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The ground space optical observation system considered as one of the main measurement methods to observe space object in Medium Earth Orbit (MEO), Highly Elliptical Orbit (HEO) and Geostationary Orbit (GEO) faces difficulties in forming high-solution images for objects located in GEO that only appears several light spots with limited pixels due to the impacts of observing distance, resolution ratio, and other atmospheric conditions. The light curve is the time history of an object’s observed brightness obtained from the ground-based optical observation system through light curves, right ascension, and declination of space objects. The characteristic of space objects such as position, velocity, size, shape, and material can be inverted. This paper has analyzed the principles, applications, merits, and demerits of several non-linear filtering methods in detail. Besides, the scientific description of inversion for characteristics of a non-resolved space object from ground photometric measurements and the essence of the non-linear filtering inversion method has also been clarified. A selection principle of the non-linear filtering inversion method for different space object characteristics is then proposed, and the developing direction of such inversion methods is also described in the end.

In the autonomous driving era, high-definition (HD) maps is an essential building block to enable fine-grained environmental perception, precise localization, and path planning. However, with rich multi-dimensional information, the size of HD map data is huge and cannot be stored on-board, where the dynamic map data needs to be distributed in real-time via vehicular networks and how to design the distribution mechanism (i.e., determining the data source for requests) becomes crucial. For the end-to-end communication protocols (i.e., TCP/IP), the main limitation is the vehicle mobility and high dynamic of the network topology, which can degrade the transmission performance dramatically. Therefore, in this paper, we propose a Reinforcement Learning-based data Source Selection scheme, named RLSS, for efficient HD map distribution in vehicular named data networking (NDN) scenarios, which aims at seeking the best data source (roadside infrastructures or nearby vehicles) in accordance with the map data requests. Specifically, in RLSS, we adopt a deep reinforcement learning-based architecture to learn a neural network as an agent to make the decision of data source selection, which can work online after offline training based on historical selection action performance. In addition, to solve the “cold start” problem for a new vehicle, we propose a model aggregation algorithm and weight update approach to learn the model parameters from its nearby vehicles, which can guarantee the performance while saving the communication cost. Finally, we implement RLSS in NS-3 by adopting the tools of the ndnSIM, SUMO, and Gym. Extensive simulations demonstrate that RLSS can significantly improve the transmission performance in terms of delay, throughput, and packet loss when compared with state-of-the-art data source selection schemes.

Masses of user-side smart meter data provide a foundation for utilities to acquire users’ electricity consumption patterns and promote refined energy management. Therefore, the charging load of electric vehicles can be identified non-intrusively through the smart meter data of residential users, which provides flexible resources for the demand side response of power grid enterprises and is beneficial to the power dispatching decision making. Meanwhile, revealing residential energy consumption behavior from smart meter data is treated as energy privacy leakage. Extensive data collection from smart meters has raised privacy risk, which could lead to the reduction of trust in utilities from households. Aiming at this problem, the paper proposes a non-intrusive method to extract residential electric vehicles, and on this basis, discusses the privacy protection. In this paper, DWT method is used to decompose the total power of smart meter to extract the charging load of electric vehicle, and similar charging load patterns are added to smart meter data to preserve the residential EV charging behavior privacy. Practical households with EVs are used to test the proposed method and the result shows its feasibility for the performance of privacy-preserving.

The paper presents a wall-climbing robot featuring self-compliance for variable curvature facades. The high payload and maneuverability make it highly potential in heavy-duty operation of industrial applications. The robot consists of two traction modules and one link module. Each traction module is equipped with magnetic adhesion and crawler traction submodules. The two traction modules are connected by the link module with 4 degrees of freedom (DoF) of passive compliance. Variable curvature facade self-compliance is achieved by the passive compliant link module, which results in the attitude change decoupling of the two traction modules. The attitude can be adjusted under the effect of magnetic adhesion force to comply with the curvature variations. High payloads are achieved by the large contact area between the crawler and the surface. Omni-directional high maneuverability is enabled by the speed difference between the motors of two traction modules. The robot can maneuver in any direction on the surface with a minimum radius of 1 m and carry 36 kg payload on vertical surfaces.

Different from cloud computing, edge computing moves computing away from the centralized data center and closer to the end-user. Therefore, with the large-scale deployment of edge services, it becomes a new challenge of how to dynamically select the appropriate edge server for computing requesters based on the edge server and network status. In the TCP/IP architecture, edge computing applications rely on centralized proxy servers to select an appropriate edge server, which leads to additional network overhead and increases service response latency. Due to its powerful forwarding plane, Information-Centric Networking (ICN) has the potential to provide more efficient networking support for edge computing than TCP/IP. However, traditional ICN only addresses named data and cannot well support the handle of dynamic content. In this article, we propose an edge computing service architecture based on ICN, which contains the edge computing service session model, service request forwarding strategies, and service dynamic deployment mechanism. The proposed service session model can not only keep the overhead low but also push the results to the computing requester immediately once the computing is completed. However, the service request forwarding strategies can forward computing requests to an appropriate edge server in a distributed manner. Compared with the TCP/IP-based proxy solution, our forwarding strategy can avoid unnecessary network transmissions, thereby reducing the service completion time. Moreover, the service dynamic deployment mechanism decides whether to deploy an edge service on an edge server based on service popularity, so that edge services can be dynamically deployed to hotspot, further reducing the service completion time.

A multi-mode flapping wing micro air vehicle (FWMAV) that uses a figure eight wingtip motion trajectory with wing flapping, rotation, and swing motion is presented in this paper. The flapping wing vehicle achieves three active degrees of freedom (DOF) wing movements only with one driving micromotor which has a good balance in the mechanism design (that is inspired by natural fliers) and total weight. Owing to these characteristics being integrated into the simple mechanism design, the aerodynamic force is improved. The aerodynamic performance of the thrust force is improved by 64.3 % compared to one that could only flap up and down with one active DOF under the condition of routine flapping frequency.

In Named Data Networking (NDN) architecture, data can be obtained from multiple content sources (i.e., producers or caching nodes) with multiple paths, making the traditional end-to-end (i.e., TCP/IP) congestion control scheme invalid. In addition, the NDN multi-path discovery and management are still an open issue as the dynamic network topology changes. In this article, we propose a multi-path congestion control mechanism, named MPCC, which includes two major components, i.e., multi-path discovery and multi-path congestion control. Particularly, for multi-path discovery, we first devise a path tag to uniquely mark each sub-path in the forwarding process, and then propose a tag-aware forwarding strategy to discover and manage sub-paths. For multi-path selection and congestion control, we first integrate the metrics of packet loss, bandwidth, round trip time, and path centrality, for path assessment, based on which, we then leverage the Upper Confidence Bound (UCB) algorithm to select sub-paths in order to maximize the network throughput. In addition, for selected sub-paths, we have devised a sub-path window adaptation algorithm to avoid multi-path congestions. At last, we implement MPCC in ndnSIM and conduct extensive experiments for performance evaluation. Our results demonstrate that MPCC can discover all sub-paths in real-time for the multi-path scenario, and can effectively avoid multi-path congestions with improving throughput and reducing transmission time.

The integration of the massive multiple-input multiple-output (MIMO) and millimeter-wave (mmWave) communication can increase the throughput of 5G networks. As an attractive technique in the MIMO systems, hybrid beamforming (HBF) can improve the 5G capacity by employing spatial domain resources. However, with the increase of the number of antennas, the traditional beamforming algorithms fail to efficiently keep a balance between the hardware complexity and beamforming gains. In this paper, with the aid of bidirectional location information, a bidirectional positioning assisted HBF (BPA-HBF) scheme is proposed. Specifically, we first propose a new scheme to decouple the optimal problem of traditional HBF as two phases. In the analog beamforming (ABF) phase, the dominated path among the multi-path components is determined by the transmitter and receiver. In addition, the codebook-based beamforming weight vectors are bidirectionally and synchronously determined according to the angle parameters of the dominated path. In the second phase, based on the ABF matrices, the digital beamformers are designed to maximize the energy efficiency. Simulation results indicate that the proposed BPA-HBF scheme can lead to a lower convergence time and complexity than the conventional schemes. In addition, the results show that the algorithm convergence time can be significantly reduced by increasing the positioning precision.

Background: In the production process of apple orchard, the labor needed for apple harvest accounts about 40% of the whole production process. Objective: The auxiliary device of apple harvest was designed to improve production efficiency and reduce labor cost. Methods: This device was composed of four parts: fruit collecting mechanism, horizontal conveying mechanism, vertical conveying mechanism and rotary packing mechanism, which assists farmers in finishing the process from picking to packaging apples. Simulation and fruit damage analyses were carried out by Solid works, fruit damage included three types: Single collision, Overlapping collision and Multi-zone collision. Results: In the experiment, PID algorithm was employed to control device operate accurately. And when the motor speed were 52 r/min, 56 r/min, 60 r/min, the collection efficiency were 4900 apples/ h, 5300 apples/h, 5700 apples/h and the fruit damage rates were 4%, 5%, 8%. Conclusion: The experimental results indicated that compared with the manual operation and visual recognition-based picking robots this device’s production efficiency was improving without increasing the fruit damage rate.

Named Data Networking (NDN) is considered as a prominent architecture towards future Wireless Local Area Networks (WLAN), and multicast plays an important role in data delivery such as media streaming, multipoint videoconferencing, etc. However, to achieve high-efficiency multicast in NDN WLAN is challenging for two significant reasons. First, without feedback mechanism in IEEE 802.11 standards, to guarantee reliability, the current multicast scheme transmits the multicast data with the basic rate (e.g., 1 Mbps for IEEE 802.11b), which inevitably increases the transmission delay for high-speed consumers. Second, as a NDN multicast group is constituted by consumers who are requesting the same content, multicast groups are easy to form and evolve rapidly, where a data rate adaptation scheme is requisite to accommodate differential multicast groups. In this paper, we propose a multi-stage multicast rate adaptation scheme for NDN WLAN, named NDN-MMRA , to minimize the total transmission time with reliability guarantee for multicast group members. In NDN-MMRA , by checking the Pending Interest Table (PIT) status information, the number of consumers in each multicast group as well as their receiving capabilities are known ahead; with the available data rates in a specific 802.11 standard, NDN-MMRA determines: 1) how many transmission stages are required; and 2) in each stage, which data rate should be adopted. The merit is that with multi-stage transmissions, the data rate can be adapted in descending order to accommodate high-speed consumers with delay minimized, and low-speed consumers with reliability guaranteed. We implement NDN-MMRA in NS-3 by adopting the ndnSIM module, and conduct extensive experiments to demonstrate its efficacy under different IEEE 802.11 standards and various underlying WLAN topologies.

By considering the application requirements of automatic welding of labels on the heads of bundled rebars in the iron and steel industry, an automatic label welding robot system is developed based on machine vision and image processing. The system consists of six modules: pickup unit, image unit, control unit, welding stud unit, label unit, and rebar unit. It can automatically pick up welding studs, pick up labels, perform welding, detect dropped labels, and detect dropped welding studs. The development process of label welding mainly includes the following steps. First, the Cascade RCNN object detection algorithm is used to detect the rebar head, so that the number of rebars and the pixel coordinates of the rebar head centers can be obtained. The detection accuracy of the number of rebars can reach 100%, and the Cascade RCNN average score is 0.9801. Then, an algorithm to identify the center of bundled rebar heads based on the variable-scale method (Davidon–Fletcher–Powell formula (DFP)) is proposed. In addition, limiting conditions, and a process for selection of weldable points for double-label welding are provided. And the coordinate conversion equations of weldable points in different coordinate systems are derived. Finally, a serious of functional verification tests of the robot system were carried out on an actual rebar production line, which verified the accuracy of the weldable point image recognition process, and the usability of the robot system. The results indicate that the accuracy of weldable point recognition can reach 98.99%.

This paper investigated the problem of multi-target tracking (MTT) over a vehicle sensor network. A novel adaptive square root cubature joint probabilistic data association (ASRCJPDA) was proposed. Motivated by enhancing the stability of joint probabilistic data association (JPDA) in practical application, the proposed methodology implemented a numerically stabled cubature Kalman filter for JPDA state estimate process. It improved numerical stability and acquired more accurate estimated results. Additionally, enlightened by enhancing the real time efficiency of JPDA, an adaptive tracking gate designed for the JPDA measurement associate process was proposed. It combined with the kinematics of vehicle to reduce the computational complexity of data association, which improved the robustness of MTT in complex scenarios. The virtual vehicle target tracking scenarios were built in PreScan software in order to better simulate the real traffic condition. Simulations of target tracking examples are presented to show great effectiveness and superiority of the proposed method.

Graph convolutional network (GCN) has been proved to be an effective framework for graph-based semi-supervised learning applications. The core operation block of GCN is the convolutional layer, which enables the network to construct node embeddings by fusing both attributes of nodes and relationships between nodes. Different features or feature interactions inherently have various influences on the convolutional layers. However, there are very limited studies about the impact of feature importance in GCN-related communities. In this work, we attempt to augment convolutional layers in GCNs with statistical attention-based feature importance by modeling the latent interactions of features, which is complementary to the standard GCNs and only needs simple calculations with statistics rather than heavy trainings. To this end, we treat the feature input of each convolutional layer as a separate multi-layer heterogeneous graph, and propose Graph Statistical Self-Attention (GSSA) method to automatically learn the hierarchical structure of feature importance. More specifically, we propose two modules in GSSA, Channel-wise Self-Attention (CSA) to capture the dependencies between feature channels, and Mean-based Self-Attention (MSA) to reweight similarities among features. Aiming at each graph convolutional layer, GSSA can be applied in a ”plug and play” way for a wide range of GCN variants. To the best of our knowledge, this is the first implementation that optimizes GCNs from the feature importance perspective. Extensive experiments demonstrate that GSSA can promote existing popular baselines remarkably in semi-supervised node classification tasks. We further employ multiple qualitative evaluations to get deep insights into our method.

Machine learning models have achieved state-of-the-art performance in various fields, from image classification to speech recognition. However, such models are trained with a large amount of sensitive training data, and are typically computationally expensive to build. As a result, many cloud providers (e.g., Google) have launched machine-learning-as-a-service, which helps clients benefit from the sophisticated cloud-based machine learning models via accessing public APIs. Such a business paradigm significantly expedites and simplifies the development circles. Unfortunately, the commercial value of such cloud-based machine learning models motivates attackers to conduct model extraction attacks for free use or as a springboard to conduct other attacks (e.g., craft adversarial examples in black-box settings). In this article, we conduct a thorough investigation of existing approaches to model extraction attacks and defenses on cloud-based models. We classify the state-of-the-art attack schemes into two categories based on whether the attacker aims to steal the property (i.e., parameters, hyperparameters, and architecture) or the functionality of the model. We also categorize defending schemes into two groups based on whether the scheme relies on output disturbance or query observation. We not only present a detailed survey of each method, but also demonstrate the comparison of both attack and defense approaches via experiments. We highlight several future directions in both model extraction attacks and its defenses, which shed light on possible avenues for further studies.

Background: The high permeability of Distributed Generation (DG) and the development of DC load represented by electric vehicle Battery Swapping Station (BSS) pose new challenges to the reliable and economic operation of DC distribution system. Methods: In order to improve the wind and solar absorption rate and the reliable operation of DC distribution network and coordinate the interests and demands of BSS and DC distribution company, the upper level takes the abandonment rate and the minimum variance of BSS charging and discharging net load as two objective functions, and the lower level takes the minimum operation cost of DC distribution network and BSS as the objective function. Secondly, this paper proposes a method that combines Genetic Algorithm (GA) with Wind-Driven Optimization algorithm (WDO). CPLEX and hybrid GA-WDO are used to solve the upper and lower models, respectively. Results: Finally, an example shows that the proposed optimization model can reduce the operation cost of DC distribution network with BSS and improve the utilization rate of wind and light, which shows the rationality and effectiveness of the optimization model. Conclusion: In this paper, considering the randomness and uncertainty of wind power generation and photovoltaic power generation, this paper establishes the upper objective function with the minimum abandonment rate and load variance and the lower objective function with the minimum operation cost of DC distribution network and BSS operators.

Service migration is an often-used approach in cloud computing to minimize the access cost by moving the service close to most users. Although it is effective in a certain sense, the service migration in existing research still suffers from some deficiencies in its evolutionary abilities in scalability , sensitivity , and adaptability to effectively react to the dynamically changing environments. This article proposes an evolutionary framework based on deep reinforcement learning for virtual service migration in large-scale mobile cloud centers. To enhance the spatio-temporal sensitivity of the algorithm, we design a scalable reward function for virtual service migration, redefine the input state, and add a Recurrent Neural Network ( RNN ) to the learning framework. Additionally, in order to enhance the adaptability of the algorithm, we also decompose the action space and exploit the network cost to adjust the number of virtual machine (VMs). The experimental results show that, compared with the existing results, the migration strategy generated by the algorithm can not only significantly reduce the total service cost and achieve the load balancing at the same time, but also address the burst situations with low cost in dynamic environments.

When a dump truck brakes, it is difficult to effectively absorb the braking energy due to the transient mutation of braking energy. At the same time, braking energy production is too high to store easily. Focusing on these problems, this paper proposes a new type of two-stage series supercapacitor and battery (SP&B) hybrid energy storage system (ESS). Using the characteristics of supercapacitors, which can charge and discharge quickly with large currents, the system combines the supercapacitors with a battery with a high energy storage capacity. This allows sudden large currents to be absorbed and released rapidly, while large amounts of energy can be stored. This system can also reduce the charge and discharge times of the SP&B to meet the working requirements of dump truck braking while improving the service life of the system. After completion of the research and implementation of the proposed SP&B hybrid ESS, a laboratory dynamic model was set up as an experimental platform and vehicle operation experiments were performed. The obtained results show that the energy recovery efficiency of the hybrid ESS can reach 53.5%, which can realize both security and stability for dump truck braking energy recovery and utilization (BER&U) systems.

Multicast is a promising technology for rapidly-increasing video streaming in Wireless Local Area Networks (WLAN). NDN's implicit multicast and caching capabilities provide strong benefits in multicast transmission and can significantly improve network transmission efficiency. However, existing WLAN multicast schemes use the basic data rate to transmit the multicast data. The basic data rate cannot support high-quality video transmission, which significantly reduces the user's Quality of Experience (QoE). In addition, the NDN architecture follows the One-Interest-One-Data transmission principle, so that the uplink will have to face fierce competition, especially when the number of stations has increased significantly. In this paper, we propose HPNM, a High Performance adaptive video streaming scheme by using NDN WLAN Multicast. To improve the Interest sending efficiency in the NDN Live video scenario, we propose an AP(Access Point)-based Interest proxy mechanism that mitigates WLAN channel competition as the number of Interest sending packets increases. In addition, we propose a layer-based NDN WLAN multicast data rate selection mechanism for the adaptive Scalable Video Coding (SVC) video streaming, which can further improve the video bitrate. We use ndnSIM to evaluate the performance of HPNM in terms of video bitrate, startup time, and stalling time. The evaluation results show that the average video bitrate of HPNM has increased significantly compared with the existing scheme in the background traffic scenario.

Resource management is challenging in clouds due to the dynamics and sharing characteristics. The crucial problem is how to allocate resources accurately and satisfy demands of workloads timely. The traditional solution is to use historical data to predict future resource usage. Although these resource prediction methods can predict the periodicity, they can not accurately predict mutation points due to the high dynamics and uncertainty of resource usage. To tackle this issue, in this paper we propose a resource usage prediction method - RPTCN, which is based on a deep learning method - temporal convolutional networks (TCNs) in cloud systems. We add a fully connected layer and attention mechanism to TCNs to improve the prediction accuracy. In order to explore the relationship between the usage of different resources in the temporal dimension, we use correlation analysis to screen performance indicators as multidimensional feature input for prediction. Finally, we evaluate the performance of this method on Alibaba trace v2018. Evaluations show that RPTCN improves the overall MAE and MSE by 6.50%~89.03% and 0.41%~68.82% respectively compared to baselines in dynamic and long-term prediction of resource usage. Moreover, the convergence and generalization of RPTCN are also better than the baselines.

Braking safety of heavy-duty engines has always been the focus of the research, and the fuel economy and after-treatment thermal management during low-load operation of heavy-duty engines have also received much attention in recent years. A variable mode valve actuation system which can realize switching between four-stroke driving, two-stroke compression release braking and cylinder deactivation modes on a traditional four-stroke engine was proposed in this article. Two-stroke compression release braking mode of the variable mode valve actuation system can greatly enhance the braking safety, while the overload of valve train was a great challenge, especially during the release event. The effects of different release opening timing on cylinder pressure and the braking performance were studied. The results indicated that a higher cylinder pressure does not always lead to higher braking power. When the release opening timing was advanced by 6 °CA, the braking power reduced by only 9 kW (2.65%) at 1900 r/min compared with the initial value, while the maximum cylinder pressure reduced by 11.4 bar (20.8%). Besides, the variable mode valve actuation system can realize alternate three-cylinder cylinder deactivation mode on a six-cylinder turbocharged engine, which can improve the brake-specific fuel consumption by 14.67% and increase the turbine outlet temperature by 63.6 °C and reduce the exhaust flow rate by 50.66% at lightly load idle. Meanwhile, when the engine load is less than 50% at the rated speed, the three-cylinder cylinder deactivation mode can improve the brake-specific fuel consumption, increase the turbine outlet temperature and reduce the exhaust flow rate. The increase of the turbine outlet temperature and the decrease of the exhaust flow rate are very beneficial to improve the efficiency of the after-treatment thermal management of heavy-duty engines.

A new variable mode valve actuation system for a heavy-duty engine was proposed and designed in this paper. The variable mode valve actuation system can significantly enhance braking safety and improve fuel economy and emission of heavy-duty engines through flexible switching among four-stroke driving mode, two-stroke compression-release braking mode, and cylinder deactivation mode on a conventional four-stroke engine. The switching was controlled by four-stroke driving modules and two-stroke braking modules, both of which have two operation states: effective state and failure state. For the control of the multi-cylinder engine, all cylinders can be divided into several groups, and all the four-stroke driving modules in the same group were controlled by one solenoid valve, as well as all the two-stroke braking modules were controlled by another solenoid valve. A hydraulic-mechanical multi-body dynamics model was established to investigate the switching response of the variable mode valve actuation system. The results indicated that when the engine operated at 2000 r/min, the switching of the four-stroke driving module and the two-stroke braking module required 30 °CA and 101 °CA at most, respectively. In addition, to avoid the conflict between the four-stroke driving valve lift and the two-stroke braking valve lift, the switching between the four-stroke driving mode and the two-stroke compression-release braking mode must have a reasonable sequence. The variable mode valve actuation system has an excellent switching response and it is convenient for the control of the multi-cylinder engine. Therefore, the variable mode valve actuation system has a good application prospect for heavy-duty engines.

In this work, a class-AB operational amplifier is to be presented, which is developed in a 0.5 μm CDMOS technology. The proposed amplifier is composed of a folded cascade input stage and a class-AB output stage. Owing to the special design of the operational amplifier, the robustness of the whole circuit has been enhanced. The proposed system can drive a wide resistance load range from 10 Ω to 100 kΩ with a 10 pF capacitance load without distortion and stability problems. In addition, a current-limit technique that stems from the design of a low dropout linear regulator is adopted to protect the output stage of the operational amplifier from being damaged by a huge transient current under unexpected conditions (when the output terminal is shorted to ground or V DD). Measurement results show that the maximum output current is nearly 390 mA with an 800 mA DC current limiting protection circuit on the single 5 V power supply at room temperature. This design can stably work at the industrial temperature range from −40 to 125°C. What is more, the total static current consumption of the whole circuit is 624 μA and the chip area is 0.419 mm2.

Geometry and texture noise make it difficult to accurately describe road image rules, which leads to the low degree of automation of traditional template matching algorithms based on internal texture homogenization. We propose a semi-automatic road extraction method based on multiple descriptors to improve the degree of automation while ensuring the accuracy of road extraction. This method aims to address the problems of incomplete road image geometric information and poor homogeneity of internal road texture. The multiscale line segment orientation histogram model and sector descriptor are established. Road points are tracked by interpolation and extension, and postprocessing is used to fit the tracking points and extract the road routes. In this article, high-resolution remote sensing images of different types, different resolutions, and different scenes are selected, and the roads exhibit curvatures, vehicle and shadow occlusions, roundabouts, and variational features. Experiments show that for roads with a certain width, completeness, and correctness of the method are more than 98%. Additionally, as compared with other algorithms, the interactive human intervention of this method is reduced by more than 2/3.

This paper focuses on the tracking control problem of Magnetic Levitation System (MLS). MLS is highly unstable nonlinear system, in the process of operation, MLS needs to have strong robustness and anti-interference ability. Due to disturbance and parameter uncertainties in MLS, it is complex to obtain the exact dynamics of MLS, and it is difficult to design a suitable controller. The dynamic equation of the system is established by Lagrange equation, and then we propose an adaptive Sliding Mode Control (SMC) based on Radial Basis Function Neural Network (RBFNN). Because of the parameter uncertainties and disturbance in MLS, RBFNN is used to approximate the unknown dynamics in MLS. The stability of the closed-loop system is strictly proved by using the Lyapunov stability theory, which can achieve the uniform ultimate boundedness (UUB) of the signals of the closed-loop MLS. MATLAB environment is used to verify the performance of the proposed controller. Considering disturbance, parameter change, or unmodeled dynamics in MLS, proposed controller is compared with other nonlinear controllers, simulation results verify the effectiveness of the proposed approach.

Interior permanent magnet synchronous motors (IPMSMs) have high power densities and speed control performance, and they are widely used in the industry. The problem of reducing the torque ripple of an IPMSM is one of the hot issues in the field of electrical machine design. In order to determine the optimal combination of the geometric parameters to reduce the torque ripple of an IPMSM, a range analysis was conducted on the data from the orthogonal experiments in this study, dividing the rotor geometric parameters into two categories (important and ordinary) based on their degree of impact on the torque ripples of the IPMSM. Thereafter, an optimization of the ordinary parameters was carried out based on the results of the range analysis, whereas the optimization of the important parameters was carried out through a method that combined a multi-island genetic algorithm (MIGA) and Radial Basis Function (RBF) neural networks. The torque ripple of the IPMSM was effectively reduced without materially affecting the output power. Finally, the results of this optimization process were verified using a finite element simulation. The optimization method used in this study divided the motor geometric parameters into two categories and applied a different method of optimization to each parameter type, so it was able to efficiently optimize multiple geometric parameters for the IPMSM.

The energy consumption of wireless interface is critical to power-constrained mobile devices. To improve energy efficiency for WLAN stations, power saving mode (PSM) is proposed, to manage the time spent in idle listening (IL) state. The hurdle is that the receiver has no knowledge about when the pending data will arrive under end-to-end communication protocols (TCP/IP), making each station spend enormous time in IL to wait for the pending data. To overcome this limitation, in this paper, we propose a named data networking (NDN) enabled PSM, namely, NDN-PSM, which leverages NDN communication architecture to cut down unnecessary IL time. In particular, we devise two new power states in NDN-PSM , i.e., light doze and deep doze, to precisely map to the underlying data arriving states. The inherent receiver-driven pattern of NDN can effectively drive the stations to the deep doze state for power saving, and to light doze state for timely data reception. Considering the IL time waste during channel contention, we further design a channel contention control mechanism in NDN-PSM , in which stations will switch to the light doze state if the channel is perceived to be busy. The power consumption model of the proposed NDN-PSM is theoretically analyzed and verified via numerical results. At last, we implement NDN-PSM in NS-3 by adopting the ndnSIM module and conduct extensive simulations to demonstrate the efficacy of NDN-PSM . Specifically, compared to the existing PSM mechanism, NDN-PSM reduces 56% power consumption by cutting down unnecessary IL time, and meanwhile enables low-delay transmission.

Background: As the multimedia service develops and the transmission rate in terrestrial communication systems increases rapidly, satellite communication needs to improve the transmission rate and throughput. Multiple Input Multiple Output (MIMO) techniques can increase the system capacity significantly by introducing the space dimension, as the system bandwidth remains the same. Therefore, utilization of MIMO for satellite communications to increase the capacity is an important research topic. So MIMO techniques for multibeam satellite communications are researched in the dissertation. Objective: The goal of this work is improving the capacity of the satellite system. Multi-beam and dual-polarized technologies are applied to a satellite system to improve the capacity further. Methods: In this paper, we first introduce a multi-beam dual-polarized satellite multi-put and multiout (MBDP-S-MIMO) system which combines the full frequency multiplexing and dual-polarization technologies. Then the system model and channel model are first constructed. At last, to improve the capacity further, BD and BD-ZF precoding algorithms are applied to MBDP-S-MIMO and their performance is verified by simulation. Results: Simulation results show the performance of the BD precoding algorithm gets better with the growth of the XPD at the receiver and is almost not affected by the growth of the channel polarization correlation coefficient. In addition, with the growth of the users’ speed, the performance becomes worse. Conclusion: The multi-beam dual-polarized satellite MIMO system has high capacity, and it has certain application prospects for satellite communication.

In order to enhance the braking safety and improve the fuel economy and emission of heavy-duty engines, a variable mode valve actuation system which can switch operation modes flexibly among four-stroke driving, two-stroke compression release braking, and cylinder deactivation modes on a four-stroke engine was developed in this article. The switching was controlled by the four-stroke driving module and the two-stroke braking module, both of which have two states: effective state and failure state. Besides, a full-cycle numerical model of a six-cylinder turbocharged engine was established for the performance analysis of two-stroke compression release braking mode. The orthogonal design method was introduced in the present study to obtain the optimum valve parameters which can result in high braking power and maintain the maximum cylinder pressure at a lower level at the same time. Then, the two-stroke compression release brake braking power with the optimum valve parameters was compared with four-stroke compression release braking power. Meanwhile, the two-stroke braking cam profile of the variable mode valve actuation system was designed according to the optimum valve parameters, and the two-stroke braking performance with the dynamic valve lift of the variable mode valve actuation system was validated by the numerical model including the hydraulic system. The results indicated that a higher engine speed leads to higher braking power at the same valve lift. Besides, two-stroke compression release brake braking power of the variable mode valve actuation system achieved 525.3 kW at 2600 r/min and 358.1 kW at 1900 r/min, 52.9% and 71.3% higher than four-stroke compression release braking power, respectively. Although the two-stroke compression release brake braking power with dynamic valve lift is slightly less than that with the optimum valve parameters, it is still much higher than that of the four-stroke compression release braking power. Therefore, it has a good application prospect for heavy-duty engines.

Aiming at the problem that the current generation method of power networksecurity defense strategy ignores the dependency relationship between nodes,resulting in closed-loop attack graph, which makes the defense strategy notgenerate attack path, resulting in poor defense effect and long generationresponse time of power network security defense strategy, a generationmethod of power network security defense strategy based on Markov decisionprocess is proposed. Based on the generation of network attack and defensediagram, the paper describes the state change of attack network by usingMarkov decision-making process correlation principle, introduces discountfactor, calculates the income value of attack and defense game process,constructs the evolutionary game model of attack and defense, solves theobjective function according to the dynamic programming theory, obtains theoptimal strategy set and outputs the final results, and generates the powernetwork security defense strategy. The experimental results show that the proposed method has good defense effect and can effectively shorten thegeneration response time of power network security defense strategy.

BackgroundSevere deep neck space infection is rare and critical.The objective is to deepen the understanding of severe deep neck space infection and improve the level of clinical diagnosis and treatment. Case presentationA retrospective analysis of the diagnosis and treatment of 5 cases of serious deep neck infections admitted to our department. The 5 patients were all diagnosed by cervical CT and surgical exploration. 3 patients with diabetes, 2 patients with diabetic ketoacidosis, 3 patients with mediastinal infection; 3 patients underwent tracheotomy and 2 patients with tracheal intubation; All patients were treated by neck incision, drainage, dressing change and targeted antibiotics. Conclusions Severe deep neck infections are prone to occur in patients with diabetes and other systemic basic diseases. Early diagnosis, timely neck incision exploration, drainage to maintain airway patency and targeted antibiotic treatment are the keys to diagnosis and treatment.

As an enabling technology to address spectrum shortage, spectrum sensing has been investigated a lot. However, the uncertainties in the detection environment, including noise uncertainty and carrier frequency (CF) mismatch, still remain as the main challenges of spectrum sensing, which greatly degrades the sensing performance of typical sensing methods, such as energy detection and cyclostationary detection. To this end, this paper proposes two robust spectrum sensing schemes by leveraging the difference between the phase difference (PD) distribution of noise-perturbed signal and that of Gaussian noise. Specifically, the compact approximation of the PD distribution is first derived to enable the extraction of the features of PD distributions, which are robust to noise uncertainty and CF mismatch. Based on these features, two sensing schemes based on the deep neural network (DNN), referred to as DNN-based PD distribution detection (PDD) and blind PDD (BPDD), are proposed to detect spectrum holes in cases with known CF and unknown CF, respectively. Simulation results show that our proposed schemes are more robust to CF mismatch and noise uncertainty in comparison with the existing sensing schemes. Furthermore, when the CF of the sensed signal is unknown, the proposed BPDD significantly outperforms existing blind sensing schemes.

At present, the common problem in the tobacco factory is whether there is yellow cardboard sandwich material in the tobacco packaging box. The KUKA unpacking robot can not independently determine the shape of the cardboard, the central point position, and whether there is any, resulting in the problem of misoperation and repeated operation, resulting in the decline in the efficiency of tobacco unpacking. So this article with the method of image processing combined with convolution neural network puts forward a kind of complex tobacco packaging sandwich materials based on machine vision detection method, shape to calibrate the strawboards interlining, determine the center position, whether there is any problem and confirm the strawboards interlining, to realize accurate positioning KUKA robot clip strawboards sandwich material. The experiment shows that the efficiency of judgment is better than that of manual inspection, and the production efficiency of the factory is increased by 82%, saving human resources to some extent.

With the rapid development of offshore wind power, its permeability in the power system increases year by year, which makes it possible for it to assume the frequency modulation function in the power system. In this paper, an optimal control method of active power for offshore wind farm frequency modulation is presented. Firstly, the mathematical model of doubly-fed wind turbine is established, and the relations among wind speed, speed, pitch angle and active power are constructed. Then, simulated annealing algorithm and offshore wind farm active power optimization model are introduced. Finally, the effectiveness of the optimization model is verified by the actual operation data. The method presented in this paper can effectively reduce the action amplitude of the fan rotor in the wind farm and improve the frequency response speed of wind farm.

In this paper, the application of intelligent anti-misoperation technology in substation one-button sequential control is studied. Firstly, an adaptive anti-misoperation verification technique for the one-button sequential control operation is proposed to judge the five-prevention system's information interaction relationship in one-sequential sequential control. The combined anti-misoperation logic based on the monitoring information of primary and secondary equipment is further studied, and a set of intelligent five-prevention systems suitable for the one-button sequential control operating system is developed. Finally, the intelligent lock control system structure and its application in one-key sequential control are studied and developed.

Ghost imaging (GI) is a technology of nonlocal optical imaging, which is widely used in the field of information encryption. However, there are some inherent shortcomings in the encryption process, such as imperfect security and low imaging precision. To solve these problems, we propose a camouflaged encryption mechanism based on sparse decomposition of principal component orthogonal basis and ghost imaging (CSP-GI). Unlike the traditional encryption method, there are two-level keys and ciphertexts. The information of the secret image is hidden in the camouflaged image to improve the security of the system. The use of the sparse decomposition of principal component orthogonal basis expands the information content of encrypted transmission, improves the accuracy of reconstructed image, and reduces the reconstruction time. The simulation and experimental results show that the proposed method not only has good security and robustness but also reduces reconstruction time.

There is currently a low precision in predicting the indirect dynamometer card with measured power cure. The indirect measured model of normal beam pumping unit based on the measured power cure was established, and taken into consideration comprehensively the effects of transmission efficiency of each node and moving part inertia on the dynamic parameters of the system, combined with the power transfer process and the balance principle of reducer, then the polished rod load can be derived. Through the calibration method, using the load sensor to demarcate the transmission efficiency of the whole ground transmission system under different crank angle, and each well established a database that contains different error function in different conditions. Based on the above method makes the calculations closer to the actual value. Two wells were continuously monitored under different dynamic fluid levels, and the results showed that the indirect measured dynamometer card of had a very high coincidence rate with the direct measured dynamometer card, and the error was within ± 5%, which could meet the actual engineering requirements.

Link prediction is one of the most important tasks in graph machine learning, which aims at predicting whether two nodes in a network have an edge. Real-world graphs typically contain abundant node and edge attributes, thus how to perform link prediction by simultaneously learning structure and attribute information from both interactions/paths between two associated nodes and local neighborhood among node’s ego subgraph is intractable.

In order to further simplify the process of economic loss assessment of voltage sag and improvethe applicability and accuracy of economic loss prediction, an estimation model based on DBN-DNN for economic loss caused by voltage sag is proposed. The characteristic factors affecting theeconomic loss of voltage sag are analyzed The 19-dimensional feature vectors are extracted from the saginformation, industrial process information, sensitive equipment information and users’ basic information asinput vectors of DBN-DNN prediction model, and the economic loss results are taken as output. Finally, the DBN-DNN model is trained and evaluated based on the actual voltage sag sampling data of alarge electronic industry enterprise in China, which shows the effectiveness of the proposed method.

Face recognition is one of the most challenging research fields in image processing, pattern recognition and artificial intelligence, and its practical application has a bright future. Face is vulnerable to illumination and expression factors in the process of image acquisition. The traditional machine learning method for extracting face features usually requires high clarity of face images to achieve ideal results. Face feature extraction and recognition based on deep learning method can effectively solve this kind of problem, but the recognition rate is greatly improved, at the same time, the size of the network and the difficulty of training are also sharply increased. It does not meet the increasingly lightweight requirements of the network. This paper studies the current mainstream DCNN architecture, especially MobileNet and ShuffleNet at light-weight mobile level. Some high-efficiency and practical structural features of convolution are summarized; A Nonlinear separable deep convolutional neural network X-Net is designed; Focusing on the loss of the sample in the real face dataset, focus loss is introduced to reduce the effect of sample deviation on network fitting. The experimental results show that the feature extraction based on deep convolutional neural network in this paper can use dynamic library DLL to play an effective role in practical commercial projects.

At present, Chinese school physical education work has made positive progress. However, in general, school physical education is still the weak link in the whole educational cause, especially in the implementation of school physical education policy. There are still some policy defects such as weak or inefficient implementation. The causes need to be clarified and countermeasures to be put forward. This study adopts literature data, mathematical statistics and other methods to analyze the factors restricting the implementation of school physical education policy in China, combined with the theory of "interactive adaption". On this basis, the research hypotheses of six second-order dimensions restricting the implementation of school physical education policy are proposed, and the conceptual model of restricting factors in school physical education policy implementation in China is constructed by using the structural equation model, and finally the solution path is proposed.

The installed capacity of new energy power generation in China has broken new records for many times in recent years. However, as the installed capacity of new energy takes up a larger proportion in the power grid, it also brings great challenges to the safe and stable operation of the power grid. The defects of endowment of the new energy, represented by wind turbine and photovoltaic, are randomness volatility and intermittent. In order to improve the control ability of new energy generation when natural resources change, it brings a new operation mode to combine wind turbine PV and second-used battery to improve the stability of the new energy generation to reach the same level with thermal power. At the same time, the paper studied the grid-connected control strategy based on PQ control, building the microgrid grid-connected MATLAB/Simulink simulation model based on PQ control, providing the construction method of each sub-module and the calculation and design method of related control parameters. Finally, the effectiveness of the control system design was verified by Simulink simulation.

High Definition (HD) map is a key technology for autonomous driving, and how to quickly distribute HD map is an open issue. Named Data Networking (NDN) is an information-centric architecture and naturally supporting consumer mobility. NDN shows strong potential in HD map distribution for au-tonomous driving. However, there are still some problems in the autonomous driving area, such as the use of Vehicle-to-everything (V2X) cooperative communication for map data delivery, and V2X interface handover. In this paper, we propose an NDN HD map distribution scheme based on Vehicle Ad-hoc Network (VANET), which improves the map distribution efficiency in the case of limited bandwidth. The scheme is divided into three parts, a fast content delivery mechanism using multiple data sources, a probabilistic handover strategy for V2X interface handover, and an Interest sending rate control scheme for different interfaces. The evaluations show that the proposed scheme can improve by about 51% of average throughput and reduce up to 50% packet loss.

With the increase of the video Quality of Experience (QoE), the demand for effective video distribution increases sharply. As the wireless environment changes, it will significantly impact the user’s QoE in video streaming. The characteristics of Named Data Networking (NDN), such as multicast and Interest aggregation, which can improve the distribution efficiency of video streaming. However, the current video streaming transmission solution requires a large number of Interest request packets for video segments, which will increase the consumer channel competition in wireless NDN. In this paper, we propose an adaptive video streaming transmission mechanism for wireless NDN, which includes Data Batch Processing Scheme (DBPS) and Adaptive Transmission Scheme (ATS). To reduce the channel competition, we propose the DBPS to reduce the number of Interest sending. In addition, we propose the ATS to adaptively select the transmission mode under the different network conditions, which can improve the user’s QoE. Evaluation results show that the proposed scheme of the NDN video streaming transmission mechanism can effectively improve the user’s QoE.

With the development of wind-solar-battery power system and the increasingly strict requirements of consumers for power quality, it is of great significance to evaluate the power quality of wind-solar-battery power system both accurately and comprehensively. This paper constructs the evaluation index system of power quality applicable to the wind-solar-battery power system. On this basis, it establishes a comprehensive evaluation model based on the fuzzy analytic hierarchy process (FAHP) and improved grey relational analysis (GRA). The model takes the existing experience into account during the decision-making process, integrating the index value coefficient into the objective correlation degree. While introducing the adjustment strategy of dynamic resolution coefficient, this model makes up for the shortcomings of the traditional grey relational analysis, and realizes the good combination of subjective expert experience and objective node data. Finally, a case study was provided to verify the feasibility and effectiveness of the proposed method.

In order to circumvent Internet censorship, more and more malicious users utilize Virtual Private Network (VPN) software to penetrate firewall and access restricted resources. It's important to identify and classify VPN traffic for network management and security. In this paper, 6 different kinds of VPN traffic are analyzed. Characterizing VPN traffic by Sample Entropy Fingerprint and Payload Length Sequence (PLS) separately, we apply Machine Learning (ML) algorithms to construct VPN traffic classifiers basing on these features. Comparing with Deep Packet Inspection (DPI), Sample Entropy Fingerprint and PLS, our result shows that the classifier based on PLS can achieve accurate VPN traffic classification, including obfuscated VPN traffic.

Datacenter jobs in a private cloud are often generated by different departments or groups of a business or other organization. In order to reflect the organizational structures of datacenter workloads, it is desirable for the datacenter scheduler to support hierarchical scheduling. The state-of-the-art hierarchical scheduling scheme, hierarchical dominant resource fairness (H-DRF), however, is expensive to implement. Specifically, the worst-case time complexity of H-DRF is $O(q^{2})$ , where $q$ is dominated by the number of jobs in the hierarchy, which is a large complexity in a practical large-scale datacenter. In this letter, we design a new online hierarchical fair scheduling scheme, multi-resource collapsed hierarchies (MCH), in the way of converting the hierarchy into a weighted flat tree. MCH takes at most $O(q)$ time and is simple enough to implement in practice. Simulations driven by Google cluster traces show that MCH consumes a nearly sqrt of the total run time of H-DRF, improving significantly in saving the computation time.