Papers by Keyword: Data Analysis

Abstract: This study presents a global digital marketing framework, developed through a case study of a UK-based B2B industrial flooring company and the review of relevant literature, focusing on enhancing its Southeast Asian market presence. Using a mixed-method approach, it combines qualitative and quantitative analyses to examine digital marketing strategies, trends, and competitor practices. Findings highlight the effective use of social media by leading competitors and emphasize the importance of digital marketing in optimizing B2B operations and cross-cultural interactions. The study recommends some actionable insights for companies aiming to remain competitive and grow in the dynamic market.

Abstract: In an open or unbounded system, when a solid sphere freely falls into a liquid, it accelerates until it reaches a terminal velocity. At this point, the gravitational force, buoyant force, and viscous drag acting on the sphere are balanced. Stokes' law describes that in a laminar flow state, the viscous drag on the sphere is proportional to its radius, velocity, and viscosity coefficient. In this paper, an experimental system was constructed and the vertical and horizontal positions of spheres were measured with different sizes and densities in liquids of different diameters and viscosities, and the vertical and horizontal positions, velocities, and accelerations of spheres were analyzed. The data analysis shows that the sphere is not only subjected to the viscous drag and but also the boundary forces from the system. This work will have a significance in modeling and computer simulation of accurate measurement of liquid viscosity.

Abstract: Optimizing the manufacturing process to increase the product quality is a major challenge most industry branches just like aluminum production have to face. To continuously improve the production quality, it is necessary to develop new methods to identify parameters which may influence the product quality. Influencing parameters can be found at various production steps. Production data is recorded by numerous sensors throughout the entire manufacturing process. The goal is to develop methods for analysing the sensor data from each step of the production process to effectively identify specific patterns that may indicate critical process parameters along the production chain. The work shows feature extraction methods to find characteristics in the sensor data that could affect the product-quality.

Abstract: Compared to discrete manufacturing, sheet material is produced in a continuous manufacturing process with several dimension and volume changes. This includes thickness reduction by rolling and width and length changes by slitting and cross-cutting. Along the process chain, this happens several times using different manufacturing facilities, where each work step is usually followed by coiling. Each of these machines records high-frequent production data in a time-based manner. General research topics in this field [1, 2] aim to assign the time-based records to the related section of the alloy sheet (length-based). This paper deals with challenges concerning the identification of strips and the assignment of the corresponding process data. In a particular application, the coil orientation for each process step is calculated and documented for a given part of the production process. This is a necessary precondition for further process data assignment. Furthermore, the effort for certain manual tasks can be reduced by using the calculated coil orientation.

Abstract: A Blowout Preventer (BOP) serves as a safety valve in the drilling process in the oil and gas industry. It will be closed if an influx of formation fluids occurs and threatens the rig. A Ram BOP is one type of widely used BOP. It is composed of two ram blades, which will move toward each other to shear the drilling pipe and to close the valve. To ensure the shearing process is completed on the rig, lab tests are often run to evaluate the BOP’s capability and the required shearing pressure. Over the last decade, Finite element analysis (FEA) based simulation method has been set up to predict the shearing process. The simulation method still requires pipe damage parameters and requires lab test. This paper presents a test-free simulation method enabled by analyzing the ram BOP pipe shearing data, which significantly reduces the lead time and test costs.

Abstract: Scientific data available on the internet is rarely labelled. Most popular research paper repository sites contain papers without any annotation for grouping data. Classification of text via words, sentences and even paragraphs has become a key resource for a lot of industries looking to help their computers understand human language – the next stage in Artificial Intelligence. Using valuable Computational Linguistics ideas, some industrial applications have been able to streamline their processes to effectively and efficiently process and interpret language data. Continuing in this trend, in this paper, we aim to effectively clustering scientific research papers into topic-based differentiators, in the most efficient manner. Using multiple algorithms that have revolutionized the industry in the previous years, we compute over 800,000 entries of scientific research articles across 200+ domains that have been uploaded to accurately predict domains for each of these articles. We use clustering techniques like the K-Means algorithm to derive the topics for these papers with an accuracy of nearly 80%. We also use BERT to create topic clusters that generate topics based on frequently occurring contexts within the text. Beyond BERT, we use offspring algorithms that tackle specific, niche issues that BERT does not account for. We also fine-tune the parameters of the algorithms used to generate over 50 stronger topics that more accurately define scientific articles.

Abstract: Forging tools must be able to withstand very strong mechanical, thermal, tribological, and chemical stresses. The extent to which a tool can withstand these stresses depends on the material used and its pre-treatment as well as the heat and surface treatment, i.e. the load capacity. The ratio of stress to load capacity determines how high the tool life of a forging tool is. This paper deals with the variations in the tool life of forging tools using the example of a specific industrial stage sequence and production conditions. Due to a large number of influencing variables that have an effect on the tool during the entire tool life history, the focus of this work is placed on influencing variables of the forming process. Based on real production parameters of a forging company, which are recorded during a period for the investigation, the process data are analyzed about an influence on the tool life. The investigation focuses on four influencing variables, namely the subjective assessment of the end of the tool life, the interaction between the forming stages, production interruptions, and the cooling and lubrication of the forming tools. For the parameters that are not yet recorded during the trials, promising available measurement methods are identified and tested under laboratory conditions. One example of this is the recording of the actual spray quantities that are sprayed onto the tool surface before the forming process. The results of the investigations show that the tool life fluctuations can be reduced by about 16% and as a consequence, the average tool life can be increased by about 13%.

Abstract: The paper proposed a practice teaching mode by making analysis on Didi data set. There are more and more universities have provided the big data analysis courses with the rapid development and wide application of big data analysis technology. The theoretical knowledge of big data analysis is professional and hard to understand. That may reduce students' interest in learning and learning motivation. And the practice teaching plays an important role between theory learning and application. This paper first introduces the theoretical teaching part of the course, and the theoretical methods involved in the course. Then the practice teaching content of Didi data analysis case was briefly described. And the study selects the related evaluation index to evaluate the teaching effect through questionnaire survey and verify the effectiveness of teaching method. The results show that 78% of students think that practical teaching can greatly improve students' interest in learning, 89% of students think that practical teaching can help them learn theoretical knowledge, 89% of students have basically mastered the method of big data analysis technology introduced in the course, 90% of students think that the teaching method proposed in this paper can greatly improve students' practical ability. The teaching mode is effective, which can improve the learning effect and practical ability of students in data analysis, so as to improve the teaching effect.

Abstract: Buildings are responsible for a major amount of the annual energy consumption. A detailed recording and evaluation of building data could provide a deeper understanding of building operation schemes and the corresponding performance. This could help building owners and operators to evaluate and better understand the actual situation. Based on this (real-time) data an optimized operation scheme can be designed and implemented for future time steps. Additionally, a more detailed understanding of the impact of previous building systems interactions will be possible. The building automation industry and the related service provider sector are actually providing proprietary solutions for data logging, visualization and energy optimization. Such solutions are regularly integrated into their own specific software of the used proprietary building management solutions. As an alternative, we suggest an Internet of Things (IoT) and web services inspired concept for the implementation of a generic web service for building diagnostics. Our suggestion encompasses a holistic performance evaluation that considers both the energy consumptions and delivered building service. In this contribution, a general design of a web service based solution is presented and the future possibilities for data access from various sources are discussed. Furthermore, details of actually developed and demonstratively implemented software components for data preprocessing are presented. Data processing examples for different types of data are included and highlight the potential of such web-based approaches. Moreover, possibilities for improved building control by the use of web services for operation schedule generation or model predictive control are illustrated and critically debated.

Abstract: Given the adverse implications of both urbanization and global climate change for cities, specifically regarding issues such as human health and comfort, local air quality, and increased summertime energy use in buildings, it is becoming imperative to develop models that can accurately predict the complex and nonlinear interactions between the surrounding urban fabric and local climatic context. Over the past years, a number of comprehensive tools have been widely applied for the generation of near-surface urban climatic information. In this paper, we report on the potential of two alternative approaches to urban climate modeling. Specifically, we compare the climatic output generated with Urban Weather Generator (UWG) and the Weather Research and Forecasting (WRF) model. The WRF model has been widely applied due to its capability of downscaling global weather data to finer resolutions, thus representing the location-specific microclimatic information, while considering the interactions with the surrounding urban and regional context. However, this approach is computationally intensive. The UWG was recently introduced as a simpler alternative to such complex models. The tool morphs rural weather data to represent urban conditions given a set of location-specific morphological parameters. In the present paper, WRF and UWG methods were compared based on empirical data pertaining to air temperature, wind speed, and humidity, collected from 12 locations in the city of Vienna, Austria, over 5 distinct time periods. In general, our results suggest that, as compared to the WRF model, the UWG model results are closer to monitored data. However, during the extreme conditions in summer, the WRF model was found to perform better. It was further noted that the discrepancy between the two models increases with decreasing temperatures, thus revealing a higher offset between UWG and WRF output during the winter period.