bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mathematical Representation Ability and Self -Efficacy
Authors: Hanifah  , Waluya S.B. , Rochmad  , Wardono  
Published: September 2020

Abstract
In mathematics, there are several abilities should be belong to students. One of the abilities should be belong to students is the ability of the mathematical representation. Mathematical representation ability makes it easy for students to solve mathematical problems, because students are required to make their mathematical models, by making mathematical models students will be easier to solve mathematical problems. This basic mathematical ability can be mastered well by students if students have affective abilities, one of them is self-efficacy. In this study, qualitative research methods are used. Descriptive research is chosen the type of literature study. Data collection method with literature study. This article examines the ability of the representation and self-efficacy in mathematics learning, which includes indicators of what needs to be developed in the ability of the mathematical representation, and indicators contained self-efficacy in mathematics learning. The brief description is based on an analysis of the representation abilities and self-efficacy in mathematics learning.
Ensemble-support vector machine-random undersampling: Simulation study of multiclass classification for handling high dimensional and imbalanced data
Authors: Rahmi Nur Silviyah 
Published: September 2020

Abstract
Microarray technology measures on a large and parallel scale to express tens of thousands of genes. It has widely applied to predict gene function, new subtypes of specific tumors and cancer classification. However, microarray data are known has feature characteristics such as high dimension, small sample, high noise, and imbalanced class distribution. Support Vector Machine (SVM) has been widely used and shows the success in major applications to improve classification performance. To overcome the high dimension, we applied the Ensemble-SVM method. This method classifies features use clustering hierarchy and each group will be classified. While the condition of imbalance data becomes a problem in classification because the classifier will tend to predict the majority class compared to the minority class. Therefore, a Random Undersampling or EnSVM-RUS method is used to balance the size of the majority class into the minority class. We uses threefold cross-validation with a feature selection method that is Fast Correlation Based Filter (FCBF). The multiclass method used is SVM One Against One (OAO). While the evaluation criteria of performance classification based on the value of accuracy, F-score and G-mean and running time. We performs a simulation study with various scenario level of ratio imbalance (IR) that is ratio 1, 5, and 8 to know the performance of the proposed method. While the application on real data using Microarray DNA data with IR 4.22, 15.00 and 23.17 The results showed that EnSVM-RUS-OAO method with 2 clusters had higher performance than the EnSVM-OAO and EnSVM-OAO methods. Increasing the ratio imbalance doesn’t affect the advantage of the EnSVM-RUS-OAO method when compared to EnSVM-OAO and EnSVM-OAO methods. While on the use of the kernel, RBF kernel and polynomials produce higher performance and shorter computation time than linear kernels.
Simulation of Traffic T-Junction at Cibiru-Cileunyi Lane Using SimEvents MATLAB
Authors: Harahap E , Darmawan D , Badruzzaman F H 
Published: September 2020

Abstract
The Cibiru-Cileunyi line (JCC) or also called as Cinunuk road located in Bandung Regency, West Java, Indonesia, is one of the most densely lane vehicles because it is the only non-toll road that connects various cities in the western and northern regions to cities in the southern and eastern regions of Java West. In addition, the activities of the city and regency of Bandung and also the local community have added to the traffic jam at JCC. JCC traffic has been complained about by many people who pass to go to school, work or other activities. Congestion occurs every day, especially at rush hour, i.e. in the morning at 6 AM to 8 AM and the afternoon at 4:00 PM to 7:00 PM. However, outside of these hours, high traffic jams are also always occur. JCC travel time can reach more than 1 hour in congested conditions, while at normal situation the travel time to cross at JCC is an average of 10 minutes. One of the causes of congestion is the high traffic in and out of vehicles at several T-junction locations at JCC, where almost every T-junction at JCC raises a very long vehicle queue. In this article a simulation is proposed to represent the JCC traffic situation. The simulation is created using the SimEvents MATLAB. In this simulation, some traffic engineering is applied, such as by manage the crossings in and out of vehicles at several T-intersections in the JCC to reduce congestion.
Mathematical reasoning in problem-solving in three dimensions
Authors: Arifanti D R 
Published: September 2020

Abstract
This research is a qualitative study, where the purpose of this study is to describe the form of student reasoning in solving three-dimensional problems. To establish a research class, namely students of class XI-IPA who have studied Geometry material, especially Dimension Three, then conducts an analyzed test to determine the subject to be selected in the study. The number of research subjects to be selected are 3 people (high-ability subjects (ST), medium-capable subjects (SS), and low-ability subjects (SR). The research process followed the stages: Reasoning students in solving three dimensional problems with the indicators are: 1) Understanding the Problem; 2) Devising a Plan; 3) Carrying Out the Plan; 4) Looking Back.
The Science of Making Torque from Wind 2020 (TORQUE 2020)
Authors: Watson Simon J 
Published: September 2020

Wind power is starting to make a significant contribution to the decarbonisation of our energy systems throughout the world, though much remains to be done. Although, wind turbine technology is seen as commercially viable and, in many cases, now, subsidy-free, there are still a number of basic grand challenges in wind energy that the scientific and industrial community must address [1]. These include: providing a better understanding of atmospheric flows and how they interact with windfarms; an improved understanding of the aerodynamic, structural and hydrodynamic behaviour of wind turbines; and how to integrate ever greater volumes of wind energy into our electrical grids.
Europe has become very much a centre for the scientific and industrial expertise to address these challenges. To provide a focus for academic research in wind energy, the European Academy of Wind Energy (EAWE) was founded. The Academy has become arguably the world’s leading association of academic institutes active in wind energy. Amongst its many activities, EAWE founded a conference where leading researchers from around the world could present their work related to wind turbine technology. The conference was called The Science of Making Torque from Wind (or TORQUE, for short) and its inaugural edition was held in Delft in 2004. The conference has gone from strength to strength and is probably the largest scientific conference devoted to wind energy in the world. History came full circle and the eighth edition, TORQUE 2020 was due to be held in Delft again in the spring of 2020. Unfortunately, the COVID-19 pandemic meant that the hosting of a physical conference was no longer possible, so the host, the TU Delft Wind Energy Institute at the Delft University of Technology moved the conference fully online.
Following the call for three-page abstracts, more than 500 submissions were made and, after a two-stage per review process by over 150 reviewers, nearly 320 full papers were accepted for publication in the proceedings. The online conference consists of three plenary session, 28 parallel oral sessions and five poster sessions. Due to the online nature of the conference, oral presentations are to be kept short (eight minutes each) and plenary keynotes are restricted to 15 minutes to allow plenty of time for discussion. All poster presenters are allowed a recorded one-minute pitch which can be accessed during the online poster sessions.
Non quadratic smooth model of fatigue for optimal fatigue-oriented individual pitch control
Authors: Collet D , Alamir M , Domenico D Di , Sabiron G 
Published: September 2020

Abstract
Operation and maintenance cost of wind turbine is intimately related to fatigue damage. However, considering fatigue directly in an optimization needs to be carefully done because its faithful model does not fit standard forms. Recent results have shown that the fatigue damage of a signal estimated using fatigue theory is non-linearly related to its corresponding variance. Therefore, this relationship is used to design a convex cost function approximating fatigue. In this paper, the sensitivity of open-loop optimizations using this fatigue-oriented non-quadratic cost function to the time horizon considered is studied, in terms of both fatigue and computational cost. Moreover, in order to alleviate the undesirable effects of the computational cost, an efficient fixed-point iteration-based way to optimize this fatigue-oriented cost function is presented. Results show that the fatigue-oriented cost function allows to obtain sensitive fatigue reduction compared to optimizations using a family of classical quadratic cost functions, especially for long optimization horizons. It is eventually, shown that the prediction horizon length required for the non-quadratic criterion to be efficient in an MPC makes its use prohibitive in such framework. However, prospects of reaping the benefits of the fatigue-oriented optimization using imitation learning are given.
Prediction and Validation of Fatigue loads using Artificial Intelligence on Real World Measurement Data
Authors: Venu Anish , Lüdde Mike , Omole John 
Published: September 2020

Abstract
Monitoring the fatigue loads acting on the crucial structural components of a turbine plays a vital role in lifetime assessment, operational maintenance, load and power optimization of the wind farm. Generic physical models used in recent times are not highly reliable and does ascertain true decision-making situations. To determine the realistic fatigue loads acting on the turbine components requires expensive instrumentation with strain gauges. This work describes using an artificial intelligent model on real measurement data to make cost-effective estimation of fatigue loads acting on the wind turbines. The model is validated with real measurement data gathered from two onshore and one offshore commercial wind turbines.
Individual Blade Pitch Control for Extended Fatigue Lifetime of Multi-Megawatt Wind Turbines
Authors: Hoghooghi H. , Chokani N. , Abhari R. S. 
Published: September 2020

Abstract
A novel experimental study on the use of individual pitch control to extend the fatigue life of wind turbines is conducted. The experiment is accomplished using a sub-scale model of a multi-megawatt turbine in both upwind and downwind configurations in a water towing tank. It is shown that individual pitch control based on a sinusoidal pitching scheme that is locked to the phase of the rotor rotation can mitigate unsteady loads caused by the velocity variation that is induced by the effect of rotor tilt and therefore extends the fatigue lifetime of blades. A positive pitching amplitude in the case of upwind configuration and a negative pitching amplitude in the case of downwind configuration eliminate the cumulative damage fractions due to the velocity variation.
Numerical Validation of Wind Plant Control Strategies
Authors: Gomez-Iradi Sugoi , Astrain David , Aparicio María , Fernández Lorena , Chávez Roberto 
Published: September 2020

Abstract
Research work carried out with the support of the CL-Windcon EU project where flow control strategies (flow redirection or yaw misalignment, and flow induction control or de-rating) are validated. These strategies were divided in optimizing a wind farm (WF) in terms of wind farm Annual Energy Production (AEP), and in optimizing the wind farm to reduce the worst Damage Equivalent Load (DEL) in blade root flap-wise moment that sees a wind turbine (WT) while keeping the WF AEP as high as possible. Two numerical methods were employed. A modified FLORIS that takes into account the DEL was used in the optimization process and the simulations, the baselines and the optimized configurations, were replicated with a higher fidelity method, SOWFA, to verify the lower fidelity tool results prior to implementation in a real system. From the studied cases, it has been found that the wake steering approaches maximized the total power by 6 to 15%, and the flow induction strategies, despite having a poor performance increasing of the overall power productions, showed their validity for reducing the flap-wise moment DEL of the WT blade. Numerically, SOWFA and FLORIS simulations showed significant discrepancies in partial wake conditions, which suggested a need of a new tuning of the FLORIS parameters for partial wake conditions. Finally, mention that now, thanks to EU CL-Windcon project there are publicly available experimental data, wind tunnel data, and SOWFA data that could be used for validation purpose.
Investigation on a Shallow Water Annular Tuned Liquid Damper for Suppressing Vibration of Wind Tower
Authors: Zhang Youlin , Li Huaxiang , Xu Yiqing 
Published: September 2020

Abstract
Although the annular tuned liquid damper (ATLD) filling with shallow water is helpful in mitigating the vibration response of wind tower, it’s challenging in predicting the interface force between the ATLD and its supporting system and evaluating the damping contribution of the ATLD to the dynamic response of tower. In this study, we developed an approach to investigate the damping performance of the ATLD base on the cooperation between the smoothed particle hydrodynamics method (SPH) and the equivalent tuned mass damper model (TMD). Herein the SPH method is employed to numerically investigate the non-linear behaviour of sloshing waves in the damper and calculate the interface force, while the TMD method is used to express the TLD properties. Based on this approach, a series of numerical simulations of liquid sloshing in a shallow water ATLD with different excitation periods are carried out. The equivalent damping ratios of ATLD are calculated to quantitatively analyse the suppression effect of ATLD on the vibration response of a wind tower.
Optimal strain gauge configurations for the estimation of mechanical loads in the main shaft of HAWT
Authors: Iriarte X. , Aginaga J. , Lerga F.J. , Gainza G. , Ros J. , Bacaicoa J. 
Published: September 2020

Abstract
In Structural Health Monitoring of wind turbines, measuring the mechanical loads is a key issue. The customary techniques for this task use a full-bridge strain gauge configuration to measure each of the six load components exerted on the shaft. However, using only six strain gauges should be sufficient to estimate the six load components if a one-to-one correspondence was achieved. In this paper a different approach to mechanical loads estimation is presented where, measuring the strain of individual gauges in quarter-bridge configurations, it is possible to estimate all the load components from a single set of gauges. The configurations are optimally determined making use of the D-optimality criterion, which maximises the observability of the estimated components. The approach also provides configurations where the apparent strain related to temperature variations is automatically compensated. Results show several optimal configurations for different measuring conditions and shows that six strain gauges are enough to estimate all the load components. The new approach also opens the possibility to obtain configurations with more strain gauges as well as configurations that have to meet other requirements.
Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data
Authors: Andersson Leif Erik , Doekemeijer Bart , Hoek Daan van der , Wingerden Jan-Willem van , Imsland Lars 
Published: September 2020

Abstract
This article investigates the optimization of yaw control inputs of a nine-turbine wind farm. The wind farm is simulated using the high-fidelity simulator SOWFA. The optimization is performed with a modifier adaptation scheme based on Gaussian processes. Modifier adaptation corrects for the mismatch between plant and model and helps to converge to the actual plan optimum. In the case study the modifier adaptation approach is compared with the Bayesian optimization approach. Moreover, the use of two different covariance functions in the Gaussian process regression is discussed. Practical recommendations concerning the data preparation and application of the approach are given. It is shown that both the modifier adaptation and the Bayesian optimization approach can improve the power production with overall smaller yaw misalignments in comparison to the Gaussian wake model.
Investigating parallel multi-step vibration processing pipelines for planetary stage fault detection in wind turbine drivetrains
Authors: Koukoura Sofia , Peeters Cédric , Helsen Jan , Carroll James 
Published: September 2020

Abstract
This paper proposes a signal processing approach for wind turbine gearbox vibration signals based on employing multiple analysis pipelines. These so-called pipelines consist of combinations of various advanced signal processing methods that have been proven to be effective in literature when applied to wind turbine vibration signals. The performance of the pipelines is examined on vibration data containing different wind turbine gearbox faults in the planetary stages. Condition indicators are extracted from every pipeline to evaluate the fault detection capability for such incipient failures. The results indicate that the multipronged approach with the different pipelines increases the reliability of successfully detecting incipient planetary stage gearbox faults. The type, location, and severity of the fault influences the choice for the appropriate processing method combination. It is therefore often insufficient to only utilize a single processing pipeline for vibration analysis of wind turbine gearbox faults. Besides investigating the performance of the different processing techniques, the main outcome and recommendation of this paper is thus to employ a diversified analysis methodology which is not limited to a sole method combination, to improve the early detection rate of planetary stage gearbox faults.
Applicability of machine learning approaches for structural damage detection of offshore wind jacket structures based on low resolution data
Authors: Cevasco D , Tautz-Weinert J , Kolios A J , Smolka U 
Published: September 2020

Abstract
Structural damage in offshore wind jacket support structures are relatively unlikely due to the precautions taken in design but it could imply dramatic consequences if undetected. This work explores the possibilities of damage detection when using low resolution data, which are available with lower costs compared to dedicated high-resolution structural health monitoring. Machine learning approaches showed to be generally feasible for detecting a structural damage based on SCADA data collected in a simulation environment. Focus is here given to investigate model uncertainties, to assess the applicability of machine learning approaches for reality. Two jacket models are utilised representing the as-designed and the as-installed system, respectively. Extensive semi-coupled simulations representing different operating load cases are conducted to generate a database of low-resolution signals serving the machine learning training and testing. The analysis shows the challenges of classification approaches, i.e. supervised learning aiming to separate healthy and damage status, in coping with the uncertainty in system dynamics. Contrarily, an unsupervised novelty detection approach shows promising results when trained with data from both, the as-designed and the as-installed system. The findings highlight the importance of investigating model uncertainties and careful selection of training data.
Tensile rotary power transmission model development for airborne wind energy systems
Authors: Tulloch O , Amiri A Kazemi , Yue H , Feuchtwang J , Read R 
Published: September 2020

Abstract
Rotary airborne wind energy (AWE) systems are a family of AWE devices that utilise networked kites to form rotors. One such device is the Daisy Kite developed by Windswept and Interesting. The Daisy Kite uses a novel tensile rotary power transmission (TRPT) to transfer power generated at the flying rotor down to the ground. Two dynamic models have been developed and compared; one with simple spring-disc representation, and one with multi-spring representation that can take account of more degrees of freedom. Simulation results show that the angular velocity responses of the two TRPT models are more closely correlated in higher wind speeds when the system shows stiffer torsional behaviour. Another interesting point is the observation of two equilibrium states, when the spring-disc TRPT model is coupled with NREL’s AeroDyn. Given the computational efficiency of the simpler model and the high correlation of the results between the two models, the simple model can be used for more demanding simulations.
Immersed boundary methods and their applicability in wind energy
Authors: Krishnan Navaneetha , Viré Axelle , Schmehl Roland , van Bussel Gerard 
Published: September 2020

Abstract
Airborne wind energy systems often use kites made of thin membranes to save material costs and increase mobility. However, this design choice increases the complexity of the aeroelastic behaviour of the system and demands high-fidelity tools. On the aerodynamic side of the multi-physics problem, it is quite challenging to create a high quality body conforming grid due to the complexity of the geometry and the degree of deformation it undergoes. Immersed boundary methods (IBMs) are quite popular in fluid-structure interaction (FSI) problems that involve arbitrarily deforming bodies with complex geometries and are more tolerant to deformations compared to mesh deforming methods like ALE. This paper will look at some of the popular IBMs, outline criteria to evaluate their applicability, and discuss the limitations they have in fulfilling those in problems involving thin membranes.
Aero-structural Design of Composite Wings for Airborne Wind Energy Applications
Authors: Candade Ashwin A. , Ranneberg Maximillian , Schmehl Roland 
Published: September 2020

Abstract
In this work we explore the initial design space for composite kites, focusing on the configuration of the bridle line system and its effect on the aeroelastic behaviour of the wing. The computational model utilises a 2D cross sectional model in conjunction with a 1D beam model (2+1D structural model) that captures the complex composite coupling effects exhibited by slender, multi-layered composite structures, while still being computationally efficient for the use at the initial iterative design stage. This structural model is coupled with a non-linear vortex lattice method (VLM) to determine the aerodynamic loading on the wing. In conjunction with the aerodynamic model, a bridle model is utilised to determine the force transfer path between the wing and the bridles connected with the tethers leading to the ground station. The structural model is coupled to the aerodynamic and bridle models in order to obtain the equilibrium aero-structural-bridle state of the kite. This computational model is utilised to perform a design space exploration to assess the effects of varied load introduction to the structure and resulting effects on the kite.