scholarly journals Preliminary design of unmanned aircraft using genetic algorithms and data mining

2021 ◽  
Author(s):  
Daniel J Neufeld
Keyword(s):  
Data Mining ◽  
Aircraft Design ◽  
Unmanned Aircraft ◽  
Preliminary Design ◽  
Commercial Aircraft ◽  
Empirical Relationships ◽  
Design Variables ◽  
Improved Performance ◽  
Using Data ◽  
Future Work

Aircraft design is a complex process involving multiple co-dependent design variables and many design decisions. For commercial aircraft design, this difficulty is offset somewhat by the wealth of knowledge available. Observing existing designs has provided useful empirical relationships and insights for the designer to apply, yielding a relatively well defined problem. The wide variety of configuration possibilities, mission profiles, and the relative lack of historical data leave the problem of unmanned aerial vehicle (UAV) design less defined. The purpose of this research was to develop a robust optimization package for UAV design using data mining to aid configuration decisions and to develop empirical relationships applicable to a wide variety of mission profiles. An optimization software package was developed using a Genetic Algorithm (GA) and Data Mining. The algorithm proved successful in carrying out the preliminary design phase of a number to test cases similar to existing UAVs. Designs produced by the algorithm promise improved performance flight performance relative to existing systems, and reduced development time when compared with conventional design methodology. Future work will introduce high fidelity analysis to the framework developed in this research.

scholarly journals Preliminary design of unmanned aircraft using genetic algorithms and data mining

2021 ◽  
Author(s):  
Daniel J Neufeld
Keyword(s):  
Data Mining ◽  
Aircraft Design ◽  
Unmanned Aircraft ◽  
Preliminary Design ◽  
Commercial Aircraft ◽  
Empirical Relationships ◽  
Design Variables ◽  
Improved Performance ◽  
Using Data ◽  
Future Work

Aircraft design is a complex process involving multiple co-dependent design variables and many design decisions. For commercial aircraft design, this difficulty is offset somewhat by the wealth of knowledge available. Observing existing designs has provided useful empirical relationships and insights for the designer to apply, yielding a relatively well defined problem. The wide variety of configuration possibilities, mission profiles, and the relative lack of historical data leave the problem of unmanned aerial vehicle (UAV) design less defined. The purpose of this research was to develop a robust optimization package for UAV design using data mining to aid configuration decisions and to develop empirical relationships applicable to a wide variety of mission profiles. An optimization software package was developed using a Genetic Algorithm (GA) and Data Mining. The algorithm proved successful in carrying out the preliminary design phase of a number to test cases similar to existing UAVs. Designs produced by the algorithm promise improved performance flight performance relative to existing systems, and reduced development time when compared with conventional design methodology. Future work will introduce high fidelity analysis to the framework developed in this research.

Multi-Disciplinary Design Optimization of Unmanned Aerial Vehicle

2011 ◽  
Author(s):  
Cheolwan Kim ◽  
Yung-Gyo Lee
Keyword(s):  
Unmanned Aerial Vehicle ◽  
General Procedure ◽  
Optimization Technique ◽  
Aircraft Design ◽  
Preliminary Design ◽  
Approximation Model ◽  
Diamond Sample ◽  
Design Variables ◽  
Aerial Vehicle ◽  
Euler Solver

A general procedure of preliminary design of aircraft and one-way fluid-structure interaction (FSI) applied to aircraft design is introduced briefly. Then, FSI and optimization technique are implemented to optimize a wing shape of an unmanned aerial vehicle (UAV) for minimum cruise drag. FSI analysis and optimization processes for minimizing drag of UAV are explained. Design variables are wing taper ratio and dihedral angle, and objective function is the cruise drag of UAV. Fluid solution is generated with Euler solver and structural analysis is performed with FEM solver, Diamond. Sample points are selected by Design of Experiment (DOE) method and Kriging method is used for generation of an approximation model.

scholarly journals Small Unmanned Aircraft Systems (SUAS) and Manned Traffic near John Wayne Airport (KSNA) Spot Check of the SUAS Facility Map: Towards a New Paradigm for Drone Safety Near Airports

Drones ◽  
2019 ◽  
Vol 3 (4) ◽  
pp. 84 ◽  
Author(s):  
Peter J. Burke
Keyword(s):  
Data Mining ◽  
Safety Analysis ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Data Driven ◽  
New Paradigm ◽  
Traffic Pattern ◽  
Aircraft Systems ◽  
Spot Check ◽  
Using Data

Using data from an Automatic Dependent Surveillance-Broadcast (ADSB) aggregator, a custom data-mining program was developed to identify all manned aircraft below 500′ AGL within 5 miles of the KSNA airport on six specific days in 2018–2019. The data (a spot check) show that several of the zero-foot grids are well outside of the traffic pattern, with no manned aircraft below 500′ AGL for at least a mile. Detailed maps showing all the traffic on those days are overlaid on the KSNA UAS facility map for comparison. This data-driven safety analysis is outlined as a new paradigm for drone safety near airports, which can be applied worldwide.

Didactical Tool for Wing Weight Estimation in a Preliminary Aircraft Design Stage

2021 ◽  
Vol 18 ◽  
pp. 60-65
Author(s):  
Giacomo Frulla
Keyword(s):  
Aircraft Design ◽  
General Aviation ◽  
Design Stage ◽  
Preliminary Design ◽  
Weight Estimation ◽  
Simple Assumption ◽  
Weight Variation ◽  
Design Variables ◽  
Starting Point ◽  
Preliminary Design Stage

Aircraft preliminary design requires a lot of complex evaluations and assumptions related to design variables that are not completely known at a very initial stage. Didactical activity becomes unclear since students ask for precise values in the starting point. A tentative in providing a simple tool for wing weight estimation is presented devoted to overcome these common difficulties and clarifies the following points: a) the intrinsic iterative nature of the preliminary design stage, b) provide useful and realistic calculation for the wing weight with very simple assumption not covered by cumbersome calculations and formulas. The procedure is applied to the calculation of wing weight for a typical general aviation aircraft in the preliminary design stage. The effect of the main variables on the wing weight variation is also presented confirming well-known results from literature and design manuals.

Using Data Mining Techniques to Predict Obstetric Fistula in Tanzania

2021 ◽  
pp. 169-191
Author(s):  
Titus Fihavango ◽  
Mustafa Habibu Mohsini ◽  
Leonard J. Mselle
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Obstetric Fistula ◽  
Evidence Based ◽  
Patient Records ◽  
Medical Sciences ◽  
Data Mining Techniques ◽  
Improved Performance ◽  
Using Data ◽  
Accuracy Performance

DM practices in medical sciences have brought about improved performance in analysis of large and complex datasets. DM facilitates evidence-based medical hypotheses. Nowadays, health diseases, especially obstetric fistula, are increasing. CCBRT reports, approximately 3,000 women suffer from obstetric fistula annually. Since efforts to eradicate obstetric fistula have been inadequate, the researcher was motivated to employ MLA in BIO informatics to detect obstetric fistula. The purpose of this chapter was to use DM techniques to predict obstetric fistula. The datasets involving 367 patient records from January 2015 to February 2019 were collected from CCBRT. The environment was used to describe the accurate of predictive model was CV, ROC, and CM. The research was performed using six different MLA. The accuracy performance between algorithms shows that LR has better accuracies of 87.678%, precision measures of 91%, recall measures of 82%, f1-score measures of 86%, and support measures of 74%. Thus, the researcher chose to use LR as the proposed obstetric fistula prediction model.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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