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With the rapid development of railways, especially high-speed railways, there is an increasingly urgent demand for new wireless communication system for railways. Taking the mature 5G technology as an opportunity, 5G-railways (5G-R) have been widely regarded as a solution to meet the diversified demands of railway wireless communications. For the design, deployment and improvement of 5G-R networks, radio communication scenario classification plays an important role, affecting channel modeling and system performance evaluation. In this paper, a standardized radio communication scenario classification, including 18 scenarios, is proposed for 5G-R. This paper analyzes the differences of 5G-R scenarios compared with the traditional cellular networks and GSM-railways, according to 5G-R requirements and the unique physical environment and propagation characteristics. The proposed standardized scenario classification helps deepen the research of 5G-R and promote the development and application of the existing advanced technologies in railways., Comment: 7 pages

In this paper, a four-phase 8/10-pole dual armature-winding variable flux reluctance generator (DAW-VFRG) with double-redundant rectifier circuit is proposed for onboard generator. The proposed generator integrating four-phase VFRG and dual armature winding structure improves fault-tolerant capability and power density. Based on the airgap field harmonics analysis and winding function theory, the no-load back-EMF model and winding inductance model are deduced for determining the optimal stator/rotor pole combination. In addition, based on magnetic energy comparison, the influence of the connection categories between two sets of armature windings and rectifier circuits on output capability is also investigated when the saturation effect is considered. The key design parameters are globally optimized to improve the output capability and fault-tolerant capability simultaneously. Moreover, the electromagnetic performance of the proposed DAW-VFRG under both normal and fault condition is evaluated and compared with single armature-winding VFRG with half bridge rectifier. Finally, two prototypes are manufactured and tested to validate the finite-element predictions.

A compact interlinking converter modular (ICM) for hybrid ac/dc microgrids with a distributed storage (DS) bus for storage integration is proposed in this article. The advanced ICM for hybrid ac/dc/ds microgrids involves the nine-switch topology with compact configuration and decreased number of active operating switches, which structurally equivalents to afford a dc interconnection port for dc subgrid, an ac interconnection port for ac subgrid, a ds interconnection port for ds subgrid, and two dc–dc converters for the interconnection ports for parallel DSs. In addition to the popular advantage of component elements saving, the suggested ICM, aggregating various interlinking converter cells (ICCs) into an ICM, is also proficient of participating computations from all ICC sensors to enhance the overall modular performance without building extra communication connection links between ICCs. With this in mind, a decentralized power management control strategy is realized by employing a coordination power control methodology to systematically handle various distributed generations (DGs), DSs, and ICM in a decentralized manner with improved communication fault ride-through capability. The controller hardware-in-the-loop experiments on a real-time digital simulator platform validates the achievement of the advanced ICM application in hybrid ac/dc/ds microgrids and the effectiveness of the associated decentralized power management scheme.

Flying devices, e.g., Unmanned Aerial Vehicles (UAV) and High Altitude Platforms (HAP) are showing great potentials to revolutionise human society with unprecedented efficiency and convenience. 5G and beyond (5GB) networks have been considered as an important infrastructure for supporting flying devices to accomplish mission-critical applications. However, most of the existing research on 5GB networks mainly focuses on technology evolution to support ground devices, paying insufficient attention to the emerging large-scale deployment of flying devices. To fill the gap, this study aims to identify the differences when 5GB networks are used to provide massive access services for the ground devices and their counterpart flying in the air and analyse in which aspects that 5GB should be enhanced to serve flying devices. In detail, a holistic 5GB architecture is presented to support both ground and flying devices. Then, the unique features of flying devices are analysed with a focus on the challenges they bring to 5GB systems. Facing these challenges, we thoroughly investigate the advantages and disadvantages of 5GB key technologies. Furthermore, a case study is presented to demonstrate that flying devices not only create new issues for 5GB design, but also bring new opportunities for 5GB to enhance their service capabilities.

Shadow removal can significantly improve the image visual quality and has many applications in computer vision. Deep learning methods based on CNNs have become the most effective approach for shadow removal by training on either paired data, where both the shadow and underlying shadow-free versions of an image are known, or unpaired data, where shadow and shadow-free training images are totally different with no correspondence. In practice, CNN training on unpaired data is more preferred given the easiness of training data collection. In this paper, we present a new Lightness-Guided Shadow Removal Network (LG-ShadowNet) for shadow removal by training on unpaired data. In this method, we first train a CNN module to compensate for the lightness and then train a second CNN module with the guidance of lightness information from the first CNN module for final shadow removal. We also introduce a loss function to further utilise the colour prior of existing data. Extensive experiments on widely used ISTD, adjusted ISTD and USR datasets demonstrate that the proposed method outperforms the state-of-the-art methods with training on unpaired data., Submitted to IEEE TIP

Wind power may become the key development area of the renewable energy industry planning. Due to the fluctuated wind output power, the system parameter uncertainties and load disturbances, these may lead to the unstable operation of renewable energy power system for the larger voltage deviation. Therefore, the novel control strategy is proposed for the load side converter (LSC) of double-fed induction generator (DFIG) to improve the stability of power system, which is designed by taking advantage of adaptive sliding mode (SM) method and sliding mode observer (SMO). Furthermore, the static synchronous compensator (STATCOM) is also controlled through the designed adaptive SM reactive power controller, which can supply the reactive power compensation for the interconnected wind-diesel power system. Moreover, the LSC state variables need to be estimated by the SMO, which is constructed according to the established mathematical state model. Based on the estimated state variables, the designed reactive power controller is established for LSC by adaptive SM algorithm and fuzzy algorithm. The reactive power controller includes two parts. One is the designed SM controller by taking advantage of the estimated state variables and state control model. The other is micro-regulated part and the control signal is calculated by using fuzzy algorithm according to the system voltage deviation online. Experiment studies are performed by using RTDS to verify the effectiveness of the proposed strategy and assure the stability of wind-diesel power system under different operation conditions.

This paper presents a novel coordinated energy management approach for hybrid AC/DC distribution system with microgrid clusters considering multiple market players, which is modeled as a bi-level optimization problem by using non-cooperative game theory and robust optimization. The hybrid AC/DC distribution network and each microgrid are regarded as different entities, which are in the upper/lower level of the model, respectively. Considering the uncertainty of renewable energy, two-stage robust optimization is added in the bi-level optimization problem to suppress the influence of uncertainty on power interaction and achieve the minimum operation costs of each entity under uncertainty. Furthermore, In the upper level model, hybrid AC/DC distribution network can control power flow in real time, promote power interaction between entities, and reduce operation cost under uncertainty by using effective control of converter station and diesel generator. And each microgrid has dealt with the impact of uncertainty on the power interaction by reasonably managing the charge/discharge of energy storage, which realizes its own minimum operation cost. The proposed model can be transformed into mathematical programming with complementarity constraints (MPCC) by using Karush-Kuhn-Tucker conditions. Finally, numerical cases on an improved IEEE 33-bus hybrid AC/DC distribution system show the effectiveness of the proposed method.

A microrobot with untethered control in 3D space is a good choice to be applied in the fields of biomedicine with in small and confined workspace. In this paper, an electromagnetic actuation system (EMA) which combined with Helmholtz coil and Maxwell coil for the microrobot 5 DOF locomotion in 3D space is built. The magnetic field analysis of the proposed 3D EMA system was analyzed by finite-element-method (FEM) with multi-physics COMSOL software. The proposed EMA system can produce magnetic field with different characteristics such as a controllable uniform gradient magnetic field, a rotating magnetic field and a oscillating magnetic field in a three-dimensional space by independently changing the current in each coil. In this paper the 3D motion dynamic equation model of microrobot was established. A novel control method for the gravity compensation for the wireless locomotive microrobot was proposed. The proposed method has the property that the direction of magnetic flux and the locomotion path of the microrobot are independent. Meanwhile, it can achieve the horizontal motions or nearly horizontal motions and overcome the gravity well at the same time. It has been verified by experiments in 3D liquid environment. With the proposed method, the microrobot shows good performance in horizontal motions as well as various motions in the 3D space.

This paper focuses on how to see through the melting behavior of solid iron tailings in molten blast furnace slag and take a new non-contact visual analytical method to predict its melting law. The optimized convolution neural network (CNN) is used to track the moving target in charge coupled device (CCD) camera system efficiently and accurately, and the melting behavior of SiO2 is described by coordinate translation transformation theory. Hierarchical agglomerative clustering (HAC) and delaunay triangulation were used to extract the characteristic parameters of the melting process of SiO2. The prediction model of the melting rate of SiO2 at high temperature was established by least square fitting (LSF) and dimensional analysis, and compared with the actual melting rate of SiO2 obtained by experiments. The results show that the melting characteristics of SiO2 at high temperature are in accordance with certain function rule. The performance of optimized CNN in terms of processing time and the accuracy are significantly improved, and the fusion rate prediction model of SiO2 is verified by 100% accuracy. It provides theoretical support and model basis for the improvement of slag cotton preparation technology.

This paper focuses on improving tracking performance of totally uncoupled compliant micromanipulator based on robust control method of elliptical hysteresis model. The hysteresis model and hysteresis compensation model for proposed mechanism based on Piezoelectric transducer (PZT) are established by using elliptical model method. The values of elliptical hysteresis model parameters are identified by simulating method with different frequencies of control input. The uncertainty model is also established, the values of corresponding estimated parameters are conformed by experiment method. Based on the uncertainty model and elliptical hysteresis model, the robust tracking control method is presented and utilized to evaluate the tracking performance of proposed mechanism under inputting different tracking curves. The proposed method can accurately control the output displacement and improve tracking performance of this mechanism, which are validated and carried out by using experimental studies. Additionally, the coupling errors between two directions are kept within 0.14% and the tracking errors for different curves are within 2.5%.

The H ∞ control problem for networked control system with data packet dropout in both S-C link and C-A link via observer-based controller is researched in this paper. Firstly, the networked control system with data packet dropout in both S-C link and C-A link is modeled as a discrete Markov jump linear system. Secondly, the sufficient and necessary conditions on the stochastic stability of the closed-loop system is established, the solution method of the controller and observer gain matrix are also given on condition that the transition probability matrix is completely known and partly unknown, respectively. The minimal disturbance suppression performance index is also obtained. Finally, two simulation examples illustrate the validity of the proposed method.

Due to frequency and voltage fluctuation resulting from the renewable energy variety, parameter uncertainty, and system disturbance, a novel coordinated control strategy is proposed to improve the dynamic stability of isolated wind–diesel hybrid power system. So, the frequency and voltage can also be regulated by taking advantage of adaptive sliding mode (SM) method and disturbance observer (DOB), which are based on the established whole hybrid system mathematical model. First, the DOB is constructed to estimate the source–load disturbance for isolated wind–diesel power system model. Second, the coordinated control strategy is constructed according to the estimated source–load disturbance value and state control model by using adaptive SM algorithm. Then, the output vector signal of adaptive SM controller is added to the synchronous generator side and the static synchronous compensator, respectively; therefore, the proposed coordinated control strategy can regulate the frequency and voltage simultaneously. Experiment studies are performed by real-time digital simulator to validate the effectiveness of the proposed coordinated control strategy for the isolated hybrid system under different operation points.

In order to reduce the frequency deviation resulting from renewable energy fluctuation and load variance, the coordination control strategy for isolated wind-diesel hybrid micro-grid is proposed by taking advantage of smart neural network observer and sliding mode method. For diesel generator system side, the sliding mode load frequency control including load variance is designed to regulate the output power. For the wind turbine generator system side, the sliding mode pitch angle control considering load variance is constructed to smooth the wind turbine generator output power fluctuation. Furthermore, the different coordinated strategies are proposed to realize the plug and play for the hybrid micro-grid, it is easy to see that the control accuracy can be improved by the designed neural network adaptive observer and considering the load variation. The effectiveness of the proposed control strategy is validated through real time digital simulator platform under different operation condition.

Availability of wind turbine (WT) is an essential parameter both for wind project feasibility analysis and operation and maintenance optimizations, but in offshore wind farms, it is greatly affected by accessibility which has been seldom considered in traditional reliability evaluations. This paper presents a Markov-chain-based availability assessing model of offshore WT considering accessibility problems. Based on the procedure analysis of corrective maintenance offshore, the contribution of poor accessibility to availability is summarized into two independent aspects: stochastic offshore weather and inadequate maintenance resources. A three-state model of offshore WT is established. A Poisson-process-based algorithm is presented to calculate a transition rate of the three-state model. A three-integer field representing a wind farm with N WT WTs is defined for the Markov chain. Mean availability of WT is obtained by solving a Markov transition matrix. The results of case study show that the proposed model provides an effective approach for the availability assessment of WT in a multi-turbine wind farm influenced by stochastic offshore weather and different logistic policies.