IET Cyber-Physical Systems: Theory and Applications最新文献

With the rapid development of biomedical research and information technology, the number of clinical medical literature has increased exponentially. At present, COVID-19 clinical text research has some problems, such as lack of corpus and poor annotation quality. In clinical medical literature, there are many medical related semantic relationships between entities. After the task of entity recognition, how to further extract the relationships between entities efficiently and accurately becomes very critical. In this study, a COVID-19 clinical trial data relationship extraction model based on deep learning method is proposed. The model adopts MPNet model, bidirectional-GRU (BiGRU) network, MAtt mechanism and Conditional Random Field inference layer integration architecture and improves the problem that static word vector cannot represent ambiguity through pre-trained language model. BiGRU network is used to replace the current Bi directional long short term memory structure and simplify the network structure of Long Short Term Memory to improve the training efficiency of the model. Through comparative experiments, the proposed method performs well in the COVID-19 clinical text entity relation extraction task.

In order to deal with the problems of complex optimisation model and computing speed in the multi-objective operation control of the power distribution network, this paper is oriented to the basic model and process of the feeder partition decoupling method for the distribution network. Firstly, the necessity of developing partition decoupling for the distribution network is expounded according to the development status and control mode of the complex distribution network. Secondly, the objective models commonly used in the distribution network optimisation control are given to illustrate the importance of partition decoupling for the complex distribution network, including line loss and voltage offset. Finally, three general decoupling equivalent models are presented, namely Ward equivalent model, virtual generator equivalent model, and radial equivalent independent model, and then the partition decoupling equivalent process is proposed.

In this study, a partition optimisation method for distribution network is proposed to realise decoupling coordination, which provides a research basis for optimising power flow and operation regulation. Firstly, a partition model of distribution network is established, in which the electrical distance, parallel computing efficiency, and operation stability indexes are considered at the same time; Secondly, the AHC algorithm is used to realise the automatic search partition of the distribution network, and the class spacing measurement factors of point-to-point, cluster-to-cluster are considered in this method. Finally, the Distributed Sequential Quadratic Programming for Distributed Generation (DSQP-DG) is introduced, and the parallel decoupling coordination of the distribution network is realised by alternate iteration of its inner and outer layers.

In order to accurately extract the fault features of arc high impedance grounding of low-voltage distribution lines and judge the fault feature types of arc high impedance grounding of low-voltage distribution lines, a fault feature extraction method for arc high impedance grounding of low-voltage distribution lines based on Bayesian network optimisation algorithm is proposed. According to the model of arc high impedance grounding fault based on Thomson’s principle, the parameter information of each transmission signal in arc high impedance grounding fault is extracted. Through the denoising method of arc high impedance grounding signal based on combined filter, the noise information of transmission signal in case of arc high impedance grounding fault is removed and the signal purity is improved. The detection and recognition method for fault characteristics of arc high impedance grounding of low-voltage distribution lines based on Bayesian network optimisation algorithm is used to detect and judge the fault characteristics of the abnormal characteristics of the denoised transmission signal, and complete the fault feature extraction. After testing, this method can accurately and real-time extract the fault characteristics of arc high impedance grounding of low-voltage distribution lines, and has application value.

Human activity recognition (HAR) with smartphone sensors is a significant research direction in human-cyber-physical systems. Aiming at the problem of feature redundancy and low recognition accuracy of HAR, this paper presents a novel system architecture comprising three parts: feature selection based on an oppositional and chaos particle swarm optimization (OCPSO) algorithm, multi-input one-dimensional convolutional neural network (MI-1D-CNN) utilizing time-domain and frequency-domain signals, and deep decision fusion (DDF) combining D-S evidence theory and entropy. The proposed architecture is evaluated on the UCI HAR and WIDSM datasets. The results highlight that OCPSO performs better than particle swarm optimization (PSO) in feature selection, convergence speed, and recognition accuracy. Moreover, it is shown that for the MI-1D-CNN classifier, the frequency-domain signals (95.96%) perform better than time-domain signals (95.66%). In addition, this paper investigates the impact of the convolution layers, feature maps, filter sizes, and decision fusion methods on recognition accuracy. The results demonstrate that the DDF method (97.81%) outperforms single-layer decision fusion in improving the recognition accuracy on the UCI HAR dataset.

The clock synchronisation of the power system is to realise the clock synchronisation of the whole network. The clock synchronisation network is composed of the clock synchronisation systems of power grids at all levels. Dispatching agencies, power plants, and substations have their clock synchronisation systems. By using computer technology, communication technology, and network technology, combined with the topological structure of the power grid and geographic information of the geographic information system, automatically process the key index data to ensure that the distribution network project can manage the power consumption and distribution 24 h without interruption. Considering the particularity of power, the accuracy of the receiver is strictly required, and so are the positioning, speed measurement, and time accuracy. Test indicators study a set of accurate and reliable test and evaluation methods. A gross error processing method based on the k-means algorithm is proposed. Experiments show that gross errors can be well identified and eliminated in one-dimensional and multidimensional data. Considering that invalid data may be hidden in the test data, to improve the identification accuracy without affecting the detection of normal gross errors, based on the proposed k-means algorithm, the number of visible satellites is added. Because the magnitude difference between the number of visible satellites and the original three-dimensional positioning error data is relatively large, it is normalised first. The processing data is extended from three-dimensional to four-dimensional. The experimental simulation shows that it can not only identify invalid data but also ensure a good effect of gross error elimination, reduce possible economic losses, bring significant direct and indirect economic benefits, and verify the feasibility and promotional value of the online monitoring platform through practice.

Cyber-physical systems (CPS) are engineered systems with built-in seamless integration of physical and cyber components. Fundamental developments in sensing, communication, control, and computing technologies endow CPS with flexibility, adaptability, scalability, and robustness. The availability and size of input-output data generated along with the control of CPS bring a unique opportunity for machine learning techniques to advance the theory of dynamical control systems, by learning control rules directly from data. Integration of input-output data into adaptive, robust, predictive, and distributed control policies holds the key to exploiting the potential of learning and optimisation in the CPS designs. There are several challenges related to sampling, transmission, synchronization, as well as associated cyber security when merging contemporary data-based and traditional model-based control techniques for CPS.

The overarching goal of this special issue is to bring together innovative developments on the interface between learning, control, and optimisation targeting cyber-physical opportunities emerging from power, transportation, and manufacturing systems. Through a rigorous peer review process, three articles have been accepted, which are summarised below.

In the study, “Learning-based distributed adaptive control of heterogeneous multi-agent systems with unknown leader dynamics”, the authors develop a distributed adaptive tracking control method for heterogeneous multi-agent systems with unknown leader dynamics in a directed graph. In contrast to the reported leader-following consensus studies, the prior knowledge of the leader is supposed to be cognised by some or all of the followers, the situation that the leader's dynamics are totally unrecognised but can be learned for each individual follower is considered. A data-driven learning algorithm using the system’s data is developed to reconstruct the unknown systems matrix. Then, an adaptive distributed dynamic compensator is exploited to provide the leader's state estimation in a directed graph. Afterwards, a dynamic output feedback control law for each agent is projected. Theoretical analysis shows that the proposed algorithms not only ensure that all followers can identify the unknown system matrix but also guarantee that the distributed output leader-following consensus control with heterogeneous dynamics is achieved without any global information.

In the study, “Sampled-data synchronisation of singular Markovian jump system (SMJS): application to a DC motor model”, the authors consider the sampled-data synchronisation problem for SMJSs subject to aperiodic sampled-data control. Firstly, by constructing mode-dependent one-sided loop-based Lyapunov functional (LBLF) and two-sided LBLF, two different stochastically admissible conditions are suggested for error SMJSs with aperiodic sampled-data. It is guaranteed that the slave system is stochastically synchronised to t

A robust optimal attitude controller for hexarotor helicopters is proposed. Compared to the previous research studies on hexarotors, the current study takes account of the influences of non-linear and coupling dynamics, structured and unstructured uncertainties, external time-varying disturbances, and input time delays. A linear time-invariant system is derived for each Euler angle by considering the actual rotational dynamic model as a nominal non-linear system plus an equivalent perturbation, including non-linear and coupling dynamics, uncertainties, disturbances, and time delays. Using this approach, a Linear Quadratic Regulation controller is first designed for the nominal linear system of each angle to accomplish the desired tracking performances. Then, a robust compensator based on the robust compensation method is proposed to counteract the effects of the equivalent perturbation on the system. Moreover, the robust attitude tracking property and uniform asymptotical stability of the closed-loop hexarotor system are proved using Lyapunov stability theory. Several simulations have been performed to demonstrate the effectiveness and robustness of the proposed controller. Finally, experimental results are provided to confirm the robust performance of the proposed controller.

The peak regulation capacity of gas-fired power plants has always been an important flexibility resource of the power grid. Under the guidance of carbon emission reduction, the coal power units are gradually shut down, making the role of gas-fired power plants more important. However, in practice, gas-fired power plants often fail to show satisfactory flexibility. The main reasons are as follows: (1) Part of the capacity mechanism fails to effectively encourage gas-fired power plants to provide reliable flexibility and (2) the unreliability of fuel supply for gas-fired power plants. Aiming at these problems, the current capacity mechanism in different countries is first summarised and the applicability of the capacity mechanism for gas-fired power plants under the government regulation and market-oriented environment is analysed, respectively. Then, the characteristics of power dispatching and gas dispatching are analysed to explore the internal reasons for the unreliable fuel supply in gas-fired power plants. Based on the above analysis, the gas-electric coordination mechanism adapted to different development stages is proposed to solve the problem that the flexibility of gas-fired power plants cannot be guaranteed. In summary, through the research of this study, it is found that the main reason for the limited flexibility of gas-fired power plants is the lack of coordination among multiple entities belonging to different energy systems, such as electricity and gas. The cooperation mechanism proposed is an attempt to realise the cooperation between the electric system and the gas system, which provides the reference for closer collaboration among multiple energy systems in the future.