Nonlinear Systems Identification via an Input-Output Model Based on a Feedforward Neural Network

This paper develops a feedforward neural network based input output model for a general unknown nonlinear dynamic system identification when only the inputs and outputs are accessible observations. In the developed model, the size of the input space is directly related to the system order. By monitoring the identification error characteristic curve, we are able to determine the system order and subsequently an appropriate network structure for systems identification. Simulation results are promising and show that generic nonlinear systems can be identified, different cases of the same system can also be discriminated by our model.

A Local Adaptation in an Output-Based Research Support Scheme(OBRSS)at University College Dublin

University College Dublin(UCD) has implemented the Output-Based Research Support Scheme(OBRSS) since 2016. Adapted from the Norwegian model, the OBRSS awards individual academic staff using a points system based on the number of publications and doctoral students. This article describes the design and implementation processes of the OBRSS, including the creation of the ranked publication list and points system and infrastructure requirements. Some results of the OBRSS will be presented, focusing on the coverage of publications reported in the OBRSS ranked publication list and Scopus, as well as information about spending patterns. Challenges such as the evaluation of the OBRSS in terms of fairness, transparency, and effectiveness will also be discussed.

Auxiliary Model Based Multi-innovation Stochastic Gradient Identification Methods for Hammerstein Output-Error System

Special input signals identification method based on the auxiliary model based multi-innovation stochastic gradient algorithm for Hammerstein output-error system was proposed.The special input signals were used to realize the identification and separation of the Hammerstein model.As a result,the identification of the dynamic linear part can be separated from the static nonlinear elements without any redundant adjustable parameters.The auxiliary model based multi-innovation stochastic gradient algorithm was applied to identifying the serial link parameters of the Hammerstein model.The auxiliary model based multi-innovation stochastic gradient algorithm can avoid the influence of noise and improve the identification accuracy by changing the innovation length.The simulation results show the efficiency of the proposed method.

Analysis on Output Teaching Mode of College English Based on Implicit Learning Theory

The application of language,to a great extant,requires learners to understand the inputted information quickly as well as automatically,and combine verbal fragments into meaningful outputted language. This type of spontaneous mechanism depends on the effective input of language and long-rang internalization of language structure,which helps to form the implicit knowledge in students' conceptual system,thus to realize the automatic use of language. Therefore,the article intends to combine implicit learning theory with the output teaching mode with a purpose of working out a practical teaching mode to enhance the teaching effect and college students' applied abilities to use English.

Compressive sensing based multiuser detector for massive MBM MIMO uplink

Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple input multiple output(MIMO),the MBM scheme achieves better performance than other conventional multiuser MIMO schemes.In this paper,the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed.This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots,which exploits the structured sparsity of these aggregate MBM signals.Under this kind of scenario,a multiuser detector with low complexity based on the compressive sensing(CS)theory to gain better detection performance is proposed.This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit(CoSaMP)and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals.By exploiting these sparsity,the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity.Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.

Key Technology of Embedded System Implementation for Software-based CNC System

The software-based computer numerical control（CNC） system includes three types of tasks： periodic real-time tasks, aperiodic real-time tasks, and non-real-time tasks. The tasks are characterized by concurrency, hybridization, and correlation, which make system implementation difficult. The conventional scheduling algorithm can not meet the demands of system implementation in the software-based CNC system completely. The uncertainty factors when running real-time tasks affect control performance by degrading manufacturing accuracy as a result of system resource and processor use restrictions. To address the technical difficulty of embedded system implementation, a novel fuzzy feedback scheduling algorithm based on output jitter of key real-time tasks for a software-based CNC system is proposed. Time characteristics, such as sampling jitter, input-output jitter, and non-schedulability are discussed, followed by quantification through simulations of the impact of time characteristics on manufacturing accuracy. On the basis of this research, the scheduler architecture is designed, and then the algorithm table is calculated. When the system resource changes, the key periodic real-time tasks meet their deadlines by means of dynamically adjusting the task period. The simulated results show that the machining precision rises by an order of magnitude for the proposed scheduler in resource-constrained software-based CNC systems. Moreover, unlike conventional feedback scheduling methods, the algorithm in this paper does not rely on the availability of task execution times and is easy to implement while incurring only a small overhead.

Soft Direct-Adaptation Based Bidirectional Turbo Equalization for MIMO Underwater Acoustic Communications

This paper proposes a soft direct-adaptation based bidirectional turbo equalizer for multiple-input multiple-output underwater acoustic communication systems. Soft, rather than hard, direct-adaptation based equalizer combined with the fast self-optimized least mean square algorithm is employed to achieve a faster convergence rate, and the second-order phase-locked loop is embedded into the equalizer to track the time-varying channel. Meanwhile, by utilizing a weighted linear combining scheme, the conventional soft direct-adaptation based equalizer is combined with the time-reversed soft direct-adaptation based equalizer to exploit bidirectional diversity and mitigate error propagation. Both the simulation and experimental results demonstrate that the soft direct-adaptation based bidirectional turbo equalizer outperforms the single-direction soft direct-adaptation based turbo equalizer, and achieves a faster convergence rate than the hard direct-adaptation based bidirectional turbo equalizer.

Effect of defects properties on InP-based high electron mobility transistors

The performance damage mechanism of InP-based high electron mobility transistors(HEMTs) after proton irradiation has been investigated comprehensively through induced defects.The effects of the defect type, defect energy level with respect to conduction band ET, and defect concentration on the transfer and output characteristics of the device are discussed based on hydrodynamic model and Shockley–Read–Hall recombination model.The results indicate that only acceptorlike defects have a significant influence on device operation.Meanwhile, as defect energy level ETshifts away from conduction band, the drain current decreases gradually and finally reaches a saturation value with ETabove 0.5 eV.This can be attributed to the fact that at sufficient deep level, acceptor-type defects could not be ionized any more.Additionally,the drain current and transconductance degrade more severely with larger acceptor concentration.These changes of the electrical characteristics with proton radiation could be accounted for by the electron density reduction in the channel region from induced acceptor-like defects.

Electronic Throttle Control System: Modeling, Identification and Model-Based Control Designs

Electronic throttle control (ETC) system has worked its way to becoming a standard subsystem in most of the current automobiles as it has contributed much to the improvement of fuel economy, emissions, drivability and safety. Precision control of the subsystem, which consists of a dc motor driving a throttle plate, a pre-loaded return spring and a set of gear train to regulate airflow into the engine, seems rather straightforward and yet complex. The difficulties lie in the unknown system parameters, hard nonlinearity of the pre-loaded spring that pulls the throttle plate to its default position, and friction, among others. In this paper, we extend our previous results obtained for the modeling, unknown system parameters identification and control of a commercially available Bosch’s DV-E5 ETC system. Details of modeling and parameters identification based on laboratory experiments, data analysis, and knowledge of the system are provided. The parameters identification results were verified and validated by a real-time PID control implemented with an xPC Target. A nonlinear control design was then proposed utilizing the input-output feedback linearization approach and technique. In view of a recent massive auto recalls due to the controversial uncontrollable engine accelerations, the results of this paper may inspire further research interest on the drive-by-wire technology.

Hybrid orthogonal and non-orthogonal pilot distribution based channel estimation in massive MIMO system

How to obtain accurate channel state information(CSI)at the transmitter with less pilot overhead for frequency division duplexing(FDD) massive multiple-input multiple-output(MIMO)system is a challenging issue due to the large number of antennas. To reduce the overwhelming pilot overhead, a hybrid orthogonal and non-orthogonal pilot distribution at the base station(BS),which is a generalization of the existing pilot distribution scheme,is proposed by exploiting the common sparsity of channel due to the compact antenna arrangement. Then the block sparsity for antennas with hybrid pilot distribution is derived respectively and can be used to obtain channel impulse response. By employing the theoretical analysis of block sparse recovery, the total coherence criterion is proposed to optimize the sensing matrix composed by orthogonal pilots. Due to the huge complexity of optimal pilot acquisition, a genetic algorithm based pilot allocation(GAPA) algorithm is proposed to acquire optimal pilot distribution locations with fast convergence. Furthermore, the Cramer Rao lower bound is derived for non-orthogonal pilot-based channel estimation and can be asymptotically approached by the prior support set, especially when the optimized pilot is employed.

Observer-Based Nonlinear Feedback Controls for Heartbeat ECG Tracking Systems

The analysis and design of observed-based nonlinear control of a heartbeat tracking system is investigated in this paper. Two of Zeeman’s heartbeat models are investigated and modified by adding the control input as a pacemaker, thereby creating the control-affine nonlinear system models that capture the general heartbeat behavior of the human heart. The control objective is to force the output of the heartbeat models to track and generate a synthetic electrocardiogram (ECG) signal based on the actual patient reference data, obtained from the William Beaumont Hospitals, Michigan, and the PhysioNet database. The formulations of the proposed heartbeat tracking control systems consist of two phases: analysis and synthesis. In the analysis phase, nonlinear controls based on input-output feedback linearization are considered. This approach simplifies the difficult task of developing nonlinear controls. In the synthesis phase, observer-based controls are employed, where the unmeasured state variables are estimated for practical implementations. These observer-based nonlinear feedback control schemes may be used as a control strategy in electronic pacemakers. In addition, they could be used in a software-based approach to generate a synthetic ECG signal to assess the effectiveness of diagnostic ECG signal processing devices.

Multi-criteria user selection scheme for learning-based multiuser MIMO cognitive radio networks

For multiuser multiple-input-multiple-output （MIMO） cognitive radio （CR） networks a four-stage transmiision structure is proposed. In learning stage, the learning-based algorithm with low overhead and high flexibility is exploited to estimate the channel state information （ CSI ） between primary （PR） terminals and CR terminals. By using channel training in the second stage of CR frame, the channels between CR terminals can be achieved. In the third stage, a multi-criteria user selection scheme is proposed to choose the best user set for service. In data transmission stage, the total capacity maximization problem is solved with the interference constraint of PR terminals. Finally, simulation results show that the multi-criteria user selection scheme, which has the ability of changing the weights of criterions, is more flexible than the other three traditional schemes and achieves a tradeoff between user fairness and system performance.

Experienced Language Learners' Perceptions of Task-based Language Teaching-A Case Study of Guangdong University of Foreign Studies in China

The aim of this dissertation was to investigate the attitudes and opinions from experienced language learners towards task-based language teaching(TBLT), a popular and widely adopted teaching approach, in the context of Guangdong University of Foreign Studies(GDUFS) in China and provide suggestion for making improvements in language teaching.

结合气象监测与地基云图的地表太阳辐射预测

高精度的太阳辐照度预测是光伏输出功率预测的基础,而云的遮挡是导致太阳辐射波动的主要原因。针对现有技术由于对云图时变特征获取能力不足,导致在复杂天气条件下预测精度显著下降的问题,提出了利用3D卷积神经网络同时提取单张云图图像特征和云图序列时变特征,建立云图图像特征与云对地表太阳辐射衰减之间的关联,实现太阳辐射高精度预测的方法。实验验证结果表明,较现有方法,所提出的方法在复杂天气条件下的未来5 min功率预测精度提高8%以上,具有很高的推广应用价值。

2011—2021年欧盟地区木材及人造板生产情况分析

欧盟是中国木材及人造板最大贸易伙伴之一。针对2011—2021年欧盟地区木材、人造板产量变化趋势以及木材加工行业的经营业绩和就业人数进行整理并加以分析,以期为我国木材加工及人造板从业人员提供参考和借鉴。

考虑投入产出的常规公交调度绩效回溯性动态评价

实时调度因其灵活性在公交运营组织中发挥着重要决策作用。现行调度(员)绩效评价虽然能够考核行车计划执行情况,却忽略了在乘客体验和企业投入产出层面对调度效果考查,也不支持回溯过往调度实施效果。为使调度员能够精准提升调度技能和绩效、解决调度决策滞后造成的运能浪费等问题并提高乘客满意度,从投入产出及回溯性动态评价2个维度提出基于三阶段Super-SBM(super-efficiency slack-based measurement)模型的常规公交调度绩效评价方法,考虑路况及客流对调度效果的影响,在现行指标基础上从乘客角度增加运营速度、候车时间变化率及满载率标准差3项产出指标,构建评价体系。对2条上海公交线路的实际调度进行回溯性评价,验证了本文评价方法能准确区分调度效果差异。结果表明,高峰时段灵活的调度策略比在平峰能产生更明显的效益,且调度策略在不同时空场景呈现差异化适用性。通过回溯分析历史调度记录的绩效,一方面可以针对各场景为调度员提供调度决策支持,另一方面可为基于机器学习的公交实时调度策略生成技术挑选合适的学习样本,从而提升常规公交运营组织效率。

“过程+产出”导向的三维联动项目式翻译课程教学实践研究

文本以某大学《计算机辅助翻译(CAT)》课程教学为例,阐述了依托企业项目,高校、企业、学生三维联动的、“过程+产出”导向的项目式翻译课程教学模式。该教学模式注重综合能力与素质的过程培养和产出质量,以学生为主体,教师为主导,校企合作教学,“课堂双导师”和“知识构建+翻译项目实践”相结合,开阔了学生视野,发展了学生思维,培养了学生的核心素养,探索了三维联动的“三观”“四早”育人路径与育人机制,提升了学生学习的主动感、胜任力和获得感,对高校语言专业人才培养有积极意义。

ErlangShen:基于流水线低访问开销的图式区块链高效事务执行机制

基于有向无环图(DAG)的图式区块链能够显著提升系统性能,已成为近年来业界的研究热点。相较于传统串行化的链式区块链,图式区块链可在单位时间内并发处理大量事务从而提升吞吐量。随着事务量的激增,图式区块链面临事务执行效率低的瓶颈问题,即海量事务执行对状态数据访问的需求大幅增加,导致高昂的输入/输出(I/O)开销。实现低I/O访问主要包括两方面的全新挑战:一方面,图式区块链若直接采用传统的事务预取机制,将因执行逻辑不一致引入大量的陈旧读;另一方面,针对不同账户的状态访问会在默克尔树的高层节点中造成重复的I/O开销。为此,本文设计基于流水线的图式区块链高效事务执行机制—ErlangShen,包括Epoch粒度的状态预取机制和默克尔高层路径缓存机制来分别减少陈旧读的数量和重复的I/O开销。具体而言,ErlangShen充分分析并利用了事务访问频次的冷热特征,将访问热事务的逻辑执行与冷事务的状态预取并行化,以避免状态预取对事务执行的影响。此外,为了进一步提升事务执行的吞吐量,根据访问冷热状态事务的特性设计了定制化的并发控制方法。实验结果表明,ErlangShen机制能够减少约90%的陈旧读数量,与最新图式区块链事务处理机制Nezha相比,可将性能提升3~4倍。