On two Random Models in Data Analysis

An amplitude correlation and differencing method for the monitoring of flaws in repeat ultrasonic inspections

Social media has become an important reference for information during the last few decades. They have been able to be effective in various fields such as business, entertainment, science, crisis management, politics, etc. For this reason, a social media analysis has become very important for researchers and large companies. The widespread use of social media leads to a complex problem called "accumulation of data". Many data science specialists seek to analyze this data in order to identify the behavioral characteristics of users, analyze interests and needs, and improve marketing processes. Different social media platforms have the ability to use all kinds of media, including text data, video, video, audio, and location information, etc. Therefore, data analysis in social networks is very important. In this research, the concepts and applications of data analysis in social networks will be investigated.

Synthetic aperture imaging of contact acoustic nonlinearity to visualize the closing interfaces using tone-burst ultrasonic waves

To optimally preserve and manage our civil structures, we need to have accurate information about their (1) geometry and dimensions, (2) boundary conditions, (3) material properties, and (4) structural conditions. The objective of this article is to show how imaging and image fusion using non-destructive testing (NDT) measurements can support structural engineers in performing accurate structural evaluations. The proposed methodology involves imaging using synthetic aperture focusing technique (SAFT)-based image reconstruction from ground penetrating radar (GPR) as well as ultrasonic echo array (UEA) measurements taken on multiple surfaces of a structural member. The created images can be combined using image fusion to produce a digital cross-section of the member. The feasibility of this approach is demonstrated using a case study of a prestressed concrete bridge that required a bridge load rating (BLR) but where no as-built plans were available. Imaging and image fusion enabled the creation of a detailed cross-section, allowing for confirmation of the number and location of prestressing strands and the location and size of internal voids. This information allowed the structural engineer of record (SER) to perform a traditional bridge load rating (BLR), ultimately avoiding load restrictions being imposed on the bridge. The proposed methodology not only provides useful information for structural evaluations, but also represents a basis upon which the digitalization of our infrastructure can be achieved.

A sufficient concrete cover is essential to ensure the durability of reinforced concrete structures. Nondestructive testing methods that can measure the concrete cover are therefore promising tools. As a part of a research project funded by the Florida Department of Transportation, the capabilities and limitations of cover meter measurements in relation to this testing problem were investigated. Researchers designed a reinforced concrete test block on which properties such as rebar depth, size, and spacing and number of reinforcement layers were varied; the effects these variations had on the measurements were studied. The use of an automated testing frame consisting of two scanners that examined the test block from both sides ensured high positioning accuracy and constant quality in data acquisition and made possible the collection of the data along an extremely dense grid. In addition to the cover meter measurements, which referred only to the cover of the layer closest to the surface, ultrasonic pulse echo measurements were conducted, and a synthetic aperture focusing technique was applied to the data to make the rebars become apparent in refined B-scan images.

McDonald, Erickson, and McDonald (2000) and Murtaugh (2000) have presented two interesting views of analysis and interpretation of data from before-after control-impact (BACI) studies. In my opinion, the general message they convey to the reader is that analysis of data from a BACI design, in combination with professional expertise, can add useful evidence as long as one is prudent about the resulting interpretations. Whether scientists who analyze BACI data will indeed use caution in interpretation, particularly in making statements regarding cause and effect, remains to be seen. McDonald et al.'s (2000) study is application specific, taking the reader through an analysis of bird count data from the Exxon Valdez oil spill (EVOS). The authors prudently phrase their interpretation in terms of a weight of evidence argument rather than the much more difficult one of causation. I see the primary usefulness of this paper as that of encouraging scientists to use more complex models to realistically describe count data through time. (Using these models in data analysis has been considerably enhanced by the increasing availability of various types of computer software.) Particularly with biological data, a conventional first step is to try an additive model on untransformed data or an additive model on log-transformed data with the hope that the log transform restores the assumptions of variance homogeneity and normally distributed error terms. The presence of data recorded on the same units through time necessitates that the Huynh-Feldt condition (also known as sphericity) holds in order to use a conventional repeated measures approach. In case this sphericity condition does not hold, there are references to make conservative adjustments to the usual F-tests. (I do wonder just how many people would take the trouble to test these assumptions and do the proper corrections if such corrections were not already built into the

This book is a collective work by several leading scientists, analysts, engineers, mathematicians and statisticians, who have been working at the forefront of data analysis and related applications, arising from data science, operations research, engineering, machine learning or statistics. Data Analysis and Related Applications 5 represents a cross-section of current concerns and research interests in the above scientific areas. The collected material has been divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.

This paper describes the continuation of the work1,2 on synthetic aperture focusing techniques (SAFT) and ultrasonic imaging as well as new work on the measurement of the amplitude and phase as a function of frequency and aperture position.

In this era of information explosion, the dissemination of new media has emerged as a significant force influencing societal discourse and commercial decisions. With the rapid advancement of internet technology, the user base of new media platforms has expanded swiftly, leading to a gradual upheaval of traditional communication models. Against this backdrop, data analysis, as an innovative technological tool, is profoundly impacting the methods of new media dissemination with its powerful information processing and insight capabilities. Through the real-time collection and processing of big data, data analysis not only provides precise user profiles for new media but also offers robust support for content production and the optimization of dissemination paths. Particularly in areas such as enhancing user engagement and evaluating dissemination effectiveness, data analysis demonstrates its irreplaceable value. However, with the explosive growth of data, addressing issues related to data privacy, security, and algorithmic bias has become a critical research topic. This paper explores the practical applications of data analysis in new media dissemination, aiming to reveal future development trends and challenges in this field, and provide theoretical support and practical references for professionals in the new media industry.

The article presents, summarizes and develops technological concepts of cyber security based on machine learning, data processing and analysis. The seven steps of cyber attacks, use of key techniques of data analysis in cyber defense, application of data processing and analysis in information security and some tools used are presented. Applying data processing and analysis as well as extracting dependencies from them in the field of cyber security has some advantages and disadvantages wich are indicated. A common scheme for data analysis is introduced. Main types of penetration of cyberattacks are involved. Cyber attack models abuse detection and anomaly detection are presented too. Тhree types of anomalies the timing, the number, and the pattern(s) are revealed and examples are pointed.

Randomized response mechanisms for guaranteeing crowdsourcing data privacy have attracted scholarly attention; aggregators can ensure privacy by collecting only randomized data, and individuals can have plausible deniability regarding their responses. With these mechanisms, analysts employed by organizations can still make predictions and conduct analyses using the randomized data. Existing randomized response-based data collection solutions have severely restricted functionality and usability, resulting in impractical and inefficient systems. Therefore, we developed a randomized response-based privacy-preserving crowdsourcing data collection and analysis mechanism. We designed a complementary randomized response (C-RR) method to guarantee individuals’ data privacy and to preserve features from the original data for analysis. We formalized a machine learning framework; our proposed method uses randomized data in the form of binary vectors to generate a learning network. Extensive experiments on real-world data sets demonstrated that our heavy-hitters estimation scheme, which applies C-RR and our data learning model, significantly outperformed existing estimation schemes in terms of data analysis.

The forest fire is one of the great threats to the operation of transmission line. The reasons and prevent measures of forest fire induced breakdown of the transmission line were investigated, and the application of data analysis in this field was discussed in the present study. According to the data statistics on the forest fire induced breakdown of the transmission line, the occurrence of transmission line trip has apparent time and space rules. The flow chart of big data analysis on transmission line breakdown induced by forest fires with the data analysis was proposed. By using big data analysis, important information can be derived, which is helpful to the design of early-warning system, as well as the proper strategic decisions to control the damage of transmission line in forest fires.

The interpretation of data from nondestructive testing (NDT) is a complex task and is typically performed by experienced operators. However, increasing numbers of inspections being performed and increasing requirements with respect to the reliability and repeatability of inspections result in a demand for automatic NDT data-interpretation systems. Reports of successful automatic interpretation systems mainly come from nuclear-power and aircraft industries. These systems typically use such techniques for data interpretation as: statistical classifiers, ANNs, or expert systems. These techniques, however, have had limited success for inspection in less well-defined environments, like petro-chemical installations or railroads. There, the large variability of the data, as well as the practical problems of economic justifiability of automating data interpretation become important. Case-based reasoning (CBR) is an AI methodology particularly suitable for NDT data interpretation in such environments. CBR systems are characterized by lower construction costs, reliability, adaptability, ease of use, and ease of maintenance. This paper presents two CBR systems which have been developed for eddy-current inspection and for ultrasonic rail inspection. The design of both systems as well as the issues encountered in the development of two prototypes are discussed. The evaluation of the performance of the systems was positive. The system for ultrasonic rail inspection is currently under evaluation by the Dutch Railways.

Exploration on Data Collection and Analysis System Based on Integrated SIFT Algorithm

In the recent decades, remote sensing data are rapidly growing in size and variety, and considered as “big geo data” because of their huge data volume, significant heterogeneity and challenge of fast analysis. In the traditional remote sensing analysis workflows, the data transfer for downloading raw image files to local workstations often costs a lot of time and slows down the data analysis workflows. Because results of remote sensing data analysis models are usually much smaller than raw data to be processed, “on-demand processing”, which tries to upload data analysis models and execute them “near” where data stores, can significantly accelerate the execution of remote sensing analysis workflows. In this paper, a framework for on-demand remote sensing data analysis is proposed based on three-layered architecture; XML/JSON based runtime environment description; and on-demand model deployment methods. The evaluation on a prototype system shows that on-demand processing framework accelerates the execution of analysis models in 2.8 ~ 12.7 times by reducing data transfers, especially for those analysis workflows which transfer data through low bandwidth Internet. By on-demand processing, classical remote sensing data service systems can evolve into remote sensing data processing infrastructures, which provide IaaS (Infrastructure-as-a-Service) and PaaS (Platform-as-a-Service) services, and make it possible to exchange knowledge among scientists by sharing models. Furthermore, a remote sensing data analysis platform for carbon satellites is designed based on the on-demand processing proposed by this paper and will soon be implemented under the support of SunWay-TaihuLight, the world’s most powerful super computer.