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.

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.image

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.

This paper proposes a deep learning method based on a COVID‐19 clinical trial data relation extraction model. The model adopts MPNet model, bidirectional‐GRU network, MAtt mechanism and CRF reasoning layer integrated architecture to improve the problem that static word vectors cannot represent ambiguity through pre‐training language models.image

Video has become an essential medium to monitoring, identification and knowledge sharing. For industrial applications, especially Industrial Internet of Things (IIoT), videos encoded with specific protocols are transferred to smart gateways. In a typical IIoT scenario, the protocol of the video is firstly recognised, which prepares for subsequent video tasks. Due to the constrained resources in such scenarios, the video quality can be deteriorated during encoding and compression processes, which is challenging for IIoT. Recently, there have been extensive works focussing on the protocol identification (PI) and video quality enhancement (VQE) tasks on IIoT edge devices using deep neural networks (DNNs). Since DNNs often require high computational resources, complex networks can hardly be deployed on edge devices. An IIoT system which can efficiently identify the stream protocol and enhance the video quality is proposed in this study. The light‐weighted network designs and inference optimisation techniques have been proposed for PI and VQE to realise efficient deployments. Our proposed system employed on an IIoT edge device can achieve an accuracy of higher than 97.52% with fast inference speed for PI. For the VQE task, our system has demonstrated superior performance (15.230 FPS, 0.773 FPS/W) in comparison with the state‐of‐the‐art methods.

imageOur proposed system consists of two flows, protocol identification (PI) and video quality enhancement (VQE). For the given video stream from edge devices or data links, the system first employs the PI flow to recognize the underlying protocol. Once the recognition is finished, the desired video content is decoded and processed with the VQE flow to improve the video quality.

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 manuscript presents a novel smartphone sensors‐based system architecture for human activity recognition comprising three parts: OCPSO feature selection, multi‐input one‐dimensional convolutional neural network (MI‐1D‐CNN) classifier, and deep decision fusion. Experiment results show that the proposed architecture can improve recognition accuracy and robustness for sensors‐based human activity recognition.image

Highly connected smart power systems are subject to increasing vulnerabilities and adversarial threats. Defenders need to proactively identify and defend new high‐risk access paths of cyber intruders that target grid resilience. However, cyber‐physical risk analysis and defense in power systems often requires making assumptions on adversary behaviour, and these assumptions can be wrong. Thus, this work examines the problem of inferring adversary behaviour in power systems to improve risk‐based defense and detection. To achieve this, a Bayesian approach for inference of the Cyber‐Adversarial Power System (Bayes‐CAPS) is proposed that uses Bayesian networks (BNs) to define and solve the inference problem of adversarial movement in the grid infrastructure towards targets of physical impact. Specifically, BNs are used to compute conditional probabilities to queries, such as the probability of observing an event given a set of alerts. Bayes‐CAPS builds initial Bayesian attack graphs for realistic power system cyber‐physical models. These models are adaptable using collected data from the system under study. Then, Bayes‐CAPS computes the posterior probabilities of the occurrence of a security breach event in power systems. Experiments are conducted that evaluate algorithms based on time complexity, accuracy and impact of evidence for different scales and densities of network. The performance is evaluated and compared for five realistic cyber‐physical power system models of increasing size and complexities ranging from 8 to 300 substations based on computation and accuracy impacts.

This manuscript proposes the use of probabilistic graphical model such as Bayesian Networks to construct Bayesian Attack Graph and further utilize it to infer cyber states for making the power grid cyber‐attack resilient. The inference techniques are evaluated for communication network modelled for WSCC 9 bus, 8 substation model and IEEE‐300 bus system.image