4,205 research outputs found
AAO Starbugs: software control and associated algorithms
The Australian Astronomical Observatory's TAIPAN instrument deploys 150
Starbug robots to position optical fibres to accuracies of 0.3 arcsec, on a 32
cm glass field plate on the focal plane of the 1.2 m UK-Schmidt telescope. This
paper describes the software system developed to control and monitor the
Starbugs, with particular emphasis on the automated path-finding algorithms,
and the metrology software which keeps track of the position and motion of
individual Starbugs as they independently move in a crowded field. The software
employs a tiered approach to find a collision-free path for every Starbug, from
its current position to its target location. This consists of three
path-finding stages of increasing complexity and computational cost. For each
Starbug a path is attempted using a simple method. If unsuccessful,
subsequently more complex (and expensive) methods are tried until a valid path
is found or the target is flagged as unreachable.Comment: 10 pages, to be published in Proc. SPIE 9913, Software and
Cyberinfrastructure for Astronomy IV; 201
The development and evaluation of a detection concept to extend the red clearance by predicting a red light running event
This study focuses on developing and evaluating a detection concept to extend the red clearance by predicting a RLR event. It will dynamically extend the red clearance several seconds when a RLR is predicted to happen otherwise zero. Therefore the time will be used more efficient. In order to evaluate the influence caused by alternative detector positions, a VISSIM network was built up, connecting Econolite ASC/3 controller and ATACID. Due to the lack of realistic data, all the data in this study are fictional, but close to real. Several parameters are artificially modified in order to gain larger RLR occurrence. The two of the most crucial changes are the changing of reaction to amber signal and decreasing yellow interval. In this study, a MATLAB program predicts the RLR violation based on the data received from VISSIM via COM Interface, and makes decisions And then, ASC/3 controller would execute every command received from the MATLAB program.Within every cycle, the MATLAB program would output data into a texture file, including the red extension type and red extension length. The result has four types: red clearance is extended while there is RLR violation (RERV); red clearance is extended no RLR violation (RENRV); red clearance not extended RLR violation (RNERV); red clearance not extended no RLR violation (RNENRV). RENRV and RNERV are two types of error that should be controlled in a reasonable range, especially RNERV. There are five scenarios while the position of the prediction detector is separately 100, 125, 150, 175 and 200ft away from the stopping bar. Each scenario has 5 runs with different simulation seed.By comparing the percentile of those four types of red extension among five scenarios, the system is more likely to extend all red as the distance increases; system accuracy will increase first and then decrease; Because detector located at 150ft has the least RNERV value, 2.1%, and least summation of RNERV and RENRV, 6.6%, it can be tentatively concluded that 150 ft is the appropriate position to locate the red extension detector, while the speed limit is 60 MPH in this study
A STUDY ON AUTONOMOUS DRIVING ADAPTIVE SIMULATION SYSTEM USING DEEP LEARNING MODEL YOLOV3
For the safety of autonomous vehicles, it is not necessary that the human driver does not have much trouble detecting other vehicles and maintaining a certain distance between them, but in the case of autonomous vehicles, that's not an easy task. The problem of detecting and recognizing the front state of autonomous vehicles is known as object detection by Yolov3 bounding boxes. Therefore, we propose this study to avoid accidents before they occur due to autonomous driving on the road and for a better future.ΓΒ Our purpose in this study is to put autonomous vehicles on the road in practice using Simulink Matlab, and it is a reflection on the ability of autonomous vehicles to ensure curve road safety And to quickly determine responses on curve road situations such as acceleration/deceleration, stopping, and keeping the same speed direction so that better decisions can be made quickly. Simulation represents a possible solution by enabling the creation of reliable bounding boxes, as a first step, in this study, we discuss the feasibility of a simulation framework to detect the speed of different autonomous vehicles using Yolov3 in the real world. We first developed the YOLOV3 algorithm for autonomous vehicle image recognition using the dataset from the Matlab site. The YOLO v3 model, with an optimal performance compared to the performances of deep learning algorithms, is applied. The training parameters are refined through experiments and in the second part we proposed an effective system using "Vision Vehicle Detector test brake adapter" adaptive HighwayLaneFollowingTestBench/Simulation 3D Scenario to prepare Matlab Simulink simulation environment and sensors, Vision Vehicle Detector. The training parameters are refined through experiments. The vehicle detection rate is approximately 95.8% As per our best knowledge, as a result of the experiment, the proposed system has shown favorable results.For the safety of autonomous vehicles, it is not necessary that the human driver does not have much trouble detecting other vehicles and maintaining a certain distance between them, but in the case of autonomous vehicles, that's not an easy task. The problem of detecting and recognizing the front state of autonomous vehicles is known as object detection by Yolov3 bounding boxes. Therefore, we propose this study to avoid accidents before they occur due to autonomous driving on the road and for a better future.ΓΒ Our purpose in this study is to put autonomous vehicles on the road in practice using Simulink Matlab, and it is a reflection on the ability of autonomous vehicles to ensure curve road safety And to quickly determine responses on curve road situations such as acceleration/deceleration, stopping, and keeping the same speed direction so that better decisions can be made quickly. Simulation represents a possible solution by enabling the creation of reliable bounding boxes, as a first step, in this study, we discuss the feasibility of a simulation framework to detect the speed of different autonomous vehicles using Yolov3 in the real world. We first developed the YOLOV3 algorithm for autonomous vehicle image recognition using the dataset from the Matlab site. The YOLO v3 model, with an optimal performance compared to the performances of deep learning algorithms, is applied. The training parameters are refined through experiments and in the second part we proposed an effective system using "Vision Vehicle Detector test brake adapter" adaptive HighwayLaneFollowingTestBench/Simulation 3D Scenario to prepare Matlab Simulink simulation environment and sensors, Vision Vehicle Detector. The training parameters are refined through experiments. The vehicle detection rate is approximately 95.8% As per our best knowledge, as a result of the experiment, the proposed system has shown favorable results
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Έμ€.The automotive industry is entering a new phase in response to changes in the external environment through the expansion of eco-friendly electric/hydrogen vehicles and the simplification of modules during the manufacturing process. However, in the existing automotive industry, conflicts between structured production guidelines and various stake-holders, who are aligned with periodic production plans, can be problematic. For example, if there is a sudden need to change either production parts or situation-specific designs, it is often difficult for designers to reflect those requirements within the preexisting guidelines.
Automotive design includes comprehensive processes that represent the philosophy and ideology of a vehicle, and seeks to derive maximum value from the vehicle specifications. In this study, a system that displays information on parts/module components necessary for real-time design was proposed. Designers will be able to use this system in automotive design processes, based on data from various sources. By applying the system, three channels of information provision were established. These channels will aid in the replacement of specific component parts if an unexpected external problem occurs during the design process, and will help in understanding and using the components in advance.
The first approach is to visualize real-time data aggregation in automobile factories using Google Analytics, and to reflect these in self-growing characters to be provided to designers. Through this, it is possible to check production and quality status data in real time without the use of complicated labor resources such as command centers.
The second approach is to configure the data flow to be able to recognize and analyze the surrounding situation. This is done by applying the vehicles camera to the CCTV in the inventory and distribution center, as well as the direction inside the vehicle. Therefore, it is possible to identify and record the parts resources and real-time delivery status from the internal camera function without hesitation from existing stakeholders.
The final approach is to supply real-time databases of vehicle parts at the site of an accident for on-site repair, using a public API and sensor-based IoT. This allows the designer to obtain information on the behavior of parts to be replaced after accidents involving light contact, so that it can be reflected in the design of the vehicle.
The advantage of using these three information channels is that designers can accurately understand and reflect the modules and components that are brought in during the automotive design process.
In order to easily compose the interface for the purpose of providing information, the information coming from the three channels is displayed in their respective, case-specific color in the CAD software that designers use in the automobile development process. Its eye tracking usability evaluation makes it easy for business designers to use as well. The improved evaluation process including usability test is also included in this study.
The impact of the research is both dashboard application and CAD system as well as data systems from case studies are currently reflected to the design ecosystem of the motors group.μλμ°¨ μ°μ
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νμλ€.1 Introduction 1
1.1 Research Background 1
1.2 Objective and Scope 2
1.3 Environmental Changes 3
1.4 Research Method 3
1.4.1 Causal Inference with Graphical Model 3
1.4.2 Design Thinking Methodology with Co-Evolution 4
1.4.3 Required Resources 4
1.5 Research Flow 4
2 Data-driven Design 7
2.1 Big Data and Data Management 6
2.1.1 Artificial Intelligence and Data Economy 6
2.1.2 API (Application Programming Interface) 7
2.1.3 AI driven Data Management for Designer 7
2.2 Datatype from Automotive Industry 8
2.2.1 Data-driven Management in Automotive Industry 8
2.2.2 Automotive Parts Case Studies 8
2.2.3 Parameter for Generative Design 9
2.3 Examples of Data-driven Design 9
2.3.1 Responsive-reactive 9
2.3.2 Dynamic Document Design 9
2.3.3 Insignts from Data-driven Design 10
3 Benchmark of Data-driven Automotive Design 12
3.1 Method of Global Benchmarking 11
3.2 Automotive Design 11
3.2.1 HMI Design and UI/UX 11
3.2.2 Hardware Design 12
3.2.3 Software Design 12
3.2.4 Convergence Design Process Model 13
3.3 Component Design Management 14
4 Vehicle Specification Design in Mobility Industry 16
4.1 Definition of Vehicle Specification 16
4.2 Field Study 17
4.3 Hypothesis 18
5 Three Preliminary Practical Case Studies for Vehicle Specification to Datadriven 21
5.1 Production Level 31
5.1.1 Background and Input 31
5.1.2 Data Process from Inventory to Designer 41
5.1.3 Output to Designer 51
5.2 Delivery Level 61
5.2.1 Background and Input 61
5.2.2 Data Process from Inventory to Designer 71
5.2.3 Output to Designer 81
5.3 Consumer Level 91
5.3.1 Background and Input 91
5.3.2 Data Process from Inventory to Designer 101
5.3.3 Output to Designer 111
6 Two Applications for Vehicle Designer 86
6.1 Real-time Dashboard DB for Decision Making 123
6.1.1 Searchable Infographic as a Designer's Tool 123
6.1.2 Scope and Method 123
6.1.3 Implementation 123
6.1.4 Result 124
6.1.5 Evaluation 124
6.1.6 Summary 124
6.2 Application to CAD for vehicle designer 124
6.2.1 CAD as a Designer's Tool 124
6.2.2 Scope and Method 125
6.2.3 Implementation and the Display of the CAD Software 125
6.2.4 Result 125
6.2.5 Evaluation: Usability Test with Eyetracking 126
6.2.6 Summary 128
7 Conclusion 96
7.1 Summary of Case Studies and Application Release 129
7.2 Impact of the Research 130
7.3 Further Study 131Docto
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