Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks

An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the nonlinear system and a recurrent neural network to minimize the difference between the linear model and the real nonlinear system. Because the current control input is not included in the input vector of recurrent neural network (RNN), the inverse control law can be calculated directly. This scheme can be used in real-time nonlinear single-input single-output (SISO) and multi-input multi-output (MIMO) system control with less computation work. Simulation studies have shown that this scheme is simple and affects good control accuracy and robustness.

Short-Term Wind Power Prediction Based on WVMD and Spatio-Temporal Dual-Stream Network

As the penetration ratio of wind power in active distribution networks continues to increase,the system exhibits some characteristics such as randomness and volatility.Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization control.Based on the spatio-temporal features of Numerical Weather Prediction(NWP)data,it proposes the WVMD_DSN(Whale Optimization Algorithm,Variational Mode Decomposition,Dual Stream Network)model.The model first applies Pearson correlation coefficient(PCC)to choose some NWP features with strong correlation to wind power to form the feature set.Then,it decomposes the feature set using Variational Mode Decomposition(VMD)to eliminate the nonstationarity and obtains Intrinsic Mode Functions(IMFs).Here Whale Optimization Algorithm(WOA)is applied to optimise the key parameters of VMD,namely the number of mode components K and penalty factor a.Finally,incorporating attention mechanism(AM),Squeeze-Excitation Network(SENet),and Bidirectional Gated Recurrent Unit(BiGRU),it constructs the dual-stream network(DSN)for short-term wind power prediction.Comparative experiments demonstrate that the WVMD_DSN model outperforms existing baseline algorithms and exhibits good generalization performance.The relevant code is available at https://github.com/ruanyuyuan/Wind-power-forecast.git(accessed on 20 August 2024).

A multi-input and multi-output design on automotive engine management system

Lookup table is widely used in automotive industry for the design of engine control units(ECU).Together with a proportional-integral controller,a feed-forward and feedback control scheme is often adopted for automotive engine management system(EMS).Usually,an ECU has a structure of multi-input and single-output(MISO).Therefore,if there are multiple objectives proposed in EMS,there would be corresponding numbers of ECUs that need to be designed.In this situation,huge efforts and time were spent on calibration.In this work,a multi-input and multi-out(MIMO) approach based on model predictive control(MPC) was presented for the automatic cruise system of automotive engine.The results show that the tracking of engine speed command and the regulation of air/fuel ratio(AFR) can be achieved simultaneously under the new scheme.The mean absolute error(MAE) for engine speed control is 0.037,and the MAE for air fuel ratio is 0.069.

Cooperative MIMO MAC Transmission Using Space Codes in Wireless Sensor Network

Wireless sensor network （WSN） requires robust and efficient communication protocols to minimise delay and save energy. The lifetime of WSN can be maximised by selecting proper medium access control （MAC） scheme depending on the contention level of the network. The throughput of WSN however reduces due to channel fading effects even with the proper design of MAC protocol. Hence this paper proposes a new MAC scheme for enabling packet transmission using cooperative multi-input multi-output （MIMO） utilising space time codes（STC） such as space time block code （STBC）, space time trellis code （STTC） to achieve higher energy savings and lower delay by allowing nodes to transmit and receive information jointly. The performance of the proposed MAC protocol is evaluated in terms of transmission error probability, energy consumption and delay. Simulation results show that the proposed cooperative MIMO MAC protocol provides reliable and efficient transmission by leveraging MIMO diversity gains.

Evaluation of Linear Precoding Schemes for Cooperative Multi-Cell MU MIMO in Future Mobile Communication Systems

In Mobile Communication Systems, inter-cell interference becomes one of the challenges that degrade the system’s performance, especially in the region with massive mobile users. The linear precoding schemes were proposed to mitigate interferences between the base stations (inter-cell). These schemes are categorized into linear and non-linear;this study focused on linear precoding schemes, which are grounded into three types, namely Zero Forcing (ZF), Block Diagonalization (BD), and Signal Leakage Noise Ratio (SLNR). The study included the Cooperative Multi-cell Multi Input Multi Output (MIMO) System, whereby each Base Station serves more than one mobile station and all Base Stations on the system are assisted by each other by shared the Channel State Information (CSI). Based on the Multi-Cell Multiuser MIMO system, each Base Station on the cell is intended to maximize the data transmission rate by its mobile users by increasing the Signal Interference to Noise Ratio after the interference has been mitigated due to the usefully of linear precoding schemes on the transmitter. Moreover, these schemes used different approaches to mitigate interference. This study mainly concentrates on evaluating the performance of these schemes through the channel distribution models such as Ray-leigh and Rician included in the presence of noise errors. The results show that the SLNR scheme outperforms ZF and BD schemes overall scenario. This implied that when the value of SNR increased the performance of SLNR increased by 21.4% and 45.7% for ZF and BD respectively.

基于动态双注意力机制的跨模态行人重识别模型

针对跨模态行人重识别图像间模态差异大的问题,大多数现有方法采用像素对齐、特征对齐来实现图像间的匹配。为进一步提高两种模态图像间的匹配的精度,设计了一个基于动态双注意力机制的多输入双流网络模型。首先,在每个批次的训练中通过增加同一行人在不同相机下的图片,让神经网络在有限的样本中学习到充分的特征信息;其次,利用齐次增强得到灰度图像作为中间桥梁,在保留了可见光图像结构信息的同时消除了颜色信息,而灰度图像的运用弱化了网络对颜色信息的依赖,从而加强了网络模型挖掘结构信息的能力;最后,提出了适用于3个模态间图像的加权六向三元组排序(WSDR)损失,所提损失充分利用了不同视角下的跨模态三元组关系,优化了多个模态特征间的相对距离,并提高了对模态变化的鲁棒性。实验结果表明,在SYSU-MM01数据集上,与动态双注意聚合(DDAG)学习模型相比,所提模型在评价指标Rank-1和平均精确率均值(mAP)上分别提升了4.66和3.41个百分点。

Multi-user rate and power analysis in a cognitive radio network with massive multi-input multi-output

This paper discusses transmission performance and power allocation strategies in an underlay cognitive radio （CR） network that contains relay and massive multi-input multi-output （MIMO）. The downlink transmission performance of a relay-aided massive MIMO network without CR is derived. By using the power distribution criteria, the kth user＇s asymptotic signal to interference and noise ratio （SINR） is independent of fast fading. When the ratio between the base station （BS） antennas and the relay antennas becomes large enough, the transmission performance of the whole system is independent of BS-to-relay channel parameters and relates only to the relay-to-users stage. Then cognitive transmission performances of primary users （PUs） and secondary users （SUs） in an underlay CR network with massive MIMO are derived under perfect and imperfect channel state information （CSI）, including the end-to-end SINR and achievable sum rate. When the numbers of primary base station （PBS） antennas, secondary base station （SBS） antennas, and relay antennas become infinite, the asymptotic SINR of the kth PU and SU is independent of fast fading. The interference between the primary network and secondary network can be canceled asymptotically.Transmission performance does not include the interference temperature. The secondary network can use its peak power to transmit signals without causing any interference to the primary network. Interestingly, when the antenna ratio becomes large enough, the asymptotic sum rate equals half of the rate of a single-hop single-antenna K-user system without fast fading. Next, the PUs＇ utility function is defined. The optimal relay power is derived to maximize the utility function. The numerical results verify our analysis. The relationships between the transmission rate and the antenna nunber, relay power, and antenna ratio are simulated. We show that the massive MIMO with linear pre-coding can mitigate asymptotically the interference in a multi-user underlay CR network. The primary and secondary networks can operate independently.

A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System

In this paper, a novel real time non-linear model predictive controller(NMPC) for a multi-variable coupled tank system(CTS) is designed. CTSs are highly non-linear and can be found in many industrial process applications. The involvement of multi-input multi-output(MIMO) system makes the design of an effective controller a challenging task. MIMO systems have inherent couplings,interactions in-between the process input-output variables and generally have an complex internal structure. The aim of this paper is to design, simulate, and implement a novel real time constrained NMPC for a multi-variable CTS with the aid of intelligent system techniques. There are two major formidable challenges hindering the success of the implementation of a NMPC strategy in the MIMO case. The first is the difficulty of obtaining a good non-linear model by training a non-convex complex network to avoid being trapped in a local minimum solution. The second is the online real time optimisation(RTO) of the manipulated variable at every sampling time.A novel wavelet neural network(WNN) with high predicting precision and time-frequency localisation characteristic was selected for an MIMO model and a fast stochastic wavelet gradient algorithm was used for initial training of the network. Furthermore, a genetic algorithm was used to obtain the optimised parameters of the WNN as well as the RTO during the NMPC strategy. The proposed strategy performed well in both simulation and real time on an MIMO CTS. The results indicated that WNN provided better trajectory regulation with less mean-squared-error and average control energy compared to an artificial neural network. It is also shown that the WNN is more robust during abnormal operating conditions.

On optimization of cooperative MIMO for underlaid secrecy Industrial Internet of Things

In this paper,physical layer security techniques are investigated for cooperative multi-input multi-output(C-MIMO),which operates as an underlaid cognitive radio system that coexists with a primary user(PU).The underlaid secrecy paradigm is enabled by improving the secrecy rate towards the C-MIMO receiver and reducing the interference towards the PU.Such a communication model is especially suitable for implementing Industrial Internet of Things(IIoT)systems in the unlicensed spectrum,which can trade off spectral efficiency and information secrecy.To this end,we propose an eigenspace-adaptive precoding(EAP)method and formulate the secrecy rate optimization problem,which is subject to both the single device power constraint and the interference power constraint.This precoder design is enabled by decomposing the original optimization problem into eigenspace selection and power allocation sub-problems.Herein,the eigenvectors are adaptively selected by the transmitter according to the channel conditions of the underlaid users and the PUs.In addition,a simplified EAP method is proposed for large-dimensional C-MIMO transmission,exploiting the additional spatial degree of freedom for a low-complexity secrecy precoder design.Numerical results show that by transmitting signal and artificial noise in the properly selected eigenspace,C-MIMO can eliminate the secrecy outage and outperforms the fixed eigenspace precoding methods.Moreover,the proposed simplified EAP method for the large-dimensional C-MIMO can significantly improve the secrecy rate.