SC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views

In recent studies, the generalization of neural radiance fields for novel view synthesis task has been widely explored. However, existing methods are limited to objects and indoor scenes. In this work, we extend the generalization task to outdoor scenes, trained only on object-level datasets. This approach presents two challenges. Firstly, the significant distributional shift between training and testing scenes leads to black artifacts in rendering results. Secondly, viewpoint changes in outdoor scenes cause ghosting or missing regions in rendered images. To address these challenges, we propose a geometric correction module and an appearance correction module based on multi-head attention mechanisms. We normalize rendered depth and combine it with light direction as query in the attention mechanism. Our network effectively corrects varying scene structures and geometric features in outdoor scenes, generalizing well from object-level to unseen outdoor scenes. Additionally, we use appearance correction module to correct appearance features, preventing rendering artifacts like blank borders and ghosting due to viewpoint changes. By combining these modules, our approach successfully tackles the challenges of outdoor scene generalization, producing high-quality rendering results. When evaluated on four datasets (Blender, DTU, LLFF, Spaces), our network outperforms previous methods. Notably, compared to MVSNeRF, our network improves average PSNR from 19.369 to 25.989, SSIM from 0.838 to 0.889, and reduces LPIPS from 0.265 to 0.224 on Spaces outdoor scenes

Multiple Chemical Sources Localization Using Virtual Physics-Based Robots with Release Strategy

This paper presents a novel method of simultaneously locating chemical sources by a virtual physics-based multirobot system with a release strategy. The proposed release strategy includes setting forbidden area, releasing the robots from declared sources and escaping from it by a rotary force and goal force. This strategy can avoid the robots relocating the same source which has been located by other robots and leading them to move toward other sources. Various turbulent plume environments are simulated by Fluent and Gambit software, and a set of simulations are performed on different scenarios using a group of six robots or parallel search by multiple groups’ robots to validate the proposed methodology. The experimental results show that release strategy can be successfully used to find multiple chemical sources, even when multiple plumes overlap. It can also extend the operation of many chemical source localization algorithms developed for single source localization

A Virtual Physics-based Approach to Multiple Odor Sources Localization

The detection of an odor source location has been enhanced by using multiple plume-tracing mobile robots. So far, many researchers focus on locating a single source in varied environments. The present study is concerned with the problem of multiple chemical sources localization using multi-robot system. In this study, multiple groups of robots were used and coordinated by a multi-robot cooperation strategy with virtual physics force, which includes structure formation force, goal force, repulsion force and rotary force. In order to test the effectiveness of the proposed strategy, plume model with two sources was constructed by computation fluid dynamics simulations. Simulation experiment discussed the influence of the varied frequencies of wind direction/ speed and methane release with different initial positions of multiple groups to the search performance. Simulation comparison experiments using three kinds of plume tracing algorithms: chemotaxis, anemotaxis and fluxotaxis were carried out respectively and the comparative result about three plume tracing algorithms was illustrated

A fault diagnosis system of mine main ventilator

A fault diagnosis model was built by use of neural network trained by extreme learning machine. A fault diagnosis system of mine main ventilator based on the model was designed, and software and hardware design schemes of the system were introduced. The test results show running time of extreme learning machine algorithm in the system is only 0.031 3 s and accuracy rate of fault diagnosis is not less than 97.35%, which has better real-time performance and accuracy than fault diagnosis systems based on BP neural network, ELMAN neural network or neural network trained by support vector machine

Fast Sampling Control of Singularly Perturbed Systems with Actuator Saturation and L

We will consider the problem of fast sampling control for singularly perturbed systems subject to actuator saturation and L2 disturbance. A sufficient condition for the existence of a state feedback controller is proposed. Under this controller, the boundedness of the trajectories in the presence of L2 disturbances is guaranteed for any singular perturbation parameter less than or equal to a predefined upper bound. To improve the capacity of disturbance tolerance and disturbance rejection, two convex optimization problems are formulated. Finally, a numerical example is presented to demonstrate the effectiveness of the main results of this paper