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Semantic segmentation in dynamic urban traffic scenes holds paramount importance for enhancing the safety and efficiency of intelligent vehicles. However, the performance of this technique can be hindered in challenging lighting conditions and intricate backgrounds, even with the aid of geometric information derived from depth-images or LiDAR point clouds alongside single-modal RGB images. To overcome this limitation, it is a feasible method to introduce thermal data alongside visible images, which could boost the accuracy and robustness of semantic segmentation in dynamic urban traffic scenes with varying illumination conditions. Considering that, we propose a novel cross-dimensional multispectral edge fusion network (CMEFNet) specifically designed for RGB-Thermal (RGB-T) semantic segmentation in dynamic traffic scenes. It incorporates the dual encoders regrouping low-level and high-level RGB-T features, the hierarchical attentional fusion module to refine multiscale multimodal features, a skip edge guidance structure for information supplement and further promoting accuracy, and a deep supervision mechanism for fine-tuning. Experimental results demonstrate that our method can significantly improves segmentation accuracy and robustness (improving 0.9-18.3% in mIoU), particularly in detecting dynamic traffic participants such as vehicles and pedestrians (improving 0.4-24.6%). Furthermore, it displays commendable accuracy and robustness in both daytime and nighttime scenarios, surpassing the performance of state-of-the-art networks.

The accurate segmentation of surgical instruments in endoscopic robotic surgery is a critical challenge due to the intricate and dynamic nature of the surgical environment. Existing segmentation techniques predominantly focus on binary classification, which often falls short in complex scenarios where multiple instruments need to be precisely identified and differentiated. This limitation significantly hampers the effectiveness of computer-assisted surgical systems, where real-time and accurate multi-class segmentation is paramount. In this study, we address these challenges by introducing a novel segmentation framework that leverages pixel-wise contrastive learning to enhance multi-class instrument segmentation. Our approach integrates the strengths of HRNet and DeepLabV3 as backbone networks to effectively capture and embed high-resolution features from endoscopic images. These feature embeddings are stored in dynamically updated dataset-wide queues, enabling the model to mitigate the class imbalance by incorporating representative samples from less prevalent classes during training. This is achieved by combining an enhanced contrastive loss function with pixel-wise cross-entropy loss, which together facilitate robust multi-class differentiation. We validate our approach using the challenging EndoVis 2017 and EndoVis 2018 datasets, where it demonstrated superior performance compared to existing methods. On the EndoVis 2017 test dataset, our HRNet-based method achieved an average mIOU of 0.682 across 10 test subsets, surpassing the official benchmark of 0.542 by a significant margin. Similarly, on the EndoVis 2018 test dataset, our method attained an average mIOU of 0.599, exceeding the official best score of 0.579. These results underscore the efficacy of our approach in significantly improving segmentation accuracy. The enhanced segmentation capability not only advances the state of the art in multi-class instrument segmentation but also has practical implications for improving surgical safety and efficiency in real-world robotic surgery.

A novel single-ended-to-balanced (SETB) filtering circuit is proposed in this paper. The proposed circuit not only integrate controllable working modes between SETB filtering and SETB filtering power dividing (FPD) into one unit, but also has multi-level reconfigurable capabilities including tunable center frequencies, bandwidths, and switchable single/dual-band operations. It is constructed by a 5-port symmetrical microstrip network. By properly engineering the transmission response of the even and odd mode of the proposed circuit, the multiple reconfigurable functions can be realized in just one circuit in a mathematically sound way. The working modes between SETB filtering and SETB FPD are switched by leaving the corresponding ports open, while the control of center frequency, bandwidth, and single/dual-band operation is realized by tuning the capacitance value of loaded varactors. Detailed theoretical and experiment results have been given with good agreement.

The short-term scheduling schemes of cascaded hydropower plants are based on day-ahead hydrological forecasting information. Affected by the accuracy of prediction, real-term hydrological information can considerably vary from previously forecasted values, especially for the power load and local inflows. As a result, short-term scheduling schemes are difficult to apply directly in real-time scheduling. To solve this problem, a rolling optimal hourly operation model for interbasin cascaded hydropower plants is proposed to rapidly balance the load demand. The model is deemed an ultrashort-term model, and it has a short-cycle schedule between short-term and real-time schemes. The basic strategy of solving the model is as follows. First, the day-ahead power load and local inflow information are corrected using a statistical method based on the most recent real-time information. Second, the water delay time, defined as the time between when water is released from upstream reservoirs and arrives at downstream reservoirs, is updated based on the most recent real-time information. Third, a heuristic method is proposed to dynamically update the short-term scheduling scheme over the next few hours. The objective is to minimize the maximum relative deviation between the actual water level of a reservoir at the end of 24 hours and the day-ahead forecasted value. This approach can keep the reservoir water level at the end of 24 hours close to the forecasted value and improve the accuracy of estimating the initial water level the next day. The developed method is applied to solve an ultrashort-term scheduling problem involving the cascaded hydropower plants located in Yunnan Province, China. The results indicate that the proposed model can achieve seamless coupling between short-term scheduling and real-time scheduling. The proposed method can provide a scientific basis for the real-time dispatching of large-scale hydropower plants and improve the practicality of short-term dispatching schemes.

Generally, accurate hydrological forecasting information plays an increasingly important role in promoting the comprehensive benefit of hydropower reservoirs. With satisfying generalization ability and search rate, the extreme learning machine (ELM), a famous single-layer feedforward neural network, has been widely used to address regression and classification problem. However, the standard ELM method often falls into second-best solutions with a high probability due to the random assignments of network parameters. In order to overcome this problem, this article aims at developing a hybrid model for monthly runoff time series forecasting. In the hybrid method, an effective swarm intelligence method, grey wolf optimizer (GWO), is adopted to optimize the input-hidden weights and hidden biases of the ELM method; and then the Moore-Penrose generalized inverse method is adopted to determine the hidden-output weights. The world's largest hydropower reservoir, Three Gorges, is chosen to compare the performances of various forecasting methods. Based on the simulation results, the presented method outperforms several traditional forecasting methods (like artificial neural network and support vector machine) in several quantitative indexes. Thus, a novel alternative is presented to predict the nonlinear hydrological time series in China.

Tampering, forgery and theft of the measurement and control messages in a smart grid could cause one breakdown in the power system. However, no security measures are employed for communications in intelligent substations. Communication services in an intelligent substation have high demands for real-time performance, which must be considered when deploying security measures. This paper studies the security requirements of communication services in intelligent substations, analyzes the security capabilities and shortages of IEC 62351, and proposes a novel security scheme for intelligent substation communications. This security scheme covers internal and telecontrol communications, in which the real-time performance of each security measure is considered. In this scheme, certificateless public key cryptography (CLPKC) is used to avoid the latency of certificate exchange in certificate-based cryptosystem and the problem of key escrow in identity-based cryptosystem; the security measures of generic object-oriented substation event, sampled measure value and manufacturing message specification in IEC 62351 are improved to meet the real-time requirements of the messages as well as to provide new security features to resist repudiation and replay attacks; and the security at transport layer is modified to fit CLPKC, which implements mutual authentication by exchanging signatures. Furthermore, a deployment of CLPKC in an intelligent substation is presented. We also evaluate the security properties of the scheme and analyze the end-to-end delays of secured services by combining theoretical calculation and simulation in this paper. The results indicate that the proposed scheme meets the requirements of security and real-time performance of communications in intelligent substations.

A novel Abel inversion method has been employed to obtain the precise electron density profile from chordal interferometric measurements on J-TEXT tokamak, in which the Gauss–Legendre integration method and dynamic boundary are introduced. To demonstrate the feasibility of this novel method, a hypothetical function \(I({x})\) is established, which resulted in a valid local value. Numerical results under different conditions are presented in the paper. [ABSTRACT FROM PUBLISHER]

We present an investigation of the magnetic entropy ΔS and resistivity ρ, and the relation between them, for La0, 67, Ca0, 33MnO3 film. We found that the maximum SS is approximately 6 J/kg·K, and the maximum magnetoresistance (defined as ρ(O)/ρ(H) - 1) is about 400% under a field μ0H of 5 T. A careful analysis gives an equation of the form δ5 = -&alfa; ∞0H = 21.74 emu/g),which relates magnetic order to transport behavior of the compounds. This result reveals the predominant role of magnetic polarons on the magnetoresistive property of manganites. It also provides an alternative method to determine magnetic entropy change on the basis of resistive measurements. [ABSTRACT FROM AUTHOR]

We present an investigation of the magnetic entropy ΔS and resistivity ρ, and the relation between them, for La0, 67, Ca0, 33MnO3 film. We found that the maximum SS is approximately 6 J/kg·K, and the maximum magnetoresistance (defined as ρ(O)/ρ(H) - 1) is about 400% under a field μ0H of 5 T. A careful analysis gives an equation of the form δ5 = -&alfa; ∞0H = 21.74 emu/g),which relates magnetic order to transport behavior of the compounds. This result reveals the predominant role of magnetic polarons on the magnetoresistive property of manganites. It also provides an alternative method to determine magnetic entropy change on the basis of resistive measurements. [ABSTRACT FROM AUTHOR]