolution in technology. A broad range of processes are available to produce various types of CNTs, depending on the rolling times of graphite sheets. In this paper, different types of CNTs, their properties, ways of synthesis such as the arc discharge method and chemical vapor deposition, and application have been covered. Outlining their respective advantages and challenges. SWCNTs exhibit high carrier mobilities and tunable bandgaps, making them suitable for transistor devices and interconnects in integrated circuits. DWCNTs offer enhanced mechanical stability and electrical conductivity, catering to applications in flexible electronics and energy storage devices. MWCNTs, though lacking distinct properties of SWCNTs and DWCNTs, find utility in composites, sensors, and biomedical devices due to their ease of synthesis and lower cost.

Analysis of carbon nanotube for low power nano electronics applications

The implementation of nanoelectronic circuits depends on technologies such as Complementary Metal-Oxide Semiconductor (CMOS) or Bipolar CMOS (BICMOS), and the length of the channel can be reduced up to a certain limit. Due to the generation of various errors nanomaterials can be an alternative solution for circuit design. In the field of nanotechnology, Carbon Nanotubes (CNTs) have become a notable and remarkable invention. Their structure is very similar to that of graphite, and its small size, lightweight, high strength, and good conductivity make them ideal building blocks for future technologies. CNTs hold great promise for being the catalyst for the next technological revolution. Today, a broad range of processes is available to produce various types of CNTs, depending on the rolling times of graphite sheets. This review paper sheds light on the different types of CNTs, their properties, methods of synthesis such as arc discharge and chemical vapor deposition, and their applications. To achieve this goal, this paper provides a review that aims to define the state-of-the-art in this field from a novel and unified perspective while elaborating insights of current developments and emerging trends.

An integrated model for crude oil forecasting: Causality assessment and technical efficiency

In light of the central role of crude oil in the economy and the complex mechanisms involved in forecasting crude oil prices, this study proposes a two-stage model that optimally selects driving predictors for crude oil price forecasting by integrating Granger causality test (GCT) and stochastic frontier analysis (SFA). In the first stage, GCT is used to perform causality assessments for 92 predictors across eight categories of factors (demand, supply, inventory, financial market, macroeconomy, economic policy uncertainty, geopolitical risk, and technical indicator). In the second stage, SFA is employed to assess the forecasting power of the preliminarily selected predictors in terms of technical efficiency by using multiple evaluation measures. By collecting a data sample which spans a 21-year period from January 1, 2000 to December 31, 2020, we conduct a comprehensive empirical study by employing rolling time window technique. The empirical results demonstrate that the two-stage model significantly outperforms eight competing models in terms of four forecasting techniques (linear regression, artificial neural network, support vector regression, and random forest). The proposed model's outperformance is robust to different time windows, different forecast horizons, alternative proxies of crude oil prices, and different business conditions. We also explore the time-varying characteristics of predictors for crude oil price forecasting and confirm that financial factors remain vital determinants affecting oil prices.

Effect of ultrasonic surface rolling processing on wear properties of Cr12MoV steel

Ultrasonic surface rolling processing (USRP) with different rolling times (2, 6 and 10 times) was introduced to the Cr12MoV steel. The surface integrity and wear resistance after USRP were investigated. The microstructural features of the surface deformed layer were characterized by some microscopic analysis methods(scanning electron microscopy, X-ray diffraction, X-Ray residual stress analyzer and microhardness analysis). Multifunctional friction and wear testing machine was used to test the wear properties of different treated samples along the rolling direction. Results indicate that after two times of USRP, the grain of the plastic deformation layer of the material is significantly refined, moreover, the surface hardness, residual compressive stress and surface roughness were improved. However, the carbon pit produced by carbide exfoliation could not be filled in time, which becomes the source of the layered exfoliation crack and reduced the strengthening effect. With the increase of rolling times, the thickness of plastic deformation layer increased and the surface carbide decreased, which is helpful to inhibit the crack initiation and propagation, effectively improving the phenomenon of layered spalling and improve the wear resistance of material.

The international spillover behaviour of implied volatilities and forecasting ability of spillover indices

ABSTRACT In this study, we investigate the spillover behaviours of implied volatilities and test the forecasting power of spillover indices. By using a Diebold and Yilmaz’s (2012) framework, we capture a better overall picture of the developments of spillover effects and create a series of spillover indices by quantifying the spillover intensity for each rolling time window such that we are able to investigate the forecasting ability of spillover indices on market returns. There are several findings in this study including (1) the spillover indices of implied volatilities not only enable us to describe the spillover effects but also possess strong forecasting power on market returns, (2) implied volatilities which own emotional-related characteristic create stronger spillover mechanisms than spillover of market returns and (3) by observing the time-varying patterns of spillover indices of implied volatilities, we find that the peaks of spillover indices occur during well-known economic events such as the Lehman Brothers’ Bankruptcy and Greek debt restructuring.

Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data.

Wearable accelerometers record physical activity with high resolution, potentially capturing the rich details of behaviour changes and habits. Detecting these changes as they emerge is valuable information for any strategy that promotes physical activity and teaches healthy behaviours or habits. Indeed, this offers the opportunity to provide timely feedback and to tailor programmes to each participant's needs, thus helping to promote the adherence to and the effectiveness of the intervention. This article presents and illustrates U-BEHAVED, an unsupervised algorithm that periodically scans step data streamed from activity trackers to detect physical activity behaviour changes to assess whether they may become habitual patterns. Using rolling time windows, current behaviours are compared with recent previous ones, identifying any significant change. If sustained over time, these new behaviours are classified as potentially new habits. We validated this detection algorithm using a physical activity tracker step dataset (N = 12,798) from 79 users. The algorithm detected 80% of behaviour changes of at least 400 steps within the same hour in users with low variability in physical activity, and of 1600 steps in those with high variability. Based on a threshold cadence of approximately 100 steps per minute for standard walking pace, this number of steps would suggest approximately 4 and 16 min of physical activity at moderate-to-vigorous intensity, respectively. The detection rate for new habits was 80% with a minimum threshold of 500 or 1600 steps within the same hour in users with low or high variability, respectively.

An Auction-Based Multiagent Simulation for the Matching Problem in Dynamic Vehicle Routing Problem with Occasional Drivers

This research incorporated an auction mechanism into the vehicle routing problem with occasional drivers and produced simulations in an agent-based environment. Auctions were used to match online orders with potential occasional drivers. While a centralized system optimizes system performance under global objectives, the novel decentralized approach presented here illustrates emergent phenomena resulting from the interaction of individual entities in highly dynamic cases. In the simulations, the auctions were executed after a fixed time interval called a rolling time horizon. Our results suggest that the appropriate rolling time horizon produces a lower average unit compensation cost because better matches can be found when the accumulation of online orders and occasional drivers is maintained at a certain level. The simulation results also indicate that the use of an auction mechanism instead of simple nonauction rules can improve the average unit compensation cost by up to 25.1%.

Analysis of Temperature Influence on Strain-Speed Parameters of Radial-Shear Rolling of Al-Zn-Mg-Ni-Fe Alloy.

The comprehensive analysis of temperature influence on the strain–speed parameters of radial-shear rolling of Al-Zn-Mg-Ni-Fe alloy including the investigation of rheological properties, FEM simulation, and in-depth analytical interpretation of results was carried out. The rolling temperature has significant effect on the kinematic of metal forming, speed parameters, configuration, and length of trajectories. With the decrease in temperature, the speed of metal movement reduces, and this is the same for different components. The greatest decrease is noted for the axial speed component. In general, according to the nature of effect on the strain kinematic state, a temperature reduction of 100 °C (from 500 to 400 °C) acts similarly to a decrease in feed angle of about 4° and, in particular, increases the rolling time, nonuniformity of deformation, tightening, and temperature effect of deformation heating.

Scalable Preparation of Sub-Millimeter Double-Shelled Al2O3 Hollow Spheres and Their Rapid Separation from Wastewater after Adsorption of Congo Red.

Porous double-shelled ceramic hollow spheres (PDSs) have attracted extensive attention due to their high specific surface areas and multifunctional designs. When used in wastewater treatment, millimeter or sub-millimeter spheres can be quickly separated from water by commercial sieves. However, the simple, scalable, and low-cost preparation of sub-millimeter PDSs in the solid phase remains a challenge. Herein, porous PDSs were facilely fabricated via a spheronization process utilizing pseudoboehmite powders and wet gelatin spheres as templates, which broke through the difficulty of preparing PDSs by one-step solid-state synthesis. Treating pseudoboehmite powder with nitric acid can improve the compressive strength of the PDSs. By controlling the rolling time and gelatin concentration of gelatin microspheres, the integrity, shell thickness, and double-shelled spacing of the gelatin microspheres were tuned. When the rolling time was 8–12 min, and the gelatin concentration in gelatin spheres was 250 g/L, and PDSs with a complete double-shelled structure, good mechanical property, and high specific surface area (327.5–509.6 m2/g) were obtained at 600 °C. The adsorption capacities of the PDSs for 100 mg/L Congo red solution (70.7 mg/g) were larger than those of single-shelled hollow spheres (49 mg/g), and larger diameters (608–862 μm) of the PDSs allow them to be rapidly separated from solution by a commercial sieve. This paper provides a facile and scalable method for the preparation of sub-millimeter PDSs and demonstrates their excellent adsorption capacity for Congo red solution.

Non-targeted metabolomics and electronic tongue analysis reveal the effect of rolling time on the sensory quality and nonvolatile metabolites of congou black tea

Rolling is a crucial step in congou black tea manufacturing. Herein, the influence of rolling time (50, 75, 100, 125 min) on the sensory quality and chemical composition of black tea was investigated by human sensory evaluation, electronic tongue, colorimetric analysis, tea pigments quantification and liquid chromatography-mass spectrometry (LC-MS) based non-targeted metabolomics. With increasing rolling time, tea liquor color attributes a*, b*, C* and pigments theaflavins (TFs), thearubigins (TRs), and theabrownins (TBs) were significantly increased. Fifty-two key differential metabolites were identified among four treatments. Catechins, dimeric/trimeric catechins, amino acids were significantly declined. The changes of flavonol/flavone glycosides depended on the linkage pattern. Flavonol O-glycosides such as myricetin 3-O-glucoside were decreased, whereas flavone C-glycosides such as vitexin were increased. These results suggested that different rolling times influenced the catechins oxidative polymerization, flavonol O-glycosides hydrolysis and amino acids oxidation. Black tea by 100-min rolling rendered optimal sensory quality, with highest umami and low bitter intensities in electronic tongue, and higher quality index (10 TFs + TRs)/TBs. Similar result was obtained in a validation batch in a different year. This study provides novel insights into the effect of rolling on black tea quality, which may serve as guidance for the manufacturing of high-quality congou black tea.

Microstructure and tribological performances of M50 bearing steel processed by ultrasonic surface rolling

To prolong service life of M50 steel for the main shaft bearing, ultrasonic surface rolling process (USRP) was utilized to improve surface performances. The effects of USRP on surface integrity and tribological behavior were investigated. The results show the effect of USRP parameters on surface performances follows the order: feed rate > load > rolling times. During the USRP treatment, plastic deformation layer with 24 µm thickness is formed, and fine grain strengthening increases the microhardness (15.56%) and residual stress (79.61%) and decreases the surface roughness (−68.09%), respectively. Friction coefficient and wear rate of USRP sample are lower than those of untreated sample, because USRP helps form a smooth tribolayer and changes the main wear mechanism to slight adhesive wear.

Damage Mechanism of Asphalt Concrete Pavement under Different Immersion Environments

With the construction of road infrastructure in China, the rapid development of expressways has provided great convenience for human travel and cargo transportation. Asphalt concrete pavement is widely used in highway construction because of its characteristics of good flatness, high strength, and convenient maintenance. But the water damage of asphalt concrete pavement is the main factor affecting the stability of highway subgrade and flatness of pavement, as well as the main cause of deformation and collapse of pavement. In view of the water damage problem of asphalt concrete pavement, this paper investigates the overall condition and water damage situation of asphalt concrete pavement with different immersed states and carries out indoor simulation test of asphalt concrete samples without water, dry and wet cycle, and saturated water. The quality, loss rate and Marshall stability of the samples under different times of cyclic rolling were obtained through the statistics of cyclic rolling pressure of the samples. The influencing factors of water loss, water resistance and failure forms of highway under different impregnation environments were analyzed, and the prevention measures of water loss of asphalt concrete were put forward. The test results show that the mass loss rate of the sample is water-free, dry-wet cycle, and water-saturated, and the Marshall stability of the sample is water-free, dry-wet cycle, and water-saturated from high to low. Under the action of rolling and friction forces, the fine aggregates in the asphalt concrete are worn off, which leads to the expansion of cracks on the surface of the sample and the formation of fissure network. Small holes appear on the surface of water-saturated sample, and the coarse aggregate is broken and disintegrated under pore water pressure. Traffic flow (cyclic rolling times) and immersion state have become the main factors of asphalt concrete pavement failure. This study has specific and important theoretical guiding significance for reducing and preventing water damage of asphalt concrete pavement of expressway, delaying road service life, and enhancing road safety.

Ultra-short-term wind power prediction based on PVMD-ESMA-DELM

The non-linearity and non-stationarity of wind power data have brought great challenges to the safe operation of the power system. It is particularly important to effectively improve the accuracy of ultra-short-term prediction of wind power. Therefore, we propose an ultra-short-term wind power prediction method that particle swarm optimization-variational mode decomposition (PVMD), enhanced slime mold algorithm (ESMA) for elite opposition-based learning strategy (EOBL) and deep extreme learning machine (DELM). First, the particle swarm optimization algorithm (PSO) is used to optimize the two core parameters of the variational mode decomposition (VMD) to obtain the PVMD algorithm. The PVMD is used to decompose the original wind power data into a series of stable sub-sequences, and the rolling time series is used to analyze the sub-sequences decomposed by PVMD. Then the DELM predictive model is established and the input weights (e) and thresholds (bc) in DELM are optimized through ESMA, and the EOBL is used to improve the diversity and population quality of the slime mold population, thereby improving the global optimization performance and convergence accuracy of the slime mold algorithm (SMA), and further improving the prediction accuracy of the DELM model. Finally, each subsequence is substituted into the DELM optimized by the elite opposition based learning-slime mold algorithm (ESMA-DELM), and the prediction components are superimposed to obtain the final prediction result. Comparing the effects of several different forecasting models with the evaluation of calculation examples proves the effectiveness of the PVMD-ESMA-DELM blended forecasting model proposed in this paper, and gives a new approach for ultra-short-term wind power prediction.

Preparation and anti-icing performance of acrylic superhydrophobic asphalt pavement coating with microwave heating function

In winter, icing greatly reduces the friction performance of pavement and brings a high safety hazard to road traffic. We prepared a superhydrophobic coating (SHC) with a microwave heating function to minimize pavement icing risk. The static contact angle of the water droplet on the SHC is 161.2°. The freezing time of water droplets on asphalt mixture coated with SHC is 1.63 times longer than that of asphalt mixture without coating. SHC can significantly delay the freezing process of water. The normal adhesion strength of 10 mm ice layer on the asphalt mixture coated with the SHC at −5 °C is only 14.3 % of that on the asphalt mixture without coating. It confirms that the SHC dramatically reduces the normal adhesion strength of the ice layer on asphalt pavement. In addition, the microwave heating rate of the asphalt mixture coated with SHC is 48.37 % faster than that of the asphalt mixture without coating. Carbon nanotube particles directly contact the ice on the SHC surface. Besides, carbon nanotube particles also have high microwave heat conversion efficiency and high thermal conductivity. The ice layer on the surface of the asphalt mixture coated with SHC can be melted and removed within 80 sec, which is 42.86 % faster than the ice removal time of asphalt mixture without coating. Carbon nanotube particles on the SHC surface were directly heated by microwaves and melted the ice layer rapidly. The acrylic coating on the asphalt mixture surface has an excellent thermal insulation performance. The load wheel rolling experiment was conducted to simulate the traffic wheel friction. The static contact angle of the water droplet on the SHC keeps at 147°, and the microwave heating performance of the SHC has no significant changes before and after 500 rolling times. This implies that the SHC has sustainable superhydrophobic properties and can melt the ice layer under microwave heating even after the traffic wheel friction. The SHC is of great significance for improving the driving safety of asphalt pavement.

Microstructure evolution of three-roll skew-rolling formed hollow axles with uniform wall thickness

The uniform thickness hollow axle is one of the lightest weight axles in the high-speed rail industry. It can effectively reduce unsprung mass and further improve the train speed. Therefore, due to its importance and significance, it is crucial to research and develop the uniform thickness hollow axle. To understand the microstructural evolution during three-roll skew-rolling (TRSR) forming, 30CrMoA alloy steel was used in this study. A constitutive model of 30CrMoA was established by SIMUFACT FORMING finite element software and utilized to simulate the deformation, heat transfer, and microstructure coupling during TRSR. Via analyzing the influence of process parameters on the average grain size, the microstructural evolution of the forming part at each deformation stage is revealed. The result shows that the dynamic recrystallization of the rolled piece produces a fine and uniform grain structure during the forming process. The refinement level is enhanced with the increase of the radial compression; the grain size is gradually increased from the outer surface to the inner surface of the rolled piece. At a higher rolling temperature, it was found the initial grain size and final average grain size were larger compared to the lower temperature. By increasing the axial traction speed, not only can the rolling time be shortened, but also the appearance of coarse grains can be avoided. Therefore, the mechanical properties can be improved.

China's GDP forecasting using Long Short Term Memory Recurrent Neural Network and Hidden Markov Model.

This paper presents a Long Short Term Memory Recurrent Neural Network and Hidden Markov Model (LSTM-HMM) to predict China's Gross Domestic Product (GDP) fluctuation state within a rolling time window. We compare the predictive power of LSTM-HMM with other dynamic forecast systems within different time windows, which involves the Hidden Markov Model (HMM), Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) and LSTM-HMM with an input of monthly Consumer Price Index (CPI) or quarterly CPI within 4-year, 6-year, 8-year and 10-year time window. These forecasting models employed in our empirical analysis share the basic HMM structure but differ in the generation of observable CPI fluctuation states. Our forecasting results suggest that (1) among all the models, LSTM-HMM generally performs better than the other models; (2) the model performance can be improved when model input transforms from quarterly to monthly; (3) among all the time windows, models within 10-year time window have better overall performance; (4) within 10-year time window, the LSTM-HMM, with either quarterly or monthly input, has the best accuracy and consistency.

An evaluation of competing methods for constructing house price indexes: The case of Warsaw

Accurate house price indexes allow governments and planners to make more effective decisions with regard to land use policy. We compare different ways of computing house price indexes for Warsaw over the period 2006–2018 using a detailed micro-level dataset of 101,182 transactions. We find that when a hedonic approach is used, the resulting index is reasonably robust to the choice of method. More problematic is the repeat-sales method, which is widely used in the US. We find that repeat-sales price indexes are unreliable, affected by sample-selection bias, and prone to significant revisions when new periods are added to the dataset. Even for the hedonic approach, the choice of method can become important for smaller datasets. In such cases, the hedonic time dummy and rolling time dummy methods tend to perform better than the hedonic repricing, hedonic imputation and average characteristics methods. In comparison with the index produced by the National Bank of Poland (NBP), we find that in 2006–7 prices did not rise as much, while since the end of 2012 prices have risen faster than indicated by the NBP index. We attribute these differences to a combination of the choice of hedonic method (we use time-dummy while the NBP index uses the repricing method) and different datasets (the NBP dataset is smaller, especially in the earlier years of the sample).