This paper investigates the application of Model Predictive Control to Dual Battery Energy Storage System so that the reference power from the actual wind farm power can be tracked satisfactorily. Operation of the two batteries is categorized into two modes. In Mode 1, the first battery charges and the second battery discharges; in Mode 2, things function in the opposite way. The power from the battery is treated as the control signal which is an optimized computation results by MPC considering certain practical constraints including the power delivered/extracted from each battery, as well as the state of charge on each battery. The significance of this work contains mainly three aspects. Firstly, the two-mode operation of batteries removes the problem of overloading battery. Secondly, the actual wind power data is used to verify the dual battery integrated wind farm performance using MPC, thereby tracking the reference power by reducing the battery switching times and extending the life of the battery. Lastly, the constrained MPC is solved by following Hildreth’s quadratic programming procedure, for its reputation of providing simple and reliable real-time implementations.

While many companies have benefited from online sales as their sole sales channel with the rapid growth of online retailing, this approach has limitations, especially for products that contain non-digital information and require a complementary service to fully attract customers. Sellers of these types of products are actively considering or have already adopted a multichannel strategy, which includes maintaining the existing online channel and opening physical brick-and-mortar stores. To stimulate sales, the service operator may consider offering a product bundle with the product manufacturer by providing subsidies to them. Decisions on product bundling could potentially facilitate or pose barriers to channel expansion. This study employs a game-theoretic model to explore the optimal pricing, multichannel and bundling strategies for a product manufacturer and a service operator who offer the core products with ancillary services in either bundled or non-bundled format. Our equilibrium analysis yields several insights. First, the manufacturer’s offline expansion allows customers who visit in-store to try and inspect the product, which raises not only the offline price but also the manufacturer’s online price. Interestingly, this price increase is more significant for the bundled format compared to the non-bundled format. Second, the bundling strategy influences the manufacturer’s decision to expand into multichannel operations. Specifically, product bundling incentivises multichannel expansion if the newly added physical stores can attract a significant number of new customers, indicating that demand spillover is significant. Conversely, product bundling may deter multichannel expansion if the online hassle cost is moderate.

This work focuses on optimizing the performance of power networks by maximizing and optimizing the utilization of renewable energy sources. To accomplish this, a cooperative distributed model predictive control scheme is used in which each microgrid subsystem consists of a controllable load, an energy storage system, and a non-renewable controllable generator. This work also investigated and studied methods of increasing the computational efficiency of previously established algorithms. Simulation results showed that our proposed method provides better utilization of available renewable energy sources while also keeping supply-demand balance satisfied all in a more computationally efficient manner than would be otherwise possible. Meanwhile, our results demonstrated that the utilization of renewable energy sources in the network is increased while also preventing deep discharging of the energy storage systems.

The Internet of Things (IoT) will be widely used in all areas of life and transportation as the 5th Generation (5G) communication technology matures and becomes commercially available. Especially in the field of railway transportation, the IoT technology can alleviate the challenge caused by insufficient wireless spectrum resources and improve the railway communication performance. However, the existing IoT is made up of a large heterogeneous network. In such a super-dense heterogeneous network scenario, how to allocate the most appropriate access point (AP) according to the needs of users has become a problem demanding prompt solution, which also brings additional challenges for the intelligent transportation system (ITS) to develop green and efficient network communication technology. Therefore, focusing on the selection and access of heterogeneous networks in the Railway IoT, this article studies the spatial characteristics of the intelligent spectrum situation of the Internet of Mobile Things in the railway scenario, and establishes the opportunistic access situation of Railway IoT based on the graph convolutional neural (GCN) network. Furthermore, we utilize the GCN network to mine the spatial correlation between different APs, and propose a railway communication AP decision algorithm based on the GCN network combined with the traditional heterogeneous network multiattribute decision algorithm. Our experimental results prove that the proposed algorithm can effectively reduce transmission delay and improve the throughput of the communication system.

The Long Term Evolution for Railway (LTE-R) communication system is providing a reliable data link for High-Speed Railway (HSR) communication. However, when the train passes through different railway environments, the channel capacity of the base station and the number of users are always in highly dynamic changes. Therefore, accurate predicting the changing law of wireless spectrum resources can make more efficient use of wireless spectrum resources. The purpose of this paper is to use the Long Short-Term Memory network ($LST\!M$) to predict the channel occupancy changes of wireless spectrum resources. Under the premise of ensuring the safe and reliable service for primary users (PU), it provides a feasible method for the secondary user’s (SU) opportunistic access to the authorized channels, thereby improving theLTE-Rsystem Utilization rate of spectrum resources. Based on the “occupied/idle” status of the authorized channel at the previous$n$historical moments, we infer the status of the authorized channel at the current moment, build the spectrum situation of the authorized channels, and guide the SU to conduct Dynamic opportunistic Spectrum Access (DSA) to the authorized channels. The simulation results show that when SU uses the channel situation constructed by the$LST\!M$network to access the authorized channels, it has fewer handovers and lower collision rates, and can obtain higher throughput.

In order to realize the reliable and safe operation of the smart railways, and provide high quality information transmission service for passengers, the railway system needs to develop innovative communication network and advanced communication technology to meet the gradually increasing service demand of multi-dimensional comprehensive information resources. The Space-Air-Ground Integrated Network (SAGIN) can provide seamless information services for land, sea, air and space users, and is an effective solution to the challenge posed by the future smart railways to the all-time, all-domain, all-air, high-reliability and high-throughput communication. This paper aims to comprehensively discuss the technical development and application examples of High-Speed Railways (HSRs) based onSAGIN. Firstly, we analysis the development of theSAGINand the mobile communication network of theHSRs, and comprehensively discuss the single network architecture of the space-based, air-based and ground-based networks, as well as the integrated network, and discuss the application scenario and network structure of the combination of the integrated networks. At the same time, the communication services, existing problems and key technologies of the space-based, air-based and ground-based networks are discussed, and the application trend of theSAGINinHSRsis presented. Furthermore, the application scenarios of Artificial Intelligence (AI) technologies in solving the efficient resource utilization of smart railways communication and theSAGINare studied. Based on these technologies, we point out the research direction for the future development of AI technologies inSAGINinHSRscommunications.

In the evolution of railway mobile communications from Long Term Evolution for Railway (LTE-R) to the future 5th Generation Wireless System (5G), the rapid increase in the number of low-power base station nodes along the railway has brought more frequent handovers. The current handover parameter selection mechanism often relies on the on-site measured results in a limited number of discrete scenarios. It cannot deal with the continuous changing characteristics of the high-speed railway mobile communication environment, which leads to a serious lack of accuracy, adaptability and intelligence. This article hopes to construct a parameter-adaptive handover mechanism suitable for5Gin the high-speed railway dedicatedLTE-Rcommunication system. The mechanism first uses the interaction of Temporal-Difference(TD)-learning-based reinforced agents to obtain high-speed railway handover performance and network performance in different combinations of speeds and handover parameters, and continuously updates the accumulated rewards used to target optimization, obtaining a Discrete TD value cube with closely related handover performance. Further, based on the Discrete TD value cube, we use the approximation function method for the completion of “continuous” situation of handover parameter selection, and construct a continuous TD value cube and the corresponding performance cubes. Our experimental results prove that TD learning agents with function approximation can accurately estimate and predict the handover performance and network performance of state combinations with different speeds and handover parameters, and further show that the handover parameter adaptation mechanism based on the Inference ability can find the optimal handover parameters to improve the handover performance and network performance.

This paper presents our experience in teaching Devices and Controls remotely during the COVID-19 pandemic in Autumn 2020, at the School of Engineering and Technology, University of Washington, Tacoma. As a core course for Computer Engineering and Systems students, and the only control course in this program’s curriculum, we tried our best to minimize the impact brought by the remote teaching and learning, bridge the gap between system theories and practical applications, and expose students to the state-of-the-art technology in the field of embedded control systems. Our efforts include distributing each student an easy-to-use Arduino Mega2560 starter kit, enhancing students’ understanding of control theories by using analysis/design tools supported by MATLAB/Simulink, and remotely supervising individual labs and a final project which implements a PID controller through both MATLAB/Simulink and Arduino IDE. In addition to four-hour lectures in the virtual classroom, students also take two-hour virtual lab each week, and complete the course project by the end of quarter. The course evaluation results showed that although challenged by the physically isolated setting, students gained knowledges of the embedded control systems, and in particular, the hands-on experience obtained from virtual labs and the individual project.

With the rapid development of railway transportation, railway communication is particularly important. In addition to meeting the communication needs of train control, it also guarantees the communication quality of passengers on board. In order to solve the problem of low utilization of spectrum resources in the special railway communication network, a spectrum sharing special railway network communication scheme based on the special railway communication network architecture and cognitive radio technology was put forward this paper. Furthermore, in order to improve the fairness of cognitive base station services, we proposed a secondary user queue management strategy based on Kalman Filter Prediction model to realize dynamic resource allocation. On the premise of satisfying the demand of real-time service for queuing delay, the cognitive base station adjusts the service rate of real-time service according to the predicted changes in the queue length, so as to minimize the queuing delay of NRT service. Finally, the simulation results show that the proposed method can reduce the queuing delay of NRT services, effectively improve the fairness of the cognitive base station services, and ensure the efficient utilization of the limited spectrum resources.

With the rapid development of high-speed railway, the mobile communication system for railway need to optimize the allocation of wireless communication resources according to the train motion, thus improving the quality of communication services. In this paper, we construct a chain base station (BS) network model based on LTE-R. A Gauss-Markov (GM) motion prediction model is used to simulate a period of train motion in the orbit, and a cell zooming (CZ) algorithm based on orbital motion prediction and load balance is proposed. The performance of our algorithm is verified by simulation results. When the CZ strategy is implemented or not implemented, the load rate, load balancing factor, blocking rate and throughput of the network model at different moments of the orbital motion are obtained. By comparing these performance indexes, it can be concluded that the proposed CZ algorithm improves these performance indexes.

The rapid construction of high-speed railway has brought great convenience to people's lives. However, in the case of emergencies such as natural disasters and network interruptions, it is urgent to quickly build emergency communication network to ensure the safety and reliability of railway communication. With the continuous improvement of the performance of unmanned aerial vehicle(UAV) network technology in terms of networking flexibility and communication reliability, it has been increasingly applied to the emergency communication. Based on this, we propose an emergency communication strategy to provide signal services by means of UAVs in case of communication signal failure of high-speed railway. In addition, the capacity of the relay UAV has been fully considered, so we propose an improved DSR routing protocol which chooses a better network route between the UAV providing signal services and the remote base stations. The high-speed railway emergency communication models of different scenarios are simulated by the platform of MATLAB GUI and we analyze the values of RSRP and SINR to evaluate the effectiveness and reliability of the proposed emergency communication networking scheme. The experimental results show that the proposed scheme meets the needs of emergency communication for high-speed railway.

Single-cell RNA sequencing (scRNA-seq) technologies facilitate the characterization of transcriptomic landscapes in diverse species, tissues, and cell types with unprecedented molecular resolution. In order to evaluate various biological hypotheses using high-dimensional single-cell gene expression data, most computational and statistical methods depend on a gene feature selection step to identify genes with high biological variability and reduce computational complexity. Even though many gene selection methods have been developed for scRNA-seq analysis, there lacks a systematic comparison of the assumptions, statistical models, and selection criteria used by these methods. In this article, we summarize and discuss 17 computational methods for selecting gene features in unsupervised analysis of single-cell gene expression data, with unified notations and statistical frameworks. Our discussion provides a useful summary to help practitioners select appropriate methods based on their assumptions and applicability, and to assist method developers in designing new computational tools for unsupervised learning of scRNA-seq data.

As the high-speed railway (HSR) industry Internet-of-Things chain matures, HSR wireless communication technology has become an increasingly important research field. The efficient management of time-frequency resources for Internet-of-Things networking is the core issue of HSR wireless communication optimization. The lack of time-frequency resources in LTE-R is still severe. In this article, a new LTE-R time-frequency resource allocation optimization method based on the probabilistic graphical theory is proposed. Considering the regularity that high-speed trains always pass by the same geographical location in similar time periods, we can do some research on opportunistic spectrum accessibility in the existing LTE time-frequency resource algorithm. The probabilistic graphical theory is suitable for finding the appropriate communication access opportunity in an HSR environment. The simulation results show that our method can effectively improve the performance of various traditional LTE time-frequency resource allocation algorithms.

With the rapid development of China's high-speed railway, the traditional railway wireless communication system technology has been difficult adapting to the requirement, and is gradually being replaced by the LTE-R communication system. However, the LTE-R handover parameters selection mainly depends on historical experience, and there is no established theory or method. Therefore, research on adaptive optimization methods of handover parameters at different speeds has great significance on improving the handover performance of LTE-R systems for high-speed railway wireless communications. Combined with the environment adaptive ability of reinforcement learning, this paper proposes an adaptive optimization method based on the Q-Learning algorithm to achieve real-time estimation of the handover parameters of the LTE-R system. And based on these, we establish a performance situation map for handover parameters for different speeds, and use the generated “handover situation” to provide a basis for mobile users accessing opportunistic channels during the handover process, thereby improving handover performance. Our simulation results show that the optimized handover parameters can significantly improve the handover performance of the LTE-R system.

As the high-speed railway industry matures, higher requirements are put forward for the railway wireless communication, and the demand for spectrum resources is also increasing gradually. When the train is moving at a high speed, it will bring about the frequent handover of wireless communication networks, which will lead to the deterioration of wireless communication quality and even the dropping of calls. Therefore, based on the Cognitive Radio, we established the cognitive base station model to enable the base station to have cognitive functions in this paper. We also proposed a multiple base station cooperative reinforcement learning to achieve dynamic spectrum management. Cognitive base station can select the optimal channel for communication services by fusing interaction information between different cognitive base stations, thus reducing the failure of handover when the train crosses the cells. The experimental results showed that the proposed algorithm can improve the utilization of spectrum resources effectively.

By combining the coupling identification concept with the gradient search, this study develops a partially coupled generalised extended projection algorithm and a partially coupled generalised extended stochastic gradient algorithm to estimate the parameters of a multivariable output‐error‐like system with autoregressive moving average noise from input–output data. The key is to divide the identification model into several submodels based on the hierarchical identification principle and to establish the parameter estimation algorithm by using the coupled relationship between these submodels. The simulation test results indicate that the proposed algorithms are effective.

Massive multiple-input multiple-output (MIMO) has been considered as one of the key technologies in 5G communication, owing to its promising potential to increase the spectrum efficiency significantly. The capacity grows rapidly with the increasing number of antennas. However, in real-time environment, the more antennas are employed, the more computing resources are required, and this increase is even in a nonlinear progression. In this work, radio computing architecture (RCA) with multiple parallel general purpose processors (GPPs) and automatic distribution system (ADS) are utilized to overcome the strict real-time constraints and establish a highly flexible prototype platform for massive MIMO research. The feasibility and performance of the GPP-based prototype has been investigated through field trial. A collocated 64-RF-channel base station (BS) with each RF channel driving 3 antennas can simultaneously serve twelve 8-antenna user equipments (UEs). The maximum overall bandwidth is 200 MHz, and the maximum flow number is pre-defined as 24. In this field trial, a peak total user throughput of 11.29 Gbps and a cell spectrum efficiency of 79.82 bps/Hz are achieved. As far as we know, this is the world's highest throughput ever achieved in the sub 6 GHz band. Furthermore, apart from multi-user MIMO, single-user MIMO is also tested in this field trial.

A large scale field trial is conducted to investigate the integrated performance of massive multiple-input multiple-output (MIMO) and polar code, which are two key technologies for 5th generation (5G) mobile system. A more simple and effective algorithm named polarization weight (PW) is used in polar construction. Based on uplink and downlink channel reciprocity in the time-division duplex (TDD) mode, the practical performance of single user (SU) massive MIMO prototype with 64 independent RF channels and 200 MHz bandwidth is investigated under different user equipment (UE) deployments, different layer numbers and moving speeds. Compared to massive MIMO with turbo code, it is shown that significant performance gains can be obtained in TDD massive MIMO system with polar code. For 3 layers scheduled in massive MIMO system with polar code, the maximum user throughput of 1.64 Gbps and spectrum efficiency of 11.64 bit/s/Hz can be achieved. Based on these observations, the feasibility and performance of TDD massive MIMO system with polar code are verified.

Micro-Aerial Vehicles (MAVs) have gained significant attention lately due to their size advantage. However, there is a drawback of MAVs - its limited payload and size don't allow adding extensive sensors. That explains why incorporating computer vision is of great significance to MAVs. One of the problems that computer-vision-driven MAVs need to overcome is obstacle avoidance, which is very important for autonomic vehicles especially for aerial vehicles as they are more vulnerable to collision compared to ground vehicles. Over the last ten years, several obstacle detection algorithms have been developed to create collision-free maneuver for MAVs. Most of them have promising results inside virtual environment; however, they fail miserably during actual flight tests. In this project, we will investigate the real-life issues affecting obstacle avoidance for MAVs and carry out the project on a physical drone. We take into consideration the limitations of the platform and derive our own obstacle avoidance algorithm by combining several existing ones. Effectiveness of the algorithm will be demonstrated through experimental results on the physical drone.

In this paper, we present a disruption-tolerant message forwarding algorithm for optimizing the message delivery ratio of an ad-hoc network in presence of multiple unknown probabilistic influences that are cyclic in nature. Our model assumes each node moves independently from other nodes in the network, and each node's mobility behavior, although not deterministic, can be considered as random variables associated with an unknown sequence of probability distributions that follows a cyclic pattern over time. Based on those assumptions, our algorithm enables any node in the network to estimate the cycle lengths of hidden events that cause variations in each node's mobility behavior using its known mobility history as well as the history information from all peer nodes at its current landmark. Subsequently using such estimates, each node can predict other nodes' most likely trajectories in future and make optimized routing decisions based on these predictions. In this paper, we also report results from experiments that measure the effectiveness of our proposed algorithm by comparing its message delivery ratio with those of two baselines on networks of different sizes.

In this paper we introduce a software and hardware structure for on-line mobile robotic systems. The system hardware configuration mainly consists of a commercially available Pioneer 2 PeopleBot mobile robot, a Sony PTZ video camera and a pair of BreezeNet indoor wireless Ethernet adaptors. The system employs a client-server software architecture in which the client server is insulated from the lower-level details of the mobile robot. This architecture is implemented on the real Internet and the preliminary result is promising. By adopting this modular structure, it will be very easy to construct an experimental platform for the research on diverse teleoperation topics such as remote control algorithms, interface designs, network protocols and applications etc.