Microsystem Technologies - Printed electronics such as solar cells, RFIDs, and display panels can be made using printed electronic technology by printing viscous liquids having various properties... 相似文献
Predicting remaining useful life (RUL) is crucial for system maintenance. Condition monitoring makes not only degradation data available for RUL estimation but also categorized health status data for health state identification. However, RUL prediction has been treated as an independent process in most cases even though potential relevance exists with health status detection process. In this paper, we propose a convolution neural network based multi-task learning method to reflect the relatedness of RUL estimation with health status detection process. The proposed method applied to the C-MAPSS dataset for aero-engine unit prognostics supported superior performances to existing baseline models.
Achieving high processing quality for chemical mechanical planarization (CMP) in semiconductor manufacturing is difficult due to the distinct process variations associated with this method, such as drift and shift. Run-to-run control aims to maintain the targeted process quality by reducing the effect of process variations. The goal of controller learning is to infer an underlying output–input reverse mapping based on input–output samples considering the process variations. Existing controllers learn reverse mapping by minimizing the total mapping error for sample data. However, this approach often fails to generate inputs for unseen target outputs because conditional input distributions on target outputs are not captured in the learning. In this study, we propose a controller based on a least squares generative adversarial network (LSGAN) that can capture the input distributions. GANs are deep-learning architectures composed of two neural nets: a generator and a discriminator. In the proposed model, the generator attempts to produce fake input distributions that are similar to the real input distributions considering the process variation features extracted using convolutional layers, while the discriminator attempts to detect the fake distributions. Competition in this game drives both networks to improve their performance until the generated input distributions are indistinguishable from the real distributions. An experiment using the data obtained from a work-site CMP tool verified that the proposed model outperformed the comparison models in terms of control accuracy and computation time.
Drawing on the mixed methods of qualitative research and agent‐based simulation, this study examines: (a) how end‐users use digital platforms to become customer–entrepreneurs undertaking commercial activities on platforms; and (b) how platform providers can convert this customer entrepreneurship into a revenue stream. Considering that end‐users have traditionally been defined as passive and uncharged actors in platform business models, an in‐depth understanding of their commercial activities and the viable revenue model to monetize this emerging customer practice is warranted. Our qualitative study reveals that customer–entrepreneurs make substantial use of platform offerings to advertise their products; communicate with end‐consumers; and accept payments. These commercial activities are largely exercised for free on platforms, even though they could otherwise serve as a source of revenue. On this point, our simulation results identify two pricing models achieving the generation of nearly identical revenues over time. First, platform providers may charge both advertising and transaction fees, which maximize the survival of professional customer–entrepreneurs. Second, platform businesses may levy advertising fees only, which maximizes the survival of informal customer–entrepreneurs operating on a micro‐scale and part‐time basis. This study offers theoretical, methodological, and managerial implications for platform studies. 相似文献
This study proposes a framework regarding the relationship between consumer trust, satisfaction, expectation, and post-expectation
in the context of electronic commerce. In particular, the framework draws together from three theories: social exchange theory,
expectation-confirmation theory, and post-acceptance model of IS continuance. Following the longitudinal pre-purchase and
post-purchase stages, this study provides a theoretical framework combining trust, expectation, satisfaction, and post-expectation
(i.e., perceived usefulness) and tests the proposed model empirically using Internet consumer behavior data collected via
two rounds of Web surveys. The empirical findings suggest that both consumer’s trust and expectation have positive influences
on consumer’s satisfaction; a significant and positive relationship is detected between consumer’s trust and expectation;
customer’s satisfaction and perceived usefulness as post-expectation belief are important predictors of repurchase intention.
In consequence, the study provides a framework explaining the subsequent relationships of trust, expectation, confirmation,
satisfaction, post-expectation, and repurchase intention (i.e., consumer trust → expectation → confirmation → satisfaction
→ post-expectation → repurchase intention) across pre-purchase and post-purchase stages. Theoretical and practical implications
are discussed. 相似文献
