A Multi-Level Approach to Waste Object Segmentation

We address the problem of localizing waste objects from a color image and an optional depth image, which is a key perception component for robotic interaction with such objects. Specifically, our method integrates the intensity and depth information at multiple levels of spatial granularity. Firstly, a scene-level deep network produces an initial coarse segmentation, based on which we select a few potential object regions to zoom in and perform fine segmentation. The results of the above steps are further integrated into a densely connected conditional random field that learns to respect the appearance, depth, and spatial affinities with pixel-level accuracy. In addition, we create a new RGBD waste object segmentation dataset, MJU-Waste, that is made public to facilitate future research in this area. The efficacy of our method is validated on both MJU-Waste and the Trash Annotation in Context (TACO) dataset.Comment: Paper appears in Sensors 2020, 20(14), 381

Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment

Medical image segmentation has made significant progress when a large amount of labeled data are available. However, annotating medical image segmentation datasets is expensive due to the requirement of professional skills. Additionally, classes are often unevenly distributed in medical images, which severely affects the classification performance on minority classes. To address these problems, this paper proposes Co-Distribution Alignment (Co-DA) for semi-supervised medical image segmentation. Specifically, Co-DA aligns marginal predictions on unlabeled data to marginal predictions on labeled data in a class-wise manner with two differently initialized models before using the pseudo-labels generated by one model to supervise the other. Besides, we design an over-expectation cross-entropy loss for filtering the unlabeled pixels to reduce noise in their pseudo-labels. Quantitative and qualitative experiments on three public datasets demonstrate that the proposed approach outperforms existing state-of-the-art semi-supervised medical image segmentation methods on both the 2D CaDIS dataset and the 3D LGE-MRI and ACDC datasets, achieving an mIoU of 0.8515 with only 24% labeled data on CaDIS, and a Dice score of 0.8824 and 0.8773 with only 20% data on LGE-MRI and ACDC, respectively.Comment: Paper appears in Bioengineering 2023, 10(7), 86

Impact of pore structure on the thermal conductivity of glass foams

The thermal conductivity (λ) of glass foams is thought to depend on pore size. We report on the impact of pore size, determined using X-ray microtomography, and percentage porosity on the λ of glass foams. Glass foams were prepared by heating powder mixtures of obsolete cathode ray tube (CRT) panel glass, Mn3O4 and carbon as foaming agents, and K3PO4 as additive, to a suitable temperature above Tg, and subsequent cooling. Here, we report for the first time a correlation between λ and pore size in the range 0.10–0.16 mm showing a decrease from 57 to 49 mW m−1 K−1 with increasing the pore size for glass foams with porosities of 87–90%. This indicates that the pore structure should be optimized in order to improve the insulating performance of glass foams

Atomic and vibrational origins of mechanical toughness in bioactive cement during setting

Bioactive glass ionomer cements (GICs) have been in widespread use for ~40 years in dentistry and medicine. However, these composites fall short of the toughness needed for permanent implants. Significant impediment to improvement has been the requisite use of conventional destructive mechanical testing, which is necessarily retrospective. Here we show quantitatively, through the novel use of calorimetry, terahertz (THz) spectroscopy and neutron scattering, how GIC’s developing fracture toughness during setting is related to interfacial THz dynamics, changing atomic cohesion and fluctuating interfacial configurations. Contrary to convention, we find setting is non-monotonic, characterized by abrupt features not previously detected, including a glass–polymer coupling point, an early setting point, where decreasing toughness unexpectedly recovers, followed by stress-induced weakening of interfaces. Subsequently, toughness declines asymptotically to long-term fracture test values. We expect the insight afforded by these in situ non-destructive techniques will assist in raising understanding of the setting mechanisms and associated dynamics of cementitious materials

On the Application of Joint-Domain Dictionary Mapping for Multiple Power Disturbance Assessment

This paper proposes a joint-domain dictionary mapping method to obtain high assessment accuracy of multiple power disturbances. Firstly, in order to achieve resolutions in both the time and frequency domains, a joint-domain dictionary is proposed which consists of a discrete Hartley base and an identity matrix. Due to the low correlation between the discrete Hartley base and the identity matrix, the joint-domain dictionary mapping can separately capture the approximations of the sinusoidal components and transients. Since the mapping coefficients contain the physical quantities, the eigenvalues of each component can be effectively estimated. A quantified eigenvalue classifier was designed for identifying power disturbances using the estimated eigenvalues. The proposed method was compared with several advanced methods through simulated power disturbances under different noise conditions, and actual data from the Institute of Electrical and Electronics Engineers Power and Energy Society database. The results reveal that the joint-domain dictionary mapping technique shows good performance on parameter estimation and recognition precision, even dealing with complicated multiple power disturbances

A Novel Approach for Searching the Upper/Lower Bounds of Uncertainty Parameters in Microgrids

In this study, a novel method based on μ analysis is presented to search for the upper/lower bounds of uncertainty parameters in microgrids (MGs). It is well known that uncertainty parameters have important effects in a MG, and they may cause instability. Previous studies have mainly focused on identifying the stability of a MG with its uncertainty parameters, but they did not address the problem of the upper/lower bounds of uncertainty parameters, i.e., how far the uncertainty parameters can be extended while the system remains stable in the small-signal sense. Thus, we developed an approach for identifying the bounds of uncertainty in MGs. In the current paper, first, a method is proposed for linear fractional transformation (LFT) configuration to express the uncertainty parameters, which makes the stability of the nominal MG system independent of any extension of the bounds. An algorithm based on this configuration is then designed to find the upper/lower bounds for both single parameter and multiple uncertainty parameters in a MG. Finally, the two cases are discussed, and the accuracy of the proposed method is confirmed using the conventional eigenvalue method

Fabrication of exfoliated graphene-based polypropylene nanocomposites with enhanced mechanical and thermal properties

Abstract: Despite the great potential of graphene as the nanofiller, to achieve homogeneous dispersion remains the key challenge for effectively reinforcing the polymer. Here, we report an eco-friendly strategy for fabricating the polymer nanocomposites with well-dispersed graphene sheets in the polymer matrix via first coating graphene using polypropylene (PP) latex and then melt-blending the coated graphene with PP matrix. A ∼75% increase in yield strength and a ∼74% increase in the Young's modulus of PP are achieved by addition of only 0.42 vol% of graphene due to the effective external load transfer. The glass transition temperature of PP is enhanced by ∼2.5 °C by incorporating only 0.041 vol% graphene. The thermal oxidative stability of PP is also remarkably improved with the addition of graphene, for example, compared with neat PP, the initial degradation temperature is enhanced by 26 °C at only 0.42 vol% of graphene loading