Search Results (21)

This study presents a bilateral control architecture that links fluoroscopic image feedback directly to the kinematics of a tendon-driven, three-joint robotic catheter and a 3-DoF motorised C-arm, intending to preserve optimal imaging geometry during autonomous catheter insertion and thereby mitigating radiation exposure. Forward and inverse kinematics for both manipulators were derived via screw theory and geometric analysis, while a calibrated projection model generated synthetic X-ray images whose catheter bending angles were extracted through intensity thresholding, segmentation, skeletonisation, and least-squares circle fitting. The estimated angle fed a one-dimensional extremum-seeking routine that rotated the C-arm about its third axis until the apparent bending angle peaked, signalling an orthogonal view of the catheter’s bending plane. Implemented in a physics-based simulator, the framework achieved inverse-kinematic errors below 0.20% for target angles between ${20}^{°}$ and ${90}^{°}$, with accuracy decreasing to 3.00% at ${10}^{°}$. The image-based angle estimator maintained a root-mean-square error $\le 3$% across most of the same range, rising to 6.4% at ${10}^{°}$. The C-arm search consistently located the optimal perspective, and the combined controller steered the catheter tip along a predefined aortic path without collision. These results demonstrate sub-degree angular accuracy under idealised, noise-free conditions and validate real-time coupling of image guidance to dual-manipulator motion; forthcoming work will introduce realistic image noise, refined catheter mechanics, and hardware-in-the-loop testing to confirm radiation-dose and workflow benefits. Full article

Advancements in Industry 4.0 and smart manufacturing have increased the demand for precise and intricate part surface geometries, making the analysis of surface morphology essential for ensuring assembly precision and product quality. This study presents an innovative fitness landscape-based methodology for extreme point analysis of part surface morphology, effectively addressing the limitations of existing techniques in accurately identifying and analyzing extremum points. The proposed approach integrates adaptive Fitness-Distance Correlation (FDC) with a roughness index to dynamically determine the number and spatial distribution of initial points within the pattern search algorithm, based on variations in surface roughness. By partitioning the feasible domain into subregions according to FDC values, the algorithm significantly reduces optimization complexity. Regions with high ruggedness are further subdivided, facilitating the parallel implementation of the pattern search algorithm within each subregion. This adaptive strategy ensures that areas with intricate surface features are allocated a greater number of initial points, thereby enhancing the probability of locating both regional and global extremum points. To validate the effectiveness and robustness of the proposed method, extensive testing was conducted using five diverse test functions treated as black-box functions. The results demonstrate the method’s capability to accurately locate extremum points across varying surface profiles. Additionally, the proposed method was applied to flatness error evaluation. The results indicate that, compared to using only the raw measurement data, the flatness error increases by approximately 3% when extremum points are taken into account. Full article

Rapid localization of ROI (Region of Interest) for tomographic medical images (TMIs) is an important foundation for efficient image reading, computer-aided education, and well-informed rights of patients. However, due to the multimodality of clinical TMIs, the complexity of anatomy, and the deformation of organs caused by diseases, it is difficult to have a universal and low-cost method for ROI organ localization. This article focuses on actual concerns of TMIs from medical students, engineers, interdisciplinary researchers, and patients, exploring a universal registration method between the clinical CT/MRI dataset and CVH (Chinese Visible Human) to locate the organ ROI in a low-cost and lightweight way. The proposed method is called Two-step Progressive Registration (TSPR), where the first registration adopts “eye–nose triangle” features to determine the spatial orientation, and the second registration adopts the circular contour to determine the spatial scale, ultimately achieving CVH anatomical knowledge automated mapping. Through experimentation with representative clinical TMIs, the registration results are capable of labeling the ROI in the images well and can adapt to the deformation problem of ROI, as well as local extremum problems that are prone to occur in inter-subject registration. Unlike the ideal requirements for TMIs’ data quality in laboratory research, TSPR has good adaptability to incomplete and non-thin-layer quality in real clinical data in a low-cost and lightweight way. This helps medical students, engineers, and interdisciplinary researchers independently browse images, receive computer-aided education, and provide patients with better access to well-informed services, highlighting the potential of digital public health and medical education. Full article

Determining precise boundaries and protective zones for historical and cultural objects enables their effective preservation. This article presents the framework for establishing protective zones around historical and cultural heritage sites existing in Ukraine, using the example of the Citadel defensive complex located in Lviv (Ukraine). It proposes general and detailed conceptual models for the combined application of remote and non-invasive methods for investigating historical and cultural heritage sites. It introduces the theory of integrating radar satellite imaging with ground-based georadar imaging. Additionally, it presents a software module that has been developed to analyze collected data on two-dimensional historical and cultural heritage objects, refine their boundaries, and establish protective zones around them. The result of the work is the determination of extremum points of vertical displacement on the territory of the historical and cultural heritage site “Lviv Citadel” in Ukraine using SAR, and the construction of a map of vertical displacements. A classification of these points was carried out, after which two of them were investigated using GPR for anomalies. Artifacts from World War II were discovered at each of these points. Using the developed software module, updated boundaries of the site were constructed, taking into account the underground artifacts and protective zones. Full article

The Tarim Basin is located in an arid inland area; the ecological environment is fragile, and it is extremely sensitive to climate change. For the purpose of studying dynamic changes in the vegetation response of vegetation in the Tarim Basin to extreme climate, this study used the Vegetation Ecological Quality Index (EQI) as a vegetation indicator and calculated 12 extreme climate indices using Rclimdex. Pearson correlation analysis was used to explore the relationship between EQI values and various extreme climate indices at both inter-annual and intra-annual scales. Additionally, geographic detector analysis was employed to examine the single and interactive effects of extreme climate on the EQI for different vegetation types. The following was found: (1) During 2000–2022, the EQI showed an upward trend in the Tarim Basin, and the increase in agricultural vegetation was the fastest. (2) Since 2000, the extreme warm temperature indices have risen, whereas the extreme cold temperature indices have declined. The warming rate of nighttime temperatures exceeds that of daytime, and the extreme precipitation rises intensively. Simultaneously, continuous dry days have also increased. (3) On an inter-annual scale, the EQI is primarily negatively correlated with the most extreme warm temperature indices, while it is positively correlated with extreme cold temperatures and extreme precipitation indices. On an intra-annual scale, there is an obvious regional concentration in the correlation between the EQI and extreme climate indices. The diurnal temperature range (DTR) and cold daytimes (TX10P) have inhibitory and promoting effects on areas with high and low EQI, respectively. The extremum indices, temperature warm indices, and precipitation intensity indices have a promoting effect on areas with a high EQI and an inhibiting effect on areas with a low EQI. The interaction between extreme climate indices has a greater impact on the EQI than the effect of a single extreme climate index, especially with a significant impact on forests and shrubs. This study provides a reference for the early warning of meteorological disasters, ecosystem protection, and sustainable management in the Tarim Basin. Full article

Ship detection has been an area of focus for high-frequency surface wave radar (HFSWR). The detection and identification of ship formation have proven significant in early warning, while studies on the formation identification are limited due to the complex background and low resolution of HFSWR. In this paper, we first establish a spatial distribution model of ship formation in HFSWR. Then, we propose a cascade identification algorithm of ship formation in the clutter edge. The proposed algorithm includes a preprocessing stage and a two-stage formation identification stage. The Faster R-CNN is introduced in the preprocessing stage to locate the clutter regions. In the first stage, we propose an extremum detector based on connected regions to extract suspicious regions. The suspicious regions contain ship formations, single-ship targets, and false targets. In the second stage, we design a network connected by a convolutional neural network (CNN) and an extreme learning machine (ELM) to identify two densely distributed ship formations from inhomogeneous clutter and single-ship targets. The experimental results based on the factual HFSWR background demonstrate that the proposed cascade identification algorithm is superior to the extremum detector combined with the classical CNN algorithm for ship formation identification. Meanwhile, the proposed algorithm performs well in weak formation and deformed formation identification. Full article

Precipitation products play an important role in monitoring rainstorm processes. This study takes a rare historical event of extreme, heavy precipitation that occurred in Henan Province, China, in July 2021 as a research case. By analyzing the distribution of the spatial and temporal characteristics of precipitation errors, using a probability density function of the occurrence of precipitation and the daily variation pattern, we assess the capability of a radar precipitation estimation product (RADAR), satellite precipitation products (IMERG and GSMAP), a reanalysis product (ERA5) and a precipitation fusion product (the CMPAS) to monitor an extreme rainstorm in the Henan region. The CMPAS has the best fit with the gauge observations in terms of the precipitation area, precipitation maximum and the evolution of the whole process, with a low spatial variability of errors. However, the CMPAS slightly underestimated the precipitation extremum at the peak moment (06:00–08:00). The RADAR product was prone to a spurious overestimation of the originally small rainfall, especially during peak precipitation times, with deviations concentrated in the core precipitation area. The IMERG, GSMAP and ERA5 products have similar performances, all of which failed to effectively capture heavy precipitation in excess of 60 mm/h, with negative deviations in precipitation at mountainfront locations west of northern Henan Province. There is still a need for terrain-specific error revisions for areas with large topographic relief. By merging and processing precipitation data from multiple sources, the accuracy of the CMPAS is better than any single-source precipitation product. The CMPAS has the characteristic advantage of high spatial and temporal resolutions (0.01° × 0.01°/1 h), which play a positive role in precipitation dynamic monitoring, providing early warnings of heavy rainfall processes and hydrological application research. Full article

This study aimed to determine the influence of time and precipitation as covariates on the flood frequency distribution in the Songhua River tributaries under the nonstationarity assumption and to investigate the possibility of nonstationary models’ application in river management scope demarcation work. Nonstationary flood frequency analysis (NS-FFA) was conducted in three typical basins of the Songhua River (in Northeastern China) based on the generalized additive models for location, scale, and shape (GAMLSS), and stationary flood frequency analysis was used as a comparison. Under the stationarity assumption, the Pearson type Ⅲ (P-Ⅲ) distribution is the main theoretical distribution for the flood extremum at hydrological stations, followed by a lognormal (LN) distribution. Under the nonstationarity assumption, when time is considered a covariate, the optimal theoretical distribution of the flood extremum is mainly LN (with 63.75%), followed by the Weibull distribution (with 18.75%). When precipitation is considered as a covariate, the optimal theoretical distribution of the flood extremum is mainly LN (with 57.5%). We attempted to apply several FFA methods to calculate the design frequency in this study, referring to the work requirements for river management scope demarcation in three typical basins, and came to the following conclusions. From the simulation results of the p = 10% flood at the export stations of typical basins, it can be seen that time-covariate NS-FFA obtained the best simulation results. Two cases of the simulation under the stationarity assumption are positive, which will lead to a high design scale. The time-covariate GAMLSS in NS-FFA has the advantages of higher calculation accuracy and simpler processes. To better balance construction costs and disaster protection requirements, NS-FFA can be used to determine the design scale of water conservation projects; additionally, it can be used to demarcate the scope of river management. The accuracy of GAMLSS for FFA is also influenced by the complexity of the terrain, with basins with relatively simple terrain having higher calculation accuracy. Full article

As precious cultural heritage sites, the state of preservation of cave temples is closely related to the geological and climatic conditions in which they are located. This paper constructed an analytical method of sized slate stability based on the equivalent support stiffness method. The stability analysis of the roof slab of Yuanjue Cave was carried out by establishing a three-dimensional numerical calculation model. Through comparative analysis of the results of stress and displacement fields under different conditions, the stress and deformation characteristics of the roof slab of Yuanjue Cave were revealed, as well as the study of the main factors affecting the stability of the roof slab of Yuanjue Cave and the key slate to be monitored. The main research results are as follows. The stress deformation of the roof plate of Yuanjue cave is mainly divided into the initial uniform change stage, the medium-term stable change stage or the medium-term accelerated change stage, and the later rapid change stage. With the increase in the number of overhanging and broken slates and the increase in the damage factor of cracked slates, the vertical stress extremum of the stones increases continuously, and the equivalent support stiffness decreases, which aggravates the uneven stress deformation of the roof of the Yuanjue Cave. This study provides a reliable reference basis for the stability analysis and evaluation of the roof slab of a large number of cave temples existing in the Sichuan and Chongqing areas in China. Full article

Accurate localization of partial discharge in GIS equipment remains a key focus of daily maintenance for substations, which can be achieved through advanced detection and location techniques, as well as regular maintenance and testing of the equipment. However, there is currently an issue with low accuracy in the localization algorithm. Aiming at the problems of low precision and local optimization of the swarm intelligence algorithm in partial discharge localization system of GIS equipment, this paper proposes a 3D localization algorithm based on a time difference of arrival (TDOA) model of the improved artificial fish swarm algorithm (IAFSA). By introducing the investigation behaviour of the artificial bee colony(ABC) algorithm into the artificial fish swarms algorithm (AFSA), this algorithm is more efficient to jump out of the local extremum, enhance the optimization performance, improve the global search ability and overcome the premature convergence. Furthermore, more precise positioning can be achieved with dynamic parameters. The results of the testing function show that IAFSA is significantly superior to AFSA and particle swarm optimization (PSO) in terms of positioning accuracy and stability. When applied to partial discharge localization experiments, the maximum relative positioning error is less than 2.5%. This validates that the proposed method in this paper can achieve high-precision partial discharge localization, has good engineering application value, and provides strong support for the safe operation of GIS equipment. Full article

Leak detection and location of water supply pipelines is an important area of research, and it is especially important to find the leakage location in time and repair them. In view of the problem, that a large amount of noise is mixed in the detection signal when the pipeline leaks, it will inevitably affect the detection and positioning effect. In this paper, a denoising algorithm based on improved module maximum is proposed. Firstly, a discrete binary wavelet transform is carried out on the noisy signal, and the module maximum point corresponding to the wavelet transform coefficients, on each scale, is obtained. Secondly, different thresholds are used for the module maximum of different scale layers and the wavelet coefficients are reconstructed according to the retained module maximum and their extremums. Thirdly, the alternative projection algorithm is used to effectively suppress the false oscillations in the reconstructed signal, improve the quality of the reconstructed signal, and obtain the noise reduction signal. Finally, according to the theory of the negative pressure wave, the inflection point of the negative pressure wave is identified by the wavelet decomposition method, and the location of leakage point is determined. In order to verify the effectiveness of the proposed algorithm, a leakage simulation experiment system of water supply pipeline is built. The analysis of the results shows that, compared with the wavelet denoising method and the EMD-based method, the method proposed in this paper achieves a better denoising effect, obtains a smoother pressure signal, retains the signal waveform characteristics, and identifies the obvious inflexion point of the negative pressure wave. The minimum relative error of leakage point location is 0.9%, and the maximum relative error is 2.5%. Full article

A novel one-dimensional parity-time-symmetric periodic ring optical waveguide network (1D PTSPROWN) is constructed using magnesium fluoride (MgF₂), by adjusting the length ratio of gain and loss materials in PT-symmetric waveguide and ordinary dielectric material, and by optimizing the program to search for the extremum spontaneous PT-symmetric breaking points. The ultra-strong transmission, reflection, and photonic location are noticed in the proposed 1DPTSPROWN as compared with the other PT-symmetric optical waveguide networks. The maximum and minimum reached 10¹⁸ and 10⁻¹⁵, respectively, which is more than 6 orders of magnitude greater and 3 orders of magnitude smaller than the best results reported so far. The ultra-strong transmission and reflection peaks, ultra-weak transmission, and reflection valleys generated by electromagnetic waves in this network were found to have interesting resonance and anti-resonance effects. Furthermore, frequency of periodic cycles and violet or redshift laws were discovered in the 1D PTSPROWN of fixed length ratio of gain and loss material in the PT-symmetric waveguide by adjusting the ratio of the upper and lower arm lengths of waveguides. The proposed optical waveguide network might have potential application in the design of CPA lasers, high-efficiency optical accumulators, and several other devices. Full article

Object recognition, as one of the most fundamental and challenging problems in high-resolution remote sensing image interpretation, has received increasing attention in recent years. However, most conventional object recognition pipelines aim to recognize instances with bounding boxes in a supervised learning strategy, which require intensive and manual labor for instance annotation creation. In this paper, we propose a weakly supervised learning method to alleviate this problem. The core idea of our method is to recognize multiple objects in an image using only image-level semantic labels and indicate the recognized objects with location points instead of box extent. Specifically, a deep convolutional neural network is first trained to perform semantic scene classification, of which the result is employed for the categorical determination of objects in an image. Then, by back-propagating the categorical feature from the fully connected layer to the deep convolutional layer, the categorical and spatial information of an image are combined to obtain an object discriminative localization map, which can effectively indicate the salient regions of objects. Next, a dynamic updating method of local response extremum is proposed to further determine the locations of objects in an image. Finally, extensive experiments are conducted to localize aircraft and oiltanks in remote sensing images based on different convolutional neural networks. Experimental results show that the proposed method outperforms the-state-of-the-art methods, achieving the precision, recall, and $F_1$-score at 94.50%, 88.79%, and 91.56% for aircraft localization and 89.12%, 83.04%, and 85.97% for oiltank localization, respectively. We hope that our work could serve as a basic reference for remote sensing object localization via a weakly supervised strategy and provide new opportunities for further research. Full article

The water distribution system has deployed several low-power IoT devices on an uneven surface where battery power is a major concern. Therefore, this paper focuses on using a UAV-enabled wireless powered communication network capable of directing energy to a target location and using it for communication, thereby reducing battery issues. In this paper, a static optimization was applied to find the initial height values using 3D clustering and beamforming method and dynamic optimization using extremum seeking method was applied to find the optimized height. The optimized height values were calculated and Travelling Salesman Problem (TSP) was applied to create the trajectory of the UAV. The overall energy consumption of the UAV was minimized by integrating dynamic optimization and dome packing method, which can find an optimal position and trajectory where the UAV will be hovering to direct energy and collect data. Moreover, we also minimized the total flight time of the UAV. Full article

Transmission towers are easily affected by various meteorological and geological disasters. In this paper, a transmission tower tilt state assessment approach—based on high precision and dense point cloud from UAV LiDAR—was proposed. First, the transmission tower point cloud was rapidly located and extracted from the 3D point cloud obtained by UAV-LiDAR line patrol. A robust histogram local extremum extraction method with additional constraints was proposed to achieve adaptive segmentation of the tower head and tower body point cloud. Second, an accurate and efficient extraction and simplification strategy of the contour of the feature plane point cloud was proposed. The central axis of the tower was constrained by the contour of the feature plane through the four-prism structure to calculate the tilt angle of the tower and evaluate the state of the tower. Finally, the point cloud of tower head from UAV-based LiDAR was accurately matched with the designed tower head model from database, and a tower head state evaluation model based on matching offset parameters was proposed to evaluate tower head tilt state. The experimental results of simulation and measured data showed that the calculation accuracy of the tilt parameters of transmission tower body was better than 0.5 degrees, that the proposed method can effectively evaluate the risk of tower head with complex structure, and improve the rapid and mass intelligent perception level of the risk state of the transmission line tower, which has a wide prospects for application. Full article