Abstract
With the improvement of socio-economic level and people’s material living standard, the distribution network changes from the traditional passive repair to the active-rush repair mode in ...order to improve the efficiency of emergency repair. This paper proposes to construct a new command system under the active-rush repair mode of distribution network in order to improve the efficiency of emergency repair. First of all, through information sharing and data fusion between different departments, we can effectively clear the obstacles and strengthen the efficiency of multi-department cooperative office. Then, with the help of highly covered distribution automation system, the monitoring and automatic evaluation of distribution equipment are realized and the original construction of the command system has been constructed.
This paper describes the technical characteristics and application status of ANITAII (Analysing Interferometer for Ambient Air), an atmospheric monitoring system currently used by ESA (European Space ...Agency), reveals the characteristics and advantages of FTIR (Fourier transform infrared spectroscopy) technology in multi-component gas monitoring, and analyzes the application prospect of FTIR technology in the field of environmental monitoring.
Pulsed magnetic fields can be generated by pulsed inductance coils and play an important role in many fields such as medical, military, and civilian. Predicated on the theories of electromagnetism ...and heat transfer, a transient calculation model of electromagnetic-thermal coupling of inductance coils is constituted. In an effort to verify the precision of the model calculation, a temperature experiment predicated on optical fiber sensing was designed for comparative analysis. Then, the distribution characteristics of coil current density and temperature are assayed by using this model. The impact of thermal conductivity of epoxy composites on the temperature of the pulsed inductance coil was investigated. Finally, thermal aging experiments were conducted on composites made of epoxy/glass fibers. The results demonstrate that with the increase in aging time and temperature, a significant collapse of the sample surface is observed, accompanied by the appearance of cracks and damage to the surface structure.
Abstract
This paper introduces a new type of toxic and harmful gas analyzer based on DOAS (differential optical absorption spectroscopy) technology. Combined with the least square method, it can ...monitor H
2
S, NH
3
, SO
2
, NO
2
and CH
3
SH online at the same time, and the prescribed minimum is as low as 0.1ppb, the detection breadth and accuracy are higher than those of the current common DOAS instruments.
Transposed convolutional layers have been widely used in a variety of deep models for up-sampling, including encoder-decoder networks for semantic segmentation and deep generative models for ...unsupervised learning. One of the key limitations of transposed convolutional operations is that they result in the so-called checkerboard problem. This is caused by the fact that no direct relationship exists among adjacent pixels on the output feature map. To address this problem, we propose the pixel transposed convolutional layer (PixelTCL) to establish direct relationships among adjacent pixels on the up-sampled feature map. Our method is based on a fresh interpretation of the regular transposed convolutional operation. The resulting PixelTCL can be used to replace any transposed convolutional layer in a plug-and-play manner without compromising the fully trainable capabilities of original models. The proposed PixelTCL may result in slight decrease in efficiency, but this can be overcome by an implementation trick. Experimental results on semantic segmentation demonstrate that PixelTCL can consider spatial features such as edges and shapes and yields more accurate segmentation outputs than transposed convolutional layers. When used in image generation tasks, our PixelTCL can largely overcome the checkerboard problem suffered by regular transposed convolutional operations.
Visualizing the details of different cellular structures is of great importance to elucidate cellular functions. However, it is challenging to obtain high quality images of different structures ...directly due to complex cellular environments. Fluorescence staining is a popular technique to label different structures but has several drawbacks. In particular, label staining is time consuming and may affect cell morphology, and simultaneous labels are inherently limited. This raises the need of building computational models to learn relationships between unlabeled microscopy images and labeled fluorescence images, and to infer fluorescence labels of other microscopy images excluding the physical staining process. We propose to develop a novel deep model for virtual staining of unlabeled microscopy images. We first propose a novel network layer, known as the global pixel transformer layer, that fuses global information from inputs effectively. The proposed global pixel transformer layer can generate outputs with arbitrary dimensions, and can be employed for all the regular, down-sampling, and up-sampling operators. We then incorporate our proposed global pixel transformer layers and dense blocks to build an U-Net like network. We believe such a design can promote feature reusing between layers. In addition, we propose a multi-scale input strategy to encourage networks to capture features at different scales. We conduct evaluations across various fluorescence image prediction tasks to demonstrate the effectiveness of our approach. Both quantitative and qualitative results show that our method outperforms the state-of-the-art approach significantly. It is also shown that our proposed global pixel transformer layer is useful to improve the fluorescence image prediction results.
In the domain of optical fiber distributed acoustic sensing, the persistent challenge of extending sensing distances while concurrently improving spatial resolution and frequency response range has ...been a complex endeavor. The amalgamation of pulse compression and frequency division multiplexing methodologies has provided certain advantages. Nevertheless, this approach is accompanied by the drawback of significant bandwidth utilization and amplified hardware investments. This study introduces an innovative distributed optical fiber acoustic sensing system aimed at optimizing the efficient utilization of spectral resources by combining compressed pulses and frequency division multiplexing. The system continuously injects non-linear frequency modulation detection pulses spanning various frequency ranges. The incorporation of non-uniform frequency division multiplexing augments the vibration frequency response spectrum. Additionally, nonlinear frequency modulation adeptly reduces crosstalk and enhances sidelobe suppression, all while maintaining a favorable signal-to-noise ratio. Consequently, this methodology substantially advances the spatial resolution of the sensing system. Experimental validation encompassed the multiplexing of eight frequencies within a 120 MHz bandwidth. The results illustrate a spatial resolution of approximately 5 m and an expanded frequency response range extending from 1 to 20 kHz across a 16.3 km optical fiber. This achievement not only enhances spectral resource utilization but also reduces hardware costs, making the system even more suitable for practical engineering applications.
The negative rejection of certain solutes is an interesting phenomenon often observed in nanofiltration (NF) process, which presents an ideal solute separation effect. In this study, the NF membrane ...pore size, electric charge property and influent ions composition were controlled to investigate the rule and mechanism of negative rejection phenomenon during SO42−/Cl− mixed salt NF. The results show that the changing of the membrane pore diameter and charge will significantly affect the NF selectivity to SO42−/Cl−, then affect the negative rejection phenomenon. Selective efficiency of Cl−/SO42− can be up to 156.04–283.20 for the negative charged NF membrane with an average pore diameter of 0.62–0.68 nm, and the Cl− was negatively rejected under many influent conditions with the lowest rejection rate of −252.36 %. The substitution of Na+ for Mg2+ and the increase of SO42−/Cl− ratio are beneficial to improve selectivity. The Donnan Steric Pore Model with dielectric exclusion model was used to calculate the rejection curve which is consistent with the experimental results. The analysis of NF mechanism shows that the negative rejection of Cl− is caused by the extra transfer power derived from the NF selectivity to Cl−, which is a compensation for the higher rejection rate of SO42−.
•Negative rejection is an extreme manifestation of NF membrane selectivity.•Pore size, chargeability and salts ratio significantly affect the NF selectivity.•The extreme selectivity factor of the PIP/TMC NF film to SO42−/Cl− is up to 283.20.•DSPM-DE can be used to calculate negative rejection and analyze the mechanism.•Osmotic force from additional cations promotes the negative rejection of Cl−.
Grouping has been commonly used in deep metric learning for computing diverse features. To improve the performance and interpretability, we propose an improved and interpretable grouping method to be ...integrated flexibly with any metric learning framework. Specifically, our method is based on the attention mechanism with a learnable query for each group. The query is fully trainable and can capture group-specific information when combined with the diversity loss. An appealing property of our method is that it naturally lends itself interpretability. The attention scores between the learnable query and each spatial position can be interpreted as the importance of that position. We formally show that our proposed grouping method is invariant to spatial permutations of features. When used as a module in convolutional neural networks, our method leads to translational invariance. We conduct comprehensive experiments to evaluate our method. Our quantitative results indicate that the proposed method outperforms prior methods consistently and significantly across different datasets, evaluation metrics, base models, and loss functions. For the first time to the best of our knowledge, our interpretation results clearly demonstrate that the proposed method enables the learning of diverse and stable semantic features across groups.
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