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This work studies the event-triggered control problem for networked control systems. The plant is controlled directly by a dynamic local controller, which receives the reference control signal from the remote controller. The measurement signal of the plant and the reference control signal of the remote controller have their separate event triggers, and thus the remote controller and the local controller can decide when to transmit signals on their own. It is proved that with the proposed control approach and the dual event triggers, the closed loop system is globally asymptotically stable, which has been illustrated by simulation results.

The finite-time coverage control problem for networked mobile agents with continuous-time dynamics and unidirectional motion constraint on a closed curve is addressed in this article. The objective is to minimize a coverage cost function in finite time which characterizes the largest arrival time from the multiagent system to any point on the curve. Low gain feedback is employed to design the distributed coverage control laws for the agents with different input constraints due to their limited movement capabilities. An upper bound on each low gain is also provided by showing that the distance between the neighboring agents is always lower and upper bounded. Under the proposed control laws, the networked mobile agents can be driven to the optimal configuration, minimizing the coverage cost function in finite time. Due to the unidirectional motion of the agents, there exists at least one agent which remains static throughout the coverage task. As a result, a less conservative upper bound on the low gains is derived, which provides the possibility of better convergence rates of the proposed coverage control laws.

For long-span cable-stayed bridges, cables serve as one of the most important components to guarantee structural integrity. Forces of stay cables indicate not only the performance of cables themselves but also the overall condition of bridges. In order to help stakeholders to make maintenance decisions, an extreme cable force estimation method was proposed based on cable force measurements and traffic data from the weighing system. First, raw monitoring data were preprocessed based on a median filtering to obtain usable cable force signals. The multiresolution wavelet method was used to extract traffic-induced force component from mixed signals. Then, a Monte Carlo-based random vehicle model was developed using traffic data from the weighing system. Based on field temperature measurements and simulation of traffic-induced effects, extreme cable forces with respect to vehicle loads and temperature effects were predicted by extreme value theory. The Generalized Pareto Distribution (GPD) was adopted to establish the probability distribution models of the daily maximum cable force. Then, the extreme value within a return period of 100 years was determined and compared with the design loading demand. Finally, the effectiveness of the proposed method was validated through a cable-stayed bridge in China. As a result, the low-frequency varying component of cable force response had positive correlation with environmental temperatures, and the extreme value of the predicted cable force under prospective traffic volumes was within limit interval value according to the design code. The conclusions can be utilized by bridge owners to make maintenance decisions.

In this paper the coverage problem of a mobile sensor network subject to time-varying communication delays is investigated. Each sensor is required to move along a fixed closed curve. The goal is to minimize a coverage cost function which is defined as the largest response time from the sensor network to any point on the curve. The maximum velocities of the sensors are assumed to be different from each other. This results in different input saturation constraints on the sensors with first-order dynamics. It is shown that even if time-varying communication delays exist, low gain feedback can be also used to design distributed coverage control laws for the mobile sensors. When the sensors’ delays and their derivative are both upper bounded, the sensor network is guaranteed to arrive at the optimal configuration which minimizes the coverage cost function. Moreover, in this work the maximal allowable upper bound on the sensors’ delays can be increased by choosing a lower upper bound on the sensors’ low control gains. Finally, the effectiveness of the proposed coverage control laws is verified by a numerical example.

For long-span cable-stayed bridges, cables are one of the most important components to resist various actions. With the application of structural health monitoring technique, real-time recording of cable forces is achieved, and hence, the warning system on cable anomaly established. However, it is still difficult and there are challenges to conduct the warning system effectively, especially due to the phenomena of false alarm or omission. A practical reason is the warning index’s sensitivity to the ambient environment. Temperature variations, for instance, usually disturb the force-based cable anomaly warning and result in the false evaluation of structural condition. In view of eliminating the effects of environmental temperature, cointegration, a statistical concept from econometrics, is employed in cable anomaly warning studies. An approach that extracts warning index by linear combination of two non-stationary time series using the cointegration algorithm is developed in order to produce a more stationary cointegrated residual series (warning index series). The calculated stationary relationship between two time series is insensitive to the influence of environmental temperature and is capable of cable anomaly warning. Specifically, the framework of the cable anomaly warning system is first proposed. Subsequently, time-series test methods are introduced to check the non-stationary order and calculate the cointegration parameters of measured cable forces and environmental temperature. The computed cointegrated residual series is fed into statistical analysis as a warning index and the procedure of cable anomaly warning under the influence of environmental temperature is illustrated in detail. Finally, a case study for a cable-stayed bridge is demonstrated with results and discussions.

Generative adversarial networks (GANs) have been successfully used for considerable computer vision tasks, especially the image-to-image translation. However, GANs are often accompanied by training instability and mode collapse in the process of image-to-image translation, which leads to the generation of low-quality images. To address the aforementioned problem, by combining CycleGAN and intra-domain reconstruction loss (IDRL), we propose an unsupervised image-to-image translation network named “Cycle-IDRL”. Specifically, the generator adopts the U-Net network with skip connections, which merges the coarse-grained and fine-grained features and the least squares loss in LSGAN is used to improve the stability of training process. Especially, the target domain features extracted from the discriminator are used as input of generator to generate reconstructed samples. Then, we construct the IDRL between the target domain samples and the reconstructed samples by using L1 norm. The experimental results on multiple datasets show that the proposed method performs better than the existing methods.

With the development of network infrastructure, a large volume of data will be exchanged with increased bandwidth. Many applications are connecting people to the rest of the world through the public network. Thus, privacy and security have become a concern. Under this circumstance, it becomes a trend that the enterprises tend to host their data and services on private clouds dedicated to their own use, rather than the public cloud services. However, in contrary to the well-investigated total cost of ownership (TCO) for public clouds, the analytic research on the cost of purchase and operation for private clouds is still a blank. In this work, we first review the state-of-the-art TCO literature to summarize the models, tools, and cost optimization techniques for public clouds. Based on our survey, we envision the TCO modeling and optimization for private clouds by comparing the differences of features between public and private clouds. Finally, we propose a heuristic algorithm, conflict-aware first-fit to optimize the total cost of ownership of private cloud by minimizing the number of racks when deploying servers.

The circle formation control problem for mobile sensor networks with bounded position measurement errors is addressed in this paper, where the sensors are required to form a prescribed formation on a given circle. Only the upper bounds of the measurement errors are assumed to be known by the sensors a priori. An estimation algorithm is firstly proposed to estimate the position difference between the sensors and its neighbors. Then, a distributed circle formation control law is developed for each sensor by using the estimated position difference. It is shown that the effect of the measurement errors on the formation task can be reduced under the proposed formation control laws. Finally, a numerical example is provided to illustrate the effectiveness of the proposed control laws.

This paper takes cube robot as the object and studies balance control on its corner. First, the mechatronic design of cube robot is presented, and its dynamics are derived on this basis. Then a balance controller using state feedback is presented. Finally, the proposed controller is implemented on the cube robot. The experimental results verify the correctness of dynamics and the effectiveness of the controller.

This work investigates the connectivity preservation problem of multi-agent systems with event-triggered controllers. The agents in the system have only limited communication ranges, and they are required to achieve rendezvous while preserving the connectivity of the communication graph. To reduce the amount of communication packages, event-triggering mechanism is employed. We propose two kinds of event triggers to realize the connectivity-preserving rendezvous of the multi-agent system, i.e., the connectivity trigger to preserve the network connectivity, and the convergence trigger to drive the agents to achieve rendezvous. By introducing a particular constraint function in the controller design, the control inputs of the agents can be bounded throughout the rendezvous process. This guarantees that the controller can be physically implemented in practice. It is proven that the agent group will achieve rendezvous while all the existing communication links can be preserved under some very mild assumptions on the controller design. Moreover, Zeno behavior can be avoided by using an event/time hybrid triggering approach. The effectiveness of the proposed event-triggered control is illustrated by simulations.

This work considers the event-triggered and self-triggered control for linear systems with periodic sampling schemes. An event-triggered control using sampled states is proposed. The asymptotic stability of the closed-loop system is guaranteed by a condition in terms of a linear matrix inequality. Moreover, a self-triggered control with sampling is presented and the next control task release time is predicted based on the current sampled data. It is noted that by introducing the periodic sampling scheme, Zeno behaviors can be naturally avoided in both of the algorithms. Finally, simulation results are provided to illustrate the theoretical effectiveness.

The microservice architecture (MSA) differs fundamentally from the monolithic, layered architecture. The use of microservices provides a high degree of autonomy, composability, scalability, and fault-tolerance. MSA is regarded by many as a promising architecture for smart-city applications; however, a number of issues remain, including (1) the management of complex call relationships among microservices; (2) ensuring the quality of the overall software system even as new microservices are added and existing ones are modified, and (3) locating existing microservices that satisfy new requirements. In this paper, we propose a novel approach to the development of microservice-based systems, referred to as GSMART (Graph-based and Scenario-driven Microservice Analysis, Retrieval and Testing). GSMART enables the automatic generation of a “Service Dependency Graph (SDG)” by which to visualize and analyze dependency relationships between microservices as well as between services and scenarios. It also enables the automatic retrieval of test cases required for system changes to reduce the time and costs associated with regression testing. A microservice retrieval method using VSM and word2vec accelerates the development of new microservices tailored specifically to the needs of users based on user-provided scenarios. Experiment results demonstrate the feasibility, effectiveness, and efficiency of all of the main features of GSMART.

When the camera is in a low-textured scene, it is difficult for the simultaneous localization and mapping (SLAM) algorithm based on point features to track sufficient effective point features, which leads to low accuracy and weak robustness, or even results in algorithm failure. To tackle this problem, a low-texture monocular visual odometer based on point-line feature is proposed in this work. Firstly, the data association accuracy and robustness are improved by using the complementation of point-feature and line-feature. Based on this, an environmental-feature map with geometric information is constructed. We thoroughly evaluate this approach and the new initialization strategy on the TUM RGB-D benchmark. The results demonstrate that the use of lines does not only improve the original ORB-SLAM solution in poorly textured frames, but also systematically improve the solution in sequence frames combining points and lines without compromising the efficiency.

Visual SLAM based on ORB features will increase the computational pressure of SLAM system due to the large amount of feature extraction and matching computation and the need to screen a large number of mismatched point pairs. It cannot completely eliminate the mismatched point pairs, which will affect the camera positioning accuracy of visual SLAM system to a certain extent. To solve the two questions, the PROSAC algorithm is used, screening point of mismatch on, all matching points by calculation of evaluation function, selection to match point to build the model with the highest quality, through continuous to join interior point, build the final model, screening point pairs of mismatch. Provide high quality data for camera pose estimation and back end optimization of SLAM system. Through the comparison of RANSAC algorithm and PROSAC algorithm false match screening time, as well as tracking error. PROSAC algorithm effectively reduced the time of mismatching screening, with a maximum improvement of 100 times. The tracking error has also improved significantly.

Long-span bridges face various threats every day due to their sophisticated operational environments. To investigate the operational state of bridges, a real-time dynamic warning method on ...

Aiming at the problem of congestion and blockage caused by the increasing number of cars in urban roads, in order to alleviate the congestion and blockage of urban main roads, the number of vehicles in each lane was calculated based on geomagnetic sensor and infrared counter. At the same time, considering the influence of the number of lanes on the capacity of the road, according to the actual situation of the road needs to establish the variable lane research program in line with the actual section, and put forward the intelligent lane control system suitable for the urban main road. By analyzing the data collected by sensors, the lane adaptive control can be realized. According to the special situation of emergency rescue, the emergency mode is put forward to save rescue time. Real-time lane information on the screen can help you plan your route in advance and reduce travel time.

This work studies the event-triggered control problem for networked control systems. The plant is controlled directly by a dynamic local controller, which receives the reference control signal from the remote controller. The measurement signal of the plant and the reference control signal of the remote controller has their separate event trigger, and thus the remote controller and the local controller can decide when to transmit signals on their own. It is proved that with the proposed control approach and the dual event trigger, the closed loop system is globally asymptotically stable, which has been illustrated by simulation results.

In this paper, an event-triggered optimal adaptive control is developed for robot trajectory tracking system. Due to the nonlinearity of Hamilton function, we apply the actor-critic neural network structure to solve it. Firstly, the critic network is used to estimate the cost function and the actor network is used to estimate the optimal event-triggered control law. Due to the advantage of event-triggered method, the weight update rate of actor-critic neural network only occurs when the triggering condition is violated, which save a lot of communication resources. Then, the event-triggered robot trajectory tracking system is ultimately bounded by Lyapunov stability analysis. Finally, the simulation show that the proposed method is effective.

For a linear time-invariant system with bounded system noise, the robust control problem based on event-triggered communication quantizer is studied in this paper. By using robust control theory and linear matrix inequality (LMI) technique, a sufficient triggering condition is proposed to guarantee the given level of robust disturbance suppression. The Lyapunov criterion for asymptotic stability analysis and event-triggered method for controller design are established. In this process, event-triggered scheme and controller are designed in collaboration. In addition, the lower bound of event-triggered interval is given, and it is proved that the proposed method can naturally avoid Zeno behavior. Finally, a simulation example is given to verify the effectiveness of the proposed method.

This paper investigates the finite-time circle formation control problem for networked mobile agents with continuous-time dynamics. The agents are constrained to move on the circle only in the counterclockwise direction. Distributed control laws are developed to achieve desired circle formation in finite time and the spatial order of the agents is preserved during the formation task. With the help of Lyapunov methods and finite-time stability theory, convergence analysis of the proposed formation control laws is provided. Finally, simulation examples are presented to illustrate the effectiveness and performance of the proposed control laws.

In recent years, development in the fields of big data and artificial intelligence has given rise to interest among scholars in neurocomputing-related applications. Neurocomputing has relatively widespread applications because it is a critical technology in numerous fields. However, most studies on neurocomputing have focused on improving related algorithms or application fields, they have failed to highlight the main technology hotspots and development trends from a comprehensive viewpoint. To fill the research gap, this study adopts a new viewpoint and employs technological fields as its main subject. Neurocomputing patents are subjected to network analysis to construct a neurocomputing technology hotspot. The results reveal that the neurocomputing technology hotspots are algorithms, methods or devices for reading or recognizing printed or written characters or patterns, and digital storage characterized by the use of particular electric or magnetic storage elements. Furthermore, the technology hotspots are discovered to not be clustered around particular fields but, rather, are multidisciplinary. The applications that combine neurocomputing with digital storage are currently undergoing the most extensive development. Finally, patentee analysis reveal that neurocomputing technology is mainly being developed by information technology corporations, thereby indicating the market development potential of neurocomputing technology. This study constructs a technology hotspot network model to elucidate the trend in development of neurocomputing technology, and the findings may serve as a reference for industries planning to promote emerging technologies.

Automated Valet Parking is foreseen as a highly automated function to be implemented on large scale in mass production in the near future. For such function, The authors have developed an innovative system concept using both real time kinematic sensing system and infrastructure IoT (Internet of things) camera to perform AVP function. As the driver activates the AVP function, the infrastructure identifies available parking spots by processing in real time camera frames and as the path is computed, the vehicle executes automated parking maneuvers. This paper introduces the system scheme and system engineering approach as well as key technology bricks to realize AVP function and proves the effectiveness and accuracy of the system through extensive vehicle testing. From system definition to final prototype validation, through Motion control algorithm description or deep learning algorithm tuning method, this paper covers a large spectrum of the engineering domains required to implement such function. Finally a specific Pursuit Model development and calibration approach is elicited for the motion control function and proven in use by AVL Vehicle Prototype platform.

A novel lens antenna based on metamaterial technology is proposed for millimeter-wave communications. High directivity is obtained owing to use true-time-delay and space filtering technology. The millimeter-wave lens antenna proposed in this paper can work in wideband. Its unit structure is larger than the traditional millimeter-wave lens which is easier to manufacture. It can be produced by PCB printing and laminating, reducing the production cost and difficulty. The antenna has wide bandwidth and stable directivity. The simulation result shows that 3dB bandwidth and maximum directivity are 28Ghz–32Ghz and 25.8dBi respectively.

In this paper, an event-triggered control scheme is developed for the systems with both time delay and quantization. Firstly, the event-triggered closed-loop system model is set up. Then, the conditions for the stability of the system with quantization are obtained by using event-triggered control method. During the event triggering interval, the controller uses a zero-order holder (ZOH) to keep the input constant. Moreover, the theoretical results are applied to time-delay systems. Finally, through simulation examples, the synthetical influence of quantization and time delay on the control system is discussed.

The coverage control problem for mobile sensor networks with bound position measurement errors on a circle is addressed in this paper. A practical measurement error model is considered in which only the upper bound on each sensor’s measurement error is assumed to be known by it a priori. Firstly, the upper bound on the measurement errors are employed to design an estimation algorithm for each sensor to estimate the difference between neighboring sensors’ positions. Then, a distributed coverage control law is developed by using the estimated difference to drive networked mobile sensors to a neighborhood of the optimal configuration. It is shown that the effect of the measurement errors on the coverage performance can be reduced or even eliminated under the proposed coverage control law.

Automated Valet Parking is foreseen as a highly automated function to be implemented on large scale in mass production in the near future. For such function, the authors have developed an innovative system concept using both real time kinematic sensing system and infrastructure IoT (Internet of things) camera to perform AVP function. As the driver activates the AVP function, the infrastructure identifies available parking spots by processing in real time camera frames and as the path is computed, the vehicle executes automated parking maneuvers. This paper introduces the system scheme and system engineering approach as well as key technology bricks to realize AVP function and proves the effectiveness and accuracy of the system through extensive vehicle testing. From system definition to final prototype validation, through Motion control algorithm description or deep learning algorithm tuning method, this paper covers a large spectrum of the engineering domains required to implement such function. The development approach is focused on a user perspective by emphasizing simplicity of usage, frugality of the solution and robustness of the system.

Imaging through opacity has captured extensive attention in recent years for its promising applications in bioimaging systems and information extraction from scrambled light. Many optimized image reconstruction algorithms and advanced imaging scenarios are introduced and intensively studied in previous researches. However, intensity-based image reconstruction under the highly coherent light illumination is still a major challenge due to the strong interference effect. Here, we demonstrate that imaging through opacity can be implemented efficiently even under the highly coherent illumination with the aid of an intensity-based/incoherent deconvolution algorithm. Based on the experimental and theoretical analysis, we find the small phase correlation length of the diffuser plays a pivotal role in enabling the incoherent deconvolution operation for highly coherent illumination. Furthermore, the deconvolution algorithm shows insensitivity to the light coherence since the spatial frequencies of the object are well preserved in the spatial frequency spectrum of the speckle pattern. Our results provide a phase-information-free scheme for imaging through opacity under the highly coherent illumination. We anticipate our work would motivate the deep understanding of imaging using highly coherent illumination, increase the structural robustness of imaging systems and facilitate wavelength-sensitive imaging applications.

This work investigates the coordination control problem for multiple distributed generation (DG) units with a hierarchical control structure. At the secondary control level, an event-triggered power sharing strategy based on the concept of multi-agent consensus has been proposed for the DG coordination control. Unlike existing consensus-based DG control approaches, the proposed control algorithm is based on sampled data. Thus the event triggering and controller updating actions can only be executed at the sampling time instants. To further reduce the amount of communication among DGs, the proposed event-triggered algorithm is extended to a self-triggered algorithm, where the inter-agent communication transmission is no longer required to be executed at each sampling time instant. The case study results show that the self-triggered algorithm can achieve nearly the same performance on DG coordination as that of the event-triggered algorithm, while significantly reduces the amount of communication.

The coverage control problem for mobile sensor networks with limited communication ranges is addressed in this paper. The goal of the problem is to minimize a coverage cost function which indicates the largest arrival time from the mobile sensor network to the points on a circle. Different input constraints are imposed on the sensors with first-order dynamics due to their different movement capabilities. To deal with this problem, low gain feedback is utilized to develop a distributed coverage control law for each sensor and an upper bound on the low gain is also provided. It is shown that networked mobile sensors can be driven to the configuration minimizing the coverage cost function as long as their communication ranges exceed a threshold and network connectivity of the sensors does not need to be preserved during the coverage task. Finally, a numerical example is given to illustrate the effectiveness of the proposed coverage control law.

The capability of identifying real-time customer needs is critical for manufacturers that provide short life cycle consumer products such as smart phones. Companies need to form research and development (R&D) strategies to improve key functional features for short lifespan products to reflect the adoption of innovative technologies and changing customer expectations. With the pervasive use of the Internet, this research crawls and analyzes the online voice of customers (VoC), overcoming the time lag of offline surveys, to identify and prioritize product functions for deployment using extended quality function deployment (eQFD) models. In this research, the novel analytics of the manufacturer’s patent portfolio is added as an additional eQFD dimension to map ranked functional improvements to a manufacturer’s R&D capabilities. Thus, a computer supported eQFD system is developed to perform the unique mappings and gap analyses between the VoC, the prioritized product functions, and the manufacturer’s patent portfolio. The newly developed eQFD methodology and its novel discoveries are demonstrated in detail using a case study of three smart phones launched during the same time frame. The products include the Samsung Galaxy S7, the Huawei Honor 5X, and the ASUS Zenfone 3. The newly developed methodology is generally applicable to support VoC-centric product function deployment and R&D strategic planning in other domains.

Image-to-image translation is an important research field in computer vision, which is widely associated with Generative Adversarial Networks (GANs) and dual learning. However, the existing methods mainly translate the global image of the source domain to the target domain, which fails to implement instance-level image-to-image translation, and the translation results in the target domain cannot be controlled. In this paper, an instance-level image-to-image translation network (IIT-GAT) is proposed, which includes attention module and feature-encoder module. The attention module is used to guide our model to focus on more interesting instance to generate instance masks, which helps to separate instance and background of an image. The feature-encoder module is used to embed the images into two different spaces: domain-invariant content space and domain-specific attribute space. The content features and attribute features of different images are used as input to generator simultaneously to improve the controllability of image-to-image translation. To this end, we introduce a local self-reconstruction loss that encourages the network to learn the style feature of target instances. Generally, our method not only improves the quality of instance-level image-to-image translation, but also increases controllability on this basis. Extensive experiments are conducted on multiple datasets to validate the effectiveness of the proposed framework, and the results show our method has better performance than previous methods.

Metasurface has demonstrated potential and novel optical properties in previous research. The prevailing method of designing a macroscale metasurface is based on the local periodic approximation. Such a method relies on the pre-calculated data library, including phase delay and transmittance of the nanostructure, which is rigorously calculated by the electromagnetic simulation. However, it is usually time-consuming to design a complex metasurface such as broadband achromatic metalens due the required huge data library. This paper combined different numbers of nanofins and used deep neural networks to train our data library, and the well-trained model predicted approximately ten times more data points, which show a higher transmission for designing a broadband achromatic metalens. The results showed that the focusing efficiency of designed metalens using the augmented library is up to 45%, which is higher than that using the original library over the visible spectrum. We demonstrated that the proposed method is time-effective and accurate enough to design complex electromagnetic problems.

The problem of coverage for a batch of mobile sensors which deployed on a ring has been addressed in this work. Firstly, a coverage cost function is minimized which guarantee the least time from a network of mobile sensors till whichever point on the prescribed trajectory. Secondly, each sensor has a velocity constraint and is limited to moving forward. Thirdly, an extra control input for collision avoidance is added into the coverage control law. Finally, a group of simulations are given to illustrate proposed control law’s performance.

This article studies the event-triggered coverage control problem for heterogeneous mobile sensor networks. We define a coverage cost function as the largest arrival time from the mobile sensor networks to the points on the circle. The purpose of our issue is to minimize this function. Decentralized event-triggered control law is proposed for each sensor by using low gain feedback approach. An event-triggered condition can also be derived. Under this strategy and decentralized event-triggered condition, the mobile sensors can move to the optimal position and minimize this cost function. Finally, we do simulation experiments to verify the feasibility of the event-triggered coverage control law.

Computer vision (CV) attempts to mimic human eyes for image processing and identifications of detailed visual information, such as object positions, features of appearances, and even human emotions and behaviors. In this research, more than one hundred literatures, relating to applying deep learning (DL) methodologies in advanced computer visions (2010∼2018), are reviewed and analyzed. The objective is to discover the state-of-the-art DL methods, topics, and trends for CV and their practical applications. DL algorithms aim at representing multi-levels of distributed neural networks. Because of the enhancement of high speed computational power, DL modeling, based on accumulated big data analytics, has found practical applications for non-supervised intelligent decision supports, such as detection of product defects and prognosis of machine malfunctions based on real-time signal or feature data analyses. There are a vast number of literature, describing DL related researches, developments, and implementations for problem solving. For the comprehensive mining of the related literature, we integrate Latent Dirichlet Allocation (LDA), K-means (Clustering), and normalized term frequency-inverse document frequency (NTF-IDF) approaches to discover, or called technology mining, of the major trends in DL for computer visions, specifically for key applications in object detection, semantic segmentation, image retrieval, and human pose estimation.

5G supports more new services, including enhanced Mobile Broadband (eMBB), Ultra-reliable and Low Latency Communications (URLLC) and massive Machine Type Communications (mMTC). The Quality of Service (QoS) requirements of these 5G service types are different. In this paper, we capture the packets from the Narrow Band-Internet of Things (NB-IoT) transmission, Unmanned Aerial Vehicle (UAV) control, 4K video and Facebook access for emulating mMTC, URLLC, eMBB and Internet traffic in 5G. With the captured packets, we investigate using the machine learning technology to classify the packets based on the payload information. Specifically, the Convolutional Neural Network (CNN) model is performed to classify the application packets into suitable groups. In addition, this paper studies the effects of various parameters such as the kernel number, kernel size, pooling window size, the dropout rate and the payload length to find the optimal values for high accuracy and low latency.

This paper investigates the finite-time circle formation control problem for a network of mobile agents with continuous-time dynamics. Distributed control protocols for each single-integrator agent dynamics are constructed to form desired configuration on a given circle in finite time. To prove our approaches, finite-time stability theory and Lyapunov methods are considered. And the expressions for the bounded finite settling time is given. Finally, numerical simulations are presented to illustrate the effectiveness and performance of our protocols.

The circular formation problem for multi-agent systems with limited interaction range and input saturation is addressed in this paper. Low gain feedback is used to develop distributed formation control laws for each agent. It is shown that the desired circular formation can be formed if the agents’ interaction range is sufficiently large and each low control gain is upper bounded by a given constant. Order preservation of the agents is also guaranteed under the formation control laws such that collision among the agents can be prevented throughout the circular formation task. Finally, a simulation example is provided to illustrate the effectiveness of the proposed control laws.

Event-triggered control and self-triggered control have recently been proposed as an alternative to more traditional periodic execution control tasks. The possibility of reducing the number of executions while guaranteeing the desired level of performance makes event triggering and self-triggering control very attractive in sensor/actuator network environments. In this case, reducing the number of feedback control executions means reducing the amount of transmission, thus reducing energy expenditures. This paper describes two new distributed implementations of event triggering and self-triggering strategies on sensor/actuator networks. The usefulness of the results is illustrated through a numerical example.

In this paper, the Zeno-free analysis for non-holonomic mobile robots tracking using event-triggered mechanism is investigated. We provide event-triggered algorithms for the actuation signals of the robot. A suitable Lyapunov function is selected to prove the stability. The event conditions are designed for controlling inputs updates. Moreover, It is proved that the Zeno behavior of the system is excluded. Finally, the algorithm is validated by simulation examples.

In this paper, we propose an event-triggered optimal control method for linear systems based on state observer. Firstly, combining with the optimal control theory, the modeling and analysis of the control systems with state feedback observer are studied. Secondly, by setting the minimum performance index in which we considering the system state and the observer state to make the system reach the equilibrium point from the initial state, the optimal feedback control law of the system is obtained on basis of the minimum principle. Thirdly, a state-feedback control system based on an event-triggered observer is studied, where the state of the plant is only transmitted to the observer when the event condition is violated. Then, the asymptotic stability of the control plant and the observer error system is guaranteed by conditions in terms of LMIs(Linear Matrix Inequalities). Finally, a numerical example is worked out to illustrate the validity and effectiveness of the method.

We put forward an optimal event-triggered optimal control method with the uncertainty of parameters. Event-triggered control is more significant in network control system because it can reduce communication costs. Firstly, the control law is designed by solving the optimal control problem, and implemented by event triggering mechanism. Then, the existence of minimum lower bound inter-event triggering intervals proves that Zeno behaivor does not occur. In addition, by using the input-state stability criterion (ISS), we can arrive at the stability of state. Finally, the algorithm is verified by numerical simulation.

Optimized event-triggered and self-triggered control are investigated in this paper. Firstly, in order to reduce computational resources, event-triggering mechanism is introduced, and an optimized event-triggered controller is designed according to the optimized time-triggered control mechanism. Secondly, the event-triggering condition which guarantees the system stable are obtained, the system will not exhibit Zeno behavior under the proposed event-triggering mechanism. Thirdly, the self-triggering condition which makes the system stable are proposed. Through the deduction of this condition, the calculation method of the next triggering time is obtained, which will naturally exclude Zeno behavior. Finally, a numerical simulation shows the correctness and effectiveness of the proposed methods.

The control method of exoskeleton system for lower limb rehabilitation is complex, and there are many internal and external factors to be considered, which makes the system model exist uncertainties. Through the analysis of the lower extremity exoskeleton, the corresponding equation is obtained, and then linearized. Considering the internal and external factors of the connecting rod, the uncertain parts are introduced and the optimal control method is adopted to control the system. In this paper, LQR control and robust optimal control are used to not only optimize the state of the lower extremity exoskeleton system, but also make the system robustness and stability. Finally, the physical parameters of the lower extremity exoskeleton are given, and simulation analysis is carried out to verify the effectiveness of the control method.

With 5G deployment, the enhanced broadband services and massive Internet of Things (IoT) devices requires larger bandwidth. To response this requirement, the US government proposes a Citizens Broadband Radio Service (CBRS). The CBRS spectrum contains a 150 MHz band ranging from 3550 MHz to 3700 MHz. Through spectrum sharing, mobile operators can utilize the CBRS spectrum to provide 5G mobile broadband services (AR/VR) or IoT services. The Spectrum Access Server (SAS) is one of the most important components in CBRS. The SAS manages the requests from the CBRS devices (CBSD) and assigns the CBRS spectrum to the authorized CBSDs. To verify whether the SAS development can conform to the CBRS standards, this paper develop a conformance test tool to examine the CBRS interface and operation of SAS.

In this paper, we propose an adaptive event-triggered control approach based on quantizer for linear systems. Firstly, an adaptive event-triggered mechanism is designed according to the state measurement error in order to reduce the number of information transmission and the occupation of communication bandwidth while ensuring the stability of the system. Secondly, a quantizer is used for reducing processing data and cutting down network congestion, which can improve the quality of control in a certain extent. Next, the co-design scheme of event-triggered mechanism and the controller is given to guarantee the asymptotically stability of the closed-loop system based on Lyapunov function and linear matrix inequality technique. Finally, a simulation example illustrates the efficiency of the presented method.