Distributed precision jamming (DPJ) is a novel blanket jamming concept in electronic warfare, which delivers the jamming resource to the opponent equipment precisely and ensures that friendly devices are not affected. Robust jamming performance and low hardware burden on the jammers are crucial for practical DPJ implementation. To achieve these goals, we study the robust design of wideband constant modulus (CM) discrete phase waveform for DPJ, where the worst-case combined power spectrum (CPS) of both the opponent and friendly devices is considered in the objective function, and the CM discrete phase constraints are used to design the wideband waveform. Specifically, the resultant mathematical model is a large-scale minimax multi-objective optimization problem (MOP) with CM and discrete phase constraints. To tackle the challenging MOP, we transform it into a single-objective minimization problem using the L p -norm and Pareto framework. For the approximation problem, we propose the Riemannian conjugate gradient for CM discrete phase constraints (RCG-CMDPC) algorithm with low computational complexity, which leverages the complex circle manifold and a projection method to satisfy the CM discrete phase constraints within the RCG framework. Numerical examples demonstrate the superior robust DPJ effectiveness and computational efficiency compared to other competing algorithms.

Load forecasting for the traction power supply system (TPSS) in urban rail transit (URT) presents new opportunities for energy management of regenerative energy utilization devices (REUDs), such as supercapacitor energy storage system (ESS) and energy feedback system (EFS). It also facilitates energy efficiency analysis for subway operators and supports the formulation of optimized power supply strategies. To address the challenge of limited information availability in practical engineering scenarios, this study proposes a method for predicting the load power of the entire adjacent power supply section only based on the local station information. By mapping local original features into a clustered spaceof operating conditions, spatial features that effectively characterize load power are extracted. Based on the composite spatial-temporal features and deep learning, online ultra-short-term load power prediction achieved. Experiments conducted on real-world subway lines, supported by comparative analyses of feature engineering and model engineering, demonstrate that the method significantly enhances prediction accuracy and generalization.

Air Traffic Control (ATC) is a complicated, time-evolving, and real-time procedure to direct flight operations in a safer and ordered manner. Although enormous data storages are available during air traffic operations for over 40 years, data-driven intelligent application in aviation is still an emerging task due to the safety-critical issue. With the prevalence of the Next Generation ATC system, artificial intelligence (AI) -empowered research topics are attracting increasing attention from both industrial and academic domains and a high-quality dataset naturally becomes the prerequisite for such practices. However, almost all ATC-related datasets are only unimodal for certain tasks, which fails to comprehensively illustrate the traffic situation to further support real-world studies. To address this gap, a multimodal air traffic situation (M2ATS) dataset is constructed to advance AI-related research in the ATC domain, including airspace information, flight plan, trajectory, and speech. M2ATS covers 10362 flights ATC situation data, involving 110000+ utterances (104 hours) with diversity golden text annotations, 16 intents, and 51 slots. Considering the real-world ATC requirements, a total of 10 multimedia-related tasks (24 baselines) are designed to validate the proposed dataset, covering automatic speech recognition, natural language processing, and spatial-temporal data processing. New ATC-related metrics corresponding to ATC applications are proposed in addition to the common metrics to evaluate task performance. Extensive experiment results demonstrate that the selective baselines can achieve designed tasks on this new dataset, and further investigations are also required to address task and data specificities. It is believed that the proposed new dataset is a new practice to advance AI applications to an industrial scene, which not only promotes ATC-related applications but also provides diverse research topics in the common multimedia community.

Based on a single converter, this paper considers the train’s operation for the extraction of the load characteristics and the curve of power versus time changing. The corresponding operating conditions of the train traction converters under different operating diagrams are analyzed. Then, this paper studies on the power loss and thermal conductivity of IGBT so as to obtain the electro-thermal coupling model for predicting and comparing the life of IGBT modules under different operation diagrams.

The modeling complexity of the traction power system and variation of traffic conditions bring challenges for the optimization of energy management strategy for supercapacitor energy storage systems in urban rail transit. Therefore, in this paper a deep-reinforcement-learning-based energy management strategy is proposed: the energy management system is modeled as an intelligent agent, the reward function is formulated comprehensively considering the energy-saving and voltage-stabilizing effects of supercapacitor, a traction power system simulator is developed to emulate the environment, and the agent's behavior is improved in each headway through the deep Q-learning algorithm, and converges to the nearly-optimal policy. The proposed strategy is verified through simulation based on the Beijing Subway Batong Line. The study results show that it dynamically adjusts the voltage thresholds so as to better allocate the supercapacitor capacity along the time horizon. The energy-saving and voltage-stabilizing effects are significantly improved compared with the fixed-threshold strategy and genetic optimization, and demonstrating to be in close proximity to the optimal benchmark deduced from dynamic programming.

Automatic Speech Recognition (ASR) is greatly developed in recent years, which expedites many applications on other fields. For the ASR research, speech corpus is always an essential foundation, especially for the vertical industry, such as Air Traffic Control (ATC). There are some speech corpora for common applications, public or paid. However, for the ATC, it is difficult to collect raw speeches from real systems due to safety issues. More importantly, for a supervised learning task like ASR, annotating the transcription is a more laborious work, which hugely restricts the prospect of ASR application. In this paper, a multilingual speech corpus (ATCSpeech) from real ATC systems, including accented Mandarin Chinese and English, is built and released to encourage the non-commercial ASR research in ATC domain. The corpus is detailly introduced from the perspective of data amount, speaker gender and role, speech quality and other attributions. In addition, the performance of our baseline ASR models is also reported. A community edition for our speech database can be applied and used under a special contrast. To our best knowledge, this is the first work that aims at building a real and multilingual ASR corpus for the air traffic related research.

Battery energy storage system (BESS) can achieve good effect of energy saving and voltage stabilization in urban rail transit system. In order to make better use of the capacity of the battery, this paper put forward an improved control strategy based on state of charge (SOC) tracking to achieve peak load shifting. The mathematical model of the traction power supply network which combined trains and BESS was established to analyze the influence of the BESS on the output of substation and the load analysis of the urban rail system under different departure intervals. Then the paper proposed a dynamic threshold strategy which can adjust the discharge threshold based on SOC tracking, using the battery to transfer parts of the recovered braking energy from the off-peak time to peak time, reducing the peak power of the substation while saving energy. The optimal values of the control parameters were obtained through the genetic algorithm. Finally, the simulation results showed the effectiveness of the proposed control strategy.

In order to achieve better energy saving effect of the super-capacitor energy storage system (SC-ESS), an on-line optimization control strategy is proposed in this paper. Firstly, the model of urban rail traction power supply system with super-capacitor energy storage system is established, and the working principle of ESS is analyzed. Then the paper analyzes the influence of train braking characteristics and headway on the energy saving effect, and gives the recommended setting range of charge threshold. A real-time optimization control strategy is proposed to adjust the charge and discharge threshold in real time according to the energy saving in a certain period of time, which can make the best energy saving effect of the SC-ESS. The control strategy based on the combination of offline optimization and real-time threshold adjustment, using genetic algorithm in multi-trains operation condition obtains threshold optimal control parameters, and the threshold is adjusted according to the working state of the ESS in actual operation. Simulation and experimental results demonstrate the effectiveness of the proposed control strategy.

Single-phase four quadrant converter is an important part of high-speed train traction drive system. High-speed train and power supply system coupling form the high-speed rail system. Focusing on the medium-frequency oscillation phenomenon between the AC power supply system and high-speed trains, this paper considers digital control delay effects in 4Q converter modeling and analysis. It presents a mathematical model for a single-phase 4Q converter, coupling with the power supply system impedance, and it uses Hopf bifurcation theory of nonlinear systems, small disturbance method and the root-locus method to study on AC power railway system stability, especially on the system stability with different circuit parameters and control parameters. The numerical simulation results and the experiments on the small scale prototype hardware platform have verified the theoretical analysis.

The reuse of the regenerated braking energy has huge potential. This paper mainly focuses on the efficient usage of regenerative energy. When one train is braking, if the regenerative energy isn't absorbed by other trains, the traction supply network voltage will increase, it leads to the regenerative braking failure and the waste of regenerative braking energy. Based on the knowing and analysis of online running trains' operation conditions, the other trains' operation curve will be adjusted correspondingly, to achieve the synchronization of acceleration and braking condition. In order to analyze the adjustment principles and adjustment strategies, the model of traction supply network and the constraint conditions of running train are put forward, then simulation in the simple traction power supply system of an actual line are analyzed to verify the correctness and validity of the methods.

So far, analysis of wireless power transfer system is usually based on the constant pure resistive loads. Meanwhile, in real applications, there are many types of different loads. This paper studies a WPT system using super-capacitor as its load. In order to simplify the analysis, the equivalent model of the WPT system is derived based on the principle of power conservation. During the charging of the super-capacitor, the variations in primary, secondary currents and system efficiency are analytically derived. Considering the possible variation of the coupling coils, the influences of coupling coefficient and quality factor on the voltage and current gain are also analyzed. Finally, the theoretical derivation and analysis are verified by simulation and experiments.

In this paper, the circuit model of the parallel connected induction motors driven by single inverter is established. According to the circuit model, the problem of unbalanced electromagnetic torque caused by the wheel diameter difference of the parallel connected motors is analyzed. The weighted vector equation of the parallel connected motors is established. To improve the control performance with unbalanced torque, the paper proposes a new torque control strategy based on weighted torque current for high speed train parallel connected induction motors. At the same time, the simulation model of the traction system of the China Railways High-speed train (CRH2A) is established. The simulation and experiment prove the feasibility and correctness of the control strategy.

High-speed train is supplied by alternating current (AC) power and bases on single-phase four quadrant (4Q) converter as one necessary part of the `AC-DC-AC' transform process. The discrete mathematical model for a single-phase 4Q converter is derived with small disturbance method and digital control delay effect is considered in 4Q converter modeling and analysis. Focusing on stability characteristics of the system, the effect of different circuit parameters and control parameters is analyzed with Hopf bifurcation theory of nonlinear systems. The Matlab simulation and small scale prototype hardware platform have been developed to confirm the theoretical analysis.

Wayside supercapacitor based energy storage system (ESS) for urban rail transit can reuse the braking energy and reduce energy consumption. Most ESSs switch over from charging to discharging states based on voltage of DC bus. But open circuit voltage of DC power supply in subway system always changes, which would influence the normal operation of ESS. This paper will show the output characteristic of substation and the basic structure of supercapacitor based ESS. At the same time, influences of substation output voltage fluctuation on ESS will be analyzed. And then, a control strategy for eliminating the influence will be come up with. Finally, this paper will present the experiment results of 200kW supercapacitor based ESS prototype field test in Beijing subway.

Applying ultracapacitor to store regenerative braking energy in urban rail transit can achieve good effects on energy efficiency and voltage regulation. But when multiple devices distributed in one rail line, it cannot be coordinated by current energy management strategy, which leads to higher power dissipation and voltage stress. The paper firstly establishes models of train, traction power grid and energy storage system combined with characteristics of ultracapacitor. Then operating features are analyzed under constant voltage-based energy management strategy, in view of the analysis a state of charge-based energy management strategy is proposed, which can be sufficiently utilize the configuration capacity of all ultracapacitors, lower the power dissipation and the voltage stress and help to extend the service life of the ultracapacitors. Finally, simulation and experiment results show the correctness of the model and the effectiveness of the proposed energy management strategy.

This paper provides an equivalent series resistance-based real-time control method for the battery-ultracapacitor hybrid system. The idea of this control method is that the dynamic load demand is distributed based on the equivalent series resistance ratio of batteries to ultracapacitors, whilst the estimated average load demand based on the past N seconds is supplied by the batteries. In addition, the energy stored in the ultracapacitors is considered for protection purpose. The simulation results verify the effectiveness of the equivalent series resistance-based real-time control method, in terms of the system efficiency, the overall energy loss, and the utilization of the ultracapacitors. Further comparison results show that the efficiency of the proposed real-time control method with well-selected parameter is only 1% lower than that of the dynamic programming method.

This paper discusses a game theory based control of an engine/generator-battery-ultracapacitor hybrid energy system. The engine/generator, the battery and ultracapacitor packs are modelled as three independent agents to show their different preferences under the Netlogo environment. The preferences of these three energy components are represented by utility functions. Then a non-cooperative current control game is set up and a Nash equilibrium is found which is used as reference solution formula to be updated at each control instant. The weights in the utility functions are chosen based on the location of the knee point in the Pareto set. The simulation results show that the game theory based control has a comparable performance with the average load demand-based control without knowing the pre-knowledge of the test trip.

This paper provides a quantitative and comparative study on efficiencies of a battery semi-active hybrid energy storage system (HESS) and a battery-only system. The discussion is based on the equivalent series resistance (ESR) circuit models and a pulsed current load. It is theoretically proved that the efficiency difference between the two systems depends on the variance of the load current, the average load current, and the internal resistance of the battery pack. A threshold variance is then accurately derived, above which the battery semi-active HESS becomes more energy efficient. This threshold decreases with an increasing internal resistance of the battery pack and/or a decreasing average load current. Therefore, the best usage of ultracapacitors is to combine with batteries designed for low cost (i.e. a large internal resistance), and supply the dynamic part of a current load with a high peak-to-average ratio. Finally, JC08 driving cycle-based simulation validates the theoretical analysis.