5G network slicing is a promising solution to prioritize time-critical protection communication in wireless networks. However, recent trends indicate that a 5G slice could encompass all smart grid applications lacking the necessary granularity. At the same time, while substation communication standards recommend prioritization of protection communication traffic to improve reliability, these recommendations are only for wired connections. Therefore, this paper investigates traffic shaping and uplink (UL) bitrate adaptation of video stream based on existing commercial solutions as methodologies for prioritizing the protection communication in a 5G slice. These methodologies are validated in an experimental setup combining controller-hardware-in-the-loop (CHIL) simulation with a quality of service (QoS) measurement system. The system under test consists of commercial 5G networks, commercial intelligent electronic devices (IEDs), and merging units to validate the methodologies on three smart grid applications: fault location, line differential, and intertrip protection. The results show improvement in protection communication when traffic shaping and UL bitrate adaptation are applied. Traffic shaping even improves prioritization with a wired connection.

Electric Vehicles (EV) have the potential to completely transform the transportation industry, slash carbon emissions significantly, and pave the road for significant climate progress. According to surveys, worries regarding range and charging accessibility are a significant barrier to EV customer adoption. The electricity grid will be significantly impacted by EV charging. A coordinated charging approach that facilitates EV charging while avoiding the power grid from overloading is required to manage EV charging. In this work, the EVs are classified based on their charging capacity. This classification includes Car, truck and bus. With this a chargingdischarging scheme has been established using Real Time Pricing (RTP) for each hour for a 20 min block. Augmenting this, in the present work the power loss is analyzed when Distributed Generation (DG) units are installed after the EVs are located in the system for charging and discharging. The DG units are installed considering the Voltage Stability Index (VSI) and the power losses incurred due to the presence of EVs. The Monetary benefit of such an investment is analyzed for different topologies of IEEE-33 and 69 bus systems and compared with the existing methods where DGs are installed before the EVs are located in the system for charging and discharging.

Abstract: Path planning is a crucial component for ensuring the safety and efficiency of flight missions, especially for fighter aircraft. To enhance the combat effectiveness of fighter aircraft, it is important to consider how to avoid danger sources and terrain obstacles, reduce fuel consumption, and utilize the aircraft’s own performance to accomplish the mission objectives. In the modern battlefield environment, the shortest path is not the only criterion for planning, but also other factors such as the threat level to the aircraft, fuel consumption, mission completion time, and minimum turning radius. In this paper, we propose a multi-constraint path planning method for fighter aircraft that incorporates these factors into an improved particle swarm algorithm. We transform the constraints of three-dimensional terrain, threat source, fuel consumption, and mission time into an aggregated fitness function. We construct a limit curvature matrix to evaluate the feasibility of the generated path. We also introduce an adaptive adjustment strategy based on the activation function for the parameters in the particle swarm algorithm. The weights of each constraint are determined according to the actual demand. The experiment results show that our method can efficiently plan the optimal path that satisfies the requirements. Compared with other improved particle swarm algorithms, our method has higher optimal search efficiency and better convergence effect. We also provide optimal values for important parameters such as mission energy consumption, mission time, flight speed and others to support the overall mission planning. Our method has certain practical application value.

Compressed sensing (CS) techniques have enabled efficient acquisition and recovery of sparse high-dimensional data via succinct low-dimensional projections, which usually consist of an encoder and a decoder. Unlike conventional CS techniques with the encoding-decoding architecture, the uncertainty autoencoder (UAE) can sample from the learned input data distribution without an explicit likelihood function, hence avoids potential uninformative latent representations. However, existing works on UAE mainly focus on the encoders and maximize the lower bound of the mutual information between input and measurements, rather than the decoders, which brings the shortcoming that the two may not cope well. In this letter, we propose a novel training scheme for UAE that blurs the measurements to learn the encoder and decoder simultaneously. Experimental results show that the proposed method improves the reconstruction performances when applied to UAE.

Driven by the demand for carbon emission reduction and environmental protection, battery exchange stations (BES) with battery energy storage stations (BESS) and renewable energy have become one of the key technologies to achieve the goal of emission peaking and carbon neutrality. Therefore, this paper proposes a strategy to optimize the operation of BES with Photovoltaics (PV) and BESS supplied by transformer spare capacity. Firstly, it introduces the operation mechanism of BES and uses the spare capacity of building special transformers and the roof PV to supply power to BES to avoid the investment of transformers. Secondly, this paper establishes the load model of BES and proposes the charging rules of battery exchange. Thirdly, a segmented pricing mechanism for the rental price of special transformers is formulated to guide BES operators to preferentially rent spare capacity during low load rate periods. Aiming at the maximum daily profit of BES, an optimization model is established to optimize the number of batteries to be charged and the charging status of BESS in each period, on this basis, the demand response model is proposed. Simulation results show that the proposed strategy can improve the daily profit of BES. And the configuration of BESS can improve the battery exchange capacity. Moreover, the exponential segmented pricing mechanism can effectively guide BES to rent the spare capacity under low and medium load rates of special transformers.

Most of the first eight pilot regions in China have entered the long-period continuous operation stage, and the second batch of six power spot pilots have started their simulation trial runs; however, the construction of power markets in the hydropower-rich regions of China (i.e., Sichuan and Yunnan) still faces multiple problems, including poor reliability of medium- and long-term power supplies, inefficient hydropower resource utilization and consumption scales, and unstable market prices. In this study, we establish a multi-scale joint clearing model for different market models (considering both long- and short-term operation strategies) and perform simulations of different market models at different time scales, to compare and analyze their optimization results and electricity price fluctuations. The model can stabilize the market price and reasonably reflect the true value of hydropower; it can also promote the consumption of clean energy and ensure the reliability of power supplies in the medium and long term. Finally, suggestions are made regarding the selection of electricity market models, the security of energy supplies, and the design of sustainable development mechanisms for hydropower in hydropower-rich regions, with a view to providing useful references for the development of China’s electricity market.

A low-power wideband self-biased phase-locked loop (SPLL) is proposed for multi-protocol SerDes applications in this letter. With the proposed adaptive fast-locking current circuit (AFLCC), the settling time is reduced significantly, and no extra power and jitter contribution. In addition, a start-up module is adopted to reset the system to an optimal initial operating frequency quickly. The proposed 1-3-GHz SPLL, fabricated in TSMC 28-nm CMOS process and occupies a compact 0.028mm2 area. It achieves a roughly constant settling time of 5 μs over all frequencies and division ratios range. Only 0.96 mW is consumed from a 0.9 V supply at 1 GHz frequency.

As self-driving cars perform more tasks, new challenges arise. One of these challenging tasks is autonomous driving decision-making due to the uncertainty of the vehicle’s complex environment. This paper provides an overview of decision-making technology and trajectory control for autonomous vehicles. The main common goal in decision-making is to consider uncertainties, unpredictable situations, and driving tasks to propose a global and robust solution adapted to each situation. The main concern is safety. Decision-making falls into three categories. The first is the traditional approach, which often consists of building a rule system and deriving optimal operations. The advantages of such an approach are well known for being easy to understand and applicable to small problems. The second category of decision-making is based on a probabilistic process and, due to its efficiency, has several applications in this area. The third category is learning-based approaches. Once a decision has been made, manipulate the steering angle or accelerator/brake pedals to perform the appropriate action. Two approaches are existing to designing autonomous driving controllers. Either based on imitating human drivers that includes approaches based on the use of driver models such as AI, or the use of approach-based models

It is shown experimentally a new digital optical decoding scheme based on the transmission or polarized light at p polarization planes using a K-Nearest Neighbor (KNN) algorithm through a single-mode optical fibre at 633 nm. The optical power signal is sent at p polarization planes which constitute p classes required for signal bit recognition. Results show that it is possible to recognize 32 polarizations planes, 5 bits, using 4 features corresponding to the measurement of optical power at 4 different angles at the photodetector side with an average assertiveness of 99.1%.

When acting as the shared energy storage and participating in electricity market services, the schedulable capacity that shared electric vehicle (shared EV) will provide to the grid needs in future time needs to be predicted accurately. This research first proposes a method to construct a schedulable capacity dataset for shared EVs based on publicly available shared vehicle rental service data. Secondly, a schedulable capacity evaluation model based on model-agnostic meta-learning, convolutional neural network, long short-term neural network and attention mechanism (MAML-CNN-LSTM-Attention) is proposed. Through the model, the aggregated schedulable capacity of shared EVs in different functional communities for the coming 60 minutes is predicted. Model uses MAML to fine-tune the meta-prediction network through multi-task training to quickly adapt to feature changes caused by different travel habits of different functional communities; CNN-LSTM is used to learn spatial features of schedulable capacity and efficiently extract high-dimensional temporal features from historical sequences; Attention mechanism is used to further improve model prediction accuracy. Simulations show that the model proposed in this paper outperforms other existing models and can reliably predict the schedulable capacity for different date types and functional areas, providing useful decision aids for shared EV operators to participate in market services.

This paper proposes a recurrent neural network (RNN) based model to segment and classify multiple combined multiple power quality disturbances (PQDs) from the PQD voltage signal. A modified bi-directional long short-term memory (BI-LSTM) model with two different types of attention mechanism is developed. Firstly, an attention gate is added to the basic LSTM cell to reduce the training time and focus the memory on important PQD signal part. Secondly, attention layer is added to the BI-LSTM to obtain the more important part of the voltage signal by assigning weightage to the output of the BI-LSTM model. Finally a SoftMax classifier is applied to classify the combined PQD signal in 96 different combinations. The performance of proposed BI-LSTM model with attention gate and attention layer mechanism is compared with the performance of baseline models based on recurrent neural network (RNN) and convolution neural network (CNN). With this model, the PQD signal is easily segmented from the voltage signal which makes the process of PQD classification more accurate with less computation complexity and in less time.

This paper considers the variability in the impact of multi-dimensional meteorological information on power load in different regions. To improve the accuracy of load forecasting in the spatial dimension, the method of spatio-temporal fusion (SF) of multi-dimensional meteorological information is proposed. The Copula theory is applied to analyze the nonlinear coupling of meteorological information such as wind speed, rainfall, temperature, and sunshine intensity from multiple meteorological stations with the power load and to achieve spatio-temporal fusion. In the time dimension, the core parameters of the variational mode decomposition (VMD) are improved by the marine predators algorithm (MPA). The weighted permutation entropy (WPE) is used to construct the MPA-VMD fitness function for the adaptive decomposition of the load sequence. In addition, the input sets of the LSTM model and MPA-LSSVM model are constructed by combining each component of the time dimension and each meteorological information of spatial dimension to obtain the prediction results of each component. The prediction model corresponding to each component is selected according to the evaluation index, and reconstructed to obtain the overall prediction results. The analysis results show that the proposed forecasting method is better than the traditional forecasting method and effectively improves the accuracy of power load forecasting.

As a key component of lithium-ion batteries (LIBs), separator plays a crucial role in the performance and safety of LIBs. In this paper, a cellulose based porous membrane modified by nano CaCO3 is prepared conveniently by electrospinning. The membrane exhibits rich fibrous porous networks and uniform distribution of nanoparticles. Strengthed by CaCO3, the tensile strength of the cellulose porous membrane elevates from 4.7 ± 0.4 MPa to 7.7 ± 0.7 MPa. Besides, the modified membranes possess improved thermal stability and can maintain their original size after treatment at 150 °C and 180 °C. Also, the electrolyte uptake of cellulose/CaCO3 membrane is 73% higher than that of the pure cellulose membrane. Thus, the ionic conductivity of membrane achieves 1.08 mS cm-1 and the electrochemical window is about 4.8V, which meets the practical requirements of LIBs. Significantly, LiFePO4/Li battery this membrane can run for 230 cycles with a capacity retention of 97.4% and a discharge capacity of 149.0 mAh g-1, demonstrating the huge potential for high safety and next-generation LIBs.

Based on the data of indexed papers from Inspec database, scientometrics and statistics-related analysis methods are used to study the overall development trend of international cooperation in advanced manufacturing, the characteristics and evolution of major countries in four dimensions of cooperation scale, cooperation intensity, cooperation network and cooperation network from 2011 to 2020, and this article focuses on the changing trend of China and the differences existing with other countries. The study shows that cooperation in advanced manufacturing has an upward trend, with China and the US having a clear lead in cooperation scale and intensity. China leads the most in the aspect of international cooperation in advanced manufacturing. And the U.S. is at the heart of a network of cooperation across the field.

The development of mega constellations inevitably brings various problems for the development of routing techniques. Most of the existing work considers end-to-end delay and load balancing problems, while the analysis of routing strategies in case of link performance degradation is neglected, and an optimization approach applicable to mega satellite networks is not developed. In this letter, we propose a robust routing strategy based on deep reinforcement learning (RRS-DRL) that regards the Age of Information (AoI) of packets as an optimization target, and ensures the effectiveness of message transmission throughout the network. Extensive simulation results show that our proposed RRS-DRL algorithm obtains a lower average AoI across the network and better utilization of the resources than the traditional shortest path algorithm, significantly increasing the robustness of the constellation.

The tracking and data relay satellite (TDRS) in the geosynchronous orbit (GEO) is capable of providing ranging and communication services for user spacecraft in low Earth orbit (LEO) through the inter-satellite link (ISL). The time delay of TDRS’ ISL transponder in orbit differs from that before launch and changes due to complex space environment, which contributes non-negligible errors to user spacecraft’s orbit determination based on the four-way ranging data. We construct a calibration system and propose an efficient on-orbit calibration method for time delay of ISL transponder in TDRS. Experimental results verify the effectiveness of the proposed method and the RMS of calibration residuals is less than 0.5 meters.

In this paper, a 2-bit digital reflection-type phase shifter working at 120 GHz is presented. It uses a compact coupled-lines coupler with low insertion loss and high isolation over a wide bandwidth. The loads are made by a microstrip-line loaded by three PIN diodes whose states are tuned ON/OFF to obtain 90°, 180° and 270° phase shift relative to the reference (0°). Measurement results show RMS phase and amplitude error equal to 10.3° and 1.2 dB, respectively. The maximum insertion loss is equal to 8.6 dB, leading to a figure of merit of 31°/dB. As shown by simulation, by flipping PIN diodes and use negative voltage for biasing, the maximum insertion loss could be reduced to 3.6 dB (figure of merit of 75°/dB) along with a great improvement in RMS phase and amplitude errors, thus showing the potential of the proposed architecture.

Chiral Iridium complexes are very important for the preparation of circularly polarized light emitting diodes (CP-OLEDs). Three novel Iridium(III) complexes, Ir(dnfppy)2(Cl/pyrrole), Ir(dfppy)2(dpp) and Ir(tfmqz)2(sdpp) have been synthesized and characterized, respectively. These Iridium(III) complexes emitted deep-blue, blue and red photoluminescence with high quantum yields, for ((Ir(dnfppy)2(Cl/Pyrrole): λmax=447 nm, F=62.4%; Ir(dfppy)2(dpp): λmax=467 nm, F= 25%; Ir(tfmqz)2(sdpp): λmax=609 nm, F=73.7%). Compared with Ir(dnfppy)2(Cl/pyrrole), HOMO energy level of Ir(dfppy)2(dpp) and Ir(tfmqz)2(sdpp) was calculated to be −5.71/-5.73 eV, and LUMO energy level increased to be -2.75/-3.36 eV, respectively. CD spectra of two pairs of the enantiomers for Ir(dfppy)2(dpp) or Ir(tfmqz)2(sdpp) displayed symmetry with opposite polarization between 300 and 600 nm. The maximum external quantum efficiency (EQEmax) of OLEDs based on Ir(tfmpqz)2(sddp) was 13.8%, showing a relatively low efficiency roll-off with the EQE of 10.7% at 5000 cd/m2.