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- Wang, Yan Ming, et al.
(författare)
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Colorado geoid computation experiment : overview and summary
- 2021
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Ingår i: Journal of Geodesy. - : Springer. - 0949-7714 .- 1432-1394. ; 95:12
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Tidskriftsartikel (refereegranskat)abstract
- The primary objective of the 1-cm geoid experiment in Colorado (USA) is to compare the numerous geoid computation methods used by different groups around the world. This is intended to lay the foundations for tuning computation methods to achieve the sought after 1-cm accuracy, and also evaluate how this accuracy may be robustly assessed. In this experiment, (quasi)geoid models were computed using the same input data provided by the US National Geodetic Survey (NGS), but using different methodologies. The rugged mountainous study area (730 km x 560 km) in Colorado was chosen so as to accentuate any differences between the methodologies, and to take advantage of newly collected GPS/leveling data of the Geoid Slope Validation Survey 2017 (GSVS17) which are now available to be used as an accurate and independent test dataset. Fourteen groups from fourteen countries submitted a gravimetric geoid and a quasigeoid model in a 1' x 1' grid for the study area, as well as geoid heights, height anomalies, and geopotential values at the 223 GSVS17 marks. This paper concentrates on the quasigeoid model comparison and evaluation, while the geopotential value investigations are presented as a separate paper (Sanchez et al. in J Geodesy 95(3):1. https://doi.org/10.1007/s00190-021-01481-0, 2021). Three comparisons are performed: the area comparison to show the model precision, the comparison with the GSVS17 data to estimate the relative accuracy of the models, and the differential quasigeoid (slope) comparison with GSVS17 to assess the relative accuracy of the height anomalies at different baseline lengths. The results show that the precision of the 1' x 1' models over the complete area is about 2 cm, while the accuracy estimates along the GSVS17 profile range from 1.2 cm to 3.4 cm. Considering that the GSVS17 does not pass the roughest terrain, we estimate that the quasigeoid can be computed with an accuracy of similar to 2 cm in Colorado. The slope comparisons show that RMS values of the differences vary from 2 to 8 cm in all baseline lengths. Although the 2-cm precision and 2-cm relative accuracy have been estimated in such a rugged region, the experiment has not reached the 1-cm accuracy goal. At this point, the different accuracy estimates are not a proof of the superiority of one methodology over another because the model precision and accuracy of the GSVS17-derived height anomalies are at a similar level. It appears that the differences are not primarily caused by differences in theory, but that they originate mostly from numerical computations and/or data processing techniques. Consequently, recommendations to improve the model precision toward the 1-cm accuracy are also given in this paper.
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- Ngabo, Desire, et al.
(författare)
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Blockchain-Based Security Mechanism for the Medical Data at Fog Computing Architecture of Internet of Things
- 2021
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Ingår i: Electronics. - : MDPI. - 2079-9292. ; 10:17
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Tidskriftsartikel (refereegranskat)abstract
- The recent developments in fog computing architecture and cloud of things (CoT) technology includes data mining management and artificial intelligence operations. However, one of the major challenges of this model is vulnerability to security threats and cyber-attacks against the fog computing layers. In such a scenario, each of the layers are susceptible to different intimidations, including the sensed data (edge layer), computing and processing of data (fog (layer), and storage and management for public users (cloud). The conventional data storage and security mechanisms that are currently in use appear to not be suitable for such a huge amount of generated data in the fog computing architecture. Thus, the major focus of this research is to provide security countermeasures against medical data mining threats, which are generated from the sensing layer (a human wearable device) and storage of data in the cloud database of internet of things (IoT). Therefore, we propose a public-permissioned blockchain security mechanism using elliptic curve crypto (ECC) digital signature that that supports a distributed ledger database (server) to provide an immutable security solution, transaction transparency and prevent the patient records tampering at the IoTs fog layer. The blockchain technology approach also helps to mitigate these issues of latency, centralization, and scalability in the fog model.
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- Rambaree, Komalsingh, 1970-, et al.
(författare)
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Qualitative stakeholder analysis for a Swedish regional biogas development : A thematic network approach
- 2021
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 13:14
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Tidskriftsartikel (refereegranskat)abstract
- The creation of pathways toward a societal transition to clean energy requires the engagement of multiple stakeholders with different and sometimes conflicting interests. In this connection, stakeholder analysis (SA) offers a technique for identifying, assessing and structuring different needs, interests and concerns related to different stakeholders within the context of sustainability. This article aims to present the findings from a qualitative stakeholder analysis (QSA) by using a thematic network approach (TNA), with the help of the ATLAS.ti software. It focuses on Project X, which was aimed at engaging multiple stakeholders and creating favorable conditions for small and medium-sized companies in a region situated in the central part of Sweden, with the potential to start biogas production. In this work, the findings and discussions of the QSA using TNA are structured by using the political, economic, social, technological, environmental and legal (PESTEL) themes of the model. The present study concludes that for the small-scale biogas industry to successfully develop an understanding of the possibilities of the biogas value chain, it is necessary to have analyzed the nature of the main themes by which various stakeholders relate to biogas production and envision their contribution to creating a sustainable society. Herein, we demonstrate that QSA by a TNA, combined with the application of a PESTEL model, are valuable analytical tools in sustainable project management. The lessons from Project X can be applied to other local biogas initiatives, as many identified threats and opportunities are shared by others.
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- Schreurs, Guido, et al.
(författare)
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Benchmarking analogue models of brittle thrust wedges
- 2016
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Ingår i: Journal of Structural Geology. - : Elsevier BV. - 0191-8141 .- 1873-1201. ; 92, s. 116-139
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Tidskriftsartikel (refereegranskat)abstract
- We performed a quantitative comparison of brittle thrust wedge experiments to evaluate the variability among analogue models and to appraise the reproducibility and limits of model interpretation. Fifteen analogue modeling laboratories participated in this benchmark initiative. Each laboratory received a shipment of the same type of quartz and corundum sand and all laboratories adhered to a stringent model building protocol and used the same type of foil to cover base and sidewalls of the sandbox. Sieve structure, sifting height, filling rate, and details on off-scraping of excess sand followed prescribed procedures. Our analogue benchmark shows that even for simple plane-strain experiments with prescribed stringent model construction techniques, quantitative model results show variability, most notably for surface slope, thrust spacing and number of forward and backthrusts. One of the sources of the variability in model results is related to slight variations in how sand is deposited in the sandbox. Small changes in sifting height, sifting rate, and scraping will result in slightly heterogeneous material bulk densities, which will affect the mechanical properties of the sand, and will result in lateral and vertical differences in peak and boundary friction angles, as well as cohesion values once the model is constructed. Initial variations in basal friction are inferred to play the most important role in causing model variability. Our comparison shows that the human factor plays a decisive role, and even when one modeler repeats the same experiment, quantitative model results still show variability. Our observations highlight the limits of up-scaling quantitative analogue model results to nature or for making comparisons with numerical models. The frictional behavior of sand is highly sensitive to small variations in material state or experimental set-up, and hence, it will remain difficult to scale quantitative results such as number of thrusts, thrust spacing, and pop-up width from model to nature.
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- Zhou, Jincheng, et al.
(författare)
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Voice spoofing countermeasure for voice replay attacks using deep learning
- 2022
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Ingår i: Journal of Cloud Computing. - : Springer. - 2192-113X. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- In our everyday lives, we communicate with each other using several means and channels of communication, as communication is crucial in the lives of humans. Listening and speaking are the primary forms of communication. For listening and speaking, the human voice is indispensable. Voice communication is the simplest type of communication. The Automatic Speaker Verification (ASV) system verifies users with their voices. These systems are susceptible to voice spoofing attacks - logical and physical access attacks. Recently, there has been a notable development in the detection of these attacks. Attackers use enhanced gadgets to record users’ voices, replay them for the ASV system, and be granted access for harmful purposes. In this work, we propose a secure voice spoofing countermeasure to detect voice replay attacks. We enhanced the ASV system security by building a spoofing countermeasure dependent on the decomposed signals that consist of prominent information. We used two main features— the Gammatone Cepstral Coefficients and Mel-Frequency Cepstral Coefficients— for the audio representation. For the classification of the features, we used Bi-directional Long-Short Term Memory Network in the cloud, a deep learning classifier. We investigated numerous audio features and examined each feature’s capability to obtain the most vital details from the audio for it to be labelled genuine or a spoof speech. Furthermore, we use various machine learning algorithms to illustrate the superiority of our system compared to the traditional classifiers. The results of the experiments were classified according to the parameters of accuracy, precision rate, recall, F1-score, and Equal Error Rate (EER). The results were 97%, 100%, 90.19% and 94.84%, and 2.95%, respectively.
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