Parallel Network Research Articles

Bidirectional Efficient Attention Parallel Network for Segmentation of 3D Medical Imaging

Currently, although semi-supervised image segmentation has achieved significant success in many aspects, further improvement in segmentation accuracy is necessary for practical applications. Additionally, there are fewer networks specifically designed for segmenting 3D images compared to those for 2D images, and their performance is notably inferior. To enhance the efficiency of network training, various attention mechanisms have been integrated into network models. However, these networks have not effectively extracted all the useful spatial or channel information. Particularly for 3D medical images, which contain rich spatial and channel information with tightly interconnected relationships between them, there remains a wealth of spatial and channel-specific information waiting to be explored and utilized. This paper proposes a bidirectional and efficient attention parallel network (BEAP-Net). Specifically, we introduce two modules: Supreme Channel Attention (SCA) and Parallel Spatial Attention (PSA). These modules aim to extract more spatial and channel-specific feature information and effectively utilize it. We combine the principles of consistency training and entropy regularization to enable mutual learning among sub-models. We evaluate the proposed BEAP-Net on two public 3D medical datasets, LA and Pancreas. The network outperforms the current state of the art in eight algorithms and is better suited for 3D medical images. It achieves the new best semi-supervised segmentation performance on the LA database. Ablation studies further validate the effectiveness of each component of the proposed model. Moreover, the SCA and PSA modules proposed can be seamlessly integrated into other 3D medical image segmentation networks to yield significant performance gains.

Few-shot bronze vessel classification via siamese fourier networks

Exploring ancient Chinese artifacts is crucial for analyzing East Asian technological development, with bronze vessel being the critical element. Bronze vessels, typically featuring intricate carvings, hold historical significance and provide valuable insights into past civilizations. However, identifying bronze patterns can be challenging for human vision, and most RGB-domain methods fail to capture periodic designs. Addressing these issues, we propose the Siamese Fourier Networks (SFN), a parallel network model designed for few-shot regular pattern classification. The Siamese network can differentiate between intricate shapes, while Fourier features enable the extraction of regular textures. To optimize parallel networks, we combine the BCE loss and focal contrastive loss, balancing positive and negative samples. Moreover, we introduce the Bronze Vessel Dataset, featuring 527 samples with diverse shapes and unbalanced distributions. Extensive experiments with advanced few-shot methods demonstrate the superiority of SFN and focal mechanism, significantly improving accuracy.

Nearby allocation of emergency repair resources for multiple faults in distribution networks based on parallel CNN algorithm

In order to improve the efficiency of emergency repair for multiple faults in distribution networks, a method for allocating emergency repair resources for multiple faults in distribution networks based on parallel convolutional neural network (CNN) algorithm is studied. This method uses a matrix operation based multiple fault detection method for distribution networks. After determining the location of multiple faults based on the direction of fault power, the principle is to allocate emergency repair resources nearby for multiple faults, with the goal of minimizing economic losses caused by the fault point and minimizing fault repair time. The objective function for allocating emergency repair resources nearby is constructed, and parallel CNN algorithm is used to solve the problem by classification, to find the feasible solution with the minimum mean square error between the objective function and the feasible solution set for nearby allocation of emergency repair resources, and it is used as the optimal solution for nearby allocation of repair resources. The experimental results show that when the proposed method is used to allocate emergency repair resources for multiple faults in the distribution network, the optimal time for setting the repair plan is 0.53 and 0.96 s, respectively, with an average allocation accuracy of 91%. It has been confirmed that this method can achieve the optimal decision of resource allocation plan in a short period of time, improving the efficiency of emergency repair.

Generative adversarial network for Multimodal Contrastive Domain Sharing based on efficient invariant feature-centric growth analysis improved brain tumor classification

ABSTRACT Efficient and accurate classification of brain tumor categories remains a critical challenge in medical imaging. While existing techniques have made strides, their reliance on generic features often leads to suboptimal results. To overcome these issues, Multimodal Contrastive Domain Sharing Generative Adversarial Network for Improved Brain Tumor Classification Based on Efficient Invariant Feature Centric Growth Analysis (MCDS-GNN-IBTC-CGA) is proposed in this manuscript.Here, the input imagesare amassed from brain tumor dataset. Then the input images are preprocesssed using Range – Doppler Matched Filter (RDMF) for improving the quality of the image. Then Ternary Pattern and Discrete Wavelet Transforms (TPDWT) is employed for feature extraction and focusing on white, gray mass, edge correlation, and depth features. The proposed method leverages Multimodal Contrastive Domain Sharing Generative Adversarial Network (MCDS-GNN) to categorize brain tumor images into Glioma, Meningioma, and Pituitary tumors. Finally, Coati Optimization Algorithm (COA) optimizes MCDS-GNN’s weight parameters. The proposed MCDS-GNN-IBTC-CGA is empirically evaluated utilizing accuracy, specificity, sensitivity, Precision, F1-score,Mean Square Error (MSE). Here, MCDS-GNN-IBTC-CGA attains 12.75%, 11.39%, 13.35%, 11.42% and 12.98% greater accuracy comparing to the existingstate-of-the-arts techniques, likeMRI brain tumor categorization utilizing parallel deep convolutional neural networks (PDCNN-BTC), attention-guided convolutional neural network for the categorization of braintumor (AGCNN-BTC), intelligent driven deep residual learning method for the categorization of braintumor (DCRN-BTC),fully convolutional neural networks method for the classification of braintumor (FCNN-BTC), Convolutional Neural Network and Multi-Layer Perceptron based brain tumor classification (CNN-MLP-BTC) respectively.

Advanced Physics-informed neural networks for numerical approximation of the coupled Schrödinger–KdV equation

Physics-informed neural networks (PINNs) has been shown to be an effective tool for solving partial differential equations (PDEs). PINNs incorporate the PDEs residual into the loss function, seamlessly integrating it as part of the neural network architecture. This novel methodology has exhibited success in tackling a wide range of both forward and inverse PDE problems. However, a limitation of the first generation of PINNs is that they usually have limited accuracy even with many training points. Here, we propose two advanced methodologies: PINNs utilizing an H1 loss function and parallel physics-informed neural networks (P-PINNs). The former involves modifying the loss function with the H1 norm, while the latter entails solving the coupled Schrödinger–Korteweg–de Vries (Sch–KdV) equation separately with two parallel networks. These advancements are designed to enhance both accuracy and training efficiency. We have extensively tested these two advanced methods through a series of experiments and demonstrated the accuracy and effectiveness in approximation of the Sch–KdV system.

Single Image Super-Resolution via Wide-Activation Feature Distillation Network.

Feature extraction plays a pivotal role in the context of single image super-resolution. Nonetheless, relying on a single feature extraction method often undermines the full potential of feature representation, hampering the model's overall performance. To tackle this issue, this study introduces the wide-activation feature distillation network (WFDN), which realizes single image super-resolution through dual-path learning. Initially, a dual-path parallel network structure is employed, utilizing a residual network as the backbone and incorporating global residual connections to enhance feature exploitation and expedite network convergence. Subsequently, a feature distillation block is adopted, characterized by fast training speed and a low parameter count. Simultaneously, a wide-activation mechanism is integrated to further enhance the representational capacity of high-frequency features. Lastly, a gated fusion mechanism is introduced to weight the fusion of feature information extracted from the dual branches. This mechanism enhances reconstruction performance while mitigating information redundancy. Extensive experiments demonstrate that the proposed algorithm achieves stable and superior results compared to the state-of-the-art methods, as evidenced by quantitative evaluation metrics tests conducted on four benchmark datasets. Furthermore, our WFDN excels in reconstructing images with richer detailed textures, more realistic lines, and clearer structures, affirming its exceptional superiority and robustness.

Mapping model of ribbon contour and tool influence function based on distributed parallel neural networks in magneto-rheological finishing

Magnetorheological finishing (MRF) stands out as a notable polishing technology, characterized by high precision and minimal damage. However, establishing an accurate and practical model for the tool influence function (TIF) of MRF poses a significant challenge. In this paper, a TIF modeling method of MRF based on distributed parallel neural networks is proposed for the first time. Assessment of the viability of this approach through multiple sets of robot-assisted MRF experiments is detailed. The experimental results conclusively demonstrate the successful intelligent prediction of TIF, with key indicators such as volume removal rate and peak removal rate achieving an average prediction accuracy exceeding 95%. This method can remarkably advance the intelligence of the TIF model in MRF and serve as a valuable reference for other optical fabrication methods.

Cnn-trans model: A parallel dual-branch network for fundus image classification

The existence of fundus diseases not only endangers people’s vision, but also brings serious economic burden to the society. Fundus images are an objective and standard basis for the diagnosis of fundus diseases. With the continuous advancement of computer science, deep learning methods dominated by convolutional neural networks (CNN) have been widely used in fundus image classification. However, the current CNN-based fundus image classification research still has a lot of room for improvement: CNN cannot effectively avoid the interference of repeated background information and has limited ability to model the whole world. In response to the above findings, this paper proposes the CNN-Trans model. The CNN-Trans model is a parallel dual-branch network, which is the two branches of CNN-LSTM and Vision Transform (ViT). The CNN-LSTM branch uses Xception after transfer learning. As the original feature extractor, LSTM is responsible for dealing with the gradient disappearance problem in neural network iterations before the classification head, and then introduces a new type of lightweight attention mechanism between Xception and LSTM: Coordinate Attention, so as to emphasize the key information related to classification and suppress the less useful repeated background information; while the self-attention mechanism in the ViT branch is not limited by local interactions, it can establish long-distance dependence on the target and extract global features. Finally, the concatenation (Concat) operation is used to fuse the features of the two branches. The local features extracted by the CNN-LSTM branch and the global features extracted by the ViT branch form complementary advantages. After feature fusion, more comprehensive image feature information is sent to the to the classification layer. Finally, after a large number of experimental tests and comparisons, the results show that: the CNN-Trans model achieved an accuracy of 80.68% on the fundus image classification task, and the CNN-Trans model has a classification that is comparable to the state-of-the-art methods. performance..

GCPDFFNet: Small Object Detection for Rice Blast Recognition.

Early detection of rice blast disease is pivotal to ensure rice yield. We collected in situ images of rice blast and constructed a rice blast dataset based on variations in lesion shape, size, and color. Given that rice blast lesions are small and typically exhibit round, oval, and fusiform shapes, we proposed a small object detection model named GCPDFFNet (global context-based parallel differentiation feature fusion network) for rice blast recognition. The GCPDFFNet model has three global context feature extraction modules and two parallel differentiation feature fusion modules. The global context modules are employed to focus on the lesion areas; the parallel differentiation feature fusion modules are used to enhance the recognition effect of small-sized lesions. In addition, we proposed the SCYLLA normalized Wasserstein distance loss function, specifically designed to accelerate model convergence and improve the detection accuracy of rice blast disease. Comparative experiments were conducted on the rice blast dataset to evaluate the performance of the model. The proposed GCPDFFNet model outperformed the baseline network CenterNet, with a significant increase in mean average precision from 83.6 to 95.4% on the rice blast test set while maintaining a satisfactory frames per second drop from 147.9 to 122.1. Our results suggest that the GCPDFFNet model can accurately detect in situ rice blast disease while ensuring the inference speed meets the real-time requirements.

A dual-head output network attack detection and classification approach for multi-energy systems

In today’s digital age, multi-energy systems (MES) have become an indispensable part of the social infrastructure, providing people with diversified energy support such as electricity, gas, water and so on. However, with the increasing popularity and networking of MES, the network security threats they face are becoming more and more serious, especially the threat of network attacks. This makes it essential to detect attacks on MES and precisely classify attack types in order to establish effective defense strategies. In this paper, a Dual-Head output network attack detection and classification method based on parallel CNN-BiLSTM network is proposed. The method adopts a parallel structure and can process different aspects of information at the same time, speeding up the training and inference process of the whole network, making the system respond more quickly to potential network attacks, and improving real-time and efficiency. The multi-model fusion structure can give full play to the advantages of CNN and BiLSTM in processing different types of data, so that the system can capture attack characteristics more comprehensively in many aspects, and improve the overall detection and classification performance. The dual-head output not only improves the system’s ability to accurately detect attacks, but also can effectively classify different types of attacks in detail, which helps to formulate more targeted defense strategies. In addition, in order to effectively evaluate our proposed method, the network traffic data required for the experiment were collected in an environment very similar to the actual operating environment of a multi-energy system. Finally, the experiment verifies that our method can not only realize effective detection of network attacks, but also accurately classify different types of attacks.

Introduction to Section of Dynamic Mathematics: Dynamic Fuzzy Sets Theory: Parallel Fuzzy Sprt – Elements and Their Applications

There is a need to develop an instrumental mathematical base for new technologies. The task of the work is to create new approaches for this by introducing new concepts and methods. Our mathematics is unusual for a mathematician, because here the fulcrum is the action, and not the result of the action as in classical mathematics. Therefore, our mathematics is adapted not only to obtain results, but also to directly control actions, which will certainly show its benefits on a fundamentally new type of neural networks with directly parallel calculations, for which it was created. Any action has much greater potential than its result. Significance of the article: in a new qualitatively different approach to the study of complex processes through new mathematical hierarchical parallel fuzzy dynamic structures, in particular those processes that are dealt with by Synergetics. Our approach is not based on deterministic equations that generate self-organization, which is very difficult to study and gives very small results for a very limited class of problems and does not provide the most important thing - the structure of self-organization. We are just starting from the assumed structure of self-organization, since we are interested not so much in the numerical calculation of this as in the structure of self-organization itself, its formation (construction) for the necessary purposes and its management. Although we are also interested in numerical calculations. Nobel laureates in physics 2023 Ferenc Kraus and his colleagues Pierre Agostini and Anna Lhuillier used a short-pulse laser to generate attosecond pulses of light to study the dynamics of electrons in matter. According to our Theory of singularities of the type synthesizing, its action corresponds to singularity ↑I↓h q , which allows one to reach the upper level of subtle energies to manipulate lower levels. In April 2023, we proposed using a short-pulse laser to achieve the desired goals by a directly parallel neural network [1]. We then proposed the fundamental development of this directly parallel neural network. In the article new mathematical structures and operators are constructed through action - “fuzzy containment” [2]. Here, the construction of new mathematical structures and operators is carried out with generalization to “parallel fuzzy containment”. The significance of our articles is in the formation of the presumptive mathematical structure of subtle energies, this is being done for the first time in science, and the presumptive classification of the mathematical structures of subtle energies for the first time. The experiments of the 2022 Nobel laureates Asle Ahlen, John Clauser, Anton Zeilinger and the experiments in chemistry Nazhipa Valitov eloquently demonstrate that we are right and that these studies are necessary. Be that as it may, we created classes of new mathematical structures, new mathematical singularities, i.e., made a contribution to the development of mathematics.

The damage localization method in the composite panel by multifrequency guided wave signal using parallel network

Fiber-reinforced composite materials find extensive application in the realm of aeronautics; nonetheless, during operational phases, structural integrity may be compromised due to external impacts or extreme environmental conditions. Consequently, the imperative is the necessity of vigilantly monitoring the structural state. The structural health monitoring (SHM) technology based on ultrasonic guided waves can achieve long-distance and wide-ranging damage detection utilizing a minimal number of cost-effective piezoelectric transducers. However, traditional damage identification methods necessitate the analysis of the multi-modal characteristics of guided waves and the deployment of dense sensor arrays. In this paper, we propose a network model that leverages multi-frequency guided-wave signals to achieve spatiotemporal parallelism, incorporating residual blocks and convolutional attention mechanisms (GCRA). This model accurately estimates damage locations with an $R^2$ value of 0.9147. The average error for the six randomly selected damage locations is 0.4275 cm. The results demonstrate that the GCRA facilitates the identification of damaged positions with a high degree of confidence.

ReLAP-Net: Residual Learning and Attention Based Parallel Network for Hyperspectral and Multispectral Image Fusion

Remote sensing applications require high-resolution images to obtain precise information about the Earth???s surface. Multispectral images have high spatial resolution but low spectral resolution. Hyperspectral images have high spectral resolution but low spatial resolution. This study proposes a residual learning and attention-based parallel network based on residual network and channel attention. The network performs image fusion of a high spatial resolution multispectral image and a low spatial resolution hyperspectral image. The network training and fusion experiments are conducted on four public benchmark data sets to show the effectiveness of the proposed model. The fusion performance is compared with classical signal processing???based image fusion techniques. Four image metrics are used for the quantitative evaluation of the fused images. The proposed network improved fusion ability by reducing the root mean square error and relative dimensionless global error in synthesis and increased the peak signal-to-noise ratio when compared to other state-of-the-art models.

Meta-Path Semantic and Global-Local Representation Learning Enhanced Graph Convolutional Model for Disease-Related miRNA Prediction.

Dysregulation of miRNAs is closely related to the progression of various diseases, so identifying disease-related miRNAs is crucial. Most recently proposed methods are based on graph reasoning, while they did not completely exploit the topological structure composed of the higher-order neighbor nodes and the global and local features of miRNA and disease nodes. We proposed a prediction method, MDAP, to learn semantic features of miRNA and disease nodes based on various meta-paths, as well as node features from the entire heterogeneous network perspective, and node pair attributes. Firstly, for both the miRNA and disease nodes, node category-wise meta-paths were constructed to integrate the similarity and association connection relationships. Each target node has its specific neighbor nodes for each meta-path, and the neighbors of longer meta-paths constitute its higher-order neighbor topological structure. Secondly, we constructed a meta-path specific graph convolutional network module to integrate the features of higher-order neighbors and their topology, and then learned the semantic representations of nodes. Thirdly, for the entire miRNA-disease heterogeneous network, a global-aware graph convolutional autoencoder was built to learn the network-view feature representations of nodes. We also designed semantic-level and representation-level attentions to obtain informative semantic features and node representations. Finally, the strategy based on the parallel convolutional-deconvolutional neural networks were designed to enhance the local feature learning for a pair of miRNA and disease nodes. The experiment results showed that MDAP outperformed other state-of-the-art methods, and the ablation experiments demonstrated the effectiveness of MDAP's major innovations. MDAP's ability in discovering potential disease-related miRNAs was further analyzed by the case studies over three diseases.

PMTT: Parallel multi-scale temporal convolution network and transformer for predicting the time to aging failure of software systems

Software aging is one of the significant factors affecting the reliability and availability of long-running software systems, such as Android, Cloud systems, etc. The time to aging failure (TTAF) prediction for software systems plays a crucial role in proactive rejuvenation scheduling through machine learning or statistical analysis techniques, due to its ability to determine when to perform rejuvenation to mitigate the aging effects. However, software aging characterization is relatively complicated, and only fitting the variations for a single aging indicator cannot grasp the comprehensive degradation process across different case systems; moreover, since software systems often exhibit long and short-term inherent degradation characteristics, existing prediction models possess a poor ability for modeling both global and local information simultaneously. To tackle the above problems, a novel TTAF prediction framework based on the parallel multi-scale temporal convolution network and transformer (named PMTT) is proposed, by mapping various system running indicators reflecting the software aging to TTAF. PMTT possesses the following distinctive characteristics. First, a local feature extraction module that contains multiple channel TCNs with different scales is developed to extract inherent local information from the raw input. Second, in a parallel manner, a global feature extraction module integrating transformer blocks is built to extract global information representation synchronously using the self-attention mechanism. Afterward, high-level global–local features extracted from different channels are fused, and TTAF is estimated through two fully connected regression layers using the fused features. The proposed PMTT has been compared to seven competitors using run-to-failure data collected from Android and OpenStack systems. The experiments have demonstrated the superiority of PMTT, showing an average improvement of 11.2%, 9.0%, and 9.3% in performance across three evaluation metrics compared with the optimal baseline model.

Energy price prediction based on decomposed price dynamics: A parallel neural network approach

The growing uncertainties of the world due to geographic tensions, and weather conditions challenge the traditional method to achieve a reliable price prediction of energy future for both asset pricing and risk management. Following the initial success of deep learning models in energy price prediction, we attempt to establish a better architecture of neural networks to improve the prediction accuracy. We propose a novel Parallel Hybrid Neural Network (PHNN) model that utilizes independent sub-networks to effectively capture the distinct features of various sequences. Empirical results demonstrate that the PHNN model exhibits a significant performance enhancement of 16.68%, 14.09%, and 2.34% over the EMD–LSTM, the informer model, and the single LSTM model, respectively. In particular, the PHNN outperforms the single LSTM, which is trained on the same inputs, by 2.34% overall while by a remarkable 4.11% during event periods. This suggests that the PHNN derives notable benefits from its distinct architecture, particularly during the initial phase of extreme events characterized by significant price trend changes. Additionally, the study explores the potential benefits of incorporating additional event-tracking indicators for energy futures price forecasting. However, the findings suggest that these indicators may not consistently provide effective complementary information.

Analog Implementation of a Spiking Neuron with Memristive Synapses for Deep Learning Processing

Analog neuromorphic prototyping is essential for designing and testing spiking neuron models that use memristive devices as synapses. These prototypes can have various circuit configurations, implying different response behaviors that custom silicon designs lack. The prototype’s behavior results can be optimized for a specific foundry node, which can be used to produce a customized on-chip parallel deep neural network. Spiking neurons mimic how the biological neurons in the brain communicate through electrical potentials. Doing so enables more powerful and efficient functionality than traditional artificial neural networks that run on von Neumann computers or graphic processing unit-based platforms. Therefore, on-chip parallel deep neural network technology can accelerate deep learning processing, aiming to exploit the brain’s unique features of asynchronous and event-driven processing by leveraging the neuromorphic hardware’s inherent parallelism and analog computation capabilities. This paper presents the design and implementation of a leaky integrate-and-fire (LIF) neuron prototype implemented with commercially available components on a PCB board. The simulations conducted in LTSpice agree well with the electrical test measurements. The results demonstrate that this design can be used to interconnect many boards to build layers of physical spiking neurons, with spike-timing-dependent plasticity as the primary learning algorithm, contributing to the realization of experiments in the early stage of adopting analog neuromorphic computing.

Efficient neural network-aided seismic life-cycle cost optimization of steel moment frames

In this paper, a novel and efficient neural network-based methodology is proposed to achieve seismic total cost optimization of steel moment-resisting frames in a timely manner. The computational burden of an optimization process based on performing nonlinear time-history analysis is prohibitively high. To address this crucial issue, a new and efficient neural network model is proposed in this paper to accurately predict the nonlinear time history response of steel frames during the optimization process. In the proposed neural network model, an ensemble of parallel neural networks is used to provide excellent prediction accuracy. In addition, a new repairability constraint is proposed to check the seismic damage level of structures during the optimization process with the aid of the proposed neural network model. Moreover, an efficient metaheuristic algorithm is used to achieve the optimization task. Two numerical examples are illustrated to demonstrate the efficiency of the proposed methodology. The results show that the proposed neural network model outperforms the existing standard models in terms of prediction accuracy. Furthermore, it is shown that by using the proposed methodology, the optimal seismic total cost of steel frames increases by less than 2.5%, yet their seismic collapse capacity increases by at least 30%.

Client selection and resource scheduling in reliable federated learning for UAV-assisted vehicular networks

Federated Learning (FL), a promising deep learning paradigm extensively deployed in Vehicular Edge Computing Networks (VECN), allows a distributed approach to train datasets of nodes locally, e.g., for mobile vehicles, and exchanges model parameters to obtain an accurate model without raw data transmission. However, the existence of malicious vehicular nodes as well as the inherent heterogeneity of the vehicles hinders the attainment of accurate models. Moreover, the local model training and model parameter transmission during FL exert a notable energy burden on vehicles constrained in resources. In view of this, we investigate FL client selection and resource management problems in FL-enabled UAV-assisted Vehicular Networks (FLVN). We first devise a novel reputation-based client selection mechanism by integrating both data quality and computation capability metrics to enlist reliable high-performance vehicles. Further, to fortify the FL reliability, we adopt the consortium blockchain to oversee the reputation information, which boasts tamper-proof and interference-resistant qualities. Finally, we formulate the resource scheduling problem by jointly optimizing the computation capability, the transmission power, and the number of local training rounds, aiming to minimize the cost of clients while guaranteeing accuracy. To this end, we propose a reinforcement learning algorithm employing an asynchronous parallel network structure to achieve an optimized scheduling strategy. Simulation results show that our proposed client selection mechanism and scheduling algorithm can realize reliable FL with an accuracy of 0.96 and consistently outperform the baselines in terms of delay and energy consumption.

Parallel camera network: Motion-compensation vision measurement method and system for structural displacement

This study proposes a motion-compensation vision measurement method and system for structural displacement, termed the parallel camera network (PCN). Based on the fixedly connected calibration cameras and measurement cameras, the PCN method effectively address the optical measurement problems of the unstable observation platform. Unlike existing ego-motion compensation methods, the PCN method allows for reference or stationary points that are distant from the camera stations, significantly reducing motion-induced measurement errors of the observation platform. Laboratory tests showed that compensating for platform motion tripled measurement accuracy using the PCN method. In practical applications, the system has been successfully deployed for monitoring subsidence of operational high-speed rail piers and the arch axis during large-span arch bridge construction. The proposed method and system significantly enhance the engineering flexibility of visual measurements.