Solid waste mixtures combustion in a circulating fluidized Bed: emission properties of NOx, Dioxin, and Heavy Metals

To efficiently and environment friendly combust the domestic garbage, sludge, and swill waste fuels, five different fuels are prepared by mixing the waste fuels together with coal, and grass biomass at different mixing ratios, and finally those fuels were combusted in a circulating fluidized bed (CFB) reactor. The emission performances of NOx, dioxin, and heavy metal during the combustion tests are studied. The results showed that a stable furnace temperature can be reached at approximately 850 °C when combusting all studied mixed fuels, benefiting the thermal processes of sludge and domestic garbage and thus realizing the purpose of waste-to-fuel. In addition, the dioxin emissions are much lower than the emission standards, and NOx emissions could be reduced significantly by adjusting the ratio of waste fuels. However, the emissions of mercury, lead, and the combinations of chromium, tin, antimony, cupper and manganese components all exceeded the pollution control standard for hazardous wastes incineration, a further technology is required for heavy metal reductions to achieve the emission standards

Dual Progressive Transformations for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS), which aims to mine the object regions by merely using class-level labels, is a challenging task in computer vision. The current state-of-the-art CNN-based methods usually adopt Class-Activation-Maps (CAMs) to highlight the potential areas of the object, however, they may suffer from the part-activated issues. To this end, we try an early attempt to explore the global feature attention mechanism of vision transformer in WSSS task. However, since the transformer lacks the inductive bias as in CNN models, it can not boost the performance directly and may yield the over-activated problems. To tackle these drawbacks, we propose a Convolutional Neural Networks Refined Transformer (CRT) to mine a globally complete and locally accurate class activation maps in this paper. To validate the effectiveness of our proposed method, extensive experiments are conducted on PASCAL VOC 2012 and CUB-200-2011 datasets. Experimental evaluations show that our proposed CRT achieves the new state-of-the-art performance on both the weakly supervised semantic segmentation task the weakly supervised object localization task, which outperform others by a large margin

DI-Net : Decomposed Implicit Garment Transfer Network for Digital Clothed 3D Human

3D virtual try-on enjoys many potential applications and hence has attracted wide attention. However, it remains a challenging task that has not been adequately solved. Existing 2D virtual try-on methods cannot be directly extended to 3D since they lack the ability to perceive the depth of each pixel. Besides, 3D virtual try-on approaches are mostly built on the fixed topological structure and with heavy computation. To deal with these problems, we propose a Decomposed Implicit garment transfer network (DI-Net), which can effortlessly reconstruct a 3D human mesh with the newly try-on result and preserve the texture from an arbitrary perspective. Specifically, DI-Net consists of two modules: 1) A complementary warping module that warps the reference image to have the same pose as the source image through dense correspondence learning and sparse flow learning; 2) A geometry-aware decomposed transfer module that decomposes the garment transfer into image layout based transfer and texture based transfer, achieving surface and texture reconstruction by constructing pixel-aligned implicit functions. Experimental results show the effectiveness and superiority of our method in the 3D virtual try-on task, which can yield more high-quality results over other existing methods

Tensor-network-assisted variational quantum algorithm

Near-term quantum devices generally suffer from shallow circuit depth and hence limited expressivity due to noise and decoherence. To address this, we propose tensor-network-assisted parametrized quantum circuits, which concatenate a classical tensor-network operator with a quantum circuit to effectively increase the circuit's expressivity without requiring a physically deeper circuit. We present a framework for tensor-network-assisted variational quantum algorithms that can solve quantum many-body problems using shallower quantum circuits. We demonstrate the efficiency of this approach by considering two examples of unitary matrix-product operators and unitary tree tensor networks, showing that they can both be implemented efficiently. Through numerical simulations, we show that the expressivity of these circuits is greatly enhanced with the assistance of tensor networks. We apply our method to two-dimensional Ising models and one-dimensional time-crystal Hamiltonian models with up to 16 qubits and demonstrate that our approach consistently outperforms conventional methods using shallow quantum circuits.Comment: 12 pages, 8 figures, 37 reference

3-D motion recovery via low rank matrix restoration on articulation graphs

This paper addresses the challenge of 3-D skeleton recovery by exploiting the spatio-temporal correlations of corrupted 3D skeleton sequences. A skeleton sequence is represented as a matrix. We propose a novel low-rank solution that effectively integrates both a low-rank model for robust skeleton recovery based on temporal coherence, and an articulation-graph-based isometric constraint for spatial coherence, namely consistency of bone lengths. The proposed model is formulated as a constrained optimization problem, which is efficiently solved by the Augmented Lagrangian Method with a Gauss-Newton solver for the subproblem of isometric optimization. Experimental results on the CMU motion capture dataset and a Kinect dataset show that the proposed approach achieves better recovery accuracy over a state-of-the-art method. The proposed method has wide applicability for skeleton tracking devices, such as the Kinect, because these devices cannot provide accurate reconstructions of complex motions, especially in the presence of occlusion

3-D motion recovery via low rank matrix analysis

Skeleton tracking is a useful and popular application of Kinect. However, it cannot provide accurate reconstructions for complex motions, especially in the presence of occlusion. This paper proposes a new 3-D motion recovery method based on lowrank matrix analysis to correct invalid or corrupted motions. We address this problem by representing a motion sequence as a matrix, and introducing a convex low-rank matrix recovery model, which fixes erroneous entries and finds the correct low-rank matrix by minimizing nuclear norm and `1-norm of constituent clean motion and error matrices. Experimental results show that our method recovers the corrupted skeleton joints, achieving accurate and smooth reconstructions even for complicated motions

Semantic-Constraint Matching Transformer for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) strives to learn to localize objects with only image-level supervision. Due to the local receptive fields generated by convolution operations, previous CNN-based methods suffer from partial activation issues, concentrating on the object's discriminative part instead of the entire entity scope. Benefiting from the capability of the self-attention mechanism to acquire long-range feature dependencies, Vision Transformer has been recently applied to alleviate the local activation drawbacks. However, since the transformer lacks the inductive localization bias that are inherent in CNNs, it may cause a divergent activation problem resulting in an uncertain distinction between foreground and background. In this work, we proposed a novel Semantic-Constraint Matching Network (SCMN) via a transformer to converge on the divergent activation. Specifically, we first propose a local patch shuffle strategy to construct the image pairs, disrupting local patches while guaranteeing global consistency. The paired images that contain the common object in spatial are then fed into the Siamese network encoder. We further design a semantic-constraint matching module, which aims to mine the co-object part by matching the coarse class activation maps (CAMs) extracted from the pair images, thus implicitly guiding and calibrating the transformer network to alleviate the divergent activation. Extensive experimental results conducted on two challenging benchmarks, including CUB-200-2011 and ILSVRC datasets show that our method can achieve the new state-of-the-art performance and outperform the previous method by a large margin

Global 3D non-rigid registration of deformable objects using a single RGB-D camera

We present a novel global non-rigid registration method for dynamic 3D objects. Our method allows objects to undergo large non-rigid deformations, and achieves high quality results even with substantial pose change or camera motion between views. In addition, our method does not require a template prior and uses less raw data than tracking based methods since only a sparse set of scans is needed. We compute the deformations of all the scans simultaneously by optimizing a global alignment problem to avoid the well-known loop closure problem, and use an as-rigid-as-possible constraint to eliminate the shrinkage problem of the deformed shapes, especially near open boundaries of scans. To cope with large-scale problems, we design a coarse-to-fine multi-resolution scheme, which also avoids the optimization being trapped into local minima. The proposed method is evaluated on public datasets and real datasets captured by an RGB-D sensor. Experimental results demonstrate that the proposed method obtains better results than several state-of-the-art methods