Готові списки джерел за темами / Mining engineering Blasting Data processing

Добірка наукової літератури з теми "Mining engineering Blasting Data processing"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 29 січня 2023

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Статті в журналах з теми "Mining engineering Blasting Data processing"

Isheyskiy, Valentin, Evgeny Martinyskin, Sergey Smirnov, Anton Vasilyev, Kirill Knyazev, and Timur Fatyanov. "Specifics of MWD Data Collection and Verification during Formation of Training Datasets." Minerals 11, no. 8 (July 22, 2021): 798. http://dx.doi.org/10.3390/min11080798.

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This paper presents a structured analysis in the area of measurement while drilling (MWD) data processing and verification methods, as well as describes the main nuances and certain specifics of “clean” data selection in order to build a “parent” training database for subsequent use in machine learning algorithms. The main purpose of the authors is to create a trainable machine learning algorithm, which, based on the available “clean” input data associated with specific conditions, could correlate, process and select parameters obtained from the drilling rig and use them for further estimation of various rock characteristics, prediction of optimal drilling and blasting parameters, and blasting results. The paper is a continuation of a series of publications devoted to the prospects of using MWD technology for the quality management of drilling and blasting operations at mining enterprises.

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Wróblewski, Adam, Jacek Wodecki, Paweł Trybała, and Radosław Zimroz. "A Method for Large Underground Structures Geometry Evaluation Based on Multivariate Parameterization and Multidimensional Analysis of Point Cloud Data." Energies 15, no. 17 (August 29, 2022): 6302. http://dx.doi.org/10.3390/en15176302.

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In underground mining, new workings (tunnels) are constructed by blasting or mechanical excavation. The blasting technique used in underground mines is supported by economic aspects, especially for deposits characterized by hard rocks. Unfortunately, the quality of the result may be different than expected in terms of the general geometry of work or the roughness of excavation surfaces. The blasting technique is also a source of vibrations that may affect other existing structures, affecting their stability. Therefore, it is of great importance to monitor both the quality of the new tunnels and changes in existing tunnels that may cause rockfall from the sidewalls and ceilings of both new and existing tunnels. The length of mining tunnels and support structures in underground mines is massive. Even if one would like to limit monitoring of tunnel geometry to those used every day for major technological processes such as transport, it is a vast amount of work. What is more, any stationary monitoring system is hard to utilize both due to everyday blasting procedures and mobile machine operation. The method proposed here is based on quick LiDAR/Terrestrial Laser Scanner measurements to obtain a cloud of points, which allows generating the spatial model of a mine’s geometry. Data processing procedures are proposed to extract several parameters describing the geometry of the tunnels. Firstly, the model is re-sampled to obtain its uniform structure. Next, a segmentation technique is applied to separate the cross sections with a specific resolution. Statistical parameters are selected to describe each cross section for final 1D feature analysis along the tunnel length. Such a set of parameters may serve as a basis for blasting evaluation, as well as long-term deformation monitoring. The methodology was tested and validated for the data obtained in a former gold and arsenic mine Zloty Stok, Poland.

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ZHARIKOV, Sergey, and Vyacheslav KUTUEV. "Interrelations between technological processes of open-pit mining." Sustainable Development of Mountain Territories 14, no. 3 (September 30, 2022): 479–85. http://dx.doi.org/10.21177/1998-4502-2022-14-3-479-485.

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Introduction. High productivity in mining processes is possible only with sufficient consistency between mining processes: drilling – blasting – excavation – transportation. The relationships between drilling and blasting processes are most well studied, although there are also a number of issues related to the interpretation of data and the choice of efficiency criteria. The further link between blasting by excavation and transportation is incomplete, therefore, there is no clear direction of systematic and coordinated cost optimization in the mining processes. Getting efficiency in one process can lead to inefficiency of adjacent processes. Therefore, it is desirable that the criteria for the efficiency of work were universal and, at the same time, could reflect the individual specifics of the process. The purpose of the research. The purpose of the research was to establish links between mining processes by taking into account their energy characteristics. Research methodology. In the course of the research, modeling of the processes of drilling wells, explosive destruction, and excavation was widely used. Methods of mathematical statistics, system analysis, synthesis, modeling and field experiments were used to identify the relationships. Model representations were compared with practical data and boundary conditions were specified. Research results. As a result of the research, a universal characteristic of the process has been established, which is the energy consumption per 1 cubic meter (J/m3 ). In the case of drilling and blasting geometrically, these are the energy costs spent on destroying the volume of rock beaten off by one well. For excavation, this is the energy for excavation and loading of rock mass, the properties of which are dependent on the energy of explosive destruction and the quality of the explosion, characterized by the geometric parameters of the collapse and the intensity of excavation work. Conclusion. It is revealed that the establishment of energy relationships between the extraction processes allows not only to evaluate their efficiency, but also opens up the possibility to regulate the specified loading intensity by calculating the number of cycles required for the face and timely delivery of the appropriate number of vehicles, and can also be the basis for more accurate statements of optimization problems, including taking into account anisotropy the mountain range and the variety of influencing organizational and technological factors of mining operations. It is advisable to develop further research in the direction of processing, as suggested by I. A. Tangaev. To link the energy parameters of drilling – blasting – excavation – transportation – crushing – crushing. Considering that modern systems of dispatching and monitoring of mining machines allow collecting real-time information from sensors of the main nodes of working mining equipment, and network technologies allow tracking this in real time, then matching energy costs in processes, or changing their balance, allows you to quickly identify the efficiency or inefficiency of work. This can significantly affect the practice of making managerial decisions.

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Li, Lifeng, and Jhon Silva-Castro. "Synthesis of single-hole signatures by group delay for ground vibration control in rock blasting." Journal of Vibration and Control 26, no. 13-14 (December 24, 2019): 1273–84. http://dx.doi.org/10.1177/1077546319892435.

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Prediction and control of ground vibrations become essential as with the development of neighborhoods in the proximity of active mining operations or the need for new infrastructure in urban centers, both requiring the use of blasting. Novel ground vibration prediction models attempt to reproduce a whole vibration waveform from a blast and are based, in most cases, on the collection of vibrational information from a single blasthole. A single blasthole should have the same characteristics (geometry and weights of explosives) as the blastholes used in production shots. In some cases, the collection of the fundamental information (the signature) is straightforward. In more complex cases, the fundamental information from ground vibration data is collected from previous production shots. This study presents a novel methodology to assess the fundamental ground vibration information (the signature) using known information such as one event waveform (a production shot waveform) and the timing sequence used (the comb function) for the shot. The methodology is based on the analysis of group delay, a concept widely used in signal processing, and is modified here for the analysis of ground vibration waveforms. The methodology is developed using real data collected in coal and quarry mining operations, and at the end of this document, one case study with step-by-step calculations is presented to show the benefits of the methodology.

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Koteleva, Natalia, and Ilia Frenkel. "Digital Processing of Seismic Data from Open-Pit Mining Blasts." Applied Sciences 11, no. 1 (January 2, 2021): 383. http://dx.doi.org/10.3390/app11010383.

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This article describes an approach of mathematical processing of signals (seismograms) from five blasthole charges from experimental blasting, each 3 m deep, with equal explosive weight (1 kg), and equidistant (3 m) from one other. The seismic explosive waves were measured at a 13 to 25 m distance. This article provides spectral analysis, wavelet analysis, and fractal analysis results. It defines the dependence of dominant frequency and amplitude on the distance to the blast center. According to the experimental data, the dominant frequency is calculated as y = 1.0262x0.2622 and the amplitude dependency as y = 18.139x−2.276. Furthermore, the analysis shows that 80% of the entire signal is concentrated in half the area of frequency range, i.e., the low frequency zone is of the most interest. This research defines the dependence of distance on the energy value of signal wavelet analysis. It is demonstrated that, according to the experimental data, the 12th frequency range is closely correlated with the distance values. This article gives the definitions of entropy, correlation dimension, and predictability time. This experiment shows that entropy and correlation dimension decrease but predictability time increases when the distance to the blast center increases. This article also describes the method for determining optimal drilling and blasting parameters, and concludes with the possibility of applying the analytical results to predicting and enhancing drilling and blasting operations.

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Chimwani, Ngonidzashe. "Editorial for Special Issue “Comminution and Comminution Circuits Optimisation”." Minerals 13, no. 1 (January 5, 2023): 81. http://dx.doi.org/10.3390/min13010081.

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Bersenev, G. P., A. V. Glebov, and V. A. Kutuev. "About scientific and practical conference explosives of the Urals." Mining Industry Journal (Gornay Promishlennost), no. 6/2021 (January 15, 2022): 58–60. http://dx.doi.org/10.30686/1609-9192-2021-6-58-60.

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The article reviews the results of the VIII Scientific and Practical Conference with international participation "Technology and Safety of Drilling and Blasting Operations in Surface and Underground Mines of the Urals" which was held at the Ekaterinburg-EXPO Exhibition Center and the Uralasbest Industrial Complex as part of the IX Urals Mining Industry Forum and the URAL MINING' 21 Exhibition dedicated to the Year of Science and Technology in the Russian Federation and the 30th Anniversary of the Urals Mining and Industrial Association. The article also informs about the winners of the Ural Mining Award 2021 and about the research and engineering reports and presentations made during the conference and dedicated to blasting operations. Results are summarized of the visiting seminar at the Uralasbest Industrial Complex, where the Chief Engineer of Promtekhvzvzryv Company N.A. Chistyakov and General Director of the Urals Explosives Association G.P. Bersenev conducted tours to the open-pit mine, the 'Poremit' Plant - the first emulsion explosives production facility in the Urals, a bulk explosives storage, the museum and training center of the Urals Asbestos Mining and Processing Complex. Following the tours of the training center site of the Processing Complex, the second part of the conference was held, during which a number of reports were delivered. Upon completion of the seminar, the Urals Explosives Association awarded honorary certificates and valuable gifts to specialists of the Promtekhvzvzryv Company for their contribution to improvement of drilling and blasting technology and organization of labor on blasting sites. The article is concluded by a summary of the Conference's decisions.

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Basargin, Andrei A., and Viktor S. Pisarev. "DESIGN OF DRILLING AND EXPLOSION WORKS IN UNDERGROUND MINING USING IN MICROMINE GGIS." Interexpo GEO-Siberia 1, no. 1 (July 8, 2020): 3–14. http://dx.doi.org/10.33764/2618-981x-2020-1-1-3-14.

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In the modern world, an increasing number of enterprises involved in geological exploration and exploration use special software and information systems in their work. The use of such systems can significantly accelerate the processing and analysis of information. They make it possible to automate the processing and interpretation of geological exploration data, as well as use them to model deposits and design underground drilling and blasting operations. GGIS Micromine will automate the design of drilling and blasting operations while ensuring well placement taking into account the block geometry and rock properties, and a rational distribution of borehole charges for the most efficient crushing of rock mass. In conditions of high intensity of mining operations at the MGIS quarries, Micromine ensures the efficiency and multivariance of design decisions when performing blasting.

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Egorov, V. V., A. N. Volokitin, N. V. Ugolnikov, and A. V. Sokolovsky. "Justification of parameters and technology of drilling and blasting operations to ensure the required lumpiness." Mining Industry Journal (Gornay Promishlennost), no. 3/2021 (July 20, 2021): 110–15. http://dx.doi.org/10.30686/1609-9192-2021-3-110-115.

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The practice of mining and blasting operations both during the development of a mineral deposits, and at the mine design stage, often involves the question of selecting the technology options and operation parameters. Virtually all recommendations for selecting the best production option are based on minimizing the total costs for the entire technological cycle of mining activities. In most cases the optimal technology and parameters of mining and blasting operations depend on the commercial, maximum permissible and average size of the blasted rock mass, which are determined by the type and capacity of the mining haulage equipment. Therefore, the total costs will mainly depend on the commercial or average lump size and the cost of mining transport equipment. The article presents a methodology to select the best technological option for the drilling and blasting operations to obtain the optimal lumpiness (particle-size distribution) of the blasted rock. The optimal range of lumpiness is defined by the total minimum costs for the entire production cycle of mining and processing of minerals. In order to select a rational technology of drilling and blasting and to calculate their parameters it is proposed to take into account the integral criteria of lumpiness in addition to the average lump size. For this purpose, we studied the particle size distribution in the rock mass and in the muck piles.

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Guo, Li, Cai Wu Lu, and Zhen Yang. "CAD Secondary Development of Medium-Depth Hole Blasting Design System Based on Object/ARX." Applied Mechanics and Materials 65 (June 2011): 285–90. http://dx.doi.org/10.4028/www.scientific.net/amm.65.285.

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The medium-depth blasting design of underground mining needs to do a lot of data- processing of geological survey and graphics rendering. It is repetitive and tedious. This paper introduces a Medium-depth hole blasting design CAD system (MHBD) based on Object/ARX. However, the system of non-pillar sublevel caving can decrease the labor intensity, reduce design time and improve labor efficiency.

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Більше джерел

Дисертації з теми "Mining engineering Blasting Data processing"

Williamson, Lance K. "ROPES : an expert system for condition analysis of winder ropes." Master's thesis, University of Cape Town, 1990. http://hdl.handle.net/11427/15982.

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Includes bibliographical references.
This project was commissioned in order to provide engineers with the necessary knowledge of steel wire winder ropes so that they may make accurate decisions as to when a rope is near the end of its useful life. For this purpose, a knowledge base was compiled from the experience of experts in the field in order to create an expert system to aid the engineer in his task. The EXSYS expert system shell was used to construct a rule-based program which would be run on a personal computer. The program derived in this thesis is named ROPES, and provides information as to the forms of damage that may be present in a rope and the effect of any defects on rope strength and rope life. Advice is given as to the procedures that should be followed when damage is detected as well as the conditions which would necessitate rope discard and the urgency with which the replacement should take place. The expert system program will provide engineers with the necessary expertise and experience to assess, more accurately than at present, the condition of a winder rope. This should lead to longer rope life and improved safety with the associated cost savings. Rope assessment will also be more uniform with changes to policy being able to be implemented quickly and on an ongoing basis as technology and experience improves. The program ROPES, although compiled from expert knowledge, still requires the further input of personal opinions and inferences to some extent. For this reason, the program cannot be assumed infallible and must be used as an aid only.

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van, Schaik Sebastiaan Johannes. "A framework for processing correlated probabilistic data." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:91aa418d-536e-472d-9089-39bef5f62e62.

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The amount of digitally-born data has surged in recent years. In many scenarios, this data is inherently uncertain (or: probabilistic), such as data originating from sensor networks, image and voice recognition, location detection, and automated web data extraction. Probabilistic data requires novel and different approaches to data mining and analysis, which explicitly account for the uncertainty and the correlations therein. This thesis introduces ENFrame: a framework for processing and mining correlated probabilistic data. Using this framework, it is possible to express both traditional and novel algorithms for data analysis in a special user language, without having to explicitly address the uncertainty of the data on which the algorithms operate. The framework will subsequently execute the algorithm on the probabilistic input, and perform exact or approximate parallel probability computation. During the probability computation, correlations and provenance are succinctly encoded using probabilistic events. This thesis contains novel contributions in several directions. An expressive user language – a subset of Python – is introduced, which allows a programmer to implement algorithms for probabilistic data without requiring knowledge of the underlying probabilistic model. Furthermore, an event language is presented, which is used for the probabilistic interpretation of the user program. The event language can succinctly encode arbitrary correlations using events, which are the probabilistic counterparts of deterministic user program variables. These highly interconnected events are stored in an event network, a probabilistic interpretation of the original user program. Multiple techniques for exact and approximate probability computation (with error guarantees) of such event networks are presented, as well as techniques for parallel computation. Adaptations of multiple existing data mining algorithms are shown to work in the framework, and are subsequently subjected to an extensive experimental evaluation. Additionally, a use-case is presented in which a probabilistic adaptation of a clustering algorithm is used to predict faults in energy distribution networks. Lastly, this thesis presents techniques for integrating a number of different probabilistic data formalisms for use in this framework and in other applications.

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Pabarškaitė, Židrina. "Enhancements of pre-processing, analysis and presentation techniques in web log mining." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2009. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2009~D_20090713_142203-05841.

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As Internet is becoming an important part of our life, more attention is paid to the information quality and how it is displayed to the user. The research area of this work is web data analysis and methods how to process this data. This knowledge can be extracted by gathering web servers’ data – log files, where all users’ navigational patters about browsing are recorded. The research object of the dissertation is web log data mining process. General topics that are related with this object: web log data preparation methods, data mining algorithms for prediction and classification tasks, web text mining. The key target of the thesis is to develop methods how to improve knowledge discovery steps mining web log data that would reveal new opportunities to the data analyst. While performing web log analysis, it was discovered that insufficient interest has been paid to web log data cleaning process. By reducing the number of redundant records data mining process becomes much more effective and faster. Therefore a new original cleaning framework was introduced which leaves records that only corresponds to the real user clicks. People tend to understand technical information more if it is similar to a human language. Therefore it is advantageous to use decision trees for mining web log data, as they generate web usage patterns in the form of rules which are understandable to humans. However, it was discovered that users browsing history length is different, therefore specific data... [to full text]
Internetui skverbiantis į mūsų gyvenimą, vis didesnis dėmesys kreipiamas į informacijos pateikimo kokybę, bei į tai, kaip informacija yra pateikta. Disertacijos tyrimų sritis yra žiniatinklio serverių kaupiamų duomenų gavyba bei duomenų pateikimo galutiniam naudotojui gerinimo būdai. Tam reikalingos žinios išgaunamos iš žiniatinklio serverio žurnalo įrašų, kuriuose fiksuojama informacija apie išsiųstus vartotojams žiniatinklio puslapius. Darbo tyrimų objektas yra žiniatinklio įrašų gavyba, o su šiuo objektu susiję dalykai: žiniatinklio duomenų paruošimo etapų tobulinimas, žiniatinklio tekstų analizė, duomenų analizės algoritmai prognozavimo ir klasifikavimo uždaviniams spręsti. Pagrindinis disertacijos tikslas – perprasti svetainių naudotojų elgesio formas, tiriant žiniatinklio įrašus, tobulinti paruošimo, analizės ir rezultatų interpretavimo etapų metodologijas. Darbo tyrimai atskleidė naujas žiniatinklio duomenų analizės galimybes. Išsiaiškinta, kad internetinių duomenų – žiniatinklio įrašų švarinimui buvo skirtas nepakankamas dėmesys. Parodyta, kad sumažinus nereikšmingų įrašų kiekį, duomenų analizės procesas tampa efektyvesnis. Todėl buvo sukurtas naujas metodas, kurį pritaikius žinių pateikimas atitinka tikruosius vartotojų maršrutus. Tyrimo metu nustatyta, kad naudotojų naršymo istorija yra skirtingų ilgių, todėl atlikus specifinį duomenų paruošimą – suformavus fiksuoto ilgio vektorius, tikslinga taikyti iki šiol nenaudotus praktikoje sprendimų medžių algoritmus... [toliau žr. visą tekstą]

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Yu, Zhiguo. "Cooperative Semantic Information Processing for Literature-Based Biomedical Knowledge Discovery." UKnowledge, 2013. http://uknowledge.uky.edu/ece_etds/33.

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Given that data is increasing exponentially everyday, extracting and understanding the information, themes and relationships from large collections of documents is more and more important to researchers in many areas. In this paper, we present a cooperative semantic information processing system to help biomedical researchers understand and discover knowledge in large numbers of titles and abstracts from PubMed query results. Our system is based on a prevalent technique, topic modeling, which is an unsupervised machine learning approach for discovering the set of semantic themes in a large set of documents. In addition, we apply a natural language processing technique to transform the “bag-of-words” assumption of topic models to the “bag-of-important-phrases” assumption and build an interactive visualization tool using a modified, open-source, Topic Browser. In the end, we conduct two experiments to evaluate the approach. The first, evaluates whether the “bag-of-important-phrases” approach is better at identifying semantic themes than the standard “bag-of-words” approach. This is an empirical study in which human subjects evaluate the quality of the resulting topics using a standard “word intrusion test” to determine whether subjects can identify a word (or phrase) that does not belong in the topic. The second is a qualitative empirical study to evaluate how well the system helps biomedical researchers explore a set of documents to discover previously hidden semantic themes and connections. The methodology for this study has been successfully used to evaluate other knowledge-discovery tools in biomedicine.

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Grenoble, B. Alex. "Microcomputer simulation of near seam interaction." Thesis, Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/90929.

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The mining of coal within 110 feet below a previously mined seam creates interaction effects which can be detrimental to work in the lower seam. These interaction effects are characterized by zones of very high stress and result in floor and roof instability and pillar crushing. Recent developments in the field of ground control make it possible to determine with a certain degree of confidence the location of these zones and estimate the degree to which the interaction will affect the lower seam. This information has been incorporated into a software package for microcomputers which will predict lower seam problems and suggest design criteria for minimizing the difficulties which will be encountered.
M.S.

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Gupta, Shweta. "Software Development Productivity Metrics, Measurements and Implications." Thesis, University of Oregon, 2018. http://hdl.handle.net/1794/23816.

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The rapidly increasing capabilities and complexity of numerical software present a growing challenge to software development productivity. While many open source projects enable the community to share experiences, learn and collaborate; estimating individual developer productivity becomes more difficult as projects expand. In this work, we analyze some HPC software Git repositories with issue trackers and compute productivity metrics that can be used to better understand and potentially improve development processes. Evaluating productivity in these communities presents additional challenges because bug reports and feature requests are often done by using mailing lists instead of issue tracking, resulting in difficult-to-analyze unstructured data. For such data, we investigate automatic tag generation by using natural language processing techniques. We aim to produce metrics that help quantify productivity improvement or degradation over the projects lifetimes. We also provide an objective measurement of productivity based on the effort estimation for the developer's work.

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Castro, Jose R. "MODIFICATIONS TO THE FUZZY-ARTMAP ALGORITHM FOR DISTRIBUTED LEARNING IN LARGE DATA SETS." Doctoral diss., University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4449.

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The Fuzzy-ARTMAP (FAM) algorithm is one of the premier neural network architectures for classification problems. FAM can learn on line and is usually faster than other neural network approaches. Nevertheless the learning time of FAM can slow down considerably when the size of the training set increases into the hundreds of thousands. In this dissertation we apply data partitioning and network partitioning to the FAM algorithm in a sequential and parallel setting to achieve better convergence time and to efficiently train with large databases (hundreds of thousands of patterns). We implement our parallelization on a Beowulf clusters of workstations. This choice of platform requires that the process of parallelization be coarse grained. Extensive testing of all the approaches is done on three large datasets (half a million data points). One of them is the Forest Covertype database from Blackard and the other two are artificially generated Gaussian data with different percentages of overlap between classes. Speedups in the data partitioning approach reached the order of the hundreds without having to invest in parallel computation. Speedups on the network partitioning approach are close to linear on a cluster of workstations. Both methods allowed us to reduce the computation time of training the neural network in large databases from days to minutes. We prove formally that the workload balance of our network partitioning approaches will never be worse than an acceptable bound, and also demonstrate the correctness of these parallelization variants of FAM.
Ph.D.
School of Electrical and Computer Engineering
Engineering and Computer Science
Electrical and Computer Engineering

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Kumar, Saurabh. "Real-Time Road Traffic Events Detection and Geo-Parsing." Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10842958.

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In the 21^st century, there is an increasing number of vehicles on the road as well as a limited road infrastructure. These aspects culminate in daily challenges for the average commuter due to congestion and slow moving traffic. In the United States alone, it costs an average US driver $1200 every year in the form of fuel and time. Some positive steps, including (a) introduction of the push notification system and (b) deploying more law enforcement troops, have been taken for better traffic management. However, these methods have limitations and require extensive planning. Another method to deal with traffic problems is to track the congested area in a city using social media. Next, law enforcement resources can be re-routed to these areas on a real-time basis.

Given the ever-increasing number of smartphone devices, social media can be used as a source of information to track the traffic-related incidents.

Social media sites allow users to share their opinions and information. Platforms like Twitter, Facebook, and Instagram are very popular among users. These platforms enable users to share whatever they want in the form of text and images. Facebook users generate millions of posts in a minute. On these platforms, abundant data, including news, trends, events, opinions, product reviews, etc. are generated on a daily basis.

Worldwide, organizations are using social media for marketing purposes. This data can also be used to analyze the traffic-related events like congestion, construction work, slow-moving traffic etc. Thus the motivation behind this research is to use social media posts to extract information relevant to traffic, with effective and proactive traffic administration as the primary focus. I propose an intuitive two-step process to utilize Twitter users' posts to obtain for retrieving traffic-related information on a real-time basis. It uses a text classifier to filter out the data that contains only traffic information. This is followed by a Part-Of-Speech (POS) tagger to find the geolocation information. A prototype of the proposed system is implemented using distributed microservices architecture.

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Sheikha, Hassan. "Text mining Twitter social media for Covid-19 : Comparing latent semantic analysis and latent Dirichlet allocation." Thesis, Högskolan i Gävle, Avdelningen för datavetenskap och samhällsbyggnad, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-32567.

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In this thesis, the Twitter social media is data mined for information about the covid-19 outbreak during the month of March, starting from the 3’rd and ending on the 31’st. 100,000 tweets were collected from Harvard’s opensource data and recreated using Hydrate. This data is analyzed further using different Natural Language Processing (NLP) methodologies, such as termfrequency inverse document frequency (TF-IDF), lemmatizing, tokenizing, Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Furthermore, the results of the LSA and LDA algorithms is reduced dimensional data that will be clustered using clustering algorithms HDBSCAN and K-Means for later comparison. Different methodologies are used to determine the optimal parameters for the algorithms. This is all done in the python programing language, as there are libraries for supporting this research, the most important being scikit-learn. The frequent words of each cluster will then be displayed and compared with factual data regarding the outbreak to discover if there are any correlations. The factual data is collected by World Health Organization (WHO) and is then visualized in graphs in ourworldindata.org. Correlations with the results are also looked for in news articles to find any significant moments to see if that affected the top words in the clustered data. The news articles with good timelines used for correlating incidents are that of NBC News and New York Times. The results show no direct correlations with the data reported by WHO, however looking into the timelines reported by news sources some correlation can be seen with the clustered data. Also, the combination of LDA and HDBSCAN yielded the most desireable results in comparison to the other combinations of the dimnension reductions and clustering. This was much due to the use of GridSearchCV on LDA to determine the ideal parameters for the LDA models on each dataset as well as how well HDBSCAN clusters its data in comparison to K-Means.

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Yang, Yimin. "Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2254.

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Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.

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Книги з теми "Mining engineering Blasting Data processing"

Oberndorfer, Thomas. Computer-aided mining method decision with special emphasis on computer-oriented mining method description. Leoben: Institut für Bergbaukunde, Bergtechnik und Bergwirtschaft, Montanuniversität Leoben, 1993.

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Sirmakessis, Spiros. Text Mining and its Applications: Results of the NEMIS Launch Conference. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.

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Georgieva, Petia. Advances in Intelligent Signal Processing and Data Mining: Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Seminário Nacional: O Computador e sua Aplicação no Setor Mineral: Pesquisa, Lavra e Beneficiamento Mineral (2nd 1986 Belo Horizonte, Brazil). 2o. Seminário Nacional--O Computador e sua Aplicação no Setor Mineral--Pesquisa, Lavra e Beneficiamento Mineral: Belo Horizonte, 18 a 20/jun/86. Belo Horizonte-Minas Gerais: IBRAM, 1986.

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International Symposium on the Application of Computers and Operations Research in the Mineral Industries (25th 1995 Brisbane, Australia). APCOM XXV 1995: Application of computers and operations research in the minerals industries, Brisbane, Australia, 9-14 July 1995. Carlton, Vic. Australia: Australasian Institute of Mining and Metallurgy, 1995.

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United States. Bureau of Mines., ed. Bureau of Mines cost estimating system handbook. [Pittsburgh, Pa.]: U.S. Dept. of the Interior, Bureau of Mines, 1987.

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Smith, Martin Lloyd. Geologic and mine modelling using Techbase and Lynx. Rotterdam: A.A. Balkema, 1999.

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Donato, D. A. MULSIM/PC: A personal computer-based structural analysis program for mine design in deep tabular deposits. Washington, D.C: U.S. Dept. of the Interior, Bureau of Mines, 1992.

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Sammarco, John J. Computer-aided software engineering (CASE) for software automation. [Washington, D.C.]: U.S. Dept. of the Interior, Bureau of Mines, 1990.

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Large scale and big data: Processing and management. Boca Raton: Taylor & Francis, 2014.

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Частини книг з теми "Mining engineering Blasting Data processing"

Xue, Qilong. "Data Processing and Mining in Seismic While Drilling." In Data Analytics for Drilling Engineering, 205–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34035-3_6.

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Cao, Zhiying. "Research on Medical Information Processing Based on Data Mining Technology." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 510–16. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18123-8_39.

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Rao, Wei, and Jian Chen. "Risk Control System of Construction Engineering Based on Data Mining and Artificial Intelligence Technology." In Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019), 1915–23. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1468-5_226.

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Görgülü, Zafer-Korcan, and Stefan Pickl. "Adaptive Business Intelligence: The Integration of Data Mining and Systems Engineering into an Advanced Decision Support as an Integral Part of the Business Strategy." In Advanced Information and Knowledge Processing, 43–58. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4866-1_4.

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Nodeh, Mohsen Jafari, M. Hanefi Calp, and İsmail Şahin. "Analyzing and Processing of Supplier Database Based on the Cross-Industry Standard Process for Data Mining (CRISP-DM) Algorithm." In Artificial Intelligence and Applied Mathematics in Engineering Problems, 544–58. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36178-5_44.

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Cheptsov, Alexey, Axel Tenschert, Paul Schmidt, Birte Glimm, Mauricio Matthesius, and Thorsten Liebig. "Introducing a New Scalable Data-as-a-Service Cloud Platform for Enriching Traditional Text Mining Techniques by Integrating Ontology Modelling and Natural Language Processing." In Web Information Systems Engineering – WISE 2013 Workshops, 62–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54370-8_6.

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Ma, Z. M. "Databases Modeling of Engineering Information." In Data Warehousing and Mining, 1182–204. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-951-9.ch067.

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Information systems have become the nerve center of current computer-based engineering applications, which hereby put the requirements on engineering information modeling. Databases are designed to support data storage, processing, and retrieval activities related to data management, and database systems are the key to implementing engineering information modeling. It should be noted that, however, the current mainstream databases are mainly used for business applications. Some new engineering requirements challenge today’s database technologies and promote their evolvement. Database modeling can be classified into two levels: conceptual data modeling and logical database modeling. In this chapter, we try to identify the requirements for engineering information modeling and then investigate the satisfactions of current database models to these requirements at two levels: conceptual data models and logical database models. In addition, the relationships among the conceptual data models and the logical database models for engineering information modeling are presented in the chapter viewed from database conceptual design.

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Zhou, Zude, Huaiqing Wang, and Ping Lou. "Data Mining and Knowledge Discovery." In Manufacturing Intelligence for Industrial Engineering, 84–110. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-864-2.ch004.

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In Chapters 2 and 3, the knowledge-based system and Multi-Agent system were illustrated. These are significant methods and theories of Manufacturing Intelligence (MI). Data Mining (DM) and Knowledge Discovery (KD) are at the foundation of MI. Humans are immersed in data, but are thirsty for knowledge. With the wider application of database technology, a dilemma has arisen whereby people are ‘rich in data, poor in knowledge’. The explosion of knowledge and information has brought great benefit to mankind, but has also carried with it certain drawbacks, since it has resulted in knowledge and information ‘pollution. Facing a vast but polluted ocean of data, a technical means to discard the bad and retain the good was sought. Data Mining and Knowledge Discovery (DMKD) was therefore proposed against the background of rapidly expanding data and databases. It is also the result of the development and fusion of database technology, Artificial Intelligence (AI), statistical techniques and visualization technology (Fayyad U., 1998). DMKD has become a research focus and cutting-edge technology in the field of computer information processing (Jef Woksem, 2001). The development background, conception, working process, classification and general application of DM and KD are firstly introduced in this chapter. Secondly, basic functions and assignment such as prediction, description, data clustering, data classification, conception description and visualization processing are discussed. Then the methods and tools for DM are presented, such as the association rule, decision tree, genetic algorithm, rough set and support vector machine. Finally, the application of DMKD in intelligent manufacturing is summarized.

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Rakesh Kumar, S., N. Gayathri, S. Muthuramalingam, B. Balamurugan, C. Ramesh, and M. K. Nallakaruppan. "Medical Big Data Mining and Processing in e-Healthcare." In Internet of Things in Biomedical Engineering, 323–39. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-817356-5.00016-4.

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Huang, Sen, Linna Li, Dongwang Zhong, Li He, and Jianfeng Si. "Vibration Signal Analysis of Chimney Blasting Based on HHT." In Advances in Transdisciplinary Engineering. IOS Press, 2021. http://dx.doi.org/10.3233/atde210182.

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In the blasting demolition processs of high-rise structures, the impact of blasting vibration to the environment and objects to be protected must be effectively controlled, so the blasting vibration signal is deeply analyzed [1]. In this paper, the blasting vibration signal of a chimney is analyzedbased on HHT. The blasting vibration signal is denoised by Empirical Mode Decomposition (EMD)-wavelet threshold, then using Hilbert-Huang Transform (HHT) [2] the measured blasting vibration waveform Hilbert spectrum, marginal spectrum and instantaneous energy graph are draw to analyze the chimney blasting vibration. The results show that the denoising effect of EMD-wavelet threshold is good for blasting vibration signal [3]. HHT method has a good feature identification ability when processing vibration signals, and can reflect the characteristics of data more comprehensively and accurately, which provides convenience for the study of vibration signal data.

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Тези доповідей конференцій з теми "Mining engineering Blasting Data processing"

Abramov, M. V., G. M. Trigubovich, and A. S. Sverkunov. "Processing and Interpretation of Marine Electrical Survey Data." In Engineering and Mining Geophysics 2021. European Association of Geoscientists & Engineers, 2021. http://dx.doi.org/10.3997/2214-4609.202152181.

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Anbananthen, S. Kalaiarasi, G. Sainarayanan, Ali Chekima, and Jason Teo. "Data mining using Artificial Neural network tree." In Signal Processing with Special Track on Biomedical Engineering (CCSP). IEEE, 2005. http://dx.doi.org/10.1109/ccsp.2005.4977180.

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McLachlan, Paul. "EMagPy: An Intuitive Application for Inverting and Processing Frequency Domain Electromagnetic (EMI) Data." In Engineering and Mining Geophysics 2021. European Association of Geoscientists & Engineers, 2021. http://dx.doi.org/10.3997/2214-4609.202152252.

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Persova, M. G., Yu G. Soloveichik, D. V. Vagin, A. P. Sivenkova, A. S. Kiseleva, and M. G. Tokareva. "The possibilities of geometric 3-D inversion for processing the UAV-TDEM data." In Engineering and Mining Geophysics 2021. European Association of Geoscientists & Engineers, 2021. http://dx.doi.org/10.3997/2214-4609.202152042.

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Prigara, A. M. "Features of processing of mine seismic data on transverse waves with reflection separation." In Engineering and Mining Geophysics 2020. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202051166.

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Iakovlev, S. V., Y. A. Davydenko, A. S. Bashkeev, E. A. Krainova, A. Y. Davydenko, I. Y. Pesterev, and M. G. Persova. "An Integrated Approach of Processing Data of Marine Electromagnetic Surveying Using a Towed Streamer." In Engineering and Mining Geophysics 2021. European Association of Geoscientists & Engineers, 2021. http://dx.doi.org/10.3997/2214-4609.202152151.

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Tagiltsev, S. N., V. S. Tagiltsev, S. V. Surganov, and A. A. Kurichenko. "Use of Hydrodynamic Characteristics of The Underground Water Flow for Processing Single Pumping Data." In Engineering and Mining Geophysics 2021. European Association of Geoscientists & Engineers, 2021. http://dx.doi.org/10.3997/2214-4609.202152023.

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Wang, Rui, Jinguo Wang, and Na Wang. "Application of data mining technology in medical image processing." In 2016 International Conference on Engineering and Advanced Technology (ICEAT 2016). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/iceat-16.2017.5.

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Liu, Nan, Peng Peng, Zuzhi Shen, Haihang Han, and Mingrong Deng. "Optimization of Traffic Information Processing Based on Data Mining from GPS Historical Data." In Second International Conference on Transportation Engineering. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41039(345)584.

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"Opinion Mining on Twitter Data for Airline Services." In 2015 The 5th International Workshop on Computer Science and Engineering-Information Processing and Control Engineering. WCSE, 2015. http://dx.doi.org/10.18178/wcse.2015.04.104.

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