Advancing critical care recovery:The pivotal role of machine learning in early detection of intensive care unit-acquired weakness

This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.

Patch-based vehicle logo detection with patch intensity and weight matrix

A patch-based method for detecting vehicle logos using prior knowledge is proposed.By representing the coarse region of the logo with the weight matrix of patch intensity and position,the proposed method is robust to bad and complex environmental conditions.The bounding-box of the logo is extracted by a thershloding approach.Experimental results show that 93.58% location accuracy is achieved with 1100 images under various environmental conditions,indicating that the proposed method is effective and suitable for the location of vehicle logo in practical applications.

Detection of Spherical Gold Fiducials in kV X-Ray Images Using Intensity-Estimation-Based Method

Fiducial marker detection algorithms in kilovoltage x-ray images using physical characteristics of transmission x-ray have been proposed. It, however, has been suggested recently that factors besides transmission x-ray affect x-ray images. The purpose of this study was to develop a new fiducial detection algorithm using fiducial intensity estimation based on physical characteristics of x-ray images with gold fiducials. First, x-ray images of a fiducial on a water-equivalent phantom were acquired. It was observed that the ratio of background to fiducial intensity in the images decreased as phantom thickness increased. Based on the negative correlation, we identified a function for estimating fiducial intensity that consists of background intensity and the amount of scattered radiation by the other x-ray source of an orthogonal imaging system and a treatment beam. Then, we developed an algorithm that extracts fiducial candidates using the estimation function. Its performance was measured using x-ray images which had 3824 fiducials altogether. The average number of false-positive detection of the proposed algorithm in single image was one-tenth of an algorithm considering only transmission x-ray. The proposed algorithm detected 99.5% of all fiducials under an error of 1.0 mm, while the other algorithm detected 94.7% or less (Clinical trial number: UMIN000005324).

Ghost-Retina Net:Fast Shadow Detection Method for Photovoltaic Panels Based on Improved Retina Net

Based on the artificial intelligence algorithm of RetinaNet,we propose the Ghost-RetinaNet in this paper,a fast shadow detection method for photovoltaic panels,to solve the problems of extreme target density,large overlap,high cost and poor real-time performance in photovoltaic panel shadow detection.Firstly,the Ghost CSP module based on Cross Stage Partial(CSP)is adopted in feature extraction network to improve the accuracy and detection speed.Based on extracted features,recursive feature fusion structure ismentioned to enhance the feature information of all objects.We introduce the SiLU activation function and CIoU Loss to increase the learning and generalization ability of the network and improve the positioning accuracy of the bounding box regression,respectively.Finally,in order to achieve fast detection,the Ghost strategy is chosen to lighten the size of the algorithm.The results of the experiment show that the average detection accuracy(mAP)of the algorithm can reach up to 97.17%,the model size is only 8.75 MB and the detection speed is highly up to 50.8 Frame per second(FPS),which can meet the requirements of real-time detection speed and accuracy of photovoltaic panels in the practical environment.The realization of the algorithm also provides new research methods and ideas for fault detection in the photovoltaic power generation system.

Is every microorganism detected in the intensive care unit a nosocomial infection?Isn’t prevention more important than detection?

The present letter to the editor is related to the study entitled“Multidrug-resistant organisms in intensive care units and logistic analysis of risk factors.”Not every microorganism grown in samples taken from critically ill patients can be considered as an infectious agent.Accurate and adequate information about nosocomial infections is essential in introducing effective prevention programs in hospitals.Therefore,the development and implementation of care bundles for frequently used medical devices and invasive treatment devices(e.g.,intravenous catheters and invasive ventilation),adequate staffing not only for physicians,nurses,and other medical staff but also for housekeeping staff,and infection surveillance and motivational feedback are key points of infection prevention in the intensive care unit.

A Novel YOLOv5s-Based Lightweight Model for Detecting Fish’s Unhealthy States in Aquaculture

Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture.Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for preventing the spread of diseases and minimizing economic losses.To address this issue,an improved algorithm based on the You Only Look Once v5s(YOLOv5s)lightweight model has been proposed.This enhanced model incorporates a faster lightweight structure and a new Convolutional Block Attention Module(CBAM)to achieve high recognition accuracy.Furthermore,the model introduces theα-SIoU loss function,which combines theα-Intersection over Union(α-IoU)and Shape Intersection over Union(SIoU)loss functions,thereby improving the accuracy of bounding box regression and object recognition.The average precision of the improved model reaches 94.2%for detecting unhealthy fish,representing increases of 11.3%,9.9%,9.7%,2.5%,and 2.1%compared to YOLOv3-tiny,YOLOv4,YOLOv5s,GhostNet-YOLOv5,and YOLOv7,respectively.Additionally,the improved model positively impacts hardware efficiency,reducing requirements for memory size by 59.0%,67.0%,63.0%,44.7%,and 55.6%in comparison to the five models mentioned above.The experimental results underscore the effectiveness of these approaches in addressing the challenges associated with fish health detection,and highlighting their significant practical implications and broad application prospects.

A NEW METHOD OF MOVING OBJECT DETECTION AND SHADOW REMOVING

This paper presents an adaptive method of objects and shadows detection in video streams. Models of background are firstly set up and adaptively updated in Hue Saturation Intensity (HSI) color space to detect motion regions. Then, detection errors are dealt with by motion continuity and velocity consistency. Finally, cast shadows are removed by the generic properties of luminance, chrominance and gradient density. Experimental results and their evaluation are presented to verify the effectiveness of this new method.

NDC-IVM:An automatic segmentation of optic disc and cup region from medical images for glaucoma detection

Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure(IOP),which damages the vision of eyes.So,detecting and classifying Glaucoma is an important and demanding task in recent days.For this purpose,some of the clustering and segmentation techniques are proposed in the existing works.But,it has some drawbacks that include ineficient,inaccurate and estimates only the affected area.In order to solve these issues,a Neighboring Differential Clustering(NDC)-Intensity V ariation Making(IVM)are proposed in this paper.The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc.This work includes three stages such as,preprocessing,clustering and segmentation.At first,the given retinal image is preprocessed by using the Gaussian Mask Updated(GMU)model for eliminating the noise and improving the quality of the image.Then,the cluster is formed by extracting the threshold and patterns with the help of NDC technique.In the segmentation stage,the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method.Here,the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identification.In experiments,the results of both existing and proposed techniques are evaluated in terms of sensitivity,specificity,accuracy,Hausdorff distance,Jaccard and dice metrics.

Detection and Ranging System of Flight Aid Lights

Dynamic detection based on optics sensors and ranging radars is a new method to detect the luminous intensity of flight aid lights. The optics sensors can get the illumination information of each light, the ranging radar gets the distance information, and then data amalgamation technology is used to compute the luminous intensity of each light. A method to modify the errors of this dynamic detection system is presented. It avoids the accumulation error and measurement carrier’s excursion error by using peak value detection based on optics sensors to estimate the accurate position of each light, then to modify the lights’ lengthways distance information and transverse position information. The performance of the detection and ranging system is validated by some experiments and shown in pictures.

Measurement of intensity difference squeezing via non-degenerate four-wave mixing process in an atomic vapor

We report the measurement of the intensity difference squeezing via the non-degenerate four-wave mixing process in a rubidium atomic vapor medium. Two pairs of balanced detection systems are employed to measure the probe and the conjugate beams, respectively. It is convenient to get the quantum shot noise limit, the squeezed and the amplified noise power spectra. We also investigate the influence of the input extra quadrature amplitude noise of the probe beam. The influence of the extra noise can be minimized and the squeezing can be optimized under the proper parameter condition. We measure the -3.7-dB intensity difference squeezing when the probe beam has a 3-dB extra quadrature amplitude noise. This result is slightly smaller than -4.1 dB when the ideal coherent light （no extra noise） for the probe beam is used.

The Measurement Accuracy of Ball Bearing Center in Portal Images Using an Intensity-Weighted Centroid Method

Medical linac based imaging modalities such as portal imaging can be utilized for highly accurate measurements. An intensity-weighted centroid method for determining object center is proposed that can detect the position of small object at subpixel accuracy. The principles and algorithms of the intensity-weighted centroid method are presented. Analytical results are derived for positional accuracy of a rod and a sphere in digital images, and the theoretical accuracy limits are calculated. The method was experimentally examined using phantoms with embedded ball bearings (BBs). Images of the phantoms were taken by the MV portal imager of a medical linac. The image pixel size was 0.26 mm when projected at the linac isocenter plane. The BB coordinates were calculated by applying the intensity-weighted centroid method after removing the background. The reproducibility of BB position detection was measured with 3 monitor unit (MU) exposures at various dose rates. A stationary BB, of 0.25 image contrast, showed position reproducibility in the range of 0.004 - 0.013 mm. When the method was used to measure the displacement of a moving BB, the difference between the measured and expected BB position had a standard deviation of 0.006 mm. The effect of image noise on the BB detection accuracy was measured using a phantom with multiple BBs. The overall detection accuracy, represented by standard deviation, steadily improved from 0.13 mm at 0.03 MU to 0.008 mm at 5.0 MU, and showed an inverse correlation with contrast-to-noise ratio. We demonstrated that intensity-weighted centroid method can achieve subpixel accuracy in position detection. With a linac based imaging system, precise mechanical measurement with accuracy of microns could be achieved.

A survey of occlusion detection method for visual object

Occlusion problem is one of the challenging issues in vision field for a long time,and the occlusion phenomenon of visual object will be involved in many vision research fields. Once the occlusion occurs in a visual system,it will affect the effects of object recognition,tracking,observation and operation,so detecting occlusion autonomously should be one of the abilities for an intelligent vision system. The research on occlusion detection method for visual object has increasingly attracted attentions of scholars. First,the definition and classification of the occlusion problem are presented.Then,the characteristics and deficiencies of the occlusion detection methods based on the intensity image and the depth image are analyzed respectively,and the existing occlusion detection methods are compared. Finally,the problems of existing occlusion detection methods and possible research directions are pointed out.

Test Study on Corona Onset Voltage of UHV Transmission Lines Based on UV Detection

Corona discharge is being detected by UV imaging detection technology at home and abroad in recent years.This technology is used in the corona tests of conductor bundles in this paper.In order to further research the corona characteristic,optimize geometry parameters and diameter of sub-conductor,and increase corona onset voltage of transmission lines,corona tests of three model conductors which are placed inside the outdoor corona cage are conducted.Corona cage could be used to simulate the corona activities on transmission lines under a low voltage and different conditions in an effective and economical way.Photon which was created by UV light as a result of corona discharge on conductors is detected by the UV detection apparatus.The photon number within unit interval,namely photon counting rate is adopted as the parameter of quantifying the intensity of corona discharge.According to the apparent change of photon number,corona onset voltage can be judged.All tests are conducted under almost same atmosphere condition.Using the method,corona onset voltage is acquired.The results indicate that the tests have a good repeatability,in other words,repeating same test twice same result can be aquired.The corona onset voltage can be acquired exactly from the curve of applied voltage vs.photon counting rate.Therefore UV detection apparatus can not only used to find discharge point exactly,but also applied on corona discharge research and live detection for power equipments.The method using in this paper is proved that is a new available method.

Multi-Energy Gamma-Ray Attenuations for Non-Destructive Detection of Hazardous Materials

We present a non-destructive method (NDM) to identify minute quantities of high atomic number (Z) elements in containers such as passenger baggage, goods carrying transport trucks, and environmental samples. This method relies on the fact that photon attenuation varies with its energy and properties of the absorbing medium. Low-energy gamma-ray intensity loss is sensitive to the atomic number of the absorbing medium, while that of higher-energies vary with the density of the medium. To verify the usefulness of this feature for NDM, we carried out simultaneous measurements of intensities of multiple gamma rays of energies 81 to 1408 keV emitted by sources 133Ba (half-life = 10.55 y) and 152Eu (half-life = 13.52 y). By this arrangement, we could detect minute quantities of lead and copper in a bulk medium from energy dependent gamma-ray attenuations. It seems that this method will offer a reliable, low-cost, low-maintenance alternative to X-ray or accelerator-based techniques for the NDM of high-Z materials such as mercury, lead, uranium, and transuranic elements etc.

Bidirectional intensity modulated/direct detection optical OFDM WDM-PON system

In this paper, we have evaluated a bidirectional wavelength division multiplexing passive optical network(WDM-PON) employing intensity modulated/direct detection optical orthogonal frequency division multiplexing(IM/DD-OFDM). The proposed system employs 100 Gbit/s 16 quadrature amplitude modulation(16-QAM) downstream and 5 Gbit/s on-off keying(OOK) upstream wavelengths, respectively. The proposed system is considered low-cost as non-coherent IM/DD OFDM technology and a simple reflective semiconductor optical amplifier(RSOA) colorless transmitter are employed and no dispersion compensating fiber(DCF) is needed. Based on the bit error rate(BER) results of WDM signals, the proposed WDM-PON system can achieve up to 1.6 Tbit/s(100 Gbit/s/λ × 16 wavelengths) downstream transmission over a 30 km single mode fiber(SMF).

改进Mask RCNN的盾构隧道渗漏水检测方法

渗漏水是盾构隧道结构存在潜在损伤或缺陷的重要表征,快速、准确检测出渗漏水位置,对隧道安全运营和维护具有重要意义。现有的方法大多采用光学影像对隧道渗漏水进行检测,受隧道内空间和光线条件限制,难以获得高质量病害图片。因此,本文提出了一种基于激光点云数据与改进Mask RCNN相结合的渗漏水检测方法。首先对激光点云反射强度进行修正;然后生成灰度图像并建立渗漏水病害数据集;最后在Mask RCNN算法中引入空洞卷积和变形卷积,实现了隧道渗漏水病害的快速检测。利用某地铁采集的数据进行验证,结果表明,本文提出的改进Mask RCNN算法相较于原始算法和FCN算法检测精度均有明显提升,在盾构隧道渗漏水识别方面性能表现较好。

结合密度图回归与检测的密集计数研究

针对基于检测以及基于密度图两种主流的密集计数方法中,基于检测的方法召回率较低、基于密度图的方法缺失目标物体位置信息的问题,将检测任务与回归任务相结合后提出一种基于密度图回归的检测计数方法,可以实现对密集场景中目标物体的计数以及定位,对两种方法进行优势互补,在提高召回率的同时,实现标定所有目标物体的位置信息。为提取出更加丰富的特征信息以面对复杂的数据场景,网络提出特征金字塔优化模块,该模块纵向融合底层高分辨特征与顶层抽象语义特征,横向融合同尺寸的特征,丰富目标物体的语义表达;考虑到密集计数场景中目标物体所占像素比例较低的问题,提出一种针对小目标的注意力机制,通过对输入图像构建掩膜以增强网络对目标物体的注意力,从而提高网络的检测敏感性。实验结果表明,所提出方法在保持准确率基本不变的情况下,大幅度提高了召回率,同时可准确标定目标物体位置,有效提供输入目标图像的计数以及定位信息,在工业以及生态等各种领域具有广泛的应用前景。

旋转物体的单光子三维重建技术

单光子探测技术具有高灵敏度和高时间分辨率,已成为三维成像领域的重要发展方向。APD单光子探测数据为表征光子脉冲事件的0-1数据,通常需要多次积累,可实现目标三维成像,但旋转物体的运动导致无法直接积累。针对旋转物体三维重建问题,提出了一种基于最大强度图清晰度的旋转物体单光子三维重建方法。首先建立旋转物体的三维重建模型,然后建立关于旋转速度的似然估计模型,最后以强度图的最大清晰度为代价函数求解旋转速度并进行重建。计算机仿真模拟了旋转十字架物体的单光子三维成像探测数据,通过三维重建算法对旋转物体实现了高精度三维重建,并在不同旋转速度下进行仿真验证,实现了不同速度下转速估计误差优于5%,深度重建误差优于0.1 m,验证了重建算法的正确性。

基于三维激光扫描技术的竣工盾构隧道渗漏水检测

渗漏水是隧道常见的病害,长期渗漏会导致隧道结构开裂、裂缝、钢筋腐蚀,威胁到隧道运营安全,因此对其检测一直受到重视。本文利用三维激光扫描技术实现了竣工盾构隧道渗漏水的位置及面积的自动检测。首先采用架站式三维激光扫描仪采集竣工隧道点云,基于修正后的反射强度值生成隧道衬砌表面灰度图,再采用膨胀法与腐蚀法对灰度图像进行预处理;然后利用连通域算法计算渗漏水位置及其面积;最后结合实际工程验证本文方法的实用性及准确性。结果表明,应用本文方法竣工隧道的渗漏水检测准确率达92%。

基于DeepSportLab的篮球检测及球员姿态估计研究

为了提升团队体育运动中的智能分析效果,提出通过结合部件强度场和空间嵌入原理来训练多任务,同时实现运动场景中的篮球检测、球员姿态预测和球员实例掩码分割的统一框架(DeepSportLab),以解决团队运动场景的复杂性和特殊性,例如强遮挡和运动模糊。首先,部件强度场提供了篮球和球员的位置信息,以及球员关节的位置。然后,采用空间嵌入技术将球员实例像素与球员各自中心点相关联,并将球员的关节点组合成骨架信息。在DeepSport篮球数据集上进行了验证,并取得了与具有独立任务的单个模型相当的良好性能。