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The optimization of an integrated coal gangue system of mining, dressing, and backfilling in deep underground mining is a multi-objective and complex decision-making process, and the factors such as spatial layout, node location, and transportation equipment need to be considered comprehensively. In order to realize the intellectualized location of the nodes for the logistics and transportation system of underground mining and dressing coal and gangue, this paper establishes the model of the logistics and transportation system of underground mining and dressing coal gangue, and analyzes the key factors of the intellectualized location for the logistics and transportation system of coal and gangue, and the objective function of the node transportation model is deduced. The PSO–QNMs algorithm is proposed for the solution of the objective function, which improves the accuracy and stability of the location selection and effectively avoids the shortcomings of the PSO algorithm with its poor local detailed search ability and the quasi-Newton algorithm with its sensitivity to the initial value. Comparison of the particle swarm and PSO–QNMs algorithm outputs for the specific conditions of the New Julong coal mine, as an example, shows that the PSO–QNMs algorithm reduces the complexity of the calculation, increases the calculation efficiency by eight times, saves 42.8% of the cost value, and improves the efficiency of the node selection of mining–dressing–backfilling systems in a complex underground mining environment. The results confirm that the method has high convergence speed and solution accuracy, and provides a fundamental basis for optimizing the underground coal mine logistics system. Based on the research results, a node siting system for an integrated underground mining, dressing, and backfilling system in coal mines (referred to as MSBPS) was developed.

Nowadays tracking a theft vehicle or monitoring continuously vehicles, tracking systems have escalated quickly. The major concern of the proposed system is identifying vehicle theft and monitoring its status. We can use this in several ways such as delivering security to vehicles such as bikes or cars and many other vehicles and if there are any goods in the vehicle, with the help of this we can keep track of the vehicle in maps. This is very useful for tracking the movement of a vehicle from any location at any time. In this, we can make a tracking system that is modelled and executed for tracking the signal of any enabled vehicle from any geographical location.

It is essential to ensure the safety, comfort, mobility, and quality of enormous traffic commonly seen in smart cities every day. Intelligent transport systems (ITS) are introduced to provide such facilities. A Vehicular Ad-Hoc Network (VANET) is a network made up of multiple vehicular nodes that can freely join and exit the network. VANET is an important part of the ITS development process for all applications. Several researchers from all over the world have been drawn to this new research subject. VANETs are mainly used to make sure the protection of vehicles on the street and to enhance visitors’ performance and luxury for individuals. Due to the growing mobility of vehicles inside VANETs, it’s far tough to set up a safe and efficient route between the source and destination nodes. To choose the path between source and destination, this study analyses two factors: the least hop count and the sequence number. Firstly, the path is established using the multi-casting method in this research work. After that, the data is routed to select the root nodes from the network in the multi-casting method. Then the path is selected amid source and destination using a root node. The projected method is deployed on Network Simulator-2 (NS2), and the analytic outcomes are obtained to evaluate specific parametric values. The result shows that packet loss is reduced by 59.1% and delay is reduced by 18.2% when the multi-casting technique is used for establishing a path between source to destination as compared to the broadcasting method besides it throughput has also increased by 74% in the multi-casting domain compared to other existing methods.

This study aims to enhance the intelligence level of power grid fault analysis to address increasingly complex fault scenarios and ensure grid stability and security. To this end, an intelligent information system for power grid fault analysis, leveraging improved computer communication technology, is proposed and developed. The system incorporates a novel fault diagnosis model, combining advanced communication technologies such as distributed computing, real-time data transmission, cloud computing, and big data analytics, to establish a multi-layered information processing architecture for grid fault analysis. Specifically, this study introduces a fusion model integrating Transformer self-attention mechanisms with graph neural networks (GNNs) based on conventional fault diagnosis techniques. GNNs capture the complex relationships between different nodes within the grid topology, effectively identifying power transmission characteristics and fault propagation paths across grid nodes. The Transformer’s self-attention mechanism processes time-series operational data from the grid, enabling precise identification of temporal dependencies in fault characteristics. To improve system response speed, edge computing moves portions of fault data preprocessing and analysis to edge nodes near data sources, significantly reducing transmission latency and enhancing real-time diagnosis capability. Experimental results demonstrate that the proposed model achieves superior diagnostic performance across various fault types (e.g., short circuits, overloads, equipment failures) in simulation scenarios. The system achieves a fault identification and location accuracy of 99.2%, an improvement of over 10% compared to traditional methods, with an average response time of 85 milliseconds, approximately 43% faster than existing technologies. Moreover, the system exhibits strong robustness in complex scenarios, with an average fault prediction error rate of just 1.1% across multiple simulations. This study provides a novel solution for intelligent power grid fault diagnosis and management, establishing a technological foundation for smart grid operations.

The origin–destination (OD) matrix describes traffic flow information between regions. It is a critical input for intelligent transportation systems (ITS). However, obtaining the OD matrix remains challenging due to high costs and privacy concerns. Synthetic data, which have the same statistical distribution of real data, help address privacy issues and data scarcity. Based on Generative Adversarial Networks (GAN), OD matrix generation models, which can effectively generate a synthetic OD matrix, help to address the challenge of obtaining OD matrix data in ITS research. However, existing OD matrix generation methods can only handle with tens of nodes. To address this challenge, this study proposes the Origin–Destination Progressive Growing Generative Adversarial Networks (OD-PGGAN) for large-scale OD matrix generation task which adapt the PGGAN architecture. OD-PGGAN adopts a progressive learning strategy to gradually learn the structure of the OD matrix from a coarse to fine scale. OD-PGGAN utilizes multi-scale generators and discriminators to perform generation and discrimination tasks at different spatial resolutions. OD-PGGAN introduces a geography-based upsampling and downsampling algorithm to maintain the geographical significance of the OD matrix during spatial resolution transformations. The results demonstrate that the proposed OD-PGGAN can generate a large-scale synthetic OD matrix with 1024 nodes that have the same distribution as the real sample and outperforms two classical methods. The OD-PGGAN can effectively provide reliable synthetic data for transportation applications.

Wireless sensor networks usually obtain the location of an unknown node by measuring the distance between the unknown node and its neighbouring anchors. To enhance both localisation accuracy and localisation success rates, the authors introduce a new neural network-based node localisation scheme. The new scheme is distinct because it can make the trained network model completely relevant to the topology via online training and correlated topology-trained data and therefore attain more efficient application of the neural networks and more accurate inter-node distance estimation. It is also distinct in adopting both received signal strength indication and hop counts to estimate the inter-node distances, to improve the distance estimation accuracy as well as localisation accuracy at no additional cost. Experimental evaluation is conducted to measure the performance of the proposed scheme and other artificial intelligent-based node localisation schemes. The results show that, at reasonable cost, the new scheme constantly produces higher localisation success rates and smaller localisation errors than other schemes.

The growing demand for bandwidth and spectrum has inspired the ongoing efforts to establish the future 5G network supporting vertical sectors such as cyber-physical systems (CPS). Cooperative communication is one of the requisite techniques to improve coverage, network capacity and reduce power consumption in the network. In this paper, a symbiotic two-phase intelligent transmission is considered. The first phase occurs between the source and the candidate relays, and involves the selection of a set of “reliable relays”. The second phase occurs between the reliable relays and the destination, and involves the selection of the “best relay” for transmission. Dynamic relay selection using k-means clustering is used to detect the most significant correlation between all the channel state information (CSI) attributes in the system. The work identified the reliable relays while reducing the number of relay nodes for the second transmission phase. Contextual scenarios are created with typical network configuration using three geographical locations Coventry, Birmingham and London. An experimental validation is done with Omnet++ environment for the scenarios of three geographical locations. A natural grouping of mobile users is carried out leveraging the relay capabilities. The results are validated using support vector machine (SVM) classification algorithm. Considering urban environment deployment of relay nodes, metrics such as signal-to-noise-plus-interference ratio (SINR), attenuation, signal to noise ratio (SNR), link quality, k-means clustering, accuracy, and root mean square error (RMSE) are investigated for the Direct-2-Direct (D2D) capable relays. It was observed that the proposed technique both outperforms the other fixed-parameter relay selection techniques and improves with larger datasets unlike the other techniques.