[Objective] Anchors are typical mass concrete structures found in large bridges, characterized by large structural sizes, complex boundary conditions with irregular shapes, low reinforcement ratios, high crack resistance requirements, and challenges in temperature control and crack prevention. The development of an adaptive, intelligent cooling control method and system is crucial for crack prevention and improving concrete pouring quality. [Methods] This paper proposes an intelligent cooling control method for bridge anchorage, including: (1) The basic control principles of heat balance of supply and use, accurate control, and online warning. (2) A fundamental intelligent control strategy involving real thermal field simulation and a temperature-flow coupling control algorithm. The combined influence of temperature and flow is considered when predicting the cooling system parameters. This study uses a hybrid approach involving a long short-term memory neural network (LSTM) and proportional integral derivative (PID) control algorithms to predict the future water flow rate based on the current concrete and cooling system state parameters, facilitating the temperature-to-flow mapping. (3) A “multiple terminal-edge computing-cloud storage” control model is implemented, which incorporates edge computing within the control cabinet, providing localized endpoint services to improve data transmission performance, ensure real-time processing, and reduce latency. Cloud computing uses machine learning to provide instructions for adjusting temperature and flow rates based on the deviations between the actual and target temperature control curves. Furthermore, fault recognition and rapid diagnosis functions are also implemented. Intelligent cooling control equipments and code platforms are developed for realizing online perception, real analysis, feedback control, remote diagnostics, and early warning systems for the cooling process. The system comprises water supply, reversing, control and heat exchange subsystems, and a multiterminal software platform based on WeChat and the web. [Results] This paper adopted simulation, equipment development, and field application methods based on the Longmen Bridge project. Real temperature field simulation calculations were conducted, the temperature distribution during the cooling process was analyzed, and the impact of heat transfer from the upper layer of concrete, as well as the design of cooling pipes, was optimized. Parameters such as water temperature, water flow, concrete temperature, and temperature gradient were analyzed. Furthermore, as part of a long-term temperature monitoring process, the impact of heat transfer from the upper layer of concrete was assessed to reduce the temperature difference between layers. A personalized water-cooling strategy was proposed, and the timing of the water supply was adjusted. [Conclusions] The established temperature-flow coupling control algorithm, model, equipment, and platform achieve real-time monitoring, analysis, control, continuous optimization, and early warning of water-cooling information online and remotely. The study results are successfully applied to the west anchorage of the Longmen Bridge. No temperature cracks are observed on the bridge site, which reduce manpower and water consumption. The results can be used as a design and construction reference for thermal cracking control in similar projects.

[Objective] The Manila subduction zone is the primary source of potential large tsunamis in the South China Sea, which may result in severe coastal disasters. This study aims to evaluate the tsunami hazards faced by South China Sea Islands caused by earthquakes with maximum magnitudes through assessing the earthquake with maximum possible magnitude in the Manila subduction zone and simulating the process of induced tsunamis. [Methods] The seismic potential was evaluated using the negative dislocation inversion model TDEFNODE based on GPS horizontal velocity field data. The acquired distribution of the locking and slip deficit along the Manila subduction zone was first used to assess the seismic potential. The earthquakes with a magnitude of 8.9 and a 500-year return period were selected as the maximum possible earthquake to design extreme earthquake tsunami events. This study comprehensively considered the impact of the epicenter, focal depth, and heterogeneity in the fault slip on tsunamis, and about 700 000 tsunami events under the condition of magnitude 8.9 were simulated for further evaluation. Both uniform and heterogeneous slip models were adopted to describe fault slips in the tsunami events. Considering that a larger fault slip is more likely to occur in areas with a higher degree of fault locking, the distribution of fault locking was also introduced into the heterogeneous slip model as a constraint for the random slip distribution. The tsunami events were simulated by the unit-source superposition method proposed by our group previously, which could efficiently simulate the propagation of tsunami waves based on a precomputed database and provided the offshore tsunami wave heights of major islands with small computational cost. [Results] The findings revealed that even under the same magnitude, the height of tsunami waves exhibited significant randomness. The tsunami wave height in Dongsha Island varied between 1.8 m and 6.2 m during 8.9-magnitude earthquake tsunami events. The heterogeneity of fault slip had a significant impact on tsunami wave height, and conventional models that neglected heterogeneous slip distribution would underestimate the tsunami wave height by approximately 20%-50%. In terms of spatial distribution, with tsunami wave heights exceeding 4 m, Nanshazhou, Nandao, and Beidao in the Xuande Islands and Dongsha Islands were worst affected, while the tsunami hazard in the Nansha Islands was much smaller. [Conclusions] This work enhances the tsunami hazard assessment model by introducing fault locking into the random slip model as a constraint, enabling the description of the fault slip to be more realistic than the conventional uniform slip assumption. The maximum possible tsunami hazard faced by major islands in the South China Sea has been quantified, which offers effective support for tsunami hazard prevention and reduction in these islands.

[Objective] The development of offshore wind power technology is of great importance to China’s dual carbon strategy, and the estimation of scour depths is a widely studied topic in the study of scour around offshore monopiles. The scale effect usually limits the applicability of the scour depth estimation equation obtained from traditional small-scale physical models. Thus, by analyzing the applicability of the existing scour depth prediction formulae under different scale conditions, this study aims to obtain a method to improve the estimation accuracy of scour depth formulae and derive a concise formula to calculate the scour hole volume according to the relationship between the scour hole volume and scour depth. [Methods] Large-scale (1∶13) experiments were conducted to study the maximum equilibrium scour depth and local scour volume around a monopile under irregular waves. The Keulegan–Carpenter (KC) numbers ranged from 4 to 9. According to the large-scale test data, the scour depth formulae under the actions of regular waves and irregular waves were compared in terms of applicability. The self-limitation of the formulae was analyzed, the influence of the KC number definition on the calculation of equilibrium scour depth was examined, and the different KC number definitions under irregular waves were compared in terms of applicability. Furthermore, through the analysis of the relationship between the scour hole volume and the scour depth, the factors affecting scour hole volume were determined, and according to this analysis, a formula for determining the scour hole volume was derived. [Results] The large-scale experimental results show that: 1) The estimation of the scour depth by the existing formulae can be guaranteed to be within the deviation range of ±50% under regular waves, whereas it fell below the deviation line of 50% under irregular waves. 2) The depth of a backfilled scour hole for a given KC is different from the scour depth obtained with an initially flat bed and with the same KC. 3) The maximum orbital velocity of the wave and peak wave period are used to redefine the KC number and applied to each formula. Moreover, the estimation accuracy is significantly improved, and predictions of the formulae were within the deviation range of ±50%, except for some formulae with limited applicability. 4) The KC number is an important factor affecting the scour hole volume. The formula for predicting the scour hole volume based on the existing equilibrium scour depth formula is within the deviation range of ±25%. [Conclusions] Through the analysis of large-scale experimental data and previous data, the limitations of traditional scour depth formulae obtained using small-scale experiments are demonstrated under irregular waves. The estimation accuracy of the existing formula under irregular waves can be improved by improving the KC number calculation method. Additionally, according to the results of three-dimensional terrain scanning, the relationship between the local scour volume around a monopile and the existing equilibrium scour depth formulae is derived. Overall, this study provides a concise and convenient method for estimating the protection material amount around monopile.

[Objective] As the global community moves toward carbon peak and carbon neutrality targets, the issue of carbon emissions related to water resources has emerged as a significant area of research. The social water cycle, characterized by intensive energy consumption and carbon emission, plays a pivotal role in this context. Factors such as water-related energy usage and efficiency directly affect the economy and carbon emissions of a society. Consequently, reducing carbon emissions during the social water cycle process has become a vital strategy in curbing greenhouse gas emissions. Therefore, it is crucial to accurately assess the energy consumption and carbon emissions throughout the entire social water cycle process and thoroughly understand the spatial distribution and intensity characteristics of energy consumption and carbon emissions at each stage. This study aims to identify key factors for energy saving and emission reduction within the social water cycle. [Methods] Using the life cycle assessment method, we first constructed a life cycle carbon accounting system for the social water cycle system, including four major segments: water withdrawal, supply, use, and drainage. We then established a comprehensive measurement model for social water cycle carbon emissions based on a distributed geographic model. Using the Yellow River Basin as an example, we calculated the energy consumption and carbon emissions of the social water cycle over the entire life cycle of the basin in 2017 and studied their spatial distribution characteristics. This provided a simulation method and scientific basis for establishing a more sustainable, low-carbon social water cycle. [Results] Our findings revealed that in 2017, the downstream area of the Yellow River Basin had the highest amount of carbon emissions per unit area, i.e., approximately 7.4 times higher than that in the upstream area. Among the four major segments, the water use segment had the highest amount of carbon emissions. In particular, residential water use accounted for 59.7% of the carbon emissions from the water use segment and 54.7% of the total carbon emissions from the social water cycle. This identifies it as a key segment for carbon emission reduction within the social water cycle. In terms of carbon emission intensity in each segment of the social water cycle in the Yellow River Basin, the order was: water use > drainage > water supply > water withdrawal. [Conclusions] The Yellow River Basin exhibits significant differences in carbon emissions between its upstream and downstream regions. Moreover, the intensity of carbon emissions varies greatly across different segments of the water cycle. In light of these findings, we propose several strategies for energy conservation and carbon reduction in key areas and segments of the social water cycle. First, water supply and drainage systems should be improved, and the energy efficiency of water supply and sewage treatment should be enhanced. Second, the development and utilization of clean energy sources, such as solar energy and wind energy, should be prioritized. Finally, in the industrial sector, the circulating cooling water system should be optimized, and water recycling systems should be implemented; in the residential sector, the promotion of water-saving and energy-saving appliances is recommended to improve the comprehensive efficiency of water and energy in domestic water use segments.

[Objective] With the continuous advancement in urbanization in recent years, the urban impervious rate has increased; hence, urban areas have to primarily rely on urban pipe networks for drainage. However, most of these pipe networks constructed in the early stages of cities cannot manage the extreme rainstorms caused by global warming, resulting in severe urban waterlogging. Low impact development (LID) facilities can effectively reduce urban waterlogging by increasing infiltration surface. However, the LID allocation method using the traditional full-range equal proportion (FREP) method, which is based on different land-use types, is usually adopted for determining the layout of LID facilities. The layout location of these facilities is determined by the distribution of land-use types. There will be no LID facilities in areas with severe waterlogging using FREP method, whereas more LID facilities will be available in areas with less overflow, thereby wasting LID facility resources. LID facilities can be fully utilized to better resolve waterlogging control effects if the layout of the waterlogging source is considered. Therefore, to address the above issue, this paper proposes the LID allocation method using the overflow point upstream tracking (OPUT) method. [Methods] OPUT method used storm water management model (SWMM) to build a drainage model, simulate pipe network overflow in different return periods, lock the overflow point, track the nodes of pipelines upstream of the overflow point layer by layer, and determine the corresponding catchment area level. LID facilities were laid on the catchment area depending on the land-use type. FREP method and OPUT method were compared from three aspects: runoff, waterlogging, and economy. [Results] The results obtained using the OPUT method show that in terms of relieving waterlogging, the reduction percentage of the overflow volume is 12.82% to 1.73% under design rainfall of 180 minutes (short-duration rainfall) with increasing return period and is 8.16% to 1.12% under design rainfall of 1,440 minutes (long-duration rainfall) for a unit LID area. Meanwhile, the FREP method yields reduction percentages of 1.87%—1.22% and 1.87%—0.83% under short- and long-duration rainfall, respectively. From the runoff reduction perspective, the reductions in runoff volume and peak runoff obtained using the two methods are different for LID per unit layout area under different return periods. The reduction obtained for peak runoff using the FREP method is always better than that of the OPUT method; however, as the return period increases, the reduction exhibited by the OPUT method is closer to that of the FREP method. For the reduction in runoff volume, the FREP method exhibits better results when the return period is small, whereas that of the OPUT method is better for a large return period. When the two methods have approximately the same reduction effect on overflow, under short- and long-duration rainfall, the cost for the layout requirement obtained using the OPUT method is 61.5—325 and 73.7—333 million yuan, respectively, as the return period increases. Meanwhile, with an increase in the return period, the short- and long-duration the costs for the layout requirement obtained using the FREP method are 66.1—423 and 137—423 million yuan, respectively. [Conclusions] The LID layout requirements obtained using the OPUT method exhibit a better reduction effect and economy for relieving waterlogging.

[Significance] Since the turn of the 21st century, artificial intelligence (AI) has advanced considerably in many domains, including government affairs. Furthermore, the emergence of deep learning has taken the development of many AI fields, including natural language processing (NLP), to a new level. Language models (LMs) are key research directions of NLP. Referred to as statistical models, LMs were initially used to calculate the probability of a sentence; however, in recent years, there have been substantial developments in large language models (LLMs). Notably, LLM products, such as the generative pretrained transformer (GPT) series, have driven the rapid revolution of large language research. Domestic enterprises have also researched LLMs, for example, Huawei’s Pangu and Baidu's enhanced language representation with informative entities (ERNIE) bot. These models have been widely used in language translation, abstract construction, named-entity recognition, text classification, and relationship extraction, among other applications, and in government affairs, finance, biomedicine, and other domains. [Progress] In this study, we observe that improving the efficiency of governance has become one of the core tasks of the government in the era of big data. With the continuous accumulation of government data, traditional statistical models relying on expert experience and local features gradually suffer limitations during application. However, LLMs, which offer the advantages of high flexibility, strong representation ability, and effective results, can rapidly enhance the intelligence level of government services. First, we review the research progress on early LMs, such as statistical LMs and neural network LMs. Subsequently, we focus on the research progress on LLMs, namely the Transformers series, GPT series, and bidirectional encoder representations from transformers (BERT) series. Finally, we introduce the application of LLMs in government affairs, including government text classification, relationship extraction, public opinion risk identification, named-entity recognition, and government question answering. Moreover, we propose that research on LLMs for government affairs must focus on multimodality, correctly benefit from the trend of “model as a service,” focus on high data security, and clarify government responsibility boundaries. Additionally, a technical path for studying LLMs for government affairs has been proposed. [Conclusions and Prospects] The application of LLMs in government affairs mainly focuses on small-scale models, lacking examples of application in large-scale models. Compared with smaller models, large models offer many advantages, including high efficiency, broader application scenarios, and more convenience. These advantages can be understood as follows. In terms of efficiency, large models are usually trained on a large amount of heterogeneous data, thus delivering better performance. In terms of application scenarios, large models gradually support multimodal data, resulting in more diverse application scenarios. In terms of convenience, we emphasize the “pretraining + fine-tuning” mode and the invocation method of interfaces, making LLMs more convenient for research and practical applications. This study also analyzes the issues suffered by LLMs, specifically from the technological and ethical perspectives, which have resulted in a panic to a certain extent. For example, ChatGPT has generated many controversies, including whether the generated files are novel, whether using ChatGPT will lead to plagiarism and ambiguity as to who are property rights owners for the generated files. Overall, it can be said that LLMs are in the stage of vigorous development. As the country promotes research on AI and its application in government affairs, LLMs will play an increasingly crucial role in the field.

[Objective] With the emerging applications of cloud computing and artificial intelligence, traditional network topology structures are no longer suitable for the new traffic pattern. Developing novel network topologies depends on high-fidelity network emulation tools; moreover, traditional network simulators and emulators find it difficult to satisfy the requirements of high-bandwidth and -throughput network scenarios. TurboNet based on programmable switches could achieve high-fidelity network topology emulation, but its application to scenarios where multiple programmable switches are employed for the network topology emulation is difficult due to the physical link bandwidth limitations between different programmable switches. [Methods] To this end, this paper proposes a solution based on a nonlinear integer programming, which can partition the network topology and accommodate the physical link bandwidth constraints, allowing the virtual ports on each subtopology to be mapped using TurboNet on a single programmable switch, thereby enabling the emulation of a large network topology. Herein, a multilevel partitioning algorithm is proposed to further enhance the solver efficiency. The algorithm first utilizes the Metis algorithm to reduce the dimensionality of the input topology, considerably decreasing the size of the topology and enhancing the efficiency of the solver. Additionally, by combining the characteristics of network emulation scenarios, this paper proposes a solution utilizing the match-action table for programmable switches to record the probe forwarding path; as a result, this path is no longer restricted by the restriction of the programmable switch on the packet header length. Specifically, the match-action table would match the current hop number of probes with the corresponding measurement task ID. Subsequently, the existing forwarding port for the corresponding measurement task could be acquired. When sending a new probe, corresponding forwarding table entries need to be added through the control plane based on the probe forwarding path. [Results] The experimental results performed on the Internet Topology Zoo and FatTree reveal that the proposed Metis-based multilevel partition algorithm can effectively improve the solution efficiency of the algorithm. However, due to the limitations of constraint-based methods, for topologies such as FatTree with a small number of switches but a large number of ports, the effect of the multilevel partition algorithm is relatively not evident. The experimental results also demonstrate that the solution for the network telemetry on larger topologies of using the match-action tables on programmable switches to record the probe forwarding paths enables the application of Netview to emulate network topologies on longer forwarding paths. Furthermore, this paper validates the feasibility of the network emulation scheme and performance evaluation with multiple programmable switches on a testbed with two Tofino programmable switches and one server. [Conclusions] By reducing the node dimensionality in the original network topology using the Metis algorithm, the number of constraints in the partitioning problem can be effectively reduced, thereby remarkably enhancing the solution efficiency of the partitioning algorithm. Moreover, the match-action table on programmable switches can be used to record the forwarding paths of the probe, thereby removing the restrictions on the forwarding paths; this solution could be applied to network telemetry on large-scale network topologies.

[Objective] In recent years, a large number of nonconvex, highly nonlinear, multimodal, and multivariable complex optimization problems have emerged in scientific and engineering technology design due to the continuous development of science and technology. Owing to their advantages such as simple programming, flexible operation, and efficient optimization, intelligent optimization algorithms have become research hotspots to address diverse complex optimization problems in engineering applications. They have been successfully used to solve practical problems such as neural networks, resource allocation, and target tracking. In this research, multiple strategies were developed to improve the existing monarch optimization algorithm to address its shortcomings, such as slow convergence speed, low optimization accuracy, and ease of falling into local extremum. [Methods] First, the forward normal cloud generator is used to perform nonlinear cloud operation on the parent monarch butterfly, increasing the number of candidate solutions and improving the local development ability of the algorithm. Subsequently, an opposition-based learning strategy based on convex lens imaging is used to the current optimal individual which is generated by normal cloud generator to generate new individuals and improve the convergence accuracy and speed of the algorithm. Finally, adaptive strategies are incorporated into the adjustment operator to diversify the population. [Results] Several experiments were performed on benchmark functions to verify the performance of the algorithm: (1) Different strategies proposed were analyzed using ablation experiments to verify their effectiveness. The results revealed that the proposed strategies can effectively improve the algorithm's performance. (2) The improved algorithm was compared with other swarm intelligent optimization algorithms, and the results revealed that the improved algorithm can achieve the best results on most test functions. (3) The improved algorithm was also compared with other improved versions of monarch optimization algorithm, and the results revealed that the improved algorithm exhibited more advantages such as fast convergence speed and high convergence precision. (4) The Wilcoxon rank sum test and Friedman test were used to verify the performance of the proposed algorithm. The results revealed that the improved algorithm is superior to other algorithms. [Conclusions] The optimization and comparison results of the pressure vessel design and welded beam design in engineering applications further verified the superiority of the improved algorithm in addressing real-world engineering problems.

[Objective] Multi-label text classification (MLTC) is a fundamental task in natural language processing. It selects the most relevant labels from the predefined label set to annotate texts. Most previous studies have been conducted on standardized and comprehensive datasets with manual annotations, which require strict quality control and are difficult to procure. In the real annotation process, some related labels are always lost, resulting in incomplete annotation. The impact of this missing label on the model is primarily divided into two forms: 1) degradation effect: numerous missing labels lead to a decrease in the number of positive example labels related to the text, and the model cannot obtain more comprehensive and complete information under the training of a few related labels; 2) misleading effect: numerous missing labels are treated as negative example labels that are unrelated to the text during model training, thereby misleading the model to learn the opposite information. MLTC for incomplete annotation aims to learn text from incomplete annotation datasets to classifiers for related labels while minimizing their impact on the model and improving the efficiency of multi label classification. All existing methods for MLTC involve supervised training on manually annotated data, which cannot solve missing incomplete labels. [Methods] This article proposes partial labeling self-training for the MLTC (PST) framework based on local annotation, which alleviates the negative impact of missing labels on the model by supplementing the use of missing labels. Particularly, the PST framework first utilized the basic multi label text classification model to train on incompletely labeled datasets to obtain a teacher model. Furthermore, the teacher model automatically scored large-scale unlabeled and incompletely labeled data. A dual threshold mechanism was then used to divide the labels into states based on their scores to obtain positive, negative, and other labels. Finally, the teacher model was updated using label information from three different states through joint training. To comprehensively evaluate the performance of the PST framework, we randomly deleted some labels from the training set of the English dataset AAPD, according to different missing ratios, to construct incomplete annotated synthetic datasets with different degrees of missing data. Meanwhile, we manually corrected the incomplete CCKS2022 Task 8 dataset with incomplete annotations and used it as the real dataset for the experiment. [Results] Experiments on synthetic datasets showed that as the problem of annotation intensifies, the performance of multi label text classification models decreases sharply, and the PST framework could alleviate the speed of decline to some extent, in which the more the missing labels, the more obvious the relief. The experimental results of different multi-label classification teacher models on real datasets showed that the PST framework has varying degrees of improvement on different teacher models on incompletely annotated datasets, which fully proves the universality of the PST framework. [Conclusions] The PST framework is a model-independent plug-in framework that is compatible with various teacher models. We could fully utilize the external unlabeled data to optimize the teacher model, while supplementing the use of missing labels from incomplete labeled data, thereby weakening the impact of missing labels on the model. The experimental results indicate that our proposed framework is universal and can alleviate the impact of incomplete data annotation to some extent.

[Objective] Deep learning methods have been widely employed to enhance microscopic diffusion prediction in social networks. However, the existing methods have the problem of insufficient extraction of features in the information dissemination process. For example, these methods do not consider the impact of the propagation chain of the most recently infected nodes on the subsequent propagation of the message or the impact of changes in the neighboring nodes on the propagation path of the message. Therefore, the prediction accuracy is not high. To solve the above problems, describe the information diffusion process from multiple perspectives, and discover more hidden features, this paper proposes a microscopic diffusion prediction framework — multifeature fusion and deep learning for prediction (MFFDLP). [Methods] The microscopic diffusion prediction framework is divided into three main parts: extracting the static features from the network topology and the information diffusion sequence, capturing dynamic diffusion characteristics from the information diffusion graph, and predicting the next infected node. (1) First, node embedding and node structure context are extracted from historical friendship graphs and information diffusion sequences. The gate recurrent unit (GRU) is applied to mine the deep global temporal features from the connected vectors. To further enhance the role of the recently infected node, GRU is used to mine the local temporal features from the structure context of the node. These two features are fused to form the information diffusion sequence features. (2) Capture dynamic diffusion characteristics from the information diffusion graph. These features represent changes in users' interaction or interest. An information diffusion graph is built based on the historical information diffusion sequence. The diffusion graph is then divided into subgraphs in chronological order. A graph attention network is applied to capture the node features from each subgraph, and the edge features are aggregated from the node features. Using an embedding lookup method and fusing the nodes and their edge features, the dynamic diffusion characteristics of the users in an information diffusion sequence are obtained. (3) Predict the next infected node. To further analyze the context interaction within the diffusion sequences, a dual multihead self-attention mechanism is applied to separately capture the contextual information from information diffusion sequence features and node dynamic diffusion characteristics. Then, a fully connected layer and Softmax are used to predict the next infected node. Finally, experiments on three real networks show that the proposed method outperforms the state-of-the-art models. The experimental results demonstrate the unique advantages of MFFDLP for microscopic diffusion prediction. [Results] Comparative experimental results on three public datasets show that the proposed method outperforms the comparative methods by up to 9.98% in the accuracy of microscopic diffusion prediction. [Conclusions] This method comprehensively combines the friendship graph, information diffusion sequence, and diffusion graph. Multiple deep learning models are used to extract multiple features from static and dynamic perspectives. Comparative experiments on multiple datasets demonstrate that MFFDLP can mine and fuse multiple features more effectively, thus improving the prediction accuracy of information diffusion.

[Objective] A model involving an unknown signal/parameter undergoing a linear transformation followed by a componentwise nonlinear transformation is known as a generalized linear model (GLM). Estimating an unknown signal/parameter from nonlinear measurements is a fundamental problem in radar and communication fields, including applications such as one-bit radar, one-bit multiple-input multiple-output communication, and phase retrieval. The generalized approximate message passing (GAMP) algorithm is an efficient Bayesian inference technique that deals with GLM. GAMP has low computational complexity, excellent reconstruction performance, and the ability to automatically estimate noise variance and nuisance parameters. However, when the elements of the measurement matrix deviate from the sub-Gaussian distribution, the performance of GAMP considerably degrades. To address this issue, the generalized vector approximate message passing (GVAMP) algorithm is proposed, which employs the vector factor graph representation and expectation propagation to achieve good performance across a broader ensemble of measurement matrices. Moreover, the generalized unitary approximate message passing (GUAMP) algorithm, which employs the singular value decomposition technique for eliminating correlation within the measurement matrix, is introduced. GUAMP demonstrates increased robustness compared to GAMP and GVAMP, particularly under scenarios involving the correlated measurement matrix. However, the signal estimation error of GUAMP may exhibit fluctuations even after a sufficient number of iterations. In addition, as the correlation of the measurement matrix exceeds a threshold, the performance of GUAMP deteriorates compared to the adaptive GAMP (AD-GAMP) algorithm. Therefore, proposing a method to further enhance the robustness and performance of GUAMP is imperative. [Methods] This paper proposes an adaptive GUAMP (AD-GUAMP) algorithm. AD-GUAMP incorporates stepsize selection rules for the approximate message passing (AMP) and GAMP modules of GUAMP, enabling AMP and GAMP algorithms to converge to their stationary points and achieve improved performance. The details of the AD-GUAMP are described. The objective functions designed for the two modules are introduced. The stepsize increases provided that the objective function value continues to increase, indicating that the AMP and GAMP modules perform well and increasing the stepsize accelerates the algorithm to converge. Otherwise, the stepsize decreases, slowing down the GUAMP algorithm for convergence. [Results] Extensive numerical experiments are performed, and the results indicate the effectiveness of AD-GUAMP. Results reveal that the performance of AD-GUAMP is almost similar to GVAMP and better than AD-GAMP and GUAMP with a low-ranked or ill-conditioned measurement matrix. For the correlated measurement matrix, AD-GUAMP performs better than AD-GAMP, GUAMP, and GVAMP. [Conclusions] The performance of AD-GUAMP is improved with adaptive stepsize selection rules. Therefore, AD-GUAMP can be used in more challenging measurement matrix scenarios compared to AD-GAMP, GUAMP, and GVAMP.

[Objective] Parallel compressive sensing magnetic resonance imaging (p-CSMRI) algorithms aim to improve and refine the reconstructed image using partial k-space data sampled from multiple coils. Recently, learning-based model-driven p-CSMRI algorithms have attracted extensive attention because of their superior reconstruction quality. Nevertheless, their prior network architectures are typically designed empirically, lacking model interpretability and hampering the analysis of algorithm convergence. To address this problem, we introduce a provably bounded denoiser based on deep learning and incorporate it as a prior module into a model-driven p-CSMRI network. Moreover, we propose a deep unrolled p-CSMRI algorithm; its convergence can be explicitly analyzed. [Methods] First, to improve the sparse representation capability and learning speed of traditional tight frames, we extend the single tight frame to a dual-frame network. Because the image pixels vary in the spatial domain, a deep threshold network is developed to adaptively extract thresholds from the input images, thereby improving the generalization ability of the dual frames. Based on the dual frames integrated with the elaborated deep threshold network, we introduce a novel provably bounded deep denoiser. Second, we describe a p-CSMRI optimization model based on dual frames. The constructed optimization model is iteratively solved via the half-quadratic splitting solver, and the corresponding iterations are unfolded into a deep neural network that can be trained by end-to-end supervised learning. Finally, under reducing noise level conditions, the convergence of model-driven p-CSMRI algorithms is explicitly proved based on the bounded denoiser theory. The convergence theory of plug-and-play (PnP) imaging methods demonstrates that methods with decreasing noise levels can realize a fixed-point convergence under the assumption of a bounded denoiser. We explicitly prove that the proposed deep denoiser as the prior network is bounded. Based on this bounded property, we develop a model-driven p-CSMRI algorithmic framework with guaranteed convergence. [Results] Theoretically, we explicitly prove that the built deep denoiser as the prior network satisfies the bounded property and perform a convergence analysis of the proposed algorithm under the mild condition of gradually decreasing noise. Simulation experiments carried out on the knee MRI dataset from New York University disclose that, compared with the Modl, VN, and VS-Net algorithms, the proposed method realizes improvements of 1.70, 1.45, and 0.46 dB, respectively, in peak signal-to-noise ratio for reconstructed images under a fourfold acceleration factor. However, a comparative assessment of the proposed model with Modl, VN, and VS-Net algorithms concerning parameter memory and average inference time reveals that the model-driven p-CSMRI method based on the dual frames recommended in this study has a high number of parameters. Furthermore, the image inference time of the proposed method is lower than those of Modl and VN and slightly higher than that of VS-Net. Therefore, our proposed method shows a moderate level of computational complexity. [Conclusions] The model-driven p-CSMRI network algorithm proposed here, based on trainable dual frames, has a theoretical convergence guarantee and demonstrates stable performance in experiments. Moreover, our algorithm proved effective in reconstructing high-quality MR images. This work offers valuable insights into future research and development in the area of magnetic resonance imaging.

[Objective] To ensure successful assembly in the digital process of aviation aircraft, it is necessary to simulate the motion and measurement capabilities of the adjustment and measurement mechanisms, respectively, for flexible posture adjustment and high-precision target point measurement. However, commonly used simulation platforms in the aviation field, such as DELMIA, CATIA, and SA, cannot achieve comprehensive simulation that combines location planning, motion control evaluation, and measurement field analysis of the equipment. To achieve a comprehensive simulation of aviation assembly digital measurement and control under a uniform system, a simulation analysis method for aviation assembly measurement and control based on robot modeling was proposed. [Methods] The proposed method first models essential elements, such as components and tooling, motion adjustment mechanisms, measurement mechanisms, and environmental interference objects, in a digital assembly environment into different robots in a robot simulation platform and subsequently compiles control programs according to assembly requirements to enable the system to control the joint motion of each robot to perform simulation analysis according to the requirements, complete the verification of measurement and control algorithms, and further set dynamic optimization issues for measuring and adjusting parameters where existing simulation platforms, such as measurement equipment stations, cannot easily emulate information for optimization. To better illustrate the specific implementation and practical effects of this method, the simulation analysis of the measurement accessibility of multiple laser trackers to a large number of measurement points under high occlusion environments and the simulation planning of the stations of multiple laser trackers are taken as examples, and the robot modeling method of the measurement mechanism, the construction method of the simulation system, the principle of the simulation laser measurement system, and the implementation method of station planning are described in detail. [Results] The proposed method is applied to location planning of laser trackers in aircraft measurement fields, where a total of 25 measurement points distributed on the surface of the aircraft need to be accurately measured by four laser trackers. Approximately 1 s is needed to achieve measurement accessibility analysis for a single measurement point, and 1 401.5 s is needed to determine the optimal combination location of four laser trackers, which are efficient and have excellent visibility compared with other methods based on modeling software. How the parameters (e.g., grid density) can affect planning efficiency is discussed by executing multiple simulations with different parameters, and the measurement robustness of the optimal combination location is verified by applying random perturbation errors to the base position of the four laser trackers. [Conclusions] A simulation analysis method for aviation assembly measurement and control based on robot modeling, which can achieve comprehensive simulation of planning, control, and measurement, is proposed. Based on this method, a detailed application case is proposed for the analysis of the measurement accessibility of multiple laser trackers in complex environments and the combined location planning of multiple laser trackers to achieve higher measurement accuracy. The feasibility of the proposed method is verified, and the method is simple, efficient, and can be quickly replanned when the environment changes.

[Objective] Laser profilers are widely utilized in various fields owing to their high precision, noncontact, and low cost. However, the lens plane for traditional laser profilers is parallel to the imaging plane. Thus, the high-precision measurement range of a traditional laser profiler is limited by the camera's restricted depth of view. To address this issue, this study optimizes the traditional laser profiler design and proposes a calibration method. [Methods] Specifically, this study establishes a constant focus optical path in the laser profiler by tilting the lens to meet the Scheimpflug condition, wherein the imaging, lens, and light planes intersect in a single line, called the Scheimpflug line. Furthermore, the traditional imaging model is not suitable for the detection principle of the laser profiler; hence, the corresponding calibration ideas must be improved and optimized. This study proposes a complete and effective calibration method for the laser profiler, which can be divided into two parts: camera calibration and light plane calibration. For the camera calibration part, a tilt camera imaging model is established based on the traditional camera imaging model using a two-dimensional tilt angle. A method of obtaining the initial parameters and a nonlinear optimization process for the parameters are presented to rapidly obtain the tilt camera imaging model parameters. For the light plane calibration part, a calibration target, which has a double-step shape, is designed. Precise subpixel coordinates of the feature points on the laser profiler are obtained through image processing algorithms by collecting the contour image of the calibration target once the laser profiler is used. The light plane parameters are acquired using the subpixel coordinates for the least squares fitting, which quickly completes the light plane calibration. This study also designs a three-degree-of-freedom automatic calibration device to address various issues, including the removal of the laser profiler's filter, the manual adjustment of the laser profiler's pose, and the complex operating procedures in traditional calibration experiments. [Results] This study used the automatic calibration device to complete the calibration and accuracy evaluation experiments and verify the correctness and effectiveness of the proposed scheme. The experimental results revealed of the following: (1) The laser profiler designed herein could clearly capture all the feature points on the light plane, thereby effectively solving the limited measurement range problem of the traditional laser profiler. (2) The reprojection errors of the laser profiler's camera were 0.487 with the traditional camera calibration method and 0.129 with the camera calibration method. (3) The calibration target could complete the light plane calibration by collecting only one image according to the expected goal. (4) After completing all the calibration steps, the average detection deviation of the laser profiler for measuring the size of the standard ceramic gauge block was approximately 0.028 0 mm. [Conclusions] Thus, this study significantly improves the profiler's high-precision measurement range by establishing a constant focus optical path in the laser profiler. A calibration method with higher accuracy and efficiency is proposed herein for the laser profiler. The detection accuracy of the calibrated laser profiler meets the actual industrial requirements.