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Nuts, Self-Locking, UNS N07001 730 °C, 1100 MPa, and 1210 MPa Procurement Specification for, Metric

2024-05-09
CURRENT
MA1943C
This procurement specification covers aircraft quality self-locking nuts for wrenching (hex, spline) and anchor (plate, gang channel, shank) types of nuts made from a corrosion and heat-resistant nickel-base alloy of the type identified under the Unified Numbering System as UNS N07001. Tension height nuts having overall length of threaded portion not less than 1.2 times the nominal thread diameter have 1210 MPa minimum tensile strength at room temperature. Shear height nuts having shorter threaded portion have 1100 MPa minimum tensile strength at room temperature. Maximum test temperature of parts is 730 °C.
Technical Paper

A Systematic Approach for Creation of SOTIF’s Unknown Unsafe Scenarios: An Optimization based Method

2024-04-09
2024-01-1966
Verification and validation (V&V) of autonomous vehicles (AVs) is a challenging task. AVs must be thoroughly tested, to ensure their safe functionality in complex traffic situations including rare but safety-relevant events. Furthermore, AVs must mitigate risks and hazards that result from functional insufficiencies, as described in the Safety of the Intended Functionality (SOTIF) standard. SOTIF analysis includes iterative identification of driving scenarios that are not only unsafe, but also unknown. However, identifying SOTIF’s unknown-unsafe scenarios is an open challenge. In this paper we proposed a systematic optimization-based approach for identification of unknown-unsafe scenarios. The proposed approach consists of three main steps including data collection, feature extraction and optimization towards unknown unsafe scenarios.
Technical Paper

Research on Vehicle Type Recognition Based on Improved YOLOv5 Algorithm

2024-04-09
2024-01-1992
As a key technology of intelligent transportation system, vehicle type recognition plays an important role in ensuring traffic safety,optimizing traffic management and improving traffic efficiency, which provides strong support for the development of modern society and the intelligent construction of traffic system. Aiming at the problems of large number of parameters, low detection efficiency and poor real-time performance in existing vehicle type recognition algorithms, this paper proposes an improved vehicle type recognition algorithm based on YOLOv5. Firstly, the lightweight network model MobileNet-V3 is used to replace the backbone feature extraction network CSPDarknet53 of the YOLOv5 model. The parameter quantity and computational complexity of the model are greatly reduced by replacing the standard convolution with the depthwise separable convolution, and enabled the model to maintain higher accuracy while having faster reasoning speed.
Technical Paper

Uniformity Identification and Sensitivity Analysis of Water Content of Each PEM Fuel Cell Based on New Online High Frequency Resistance Measurement Technique

2024-04-09
2024-01-2189
Water content estimation is a key problem for studying the PEM fuel cell. When several hundred fuel cells are connected in serial and their active surface area is enlarged for sufficient power, the difference between cells becomes significant with respect to voltage and water content. The voltage of each cell is measurable by the cell voltage monitor (CVM) while it is difficult to estimate water content of the individual. Resistance of the polymer electrolyte membrane is monotonically related to its water content, so that the new online high frequency resistance (HFR) measurement technique is investigated to identify the uniformity of water content between cells and analyze its sensitivity to operating conditions in this paper. Firstly, the accuracy of the proposed technique is experimentally validated to be comparable to that of a commercialized electrochemical impedance spectroscopy (EIS) measurement equipment.
Technical Paper

Research on Garbage Recognition of Road Cleaning Vehicle Based on Improved YOLOv5 Algorithm

2024-04-09
2024-01-2003
As a key tool to maintain urban cleanliness and improve the road environment, road cleaning vehicles play an important role in improving the quality of life of residents. However, the traditional road cleaning vehicle requires the driver to monitor the situation of road garbage at all times and manually operate the cleaning process, resulting in an increase in the driver 's work intensity. To solve this problem, this paper proposes a road garbage recognition algorithm based on improved YOLOv5, which aims to reduce labor consumption and improve the efficiency of road cleaning. Firstly, the lightweight network MobileNet-V3 is used to replace the backbone feature extraction network of the YOLOv5 model. The number of parameters and computational complexity of the model are greatly reduced by replacing the standard convolution with the deep separable convolution, which enabled the model to have faster reasoning speed while maintaining higher accuracy.
Technical Paper

Spatio-Temporal Trajectory Planning Using Search And Optimizing Method for Autonomous Driving

2024-04-09
2024-01-2563
In the field of autonomous driving trajectory planning, it’s virtual to ensure real-time planning while guaranteeing feasibility and robustness. Current widely adopted approaches include decoupling path planning and velocity planning based on optimization method, which can’t always yield optimal solutions, especially in complex dynamic scenarios. Furthermore, search-based and sampling-based solutions encounter limitations due to their low resolution and high computational costs. This paper presents a novel spatio-temporal trajectory planning approach that integrates both search-based planning and optimization-based planning method. This approach retains the advantages of search-based method, allowing for the identification of a global optimal solution through search. To address the challenge posed by the non-convex nature of the original solution space, we introduce a spatio-temporal semantic corridor structure, which constructs a convex feasible set for the problem.
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