Reviewerly/exHarmony huggingface dataset:
Fault identification in SE Limerick basin (Ireland) using a multi-geophysical data integration and unsupervised machine learning approach

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyFault identification in SE Limerick basin (Ireland) using a multi-geophysical data integration and unsupervised machine learning approachAuthors: Prithwijit ChakrabortiAline MeloEoin DunlevyPrithwijit ChakrabortiUniversity College Dublin (UCD)Search for more papers by this author, Aline MeloUniversity College Dublin (UCD) and Science Foundation Ireland (SFI) Research Centre in Applied Geosciences (iCRAG)Search for more papers by this author, and Eoin DunlevyUniversity College Dublin (UCD) and Science Foundation Ireland (SFI) Research Centre in Applied Geosciences (iCRAG)Search for more papers by this authorhttps://doi.org/10.1190/image2022-3751110.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractIrish type Zn-Pb deposits exhibit a spatial association with normal fault systems which makes the identification of these linear trending features on a regional scale critical for finding new mineral prospects. In this study we used a novel geophysical data integration technique employing Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction and HDBSCAN clustering to identify faults in the SE Shannon Trough (Ireland). A database of previously known faults in the study area has been used to understand the signature of preexisting faults and identify potential new faults from the final classification maps obtained by clustering of multiple geophysical data layers.Keywords: fault identification, machine learning, mineral explorationPermalink: https://doi.org/10.1190/image2022-3751110.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Prithwijit Chakraborti, Aline Melo, and Eoin Dunlevy, (2022), "Fault identification in SE Limerick basin (Ireland) using a multi-geophysical data integration and unsupervised machine learning approach," SEG Technical Program Expanded Abstracts : 1600-1604. https://doi.org/10.1190/image2022-3751110.1 Plain-Language Summary Keywordsfault identificationmachine learningmineral explorationPDF DownloadLoading ...
Pengaruh Media Pembelajaran Audio Visual terhadap Hasil Belajar Siswa Kelas VII pada Mata Pelajaran Fiqih MTs di Kota Bogor

One of the factors that can affect learning outcomes is the use of learning media, one of which is audio-visual media. This study aims to determine the effect of using audio-visual learning media on the learning outcomes of class VII students in fiqh subjects at one of the MTS in Bogor City. This research method uses a quantitative method with a quasi-experimental design of the type of nonequivalent control group pretest-posttest design. Researchers obtained data from 50 students by conducting experiments through pretest and posttest tests in the control class and the experimental class. The results of this study indicate that there is an effect of using audio-visual learning media on the learning outcomes of class VII students in fiqh subjects at one of the MTS in Bogor City with an average learning improvement of 14.6 while the experimental class with an average learning improvement result of 23,8 and it is also proven by the Simple Paired T-Test, it is known that the significance value of the students' learning outcomes is = Sig. (2-tailed) 0.000 &lt;0.05, which means that there is a significant effect so that the use of audio-visual learning media can improve student learning outcomes well. &#x0D; Keywords: influence, audio visual media, learning outcomes.
Robust Estimation in Continuous–Discrete Cubature Kalman Filters for Bearings-Only Tracking

The model of bearings-only tracking is generally described by discrete–discrete filtering systems. Discrete robust methods are also frequently used to address measurement uncertainty problems in bearings-only tracking. The recently popular continuous–discrete filtering system considers the state model of the target to be continuous in time, and is more suitable for bearings-only tracking because of its higher mathematical solution accuracy. However, the sufficient evaluation of robust methods in continuous–discrete systems is not available. In addition, in the different continuous–discrete measurement environments, the choice of a robust algorithm also needs to be discussed. To fill this gap, this paper firstly establishes the continuous–discrete target tracking model, and then evaluates the performance of proposed robust square-root continuous–discrete cubature Kalman filter algorithms in the measurement of uncertainty problems. From the simulation results, the robust square-root continuous–discrete maximum correntropy cubature Kalman filter algorithm and the variational Bayesian square-root continuous–discrete cubature Kalman filter algorithm have better environmental adaptability, which provides a promising means for solving continuous–discrete robust problems.
A Dead Reckoning Calibration Scheme Based on Optimization with an Adaptive Quantum-Inspired Evolutionary Algorithm for Vehicle Self-Localization

Parameter calibration is critical for self-localization based on dead reckoning in the control of intelligent vehicles such as autonomous driving. Most traditional calibration methods for robotics control based on dead reckoning rely on data collection with specially designed paths. For the calibration of parameters in the control of intelligent vehicles, the design of such paths is considered impossible due to the complexity of road conditions. To solve this problem, an optimization-based dead reckoning calibration scheme is introduced in this research using the differential global positioning system to obtain the actual positions of the intelligent vehicle. In this scheme, the difference between the positions obtained through dead reckoning and the positions obtained through the differential global positioning system is selected as the optimization objective function to be minimized. An adaptive quantum-inspired evolutionary algorithm is developed to improve the quality and efficiency of optimization. Experiments with an intelligent vehicle were also conducted to demonstrate the effectiveness of the developed calibration scheme. In addition, the newly introduced adaptive quantum-inspired evolutionary algorithm is compared with the classic genetic algorithm and the classic quantum-inspired evolutionary algorithm using eight benchmark test functions considering computation quality and efficiency.
Distinguishing and interpreting salt suture zones in the Jurassic Louann Salt: Mapping geochemical data in Southern Green Canyon, Gulf of Mexico, and the presence of REEs in the Louann

PreviousNext You have accessSecond International Meeting for Applied Geoscience & EnergyDistinguishing and interpreting salt suture zones in the Jurassic Louann Salt: Mapping geochemical data in Southern Green Canyon, Gulf of Mexico, and the presence of REEs in the LouannAuthors: Julie BloxsonBrian LeshRodrigo VelasquezMelinda Shaw FaulknerChristine Piela CoxMatthew HoffmanJulie BloxsonStephen F. Austin State University,Search for more papers by this author, Brian LeshStephen F. Austin State University,Search for more papers by this author, Rodrigo VelasquezStephen F. Austin State University,Search for more papers by this author, Melinda Shaw FaulknerStephen F. Austin State University,Search for more papers by this author, Christine Piela CoxBP Exploration and ProductionSearch for more papers by this author, and Matthew HoffmanChevron North America Exploration and ProductionSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3751989.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractSuture zones in allochthonous salt bodies occur where two or more bodies collide, creating zones of uncertain pressure, mineralogy, and extent that can be potential drilling hazards. Here, we characterize a suture zone in the Green Canyon protraction area, Gulf of Mexico, map the structure of the salt body across the diapir and interpret the geologic history based upon the geochemistry and structure. Furthermore, we discuss changes in the geochemistry within the Louann, both bulk and trace, and how these salt bodies could have acquired these enrichments in REEs and other trace metals.Keywords: Louann Salt, suture zones, geochemistry, REEs, Gulf of MexicoPermalink: https://doi.org/10.1190/image2022-3751989.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Julie Bloxson, Brian Lesh, Rodrigo Velasquez, Melinda Shaw Faulkner, Christine Piela Cox, and Matthew Hoffman, (2022), "Distinguishing and interpreting salt suture zones in the Jurassic Louann Salt: Mapping geochemical data in Southern Green Canyon, Gulf of Mexico, and the presence of REEs in the Louann," SEG Technical Program Expanded Abstracts : 446-448. https://doi.org/10.1190/image2022-3751989.1 Plain-Language Summary KeywordsLouann Saltsuture zonesgeochemistryREEsGulf of MexicoPDF DownloadLoading ...
A power-law fault model for Bayesian network geosteering in complex geology

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyA power-law fault model for Bayesian network geosteering in complex geologyAuthors: Hugh WinklerHugh WinklerFactor TechnologySearch for more papers by this authorhttps://doi.org/10.1190/image2022-3745753.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWe introduce a power-law distribution to describe the fault characteristics of the prior information in the geosteering inverse problem. For two faulted examples in the Marcellus shale, we retrospectively compared our computed geosteering results to the original, manual interpretations constructed by the geologists responsible for positioning the wells. We found good agreement between the computed and the manual interpretations. Incorporating this technique into real time wellpositioning systems can improve the conformance of the well shape to complex, faulted geology. Accumulating a corpus of fault statistics derived from computed geosteering interpretations for a play would allow building more accurate models, further improving well positions.Keywords: geosteernig, Bayesian, inversionPermalink: https://doi.org/10.1190/image2022-3745753.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Hugh Winkler, (2022), "A power-law fault model for Bayesian network geosteering in complex geology," SEG Technical Program Expanded Abstracts : 302-306. https://doi.org/10.1190/image2022-3745753.1 Plain-Language Summary KeywordsgeosteernigBayesianinversionPDF DownloadLoading ...
Implementasi Aplikasi Penggajian Karyawan Pada PT. Kemasan Cipta Utama Berbasis VB.Net

PT. Ciptatama Perfect Packaging (KCS) was founded in 1995 starting from the production of styrofoam by prioritizing customer satisfaction, PT. Packaging Cipta Utama has human resources in this case employees who are divided into 2 (two) namely permanent employees and contract employees. The purpose of this study was to analyze and design a computer-based payroll system at PT. Cipta Utama packaging using Microsoft Visual Studio 2008. The research method used is SDLC (System Development Life Cycle) with a waterfall approach model that starts with system analysis, system design, program code generation and testing. PT. Cipta Utama packaging in processing payroll data still uses Microsoft Excel. All payroll calculation activities are carried out manually based on documents related to employee salary calculations. This study discusses the payroll system starting from the document. The object of research uses a computerized payroll system, performs analysis and makes applications using the Microsoft Visual Studio 2008 programming language so that it provides a lot of efficiency and effectiveness in processing payroll. With the payroll system at PT. It is hoped that the management of Cipta Utama can improve the company's internal control.
Global exponential stabilization of quantum spin-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si18.svg"><mml:mfrac><mml:mn>1</mml:mn><mml:mn>2</mml:mn></mml:mfrac></mml:math> systems via improved feedback control

In this paper, we propose two state feedback strategies to exponentially stabilize eigenstates for quantum spin-12 systems based on the state feedback designed in Liang et al. (2018)[1] and Wen et al. (2022)[2], respectively. In order to obtain faster state convergence, we improve the state feedback by increasing the real-time state convergence rate, and prove the exponential convergence. On this basis, we present the way of achieving global exponential stabilization of quantum spin-12 systems under the improved state feedback with the help of noise-assisted feedback. Besides, we redesign the division of state space to improve the state convergence rate further, and compare the state convergence rates of all state feedback strategies that can exponentially stabilize eigenstates for quantum spin-12 systems. The effectiveness and the superiority of the improved feedback control are also verified in numerical simulations.
A Development of Flowchart Media Model Stimulated Images Containing Character Values and Regional Culture for Writing Text Learning in Junior High School

This study was motivated by the low learning outcomes of students in writing various kinds of texts, lack of teachers' innovation in designing learning media, lack of motivation of students in learning, lack of love and students' awareness of regional culture, and the lack of introduction and preservation of regional culture.This deficiency is expected to be improved by learning media and teachers' innovation in writing text learning with flow chart media that stimulates images with character values and regional culture.The results obtained from this study are the 1) analysis of the condition of the learning flowchart media for writing text in circulation, 2) analysis of media needs is needed flowchart media containing messages/ information that can provide an overview of the skills process (flow chart/ scheme) in writing text learning of Junior High School; 3) principles in the development of instructional media; 4) development of graphic media design includes determining basic competencies (KD) as reference for the development of learning media; determining the suitable media to be made; determining the aspects of local wisdom (regional culture) to be published and how to publish them; determining the appropriate drawing; and compiling/ producing media that are loaded with regional cultural content.
Temporal3D: 2D-to-3D Video Conversion Network with Multi-frame Fusion

This paper proposes a 2D-to-3D video conversion network, which aggregate the information of adjacent frames to synthesize the novel view. To solve the occlusion problem in novel view, adjacent frames are used to reproduce the missing data caused by view switching. Especially, a temporal aggregated attention module is proposed to make the network focus on the useful information for synthesizing viewpoints. Mask fusion module is introduced to produce the novel view according to corresponding relationship between each frame and the estimated view. The proposed network is trained by the left-right pairs extracted from 3D movies, without extra real depths. Experimental results show that the proposed method can produce high quality novel view which is superior to the state-of-the-art approach both subjective and objective quality.
Reversible data hiding in images: a brief survey

Reversible data hiding technology is an important branch of information hiding technology, and has become an important means to solve problems in information security and privacy protection. With the increasing demand of image content security and privacy protection, reversible data hiding in images has attracted more and more attention from both the academic and industry community in recent years. This paper introduces the basic concept and background of this technology, and summarizes the research results and research status of this technology in recent years from three main types: spatial domain, compression domain and encryption domain. At last, the future development trend of this technology is prospected.
Fast Nondominated Sorting Genetic Algorithm For Multi-Objective Clustering Ensemble

Clustering ensemble is to integrate the results of multiple base clustering by consensus function, which can improve the robustness, stability, and accuracy of the clustering algorithm. The existing clustering ensemble methods are basically to design a single consensus algorithm for integration. This paper proposes a fast nondominated sorting genetic algorithm for multi-objective clustering ensemble (NSGAMCE). In this model, two consensus functions are designed to act on the original data and the base clustering results respectively. Secondly, the nondominated sorting genetic algorithm II (NSGA-II) is used to solve the model, and the gene mutation strategy is changed to improve the convergence speed. Then, according to the solving process of the model, the corresponding clustering ensemble algorithm is designed. Finally, the standard data set and the comparison algorithm are used to carry out experiments. The experimental results show that the clustering ensemble algorithm has good performance.
Dioxide: A Proof-of-Stake Blockchain Consensus on Asynchronous Consensus Zones

Blockchain is a distributed and decentralized ledger for recording transactions. Although blockchain has the advantages of transparency, decentralization, and immutability, it also faces low scalability. Recently, sharding has been emerged as an elegant solution to overcome the scalability problem in blockchain, where Asynchronous Consensus Zones itself has been shown to be a state-of-the-art sharding protocol. However, this protocol will generate a relatively high orphan rate when the throughput is high, which leads to block waste. In this paper, to avoid orphan blocks and further improve throughput, we design a sharding consensus protocol with a fixed block interval named Dioxide. We implement the protocol prototype in a simulated environment and evaluate the performance of the sharding protocol. Compared with Asynchronous Consensus Zones, our experiments show that average throughput is increased by approximately 9%.
A Refused ICU Medical Prediction Model based on Time Series State Monitoring

ICU medical prediction has an important impact on the treatment of severe patients. The existing medical prediction methods are less adaptable to the patient&#x2019;s time series medical data. In order to better monitor and utilize the real-time data fluctuation, this paper proposes a refused ICU medical prediction model based on time series state monitoring. Firstly, fast dynamic time warping is introduced to solve the problem of sample imbalance in medical data. Secondly, the time series state monitoring mechanism is used to construct the medical prediction model. Finally, refused classification model is established to improve the analysis effect. Several cross-comparison experiments are designed, and the results show that the prediction accuracy is improved compared with the existing models.
Robust Algorithm for Euclidean Upgrading from a Minimal Number of Segments

In this paper, a robust algebraic approach for Euclidean upgrading from a minimal number of segments is proposed, which requires just 9 segments of known length. Euclidean Upgrading based on segments have been widely used because of the advantage that self-occlusion is not easy to occur in calibration. Since the projective reconstruction can be easily obtained according to corresponding relation of multiple camera system, the key of upgrading projective reconstruction to Euclidean reconstruction is to extract infinite plane and the image of the absolute conic. The existing method is to constitute a homogeneous polynomial equation of degree 4 in 9 variables based on the segments of known length. However, the existing solver for the polynomial equations of degree 4 in 9 variables is sensitive to noise and not robust. The algorithm proposed in this paper constrains different parameters, uses elimination method to reduce the number of unknowns, and finally construct a general solving template to solve the polynomial equations with the Gr&#x00F6;bner base method. In general, this algorithm has the advantages of robustness against noise and easy to calculate compared with the existing method. Moreover, we present experiments to demonstrate the feasibility and precision of this algorithm.
Robustness of classifier to adversarial examples under imbalanced data

Adversarial examples (AE) are used to fool classifier recently, which poses great challenges for classifier design. Therefore, it is theoretically crucial to evaluate the robustness of classifier to AE for a better classifier design. In this paper, we provide a theoretical framework to analyze the robustness of classifier to AE under imbalanced dataset from the perspective of AUC (Area under the ROC curve), and derive an interpretable upper bound. Specifically, we illustrate the obtained upper bound of linear classifier, which indicates that the upper bound depends on the difficulty of the classification task and the risk of the classifier. Experimental results on MNIST and CIFAR-10 datasets show that the classifiers designed with pairwise surrogate losses of AUC are not robust to adversarial attack. The nonlinear classifier has a higher robustness to AE compared to the linear one, which indicates that more flexible classifier can be used to improve adversarial robustness.
A Tampering Detection Algorithm Based on Multidirectional Authentication

As an important part of digital watermark technology, semi-fragile watermarking technology can not only effectively detect the tampered area, but also tolerate some conventional signal processing without changing the main content. In order to improve the tampering detection accuracy of semi-fragile watermarking algorithm, this paper proposes a multidirectional authentication strategy. The scrambled bit planes of image feature are embedded into multiple mapping blocks. When tampering occurs, some equations of the image feature and the embedded feature are established for the tampering localization. Based on the proposed multidirectional authentication strategy, a semi-fragile watermarking algorithm is proposed. The algorithm extracts the maximum singular values of image block as feature, and then encodes the feature by multidirectional scrambling to obtain the authentication watermark. In order to improve the robustness for JPEG compression, the authentication watermark is embedded into the wavelet intermediate frequency coefficients of the image by significant difference quantization. The experimental results show that the proposed algorithm has excellent ability of tampering localization.