Comparison of Microstructures and Properties of Ae42 Magnesium Alloy and Its Composites

Magnesium as an energy proficient material has the potential to replace steel, aluminum and some plastic-based materials. There is a great interest in using magnesium (Mg) alloys in the automotive industry due to greater environmental concern. Fuel resources are limited so it should be conserved and the harmful emissions in the environment should be reduced. Magnesium with a density of 1.74 gm/cm3 is a light metal and is suitable for automotive purpose. In this investigation the microstructure and properties of AE42 magnesium alloy and its composites reinforced with saffil short (essentially d-Al2O3) fibers and Sic particles has been studied. Both optical and SEM characterization study is carried out. Hardness values reveal that the composites are more promising than the alloy. Wear study is carried out on Ball on Plate Wear Tester at a normal load of 5 N and 10 N at rotational speed of 25 rpm. Though wear rate increases with the normal load, composites show more resistance to indentation than the AE42 alloy. Large curly chips are observed in case of magnesium alloy. Immersion test reveals that composites are more prone to corrosion due to galvanic cell creation within itself because of the presence of fibers and Sic particles

Development and Integration of Tactile Sensing System

To grasp and manipulate complex objects, robots require information about the interaction between the end effector and the object. This work describes the integration of a low-cost 3-axis tactile sensing system into two different robotic systems and the measurement of some of these complex interactions. The sensor itself is small, lightweight, and compliant so that it can be integrated within a variety of end effectors and locations on those end effectors (e.g. wrapped around a finger). To improve usability and data collection, a custom interface board and ROS (Robot Operating System) package were developed to read the sensor data and interface with the robots and grippers. Sensor data has been collected from four different tasks: 1. pick and place of non-conductive and conductive objects, 2. wrist-based manipulation, 3. peeling tape, and 4. human interaction with a grasped object. In the last task, a closed loop controller is used to adjust the grip force on the grasped object while the human interacts with it

CGuard: Efficient Spatial Safety for C

Spatial safety violations are the root cause of many security attacks and unexpected behavior of applications. Existing techniques to enforce spatial safety work broadly at either object or pointer granularity. Object-based approaches tend to incur high CPU overheads, whereas pointer-based approaches incur both high CPU and memory overheads. SGXBounds, an object-based approach, is so far the most efficient technique that provides complete out-of-bounds protection for objects. However, a major drawback of this approach is that it can't support address space larger than 32-bit. In this paper, we present CGuard, a tool that provides object-bounds protection for C applications with comparable overheads to SGXBounds without restricting the application address space. CGuard stores the bounds information just before the base address of an object and encodes the relative offset of the base address in the spare bits of the virtual address available in x86_64 architecture. For an object that can't fit in the spare bits, CGuard uses a custom memory layout that enables it to find the base address of the object in just one memory access. Our study revealed spatial safety violations in the gcc and x264 benchmarks from the SPEC CPU2017 benchmark suite and the string_match benchmark from the Phoenix benchmark suite. The execution time overheads for the SPEC CPU2017 and Phoenix benchmark suites were 42% and 26% respectively, whereas the reduction in the throughput for the Apache webserver when the CPUs were fully saturated was 30%. These results indicate that CGuard can be highly effective while maintaining a reasonable degree of efficiency

The Dormant Neuron Phenomenon in Deep Reinforcement Learning

In this work we identify the dormant neuron phenomenon in deep reinforcement learning, where an agent's network suffers from an increasing number of inactive neurons, thereby affecting network expressivity. We demonstrate the presence of this phenomenon across a variety of algorithms and environments, and highlight its effect on learning. To address this issue, we propose a simple and effective method (ReDo) that Recycles Dormant neurons throughout training. Our experiments demonstrate that ReDo maintains the expressive power of networks by reducing the number of dormant neurons and results in improved performance.Comment: Oral at ICML 202