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At present, most stability control systems based on active steering are superposition control of steering Angle. For the steering system configuration commonly used in automatic driving vehicles, the active steering control system of steering angle superposition cannot be realized. In order to achieve man-machine collaborative driving, the co-driving intelligent vehicles usually realize automatic steering control based on the torque superposition, which solves the drawback that the driver of the Angle superposition cannot participate in the steering. In this paper, an active steering control method of superposition of steering torque based on Receding horizon control is proposed for man-machine collaborative driving of co-drive intelligent vehicles. This control method combines steering system with vehicle dynamics system, and takes steering motor torque as the sole control input, which ensures that the driver has the ultimate control right and makes the control of the system more stable. In order to solve the system uncertainties caused by model mismatch and external disturbances, the optimal control rate in predictive time domain can be calculated according to real-time vehicle state parameters by receding horizon control. In the design of the controller, the yaw rate deviation is the input of the controller, and then the calculated superposition torque is input into the vehicle model in real time, and the steering wheel angle is observed as a state variable. The control strategy can effectively compensate for the uncertainty of the system such as model mismatch and external disturbance, and enhance the robustness of the system.

In DC microgrids, the piecewise droop control is an advanced coordinative control strategy, realizing plug-and-play capability of parallel converters and improved DC bus voltage regulation. However, it is difficult to realize smooth and accurate switch among different droop curves. The traditional hysteresis-based piecewise droop control fails to switch droop curves when difference among line resistances of parallel converters is not negligible. To handle this issue, this paper proposes an adaptive piecewise droop control strategy to switch droop curves reliably. First, the control architecture and basic principle of the proposed strategy are illustrated. Then, the transition process among different droop curves is analyzed. Furthermore, the practical implementation of the proposed control strategy is presented. Finally, the effectiveness and advantages of the strategy are verified, based on the laboratory-scale DC microgrid prototype.

It is helpful to obtain accurate and effective energy information for energy monitoring and controlling by laying out the wireless sensor network (WSN) in a stable energy region. Usually, the temperature and humidity information represents the environmental energy. That is, the stable region of temperature and humidity is the stable energy region. Therefore, this study proposes a WSN layout strategy in a stable temperature and humidity region. Firstly, the temperature and humidity model is built, and then the stable temperature and humidity region are obtained by cluster analysis and mathematical statistics. Finally, the WSN system is designed. WSN nodes are designed and manufactured, and these nodes form the WSN in the stable temperature and humidity region. For example, take the tobacco industry; a data processing system is designed to analyze and manage the temperature and humidity data based on the WSN layout strategy. WSN layout strategy provides a theoretical basis for WSN layout, contributes to data analysis, management, and optimization, and also helps to improve the control system capability and reduce production energy consumption.

Intrusion detection technology has always been a hot topic in the field of network security research. Now different types of intrusion traffic in the real network environment are imbalanced, which lead to poor recognition rate of classification models when dealing with rare attack types with few training samples. In order to solve the above problems, a new VAE-GAN model is proposed by combining VAE with GAN and optimizing the loss function to generate high-quality intrusion samples more efficiently and stably. In the training phase, original samples and generated samples are mixed as the input of classifiers. The experimental results show that the proposed VAE-GAN model has stronger stability and faster model convergence speed in the process of generating data. For small-sample data, the Recall and F1-score are significantly improved by using the generated samples of VAE-GAN model as the training set of classifiers.

In this paper, a novel nonsingular finite-time backstepping controller is constructed for trajectory tracking of marine vessel subject to unknown external disturbances. Firstly, in the presence of disturbances, a disturbance observer (DO) is proposed to estimate and compensate the disturbances exactly in finite time. Secondly, a finite-time tracking controller is designed in the classical backstepping procedure, however, the inevitable singularity appears in calculating the derivative of virtual control. Furthermore, for overcoming this singularity, a nonsingular finite-time backstepping controller is designed by adopting a finite-time command filter to estimate the derivative, instead of calculating it directly. Theoretical analysis demonstrates the closed-loop system is finite-time stable. Finally, simulation results and comparisons illustrate the effectiveness of the proposed method.

This paper investigates the tracking control problem of a class of high-order distributed systems subjected to limited communication bandwidth. An event-triggered control method is proposed, where the controller is triggered only when specific events happen. Moreover, the computational complexity is reduced by introducing command filters for virtual control signals. Specifically, the backstepping scheme is adopted as the main design framework, by which the n-th order nonlinear system is divided into n command-cascaded first order subsystems. And virtual control commands are sent through a second-order low-pass filter, by which the time derivatives of the virtual commands can be obtained directly. The theoretical analysis shows the stability of the proposed method. The tracking performance is illustrated by a simulation example.

Integrated Energy System (IES) can integrate various forms of energy, bringing new developments to demand response (DR). The demand response of a single type of energy system cannot satisfy user satisfaction and corresponding effects at the same time, traditional demand response is developing towards integrated demand response (IDR). This paper first proposes the concept of integrated energy system and the frame of integrated demand response for multi-energy coordination. Then, the communication service level for IDR is specified, and the idea of communication service optimization is proposed. This paper designs an improved communication service optimization algorithm, which can solve the communication quality problem in the user-side integrated demand response. Case study illustrates the benefits of the algorithm proposed in this paper.

In this paper, a leader-follower formation control strategy based on non-singular terminal sliding mode control under complex unknown disturbances is proposed. The contributions of this paper are: (1) An unmanned surface vehicles (USVs) formation control strategy is proposed based on fixed-time non-singular terminal sliding mode, which greatly improves the convergence rate of the formation system; (2) A finite-time extended state observer is designed to handle external disturbances, which greatly improves the formation performance of the system; (3) Stability of the closed-loop control system is proved by Lyapunov stability analysis. Simulation results demonstrate that the proposed control strategy is highly efficient and effective.

This research aims to systematically analyze the causes of the digital divide among the elderly and propose a strategy that can bridge the digital divide among the elderly at three levels in an integrated way by integrating online and offline resources to improve the Internet access, usability and overall cognitive level of the elderly, based on the design and implementation of an elderly-friendly information system. Trial results show that the system can improve the elderly's ability to use mainstream applications and knowledge level of mobile Internet and help them to gradually cultivate information literacy, so that they could integrate into the information society in a better way.

The development of information technology has provided opportunities for the application of Ultra High Definition (UHD) video, but the security problems such as privacy leakage cannot be ignored. Video selective encryption algorithm is an efficient and effective way to protect the privacy of users. However, the previous studies on the performance evaluation of video selective encryption scheme are insufficient. PSNR and SSIM, which are commonly used to evaluate the image quality, cannot fully reflect the encryption effect of selective encryption algorithm, which makes it difficult to evaluate and compare the security protection ability of selective encryption scheme for video content. To build an accurate security evaluation scheme of video selective encryption algorithm needs to rely on a large number of encrypted video data, but the existing datasets have the problems of small number of videos, single video resolution and selective encryption method, and the subjective experiment scheme is lack of scientific. In order to solve this problem, this paper selects a variety of resolutions and common scene video sequences, and uses different selective encryption schemes to encrypt them, so as to build a dataset for selective encryption. The method of double-blind subjective experiment provides a real and accurate subjective score for the perception security and visual quality of the video in the dataset. The subjective score shows that the dataset covers the encryption sequence of each security level, which can provide an effective reference for the follow-up research on video selective encryption and perceptual security index.

With the rapid development of the next-generation sequencing technology, a large amount of genomics information has been obtained. The scale of biological sequencing data is particularly large and complex. The tensor robust principal component analysis (TRPCA) method can effectively preserve the spatial structure of tensor data, so it has received extensive attention. However, the low-rank tensor obtained by TRPCA may be damaged to a certain extent. To solve this problem, this paper proposes a model for weighting low-rank data based on the method of TRPCA. This model has an additional constraint penalty term that can repair corrupted low-rank data and the effective information in it can be fully utilized. In addition, the norm is used to constrain the sparse tensor to make the sparse effect better. In the experimental part, TRPCA model clusters samples by low-rank tensor. The experimental results on cancer omics data show that our method is superior to other methods.

Genetic information becomes more and more important in the process of biological research. Gene analysis is an effective mean in biological research, especially the analysis of differentially expressed genes. Robust principal component analysis (RPCA) is an effective method to identify differentially expressed genes. But tensor robust principal component analysis (TRPCA) performs better than RPCA when processing multi-dimensional data. The traditional TRPCA method also has limitations in restoring low-rank sparse components. To further improve the accuracy of the TRPCA method in restoring low-rank components and sparse components, we propose a novel TRPCA method to obtain high-order correlations information of multi-dimensional data. It uses a new nuclear norm based on t-product operator to approximate the rank function. The $L_{2,1}$ -norm is used to improve the sparsity of tensors and reduce the negative effects caused by noises and outliers. At the same time, the introduction of $L_{2,1}$ -norm enhances the sparsity of error components, and improves the accuracy of low-rank component recovery. The low-rank sparse components are obtained by solving the convex problem of the new tensor nuclear norm. It can well preserve the spatial structure and make full use of complementary information to improve the clustering effect. Alternating direction method of multiplier (ADMM) is used to solve the optimization problem of this method. Experimental results on different cancer genomic datasets indicate that our method is superior to other methods.

For the first time, we proposed a continuous analytical surface potential-based compact model of the independent Dual Gate amorphous In-Ga-Zn-O thin film transistors (IDG a-IGZO TFTs), where percolation conduction, trap-limited conduction (TLC) and variable rang hopping (VRH) transport theories in the extended and localized states are both considered via Schroder method, physically describing the transport mechanism under different conditions of temperature and carrier density. Moreover, a single formulation of front and back surface potentials that is valid and extremely accurate in all operation regimes is developed. Based on the transport theories and surface potential, complete compact model dedicated to IDG TFTs is presented. Furthermore, the threshold compensation effect is also included in this model. To calibrate the model, we fabricated asymmetric dual gate a-IGZO TFTs. The model is validated by an excellent agreement with numerical solutions and experimental results. Finally, the compact model is coded in Verilog-A, and implemented in a vendor CAD environment. A systemically simulation of both ring oscillator (RO) and pixel circuit proves the potential application of this model in circuit design.

For the first time, we experimentally prove that MoS 2 negative-capacitance field-effect transistor (NCFET) can benefit from device scaling. In the short-channel device (83 nm channel length), ultra-low subthreshold swing (SS) of 17.28 mV/dec minimum and 39 mV/dec in average, is demonstrated without suffering from hysteresis. The average SS (SS ave ) improvement factor of MoS 2 NCFET with respect to reference MoS 2 FET, which is quantified by SS ave /SS Ref ., reaches a record-low value of 0.177 among all hysteresis-free 2D NCFETs reported so far. Furthermore, 364 and 26 times improvements of I DS are achieved at V GS = 0 V and 1.5 V, respectively.

A complex enterprise includes multiple subsystems and organizations. The U.S. Marine Corps (USMC) maintenance and supply chain is a complex enterprise and exemplifies a socio-technological infrastructures. It is imperative for the USMC to adopt more advanced data sciences including ML/AI techniques to the entire spectrum or end-to-end (E2E) logistic planning as a complex enterprise including maintenance, supply, transportation, health services, general engineering, and finance. In this paper, we first review an overall framework of leveraging artificial Intelligence to learn, optimize, and win (LAILOW) for a complex enterprise, and then show how a LAILOW framework is applied to the USMC maintenance and supply chain data as a use case. We also compare various machine learning (ML) algorithms such as supervised machine learning/predictive models and unsupervised machine learning algorithms such as lexical link analysis (LLA). The contribution of the paper is that LLA computes stable and sensitive components of a complex system with respective to a perturbation. LLA allows to discover and search for associations, predict probability of demand and fail rates, prepare spare parts, and improve operational availability and readiness.

Purpose. To assess the recognition pattern of dynamic action by visual prosthesis' wearers. Methods. Twenty volunteers (classified by gender and experience) were recruited to carried out action recognition test in simulated prosthetic vision. Twenty-three common dynamic actions video clips usually see in daily life were selected and formed the experimental action set which was divided into three parts including combined actions, simple actions and difficult actions in turn. The action animations made by 3D Studio Max were recorded by Banicam to convert to MP4 video format and processed to phosphene video clips by MATLAB in different resolutions (16 × 16, 24 × 24, 32 × 32, 48 × 48, 64 × 64, 128 × 128). Result. Comparing the recognition results, male group and female group had no significant difference. 48 × 48 resolution was the considerable latent capacity which subjects had stable performance. After a period of study, the performance of prosthesis wearers could be improved significantly.