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In the world of digitization, different objects cooperate with the Internet of Things (IoT); these objects also amplify using sensing and data processing structures. Radio frequency identification (RFID) has been identified as a key enabler technology for IoT. RFID technology has been used in different conventional applications for security, goods storage, transportation and asset management. In this paper, a fully inkjet-printed chipless radio frequency identification (RFID) sensor tag is presented for the wireless identification of tagged objects. The dual polarized tag consists of two resonating structures functioning wirelessly. One resonator works for encoding purpose and other resonator is used as a CO2/temperature sensor. The sensing behavior of the tag relies on the integration of a meandered structure comprising of multi-wall carbon nanotubes (MWCNT). The MWCNT is highly sensitive to CO2 gas. The backscattered response of the square-shaped cascaded split ring resonators (SRR) is analyzed through a radar cross-section (RCS) curve. The overall tag dimension is 42.1 mm × 19.5 mm. The sensing performance of the tag is examined and optimized for two different flexible substrates, i.e., PET and Kapton®HN. The flexible tag structure has the capability to transmit 5-bit data in the frequency bands of 2.36–3.9 GHz and 2.37–3.89 GHz, for PET and Kapton®HN, respectively. The proposed chipless RFID sensor tag does not require any microchip or a power source, so it has a great potential for low-cost and automated temperature/CO2 sensing applications.

The model of the damaged blade is essential and needs to be reconstructed when repairing the blade. Due to the blade deformation, the performance of the reconstructed model may not satisfy the service requirement, which may result in the failure of repair. To obtain the reconstructed model with desired performance, the geometric parameters of the reconstructed model must be changed and tested constantly during the reconstruction process, causing the urgent demand for the reconstruction method which can adjust the geometric parameters of the reconstructed model to given values. Therefore, in this paper, a model reconstruction strategy with geometric parameters adjustment function is proposed. The strategy extracts the damaged cross-section curves (CSCs) of the blade first and then repairs them through a parametric algorithm. The parametric algorithm reconstructs the damaged CSCs through the camber line and can ensure the geometric parameters of repaired CSCs are consistent with the given values. Subsequently, the blade model with given geometric parameters is obtained by lofting repaired CSCs. Finally, an experiment is conducted to validate the strategy. The result shows that the geometric parameters of the reconstructed model are adjusted effectively to the given values. Most of the deviations between the reconstructed and scanning model in the undamaged area are less than 0.01 mm and the surface of the reconstructed model is smooth. Thus, it is concluded that the proposed strategy can reconstruct the damaged blade precise model and adjust the geometric parameters of the reconstructed model to the given values.

Recently, the NURBS technique has been widely used in the 3D design software for ships. However, in most research, the NURBS technique is only applied to the mathematical representation of hull curves and surfaces, and the parametric deformation of hull surfaces based on geometric feature parameters is less understood. The aims of this paper are to establish the parametric design process of hull surfaces through the classification of geometric feature parameters and the design of feature curves, apply the NURBS technique to the parametric geometric modeling of hull curves and surfaces, and finally achieve the parametric deformation of hull surfaces driven by geometric feature parameters and develop the parametric deformation software. Taking the Series 60 ship as an example, we first analyze the hull geometric features and parameters, then design the longitudinal feature curves and cross-section curves based on the NURBS technique and establish the correlation between them, and finally generate the smooth hull surface by the skinning technique to achieve the parametric geometric deformation of the Series 60 ship. The research in this paper shows that the smoothness of the surfaces generated by the NURBS-based parametric design method is good. Additionally, the extracted feature parameters have a clear geometric meaning and can automatically generate hull forms to meet the design requirements quickly and effectively, which has some practical engineering value.

This work is concerned with the vulnerability of spaceborne microelectronics to single-event upset, which is a change of state caused by high-energy charged particles in the solar wind or the cosmic ray environment striking a sensitive node. To measure the susceptibility of a semiconductor device to single-event upsets, testing is conducted by exposing it to high-energy heavy ions or protons produced in a particle accelerator. The number of upsets is characterized by the interaction cross-section, which is an increasing function of linear energy transfer. The prediction of the on-orbit upset rate is made by combining the device geometry and cross-section versus linear energy transfer curve with a model for the orbit-specific radiation environment. We develop a semiparametric isotonic regression method for the upset count responses, based on a Dirichlet process prior for the cross-section curve. The methodology proposed allows the data to drive the shape of the cross-section versus linear energy transfer relationship, resulting in more robust predictive inference for the on-orbit upset rate than conventional models based on Weibull or log-normal parametric forms for the cross-section curve. We illustrate the modelling approach with data from two particle accelerator experiments.

Classical approaches of estimating cross-section Engel curves are based on parametric models. However, misspecification of a parametric model implies that information of structural nature might be masked. An alternative avoiding problems related to predetermined functional relations is the nonparametric approach. This paper surveys recent advances of nonparametric statistics in their relevance to estimating cross-section Engel curves.

In this paper, we revisit the environmental Kuznets curve (EKC) hypothesis by using estimations that account for cross-sectional dependency (CSD) and asymmetry effect in 76 countries for the period 1971–2014. Our results lend moderate support to the EKC hypothesis. The country-specific results unfold that a total of 16 out of 76 countries support the EKC hypothesis using CCEMG estimator. Results from AMG reveal that the EKC hypothesis holds in 24 out of 76 countries. It is worth highlighting that 11 countries (Australia, China, Congo Dem. Rep., Costa Rica, Gabon, Hong Kong, India, Korea, Myanmar, Turkey, and Uruguay) exhibit an inverted U-shaped curve regardless of whether CCEMG or AMG is used. The asymmetry analysis using PMG is also able to support the EKC hypothesis. We conclude that the EKC hypothesis does not fit all countries. Policy implication and recommendation in designing appropriate energy and economic policies are provided.

Full-waveform lidar systems capture the complete backscattered signal from the interaction of the laser beam with targets located within the laser footprint. The resulting data have advantages over discrete return lidar, including higher accuracy of the range measurements and the possibility of retrieving additional returns from weak and overlapping pulses. In addition, radiometric characteristics of targets, e.g., target cross-section, can also be retrieved from the waveforms. However, waveform restoration and removal of the effect of the emitted system pulse from the returned waveform are critical for precise range measurement, 3D reconstruction and target cross-section extraction. In this paper, a sparsity-constrained regularization approach for deconvolution of the returned lidar waveform and restoration of the target cross-section is presented. Primal-dual interior point methods are exploited to solve the resulting nonlinear convex optimization problem. The optimal regularization parameter is determined based on the L-curve method, which provides high consistency in varied conditions. Quantitative evaluation and visual assessment of results show the superior performance of the proposed regularization approach in both removal of the effect of the system waveform and reconstruction of the target cross-section as compared to other prominent deconvolution approaches. This demonstrates the potential of the proposed approach for improving the accuracy of both range measurements and geophysical attribute retrieval. The feasibility and consistency of the presented approach in the processing of a variety of lidar data acquired under different system configurations is also highlighted.

The cross-section profile is a key signal for evaluating hot-rolled strip quality, and ignoring its defects can easily lead to a final failure. The characteristics of complex curve, significant irregular fluctuation and imperfect sample data make it a challenge of recognizing cross-section defects, and current industrial judgment methods rely excessively on human decision making. A novel stacked denoising autoencoders (SDAE) model optimized with support vector machine (SVM) theory was proposed for the recognition of cross-section defects. Firstly, interpolation filtering and principal component analysis were employed to linearly reduce the data dimensionality of the profile curve. Secondly, the deep learning algorithm SDAE was used layer by layer for greedy unsupervised feature learning, and its final layer of back-propagation neural network was replaced by SVM for supervised learning of the final features, and the final model SDAE_SVM was obtained by further optimizing the entire network parameters via error back-propagation. Finally, the curve mirroring and combination stitching methods were used as data augmentation for the training set, which dealt with the problem of sample imbalance in the original data set, and the accuracy of cross-section defect prediction was further improved. The approach was applied in a 1780-mm hot rolling line of a steel mill to achieve the automatic diagnosis and classification of defects in cross-section profile of hot-rolled strip, which helps to reduce flatness quality concerns in downstream processes.

PurposeThe inward displacement perpendicular to the body surface produced by compression garment is an important index to evaluate pressure comfort and optimal design of tight clothing products. The purpose of this study is to explore the pressure distribution state at waist position of elastic legwear and then to solve the common problem of excessive pressure or easy slippage for waist of elastic legwear.Design/methodology/approachIn this paper, the authors obtained the waist cross-section model of human body using CT scanning and mimics modeling and then simulated the pressure and displacement distribution after wearing sample four elastic legwear using finite element method. The dressing process of elastic legwear was divided into six periods (instantaneous, 1, 2, 4, 8 and 12 h) in this study, and the finite element software ANSYS was used to simulate the displacement and deformation of the waist cross section. The authors finally obtained the functional relationship between pressure/displacement ratio and angle using curve fitting.FindingsIn this paper, the authors obtained the functional relationship between pressure/displacement ratio and angle using curve fitting. Comparison found that the “pressure/displacement–angle” function curve showed an almost consistent trend at any time. That was to say, when the human body was in the state of clothing pressure, the corresponding displacement value of the human body can be calculated by the curve equation under the premise of known pressure value.Originality/valueThis study solves the difficult problem which hard to measure displacement values by conventional methods due to the small deformation of the human body after dressing the compression garment. Conclusions also provide a theoretical reference for evaluating pressure comfort and optimizing clothing structure for the elastic legwear, and this method is also applicable to other types of compression garment.

This study develops a pore-scale model to analyze how particle arrangement and variable cross-sectional pore geometry on the hydraulic behavior of unsaturated soils. The particle-size distribution is partitioned into groups, within which pore systems of three, four, and five-particle arrangements were constructed. Using the pressure-difference method for connected channels and the Young–Laplace equation, a calculation framework for the unsaturated permeability coefficient and soil–water characteristic curve was established, explicitly considering soil micropore distribution. Model performance was verified with 18 soil datasets from the Unsaturated Soil Hydraulic Database and further examined through variable-head permeability tests on extremely fine sands with varying silty fine sand and fine-particle contents. Results show that the model reliably predicts the unsaturated permeability coefficient and Soil Water Characteristic Curve (SWCC) for loamy sand, sand, and sandy loam across both high and low water contents when suitable particle arrangement systems are applied. The five-particle system is more effective for high water content in permeability calculations and for coarse-grained soils in SWCC prediction, whereas the three-particle system better represents low water contents and fine-grained soils. The model requires only particle-size distribution, dry density, saturated permeability coefficient, and saturated water content, without introducing additional empirical parameters, thereby offering a concise and practical approach for characterizing unsaturated soil hydraulic properties.