This study focuses on evaluating the load time and link structure of the websites of private universities in Maharashtra. The objectives of the study include measuring the load time of the websites ...and comparing them to industry benchmarks, conducting a link mapping of the internal and external links and analyzing their structure and hierarchy, and providing recommendations for improving the load time and link structure based on the findings. Additionally, the study aims to analyze the structure and distribution of internal and external links on the websites of private universities in Maharashtra for search engine optimization. This study used python coding for data scraping. The findings of this study will help private universities in Maharashtra to enhance website performance, improve the user experience, and attract more potential students. By optimizing their websites for search engine optimization, these universities will be able to stay competitive and increase their online visibility. This study contributes to the body of knowledge on website optimization for private universities and provides practical recommendations for improving website performance and link structure.
•Autocorrelation incremental fuzzy C-means power load clustering method is proposed.•Effective clustering model carry out higher behavior evolution and pattern updating.•Incremental DCS model ...clustering algorithm for dynamically hidden information mining.•Higher clustering accuracy for dynamically power load data stream.
This paper focuses on the real-time dynamic clustering analysis of power load data based on the dynamic conditional score (DCS) model, and an autocorrelation increment fuzzy C-means clustering algorithm based on the DCS model is proposed. (1) The paper addresses the problem that current power load clustering methods, when performing time series data mining, tend to focus on capturing the mean structure while ignoring the variance characteristics of the data, making it difficult to effectively capture the structural information of time series data. The DCS model is used as the statistical model basis for clustering analysis, and the time series is clustered based on the estimated conditional moment characteristics of the model, dynamically capturing data features such as the mean, variance, and sequence correlation of time series data, effectively improving the clustering performance. (2) This paper also addresses the issue that current power load clustering methods tend to focus on static datasets of user power loads and cannot effectively handle the data stream clustering problem with time series characteristics in practical applications. The DCS model parameter dataset and the autocorrelation increment fuzzy clustering algorithm are used to conduct a dynamic data flow analysis of user electricity behaviour evolution and pattern continuous updating research for power loads. The clustering results are dynamically updated based on the user's power load data stream using the proposed algorithm, achieving research on a universal clustering model and secure and efficient algorithms in a big data environment. (3) The paper verifies the clustering performance of the proposed method using power load time series data provided by a Chinese power supply company as a case dataset. The clustering evaluation index shows that the proposed algorithm has high clustering accuracy and good clustering performance. Additionally, different power supply recommendations are proposed for different customer electricity types in the obtained clustering results to provide more personalized power services.
There are many publications about variable amplitude test results. However, very often information on load–time histories, spectra and testing details are missing. This fact does not allow the ...interpretation of test results with regard to fatigue lifing and structural durability design. Therefore, this paper aims at presenting how spectra and test conditions should be clearly described and how statistics can be applied when variable amplitude test results are presented.
•Self-sensing of crack localization of the concrete with macro fibers is studied.•Self-monitoring of the bending crack appearance and widening of concrete is investigated.•The relationship between ...Voltage and COD during the loading process of concrete is found.•The different resistivity of steel fiber and PP fiber may show different effect on the self-sensing ability of concrete crack.
In order to localize the crack and to sense the crack widening of concrete, an alternative method is proposed in this study. The basic element of this method consists of six electrodes. To verify the validity of this method, four-point bending test is carried out on six types of fiber reinforced concrete beams with different fiber types. The voltage between two inner electrodes is used as the indicator for crack localization and widening. Both the voltage–time curve and the load-time curve can be divided into three stages by two turning points corresponding to the uncracked stage, the cracking stage and the crack propagation stage, respectively. In addition, the relationship between crack opening displacement (COD) and voltage may be established by an exponential function. The method is experimentally proved to be highly effective given that the voltage change and crack occur simultaneously. By increasing the number of electrodes, the method proposed in this paper can be applied to sense the crack appearance and widening in concrete structure. The method can be used to localize the cracks in fiber reinforced concrete beams.
A comprehensive understanding of blast load information is crucial for evaluating structural response. Machine learning has demonstrated its efficiency in predicting blast loads. However, the high ...costs associated with acquiring blast load data limit the development of machine learning. A novel method, PCA-TANN, is proposed for predicting blast load time series on structures and improving the problem of insufficient training data by integrating transfer learning concepts and combining artificial neural networks (ANN) with principal component analysis (PCA). The main idea of this model involves preprocessing the time series data using PCA to extract essential features, employing artificial neural networks based on transfer learning (TANN) for knowledge transfer from the previous task to the new task, and integrating a few preprocessed data of the new task for training. The blast load data obtained from a single square column is utilized to assess the performance of PCA-TANN and PCA-ANN models. The findings demonstrate that in situations with limited data availability, PCA-TANN reasonably replicates the blast load characteristic, resulting in accurate time series predictions of overpressure with R2 scores exceeding 0.85. This performance significantly surpasses that of the used PCA-ANN model, which achieves R2 scores below 0.4.
•Propose an estimating combination method for electrical load interval forecasting.•Conduct feature selection to identify characteristics of original time series.•Combine Gaussian distribution with ...deep neural network.•Build an index table to include all information of prediction intervals.
Due to the failure of deterministic point forecasting to capture the uncertainty associated with the original time series, and because it can reflect the range of electrical load fluctuation, the importance of probabilistic interval forecasting has gradually increased. However, the existing theoretical system of interval forecasting is still incomplete, is a complicated process, and has relatively low accuracy. The objective of this study is to propose an interval forecasting approach based on feature selection, the optimized machine learning method, and correction of the Gaussian distribution. By applying this approach, the distribution of predictions can be established to include all the information of prediction intervals at each confidence level, making the best use of the known information. The electrical load time series of Australia are used to examine the effectiveness of the proposed approach, and compared with other models, it is proven to not only simplify the forecasting process and shorten the processing time, but also significantly improve the forecasting efficiency, flexibility, and accuracy.
The electricity tariffs available to customers in Poland depend on the connection voltage level and contracted capacity, which reflect the customer demand profile. Therefore, before connecting to the ...power grid, each consumer declares the demand for maximum power. This amount, referred to as the contracted capacity, is used by the electricity provider to assign the proper connection type to the power grid, including the size of the security breaker. Maximum power is also the basis for calculating fixed charges for electricity consumption, which is controlled and metered through peak meters. If the peak demand exceeds the contracted capacity, a penalty charge is applied to the exceeded amount, which is up to ten times the basic rate. In this article, we present several solutions for entrepreneurs based on the implementation of two-stage and deep learning approaches to predict maximal load values and the moments of exceeding the contracted capacity in the short term, i.e., up to one month ahead. The forecast is further used to optimize the capacity volume to be contracted in the following month to minimize network charge for exceeding the contracted level. As confirmed experimentally with two datasets, the application of a multiple output forecast artificial neural network model and a genetic algorithm (two-stage approach) for load optimization delivers significant benefits to customers. As an alternative, the same benefit is delivered with a deep learning architecture (hybrid approach) to predict the maximal capacity demands and, simultaneously, to determine the optimal capacity contract.
Energy system models involve various input data sets representing the generation, consumption and transport infrastructure of electricity. Especially energy system models with a focus on the ...transmission grid require time series of electricity feed-in and consumption in a high spatial resolution. In general, there are two approaches to obtain regionalized time series: top-down and bottom-up. In many cases, both methodologies may be combined to aggregate or disaggregate input data. Furthermore, there exist various approaches to assign regionalized feed-in of renewable energy sources and electrical load to the model’s grid connection points. The variety in the regionalization process leads to significant differences on a regional scope, even if global values are the same.
We develop a Methodology to compare regionalization techniques of input data for photovoltaics, wind and electrical load between various models as well as data assignment techniques to the power grid nodes. We further define two invariants to evaluate the outcome of the regionalization process at the NUTS 3 level, one invariant for the annual profiles and one for the installed capacities. This Methodology enabled us to compare different regionalization and assignment workflows using simple parameters, without explicit knowledge of grid topology. Our results show that the resolution of the input data and the use of a top-down or a bottom-up approach are the most determinant factors in the regionalization process.
•Methodology for data regionalization comparison for transmission models is developed.•Methodology extended for comparison of input data assignment to ehv nodes.•Eight different models and their regionalization workflows were compared in details.•Top-down or bottom-up regionalization determine the regionalization outcome.•Akin PV and load regionalization outputs due to their daily and weekly patterns.
Corrosion, abrasion and fretting fatigue may cause deterioration and, eventually, the failure of a post-tensioning tendon or a stay cable on a cable supported structure. In the present study, the ...stress acting during the rupture on the remaining portion of the stay which fails is derived, and the role of the rupture time on the response of the structure is discussed from a theoretical and a numerical point of view. In addition, the load–time curve during the rupture and the total time of the rupture of undamaged and damaged wires of seven-wire steel strands are investigated in an experimental program defined on the basis of the previous theoretical results. In the locally damaged specimens, a notch is machined into their outer wires. The specimens are tested under tension at three different strain rates in order to determine the influence of this parameter on the rupture time and on the load–time curve. The damaged specimens also allow us to determine the influence of a local reduction of the cross section on the stiffness and ultimate load of the specimen.
The quality of user experience is the cornerstone of any organization’s successful digital transformation journey. Web pages are the main touchpoint for users to access services in a digital mode. ...Web page performance is a key determinant of the quality of user experience. The negative impact of poor web page performance on the productivity, profits, and brand value of an organization is well-recognized. The use of realistic prediction models for predicting page load time at the early stages of development can help minimize the effort and cost arising out of fixing performance defects late in the lifecycle.
We present a comprehensive evaluation of models based on 18 widely used machine learning techniques on their capability to predict page load times. The models use only those metrics which relate to the form and structure of a page because such metrics are easy to ascertain during the early stages with minimal effort.
The machine learning techniques are trained on more than 8,700 pages from HTTP Archive data, a database of web performance information widely used to conduct web performance research. The trained models are then validated using the 10-fold cross-validation method and accuracy measures like the Pearson correlation coefficient (r), Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE) are reported.
Radial Basis Function regression and Random Forest outperform all other techniques. The value of r ranges from 0.69-0.92, indicating a high correlation between the observed and predicted values. The NRMSE varies between 0.11-0.16, implying that RMSE is less than 16% of the range of actual value. The RMSE improves by 41%-54% compared to the best baseline prediction model.
It is possible to build realistic prediction models using machine learning techniques that can be used by practitioners during the early stages of development with minimal effort.
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