3D Model Multiple Semantic Automatic Annotation for Small Scale Labeled Data Set

2011 ◽  
Author(s):  
Feng Tian ◽  
Xu-kun Shen ◽  
Liu Xian-mei ◽  
Xie Hong-tao
Keyword(s):  
3D Model ◽  
Small Scale ◽  
Automatic Annotation ◽  
Data Set

scholarly journals Dark energy survey internal consistency tests of the joint cosmological probes analysis with posterior predictive distributions

2021 ◽  
Vol 503 (2) ◽  
pp. 2688-2705
Author(s):  
C Doux ◽  
E Baxter ◽  
P Lemos ◽  
C Chang ◽  
A Alarcon ◽  
...  
Keyword(s):  
Dark Energy ◽  
Internal Consistency ◽  
Goodness Of Fit ◽  
Small Scale ◽  
P Value ◽  
Posterior Predictive Distribution ◽  
Data Set ◽  
Dark Energy Survey ◽  
Data Vector ◽  
Energy Survey

ABSTRACT Beyond ΛCDM, physics or systematic errors may cause subsets of a cosmological data set to appear inconsistent when analysed assuming ΛCDM. We present an application of internal consistency tests to measurements from the Dark Energy Survey Year 1 (DES Y1) joint probes analysis. Our analysis relies on computing the posterior predictive distribution (PPD) for these data under the assumption of ΛCDM. We find that the DES Y1 data have an acceptable goodness of fit to ΛCDM, with a probability of finding a worse fit by random chance of p = 0.046. Using numerical PPD tests, supplemented by graphical checks, we show that most of the data vector appears completely consistent with expectations, although we observe a small tension between large- and small-scale measurements. A small part (roughly 1.5 per cent) of the data vector shows an unusually large departure from expectations; excluding this part of the data has negligible impact on cosmological constraints, but does significantly improve the p-value to 0.10. The methodology developed here will be applied to test the consistency of DES Year 3 joint probes data sets.

scholarly journals AN EXPERIMENTAL STUDY ON SAND DUNE SEDIMENT TRANSPORT DUE TO TSUNAMI OVERWASH

2017 ◽  
pp. 15
Author(s):  
K M Ahtesham Hossain Raju ◽  
Shinji Sato
Keyword(s):  
Experimental Study ◽  
Sediment Transport ◽  
Mathematical Models ◽  
Storm Surge ◽  
Laboratory Study ◽  
Sand Dune ◽  
Small Scale ◽  
Data Set ◽  
Grain Sizes ◽  
Dry Sand

Response of sand dune when overwashed by tsunami or storm surge, is investigated by conducting small scale laboratory study. Dune consisting of initially wet sand and initially dry sand are tested for three different sand grain sizes. Overtopping of water and the corresponding sediment transport are analyzed. These data set can be used to validate mathematical models associated with dune sediment transport as well as prediction of dune profile.

scholarly journals Coastal Change Patterns from Time Series Clustering of Permanent Laser Scan Data

2020 ◽  
Author(s):  
Mieke Kuschnerus ◽  
Roderik Lindenbergh ◽  
Sander Vos
Keyword(s):  
Time Series ◽  
Laser Scanning ◽  
Clustering Algorithm ◽  
Coastal Areas ◽  
Small Scale ◽  
Temporal Data ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Deformation Processes ◽  
Spatio Temporal

Abstract. Sandy coasts are constantly changing environments governed by complex interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and support analysis of geomorphological deformation processes. This novel technique delivers 3D representations of a part of the coast at hourly temporal and centimetre spatial resolution and allows to observe small scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatio-temporal data set is needed. In order to allow data mining in an automated way, we extract time series in elevation or range and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well known clustering algorithms, k-means, agglomerative clustering and DBSCAN, and identify areas that undergo similar evolution during one month. We test if they fulfil our criteria for a suitable clustering algorithm on our exemplary data set. The three clustering methods are applied to time series of 30 epochs (during one month) extracted from a data set of daily scans covering a part of the coast at Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer is used to evaluate the performance of the algorithms against a ground truth. The k-means algorithm and agglomerative clustering deliver similar clusters, and both allow to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused for example by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatio-temporal data set for predominant deformation patterns with the associated regions, where they occur.

scholarly journals Effects of agricultural credit on wheat productivity of small farms in Sindh, Pakistan

2018 ◽  
Vol 78 (5) ◽  
pp. 592-610 ◽  
Author(s):  
Abbas Ali Chandio ◽  
Yuansheng Jiang ◽  
Feng Wei ◽  
Xu Guangshun
Keyword(s):  
Wheat Yield ◽  
Considerable Proportion ◽  
Small Scale ◽  
Small Farms ◽  
Short Term ◽  
Agricultural Credit ◽  
Data Set ◽  
Content Type ◽  
The Impact

Purpose The purpose of this paper is to evaluate the impact of short-term loan (STL) vs long-term loan (LTL) on wheat productivity of small farms in Sindh, Pakistan. Design/methodology/approach The econometric estimation is based on cross-sectional data collected in 2016 from 18 villages in three districts, i.e. Shikarpur, Sukkur and Shaheed Benazirabad, Sindh, Pakistan. The sample data set consist of 180 wheat farmers. The collected data were analyzed through different econometric techniques like Cobb–Douglas production function and Instrumental variables (two-stage least squares) approach. Findings This study reconfirmed that agricultural credit has a positive and highly significant effect on wheat productivity, while the short-term loan has a stronger effect on wheat productivity than the long-term loan. The reasons behind the phenomenon may be the significantly higher usage of agricultural inputs like seeds of improved variety and fertilizers which can be transformed into the wheat yield in the same year. However, the LTL users have significantly higher investments in land preparation, irrigation and plant protection, which may lead to higher wheat production in the coming years. Research limitations/implications In the present study, only those wheat farmers were considered who obtained agricultural loans from formal financial institutions like Zarai Taraqiati Bank Limited and Khushhali Bank. However, in the rural areas of Sindh, Pakistan, a considerable proportion of small-scale farmers take credit from informal financial channels. Therefore future researchers should consider the informal credits as well. Originality/value This is the first paper to examine the effects of agricultural credit on wheat productivity of small farms in Sindh, Pakistan. This paper will be an important addition to the emerging literature regarding effects of credit studies.

A method of locating the 3D centers of retroreflectors based on deep learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
BinBin Zhang ◽  
Fumin Zhang ◽  
Xinghua Qu
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Measurement Techniques ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Large Field ◽  
Small Scale ◽  
Data Set ◽  
Content Type ◽  
Ellipse Detection

Purpose Laser-based measurement techniques offer various advantages over conventional measurement techniques, such as no-destructive, no-contact, fast and long measuring distance. In cooperative laser ranging systems, it’s crucial to extract center coordinates of retroreflectors to accomplish automatic measurement. To solve this problem, this paper aims to propose a novel method. Design/methodology/approach We propose a method using Mask RCNN (Region Convolutional Neural Network), with ResNet101 (Residual Network 101) and FPN (Feature Pyramid Network) as the backbone, to localize retroreflectors, realizing automatic recognition in different backgrounds. Compared with two other deep learning algorithms, experiments show that the recognition rate of Mask RCNN is better especially for small-scale targets. Based on this, an ellipse detection algorithm is introduced to obtain the ellipses of retroreflectors from recognized target areas. The center coordinates of retroreflectors in the camera coordinate system are obtained by using a mathematics method. Findings To verify the accuracy of this method, an experiment was carried out: the distance between two retroreflectors with a known distance of 1,000.109 mm was measured, with 2.596 mm root-mean-squar error, meeting the requirements of the coarse location of retroreflectors. Research limitations/implications The research limitations/implications are as follows: (i) As the data set only has 200 pictures, although we have used some data augmentation methods such as rotating, mirroring and cropping, there is still room for improvement in the generalization ability of detection. (ii) The ellipse detection algorithm needs to work in relatively dark conditions, as the retroreflector is made of stainless steel, which easily reflects light. Originality/value The originality/value of the article lies in being able to obtain center coordinates of multiple retroreflectors automatically even in a cluttered background; being able to recognize retroreflectors with different sizes, especially for small targets; meeting the recognition requirement of multiple targets in a large field of view and obtaining 3 D centers of targets by monocular model-based vision.

On the impact of alternative seismic time imaging methods on subsurface fault mapping in the northernmost Molasse Basin, Switzerland

2020 ◽  
Vol 8 (4) ◽  
pp. SQ1-SQ13
Author(s):  
Christoph G. Eichkitz ◽  
Sarah Schneider ◽  
Andreas B. Hölker ◽  
Philip Birkhäuser ◽  
Herfried Madritsch
Keyword(s):  
Fault Zone ◽  
Three Dimensions ◽  
Small Scale ◽  
Zone Model ◽  
Molasse Basin ◽  
Data Set ◽  
Volume Comparison ◽  
Imaging Methods ◽  
Seismic Amplitude ◽  
The Impact

The identification and characterization of tectonic faults in the subsurface represent key aspects of geologic exploration activities across the world. We have evaluated the impact of alternative seismic time imaging methods on initial subsurface fault mapping in three dimensions in the form of a case study situated in the most external foreland of the European Central Alps (the northernmost Molasse Basin). Four different seismic amplitude volumes of one and the same 3D seismic data set, differing in imaging technologies and parameterizations applied, were considered for the interpretation of a fault zone dissecting a Mesozoic sedimentary sequence that is characterized by a pronounced mechanical stratigraphy and has witnessed a multiphase tectonic evolution. For this purpose, we interpreted each seismic amplitude volume separately. In addition, we computed a series of seismic attributes individually for each volume. Comparison of the different data interpretations revealed consistent results concerning the mapping of the seismic marker horizons and main fault segments. Deviations concern the apparent degree of vertical and lateral fault zone segmentation and the occurrence of small-scale fault strands that may be regarded as important fault kinematic indicators. The compilation of all fault interpretations in map form allows the critical assessment of the robustness of the initial seismic fault mapping, highlighting well-constrained from poorly defined fault zone elements. We conclude that the consideration of multiple seismic processing products for subsurface fault mapping is advisable to evaluate general imaging uncertainties and potentially guide the development of fault zone model variants to tackle previously discussed aspects of conceptual interpretation uncertainties.

Nonlinear velocity inversion by a two‐step Monte Carlo method.

Geophysics ◽  
1994 ◽  
Vol 59 (4) ◽  
pp. 577-590 ◽  
Author(s):  
Side Jin ◽  
Raul Madariaga
Keyword(s):  
Monte Carlo ◽  
Velocity Model ◽  
Small Scale ◽  
Inversion Method ◽  
Nonlinear Inversion ◽  
Ray Theory ◽  
Data Set ◽  
Seismic Reflection Data ◽  
Velocity Models ◽  
Reference Velocity

Seismic reflection data contain information on small‐scale impedance variations and a smooth reference velocity model. Given a reference velocity model, the reflectors can be obtained by linearized migration‐inversion. If the reference velocity is incorrect, the reflectors obtained by inverting different subsets of the data will be incoherent. We propose to use the coherency of these images to invert for the background velocity distribution. We have developed a two‐step iterative inversion method in which we separate the retrieval of small‐scale variations of the seismic velocity from the longer‐period reference velocity model. Given an initial background velocity model, we use a waveform misfit‐functional for the inversion of small‐scale velocity variations. For this linear step we use the linearized migration‐inversion method based on ray theory that we have recently developed with Lambaré and Virieux. The reference velocity model is then updated by a Monte Carlo inversion method. For the nonlinear inversion of the velocity background, we introduce an objective functional that measures the coherency of the short wavelength components obtained by inverting different common shot gathers at the same locations. The nonlinear functional is calculated directly in migrated data space to avoid expensive numerical forward modeling by finite differences or ray theory. Our method is somewhat similar to an iterative migration velocity analysis, but we do an automatic search for relatively large‐scale 1-D reference velocity models. We apply the nonlinear inversion method to a marine data set from the North Sea and also show that nonlinear inversion can be applied to realistic scale data sets to obtain a laterally heterogeneous velocity model with a reasonable amount of computer time.

Disaster communication

2019 ◽  
Vol 2 (3) ◽  
pp. 241-259
Author(s):  
Alicia Mason ◽  
Lynzee Flores ◽  
Pan Liu ◽  
Kenzie Tims ◽  
Elizabeth Spencer ◽  
...  
Keyword(s):  
Social Media ◽  
Crisis Communication ◽  
Medical Tourism ◽  
Tourism Industry ◽  
Small Scale ◽  
Data Set ◽  
Content Type ◽  
Disaster Communication ◽  
The Caribbean ◽  
Post Disaster

Purpose The purpose of this paper is to understand the crisis communication strategies used by the Caribbean medical tourism industry in the 2017 hurricane season, and also evaluate the quality of the disaster communication messages delivered via digital mediums. Design/methodology/approach This study includes a comprehensive, qualitative content analysis of 149 risk and crisis messages from 51 healthcare organizations distributed through digital media. The medical tourism providers (MTPs) include hospitals, medical tourism facilitators, practitioners/private physicians, specialty clinics, and dental and cosmetic providers. Findings Nearly half of the MTPs included in the data set delivered no post-disaster information to external audiences. The most prominent post-disaster message strategy utilized was conveying operational messages. Furthermore, an unexpected finding was the sheer magnitude of unrelated health-oriented and promotional destination marketing content disseminated before, during and after these events. Research limitations/implications This analysis excludes internal organizational channels of communication which may have been used to communicate risk and crisis messages during these events (i.e. employee e-mails, announcements made through intercom systems, etc.). Our analysis does not include content disseminated through medical tourism forums (i.e. Realself.com, Health Traveler’s Forum, FlyerTalk Forum). Practical implications Small-scale MTPs can improve on any weaknesses through proactive planning and preparation by creating organizational goals to complete basic crisis communication training courses and in doing so support the applied professional development of disaster and crisis responders in the Caribbean region. Second, MTPs exposed to similar risks of natural disasters may use these findings for comparative analysis purposes to support their own organizational planning. Finally, this study supports the continued utility of the National Center for Food Protection & Defense guidelines for analyzing and evaluating organizational performance. Originality/value Currently much of the academic scholarship of applied disaster communication narrowly focuses on the response strategies of one organization, or analyzes one social media platform at a time (i.e. Twitter). A strength of this analysis is the inclusion of an organizational sector (i.e. Caribbean medical tourism providers) and the range of platforms from which the content was captured (e.g. websites, org. blogs and social media networks).

Footstep detection in urban seismic data with a convolutional neural network

2020 ◽  
Vol 39 (9) ◽  
pp. 654-660 ◽  
Author(s):  
Srikanth Jakkampudi ◽  
Junzhu Shen ◽  
Weichen Li ◽  
Ayush Dev ◽  
Tieyuan Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Seismic Data ◽  
Earthquake Hazard ◽  
Small Scale ◽  
State University ◽  
Data Set ◽  
Near Surface ◽  
Privacy Concerns ◽  
Seismic Arrays

Seismic data for studying the near surface have historically been extremely sparse in cities, limiting our ability to understand small-scale processes, locate small-scale geohazards, and develop earthquake hazard microzonation at the scale of buildings. In recent years, distributed acoustic sensing (DAS) technology has enabled the use of existing underground telecommunications fibers as dense seismic arrays, requiring little manual labor or energy to maintain. At the Fiber-Optic foR Environmental SEnsEing array under Pennsylvania State University, we detected weak slow-moving signals in pedestrian-only areas of campus. These signals were clear in the 1 to 5 Hz range. We verified that they were caused by footsteps. As part of a broader scheme to remove and obscure these footsteps in the data, we developed a convolutional neural network to detect them automatically. We created a data set of more than 4000 windows of data labeled with or without footsteps for this development process. We describe improvements to the data input and architecture, leading to approximately 84% accuracy on the test data. Performance of the network was better for individual walkers and worse when there were multiple walkers. We believe the privacy concerns of individual walkers are likely to be highest priority. Community buy-in will be required for these technologies to be deployed at a larger scale. Hence, we should continue to proactively develop the tools to ensure city residents are comfortable with all geophysical data that may be acquired.

Multiattribute analysis for channel element discrimination in the Taranaki Basin, offshore New Zealand

2019 ◽  
Vol 7 (2) ◽  
pp. SC45-SC61 ◽  
Author(s):  
Phinphorn Amonpantang ◽  
Heather Bedle ◽  
Jonny Wu
Keyword(s):  
Small Scale ◽  
Root Mean Square Amplitude ◽  
Small Individual ◽  
Data Set ◽  
Channel System ◽  
Lithology Identification ◽  
Structure Channel ◽  
Multiattribute Analysis ◽  
Insight Into ◽  
Channel Systems

A detailed study of Pliocene channel systems within the Taranaki Basin was undertaken from the Parihaka 3D seismic volume to improve our understanding of the Plio-Pleistocene channel elements in terms of structure, channel evolution, and lithology. Seismic picking parameters were chosen based on the lateral resolution for optimal mapping of the channels. Individual and multiattribute studies were performed on single, combined, and complex channel systems with the goal of identifying channel features and discriminating between shale- and sand-rich regions of the channels. For this target and data set, the variance attribute provided key insights into channel features, such as the edge of the channel, meander scrolls, and point bars. Root-mean-square amplitude and sweetness performed equally well in lithology identification, and, combined with variance, it aided in identifying sand-rich channels, as well as small individual channels that could provide sediment pathways into the deepwater Taranaki Basin. Depending on the complexity of the channel system, different attribute analyses had varying success with each system. Therefore, it is important to combine various attributes to discriminate channel elements as fully as possible. The lithologies of individual channels and their elements can be determined using seismic attributes, although it becomes increasingly difficult to discriminate small-scale features within the channel as the complexity of the channel system increases. Chronostratigraphic studies using stratal slicing techniques provided insight into the evolution of the channel system through time, demonstrating an overall sand-rich base of the channel, with a shallower shale-rich lithology at the top of the channel fill.

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