Race Track Research Articles

A Data-driven Race Strategy Tool for Olympic Sailing Competitions

_ Venue-specific training has, over the years, proven to be a key asset for sailing teams performing at major championships and at the Olympics. A comprehensive understanding of the environmental features and possible weather scenarios in an outdoor sport like sailing can provide athletes with a significant strategic advantage. At the same time, GPS tracking is becoming a readily available technology that athletes use both in training and during races to analyse their own and their competitor's performance. This work couples environmental and meteorological data with GPS tracks for Olympic sailing classes, linking weather features with strategic decisions on the race track. We propose a greedy algorithm to search for an optimal route based on weather forecasts to present the best strategy prior to the race. Our results show the potential of this approach to provide valuable decision support for athletes in Olympic sailing competitions, demonstrated for the 470 Olympic class. Keywords Olympic sailing; path planning; data-driven methods; meteorology; A* algorithm

Influence of annealing temperature on 3D surface stereometric analysis in C-Ni films

PurposeThe purpose of this paper is to study the correlation between the thicknesses of the C–Ni films that have been prepared by RF-magnetron sputtering on quartz substrates and their three-dimensional (3D) micro morphology. In this work by AFM images, this paper studied stereo metric analysis of these films.Design/methodology/approachThe C–Ni films have been prepared by RF-magnetron sputtering on quartz substrates using a mosaic target consisting of pure graphite and strips of pure nickel approximately 2 cm2 attached to the graphite race track. The field emission scanning electronic microscopy (FESEM) images were used for the morphological characterization.FindingsThe histogram peaks are zero for all samples and the histograms are almost symmetric around zero. Temperature did not have much effect on the degree of isolation, so all four diagrams have similar results. The qualitative observations through statistical parameters of the 3D surface texture revealed that the smoothest surface has been obtained for C-Ni films annealed at 500 °C (Sa, Sq, Sz and Sv have the lower values), while the most irregular topography has been found for C-Ni films annealed at 300 °C (the fractal dimension D = 2.01 ± 0.131).Originality/valueAs shown in FESEM images, the size of the particles was increased for films deposited from 300 ºC to 800ºC; however, at 1000ºC, it decreased significantly. The histogram peaks are zero for all samples and the histograms were almost symmetric around zero. Also, the largest and lowest root mean heights (Sq) belong to films at 300 °C and 500 °C. Furthermore, the more irregular surface was found at 300 °C, and the more regular surface was found at 500 °C. As the temperature was increased to 800 °C, the values of the IAPSD function increased systematically, and then the values of the IAPSD function was decreased in the fourth sample. The surface skewness of samples annealed at 1000 °C was positive which confirms the lack of dominance of cavities on their surface with the highest amount of C-Ni films at 800 °C.

Comparative Analysis of Metaheuristic Algorithms for Procedural Race Track Generation in Games

Procedural Content Generation (PCG) aims to automatically generate the content of games using algorithmic approaches, as this can reduce the cost of game design and development. PCG algorithms can be applied to all elements of a game, including terrain, maps, stories, dialogues, quests, and characters. A wide variety of search algorithms can be applied to PCG problems; however, those most often used are variations of evolutionary algorithms. This study focuses on comparing three metaheuristic approaches applied to racetrack games, with the specific goal of evaluating the effectiveness of different algorithms in producing game content. To that end, a Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO) are applied to a game-level design task to attempt to identify any discernible differences in their performance and identify whether alternative algorithms offer desirable performance characteristics. The results of the study indicate that both the ABC and PSO approaches offer potential advantages to Genetic Algorithm implementation.

Research on the hybrid teaching model of "race track + basic accounting practice"

To enter the information age, we must adapt to the requirements of the times, and we must have the ability to learn by using information technology in order not to be abandoned by the times. Only by adapting to the development of the times can students have their own stage in society in the future, so as to assume historical responsibilities for society and the people and bloom their own life value. The mixed teaching mode is the product of the information age and the only way for the teaching mode to develop forward. Therefore, the implementation of mixed teaching mode is the need of social development and the development of modern teaching. Autonomous learning ability is a comprehensive ability that students show in their learning activities. Students with this ability have a strong thirst for knowledge, are good at using scientific learning methods, reasonably arranging their own learning activities, are good at positive thinking, dare to question questions, and show a strong spirit of exploration and enterprising in the learning process. Cultivating students' autonomous learning ability is the requirement of quality education and the need of cultivating students' all-round development. Promoting the formation of students' autonomous learning consciousness, autonomous learning ability, autonomous learning habits and autonomous learning quality is an important purpose of classroom teaching reform. Can promote students to achieve excellent results in various competitions.

Comprehensive Multidisciplinary Electric Vehicle Modeling: Investigating the Effect of Vehicle Design on Energy Consumption and Efficiency

In this study, an electric vehicle (EV) dynamic model is devised that amalgamates mechanical design aspects—such as aerodynamic effects, tire friction, and vehicle frontal area—with crucial components of the electrical infrastructure, including the electric motor, power converters, and battery systems. Verification of the model is executed through a comprehensive multidisciplinary analysis utilizing CATIA, ANSYS Electromagnetics, ANSYS Fluent, and MATLAB–Simulink tools, which are applied to evaluate two alternative lightweight EV prototypes. The process involves initial computations of critical inputs for the dynamic model, including aerodynamic lift (C1), drag coefficients (Cd), and frontal area (Af). Subsequent stages entail the detailed design and analysis of a 2 kW brushless permanent magnet electric motor in ANSYS Electromagnetics to map efficiency contours across various speed–torque values. Integration of these parameters into a MATLAB–Simulink dynamic model, connected with motor drive inverter and battery models, allows for simulation-based energy consumption analysis under race track slope profiles. Remarkably, the findings underscore the considerable impact of neglected parameters on energy consumption, often exceeding fifty percent of the total. Consequently, an energy-efficient EV prototype is manufactured and rigorously tested under specified drive conditions, affirming the validation of the comprehensive multidisciplinary EV dynamic model.

Probing Effect of 6MeV Electron Beam Irradiation on Haemoglobin Protein Using Spectroscopic Techniques.

In this work, we study the effect of 6MeV electron beam irradiation on the physicochemical properties of lyophilized Human Haemoglobin A (HbA). Electron beams generated from Race Track Microtron accelerator with energy 6MeV were used to irradiate HbA at fluences of 5 × 1014 e-/cm2 and 10 × 1014 e-/cm2. Pristine and electron beam irradiated HbA were characterized using UV-visible and Fourier transform infrared spectroscopy (FTIR) spectroscopy. The interfacial tension of the aqueous solutions of HbA are also analysed by pendant drop method. Absorbance intensity, % transmittance and interfacial tension decrease with fluence. The peak position of the Soret band (λsoret = 404nm) remains unaffected by the fluences. FTIR spectroscopy confirms the changes in the secondary structure of the haemoglobin. In the amide band I, the percentage of α-helix reduced from 8% to 1%, and an increase in β-sheet (19% to 29%) and β helix (6.3% to 15%) is observed. Interfacial tension decreases from 46.0mN/m and 44.0mN/m with increase in irradiation dose. These finding provides realistic guideline for biological cells exposure to electron beam radiation doses.

Effective training to improve DeepPilot

We present an approach to autonomous drone racing inspired by how a human pilot learns a race track. Human pilots drive around the track multiple times to familiarise themselves with the track and find key points that allow them to complete the track without the risk of collision. This paper proposes a three-stage approach: exploration, navigation, and refinement. Our approach does not require prior knowledge about the race track, such as the number of gates, their positions, and their orientations. Instead, we use a trained neural pilot called DeepPilot to return basic flight commands from camera images where a gate is visible to navigate an unknown race track and a Single Shot Detector to visually detect the gates during the exploration stage to identify points of interest. These points are then used in the navigation stage as waypoints in a flight controller to enable faster flight and navigate the entire race track. Finally, in the refinement stage, we use the methodology developed in stages 1 and 2, to generate novel data to re-train DeepPilot, which produces more realistic manoeuvres for when the drone has to cross a gate. In this sense, similar to the original work, rather than generating examples by flying in a full track, we use small tracks of three gates to discover effective waypoints to be followed by the waypoint controller. This produces novel training data for DeepPilot without human intervention. By training with this new data, DeepPilot significantly improves its performance by increasing its flight speed twice w.r.t. its original version. Also, for this stage 3, we required 66 % less training data than in the original DeepPilot without compromising the effectiveness of DeepPilot to enable a drone to autonomously fly in a racetrack.

Exploring dental faculty perceptions of current strategies and barriers to retention.

The aims of the study were to identify perceived strategies for and barriers to faculty retention and examine differences regarding age, gender, and race. Cross-sectional data was captured from the eight author-affiliated dental schools. A Qualtrics survey was emailed to all faculty members at these institutions (1467 possible participants) between November 2021 and February 2022. The survey was formulated from best practices listed in the American Dental Education Association Faculty Diversity Toolkit. It consisted of 18 questions, including demographic information and faculty perceptions of their respective workplaces. These responses were then evaluated with descriptive statistics, Chi-squared analysis, Pearson Correlation, and Fleiss' kappa. The survey's response rate was 19.2%. There was no significant difference in perceived retention strategies across race, age, gender, practice tenure, practice type, or clinical versus tenure track. The top four barriers identified were inadequate financial compensation, workload, poor work environment, and burnout. There was a significant difference between racial groups and their perceived barrier of support for promotion (p=0.048). This was more prevalent among clinical faculty (47.7%) than tenure track faculty (16.2%). The work environment was listed as a leading factor for both promoting retention and encouraging the exodus of faculty members. Strategies aimed at improving financial compensation, career recognition, and transparency of the promotion process, along with those aimed at improving the work environment were shown to be vital to retaining faculty. Though the low response rate is a study limitation, these findings provide valuable information and a framework for future studies regarding dental faculty recruitment and retention.

Powerful End-To-End Technology Solutions

Hexagon held a Power of One event at the Silverstone Innovation Centre in May next to the legendary race track. The event gave current and potential customers the chance to learn about its end-toend digital manufacturing solutions

소셜 빅데이터 분석을 활용한 영암F1경주장 인식 분석 : F1 대회기간과 이후 비교를 중심으로

Purpose: The purpose of this study is to provide basic data that will inform future utilization plans based on the current awareness by analyzing the public’s perception of Yeongam F1 RacecourseBR Method: This study conducted text mining and semantic network analysis based on three main words : Yeongam F1 Racecourse, International Car Racecourse, and International Car Racecourse Problem/Deficit.BR Results: As a result of keyword analysis, keywords related to F1 competitions, such as racecourse, Yeongam, Korea Grand Prix, and hosting, were mainly searched for in relation to the perception of the Yeongam F1 racetrack during the competition. Even after analyzing the competition, racecourse, Yeongam, and circuit the same way, keywords related to post-use and experience such as motorsports, festivals, activation, super races, and participation were reanalyzed. In the perception related to the problem/deficit of Yeongam F1 racecourse, keywords related to the operation of the racecourse were the main focus. As a result of analyzing the semantic network, it was associated with four categories: F1 competition, race track, F1 competition operation, and facility management during the competition. After the competition, it was divided into five categories: race track utilization, F1 competition, competition hosting, sponsorship, and industrial development. Finally, in the analysis of the semantic network related to Yeongam F1 racecourse problems/deficit, five categories were extracted: facility-related problems, facility-related projects, deficits, racecourse utilization, and F1 competition.BR Conclusion: Based on the results of the Big Data analysis of Yeongam F1 racetrack, it is necessary for Yeongam F1 racetrack to develop complex auxiliary facilities involving both public and the private sectors, instead of just holding one-off competitions and festivals. In addition, it is necessary to develop high-quality sports events, combine them with various automobile-related industries, and attempt functional changes in integrated tourism complexes by introducing facilities such as golf courses, marinas, and amusement parks.

Kehidupan Sosial Ekonomi Pedagang Di Kawasan Arena Pacu Jalur Kota Teluk Kuantan (2006 - 2022)

Abstract
 This study discusses the Socio-Economic Life of Traders in the Kuantan Bay City Racetrack Area (2006-2022). The purpose of this study was to determine the background of the trading community in the area of ​​the runway area, the social and economic life of the merchant community in the area of ​​the runway area of ​​Teluk Kuantan City and to explain the government's role in advancing traders in the area of ​​the runway area of ​​the City of Teluk Kuantan. In this study using the historical method through four stages, namely heuristics, interpretation, and historiography. The results and discussion obtained from this study are (1) Kuantan Singingi Regency has a very well-known tradition, namely the Pacu Balap tradition (2) The Pacu Balap tradition is held every year in order to celebrate the Republic of Indonesia's Independence Day which is carried out on the banks of the Kuantan River (3) The more it is known race track event the government is trying to carry out developments in the area on the edge of the Kuantan River which is named the track runway area (4) The construction of this track runway area, causing capturing business opportunities can affect their socio-economic life, namely by trading.
 Keywords: Socio-Economic Life, Traders, Racetrack

Integrating Recurrent Neural Networks into Model Predictive Control for Thermal Torque Derating of Electric Machines

For cost and space reasons, the electric machine (EM) and its cooling system in electrified powertrains for road vehicles are usually designed in such a way that the maximum power cannot be called up continuously. For this purpose, control systems are used that derate the power of the EM depending on its temperature. In this context, this paper presents the integration of a Recurrent Neural Network (RNN) model into a nonlinear model predictive control (MPC) to efficiently control the driving performance of an electric vehicle (EV) on a race track and reduce thermal derating effects. The discrete black-box RNN model incorporating a Long Short-Term Memory (LSTM) layer describing the thermal dynamics of the electric machine is combined with the continuous-time one-dimensional vehicle dynamics to form the hybrid MPC. For the selected example, the RNN-MPC outperforms a standard linear derating strategy by 6.23% in terms of energy consumption while achieving equal lap speed and maximum machine temperatures. Compared to an existing grey-box thermal network model, the RNN model significantly reduces the temperature prediction error by 28%. The trajectory tracking problem is formulated using acados and deployed on a dSPACE SCALEXIO embedded system to meet real-time requirements.

Learning Model Predictive Control for Quadrotors Minimum-Time Flight in Autonomous Racing Scenarios

In this paper, we design a Learning Model Predictive Control (LMPC) algorithm for quadrotors autonomous racing. The proposed algorithm allows to define a highly customizable 3D race track, in which multiple types of obstacles can be inserted. The controller is then able to autonomously find the best trajectory minimizing the quadrotor lap time, by learning from data coming from previous flights within the track, ensuring also the avoidance of all the obstacles therein. We also present novel relaxation approaches for the LMPC optimization problem, that allow to reduce it from a mixed-integer nonlinear program to a quadratic program. The LMPC algorithm is tested via several software-in-the-loop simulations, showing that the algorithm has learned to fly the quadrotor aggressively and dexterously, managing to both find the minimum-time trajectory and avoid the obstacles inside the track.

Optimization-based path planning and collision avoidance for autonomous racing

This paper presents a hierarchical motion planner for autonomous racing. The long-term motion planner functions offline and formulates the optimal motion plan for the entire race track. The short-term collision avoidance planner functions online and formulates a motion plan for a limited horizon ahead of the autonomous car when an obstacle is detected in the path of the vehicle. The motion planners formulate the planning problems as optimal control problems and solve the resulting optimizations using an interior point optimizer (IPOPT). Simulation experiments show that an autonomous vehicle using the motion planner is able to race around the track with minimum lap time while avoiding unexpected obstacles.

Smooth Trajectory Planning at the Handling Limits for Oval Racing

In motion planning for autonomous racing, the challenge arises in planning smooth trajectories close to the handling limits of the vehicle with a sufficient planning horizon. Graph-based trajectory planning methods can find the global discrete-optimal solution, but they suffer from the curse of dimensionality. Therefore, to achieve low computation times despite a long planning horizon, coarse discretization and simple edges that are efficient to generate must be used. However, the resulting rough trajectories cannot reach the handling limits of the vehicle and are also difficult to track by the controller, which can lead to unstable driving behavior. In this paper, we show that the initial edges connecting the vehicle’s estimated state with the actual graph are crucial for vehicle stability and race performance. We therefore propose a sampling-based approach that relies on jerk-optimal curves to generate these initial edges. The concept is introduced using a layer-based graph, but it can be applied to other graph structures as well. We describe the integration of the curves within the graph and the required adaptation to racing scenarios. Our approach enables stable driving at the handling limits and fully autonomous operation on the race track. While simulations show the comparison of our concept with an alternative approach based on uniform acceleration, we also present experimental results of a dynamic overtake with speeds up to 74 m/s on a full-size vehicle.

Efficient Spatiotemporal Graph Search for Local Trajectory Planning on Oval Race Tracks

Autonomous racing has increasingly become a research subject as it provides insights into dynamic, high-speed situations. One crucial aspect of handling these situations, especially in the presence of dynamic obstacles, is the generation of a collision-free trajectory that represents a safe behavior and is also competitive in the case of racing. We propose a local planning approach that generates such trajectories for a racing car on an oval race track by searching a spatiotemporal graph. A considerable challenge of search-based methods in a spatiotemporal domain is the curse of dimensionality. Therefore, we propose how a previously presented graph structure that is based on intervals instead of discrete values can be searched more efficiently without losing optimality by using a uniform-cost search strategy. We extend the search method to make it anytime-capable so that it can provide a suboptimal trajectory even if the search has to be terminated early. The graph-based planning approach allows us to apply a flexible cost function so that our approach can operate fully autonomously on an oval race track, including the pit lane. We present a cost function for oval racing and explain how the terms contribute to the desired behaviors. This is supported by results with a full-scale prototype.

Facial expression analysis in a wild sporting environment

The scientific community and mass media have already reported the use of nonverbal behavior analysis in sports for athletes’ performance. Their conclusions stated that certain emotional expressions are linked to athlete’s performance, or even that psychological strategies serve to improve endurance performance. This paper examines the portrayal of well-known emotions and their relationship to the participants of an ultra-distance race in a high-stake environment. For this purpose, we analyzed almost 600 runners captured when they passed through a set of locations placed along the race track. We have observed a correlation between the runners’ facial expressions and their performance along the track. Moreover, we have analyzed Action Unit activations and aligned our findings with the state-of-the-art psychological baseline.

Photonic crystal race track ring resonator design for the detection of cancer cells

Some of body’s cells grow in an uncontrolled manner and spreads to the other parts of the body due to cancer. Also in cancer, normal body cells infiltrate and destroy rapidly. Though the advanced treatment process has improved the survival rates of cancer, but still the cancer holds the second highest place as the leading cause of death throughout the world. But diagnosis at the earlier stage may cure the disease and improve the survival rates. In the reported article, photonic crystal based racetrack shaped ring resonator is designed using FDTD simulation to conduct study of cancer cell detection and a very high sensitivity of 900 nm/RIU is obtained from the analysis.

Visual attention prediction improves performance of autonomous drone racing agents.

Humans race drones faster than neural networks trained for end-to-end autonomous flight. This may be related to the ability of human pilots to select task-relevant visual information effectively. This work investigates whether neural networks capable of imitating human eye gaze behavior and attention can improve neural networks' performance for the challenging task of vision-based autonomous drone racing. We hypothesize that gaze-based attention prediction can be an efficient mechanism for visual information selection and decision making in a simulator-based drone racing task. We test this hypothesis using eye gaze and flight trajectory data from 18 human drone pilots to train a visual attention prediction model. We then use this visual attention prediction model to train an end-to-end controller for vision-based autonomous drone racing using imitation learning. We compare the drone racing performance of the attention-prediction controller to those using raw image inputs and image-based abstractions (i.e., feature tracks). Comparing success rates for completing a challenging race track by autonomous flight, our results show that the attention-prediction based controller (88% success rate) outperforms the RGB-image (61% success rate) and feature-tracks (55% success rate) controller baselines. Furthermore, visual attention-prediction and feature-track based models showed better generalization performance than image-based models when evaluated on hold-out reference trajectories. Our results demonstrate that human visual attention prediction improves the performance of autonomous vision-based drone racing agents and provides an essential step towards vision-based, fast, and agile autonomous flight that eventually can reach and even exceed human performances.

Safe and Resilient Practical Waypoint-Following for Autonomous Vehicles

We combine theorem proving and reachability analysis for cyber-physical systems verification to arrive at a practical approach to safe waypoint-following for an autonomous mobile vehicle controlled by a learning-enabled controller. We propose a robust monitor verifying short-term and long-term safety simultaneously at runtime, thereby combining the benefits of both theorem proving and reachability analysis. The proposed novel monitor architecture allows temporary violation of long-term safety while maintaining short-term safety to recover to a state with long-term safety. The recovery is based on a fallback model predictive controller. The experiments conducted in a high-fidelity racing car simulator demonstrate that our framework is safe and resilient in path tracking scenarios, in which avoiding collision with the race track boundary and obstacles is required.