Analysis and evaluation of uncertainty for conducted and radiated emissions tests

Whenever an EMC measurement is made, there are numerous uncertainties in different parts of the measurement system and even in the EMC performance of the equipment under test (EUT) which is being measured. It is important to be able to estimate the overall uncertainty, in particular, the test setup and measurement equipment uncertainty. However, making repetitive measurements can reduce the measurement uncertainty, but often economics of time do not permit that. Therefore, a practical process, which is used to evaluate uncertainty in EMC measurement a, according to the principle of uncertainty and conditions in EMC measurement is presented. In this study, an efficient analysis of uncertainty for both radiated and conducted emissions tests is performed. The uncertainty of each contributor had been calculated and evaluating the reported expanded uncertainty of measurement is stated as the standard uncertainty of measurement. This standard uncertainty is multiplied by the coverage factor k=2, which for a normal distribution corresponds to a coverage probability of approximately 95%. The result of calculating the uncertainty for both conducted and radiated emission tests showed that the overall uncertainty of the system is high and it must be lowered by reducing the expanded uncertainty for the dominant contributors for both tests. In addition, the result of applying the concept of CISPR uncertainty for both conducted and radiated emission tests showed that non-compliance is deemed to occur for both EUT of both tests. This is due to the result that the measured disturbances increased by ( ), above the disturbance limit

Scoping analytical usability evaluation methods: A case study

Analytical usability evaluation methods (UEMs) can complement empirical evaluation of systems: for example, they can often be used earlier in design and can provide accounts of why users might experience difficulties, as well as what those difficulties are. However, their properties and value are only partially understood. One way to improve our understanding is by detailed comparisons using a single interface or system as a target for evaluation, but we need to look deeper than simple problem counts: we need to consider what kinds of accounts each UEM offers, and why. Here, we report on a detailed comparison of eight analytical UEMs. These eight methods were applied to it robotic arm interface, and the findings were systematically compared against video data of the arm ill use. The usability issues that were identified could be grouped into five categories: system design, user misconceptions, conceptual fit between user and system, physical issues, and contextual ones. Other possible categories such as User experience did not emerge in this particular study. With the exception of Heuristic Evaluation, which supported a range of insights, each analytical method was found to focus attention on just one or two categories of issues. Two of the three "home-grown" methods (Evaluating Multimodal Usability and Concept-based Analysis of Surface and Structural Misfits) were found to occupy particular niches in the space, whereas the third (Programmable User Modeling) did not. This approach has identified commonalities and contrasts between methods and provided accounts of why a particular method yielded the insights it did. Rather than considering measures such as problem count or thoroughness, this approach has yielded insights into the scope of each method

Why Do Makers Make? Examining Designer Motivations on Thingiverse.com

Technological advancements have made a once fictitious dream into a reality. 3D printing has become a popular manufacturing and design technique used all over the world. As this industry becomes more popular, users of these 3D printers are reaching out across the web to share designs, seek help, and build communities of users with similar interests. This study is meant to look at what motivates 3D printing users to participate in online user innovation communities such as Thingiverse.com. This study will explore motivations such as personal needs, financial gains, approval of peers, skill development, and enjoyment. Moreover, it will assess the impact of each of these motivations on the number of designs created by designers within the observation period (May 2017-May 2018) and on the market response to these designs. To study these elements, we first perused research done in previous studies on motivations in brand communities, transactional communities, and user innovation communities to create a literature review. Following the literature review, a survey was created which asked Thingiverse makers 5 sets of questions related to their specific motivations for creating and sharing designs and asked them to provide demographic data as well. The results obtained from this research indicate that the motivation to satisfy a personal need has a marginally significant, negative impact on the number of designs created by a maker while the desire to gain approval from others in the community has a significant, positive effect on market response to those designs. Additionally, it was found that a desire for financial gain has little to no effect on the number of designs created or on the market response, a result which was surprising considering that 25% of the respondents reported earning money from 3D printing. These results and their implications as well as future research directions are outlined in the concluding discussion section

Risk analysis in manufacturing footprint decisions

A key aspect in the manufacturing footprint analysis is the risk and sensitivity analysis of critical parameters. In order to contribute to efficient industrial methods and tools for making well-founded strategic decisions regarding manufacturing footprint this paper aims to describe the main risks that need to be considered while locating manufacturing activities, and what risk mitigation techniques and strategies that are proper in order to deal with these risks. It is also proposed how the risk analysis should be included in the manufacturing location decision process

Crystalline phase, surface morphology and electrical properties of monovalent-doped Nd0.75Na0.25Mn1-yCoyO3 manganites

Perovskite-type manganites Nd0.75Na0.25Mn1-yCoyO3 (y = 0 – 0.05) have been investigated to clarify the influence of Co-doped on crystal phase and morphological study as well as electrical transport properties. The Nd0.75Na0.25Mn1-yCoyO3 samples are prepared via solid state synthesis method. X-ray diffraction analysis revealed all the samples are essentially single phased and the peaks are indexed to an orthorhombic structure with Pnma space. The morphological study from scanning electron microscope shows the improvement of the grains boundaries and sizes as well as the compaction of particles can be seen as cobalt doping increased. On the other hand, the temperature dependence of electrical resistivity measurements using four-point-probe technique indicates all samples maintained an insulator like behaviour down to low temperature. Analysis of the resistivity change with respect to temperature, dlnρ/dT-1 versus T reveals a slope changes of resistivity has been observed and a boarder peak exist for y = 0 sample and the peaks become significantly obvious for y = 0.02 and 0.05 samples. The peaks are observed in the range of charge ordering (CO) transition indicate the existence of CO in the system

Project pathogens: The anatomy of omission errors in construction and resource engineering project

Construction and engineering projects are typically complex in nature and are prone to cost and schedule overruns. A significant factor that often contributes to these overruns is rework. Omissions errors, in particular, have been found to account for as much as 38% of the total rework costs experienced. To date, there has been limited research that has sought to determine the underlying factors that contribute to omission errors in construction and engineering projects. Using data derived from59 in-depth interviews undertaken with various project participants, a generic systemic causal model of the key factors that contributed to omission errors is presented. The developed causal model can improve understanding of the archetypal nature and underlying dynamics of omission errors. Error management strategies that can be considered for implementation in projects are also discussed