Academic literature on the topic 'Data capturing techniques'
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Journal articles on the topic "Data capturing techniques"
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Bolton, Amy E., Randolph S. Astwood, and Gwendolyn E. Campbell. "Policy Capturing and Fuzzy Logic: A Better Approach to Representing Judgment Data?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 3 (September 2002): 541–45. http://dx.doi.org/10.1177/154193120204600368.
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At the 45th annual meeting of HFES, we conducted an alternative format session in which fuzzy logic was introduced as an alternative approach to analyzing judgment data and representing decision-making policies (see Buff et al., 2001). During the alternative format session, usability judgments were collected on-site for Advanced Distance Learning (ADL) applications. These data provided the basis for an empirical assessment of the value added of one modeling technique, fuzzy logic, over the more traditional approach to analyzing policy capturing data, multiple linear regression. This paper describes the results of an empirical assessment of the two modeling techniques. For a discussion of the empirical results of the impact of the different usability dimensions on the learning effectiveness of ADL applications, see Holness, Pharmer, and Buff (2002).
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Kelber, C., S. Marke, U. Trommler, C. Rupprecht, and S. Weis. "Development of process data capturing, analysis and controlling for thermal spray techniques - SprayTracker." IOP Conference Series: Materials Science and Engineering 181 (March 2017): 012010. http://dx.doi.org/10.1088/1757-899x/181/1/012010.
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SARFRAZ, MUHAMMAD. "SOME ALGORITHMS FOR CURVE DESIGN AND AUTOMATIC OUTLINE CAPTURING OF IMAGES." International Journal of Image and Graphics 04, no. 02 (April 2004): 301–24. http://dx.doi.org/10.1142/s0219467804001427.
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A new multipurpose curve technique has been introduced which is meant to automatically provide a fit to any ordered data in a plane. The technique is particularly economical for designing purposes as well as for the visualization of a large amount of data sets. A more flexible class of cubic functions is the basis of this technique. This class of functions involves two control parameters, to produce more flexible shapes than ordinary Bézier cubics or Hermite cubics, in each segment. These functions, together with the control parameters, are utilized to fit a design curve in an interactive way. These functions are also utilized in an optimal way to fit a design curve to the data arose from any image or a scientific phenomenon. The design curve method is highly useful to capture the outlines of images. It differs, in its methodology, from the existing techniques in the literature using Bézier cubics. The curve technique has used various ideas in its construction. These ideas include end-point interpolation, detection of characteristic points, least squares approximation. The final shape is achieved by stitching the generalized Bézier cubic pieces with GC1 smoothness. Finally, three algorithms have been proposed for various applications.
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De Ruiter, Naomi M. P., Steffie Van Der Steen, Ruud J. R. Den Hartigh, and Paul L. C. Van Geert. "Capturing moment-to-moment changes in multivariate human experience." International Journal of Behavioral Development 41, no. 5 (June 6, 2016): 611–20. http://dx.doi.org/10.1177/0165025416651736.
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In this article, we aim to shed light on a technique to study intra-individual variability that spans the time frame of seconds and minutes, i.e., micro-level development. This form of variability is omnipresent in behavioural development and processes of human experience, yet is often ignored in empirical studies, given a lack of proper analysis tools. The current article illustrates that a clustering technique called Kohonen’s Self-Organizing Maps (SOM), which is widely used in fields outside of psychology, is an accessible technique that can be used to capture intra-individual variability of multivariate data. We illustrate this technique with a case study involving self-experience in the context of a parent–adolescent interaction. We show that, with techniques such as SOM, it is possible to reveal how multiple components of an intra-individual process (the adolescent’s self-affect and autonomy) are non-linearly connected across time, and how these relationships transition in accordance with a changing contextual factor (parental connectedness) during a single interaction. We aim to inspire researchers to adopt this technique and explore the intra-individual variability of more developmental processes, across a variety of domains, as deciphering such micro-level processes is crucial for understanding the nature of psychological and behavioural development.
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Suzuki, K., U. Rin, Y. Maeda, and H. Takeda. "FOREST COVER CLASSIFICATION USING GEOSPATIAL MULTIMODAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 1091–96. http://dx.doi.org/10.5194/isprs-archives-xlii-2-1091-2018.
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To address climate change, accurate and automated forest cover monitoring is crucial. In this study, we propose a Convolutional Neural Network (CNN) which mimics professional interpreters’ manual techniques. Using simultaneously acquired airborne images and LiDAR data, we attempt to reproduce the 3D knowledge of tree shape, which interpreters potentially make use of. Geospatial features which support interpretation are also used as inputs to the CNN. Inspired by the interpreters’ techniques, we propose a unified approach that integrates these datasets in a shallow layer in the CNN network. With the proposed CNN, we show that the multi-modal CNN works robustly, which gets more than 80 % user’s accuracy. We also show that the 3D multi-modal approach is especially suited for deciduous trees thanks to the ability of capturing 3D shapes.
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Boonchoo, Thapana. "Capturing Spatial Relationship Mapping Patterns between GPS Coordinates and Road Network Using Machine Learning and Partitioning Techniques." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 16, no. 3 (June 18, 2022): 277–88. http://dx.doi.org/10.37936/ecti-cit.2022163.247801.
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Map matching is a technique used to identify which path a vehicle is travelling on in a road map. Since it is a crucial fundamental step for a wide range of transportation applications, many map-matching algorithms have been devised ranging from simple geometric calculation methods to more sophisticated methods. However, the study of spatial relationship patterns between GPS coordinates and road segments mapped with map-matching has not received enough attention from researchers. This paper presents a framework, called Proxy Map Matching (PMM), to learn such patterns using machine learning techniques. However, we find that solely employing machine learning techniques on such data is not sufficient to capture the patterns. Solving this problem that way results in an inaccurate proxy model. In PMM, we construct several proxy map matchers and assign them to each group of data based on their spatial proximity, thereby achieving accuracy improvement. An experiment on real-world data shows that the framework achieves above 85% accuracy with integration of machine learning techniques, and outperforms the methods which solely employ machine learning techniques significantly. Moreover, the proposed proxy model can perform very fast matching. For 14,177 GPS coordinate pairs per second, we achieve 88.4% accuracy.
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Aghili, Maryamossadat, and Ruogu Fang. "Mining Big Neuron Morphological Data." Computational Intelligence and Neuroscience 2018 (June 24, 2018): 1–13. http://dx.doi.org/10.1155/2018/8234734.
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The advent of automatic tracing and reconstruction technology has led to a surge in the number of neurons 3D reconstruction data and consequently the neuromorphology research. However, the lack of machine-driven annotation schema to automatically detect the types of the neurons based on their morphology still hinders the development of this branch of science. Neuromorphology is important because of the interplay between the shape and functionality of neurons and the far-reaching impact on the diagnostics and therapeutics in neurological disorders. This survey paper provides a comprehensive research in the field of automatic neurons classification and presents the existing challenges, methods, tools, and future directions for automatic neuromorphology analytics. We summarize the major automatic techniques applicable in the field and propose a systematic data processing pipeline for automatic neuron classification, covering data capturing, preprocessing, analyzing, classification, and retrieval. Various techniques and algorithms in machine learning are illustrated and compared to the same dataset to facilitate ongoing research in the field.
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Bordagaray, Maria, Luigi dell’Olio, Achille Fonzone, and Ángel Ibeas. "Capturing the conditions that introduce systematic variation in bike-sharing travel behavior using data mining techniques." Transportation Research Part C: Emerging Technologies 71 (October 2016): 231–48. http://dx.doi.org/10.1016/j.trc.2016.07.009.
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Adamopoulos, Efstathios, Fulvio Rinaudo, and Liliana Ardissono. "A Critical Comparison of 3D Digitization Techniques for Heritage Objects." ISPRS International Journal of Geo-Information 10, no. 1 (December 30, 2020): 10. http://dx.doi.org/10.3390/ijgi10010010.
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Techniques for the three-dimensional digitization of tangible heritage are continuously updated, as regards active and passive sensors, data acquisition approaches, implemented algorithms and employed computational systems. These developments enable higher automation and processing velocities, increased accuracy, and precision for digitizing heritage assets. For large-scale applications, as for investigations on ancient remains, heritage objects, or architectural details, scanning and image-based modeling approaches have prevailed, due to reduced costs and processing durations, fast acquisition, and the reproducibility of workflows. This paper presents an updated metric comparison of common heritage digitization approaches, providing a thorough examination of sensors, capturing workflows, processing parameters involved, metric and radiometric results produced. A variety of photogrammetric software were evaluated (both commercial and open sourced), as well as photo-capturing equipment of various characteristics and prices, and scanners employing different technologies. The experimentations were performed on case studies of different geometrical and surface characteristics to thoroughly assess the implemented three-dimensional modeling pipelines.
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Utomo, Hanggara Budi, Dewi Retno Suminar, and Hamidah Hamidah. "CAPTURING TEACHING MOTIVATION OF TEACHER IN THE DISADVANTAGED AREAS." Jurnal Cakrawala Pendidikan 38, no. 3 (October 24, 2019): 398–410. http://dx.doi.org/10.21831/cp.v38i3.26411.
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Teaching motivation of teachers is very important for student’s development. The purpose of this study is to test the teaching motivation of teachers in disadvantaged areas affected by the school climate and self-concept through the satisfaction of basic psychological needs. The subjects of this research were 241 teachers. Data collection techniques used instruments in the form of a school climate scale, a scale of self-concept, a scale of basic psychological need satisfaction, and a scale of teaching motivation. The data analysis techniques used a structural equation modeling. The results showed that the attention of teachers who teach in disadvantaged areas shows that the school climate and teacher’s self-concept differently provide an important role in teaching motivation mediated by the basic psychological needs satisfaction. This means that teaching motivation is a result of the role of the school climate as an external factor and satisfaction of basic psychological needs and self-concept as an internal factor. The implication of this research is the need for programs to develop teacher motivation in disadvantaged areas by optimizing and considering school climate, self-concept, and satisfaction of basic psychological needs as influential factors.
Dissertations / Theses on the topic "Data capturing techniques"
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Lemp, Jason David. "Capturing random utility maximization behavior in continuous choice data : application to work tour scheduling." 2009. http://hdl.handle.net/2152/18643.
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Recent advances in travel demand modeling have concentrated on adding behavioral realism by focusing on an individual’s activity participation. And, to account for trip-chaining, tour-based methods are largely replacing trip-based methods. Alongside these advances and innovations in dynamic traffic assignment (DTA) techniques, however, time-of-day (TOD) modeling remains an Achilles’ heel. As congestion worsens and operators turn to variable road pricing, sensors are added to networks, cell phones are GPS-enabled, and DTA techniques become practical, accurate time-of-day forecasts become critical. In addition, most models highlight tradeoffs between travel time and cost, while neglecting variations in travel time. Research into stated and revealed choices suggests that travel time variability can be highly consequential. This dissertation introduces a method for imputing travel time variability information as a continuous function of time-of-day, while utilizing an existing method for imputing average travel times (by TOD). The methods employ ordinary least squares (OLS) regression techniques, and rely on reported travel time information from survey data (typically available to researchers), as well as travel time and distance estimates by origin-destination (OD) pair for free-flow and peak-period conditions from network data. This dissertation also develops two models of activity timing that recognize the imputed average travel times and travel time variability. Both models are based in random utility theory and both recognize potential correlations across time-of-day alternatives. In addition, both models are estimated in a Bayesian framework using Gibbs sampling and Metropolis-Hastings (MH) algorithms, and model estimation relies on San Francisco Bay Area data collected in 2000. The first model is the continuous cross-nested logit (CCNL) and represents tour outbound departure time choice in a continuous context (rather than discretizing time) over an entire day. The model is formulated as a generalization of the discrete cross-nested logit (CNL) for continuous choice and represents the first random utility maximization model to incorporate the ability to capture correlations across alternatives in a continuous choice context. The model is then compared to the continuous logit, which represents a generalization of the multinomial logit (MNL) for continuous choice. Empirical results suggest that the CCNL out-performs the continuous logit in terms of predictive accuracy and reasonableness of predictions for three tolling policy simulations. Moreover, while this dissertation focuses on time-of-day modeling, the CCNL could be used in a number of other continuous choice contexts (e.g., location/destination, vehicle usage, trip durations, and profit-maximizing production). The second model is a bivariate multinomial probit (BVMNP) model. While the model relies on discretization of time (into 30-minute intervals), it captures both key dimensions of a tour’s timing (rather than just one, as in this dissertation’s application of the CCNL model), which is important for tour- and activity-based models of travel demand. The BVMNP’s ability to capture correlations across scheduling alternatives is something no existing two-dimensional choice models of tour timing can claim. Both models represent substantial contributions for continuous choice modeling in transportation, business, biology, and various other fields. In addition, the empirical results of the models evaluated here enhance our understanding of individuals’ time-of-day decisions. For instance, average travel time and its variance are estimated to have a negative effect on workers’ utilities, as expected, but are not found to be that practically relevant here, probably because most workers are rather constrained in their activity scheduling and/or work hours. However, correlations are found to be rather strong in both models, particularly for home-to-work journeys, suggesting that if models fail to accommodate such correlations, biased application results may emerge.text
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黃冠蓉. "Applying Image-based Data Capturing Technique on BIM Model Creation of the Existing Building." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/3kurx4.
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碩士國立高雄應用科技大學
土木工程與防災科技研究所
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Although BIM processes for new buildings are well established, most existing buildings are not yet maintained, refurbished, or deconstructed with BIM. Therefore, this research is applying Image-based Data Capturing Technique on BIM model creation of the Existing Building and it proposes a method and workflow to create 3D model for BIM model integration. First, a detailed 3D point-cloud models of an existing building is developed from photographs obtained by Unmanned Aerial Vehicle (UAV), but the 3D reconstruction was incomplete. This research attempted the different mothed of digital camera photographing to supplement the point-cloud models to become more perfect. The final point-cloud models can not only to visualize the reality, but also can facilitate the BIM integration and application in the future.
Book chapters on the topic "Data capturing techniques"
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Kutty, Sangeetha, Richi Nayak, and Tien Tran. "A Study of XML Models for Data Mining." In Data Mining, 1–27. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2455-9.ch001.
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With the increasing number of XML documents in varied domains, it has become essential to identify ways of finding interesting information from these documents. Data mining techniques can be used to derive this interesting information. However, mining of XML documents is impacted by the data model used in data representation due to the semi-structured nature of these documents. In this chapter, we present an overview of the various models of XML documents representations, how these models are used for mining, and some of the issues and challenges inherent in these models. In addition, this chapter also provides some insights into the future data models of XML documents for effectively capturing its two important features, structure and content, for mining.
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Foufopoulos, Johannes, Gary A. Wobeser, and Hamish McCallum. "Capture, Restraint, and Euthanasia of Target Species." In Infectious Disease Ecology and Conservation, 76–96. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780199583508.003.0005.
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Despite advances in noninvasive data collection, the practice of capturing wildlife provides a wealth of information and remains a fundamental component of wildlife disease and conservation studies. Six principal considerations are discussed in the context of capturing wild animals for disease sampling, including: whether the captured animals are representative of the entire population and whether the biological samples are indicative of the physiology rather than the capturing method; whether capture techniques will influence the animal’s subsequent behavior or endanger the investigators; whether the chosen sampling frequency and magnitude are representative of the host population demographics and disease dynamics; and finally, how the capturing and sampling process can be continually improved.
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Srivastava, Prashant, and Ashish Khare. "On Visual Information Retrieval Using Multiresolution Techniques for Web Usage Mining Applications." In Advances in Data Mining and Database Management, 297–323. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0613-3.ch012.
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The proliferation of huge amount of information has made it essential to develop systems that organize and index them for easy access. The advent of World Wide Web has provided immense opportunity to the people across the world to access and share information for different uses ranging from personal to professional. Various web mining techniques are applied to retrieve useful information as well as improvement of existing techniques of mining to search and retrieve useful information from the web. With the growth in the number of devices producing various forms of information, the amount of information is increasing exponentially. Also, these huge amount of information are being shared in the world through various means. Hence, it has become necessary to organize information in such a manner so that access to them is easy and feasible. As the amount of information is increasing rapidly, efficient indexing of information for easy access is becoming quite challenging. Hence, there is a need to search for solutions to solve this problem. The field of information retrieval attempts to solve this problem. Information retrieval is concerned with storage, organization, indexing, and retrieval of information. Information retrieval techniques incorporate several aspects of information to achieve the target of efficient indexing. Since there are several forms of information, their characteristics vary a lot from each other. Image is one such popular form of information which is shared the most among the people around the world. Also, with the presence of numerous image capturing devices, acquisition of image is no longer a difficult task. People enjoy capturing and sharing images through social network. Although image is a complex structure, it is easily understood by people across the world. Also, it has become a popular means of information sharing among people. This chapter discusses information retrieval techniques for image data. Visual Information Retrieval or Content-Based Image Retrieval (CBIR) accepts query in the form of image or image features instead of text. It is concerned with searching and retrieval of images similar to the query given in the form of images. Most of the visual information retrieval techniques are based on processing single resolution of an image. But processing of single resolution of image is not sufficient for efficient retrieval as image is a complex structure and contains varying level of details. Hence, there is a need of multiresolution processing of images. Today, it is very difficult to keep track of number of research papers based on multiresolution analysis as it is widely used for various image-based applications. Also, there are a number of multiresolution techniques available to achieve this. Multiresolution processing has one big advantage that features that are left undetected at one level get detected at another level which is not the case with single resolution analysis. We demonstrate this fact with the help of an experiment using Discrete Wavelet Transform along with the discussion of various multiresolution techniques for visual information retrieval. The experiment helps in explaining the important properties of multiresolution analysis and also provides future scope of research in this field.
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Chen, Shu-Ching. "Multimedia Databases and Data Management." In Methods and Innovations for Multimedia Database Content Management, 1–11. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1791-9.ch001.
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The exponential growth of the technological advancements has resulted in high-resolution devices, such as digital cameras, scanners, monitors, and printers, which enable the capturing and displaying of multimedia data in high-density storage devices. Furthermore, more and more applications need to live with multimedia data. However, the gap between the characteristics of various media types and the application requirements has created the need to develop advanced techniques for multimedia data management and the extraction of relevant information from multimedia databases. Though many research efforts have been devoted to the areas of multimedia databases and data management, it is still far from maturity. The purpose of this article is to discuss how the existing techniques, methodologies, and tools addressed relevant issues and challenges to enable a better understanding in multimedia databases and data management. The focuses include: (1) how to develop a formal structure that can be used to capture the distinguishing content of the media data in a multimedia database (MMDB) and to form an abstract space for the data to be queried; (2) how to develop advanced content analysis and retrieval techniques that can be used to bridge the gaps between the semantic meaning and low-level media characteristics to improve multimedia information retrieval; and (3) how to develop query mechanisms that can handle complex spatial, temporal, and/or spatio-temporal relationships of multimedia data to answer the imprecise and incomplete queries issued to an MMDB.
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Gebremeskel, Gebeyehu Belay, Chai Yi, and Zhongshi He. "The Integral of Spatial Data Mining in the Era of Big Data." In Geospatial Intelligence, 863–99. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8054-6.ch038.
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Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths of data characterized as high volume, velocity, variety, variability, value and others that collected and generated from various sources that are too complex and big to capturing, storing, and analyzing and challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valuable information and knowledge in large volumes of spatial data, which draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Using DM techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research field in Geosciences because of the increasing amount of data, which lead to new promising applications. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.
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Gebremeskel, Gebeyehu Belay, Chai Yi, and Zhongshi He. "The Integral of Spatial Data Mining in the Era of Big Data." In Advances in Business Information Systems and Analytics, 90–126. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2031-3.ch006.
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Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths of data characterized as high volume, velocity, variety, variability, value and others that collected and generated from various sources that are too complex and big to capturing, storing, and analyzing and challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valuable information and knowledge in large volumes of spatial data, which draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Using DM techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research field in Geosciences because of the increasing amount of data, which lead to new promising applications. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.
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McMillan, Tyson DeShaun. "Micro-Feedback via the Cloud." In Increasing Productivity and Efficiency in Online Teaching, 195–215. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0347-7.ch012.
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This chapter emphasizes online tools and techniques for capturing instantaneous electronic student feedback, sustainable online teacher monitoring of student feedback, and on-demand teacher access to spreadsheet data for analysis. This chapter presents muddiest point feedback data as described in Mosteller as a possible avenue for effectively and efficiently capturing points of confusion of online students. Traditional methods for feedback collection involve gathering feedback at fixed intervals in the semester that are limited by the on-campus class time interval or are paper-based. The method of this chapter involves collecting feedback via an online form situated in the cloud. The online teacher can then access and assess the feedback data at regular intervals as the teacher's schedule permits from any computer or mobile device thereby saving valuable time. The chapter focuses on online tools and techniques for: Google Drive™ cloud-based forms, Google Drive™ cloud-based spreadsheets, and sustainable monitoring of online student responses.
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Bhowmik, Mrinal, Payal Banerjee, and Manoj Kumar Bhowmik. "A Theoretical Study of Thermal Stress for Engineering Applications." In Applications and Techniques for Experimental Stress Analysis, 176–201. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1690-4.ch012.
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The stress generated due to the temperature difference is called thermal stress. Generally, the temperature gradients, thermal shocks, and thermal expansion or contraction are most effective contributors to thermal stress. The improper temperature profile of a body results in the formation of cracks, fractures, or plastic deformations at single or multiple spots depending upon two factors (i.e., the magnitude of temperature distribution or other variables of heating and material properties). So, this chapter analyses the causes of thermal stress and their measurement techniques. However, as most of the engineering problems, the thermal stress is due to the thermal expansion or sudden temperature changes happening in the body. Therefore, a brief analysis of temperature measurement devices with their proper data capturing methodology is also discussed.
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Anand, Rohit, Akash Sinha, Abhishek Bhardwaj, and Aswin Sreeraj. "Flawed Security of Social Network of Things." In Handbook of Research on Network Forensics and Analysis Techniques, 65–86. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4100-4.ch005.
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This chapter deals with the security flaws of social network of things. The network of things (NoT) is a dynamic structure that is basically an interface of real world and virtual world having capabilities of collection and sharing data over a shared network. The social network of things (SNoT) is a versatile way of connecting virtual and real world. Like any other device connected to internet, objects in SNoT are also vulnerable to the various security and privacy attacks. Generally, to secure Social Network of Things in which human intervention is absent, data capturing devices must be avoided. Types of security attacks that are huge threats to NoT as well as SNoT will be discussed in the chapter. The huge collection of information without necessary security measures allows an intruder to misuse the personal data of owner. Different types of attacks with reference to the different layers are also discussed in detail. The best possible potential solutions for the security of devices in SNoT will be considered.
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Aubin, Verónica Inés, and Jorge Horacio Doorn. "Exploring New Handwriting Parameters for Writer Identification." In Encyclopedia of Information Science and Technology, Fourth Edition, 4643–51. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2255-3.ch403.
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The automatic processing of handwriting samples is part of the computational biometric. It applies qualitative and quantitative techniques by means of capturing, visualizing and analyzing handwriting. The main applications are writer identification and text understanding. Two significantly different situations appear: on line and off line data capturing. In the former the samples are obtained in a dedicated framework, where the writing instrument and the surface have several sensors. In the latter, the unique information available comes from the residues left on the paper. This chapter deals with the second situation. Width, grey value, direction and other parameters of the residual manuscript text are influenced by the psychomotor characteristics of the writer. Some of these personal parameters may be estimated from the observable properties of the written text.
Conference papers on the topic "Data capturing techniques"
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Chen, Jiarong, and Qingze Zou. "Data-Driven Post-Filtering of Acoustics Noise in Atomic Force Microscope Imaging." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3265.
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Abstract This paper presents a data-driven dynamics-based filtering approach to eliminate acoustic noise-caused distortion in images of atomic force microscope (AFM). AFM operations are sensitive to external noise as disturbances including acoustic noise as disturbances to the probe-sample interaction directly results in distortions in AFM imaging. Although conventional passive noise cancellation has been employed, limitation exists and residual noise still persists. Advanced online control techniques face difficulty in capturing the complex noise dynamic and limited system bandwidth imposed by the robustness requirement. Therefore, In this work, we propose a finite-impulse-response (FIR) based post-filtering approach to remove the noise-caused distortion in AFM images. Experimental implementation is presented and discussed to illustrate the proposed technique.
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Rocha, J., and A. Sattineni. "Safety in the maritime construction site: Capturing lessons learned." In 10th World Construction Symposium. Building Economics and Management Research Unit (BEMRU), University of Moratuwa, 2022. http://dx.doi.org/10.31705/wcs.2022.57.
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The aim of this research is to examine the safety culture of the United States Army Corps of Engineers staff and contractors working on marine construction projects including dredging, beach nourishment, breakwaters, and jetties. A literature review was performed to understand existing issues and to develop a research methodology for the study. Qualitative data research was used to uncover significant themes and help gain a deeper understanding of safety related issues. Twelve construction professions were interviewed using semi-structured interview format. The data was analysed using thematic analysis techniques with an inductive approach to draw conclusions. The results were used to conclude that issues causing safety accidents in the maritime construction sector can be broadly attributed to unforeseen conditions, training, complacency, lack of support of safety and crew unpreparedness. Data from the interviews also indicates ways to improve maritime construction can be broadly categorised under training, improved communication, improved preparation and organisational support for safety.
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Bernstein, William Z., David Lechevalier, and Don Libes. "UMP Builder: Capturing and Exchanging Manufacturing Models for Sustainability." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6331.
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Targeting the improvement of environmental analysis of manufacturing systems, ASTM 3012-16 provides guidelines for formally characterizing manufacturing processes. However, the difficulty that has arisen in the early use of the standard illustrates the need for intuitive tools for helping modeling experts to conform to the specified information model. In response, we present the Unit Manufacturing Process (UMP) Builder, a browser-based tool integrating symbolic mathematical and guided textual inputs, helping to consistently record and exchange manufacturing process models for environmental sustainability. The tool provides an initial layer of governance and verification with respect to the conformance to ASTM 3012-16. In this paper, we (1) detail the requirements with developing such a tool, (2) propose an improved schema to represent UMP models accommodating data-driven techniques, and (3) demonstrate the tool using a contributed model from an open challenge for modeling manufacturing processes.
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Japikse, David, and Kerry N. Oliphant. "Turbomachinery Modeling: Explicit and Impliit Knowledge Capturing (2005A)." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68099.
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Design engineers rely on quality performance models to establish the physical relationship between diverse thermodynamic, geometric, and fluid dynamic parameters that govern turbomachinery performance. If these models are based on a rigorous, scientific foundation, they permit the designer to thoroughly optimize a new configuration and establish with confidence the performance levels to be expected when the product is introduced in the market. The process of developing advanced models has endured more than a full century, and models of increased complexity have been introduced. However, many aspects of model development have not received thorough scientific evaluation. In the turbomachinery field, meanline performance models for axial turbines have been well developed and widely published; nearly the same can be said for the field of axial compressors. Beyond these two examples, there is a need for more model development and improvement, particularly emphasizing radial and mixed-flow turbomachines. This paper shows a systematic method, now fully integrated into a computerized methodology with optimization search techniques, for extracting the greatest useful knowledge from diverse datasets suitable for subsequent model development. The process focuses on modeling eight dependent variables based on five or six independent variables that have been found to be essential for understanding the performance of these machines. This paper emphasizes the scientific and numerical approach taken to process the data such that advanced models can be developed. The actual model development is presented in subsequent papers.
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Karunanayake, KPAN, MLSS Fernando, and U. Kulatunga. "Knowledge management practices to minimize the impact of staff turnover." In 10th World Construction Symposium. Building Economics and Management Research Unit (BEMRU), University of Moratuwa, 2022. http://dx.doi.org/10.31705/wcs.2022.49.
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Skilled staff turnover plays a wide role in continuous knowledge loss in manufacturing organizations. The result of staff turnover impacts organizational performance, productivity, effectiveness, employee performance and knowledge. The importance of managing an organization’s knowledge is a need in organizations. This research identified the importance of knowledge management in the trailer manufacturing sector with high staff turnover. This enables project managers to take project knowledge management into practice within the organization. This study uses a qualitative research approach. The aim of the study was achieved by a case study research strategy along with 16 semi-structured interviews, which were performed as a data collection technique. Data were collected from the case study organisation, focusing on knowledge management methodologies used during the project phases and identifying the impact of staff turnover on organizational knowledge. The knowledge management techniques vary from one project phase to another project phase. Based on the outcome of this research, project managers can identify the most effective knowledge management techniques to be used at each phase. According to the study, the most frequently used KM techniques in the planning stage were “Learning & Idea Capturing” and “Refer Knowledge Base”. The most prominently used KM techniques in the designing stage were “Brainstorming” and “Learning & Idea Capturing”. While the most frequently used KM technique in the building, testing, and launching stages was found to be “Refer Knowledge Base”. From this research study, project managers can identify the critical areas affected by skilled staff turnover, how to prepare in advance and minimize knowledge loss.
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Kim, Kyusung, Girija Parthasarathy, Onder Uluyol, Wendy Foslien, Shuangwen Sheng, and Paul Fleming. "Use of SCADA Data for Failure Detection in Wind Turbines." In ASME 2011 5th International Conference on Energy Sustainability. ASMEDC, 2011. http://dx.doi.org/10.1115/es2011-54243.
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High operations and maintenance costs for wind turbines reduce their overall cost effectiveness. One of the biggest drivers of maintenance cost is unscheduled maintenance due to unexpected failures. Continuous monitoring of wind turbine health using automated failure detection algorithms can improve turbine reliability and reduce maintenance costs by detecting failures before they reach a catastrophic stage and by eliminating unnecessary scheduled maintenance. A SCADA (Supervisory Control and Data Acquisition System) -data based condition monitoring system uses data already collected at the wind turbine controller. It is a cost-effective way to monitor wind turbines for early warning of failures and performance issues. In this paper, we describe our exploration of existing wind turbine SCADA data for development of fault detection and diagnostic techniques for wind turbines. We used a number of measurements to develop anomaly detection algorithms and investigated classification techniques using clustering algorithms and principal components analysis for capturing fault signatures. Anomalous signatures due to a reported gearbox failure are identified from a set of original measurements including rotor speeds and produced power.
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Abuhamdia, Tariq, Saied Taheri, Abe Meddah, and David Davis. "Rail Defect Detection Using Data From Tri-Axial Accelerometers." In 2014 Joint Rail Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/jrc2014-3703.
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This study explores detecting rail defects (track ride quality exceptions) during train operations using real-time x, y and z acceleration data measured and collected on the side frame of a 315,000-lb gross rail load instrumented freight car. Different analysis tools were developed and employed to capture the peculiar characteristics of the data. This includes Harmonic analysis of the data, Wavelet analysis, energy density analysis, and correlation analysis. Based on the analysis results, different filtration and processing techniques were tried to identify the defects throughout the test data. A method that augments autocorrelation to wavelet based singularity detection showed promising results in capturing three types of exceptions: rail fractures, chipped rails, and broken concrete foundations. In addition, blind tests were conducted with several datasets and the algorithm proved to be 100% accurate in detecting the studied defects.
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Andersen, Arthur, Ralph E. Chapman, and Brian Wilcox. "Three-dimensional techniques for capturing and building virtual models of complex objects for use in scientific and industrial applications, data archiving, and the entertainment industry." In Electronic Imaging 2003, edited by Andrew J. Woods, Mark T. Bolas, John O. Merritt, and Stephen A. Benton. SPIE, 2003. http://dx.doi.org/10.1117/12.474055.
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Garcia-Hansen, V., F. Rodriguez, and R. Ong. "CAPTURING THE LUMINOUS ENVIRONMENT IN HOSPITAL ROOMS: AN OVERVIEW OF OCCUPANT-CENTERED METHODS TO INFORM DESIGN PRACTICE." In CIE 2021 Conference. International Commission on Illumination, CIE, 2021. http://dx.doi.org/10.25039/x48.2021.op06.
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Hospitals are complex environments having to balance out contradictory requirements from patients and health workers. Despite evidence demonstrating the critical role of the hospital’s luminous environment in responding to occupants’ requirements and needs, and the importance of engaging with the patient experience to improve the quality of patient care, to date, no studies have articulated approaches to study conflicting occupants’ needs for lighting in context. This paper presents a semi-systematic literature review of disciplinary approaches to investigate quality lighting conditions in hospitals and subjective impressions of occupants. Research articles from the clinical, built environment, and lighting fields were selected and evaluated. The review outlined appropriate physical (i.e., photometric) and self-report (e.g., preferences) approaches for data collection in context and potential articulations between them. Mixed data collection and data analysis techniques are deemed essential to articulate lighting design strategies engaging with the requirements of hospital occupants on a 24-hour basis.
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Nourgaliev, Robert, Nam Dinh, and Theo Theofanous. "The ‘Characteristics-Based Matching’ (CBM) Method for Compressible Flow With Moving Boundaries and Interfaces." In ASME/JSME 2003 4th Joint Fluids Summer Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/fedsm2003-45550.
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Recently, Eulerian methods for capturing interfaces in multi-fluid problems become increasingly popular. While these methods can effectively handle significant deformations of interface, they have been known to produce nonphysical oscillations near material interfaces due to the smeared out density profile and radical change in equation of state across a material interface. One promising recent development to overcome these problems is the ‘Ghost Fluid Method’ (GFM). While being able to produce excellent results for simulation of gas-gas flows, the GFM boundary treatment is unsatisfactory for the case of liquid-liquid or liquid-gas compressible flows. The present study devotes to a new methodology for boundary condition capturing in multi-fluid compressible flows. The method, named ‘Characteristics-Based Matching (CBM)’, capitalizes on the recent development of the level set method and related techniques, i.e., PDE-based re-initialization and extrapolation, and the ‘Ghost Fluid Method’ (GFM). Specifically, the CBM utilizes the level set function to ‘capture’ interface position and a GFM-like strategy to tag computational nodes. In difference to the GFM method, which employs a boundary condition capturing in primitive variables, the CBM method implements boundary conditions based on a characteristic decomposition in the direction normal to the boundary. Since the method allows to avoid over-specification of boundary conditions by respecting the information flow, we believe that the CBM is able to ‘cure’ above-mentioned problems of the GFM boundary condition capturing technique. In this paper, the method’s performance is examined on fluid dynamics problems with stationary and moving boundaries. Numerical results agree well with known analytical or computational solutions and experimental data. Robust and accurate solutions were obtained. In particular, spurious over/under-heating errors, typical for moving boundary treatment by other methods, are essentially eliminated in the CBM solutions.
Reports on the topic "Data capturing techniques"
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Pailino, Lia, Lihua Lou, Alberto Sesena Rubfiaro, Jin He, and Arvind Agarwal. Nanomechanical Properties of Engineered Cardiomyocytes Under Electrical Stimulation. Florida International University, October 2021. http://dx.doi.org/10.25148/mmeurs.009775.
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Engineered cardiomyocytes made of human-induced pluripotent stem cells (iPSC) present phenotypical characteristics similar to human fetal cardiomyocytes. There are different factors that are essential for engineered cardiomyocytes to be functional, one of them being that their mechanical properties must mimic those of adult cardiomyocytes. Techniques, such as electrical stimulation, have been used to improve the extracellular matrix's alignment and organization and improve the intracellular environment. Therefore, electrical stimulation could potentially be used to enhance the mechanical properties of engineered cardiac tissue. The goal of this study is to establish the effects of electrical stimulation on the elastic modulus of engineered cardiac tissue. Nanoindentation tests were performed on engineered cardiomyocyte constructs under seven days of electrical stimulation and engineered cardiomyocyte constructs without electrical stimulation. The tests were conducted using BioSoft™ In-Situ Indenter through displacement control mode with a 50 µm conospherical diamond fluid cell probe. The Hertzian fit model was used to analyze the data and obtain the elastic modulus for each construct. This study demonstrated that electrically stimulated cardiomyocytes (6.98 ± 0.04 kPa) present higher elastic modulus than cardiomyocytes without electrical stimulation (4.96 ± 0.29 kPa) at day 7 of maturation. These results confirm that electrical stimulation improves the maturation of cardiomyocytes. Through this study, an efficient nanoindentation method is demonstrated for engineered cardiomyocyte tissues, capable of capturing the nanomechanical differences between electrically stimulated and non-electrically stimulated cardiomyocytes.
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DeMarle, David, and Andrew Bauer. In situ visualization with temporal caching. Engineer Research and Development Center (U.S.), January 2022. http://dx.doi.org/10.21079/11681/43042.
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In situ visualization is a technique in which plots and other visual analyses are performed in tandem with numerical simulation processes in order to better utilize HPC machine resources. Especially with unattended exploratory engineering simulation analyses, events may occur during the run, which justify supplemental processing. Sometimes though, when the events do occur, the phenomena of interest includes the physics that precipitated the events and this may be the key insight into understanding the phenomena that is being simulated. In situ temporal caching is the temporary storing of produced data in memory for possible later analysis including time varying visualization. The later analysis and visualization still occurs during the simulation run but not until after the significant events have been detected. In this article, we demonstrate how temporal caching can be used with in-line in situ visualization to reduce simulation run-time while still capturing essential simulation results.