On-site Smart Operation and Maintenance System for Substation Equipment Based on Mobile Network

The maintenance of substations is crucial for the safety of the electrical grid and power industry. However, for long time, the maintenance teams in the field and the experts in the power companies are divided. The data and expertise exchanges between the on-site maintenance teams and data center are delayed due to the lack of effective communication. This paper introduces an on-site smart operation maintenance system for substation equipment based on mobile network. It is able to establish real-time communication and data exchange channels between the maintenance teams and data center. It consists of an operation and maintenance system platform located on the data center side and smart operation and maintenance boxes with mobile APP which are carried to the field side by the maintenance teams. As the kernel of the system, the smart boxes are bridges between the data center and operation sites. On one hand, it is able to formally upload data to the data center in real-time. One the other hand, the operation and maintenance personnel are able to call for help from the resource on the data center anytime. Using the system proposed in the paper, both efficiency of the operation and maintenance and the normalization of the data can be improved

Spiking NeRF: Making Bio-inspired Neural Networks See through the Real World

Spiking neuron networks (SNNs) have been thriving on numerous tasks to leverage their promising energy efficiency and exploit their potentialities as biologically plausible intelligence. Meanwhile, the Neural Radiance Fields (NeRF) render high-quality 3D scenes with massive energy consumption, and few works delve into the energy-saving solution with a bio-inspired approach. In this paper, we propose spiking NeRF (SpikingNeRF), which aligns the radiance ray with the temporal dimension of SNN, to naturally accommodate the SNN to the reconstruction of Radiance Fields. Thus, the computation turns into a spike-based, multiplication-free manner, reducing the energy consumption. In SpikingNeRF, each sampled point on the ray is matched onto a particular time step, and represented in a hybrid manner where the voxel grids are maintained as well. Based on the voxel grids, sampled points are determined whether to be masked for better training and inference. However, this operation also incurs irregular temporal length. We propose the temporal condensing-and-padding (TCP) strategy to tackle the masked samples to maintain regular temporal length, i.e., regular tensors, for hardware-friendly computation. Extensive experiments on a variety of datasets demonstrate that our method reduces the $76.74\%$ energy consumption on average and obtains comparable synthesis quality with the ANN baseline

Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation

Unsupervised domain adaptation (UDA) aims to adapt existing models of the source domain to a new target domain with only unlabeled data. Many adversarial-based UDA methods involve high-instability training and have to carefully tune the optimization procedure. Some non-adversarial UDA methods employ a consistency regularization on the target predictions of a student model and a teacher model under different perturbations, where the teacher shares the same architecture with the student and is updated by the exponential moving average of the student. However, these methods suffer from noticeable negative transfer resulting from either the error-prone discriminator network or the unreasonable teacher model. In this paper, we propose an uncertainty-aware consistency regularization method for cross-domain semantic segmentation. By exploiting the latent uncertainty information of the target samples, more meaningful and reliable knowledge from the teacher model can be transferred to the student model. In addition, we further reveal the reason why the current consistency regularization is often unstable in minimizing the distribution discrepancy. We also show that our method can effectively ease this issue by mining the most reliable and meaningful samples with a dynamic weighting scheme of consistency loss. Experiments demonstrate that the proposed method outperforms the state-of-the-art methods on two domain adaptation benchmarks, $i.e.,$ GTAV $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes

Portable dual-mode microfluidic sensor for rapid and sensitive detection of DPA on chip

In this work, we developed a dual-mode portable device that integrated a 3D-printed microfluidic chip for detection of dipicolinic acid (DPA) on chip. The system uses a ratiometric fluorescence nanoprobe formed by embedding carbon dots (CDs) into an Eu3⁺ metal–organic framework (Eu-MOF). Upon reaction with DPA in the microchannel, red fluorescence was enhanced and blue fluorescence suppressed, enabling sensitive ratiometric detection of DPA on chip with a detection limit (LOD) of 0.04 µM. Interestingly, the composite EuMOF/CDs/DPA also exhibits peroxidase-like activity, catalyzing the oxidation of TMB into a blue-colored product (oxTMB), which allows for colorimetric detection with an LOD of 10.14 µM. To improve usability and reduce environmental or instrumental variability, incorporating a microfluidic chip into a semi-portable device and utilizing a smartphone, making the system portable and miniaturized for easy operation. In the smartphone-assisted mode, the LODs were 0.33 µM (ratiometric fluorescence) and 12.27 µM (colorimetry), determined by RGB signal analysis, respectively. Moreover, satisfactory recoveries (85–104.6%) were achieved in the spiked real samples. Overall, this platform offers a straightforward, cost-effective, and versatile approach for DPA detection, with promising applications in food safety, environmental monitoring, and clinical diagnostics

DMT: Dynamic Mutual Training for Semi-Supervised Learning

Recent semi-supervised learning methods use pseudo supervision as core idea, especially self-training methods that generate pseudo labels. However, pseudo labels are unreliable. Self-training methods usually rely on single model prediction confidence to filter low-confidence pseudo labels, thus remaining high-confidence errors and wasting many low-confidence correct labels. In this paper, we point out it is difficult for a model to counter its own errors. Instead, leveraging inter-model disagreement between different models is a key to locate pseudo label errors. With this new viewpoint, we propose mutual training between two different models by a dynamically re-weighted loss function, called Dynamic Mutual Training (DMT). We quantify inter-model disagreement by comparing predictions from two different models to dynamically re-weight loss in training, where a larger disagreement indicates a possible error and corresponds to a lower loss value. Extensive experiments show that DMT achieves state-of-the-art performance in both image classification and semantic segmentation. Our codes are released at https://github.com/voldemortX/DST-CBC .Comment: Reformatte