A machine learning-based methodology for computational aeroacoustics predictions of multi-propeller drones

2021 ◽  
Vol 263 (3) ◽  
pp. 3467-3478
Author(s):  
Cesar Legendre ◽  
Vincent Ficat-Andrieu ◽  
Athanasios Poulos ◽  
Yuya Kitano ◽  
Yoshitaka Nakashima ◽  
...  

The rapid progress in technological developments of small Unmanned Aircraft Systems (sUAS) or simply "drones" has produced a significant proliferation of this technology. From multinational businesses to drone enthusiasts, such a technology can offer a wide range of possibilities, i.e., commercial services, security, and environmental applications, while placing new demands in the already-congested civil airspace. Noise emission is a key factor that is being addressed with high-fidelity computational fluid dynamics (CFD) and aeroacoustics (CAA) techniques. However, due to uncertainties of flow conditions, wide ranges of propellers' speed variations, and different payload requirements, a complete numerical prediction varying such parameters is unfeasible. In this study, a machine learning-based approach is proposed in combination with high-fidelity CFD and CAA techniques to predict drone noise emission given a wide variation of payloads or propellers' speeds. The transient CFD computations are calculated using a time-marching LES simulation with a WALE sub-grid scale. In contrast, the acoustic propagation is predicted using a finite element method in the frequency domain. Finally, the machine learning strategy is presented in the context of fulfilling two goals: (i) real-time noise prediction of drone systems; and (ii) determination of propeller's rotation speeds leading to a noise prediction matching experimental data.

Algorithms ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 17 ◽  
Author(s):  
Emmanuel Pintelas ◽  
Ioannis E. Livieris ◽  
Panagiotis Pintelas

Machine learning has emerged as a key factor in many technological and scientific advances and applications. Much research has been devoted to developing high performance machine learning models, which are able to make very accurate predictions and decisions on a wide range of applications. Nevertheless, we still seek to understand and explain how these models work and make decisions. Explainability and interpretability in machine learning is a significant issue, since in most of real-world problems it is considered essential to understand and explain the model’s prediction mechanism in order to trust it and make decisions on critical issues. In this study, we developed a Grey-Box model based on semi-supervised methodology utilizing a self-training framework. The main objective of this work is the development of a both interpretable and accurate machine learning model, although this is a complex and challenging task. The proposed model was evaluated on a variety of real world datasets from the crucial application domains of education, finance and medicine. Our results demonstrate the efficiency of the proposed model performing comparable to a Black-Box and considerably outperforming single White-Box models, while at the same time remains as interpretable as a White-Box model.


Author(s):  
Jan Heyse ◽  
Aashwin Mishra ◽  
Gianluca Iaccarino

In this work we present a machine-learning strategy developed to estimate the uncertainty introduced by a turbulence model for the prediction of a turbulent separated flows. The approach is based on the introduction of eigenvalue perturbations of the Reynolds stress anisotropy; the amount of perturbation is predicted by a random forest algorithm trained on high-fidelity simulations of the flow over a wavy wall. The proposed method is applied to the flow in an asymmetric diffuser and demonstrates how the approach correctly identifies the regions in which modeling errors occur and accurately quantifies the amount of errors when compared to experimental observations.


2021 ◽  
Author(s):  
Ruhollah Taghizadeh-Mehrjardi ◽  
Nikou Hamzehpour ◽  
Maryam Hassanzadeh ◽  
Karsten Schmidt ◽  
Thomas Scholten

<p>The digital soil mapping (DSM) approach predicts soil characteristics based on the relationship between soil observations and related covariates using machine learning (ML) models. In this research, we applied a wide range of machine learning models (12 base learners) to predict and map soil characteristics. To enhance accuracy and interpretability we combined the base learner predictions using super learning strategy. However, a major problem of using super learning and complex models is that the explicit share of individual covariates persons in the overall result cannot be explicitly quantified. To overcome this restriction and make the super learning models interpretable, we employed model-agnostic interpretation tools, for example, permutation feature importance. Particularly, we integrated the weight assigned to each ML base learner obtained by super learning and the ranked ML base learner’s covariates obtained by permutation feature importance to explore the contribution of covariates on the final prediction. We tested our super learning and permutation feature importance techniques to predict and mapping physicochemical soil characteristics of Urmia Playa Lake (UPL) sediments in Iran. As expected, our results indicated that super leaning could significantly improve the ML accuracies for predicting soil characteristics of single base learners. In terms of root mean square error, super learning improved over the performance of the linear regression by an average of 45.7%. Furthermore, the permutation feature importance allowed us to interpret our results better and prove the significant contribution of geomorphological features and groundwater data in predicting soil characteristics of UPL sediments.</p>


2018 ◽  
Author(s):  
Sherif Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joseph Shapter ◽  
David Winkler ◽  
...  

Materials constructed from different van der Waals two-dimensional (2D) heterostructures offer a wide range of benefits, but these systems have been little studied because of their experimental and computational complextiy, and because of the very large number of possible combinations of 2D building blocks. The simulation of the interface between two different 2D materials is computationally challenging due to the lattice mismatch problem, which sometimes necessitates the creation of very large simulation cells for performing density-functional theory (DFT) calculations. Here we use a combination of DFT, linear regression and machine learning techniques in order to rapidly determine the interlayer distance between two different 2D heterostructures that are stacked in a bilayer heterostructure, as well as the band gap of the bilayer. Our work provides an excellent proof of concept by quickly and accurately predicting a structural property (the interlayer distance) and an electronic property (the band gap) for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.


GIS Business ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. 597-606
Author(s):  
Dr. Maha Mustafa Omer Abdalaziz

The study aims at the technological developments that are taking place in the world and have impacted on all sectors and fields and imposed on the business organizations and commercial companies to carry out their marketing and promotional activities within the electronic environment. The most prominent of these developments is the emergence of the concept of electronic advertising which opened a wide range of companies and businessmen to advertise And to promote their products and their work easily through the Internet, which has become full of electronic advertising, and in light of that will discuss the creative strategy used in electronic advertising;


2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.


2018 ◽  
Vol 35 (4) ◽  
pp. 62-64
Author(s):  
Nazar Ul Islam Wani

Pilgrimage in Islam is a religious act wherein Muslims leave their homes and spaces and travel to another place, the nature, geography, and dispositions of which they are unfamiliar. They carry their luggage and belongings and leave their own spaces to receive the blessings of the dead, commemorate past events and places, and venerate the elect. In Pilgrimage in Islam, Sophia Rose Arjana writes that “intimacy with Allah is achievable in certain spaces, which is an important story of Islamic pilgrimage”. The devotional life unfolds in a spatial idiom. The introductory part of the book reflects on how pilgrimage in Islam is far more complex than the annual pilgrimage (ḥajj), which is one of the basic rites and obligations of Islam beside the formal profession of faith (kalima); prayers (ṣalāt); fasting (ṣawm); and almsgiving (zakāt). More pilgrims throng to Karbala, Iraq, on the Arbaeen pilgrimage than to Mecca on the Hajj, for example, but the former has received far less academic attention. The author expands her analytic scope to consider sites like Konya, Samarkand, Fez, and Bosnia, where Muslims travel to visit countless holy sites (mazarāt), graves, tombs, complexes, mosques, shrines, mountaintops, springs, and gardens to receive the blessings (baraka) of saints buried there. She reflects on broader methodological and theoretical questions—how do we define religion?—through the diversity of Islamic traditions about pilgrimage. Arjana writes that in pilgrimage—something which creates spaces and dispositions—Muslim journeys cross sectarian boundaries, incorporate non-Muslim rituals, and involve numerous communities, languages, and traditions (the merging of Shia, Sunni, and Sufi categories) even to “engende[r] a syncretic tradition”. This approach stands against the simplistic scholarship on “pilgrimage in Islam”, which recourses back to the story of the Hajj. Instead, Arjana borrows a notion of ‘replacement hajjs’ from the German orientalist Annemarie Schimmel, to argue that ziyārat is neither a sectarian practice nor antithetical to Hajj. In the first chapter, Arjana presents “pilgrimage in Islam” as an open, demonstrative and communicative category. The extensive nature of the ‘pilgrimage’ genre is presented through documenting spaces and sites, geographies, and imaginations, and is visualized through architectural designs and structures related to ziyārat, like those named qubba, mazār (shrine), qabr (tomb), darih (cenotaph), mashhad (site of martyrdom), and maqām (place of a holy person). In the second chapter, the author continues the theme of visiting sacred pilgrimage sites like “nascent Jerusalem”, Mecca, and Medina. Jerusalem offers dozens of cases of the ‘veneration of the dead’ (historically and archaeologically) which, according to Arjana, characterizes much of Islamic pilgrimage. The third chapter explains rituals, beliefs, and miracles associated with the venerated bodies of the dead, including Karbala (commemorating the death of Hussein in 680 CE), ‘Alawi pilgrimage, and pilgrimage to Hadrat Khidr, which blur sectarian lines of affiliation. Such Islamic pilgrimage is marked by inclusiveness and cohabitation. The fourth chapter engages dreams, miracles, magical occurrences, folk stories, and experiences of clairvoyance (firāsat) and the blessings attached to a particular saint or walī (“friend of God”). This makes the theme of pilgrimage “fluid, dynamic and multi-dimensional,” as shown in Javanese (Indonesian) pilgrimage where tradition is associated with Islam but involves Hindu, Buddhist and animistic elements. This chapter cites numerous sites that offer fluid spaces for the expression of different identities, the practice of distinct rituals, and cohabitation of different religious communities through the idea of “shared pilgrimage”. The fifth and final chapter shows how technologies and economies inflect pilgrimage. Arjana discusses the commodification of “religious personalities, traditions and places” and the mass production of transnational pilgrimage souvenirs, in order to focus on the changing nature of Islamic pilgrimage in the modern world through “capitalism, mobility and tech nology”. The massive changes wrought by technological developments are evident even from the profusion of representations of Hajj, as through pilgrims’ photos, blogs, and other efforts at self documentation. The symbolic representation of the dead through souvenirs makes the theme of pilgrimage more complex. Interestingly, she then notes how “virtual pilgrimage” or “cyber-pilgrimage” forms a part of Islamic pilgrimage in our times, amplifying how pilgrimage itself is a wide range of “active, ongoing, dynamic rituals, traditions and performances that involve material religions and imaginative formations and spaces.” Analyzing religious texts alone will not yield an adequate picture of pilgrimage in Islam, Arjana concludes. Rather one must consider texts alongside beliefs, rituals, bodies, objects, relationships, maps, personalities, and emotions. The book takes no normative position on whether the ziyāratvisitation is in fact a bid‘ah (heretical innovation), as certain Muslim orthodoxies have argued. The author invokes Shahab Ahmad’s account of how aspects of Muslim culture and history are seen as lying outside Islam, even though “not everything Muslims do is Islam, but every Muslim expression of meaning must be constituting in Islam in some way”. The book is a solid contribution to the field of pilgrimage and Islamic studies, and the author’s own travels and visits to the pilgrimage sites make it a practicalcontribution to religious studies. Nazar Ul Islam Wani, PhDAssistant Professor, Department of Higher EducationJammu and Kashmir, India


2021 ◽  
Vol 15 ◽  
Author(s):  
Alhassan Alkuhlani ◽  
Walaa Gad ◽  
Mohamed Roushdy ◽  
Abdel-Badeeh M. Salem

Background: Glycosylation is one of the most common post-translation modifications (PTMs) in organism cells. It plays important roles in several biological processes including cell-cell interaction, protein folding, antigen’s recognition, and immune response. In addition, glycosylation is associated with many human diseases such as cancer, diabetes and coronaviruses. The experimental techniques for identifying glycosylation sites are time-consuming, extensive laboratory work, and expensive. Therefore, computational intelligence techniques are becoming very important for glycosylation site prediction. Objective: This paper is a theoretical discussion of the technical aspects of the biotechnological (e.g., using artificial intelligence and machine learning) to digital bioinformatics research and intelligent biocomputing. The computational intelligent techniques have shown efficient results for predicting N-linked, O-linked and C-linked glycosylation sites. In the last two decades, many studies have been conducted for glycosylation site prediction using these techniques. In this paper, we analyze and compare a wide range of intelligent techniques of these studies from multiple aspects. The current challenges and difficulties facing the software developers and knowledge engineers for predicting glycosylation sites are also included. Method: The comparison between these different studies is introduced including many criteria such as databases, feature extraction and selection, machine learning classification methods, evaluation measures and the performance results. Results and conclusions: Many challenges and problems are presented. Consequently, more efforts are needed to get more accurate prediction models for the three basic types of glycosylation sites.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.


2021 ◽  
pp. 1-27
Author(s):  
Tiberiu Dragu ◽  
Yonatan Lupu

Abstract How will advances in digital technology affect the future of human rights and authoritarian rule? Media figures, public intellectuals, and scholars have debated this relationship for decades, with some arguing that new technologies facilitate mobilization against the state and others countering that the same technologies allow authoritarians to strengthen their grip on power. We address this issue by analyzing the first game-theoretic model that accounts for the dual effects of technology within the strategic context of preventive repression. Our game-theoretical analysis suggests that technological developments may not be detrimental to authoritarian control and may, in fact, strengthen authoritarian control by facilitating a wide range of human rights abuses. We show that technological innovation leads to greater levels of abuses to prevent opposition groups from mobilizing and increases the likelihood that authoritarians will succeed in preventing such mobilization. These results have broad implications for the human rights regime, democratization efforts, and the interpretation of recent declines in violent human rights abuses.


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