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A novel method for Single Event Effect (SEE) Radiation Testing using Built-In Self-Test (BIST) feature of indigenously developed Vikram1601 processor is discussed. The novelty is that the usage of BIST avoids need of exhaustive test vectors to ensure test coverage of all the internal registers and physical memory to store them. So processor is the only element vulnerable to radiation damage during testing. The test design was carried out at VSSC, Trivandrum and the testing was carried out at IUAC, Delhi. In the first part, a brief introduction, need and methods of radiation testing of electronics especially SEE of radiation on Silicon based devices, different radiation effects, radiation damage mechanisms and testing methods are described. A brief introduction to Vikram1601 processor, the instruction – TST, used as BIST and testing scheme implementation using TST for studying the SEE is explained.

This document covers the recommended practice for determining the acceptability of the dendrite arm spacing (DAS) of D357-T6 aluminum alloy castings required to have tensile strength not lower than 50 ksi (345 MPa).

This document covers all metal, self-locking wrenching nuts, plate nuts, shank nuts, and gang channel nuts made from a corrosion and heat resistant steel of the type identified under the Unified Numbering System as UNS S66286 and of 160 ksi tensile strength at room temperature, with maximum test temperature of parts at 1200 °F.

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.

This specification covers unalloyed copper in the form of sheet, strip, and plate at least 0.015 inch (0.38 mm) in nominal thickness.

This specification covers an aluminum alloy in the form of alclad sheet and plate supplied in the -T861 temper.

This specification covers an aluminum alloy in the form of alclad coiled sheet from 0.010 to 0.128 inch (0.25 to 3.25 mm), inclusive, in thickness supplied in the -T4 temper (see 8.5).

This specification covers a titanium alloy in the form of welding wire (see 8.5).

This procurement specification covers tubular, blind rivets fabricated from a corrosion resistant nickel-copper alloy of the type identified under the Unified Numbering System as UNS N04405, and of 52 ksi minimum shear strength for self-plugging style rivets.

This procurement specification covers solid rivets and hollow end rivets made from a corrosion and heat resistant steel of the type identified under the Unified Numbering System as UNS S66286 and of 80 ksi single shear strength at room temperature.

This procurement specification covers aircraft-quality solid rivets made from a corrosion resistant nickel-copper alloy of the type identified under the Unified Numbering System as UNS N04400 and of 46 ksi minimum shear strength.

This specification covers a copper alloy in the form of wire, rod, sheet, strip, foil, and powder and a viscous mixture (paste) of powder in a suitable binder (see 8.6).

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.

Focused on the permanent magnet synchronous motor (PMSM) used in electric, this paper proposes an online insulation testing method based on voltage injection under high-temperature and high-humidity conditions. The effect of constant humidity and temperature on the insulation performance has been also studied. Firstly, the high-voltage insulation structure and principle of PMSM are analyzed, while an electrical insulation testing method considered constant humidity and temperature is proposed. Finally, a temperature and humidity experimental cycling test is carried out on a certain prototype PMSM, taking heat conduction and radiation models, water vapor, and partial discharge into account. The results show that the electrical insulation performance of the motor under constant humidity and temperature operation environment exhibits a decreasing trend. This study can provide theoretical and practical references for the reliable durability design of PMSM.

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.