Two-Dimensional Images of Current and Active Power Signals for Elevator Condition Recognition

In this paper, an improved two-dimensional convolution neural network(2DCNN) is proposed to monitor and analyze elevator health, based on the distribution characteristics of elevator time series data in two-dimensional images. The current and effective power signals from an elevator traction machine are collected to generate gray-scale binary images. The improved two-dimensional convolution neural network is used to extract deep features from the images for classification, so as to recognize the elevator working conditions. Furthermore, the oscillation criterion is proposed to describe and analyze the active power oscillations. The current and active power are used to synchronously describe the working condition of the elevator, which can explain the co-occurrence state and potential relationship of elevator data. Based on the improved integration of local features of the time series, the recognition accuracy of the proposed 2DCNN is 97.78%, which is better than that of a one-dimensional convolution neural network. This research can improve the real-time monitoring and visual analysis performance of the elevator maintenance personnel, as well as improve their work efficiency.

Using restored two-dimensional X-ray images to reconstruct the three-dimensional magnetopause

Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)aims to capture two-dimensional(2-D)images of the Earth’s magnetosheath by using soft X-ray imaging.However,the observed 2-D images are affected by many noise factors,destroying the contained information,which is not conducive to the subsequent reconstruction of the three-dimensional(3-D)structure of the magnetopause.The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models.This makes it difficult to establish the mapping relationship between SXIsimulated observation images and target images by using mathematical models.We propose an image restoration algorithm for SXIsimulated observation images that can recover large-scale structure information on the magnetosphere.The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image,whose mapping relationship with the target image is established by the patch estimator.The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator.Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task,according to the peak signal-to-noise ratio and structural similarity.The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques,significantly improving the reconstruction results.Hence,the proposed technology may be feasible for processing SXI-simulated observation images.

Two-Dimensional Perovskite Single Crystals for High-Performance X-ray Imaging and Exploring MeV X-ray Detection

Scintillation semiconductors play increasingly important medical diagnosis and industrial inspection roles.Recently,two-dimensional(2D)perovskites have been shown to be promising materials for medical X-ray imaging,but they are mostly used in low-energy(≤130 keV)regions.Direct detection of MeV X-rays,which ensure thorough penetration of the thick shell walls of containers,trucks,and aircraft,is also highly desired in practical industrial applications.Unfortunately,scintillation semiconductors for high-energy X-ray detection are currently scarce.Here,This paper reports a 2D(C_(4)H_(9)NH_(3))_(2)PbBr_(4)single crystal with outstanding sensitivity and stability toward X-ray radiation that provides an ultra-wide detectable X-ray range of between 8.20 nGy_(air)s^(-1)(50 keV)and 15.24 mGy_(air)s^(-1)(9 MeV).The(C_(4)H_(9)NH_(3))_(2)PbBr_(4)single-crystal detector with a vertical structure is used for high-performance X-ray imaging,delivering a good spatial resolution of 4.3 Ip mm^(-1)in a plane-scan imaging system.Low ionic migration in the 2D perovskite enables the vertical device to be operated with hundreds of keV to MeV X-ray radiation at high bias voltages,leading to a sensitivity of 46.90μC Gy_(air)-1 cm^(-2)(-1.16 Vμm^(-1))with 9 MeV X-ray radiation,demonstrating that 2D perovskites have enormous potential for high-energy industrial applications.

Image segmentation of exfoliated two-dimensional materials by generative adversarial network-based data augmentation

Mechanically cleaved two-dimensional materials are random in size and thickness.Recognizing atomically thin flakes by human experts is inefficient and unsuitable for scalable production.Deep learning algorithms have been adopted as an alternative,nevertheless a major challenge is a lack of sufficient actual training images.Here we report the generation of synthetic two-dimensional materials images using StyleGAN3 to complement the dataset.DeepLabv3Plus network is trained with the synthetic images which reduces overfitting and improves recognition accuracy to over 90%.A semi-supervisory technique for labeling images is introduced to reduce manual efforts.The sharper edges recognized by this method facilitate material stacking with precise edge alignment,which benefits exploring novel properties of layered-material devices that crucially depend on the interlayer twist-angle.This feasible and efficient method allows for the rapid and high-quality manufacturing of atomically thin materials and devices.

Analysis of Two-Dimensional Gel Electrophoresis Images of Protein from Posterior Silk Gland of Silkworm (Bombyx mori) on Day 1 and Day 4 in the 5th Instar Stage

The posterior silk gland （PSG） of silkworm is an important organ where fibroin is synthesized and secreted exclusively. Because fibroin constitutes 75-80% of the silk filament, the mechanism governing fibroin secretion, quality and yield of cocoon can be elucidated by the study on the PSG. Using two-dimensional gel electrophoresis （2-DE） and image analysis system, the changes in the protein composition in the PSG cell were investigated on the day 1 （D1） and day 4 （D4） in the 5th instar stage from five different strains of silkworm （Bombyx mori）. While differences at protein level between days and strains were far less than those observed at the gene level using EST analysis. The change trends in protein composition from D1 to D4 were diverse among the different strains. The results suggest that the secretion of fibroin is regulated by multiple proteins. The site of regulation and the proteins responsible for the regulation vary with the strain, which leads to differences between strains in the capacity of fibroin secretion in the PSG cell.

Survey of methods and principles in three-dimensional reconstruction from two-dimensional medical images

Three-dimensional(3D)reconstruction of human organs has gained attention in recent years due to advances in the Internet and graphics processing units.In the coming years,most patient care will shift toward this new paradigm.However,development of fast and accurate 3D models from medical images or a set of medical scans remains a daunting task due to the number of pre-processing steps involved,most of which are dependent on human expertise.In this review,a survey of pre-processing steps was conducted,and reconstruction techniques for several organs in medical diagnosis were studied.Various methods and principles related to 3D reconstruction were highlighted.The usefulness of 3D reconstruction of organs in medical diagnosis was also highlighted.

Early prediction of myocardial viability after acute myocardial infarction by two-dimensional speckle tracking imaging

Background Identifying the transmural extent of myocardial necrosis and the degree of myocardial viability in acute myocardial infarction （AMI） is important clinically. The aim of this study was to assess myocardial viability using two-dimensional speckle tracking imaging （2D-STI） in patients with AMI. Methods 2D-STI was performed at initial presentation, three days, and six months after primary percutaneous coronary intervention （PCI） in 30 patients with AMI, who had a left anterior descending coronary artery （LAD） culprit lesion. In addition, 20 patients who had minimal stenotic lesions （〈 30% stenosis） on coronary angiography were also included in the control group. At six months dobutamine echocardiography was performed for viability assessment in seven segments of the LAD territory. According to the recovery of wall motion abnormality, segments were classified as viable or non-viable. Results A total of 131 segments were viable, and 44 were nonviable. Multivariate analysis revealed significant differences between the viable and nonviable segments in the peak systolic strain, the peak systolic strain rate at initial presentation, and peak systolic strain rate three days after primary PCI. Among these, the initial peak systolic strain rate had the highest predictive value for myocardial viability （hazard ratio： 31.22, P 〈 0.01）. Conclusions 2D-STI is feasible for assessing myocardial viability, and the peak systolic strain rate might be the most reliable predictor of myocardial viability in patients with AMI.

Background removal from global auroral images:Data-driven dayglow modeling

Global images of auroras obtained by cameras on spacecraft are a key tool for studying the near-Earth environment.However,the cameras are sensitive not only to auroral emissions produced by precipitating particles,but also to dayglow emissions produced by photoelectrons induced by sunlight.Nightglow emissions and scattered sunlight can contribute to the background signal.To fully utilize such images in space science,background contamination must be removed to isolate the auroral signal.Here we outline a data-driven approach to modeling the background intensity in multiple images by formulating linear inverse problems based on B-splines and spherical harmonics.The approach is robust,flexible,and iteratively deselects outliers,such as auroral emissions.The final model is smooth across the terminator and accounts for slow temporal variations and large-scale asymmetries in the dayglow.We demonstrate the model by using the three far ultraviolet cameras on the Imager for Magnetopause-to-Aurora Global Exploration(IMAGE)mission.The method can be applied to historical missions and is relevant for upcoming missions,such as the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.

Deep learning-based inpainting of saturation artifacts in optical coherence tomography images

Limited by the dynamic range of the detector,saturation artifacts usually occur in optical coherence tomography(OCT)imaging for high scattering media.The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images.We proposed a deep learning-based inpainting method of saturation artifacts in this paper.The generation mechanism of saturation artifacts was analyzed,and experimental and simulated datasets were built based on the mechanism.Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs.The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility,strong generalization,and robustness.

Artificial Intelligence and Computer Vision during Surgery: Discussing Laparoscopic Images with ChatGPT4—Preliminary Results

Introduction: Ultrafast latest developments in artificial intelligence (ΑΙ) have recently multiplied concerns regarding the future of robotic autonomy in surgery. However, the literature on the topic is still scarce. Aim: To test a novel AI commercially available tool for image analysis on a series of laparoscopic scenes. Methods: The research tools included OPENAI CHATGPT 4.0 with its corresponding image recognition plugin which was fed with a list of 100 laparoscopic selected snapshots from common surgical procedures. In order to score reliability of received responses from image-recognition bot, two corresponding scales were developed ranging from 0 - 5. The set of images was divided into two groups: unlabeled (Group A) and labeled (Group B), and according to the type of surgical procedure or image resolution. Results: AI was able to recognize correctly the context of surgical-related images in 97% of its reports. For the labeled surgical pictures, the image-processing bot scored 3.95/5 (79%), whilst for the unlabeled, it scored 2.905/5 (58.1%). Phases of the procedure were commented in detail, after all successful interpretations. With rates 4 - 5/5, the chatbot was able to talk in detail about the indications, contraindications, stages, instrumentation, complications and outcome rates of the operation discussed. Conclusion: Interaction between surgeon and chatbot appears to be an interesting frontend for further research by clinicians in parallel with evolution of its complex underlying infrastructure. In this early phase of using artificial intelligence for image recognition in surgery, no safe conclusions can be drawn by small cohorts with commercially available software. Further development of medically-oriented AI software and clinical world awareness are expected to bring fruitful information on the topic in the years to come.

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.

DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

New two-dimensional fuzzy C-means clustering algorithm for image segmentation

To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this method, the image segmentation was converted into an optimization problem. The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixels described by the improved two-dimensional histogram. By making use of the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center could be obtained by iterative optimization, and the image segmentation could be accomplished. The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%. The proposed algorithm has strong anti-noise capability, high clustering accuracy and good segment effect, indicating that it is an effective algorithm for image segmentation.

Reconstruction of Knowledge and Medical Images in the Convergence of Chinese and Western Medicine:Taking “Sweet Meat” as an Example

The pancreas is neither part of the five Zang organs(五脏) nor the six Fu organs(六腑).Thus,it has received little attention in Chinese medical literature.In the late 19th century,medical missionaries in China started translating and introducing anatomical and physiological knowledge about the pancreas.As for the word pancreas,an early and influential translation was “sweet meat”(甜肉),proposed by Benjamin Hobson(合信).The translation “sweet meat” is not faithful to the original meaning of “pancreas”,but is a term coined by Hobson based on his personal habits,and the word “sweet” appeared by chance.However,in the decades since the term “sweet meat” became popular,Chinese medicine practitioners,such as Tang Zonghai(唐宗海),reinterpreted it by drawing new medical illustrations for “sweet meat” and giving new connotations to the word “sweet”.This discussion and interpretation of “sweet meat” in modern China,particularly among Chinese medicine professionals,is not only a dissemination and interpretation of the knowledge of “pancreas”,but also a construction of knowledge around the term “sweet meat”.

TWO-DIMENSIONAL PARTICLE IMAGE VELOCIMETRY(PIV) MEASUREMENTS IN A TRANSPARENT CENTRIFUGAL PUMP

A special transparent centrifugal pump is designed. Detailed opticalmeasurements of the flow inside the rotating passages of a five-bladed shroud centrifugal pumpimpeller have been performed by using two-dimensional particle image velocimetry (PIV). The flow issurveyed at three load conditions q_V/q_(Vd) = 0.4, q_V/q_(Vd) = 1.0, q_V/q_(Vd) = 1.5,respectively. As a result, phase averaged PIV velocity vector maps on three planes between hub andshroud of the impeller are presented. At design load, the mean field of relative velocity ispredominantly vane congruent, showing well-behaved flow without separation. The distributions of therelative velocity on different plane along the pump shaft are very different and there is always alow velocity zone near the pressure-side of the blade at both low and design flow rate, but thelow-velocity-zone at the low flow rate is much larger than that at the design one. The studydemonstrates that the PIV technique is efficient in providing reliable and detailed velocity dataover a full impeller passage.

Assessment of Regional Left Ventricular Myocardial Function in Rats after Acute Occlusion of Left Anterior Descending Artery by Two-dimensional Speckle Tracking Imaging

This study evaluated the change in regional left ventricular myocardial function in rats following acute occlusion of the left anterior descending coronary artery （LAD） by using two-dimensional speckle tracking imaging （2D-STI）. Sixty Wistar rats were randomly divided into two groups, a myocardial infarction （MI） group, in which 50 rats were subjected to LAD occlusion for 30–45 min, and a sham-operated （SHAM） group that contained 10 rats serving as control. Echo-cardiography was performed at baseline and 1, 4 and 8 week（s） after the operation. High frequency two-dimensional images of left ventricular short axis at papillary muscle level were recorded. Peak systolic radial strain （PRS） and circumferential strain （PCS） were measured in the mid-ventricle in short-axis view by using EchoPAC workstation. Left ventricular internal diameter at diastole （LVIDd） and systole （LVIDs）, fractional shortening （FS）, ejection fraction （EF） and left ventricular mass （LVM） were measured by anatomical M-model echocardiography. Infarct size was measured using triphenyl tetrazolium chloride （TTC） staining 1 week and 8 weeks after the operation. Fibrosis of left ventricu-lar myocardium was displayed using Van Gieson staining 1 week after the infarction. In terms of the TTC staining results, the left ventricle fell into three categories： infarcted, peri-infarcted and remote myocardial regions. Compared with those at baseline and in the SHAM group, （1） PRS and PCS in the infarcted, peri-infarcted and remote myocardial regions were significantly decreased in the MI group within 1 week after the operation （P〈0.05） and the low levels lasted 8 weeks; （2） Compared with those at baseline, LVIDd, LVIDs, FS, EF and LVM in the MI group showed no significant dif-ference 1 week after the operation （P〉0.05）. However, LVIDd, LVIDs and LVM were increased sig-nificantly 4 and 8 weeks after the operation （P〈0.05）, and FS and EF were decreased substantially （P〈0.05）. Van Gieson staining showed that fibrosis developed in all the three myocardial regions to varying degrees. It is concluded that 2D-STI is non-invasive and can be used to assess regional func-tion of myocardium with different blood supply in rats following acute occlusion of the LAD, and can be used as a sensitive and reliable means to follow up the process of left ventricular remodeling.

Two-dimensional cross entropy multi-threshold image segmentation based on improved BBO algorithm

In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.

Assessment of Age-related Changes in Left Ventricular Twist by Two-dimensional Ultrasound Speckle Tracking Imaging

To assess the normal value of left ventricular twist （LVtw） and examine the changes with normal aging by 2-dimensional ultrasound speckle-tracking imaging （STI）, 121 healthy volunteers were divided into three age groups： a youth group （19-45 y old）, a middle-age group （46-64 y old ） and an old-age group （≥65 y old）. Basal and apical short-axis images of left ventricular were acquired to analyse LV rotation （LVrot） and LVrot velocity. LVtw and LVtw velocity was defined as apical LVrot and LVrot velocity relative to the base. Peak twist （Ptw）, twist at aortic valve closure （AVCtw）, twist at mitral valve opening （MVOtw）, untwisting rate （UntwR）, half time of untwisting （HTU）, peak twist velocity （PTV）, time to peak twist velocity （TPTV）, peak untwisting velocity （PUV）, time to peak untwisting velocity （TPUV） were separately measured. The results showed that the normal LV performs a wringing motion with a clockwise rotation at the base and a counterclock- wise rotation at the apex （as seen from the apex）. The LVtw velocity showed a systolic counterclockwise twist followed by a diastolic clockwise twist. Peak twist develops near the end of systole （96%±4.2% of systole）. With aging, Ptw, AVCtw, MVOtw, HTU and PUV increased significantly （P〈0.05） and UntwR decreased significantly （P〈0.05）. However, no significant differences in TPUV, PTV and TPTV were noted among the 3 groups （P〉0.05）. It is concluded that LV twist can be measured non-invasively by 2-dimensional ultrasound STI imaging. The age-related changes of LVtw should be fully taken into consideration in the assessment of LV function.

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

Progress on two-dimensional ferrovalley materials

The electron's charge and spin degrees of freedom are at the core of modern electronic devices. With the in-depth investigation of two-dimensional materials, another degree of freedom, valley, has also attracted tremendous research interest. The intrinsic spontaneous valley polarization in two-dimensional magnetic systems, ferrovalley material, provides convenience for detecting and modulating the valley. In this review, we first introduce the development of valleytronics.Then, the valley polarization forms by the p-, d-, and f-orbit that are discussed. Following, we discuss the investigation progress of modulating the valley polarization of two-dimensional ferrovalley materials by multiple physical fields, such as electric, stacking mode, strain, and interface. Finally, we look forward to the future developments of valleytronics.