1,721,432 research outputs found
THE INVESTIGATION OF EFFECTIVE MATERIAL CONCEPT FOR THE TRANSIENT WAVE PROPAGATION IN MULTILAYERED MEDIA
No full textRemaining useful life prediction for complex systems considering varying future operational conditions
No full textRemaining useful life (RUL) prediction technology is important for optimizing maintenance schedules. With the advancement of sensing technology, several deep learning approaches have been proposed to predict RUL without relying on prior knowledge about systems. However, previous deep learning-based approaches rarely consider the future operational conditions, which can be known according to the future work plan and is an important influential factor for RUL prediction. This paper proposes a multi-input neural network based on long short-term memory for RUL prediction considering the temporal dependencies among the measurements when the future operational conditions are known. The sliding window approach is employed for determining the input time sequences of previous monitoring data (including operational condition and sensor measurements), and the length of input time sequences of the future operational conditions are determined based on the prior estimated RUL. Fine-tuning strategy is proposed to make the training of the multi-input network more effective. To illustrate the effectiveness of the proposed methods, a case study referring to the C-MAPSS dataset is used and a sensitivity analysis is also conducted on the future operational conditions
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Quartz superoscillatory lenses with NA > 1
No full textWe developed design algorithms and manufacturing processes for quartz visible-range superoscillatory lenses. Lenses with focal spot size of 0.4λ, effective numerical aperture of 1.25 in air and working distance of 1 mm are demonstrated
Prediction Model of PTSD Symptoms One Year post the Taiwan 921 Earthquake—A Case of Adolescents Living close to the Epicenter
No full textA Semisupervised Deep Hybrid Multitask Model for RUL Prediction
No full textDeep learning (DL) methods can be used to construct health indicators (HIs) for remaining useful life (RUL) prediction. The existing DL methods consider previous and current sensor signals and utilize labeled data, which are limited in practice. To leverage unlabeled data for extracting HIs, semisupervised methods, especially hybrid methods, can be employed. In this article, a semisupervised deep hybrid multitask model (DHMTM) for RUL prediction is developed. The DHMTM contains two temporal models for unlabeled and labeled multivariate time-series data, respectively. In the model training process, adding an extra task of prediction of future sensor signal values, the DHMTM can obtain His, which improve the RUL prediction accuracy. Besides, temporal dependency of sensor signals is captured in the proposed DHMTM. The effectiveness of the proposed model is validated using the commercial modular aero-propulsion system simulation (C-MAPSS) and the lithium-ion batteries datasets. The results show that using the proposed method, the prediction errors for the two datasets have been reduced by 2.5% and 23.5% on average, respectively, compared with the fully supervised regression model, and 17% and 44%, respectively, on average compared with three other widely used semisupervised methods
Superoscillatory quartz lens with effective numerical aperture greater than one
No full textWe report super-resolution high-numerical-aperture and long-working-distance superoscillatory quartz lenses for focusing and imaging applications. At the wavelength of λ = 633 nm, the lenses have an effective numerical aperture of 1.25, a working distance of 200 μm, and a focus into a hotspot of 0.4λ. Confocal imaging with resolution determined by the superoscillatory hotspot size is experimentally demonstrated.</p
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