Analysis Of Industrial Data Research Articles

Unveiling fluctuation patterns of tourist volume: Insights from 56 major attractions in China

Understanding the spatiotemporal dynamics of tourist volume is crucial for effective tourism management and planning. However, existing tourism data analysis methods often fail to capture the complex, continuous fluctuations and temporal variations in tourist behavior. To address this challenge, we apply functional data analysis (FDA) in the tourism industry to provide a more nuanced understanding of tourist volume dynamics. Specifically, we perform FDA on real-time tourist volume data from 56 major attractions in Beijing, China, revealing intrinsic fluctuation patterns, key factors driving tourist arrivals, and the dynamic characteristics of attractions across temporal scales. Our findings enhance the ability to optimize attraction management, marketing strategies, and policy-making, while also advancing tourism data science by integrating FDA. This approach fills the methodological gap and offers a comprehensive framework for exploring the spatiotemporal complexities of tourism data.

Cache-assisted computation offloading for workflow applications in industrial internet of things

The Internet of Things plays an important role in the process of industrial intelligence. It facilitates the connection between devices and the Internet to enable the gathering and analysis of industrial data. However, industrial Internet of Things (IIoT) applications are highly susceptible to latency, have strict energy consumption limitations, and impose specific demands on the resources of IIoT devices. Fortunately, edge computing can migrate tasks to the edge server or cloud platform to deal with the above shortcomings. Nevertheless, tasks generated by IIoT devices in industrial production have a higher rate of repetition, and repetitive execution of the same task diminishes the operational efficiency of the edge system. In this regard, we can cache high-value tasks through task caching to reduce redundant execution. The aforementioned idea can be described as a joint optimization problem of computation offloading and task caching with resource constraints in IIoT. To address this challenge, we establish an edge-empowered service model and propose a cache-assisted computation offloading algorithm for IIoT applications. Extensive experiments demonstrate that the proposed algorithm outperforms some critical methods in terms of energy consumption and latency.

Quantum Machine Learning for Advanced Data Processing in Business Analytics: A Path Toward Next-Generation Solutions

Quantum Machine Learning (QML) is a rising paradigm of computing that holds high possibilities to revolutionise data analysis in the business analytics industry. This paper aims at presenting how QML can solve the increasingly challenging demands of big contributions to large-scale data analysis in business contexts, achieving better performance than orthodox machine learning methods in terms of time and efficacy. LIQUID: This research utilises actual business data and scenarios and solving problems by using quantum algorithms – VQE and QSVM. At the core of our approach, we perform extensive validation of QML models on platforms such as IBM’s Quantum Experience and D-Wave systems pertaining to critical use cases for businesses would include financial modelling, supply chain management, and customer behaviour prediction. The results presented here suggest that quantum algorithm speeds processing by 30-50% when compared with conventions approaches of computation. Moreover, QML has shown better results for prediction in analytics for various firms and provide them better aptitudes for decision-making. Hence the uniqueness of this research by grounding real world business problems in quantum algorithms and demonstrating empirically how superior they are compared to the classical alternatives in dealing with large datasets. Considering the future of business analytics, QML looks set to be a real game changer for industries that heavily rely on big data analysis. In this it has filled a gap which would cause a scholarly without hard gap between theoretical quantum computing and real business applications.

Overcoming barriers to proactive plastic recycling toward a sustainable future

The plastics sector, accounting for a significant portion of global emissions, presents a challenge and an opportunity in achieving carbon neutrality. Despite Japan's commendable polyethylene terephthalate (PET) bottle recycling rates, most plastics are thermally recycled, creating environmental issues. This study proposes an evaluation framework to enhance recycling, aligned with end-user preferences and fostering a circular plastics economy. Employing a mixed-methods approach, this study conducts fieldwork including interviews with plastic recyclers and analysis of industry data. A weighted sum multicriteria analysis integrating end-user preferences, recycling effectiveness, and market dynamics is utilized. Systemic, process, and policy challenges were shown to hinder sustainable recycling practices, while varying willingness to pay, emission and cost reduction potentials, among acceptability and sectoral diversity informed priority plastic types for recycling. Multicriteria analysis showed that although PET is favored by end users, Polyoxymethylene (POM) emerges as a potential priority target for manufacturers and recyclers. Sensitivity analysis underscores the potential impact of establishing or enhancing willingness to pay (WTP) toward certain plastic types. Moreover, manufacturer and recycler evaluations suggest a broader willingness to recycle plastics than previously assumed. The proposed evaluation framework offers insights toward plastic recycling strategies. Policy interventions such as sustained subsidies for recyclers, market incentives leveraging WTP preferences, and technological advances, including chemical recycling and the broadening of plastic type recycling in line with user and manufacturer preferences, could all contribute to promoting sustainable plastic recycling practices.

Deep Information Retention Network‐Enabled Data Modeling for Key Quality Indicator Prediction in the Chemical Industry

ABSTRACTDeep learning has attracted widespread attention in data modeling and key quality indicator prediction in the chemical industry. However, traditional deep learning networks usually distort the original data distribution due to the superposition effect of multiple layers of nonlinear activation functions. In this case, multivariate statistical learning techniques present an avenue to reveal the intrinsic relationship of the data by combining the linear trends between input and predictor variables. To comprehensively capture data features from multiple perspectives, this study proposes a deep learning‐based data modeling network called the information retention unit (IRU). This network combines intrinsic attributes to partial least squares (PLS) and autoencoder (AE) modalities, thus engendering an adaptive response to the complex linear and nonlinear data features. Furthermore, multiple IRUs can be stacked to construct a deep information retention network (DIRN), which enhances the robust extraction of deep data features. Finally, the effectiveness of the proposed network is validated through its prediction application on a dataset obtained from a real‐world chemical industrial process. This method combines multivariate statistical learning techniques based on deep learning, providing an innovative and practical solution for data analysis and prediction in the chemical industry.

Role of Big Data Analytics In Industry 4.0

Role of Big Data Analytics In Industry 4.0

Utilizing Selected Machine Learning Methods for Conicity Prediction in the Process of Producing Radial Tires for Passenger Cars

This article presents the current state and development directions of the tire industry. One of the main requirements that a tire must meet before it can leave the factory is achieving values of quantities describing uniformity at a defined level. Of particular importance areconicity and the components of the tire with the greatest impact on its value. This research is based on the possibility of using an ANN to meet contemporary challenges faced by tire manufacturers. In order to achieve a satisfactory level of prediction, we compared the use of a multi-layer perceptron and decision trees XGBoost, LightGbmRegression, and FastTreeRegression. Based on data analysis and similar examples from the literature, metrics were selected to evaluate the models’ ability to solve regression problems in relation to the described problem. We selected the best possible solution, standing at the top of the features covered by the criterion analysis. The proposed solutions can be the basis for acquiring new knowledge and contributions in the field of the computational analysis of industrial data in tire production. These solutions are characterized by the required accuracy and efficiency for online work, and they also contribute to the creation of the best fit elements of complex systems (including computational models). The results of this study will contribute to reducing the volume of waste in the tire industry by eliminating defective tire parts in the early stages of the production process.

The Confluence of AI and Big Data Analytics in Industry 4.0: Fostering Sustainable Strategic Development

The Confluence of AI and Big Data Analytics in Industry 4.0: Fostering Sustainable Strategic Development

STUDY OF THE EFFECTIVENESS OF THE APPLICATION OF COMPLEX GEOLOGICAL AND TECHNOLOGICAL MEASURES ON THE EXAMPLE OF GAS-CONDENSATE GENERATION IN THE CENTRAL PART OF THE PRIAXIAL ZONE OF THE DNIPROV-DNETZ BASIN

The field development system is the basis of hydrocarbon production, as it ensures the achievement of the necessary technical and economic indicators. Therefore, a high-perfor- mance development system is necessary to ensure maximum profit in the shortest possible time. Empirical studies, analysis and assessment of the effect of implementing a complex of geological and technological measures on the operational facility of a gas condensate field in the central part of the peri-axial zone of the Dnieper-Donets Basin were carried out in order to find methods for improving the existing gas and gas condensate field development systems and increasing gas extraction rates. It is shown that due to hydraulic fracturing, drilling and transfer of wells to the production facility during the five years of production of the produced gas, it is 7.8 % more than in the previous 21 hours, the rate of development of the field from 2015 to 2019 increased from 2.52 %. to 7.9 %, the average well yield increased by 83 % with an increase in the operational fund by 55 %. The results of studies of the effect of the application of geo- logical-technological measures are shown in the form of graphical dependences of the change in the rate of sampling on the number of production wells over time, the rate of daily gas sampling on the number of conducted geological-technological measures on the researched operational object over time. It has been determined that the main driver of the increase in gas production is the hydraulic fracturing of the formation in most of the operational wells, thanks to which the average increase in the output of the wells is 425 %. According to the results of geological and industrial data processing and analysis of changes in the rate of daily gas extraction over time, the effect of the performed operations is divided into three time segments lasting 15–18 months. The decrease in the duration of the effect from the increase in the fund of production wells and the conduct of hydraulic fracturing of the reservoir is due to the decrease in the volume of reservoir energy, therefore the investigated complex of measures will have the greatest effect when implemented at the initial stage of the development of a reservoir with similar geological parameters.

Anomaly Detection for Industrial Time Series Data Based on Correlation Analysis and CNN-BiLSTM with Self-attention

This paper aims to propose an anomaly detection model for industrial time series data based on correlation analysis and CNN-BiLSTM with self-attention to solve the problem of abnormal data detection in the field of industrial data analysis. Industrial data anomaly detection is an important task in the industrial field, which can help people to timely understand the production operation status and real-time record and perception of the operating environment. This paper introduces two key technologies: correlation analysis and CNN-BiLSTM with self-attention, and how to combine them to build an effective anomaly detection model for industrial time series data. Through experimental evaluation, this paper proves the effectiveness and superiority of the proposed model in industrial data anomaly detection tasks.

Research on Investment and Financing Data Analysis in the Chinese Robotics Industry

Research on Investment and Financing Data Analysis in the Chinese Robotics Industry

Fault detection of complicated processes based on an enhanced transformer network with graph attention mechanism

Recently, deep learning becomes increasingly popular in the industrial data analysis field due to its distinguished feature representation capability. As an emerging deep learning technology, transformer network has attracted extensive attention, but its application in industrial fault detection is still not sufficiently exploited. Furthermore, the traditional transformer network mainly focuses on the time series information of the data, while ignoring the spatial characteristic of the data. For this problem, an improved transformer network model with graph attention mechanism (GA-Tran) is proposed and used for complicated process fault detection. Considering the strong coupling property of process variables, the presented method performs joint spatial-temporal learning by mining the data information in views of both time-dimension and variable-dimension. On the one hand, a transformer encoder module equipped with a self-attention mechanism is utilized to capture long-term temporal dependencies, while multi-scale convolution is integrated to abstract short-term time dependencies. On the other hand, a graph attention network is introduced into the transformer network to meticulously analyze the spatial relationships among variables. Considering the topology of the industrial process variables, we use spectral clustering to infer prior topology information, and remove intelligently irrelevant spatial information. Different from the existing transformer models with the anomaly scores as the monitoring indicator, two monitoring statistics based on encoder features and reconstruction errors are constructed for process status monitoring. Studies on the Tennessee Eastman chemical process demonstrate the effectiveness of the proposed GA-Tran method.

A New Particle Swarm Optimization Algorithm for Outlier Detection: Industrial Data Clustering in Wire Arc Additive Manufacturing

In this paper, a novel outlier detection method is proposed for industrial data analysis based on the fuzzy C-means (FCM) algorithm. An adaptive switching randomly perturbed particle swarm optimization algorithm (ASRPPSO) is put forward to optimize the initial cluster centroids of the FCM algorithm. The superiority of the proposed ASRPPSO is demonstrated over five existing PSO algorithms on a series of benchmark functions. To illustrate its application potential, the proposed ASRPPSO-based FCM algorithm is exploited in the outlier detection problem for analyzing the real-world industrial data collected from a wire arc additive manufacturing pilot line in Sweden. Experimental results demonstrate that the proposed ASRPPSO-based FCM algorithm outperforms the standard FCM algorithm in detecting outliers of real-world industrial data. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Electric arc (which is governed by the current and arc voltage) plays a significant role in monitoring the operating status of the wire arc additive manufacturing (WAAM) process. The nominal periodic current and voltage may occasionally change abruptly due to anomalies (such as arc instability, unstable metal transfer, geometrical deviations, and surface contaminations), which would affect the quality of the fabricated component. This paper focuses on detecting possible anomalies by analyzing the current and voltage during the WAAM process. A novel clustering-based outlier detection method is proposed for anomaly detection where abnormal and normal instances are categorized into two separate clusters. A new particle swarm optimization algorithm is put forward to optimize the initial cluster centroid so as to improve the detection accuracy. The proposed outlier detection method is applied to real-world data collected from a WAAM pilot line for detecting abnormal instances. Experimental results demonstrate the effectiveness of the proposed outlier detection method. The proposed outlier detection method can be applied to other industrial applications including electrical engineering, mechanical engineering and medical engineering. In the future, we aim to develop an online outlier detection system based on the proposed method for real-time for anomaly detection and defect prediction.

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Over the past years, machine learning and big data analysis have emerged, starting as a scientific and fictional domain, very interesting but difficult to test, and becoming one of the most powerful tools that is part of Industry 5.0 and has a significant impact on sustainable, resilient manufacturing. This has garnered increasing attention within scholarly circles due to its applicability in various domains. The scope of the article is to perform an exhaustive bibliometric analysis of existing papers that belong to machine learning and big data, pointing out the capability from a scientific point of view, explaining the usability of applications, and identifying which is the actual in a continually changing domain. In this context, the present paper aims to discuss the research landscape associated with the use of machine learning and big data analysis in Industry 5.0 in terms of themes, authors, citations, preferred journals, research networks, and collaborations. The initial part of the analysis focuses on the latest trends and how researchers lend a helping hand to change preconceptions about machine learning. The annual growth rate is 123.69%, which is considerable for such a short period, and it requires a comprehensive analysis to check the boom of articles in this domain. Further, the exploration investigates affiliated academic institutions, influential publications, journals, key contributors, and most delineative authors. To accomplish this, a dataset has been created containing researchers’ papers extracted from the ISI Web of Science database using keywords associated with machine learning and big data, starting in 2016 and ending in 2023. The paper incorporates graphs, which describe the most relevant authors, academic institutions, annual publications, country collaborations, and the most used words. The paper ends with a review of the globally most cited documents, describing the importance of machine learning and big data in Industry 5.0.

Velocity and Temperature Simulation for a Cylindrical Regenerative Thermal Oxidizer.

The cylindrical regenerative thermal oxidizer (CRTO) came into being later than the three-chamber regenerative thermal oxidizer (TRTO). Compared with TRTO, CRTO has a smaller size and a larger regenerator volume for absorbing and releasing heat. There are few studies on CRTO despite its numerous applications. A CRTO was selected in industrial applications for simulation research. The velocity and temperature of the CRTO were investigated after error analysis of industrial and simulated data. It was found that the velocity and temperature in the regenerative chamber had obvious stratification and gradients after homogenization by the regenerator unit. The velocity and temperature distribution in the oxidation chamber were independent of the position of the CRTO inlet and outlet or the structure below the regenerator, and there were identical periodic changes in each period. A TRTO with primary parameters as those of the CRTO was employed for comparison. The time of the intake and exhaust periods of a CRTO regenerative chamber were 30 s longer than those of a TRTO. The regenerator volume of heat storage used by CRTO for heat exchange increased by 1/6 compared to that of TRTO at the same total regenerator volume. Simulation shows that CRTO had a more uniform velocity and temperature in the regenerative chamber compared to those in TRTO, increasing by approximately 2%; the thermal efficiency is higher, with an average increase of about 3%.

Culture as window dressing? A threefold methodological framework for researching the locality of Netflix series

Considering the implications of Netflix’s role as a content producer for cultural diversity in Europe, this methodological article investigates how to define and measure the locality of Netflix Originals. We employ a threefold methodological study based on industry data analysis, audience reception research, and content analysis. This replicable and scalable methodological design provides a solid analytical framework for future studies examining Netflix Originals from the normative perspective of cultural diversity. Demonstrating the steps of our exploratory study, we also find that Netflix’s locally-produced but globally-oriented content uses culture as window dressing, warranting further research.

A Slacks-Based Measure Model for Computing Game Cross-Efficiency

This paper introduces an improved slack-based game cross-efficiency measurement model that enhances the existing cross-efficiency framework and integrates it with the Data Envelopment Analysis (DEA) game cross-efficiency. The model ensures the fairness of its results through the implementation of a more stringent selection of frontier face weights. It accounts for the competitive relationships among Decision Making Units (DMUs), achieving a Nash equilibrium solution through continuous iterations. Furthermore, the model accounts for undesirable outputs and various strategic orientations, enhancing its applicability. The model’s effectiveness is validated through comparative analyses of diverse case studies. Additionally, the model’s practical utility is demonstrated through the analysis of industrial data from various Chinese provinces between 2010 and 2019. Analysis results show that the proposed model measures production efficiency with greater precision and comparability than alternative models.

Digital Economy and Baumol’s Cost Disease of Performing Arts: An Empirical Research in China

In the past decade, the Chinese government’s financial subsidies for performing arts troupes accounted for an average of 50.57% of their total income. By the end of 2021, China has 18,370 performing arts troupes and 3,093 performing arts venues. The research conclusions of this paper include the following four points: (1) Digital live-streaming theater(DLT) is an “innovation” for traditional Stage Theater(ST) performance, and its changes in creation mechanism, audience-actors relationship, technical esthetics and business model are worthy of further study. (2) Data from 2011 to 2021 show that Baumol’s cost disease also exists in Chinese performing arts. (3) The Chinese government has been reducing its direct management and control of arts troupes, trying to move toward “arm’s length” governance. (4) During the COVID-19 epidemic, performance case data from China show that DLT can indeed reduce the marginal cost, attract more young audiences, and increase performance revenue. However, considering the high technical cost input, whether digital technology can alleviate the cost disease of performing arts still needs more annual industry data analysis.

The application of artificial intelligence in aerospace engineering

In recent years, there has been considerable interest in applying Artificial Intelligence (AI) in the field of aerospace engineering. However, the existing literature on this topic is not sufficiently comprehensive. This paper is purposed to solve this problem by providing a thorough analysis and overview of the current state of AI in aerospace engineering. The paper is divided into four sections. Firstly, the use of AI in autonomous navigation and flight control is explored, focusing on advanced algorithms and sensor technologies that enable highly autonomous and efficient aircraft navigation. Secondly, the application of AI in image recognition and computer vision is discussed, highlighting its significance in remote sensing and aerospace component quality inspection. The third section examines the integration of AI in unmanned aerial vehicles (UAV), covering the control system and the utilization of machine learning techniques for improved UAV capabilities. Lastly, the paper explores the impact of AI on data analysis and prediction in the aerospace industry, encompassing weather forecasting, resource allocation, and decision-making processes. Finally, this paper gives a general overview of the nowadays application of AI in aerospace engineering.

Multichannel energy monitoring based on the sliding window method in an industrial environment

The research on nonintrusive load monitoring (NILM) typically focuses on residential and commercial users. However, high-energy industrial users also require load monitoring analysis to understand their electricity consumption patterns and load operating conditions. Currently used load monitoring methods and parameters are not applicable to industrial environments. In this paper, we propose an innovative multichannel low window sequence-to-point (MLSP) method based on deep neural networks for NILM of high-energy industrial users. First, the data from each channel of the main line terminal are normalized and then aggregated into a time series, which is several times longer than the original data to expand its data volume. Then, the multichannel low window sequence-to-point method is used to select the window data of several times the length, corresponding to the active power of the target device. Finally, the discriminative features of the data are learned using a deep neural network model. The analysis of actual industrial user data shows that the lowest mean absolute error (MAE) of the original sequence-to-point method for related branches is 20.38, and the highest correlation coefficient (CC) is 90.23%. Our proposed method reduces the MAE to 11.02 and increases the CC to 97.42%. The MAE and CC indicators of all branches decreased by 25.8% and increased by 22.4%, respectively. According to Mean Absolute Percentage Error (MAPE), the performance of this model is improved by 39.6% compared with Seq2point. Compared with the original sequence-to-point method, our proposed method achieves better load monitoring results and can more accurately decompose load fluctuations, which are closer to the actual load curve.