A Novel Method of the Generalized Interval-Valued Fuzzy Rough Approximation Operators

Rough set theory is a suitable tool for dealing with the imprecision, uncertainty, incompleteness, and vagueness of knowledge. In this paper, new lower and upper approximation operators for generalized fuzzy rough sets are constructed, and their definitions are expanded to the interval-valued environment. Furthermore, the properties of this type of rough sets are analyzed. These operators are shown to be equivalent to the generalized interval fuzzy rough approximation operators introduced by Dubois, which are determined by any interval-valued fuzzy binary relation expressed in a generalized approximation space. Main properties of these operators are discussed under different interval-valued fuzzy binary relations, and the illustrative examples are given to demonstrate the main features of the proposed operators

Neural-Network-Driven Method for Optimal Path Planning via High-Accuracy Region Prediction

Sampling-based path planning algorithms suffer from heavy reliance on uniform sampling, which accounts for unreliable and time-consuming performance, especially in complex environments. Recently, neural-network-driven methods predict regions as sampling domains to realize a non-uniform sampling and reduce calculation time. However, the accuracy of region prediction hinders further improvement. We propose a sampling-based algorithm, abbreviated to Region Prediction Neural Network RRT* (RPNN-RRT*), to rapidly obtain the optimal path based on a high-accuracy region prediction. First, we implement a region prediction neural network (RPNN), to predict accurate regions for the RPNN-RRT*. A full-layer channel-wise attention module is employed to enhance the feature fusion in the concatenation between the encoder and decoder. Moreover, a three-level hierarchy loss is designed to learn the pixel-wise, map-wise, and patch-wise features. A dataset, named Complex Environment Motion Planning, is established to test the performance in complex environments. Ablation studies and test results show that a high accuracy of 89.13% is achieved by the RPNN for region prediction, compared with other region prediction models. In addition, the RPNN-RRT* performs in different complex scenarios, demonstrating significant and reliable superiority in terms of the calculation time, sampling efficiency, and success rate for optimal path planning.Comment: 9 pages, 8 figure

TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images

In the remote sensing field, Change Detection (CD) aims to identify and localize the changed regions from dual-phase images over the same places. Recently, it has achieved great progress with the advances of deep learning. However, current methods generally deliver incomplete CD regions and irregular CD boundaries due to the limited representation ability of the extracted visual features. To relieve these issues, in this work we propose a novel Transformer-based learning framework named TransY-Net for remote sensing image CD, which improves the feature extraction from a global view and combines multi-level visual features in a pyramid manner. More specifically, the proposed framework first utilizes the advantages of Transformers in long-range dependency modeling. It can help to learn more discriminative global-level features and obtain complete CD regions. Then, we introduce a novel pyramid structure to aggregate multi-level visual features from Transformers for feature enhancement. The pyramid structure grafted with a Progressive Attention Module (PAM) can improve the feature representation ability with additional inter-dependencies through spatial and channel attentions. Finally, to better train the whole framework, we utilize the deeply-supervised learning with multiple boundary-aware loss functions. Extensive experiments demonstrate that our proposed method achieves a new state-of-the-art performance on four optical and two SAR image CD benchmarks. The source code is released at https://github.com/Drchip61/TransYNet.Comment: This work is accepted by TGRS2023. It is an extension of our ACCV2022 paper and arXiv:2210.0075

Co3O4@CoS core-shell nanosheets on carbon cloth for high performance supercapacitor electrodes

In this work, a two-step electrodeposition strategy is developed for the synthesis of core-shell Co3O4@CoS nanosheet arrays on carbon cloth (CC) for supercapacitor applications. Porous Co3O4 nanosheet arrays are first directly grown on CC by electrodeposition, followed by the coating of a thin layer of CoS on the surface of Co3O4 nanosheets via the secondary electrodeposition. The morphology control of the ternary composites can be easily achieved by altering the number of cyclic voltammetry (CV) cycles of CoS deposition. Electrochemical performance of the composite electrodes was evaluated by cyclic voltammetry, galvanostatic charge-discharge and electrochemical impedance spectroscopy techniques. The results demonstrate that the Co3O4@CoS/CC with 4 CV cycles of CoS deposition possesses the largest specific capacitance 887.5 F·g-1 at a scan rate of 10 mV·s-1 (764.2 F·g-1 at a current density of 1.0 A·g-1), and excellent cycling stability (78.1% capacitance retention) at high current density of 5.0 A·g-1 after 5000 cycles. The porous nanostructures on CC not only provide large accessible surface area for fast ions diffusion, electron transport and efficient utilization of active CoS and Co3O4, but also reduce the internal resistance of electrodes, which leads to superior electrochemical performance of Co3O4@CoS/CC composite at 4 cycles of CoS deposition. © 2017 by the authors.National Natural Science Foundation of China [21371057]; International Science and Technology Cooperation Program of China [2016YFE0131200, 2015DFA51220]; International Cooperation Project of Shanghai Municipal Science and Technology Committee [15520721100

Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration

Semi-implicit variational inference (SIVI) has been introduced to expand the analytical variational families by defining expressive semi-implicit distributions in a hierarchical manner. However, the single-layer architecture commonly used in current SIVI methods can be insufficient when the target posterior has complicated structures. In this paper, we propose hierarchical semi-implicit variational inference, called HSIVI, which generalizes SIVI to allow more expressive multi-layer construction of semi-implicit distributions. By introducing auxiliary distributions that interpolate between a simple base distribution and the target distribution, the conditional layers can be trained by progressively matching these auxiliary distributions one layer after another. Moreover, given pre-trained score networks, HSIVI can be used to accelerate the sampling process of diffusion models with the score matching objective. We show that HSIVI significantly enhances the expressiveness of SIVI on several Bayesian inference problems with complicated target distributions. When used for diffusion model acceleration, we show that HSIVI can produce high quality samples comparable to or better than the existing fast diffusion model based samplers with a small number of function evaluations on various datasets.Comment: 25 pages, 13 figures, NeurIPS 202