Irregular Components Research Articles

A comparative study of different deep learning methods for time-series probabilistic residential load power forecasting

The widespread adoption of nonlinear power electronic devices in residential settings has significantly increased the stochasticity and uncertainty of power systems. The original load power data, characterized by numerous irregular, random, and probabilistic components, adversely impacts the predictive performance of deep learning techniques, particularly neural networks. To address this challenge, this paper proposes a time-series probabilistic load power prediction technique based on the mature neural network point prediction technique, i.e., decomposing the load power data into deterministic and stochastic components. The deterministic component is predicted using deep learning neural network technology, the stochastic component is fitted with Gaussian mixture distribution model and the parameters are fitted using great expectation algorithm, after which the stochastic component prediction data is obtained using the stochastic component generation method. Using a mature neural network point prediction technique, the study evaluates six different deep learning methods to forecast residential load power. By comparing the prediction errors of these methods, the optimal model is identified, leading to a substantial improvement in prediction accuracy.

Upper limit assessment of GIS equipment failure based on time series model and deep learning algorithm

If a sudden fault of gas-insulated switchgear is not handled in time, it will delay production and cause certain economic losses to the enterprise. Therefore, this study proposes an upper limit assessment algorithm for equipment faults based on time series model and deep learning. The irregular fluctuation components in the time series model are stabilized by using empirical mode decomposition, and the long short-term memory network in the deep learning algorithm is introduced to combine the two. The equipment fault data is obtained by the joint algorithm. After empirical mode decomposition, the intrinsic mode function component of the data signal is obtained, and the sensitive component is extracted from it. The relationship value and mutual information between it and the original data signal are analyzed to complete the fault data feature extraction. In the process of equipment fault upper limit assessment, the parameter optimization and convergence are controlled, and the fault deep learning model is constructed. After the refraction coefficient and reflection coefficient are determined, the upper limit assessment of equipment faults is realized. The proposed method is experimentally tested. The results show that the proposed algorithm has an ideal upper limit assessment result of faults, and the obtained curve has a high degree of fit with the actual result curve. It can be proved that the proposed algorithm can scientifically evaluate the upper limit of equipment faults and has certain application value.

Magnetic field morphology and evolution in the Central Molecular Zone and its effect on gas dynamics

The interstellar medium in the Milky Way's Central Molecular Zone (CMZ) is known to be strongly magnetised, but its large-scale morphology and impact on the gas dynamics are not well understood. We explore the impact and properties of magnetic fields in the CMZ using three-dimensional non-self gravitating magnetohydrodynamical simulations of gas flow in an external Milky Way barred potential. We find that: (1) The magnetic field is conveniently decomposed into a regular time-averaged component and an irregular turbulent component. The regular component aligns well with the velocity vectors of the gas everywhere, including within the bar lanes. (2) The field geometry transitions from parallel to the Galactic plane near $z=0$ to poloidal away from the plane. (3) The magneto-rotational instability (MRI) causes an in-plane inflow of matter from the CMZ gas ring towards the central few parsecs of $0.01 that is absent in the unmagnetised simulations. However, the magnetic fields have no significant effect on the larger-scale bar-driven inflow that brings the gas from the Galactic disc into the CMZ. (4) A combination of bar inflow and MRI-driven turbulence can sustain a turbulent vertical velocity dispersion of $ on scales of $20 in the CMZ ring. The MRI alone sustains a velocity dispersion of $ Both these numbers are lower than the observed velocity dispersion of gas in the CMZ, suggesting that other processes such as stellar feedback are necessary to explain the observations. (5) Dynamo action driven by differential rotation and the MRI amplifies the magnetic fields in the CMZ ring until they saturate at a value that scales with the average local density as $B 102 \, (n/10^3 \ G $. Finally, we discuss the implications of our results within the observational context in the CMZ.

An Assessment of Changes in the Thermal Environment during the COVID-19 Lockdown: Case Studies from the Greenland and Norwegian Seas

The COVID-19 lockdown had a significant impact on human activities, reducing anthropogenic heat and CO2 emissions. To effectively assess the impact of the lockdown on the thermal environment, we used the sliding paired t-test, which we improved from the traditional sliding t-test by introducing the paired t-test for sliding statistical tests, to test the abrupt change in the thermal environment. Furthermore, an additive decomposition model and wavelet analysis method were used to analyze the characteristics of trend and irregular change, coherence, and phase difference of the time series data with respect to the thermal environment. We chose the Greenland Sea and the Norwegian Sea, regions highly sensitive to changes in climate and ocean circulation, as case studies and used remote sensing data of the sea surface temperature (SST) and the atmospheric CO2 concentration data obtained from the Goddard Earth Sciences Data and Information Services Center from January 2015 to December 2021 for the analysis. The results show that although the annual spatial mean SST in 2020 is lower than the mean of all 7 years in most areas of the two seas, there is no evidence of a significant mutation in the decrease in the SST during the lockdown in 2020 compared with the temperatures before, according to the sliding paired t-test. The analysis of the irregular components of the monthly mean SST decomposed by an additive decomposition model also does not show the anomalously low SST during the lockdown in 2020. In addition, the lockdown had almost no impact on the increasing trend of CO2 concentration. The wavelet analysis also shows that there is no obvious anomaly in coherence or phase difference between the periodic variation of the SST and the CO2 concentrations in 2020 compared with other years. These results suggest that the direct effect of the COVID-19 lockdown on the thermal environment of the study area could be negligible.

A 3D Parameterized BIM-Modeling Method for Complex Engineering Structures in Building Construction Projects

The structural components of large-scale public construction projects are more complex than those of ordinary residential buildings, with irregular and diverse components, as well as a large number of repetitive structural elements, which increase the difficulty of BIM-modeling operations. Additionally, there is a significant amount of inherent parameter information in the construction process, which puts forward higher requirements for the application and management capabilities of BIM technology. However, the current BIM software still has deficiencies in the parameterization of complex and irregular structural components, fine modeling, and project management information. To address these issues, this paper takes Grasshopper as the core parametric tool and Revit as the carrier of component attribute information. It investigates the parametric modeling logic of Grasshopper and combines the concepts of parameterization, modularization, standardization, and engineering practicality to create a series of parametric programs for complex structural components in building projects. This approach mainly addresses intricate challenges pertaining to the parametric structural shapes (including batch processing) and parametric structural attributes (including the batch processing of diverse attribute parameters), thereby ensuring the efficiency in BIM modeling throughout the design and construction phases of complex building projects.

Decomposition of full-waveform LiDAR data utilizing an adaptive B-spline-based model and particle swarm optimization

High-precision waveform decomposition is crucial for LiDAR applications. Existing methods encounter challenges including poor target detection and low accuracy in extracting parameters of irregular components, especially in complex echoes. We introduce an adaptive B-spline-based decomposition (AdaptB-spline) method, which uses B-spline curves to adaptively adjust the shape and position of component through the particle swarm optimization (PSO); and proposes an initial parameter estimation method based on the B-spline and Richardson-Lucy (RL) deconvolution, which improves the noise immunity and component detection. Experiments were conducted on synthetic waveforms and satellite LiDAR waveforms by AdaptB-spline and other four methods (Gaussian (Gauss), B-spline-based (B-spline), skew-normal (SkewN), and multi-Gaussian (MultiGauss) decomposition). We concluded that AdaptB-spline exhibits superior performance in terms of component RMSE, CC, R2, component parameter error and range error metrics compared to the four methods. So AdaptB-spline can enhance component detection and accurately fit Gaussian or non-Gaussian waveforms, demonstrating outstanding target detection and ranging precision.

Repeatability and Agreement of 4 Biometers Measuring Corneal Astigmatism in Eyes With Irregular Corneal Astigmatism Component

To investigate the repeatability and agreement of corneal astigmatism measurements in eyes with irregular corneal astigmatism component (ICAC) using four devices: IOLMaster 700 biometer, Lenstar 900 biometer, iTrace, and Pentacam. Prospective cross-sectional reliability analysis. Sixty-four eyes (52 patients) with ICAC were examined three times using the four devices. The eye with ICAC in this study is defined as the cornea has a certain degree of irregular astigmatism (asymmetric and/or skewed bowtie pattern of corneal topography according to corneal topography classification), accompanied with total corneal higher-order aberrations in the 4 mm zone of 0.3 µm or greater. Corneal astigmatism was evaluated using three categories: anterior corneal astigmatism (ACA), posterior corneal astigmatism, and total corneal astigmatism (TCA). The repeatability was determined using the ∆Ast (arithmetic mean of vector differences among three repeated corneal astigmatism measurements). Bland-Altman plots and astigmatism vector analyses were employed to assess agreement. The IOLMaster 700 (∆Ast = 0.27 ± 0.20 D) showcased higher repeatability in ACA measurements compared to iTrace (∆Ast = 0.37 ± 0.38 D, P = .040) and Pentacam (∆Ast = 0.50 ± 0.22 D, P < .001), and paralleled the performance of Lenstar 900 (∆Ast = 0.31 ± 0.26 D, P = .338). The Pentacam (∆Ast = 0.09 ± 0.07 D, P < .001) demonstrated superior repeatability in posterior corneal astigmatism, whereas the IOLMaster 700 (∆Ast = 0.33 ± 0.23 D, P < .001) excelled in TCA. The IOLMaster 700 exhibited good agreement with either Lenstar 900 or iTrace, characterized by narrow 95% limits of agreement and clinically acceptable vector differences. Conversely, vector differences between Pentacam and the other three devices in ACA and TCA measurements were clinically significant, exceeding 0.50 D (all P < .05). In terms of repeatability of corneal astigmatism measurements in eyes with ICAC, the IOLMaster 700 and Lenstar 900 outperformed iTrace and Pentacam. While the IOLMaster 700 can be used interchangeably with either Lenstar 900 or iTrace, the Pentacam is not interchangeable with the other three devices.

Trends in medication use after the onset of the COVID-19 pandemic in the Republic of Ireland: an interrupted time series study

Abstract Introduction The COVID-19 pandemic had a substantial impact on a range of health services, particularly in primary care. Research from Ireland suggests while community pharmacies remained open, restrictions for in-person healthcare appointments impacted patients.[1] Although there is evidence that the onset of the pandemic affected medicines utilisation internationally,[2] it is unclear how prescribing in Ireland changed following March 2020. Aim To evaluate how dispensing of medications in primary care in Ireland changed following the onset of the COVID-19 pandemic compared to expected trends. Methods This was an interrupted time series study, and the protocol was pre-registered (doi.org/10.17605/OSF.IO/WNQHR). It used publicly available data from the Health Service Executive Primary Care Reimbursement Services for each month between January 2016 and July 2022. Data covered dispensing on the General Medical Services (i.e. medical card) scheme for all medication therapeutic subgroups and commonly dispensed medications (based on the top 100 individual medications per month). Pre-pandemic data (January 2016-November 2019) was used to forecast expected trends from December 2019 onwards for each subgroup and medication with 99% prediction intervals. The Holt-Winters method was used, which decomposes time series data into seasonal, trend, and irregular components. Three months of observed data unaffected by the pandemic (December 2019-February 2020, validation period) were compared to the forecast to validate accuracy of predictions. Observed data were compared to forecasts in March 2020 (as the first month of pandemic restrictions in Ireland) and over the remainder of the study period from April 2020-July 2022. Statistical significance was defined as observed data outside the 99% prediction interval (p&amp;lt;0.01). Results The overall number of dispensings was 7.6% (99%CI 2.5% to 13.2%) higher than forecast in March 2020. Many (31/77) therapeutic subgroups had dispensing significantly different from forecast in March 2020. Drugs for obstructive airway disease had the largest difference, with dispensing 26.2% (99%CI 19.5%-33.6%) higher than forecasted. Other subgroups with dispensing significantly higher than forecasted included minerals, analgesics, thyroid therapy, serum lipid-reducing agents and diuretics. Dispensing was significantly lower than forecasted for other gynaecologicals (17.7% lower, 99%CI 6.3%-26.6%) and dressings (11.6%, 99%CI 9.4%-41.6%). Similarly, many individual medications had significantly higher dispensing in March 2020. Notably, dispensing of amoxicillin (with/without clavulanic acid) and oral prednisolone were lower than forecasted in the months following the onset of the pandemic, particularly during winter 2020/2021. Conclusion There was a peak in dispensing for many long-term medications in March 2020 suggesting patients obtained additional supplies of their regular medicines as pandemic restrictions were introduced. Exceptions were dispensing of two therapeutic subgroups (dressings/intrauterine devices) linked to in-person healthcare consultations, illustrating the disruption to service delivery. Lower than expected dispensing of antimicrobials and reduced seasonal peaks may be partly due to reduced infection transmission generally. Although we were limited by lacking data on the number of people dispensed each medication, this is the first study to examine changes in medication use during the COVID-19 pandemic in Ireland, considering all therapeutic areas. This study provides evidence to inform planning for medication demand and supply for future major health events.

Surface reconstruction and thickness error calculation of optical components with a complex curved surface

The increasing demand for free-form irregular optical components in both military and civilian sectors has made the inspection of such unique shapes a central challenge that hinders their production and use. In particular, the shape and thickness errors of low- and medium-precision components thermally pressed from flat optical materials are greater than those of hard brittle optical components fabricated by subtractive manufacturing, and the resulting impact on human vision is more severe. Reasonable, convenient, efficient, and accurate 3D scanning and data processing for surface reconstruction that combines application scenarios and batch manufacturing needs are urgently needed. Based on the principles of optical ray tracing and triangulation processing, the sampling and calculation of optical path thickness proposed in this paper effectively establish a theoretical model for macroscopic distortion, providing a reasonable solution for distortion correction, batch manufacturing of free-form surface pressing formed components, and defect repair.

Detecting Change in a Volatile Curve United State Stock Market (US SM) with the Use of Automated Decomposition for Time Series Components

The main reason for this investigation is to use manual process of identification of time series components with two types of automated decomposition for time series known as automated BFTSC (break for time series components) and, automated GFTSC (Group for time series components) in detecting change in a volatile curve united states stock market (US SM). In identification of components of time series present in the seasonal data of US stock market. The data US Stock Market was a monthly data from January 2001 until December 2018 and a total of 18 years. The stock market of US is also available as a secondary data at the DataBank of University Utara Malaysia Library. The weaknesses of BFAST were corrected by the extension of BFAST to BFTSC and GFTSC. Both were created to capture the cyclical and irregular components that were not captured by BFAST technique and it was included in the methodology of this study. BFTSC and GFTSC were considered to provide a combined image of all the four components of time series while GFTSC had additional advantage of providing equations to the components automated. Evaluation using simulation data and empirical data vindicated the accuracy of BFTSC and GFTSC based on linear trend less volatile data. They are effective and better than BFAST because it was able to identify 100% of the data with the basic four time series components monthly. Both techniques detects 99 % of the entire components in the time series data in a linear trend data.

Design of fast braiding process based on reconstructing mesh with complex and irregular mandrels

Circular braiding of large-sized and complex irregular structural components is widely used in fields such as aerospace and rail transit. However, existing braiding process design and simulation algorithms lack applicability to mandrels with complex features, such as flanges, concave corners, holes, and hollows, which also have low calculation efficiency in the braiding process simulation. This study first proposes an optimization algorithm for the complex features on the mandrel surface, improving the applicability for the braiding process design of complex and irregular mandrels. Then the mandrel mesh was reconstructed, greatly improving the computational efficiency of the braiding process design process. Finally, a fast iteration method based on the mandrel reconstructed mesh was proposed to achieve the fast calculation of process control parameters, including traction trajectory and traction speed, as well as the prediction of forming quality for the braiding angle and coverage rate. The experiment shows that the algorithm improves the applicability of the multi-braiding angle process and shortens the sampling cycle of braiding process design by more than 60%.

An optimization model for just-in-time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time

PurposeAlthough extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for transportation cost optimization. Traditional irregular component loading methods are based on past performance, which frequently wastes vehicle space. Additionally, real-time road conditions, precast component assembly times, and delivery vehicle waiting times due to equipment constraints at the construction site affect transportation time and overall transportation costs. Therefore, this paper aims to provide an optimization model for Just-In-Time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time.Design/methodology/approachIn order to propose a JIT (just-in-time) delivery optimization model, the effects of the sizes of irregular precast components, the assembly time, and the loading methods are considered in the 3D loading constraint model. In addition, for JIT delivery, incorporating real-time road conditions in the transportation process is essential to mitigate delays in the delivery of precast components. The 3D precast component loading problem is solved by using a hybrid genetic algorithm which mixes the genetic algorithm and the simulated annealing algorithm.FindingsA real case study was used to validate the JIT delivery optimization model. The results indicated this study contributes to the optimization of strategies for loading irregular precast components and the reduction of transportation costs by 5.38%.Originality/valueThis study establishes a JIT delivery optimization model with the aim of reducing transportation costs by considering 3D loading constraints, real-time road conditions and assembly time. The irregular precast component is simplified into 3D bounding box and loaded with three-space division heuristic packing algorithm. In addition, the hybrid algorithm mixing the genetic algorithm and the simulated annealing algorithm is to solve the 3D container loading problem, which provides both global search capability and the ability to perform local searching. The JIT delivery optimization model can provide decision-makers with a more comprehensive and economical strategy for loading and transporting irregular precast components.

Mitigation of Cutting-Induced Artificial Stresses in Contour Method Analysis of Irregular Components

Mitigation of Cutting-Induced Artificial Stresses in Contour Method Analysis of Irregular Components

Price Fluctuation Analysis of Mutton in Qinghai Based on X-13A-S Method

The study of mutton price fluctuation characteristics is of great significance to promote the healthy development of mutton industry. This paper applies the X-13A-S method to decompose Qinghai mutton price during the period from January 2013 to December 2022, analyzes its trend component, seasonal component, cyclical component, and irregular component, compares the similarities and differences between Qinghai’s and the nation’s mutton price fluctuations, and finally puts forward the development suggestions. The analysis results show that: the trend fluctuation characteristics of Qinghai and the national mutton price are basically the same; the seasonal fluctuations of Qinghai mutton price are significant; the cyclical fluctuations of Qinghai mutton price are more significant than that of the national cyclical fluctuations; and the irregular fluctuations of Qinghai mutton price are random.

Fractal Dimension Analysis in Neurological Disorders: An Overview.

Fractal analysis has emerged as a powerful tool for characterizing irregular and complex patterns found in the nervous system. This characterization is typically applied by estimating the fractal dimension (FD), a scalar index that describes the topological complexity of the irregular components of the nervous system, both at the macroscopic and microscopic levels, that may be viewed as geometric fractals. Moreover, temporal properties of neurophysiological signals can also be interpreted as dynamic fractals. Given its sensitivity for detecting changes in brain morphology, FD has been explored as a clinically relevant marker of brain damage in several neuropsychiatric conditions as well as in normal and pathological cerebral aging. In this sense, evidence is accumulating for decreases in FD in Alzheimer's disease, frontotemporal dementia, Parkinson's disease, multiple sclerosis, and many other neurological disorders. In addition, it is becoming increasingly clear that fractal analysis in the field of clinical neurology opens the possibility of detecting structural alterations in the early stages of the disease, which highlights FD as a potential diagnostic and prognostic tool in clinical practice.

Fractal Time Series: Background, Estimation Methods, and Performances.

Over the past 40years, from its classical application in the characterization of geometrical objects, fractal analysis has been progressively applied to study time series in several different disciplines. In neuroscience, starting from identifying the fractal properties of neuronal and brain architecture, attention has shifted to evaluating brain signals in the time domain. Classical linear methods applied to analyzing neurophysiological signals can lead to classifying irregular components as noise, with a potential loss of information. Thus, characterizing fractal properties, namely, self-similarity, scale invariance, and fractal dimension (FD), can provide relevant information on these signals in physiological and pathological conditions. Several methods have been proposed to estimate the fractal properties of these neurophysiological signals. However, the effects of signal characteristics (e.g., its stationarity) and other signal parameters, such as sampling frequency, amplitude, and noise level, have partially been tested. In this chapter, we first outline the main properties of fractals in the domain of space (fractal geometry) and time (fractal time series). Then, after providing an overview of the available methods to estimate the FD, we test them on synthetic time series (STS) with different sampling frequencies, signal amplitudes, and noise levels. Finally, we describe and discuss the performances of each method and the effect of signal parameters on the accuracy of FD estimation.

Forecasting Australia Gross Domestic Product (GDP) under Structural Change (SC) Using Break for Time Series Components (BFTSC)

The reason for this research is to enable us know the use BFTSC (break for time series components) in identification of the structural change and the time series components existing in Australia GDP. The data (Australia GDP) statistics spanned for period of fifty five years. The GDP of Australia is a higher information gotten from the StreamData of Universiti Utara Malaysia Library. The precincts of BFAST in terms of structural change was advanced to become BFTSC. BFTSC was created from basic research conducted on BFAST, results shows an innovative technique that captures the recurring (cyclicals) and non-recurring cyclical (irregular) components that was not included in the original BFAST technique and it was included in the methodology of this study. BFTSC was created to give a mutual image of all the required time series components. The subsequently forecasting technique was determined and forecast is made.

Markov-Switching Models with Unknown Error Distributions: Identification and Inference Within the Bayesian Framework

Abstract The basic Markov-switching model has been extended in various ways ever since the seminal work of Hamilton (1989. “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica 57: 357–84). However, the estimation of Markov-switching models in the literature has relied upon parametric assumptions on the distribution of the error term. In this paper, we present a Bayesian approach for estimating Markov-switching models with unknown and potentially non-normal error distributions. We approximate the unknown distribution of the error term by the Dirichlet process mixture of normals, in which the number of mixtures is treated as a parameter to estimate. In doing so, we pay special attention to the identification of the model. We then apply the proposed model and MCMC procedure to the growth of the postwar U.S. industrial production index. Our model can effectively control for irregular components that are not related to business conditions. This leads to sharp and accurate inferences on recession probabilities.

The Application Research of BIM Technology in the Construction Process of Yancheng Nanyang Airport

The application of BIM technology in building construction provides the possibility to realize design accuracy, to visualize the construction details, to optimize construction schemes, and to enhance cooperation among various professionals. The Yancheng Nanyang Airport terminal 2 project, with its large span of steel roof structure, complex installation in mechanical and electrical pipeline (MEP) engineering, and difficulty in construction organization, is taken as the engineering background. The whole process application of BIM technology in the construction process is introduced. In structural engineering construction, the application of BIM technology can provide guidance for plane layout of the construction site, and can also assist in deepening the designs of irregular steel components. In steel construction, the application of BIM technology gives a commendable visual demonstration of the construction process of the metal roof system and the single-layer reticulated shell. In MEP engineering, the application of BIM technology provides a great approach to establish a synthesis of pipeline drawings to further form pipeline section diagrams and operation drawings. By integrating the dimension of time, precision control, and deviation rectification, a recursive construction drawing can be built. With respect to synergistic management, the quality and safety management in the construction site can be implemented on the basis of BIM terminal equipment as well. This paper will give a great reference on the application of BIM technology in the airport terminal construction.

Simulation and Experimental Study on the Precision Molding of Irregular Vehicle Glass Components.

The high level of stress and dimension deviation induced by glass molding are the main causes of the low yield rate of large, irregular glass components on vehicles. To solve this issue, a numerical model of large glass component molding was established in this study, which aimed to analyze the dominant factors of molding quality and achieve a synergistic balance between quality characteristics and energy consumption. The results show that molding temperature is the dominant factor affecting the energy consumption and residual stress, and the molding pressure is the main factor affecting the dimension deviation. Furthermore, the NSGA-II optimization algorithm was used to optimize the maximum residual stress, dimension deviation, and energy consumption with the numerical results. The combination of a heating rate of 1.95 °C/s, holding time of 158 s, molding temperature of 570 °C, molding pressure of 34 MPa, and cooling rate of 1.15 °C/s was determined to be the optimized scheme. The predictive error of the numerical result, based on the optimized scheme, was experimentally verified to be less than 20%. It proved the accuracy of the model in this study. These results can provide guidance for the subsequent precision molding of large, irregular glass components.