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

The dimension of relevant text feature space and feature weight of substation main equipment defect information is high, so it is difficult to accurately select mining features. The Natural Language Processing (NLP) medium and short-term neural network model is used to realise the defect information text feature word segmentation in the log. After extracting the text features of defect information of main substation equipment with high categories to form the feature space; the TF-IDF algorithm is designed to calculate the importance weight of text keywords, judge the criticality of defect information text feature vocabulary, accurately locate defect information text features, and realise defect information text feature mining. Experiments show that the algorithm has high precision for specific word segmentation of massive substation main equipment log information.

The authors propose a novel object detection algorithm for identifying bird nests in medium voltage power line aerial images, which is crucial for ensuring the safe operation of the power grid. The algorithm utilises an improved Swin Transformer as the main feature extraction network of Fast R-CNN, further enhanced with a channel attention and modified binary self-attention mechanism to improve the feature representation ability. The proposed algorithm is evaluated on a newly constructed image dataset of medium voltage transmission lines containing bird nests, which are annotated and classified. Experimental results show that the proposed algorithm achieves satisfied accuracy and robustness in recognising bird nests compared to traditional algorithms.

An asymmetric semantic multi-level segmentation method based on depth learning is proposed in order to improve the precision and effect of semantic segmentation. A ‘content tree’ structure and an adjacency matrix are constructed to represent the parent-child relationship between each image sub region in a complete image. Through multiple combinations of spatial attention mechanism and channel attention mechanism, the similarity semantic features of the target object can be selectively aggregated, so as to enhance its feature expression and avoid the impact of significant objects. The asymmetric semantic segmentation model asymmetric pyramid feature convolutional network (APFCN) is constructed, and the path feature extraction and parameter adjustment are realised through APFCN. On the basis of APFCN network, a full convolution network is introduced for end-to-end image semantic segmentation. Combining the advantages of convolution network in extracting image features and the advantages of short-term and short-term memory network in solving long-term dependence, an end-to-end hybrid depth network is constructed for image semantic multi-level segmentation. The experimental results show that the mean intersection over Union value and mean pixel accuracy value are higher than that of the literature method, both of which are increased by more than 3%, and the segmentation effect is good.

In industrial environments, workers should wear workwear for safety considerations. For the same reason, smoking is also prohibited. Due to the supervision of monitoring devices, workers have reduced smoking behaviours and started wearing workwear. To meet the requirements for detecting these behaviours in real-time monitoring videos with high speed and accuracy, the authors proposed an improved YOLOv5 model with the Triplet Attention mechanism. This mechanism strengthens the connection between channel and spatial dimensions, focuses the network on important parts, and improves feature extraction. Compared to the original YOLOv5 model, the addition of the mechanism increases the parameters by only 0.04%. The recall rate of the YOLOv5 model is enhanced while its prediction speed is maintained with only a minimal increase in parameters. Experiment results show that, compared to the original model, the improved YOLOv5 has a recall rate of 78.8%, 91%, and 89.3% for detecting smoking behaviour, not wearing helmets, and inappropriate workwear, respectively.

Within the Industry 4.0 landscape, humans collaborate with cyber and physical elements to form human-cyber-physical systems (HCPS). These environments are increasingly complex and challenging workspaces due to increasing levels of automation and data availability. An effective system design requires suitable frameworks that consider human activities and needs whilst supporting overall system efficacy. Although several reviews of frameworks for technology were identified, none of these focused on the human in the system (moving towards Industry 5). The critical literature review presented provides a summary of HCPS frameworks, maps the considerations for a human in HCPS, and provides insight for future framework and system development. The challenges, recommendations, and areas for further research are discussed.

Online presence is becoming an important part of everyday's life and online communities may represent a significant source of engagement for the elderlies. Nevertheless, many may struggle to be online due to a lack of expertise, and a decentralised architecture may provide a solution by removing intermediaries, such as a webmaster, while not requiring expensive cloud solutions. However, issues concerning accessibility, security, and user experience have to be tackled. The paper focuses mainly on three issues: providing a human-readable domain, moderating content, and creating a reward system based on user reputation. An architecture is proposed based on Ethereum and Swarm. Smart contracts provide an automated set of rules to handle enterprise registration, content creation, and decision-making process, while Swarm serves both as distributed storage and the web host. Besides, in combination with Ethereum Name Service, Swarm provides a secure, distributed, and human-readable point of access to the web interface. The paper also describes an innovative two-token system where one token is meant to be a trustworthy reputation metre and the other is a spendable coin to get rewards. The final result is a fully decentralised, authenticated and moderated platform where users can aggregate and share their content presentations on the Internet.

Electricity substitution is an effective measure for reducing pollutant emissions and promoting the consumption of renewable energy. The current research lacks a quantitative analysis method for the factors affecting regional electricity substitution. This article proposes a decomposition model of the factors affecting electricity substitution based on Logarithmic Mean Divisia Index method to expand the depth and breadth of electricity substitution. Results show that: (1) Macro-level electricity substitution in Guangdong province develops steadily, with 1694.93 × 10⁸ kWh of total electricity substitution quantity during the period 2006–2020. The electricity substitution quantity in production sector accounts for 83.92% of the total, which is much larger than that in household sector. (2) Sustained improvement of labour productivity gives the largest contribution (3592.32 × 10⁸ kWh) to the increase of electricity substitution in production sector during the period 2006–2020. Living standard effect has the largest contribution (452.21 × 10⁸ kWh) to the increase of electricity substitution in household sector during the period 2006–2020. Electrification level effect and population effect have significant impact on electricity substitution development, while industrial structure has little impact on electricity substitution. (3) Energy intensity effect and energy consumption growth effect negatively influence electricity substitution in Guangdong province. The decline of energy intensity in the production sector has driven the decrease of electricity substitution quantity, contributing −2540.36 × 10⁸ kWh to electricity substitution during the period 2006–2020. Policy implications like continuously promoting breakthroughs in electricity substitution technologies, improvement of electrification rate in the sectors with potential for substitution, and improvement the price mechanism for fossil fuels and electricity can promote the deepening development of electricity substitution and the realisation of the carbon neutrality goal.

This article studies a distributed consensus-based estimation problem for discrete time-varying non-linear systems with missing measurements and Denial of Service (DoS) attacks. The probability of missing measurements is independent for each sensor. The communication link between sensor nodes is unreliable and subjected to DoS attacks. To achieve accurate state estimation against missing measurements, a local estimator with compensation mechanism is designed for each sensor node. A stochastic event-triggered mechanism is used to lessen additional information transfer. Based on this, a distributed consensus-based estimator is constructed by continually fusing local neighbours information matrixs and vectors. Moreover, the analysis of the designed estimator boundedness is presented. Finally, the effectiveness of the proposed algorithm is verified by three numerical examples.

A non-linear large scale stochastic optimisation model for enhancing the oil production and the recovery factor of the offshore oil reservoirs is proposed. The model aims at minimising the miss-match between mathematical model and the actual dynamic behaviour of the reservoir and the exploitation time, while maximising the oil production and the recovery factor. The model involves the three dimension (3D) oil reservoirs equipped with a few vertical injection and production wells. The limited number of wells is one of the major features of the common oil reservoirs in the middle-east region. The proposed model consists of the primarily mathematical model of the 3D reservoir, a model update algorithm and a large scale constrained non-linear optimisation algorithm. The input to this model is the daily production rate of the oil, natural gas and water produced from the oil reservoir and the output is the optimal injection rate to be injected to the injection wells in order to maximise the oil production and the recovery factor. In order to evaluate the performance of this model, the authors apply this model on part of one of the Iran's offshore oil reservoirs and study the performance improvement due to the proposed model and compare its performance with the performance of the available Improved Oil Recovery (IOR) technique. It is illustrated that the proposed model can increase the oil production from the reservoir up to 47.96% and reduce the exploitation period up to 66.66% compared with those of the available technique.

With the emergence of powerful and low-energy Internet of Things devices, deep learning computing is increasingly applied to resource-constrained edge devices. However, the mismatch between hardware devices with low computing capacity and the increasing complexity of Deep Neural Network models, as well as the growing real-time requirements, bring challenges to the design and deployment of deep learning models. For example, autonomous driving technologies rely on real-time object detection of the environment, which cannot tolerate the extra latency of sending data to the cloud, processing and then sending the results back to edge devices. Many studies aim to find innovative ways to reduce the size of deep learning models, the number of Floating-point Operations per Second, and the time overhead of inference. Neural Architecture Search (NAS) makes it possible to automatically generate efficient neural network models. The authors summarise the existing NAS methods on resource-constrained devices and categorise them according to single-objective or multi-objective optimisation. We review the search space, the search algorithm and the constraints of NAS on hardware devices. We also explore the challenges and open problems of hardware NAS.