Hardware Design Changes Research Articles

Design of a Tree Micro Drill Instrument to Improve the Accuracy of Wood Density Estimation

To improve the estimation accuracy of wood density and study the linear correlation between drill feed resistance and wood density, a new micro drill instrument prototype that can simultaneously measure the rotation resistance and feed resistance of the drill needle was designed. The test tree species included hard and soft broad-leaved trees and coniferous trees, and the absolute dry density of each wood sample was measured. The drill resistance data were tested by our newly proposed prototype and a Resistograph 650-SC, and four linear models were established to define the relation between drill resistance and the absolute dry density of the wood. The results showed that (1) the statistical indicators of each model for our proposed prototype were better than the corresponding indicators of the Resistograph 650-SC for three of the four species tested; (2) the coefficient of determination of the linear regression model between the feed resistance of our proposed prototype and the absolute dry density of wood was 0.946; and (3) the statistical indicators of the model that included rotation resistance and feed resistance were better than those of the model that only including rotation resistance. Although the proposed prototype produced a competitive level of accuracy and explicitly demonstrated that including feed resistance improved wood density measurement accuracy, the prototype should be considered a first iteration as further hardware design changes and in-forest performance assessments across wider diverse set of test species are required before a conclusive evaluation can be rendered.

Application of hardware reliability calculation procedures according to ISO 26262 standard

AbstractThe second edition of standard ISO 26262 (ed. 2018) for functional safety assessment in the automotive industry requires a hardware evaluation using the probabilistic metric for random hardware failures (PMHF). The standard for mentioned purpose highly recommends the fault tree analysis (FTA) utilization but does not give any specific calculation example. Therefore, this article describes computational procedures with derivation and explanation of mathematical formulas for various hardware architectures of electronic systems. Described formulas consider impact of multiple failures and impact of elf‐tests, but formulas are relatively simple. This simplicity allows them to be used in the early stages of hardware development when frequent hardware design changes can be expected. Thus, the article with attached case study is intended not only for scientists but also for developers of critical safety–related electronic systems in the automotive industry.

Sodium Metal Halide Battery Thermal Design for Improved Reliability

Cell temperature uniformity inside most batteries is important, because temperature variation leads to cell resistance variation and thus cell voltage variation during discharge–charge cycling. Voltage variation among the cells leads to accelerated degradation of the overall battery. Goal of this work was to improve cell temperature uniformity of the General Electric DurathonTM E620 battery module (600 V class, 20 kWh, 280 °C nominal temperature), which uses the sodium metal halide chemistry and convection air cooling. Computation fluid dynamics (CFD) study and bench-top testing were used to evaluate multiple battery design options. The optimized battery design was prototyped and tested, which demonstrated 3.5× increase in cooling power and 30% reduction in cell temperature difference during discharge–charge cycling. Cell temperature difference during battery float was reduced 50%. The hardware design changes were implemented into production batteries, which showed 450% improvement in reliability performance during discharge–charge cycling.

Efficient Processing of Deep Neural Networks: A Tutorial and Survey

Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems. This article aims to provide a comprehensive tutorial and survey about the recent advances toward the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic codesigns, being proposed in academia and industry. The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the tradeoffs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.

How (and When) to Make Hardware Design Changes in Released Products

Occasionally, perhaps more often than we’d like to admit, less-than-optimal product designs are released to the public and result in usability problems. There are several ways this can happen such as obtaining vendor products, expedited time to market, unforeseeable misuse, etc. The way to address field issues such as these is to develop a solution that solves the problem and is economically feasible to implement. This paper describes the use of cost-benefit analysis and provides guidelines to minimize solution-development costs in order to increase the probability that the solution can be implemented efficiently.

Using failure mode and effects analysis to plan implementation of smart i.v. pump technology

Failure mode and effects analysis (FMEA) was used to evaluate a smart i.v. pump as it was implemented into a redesigned medication-use process. A multidisciplinary team conducted a FMEA to guide the implementation of a smart i.v. pump that was designed to prevent pump programming errors. The smart i.v. pump was equipped with a dose-error reduction system that included a pre-defined drug library in which dosage limits were set for each medication. Monitoring for potential failures and errors occurred for three months postimplementation of FMEA. Specific measures were used to determine the success of the actions that were implemented as a result of the FMEA. The FMEA process at the hospital identified key failure modes in the medication process with the use of the old and new pumps, and actions were taken to avoid errors and adverse events. I.V. pump software and hardware design changes were also recommended. Thirteen of the 18 failure modes reported in practice after pump implementation had been identified by the team. A beneficial outcome of FMEA was the development of a multidisciplinary team that provided the infrastructure for safe technology implementation and effective event investigation after implementation. With the continual updating of i.v. pump software and hardware after implementation, FMEA can be an important starting place for safe technology choice and implementation and can produce site experts to follow technology and process changes over time. FMEA was useful in identifying potential problems in the medication-use process with the implementation of new smart i.v. pumps. Monitoring for system failures and errors after implementation remains necessary.

Role of refrigerant mixtures as alternatives to CFCs

Refrigerant mixtures may provide some solutions to the problem of the very limited number of fluids which have suitable properties to provide alternatives to CFCs. Mixing of refrigerants allows the adjustment or tuning of the most desirable properties of the mixture by varying the molar fraction of the components. There are three categories of mixtures which can be used as working fluids: azeotropes, near-azeotropes and zeotropes. Azeotropes are usually binary and have long been used in the refrigeration industry; it is unlikely that new combinations will now be found. Near-azeotropes have a much greater potential for development, but under leakage conditions may alter their composition and properties. Near-azeotropes have the most potential as drop-in alternatives to CFCs. Zeotropes have potential for improvements in energy efficiency and capacity modulation, but, as they require hardware design changes can only be considered in new system designs. However, with the long-term concerns about global warming, energy efficiency, and therefore the use of zeotropes, will probably have a great influence on the refrigeration industry.