I-DCGAN and TOPSIS-IFP:A simulation generation model for radiographic flaw detection images in light alloy castings and an algorithm for quality evaluation of generated images

The intelligent detection technology driven by X-ray images and deep learning represents the forefront of advanced techniques and development trends in flaw detection and automated evaluation of light alloy castings.However,the efficacy of deep learning models hinges upon a substantial abundance of flaw samples.The existing research on X-ray image augmentation for flaw detection suffers from shortcomings such as poor diversity of flaw samples and low reliability of quality evaluation.To this end,a novel approach was put forward,which involves the creation of the Interpolation-Deep Convolutional Generative Adversarial Network(I-DCGAN)for flaw detection image generation and a comprehensive evaluation algorithm named TOPSIS-IFP.I-DCGAN enables the generation of high-resolution,diverse simulated images with multiple appearances,achieving an improvement in sample diversity and quality while maintaining a relatively lower computational complexity.TOPSIS-IFP facilitates multi-dimensional quality evaluation,including aspects such as diversity,authenticity,image distribution difference,and image distortion degree.The results indicate that the X-ray radiographic images of magnesium and aluminum alloy castings achieve optimal performance when trained up to the 800th and 600th epochs,respectively.The TOPSIS-IFP value reaches 78.7%and 73.8%similarity to the ideal solution,respectively.Compared to single index evaluation,the TOPSIS-IFP algorithm achieves higher-quality simulated images at the optimal training epoch.This approach successfully mitigates the issue of unreliable quality associated with single index evaluation.The image generation and comprehensive quality evaluation method developed in this paper provides a novel approach for image augmentation in flaw recognition,holding significant importance for enhancing the robustness of subsequent flaw recognition networks.

Rail-Pillar Net:A 3D Detection Network for Railway Foreign Object Based on LiDAR

Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.

Endoscopic detection and diagnostic strategies for minute gastric cancer:A real-world observational study

BACKGROUND Minute gastric cancers(MGCs)have a favorable prognosis,but they are too small to be detected by endoscopy,with a maximum diameter≤5 mm.AIM To explore endoscopic detection and diagnostic strategies for MGCs.METHODS This was a real-world observational study.The endoscopic and clinicopathological parameters of 191 MGCs between January 2015 and December 2022 were retrospectively analyzed.Endoscopic discoverable opportunity and typical neoplastic features were emphatically reviewed.RESULTS All MGCs in our study were of a single pathological type,97.38%(186/191)of which were differentiated-type tumors.White light endoscopy(WLE)detected 84.29%(161/191)of MGCs,and the most common morphology of MGCs found by WLE was protruding.Narrow-band imaging(NBI)secondary observation detected 14.14%(27/191)of MGCs,and the most common morphology of MGCs found by NBI was flat.Another three MGCs were detected by indigo carmine third observation.If a well-demarcated border lesion exhibited a typical neoplastic color,such as yellowish-red or whitish under WLE and brownish under NBI,MGCs should be diagnosed.The proportion with high diagnostic confidence by magnifying endoscopy with NBI(ME-NBI)was significantly higher than the proportion with low diagnostic confidence and the only visible groups(94.19%>56.92%>32.50%,P<0.001).CONCLUSION WLE combined with NBI and indigo carmine are helpful for detection of MGCs.A clear demarcation line combined with a typical neoplastic color using nonmagnifying observation is sufficient for diagnosis of MGCs.MENBI improves the endoscopic diagnostic confidence of MGCs.

Traffic light detection and recognition in intersections based on intelligent vehicle

To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transformation. Then, the colors of traffic lights are detected with color space transformation. Finally, self-associative memory is used to recognize the countdown characters of the traffic lights. Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%;and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively. The research proves that the method is efficient for the detection of stabling siding and is robust enough to recognize the characters from images with noise and broken edges.

An Intrusion Detection System for SDN Using Machine Learning

Software Defined Networking(SDN)has emerged as a promising and exciting option for the future growth of the internet.SDN has increased the flexibility and transparency of the managed,centralized,and controlled network.On the other hand,these advantages create a more vulnerable environment with substantial risks,culminating in network difficulties,system paralysis,online banking frauds,and robberies.These issues have a significant detrimental impact on organizations,enterprises,and even economies.Accuracy,high performance,and real-time systems are necessary to achieve this goal.Using a SDN to extend intelligent machine learning methodologies in an Intrusion Detection System(IDS)has stimulated the interest of numerous research investigators over the last decade.In this paper,a novel HFS-LGBM IDS is proposed for SDN.First,the Hybrid Feature Selection algorithm consisting of two phases is applied to reduce the data dimension and to obtain an optimal feature subset.In thefirst phase,the Correlation based Feature Selection(CFS)algorithm is used to obtain the feature subset.The optimal feature set is obtained by applying the Random Forest Recursive Feature Elimination(RF-RFE)in the second phase.A LightGBM algorithm is then used to detect and classify different types of attacks.The experimental results based on NSL-KDD dataset show that the proposed system produces outstanding results compared to the existing methods in terms of accuracy,precision,recall and f-measure.

A NOVEL BITTER DETECTION BIOSENSOR BASED ON LIGHT ADDRESSABLE POTENTIOMETRIC SENSOR

This paper presents a novel biosensor for bitter substance detection on the basis of light addressable potentiometric sensor(LAPS).Taste receptor cells(TRCs)were used as sensitive elements,which can respond to different bitter stimuli with extreme high sensitivity and speci-ficity.TRCs were isolated from the taste buds of rats and cultured on the surface of LAPS chip.Due to the unique advantages such as single-cell recording,light addressable capability,and noninvasiveness,LAPS chip was used as secondary transducer to monitor the responses of TRCs by recording extracelluar potential changes.The results indicate LAPS chip can effectively record the responses of TRCs to different bitter substances used in this study in a real-time manner for a long-term.In addition,by performing principal component analysis on the LAPS recording data,different bitter substances tested can be successfully discriminated.It is suggested this TRCs
LAPS hybrid biosensor could be a valuable tool for bitter substance detection.With further improvement and novel design,it has great potentials to be applied in both basic research and practical applications related to bitter taste detection.

Light Field Flow Estimation Based on Occlusion Detection

Light field cameras have a wide area of applications, such as digital refocusing, scene depth information extraction and 3-D image reconstruction. By recording the energy and direction information of light field, they can well solve many technical problems that cannot be done by conventional cameras. An important feature of light field cameras is that a microlens array is inserted between the sensor and main lens, through which a series of sub-aperture images of different perspectives are formed. Based on this feature and the full-focus image acquisition technique, we propose a light-field optical flow calculation algorithm, which involves both the depth estimation and the occlusion detection and guarantees the edge-preserving property. This algorithm consists of three steps: 1) Computing the dense optical flow field among a group of sub-aperture images;2) Obtaining a robust depth-estimation by initializing the light-filed optical flow using the linear regression approach and detecting occluded areas using the consistency;3) Computing an improved light-field depth map by using the edge-preserving algorithm to realize interpolation optimization. The reliability and high accuracy of the proposed approach is validated by experimental results.

Volumetric assessment of hepatic grafts using a light detection and ranging system for 3D scanning:Preliminary data

BACKGROUND Liver transplantation has evolved into a safe life-saving operation and remains the golden standard in the treatment of end stage liver disease.The main limiting factor in the application of liver transplantation is graft shortage.Many strategies have been developed in order to alleviate graft shortage,such as living donor partial liver transplantation and split liver transplantation for adult and pediatric patients.In these strategies,liver volume assessment is of paramount importance,as size mismatch can have severe consequences in the success of liver transplantation.AIM To evaluate the safety,feasibility,and accuracy of light detection and ranging(LIDAR)3D photography in the prediction of whole liver graft volume and mass.METHODS Seven liver grafts procured for orthotopic liver transplantation from brain deceased donors were prospectively measured with an LIDAR handheld camera and their mass was calculated and compared to their actual weight.RESULTS The mean error of all measurements was 17.03 g(range 3.56-59.33 g).Statistical analysis of the data yielded a Pearson correlation coefficient index of 0.9968,indicating a strong correlation between the values and a Student’s t-test P value of 0.26.Mean accuracy of the measurements was calculated at 97.88%.CONCLUSION Our preliminary data indicate that LIDAR scanning of liver grafts is a safe,cost-effective,and feasible method of ex vivo determination of whole liver volume and mass.More data are needed to determine the precision and accuracy of this method.

Experimental Detection of Depth of Field for a Thermal Light Lensless Ghost Imaging System

We propose optical experiments to study the depth of field for a thermal light lensless ghost imaging system. It is proved that the diaphragm is an important factor to influence the depth of field, and the ghost images of two detected objects with longitudinal distance less than the depth of field can be achieved simultaneously. The longitudinal coherence scale of the thermal light lensless ghost imaging determines the depth of field. Theoretical analysis can well explain the experimental results.

Effects of Pupil Diameter on Light Detection and Temporal Modulation

Purpose: This study compared the effects of pupil variation on light detection and temporal modulation across the central visual field.Methods:Light detection sensitivity (LDS) and low flickering frequency (6Hz) temporal modulation sensitivity (TMS) of 20 young subjects were measured from the central visual field of the right eyes using an automated perimeter (Medmont M600). The measurements were taken under 3 artificial pupils, I.e. 3 mm, 4.3 mm and 6 mm diameters.The sensitivities were grouped and averaged for different retinal eccentricities(3°, 6°, 10° and 15°).Results:TMS and LDS were reduced with increasing retinal eccentricities( P ＜ 0.001)and decreasing pupil diameters( P ＜ 0.001). TMS collected from all pupil diameters were significantly different from each other( P ＜ 0.001). Similarly, LDS under 3 mm pupil was statistically different from those of 4.3 mm and 6 mm(P ＜ 0.003). Comparison of the hills of vision showed that pupil variation resulted in significantly different slopes (P=0.001).The slopes were also found to be significantly different between TMS and LDS (P=0.012).Conclusions: The data showed that dilated pupil resulted in significantly higher sensitivities than those of smaller pupil for both visual functions. The difference in the slopes of hills of vision also suggested that the variation in retinal illumination affected the visual responses differently at various retinal eccentricitities for TMS and LDS.

Methane concentration detection system based on differential infrared absorption

The infrared absorption method for methane concentration detection is an ideal way to detect methane at present. However, it is difficult to spread this method due to its high cost. In this paper, by using a wideband infrared light emitting di- ode （LED） accompanied with a PIN photo electric diode, a low-cost methane detection system was designed. To overcome the shortcomings caused by the wide working band, a differential light path was designed. By means of a differential ratio algo- rithm, the stability and the accuracy of the system were guaranteed. Finally, the validity of the system with the proposed algo- rithm was verified by the experiment results.

Improved High Speed Flame Detection Method Based on YOLOv7

In order to solve the problems of the traditional flame detection method, such as low detection accuracy, slow detection speed and lack of real-time detection ability. An improved high speed flame detection method based on YOLOv7 is proposed. Based on YOLOv7 and combined with ConvNeXtBlock, CN-B network module was constructed, and YOLOv7-CN-B flame detection method was proposed. Compared with the YOLOv7 method, this flame detection method is lighter and has stronger flame feature extraction ability. 2059 open flame data sets labeled with single flame categories were used to avoid the enhancement effect brought by high-quality data sets, so that the comparative experimental effect completely depended on the performance of the flame detection method itself. The results show that the accuracy of YOLOv7-CN-B method is improved by 5% and mAP is improved by 2.1% compared with YOLOv7 method. The detection speed reached 149.25 FPS, and the single detection speed reached 11.9 ms. The experimental results show that the YOLOv7-CN-B method has better performance than the mainstream algorithm.

Automatic detection and removal of static shadows

In case of complex textures,existing static shadow detection and removal algorithms are prone to false detection of the pixels.To solve this problem,a static shadow detection and removal algorithm based on support vector machine(SVM)and region sub-block matching is proposed.Firstly,the original image is segmented into several superpixels,and these superpixels are clustered using mean-shift clustering algorithm in the superpixel sets.Secondly,these features such as color,texture,brightness,intensity and similarity of each area are extracted.These features are used as input of SVM to obtain shadow binary images through training in non-operational state.Thirdly,soft matting is used to smooth the boundary of shadow binary graph.Finally,after finding the best matching sub-block for shadow sub-block in the illumination region based on regional covariance feature and spatial distance,the shadow weighted average factor is introduced to partially correct the sub-block,and the light recovery operator is used to partially light the sub-block.The experimental results show the number of false detection of the pixels is reduced.In addition,it can remove shadows effectively for the image with rich textures and uneven shadows and make a natural transition at the boundary between shadow and light.

A nanoresonant gold-aptamer probe for rapid and sensitive detection of thrombin

Resonance light scattering (RLS) is a sensitive technique for monitoring scattered light induced by extended aggregates of chromophores. It has been widely used to study aggregations for its simple manipulation, high sensitivity and great versatility. Gold nanoparticles generate colorful light-scattering signals due to their unique surface plasmon resonances, hence extraordinary light scattering upon aggregation. In this paper we report a rapid and sensitive method based on gold nanoparticles and DNA aptamer to detect protein biomarkers by RLS. Thiol modified thrombin aptamer was covalently assembled to the surface of gold nanoparticles as nanobio probes. As thrombin has two specific binding sites for its aptamer, it can bridge the well dispersed nanoparticles and lead to a network of particle aggregations. The formation of aggregation ia measured by RLS, and the specific detection of thrombin at nM level is achieved. The method has good specificity.

Detecting Spin Bias with Circularly Polarized Light

We theoretically study the spin transport through a two-terminal quantum dot device under the influence of a symmetric spin bias and circularly polarized light. It is found that the combination of the circularly polarized light and the applied spin bias can result in a net charge current. The resultant charge current is large enough to be measured when properly choosing the system parameters. The resultant charge current can be used to deduce the spin bias due to the fact that there exists a simple linear relation between them. When the external circuit is open, a charge bias instead of a charge current can be induced, which is also measurable by present technologies. These findings indicate a new approach to detect the spin bias by using circularly polarized light.

Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)network.The followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected problem.Here,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and efficiency.Simulation outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

结合主动光源和改进YOLOv5s模型的夜间柑橘检测方法

【目的】解决夜间环境下遮挡和较小柑橘难以准确识别的问题,实现采摘机器人全天候智能化作业。【方法】提出一种结合主动光源的夜间柑橘识别方法。首先,通过分析主动光源下颜色特征不同的夜间柑橘图像,选择最佳的光源色并进行图像采集。然后,提出一种夜间柑橘检测模型BI-YOLOv5s,该模型采用双向特征金字塔网络(Bi-FPN)进行多尺度交叉连接和加权特征融合,提高对遮挡和较小果实的识别能力;引入Coordinate attention(CA)注意力机制模块,进一步加强对目标位置信息的提取;采用融入Transformer结构的C3TR模块,在减少计算量的同时更好地提取全局信息。【结果】本文提出的BI-YOLOv5s模型在测试集上的精准率、召回率、平均准确率分别为93.4%、92.2%和97.1%,相比YOLOv5s模型分别提升了3.2、1.5和2.3个百分点。在所采用的光源色环境下,模型对夜间柑橘识别的正确率为95.3%,相比白光环境下提高了10.4个百分点。【结论】本文提出的方法对夜间环境下遮挡和小目标柑橘的识别具有较高的准确性,可为夜间果蔬智能化采摘的视觉精准识别提供技术支持。

基于Transformer和动态3D卷积的多源遥感图像分类

多源遥感数据具有互补性和协同性,近年来,基于深度学习的方法已经在多源遥感图像分类中取得了一定进展,但当前方法仍面临关键难题,如多源遥感图像特征表达不一致,融合困难,基于静态推理范式的神经网络缺乏对不同类别地物的适应性。为解决上述问题,提出了基于跨模态Transformer和多尺度动态3D卷积的多源遥感图像分类模型。为提高多源特征表达的一致性,设计了基于Transformer的融合模块,借助其强大的注意力建模能力挖掘高光谱和LiDAR数据特征之间的相互作用;为提高特征提取方法对不同地物类别的适应性,设计了多尺度动态3D卷积模块,将输入特征的多尺度信息融入卷积核的调制,提高卷积操作对不同地物的适应性。采用多源遥感数据集Houston和Trento对所提方法进行验证,实验结果表明:所提方法在Houston和Trento数据集上总体准确率分别达到94.60%和98.21%,相比MGA-MFN等主流方法,总体准确率分别至少提升0.97%和0.25%,验证了所提方法可有效提升多源遥感图像分类的准确率。

基于改进YOLOv7-tiny的高空作业人员安防装备检测算法

针对现有高空作业人员安防装备检测算法参数量较大且检测目标相对单一,难以适应复杂的高空作业场景等问题,提出一种基于改进YOLOv7-tiny的高空作业人员安防装备检测算法。首先,将主干网络重新设计为更轻量的YOLOv7-FasterNet,并调整空间金字塔池化结构,实现模型参数量的压缩;其次,在ELAN-L模块中扩展梯度传输路径的分支,解决了模型压缩造成的通道信息缺失问题,提升了特征信息的提取能力;最后,将网络中下采样部分替换为Involution模块,降低参数冗余,增强网络对全局的捕获能力。实验结果表明,改进的YOLOv7-tiny算法能够更好地适应复杂高空作业场景,在开源数据集上具备良好的性能。该算法的平均检测精度达到94.7%,较原模型提升1.5%,参数量较原模型下降11.6%,实验结果验证了算法改进措施的有效性。

低光照下的无人机异物检测与定位

为解决无人机在低光照环境下的巡检过程中,不能对场景中的异物进行识别与定位,导致后续智能算法无法获得环境语义信息的问题.本文提出一种将ORB-SLAM2算法与适用于低光照目标检测改进的YOLOv5模型进行信息融合的方法.首先,通过RGB-D相机自采集低光照数据集进行深度学习训练及融合算法验证.然后,结合关键帧信息、目标检测模块的输出结果以及相机的固有信息完成目标像素坐标提取.最后,通过关键帧信息和像素坐标完成目标物体相对世界坐标系的位置解算.本文实现了低光照环境下目标物体较为准确的识别和目标物体在世界坐标系中分米级的定位,为低光照环境下无人机智能巡检提供了一种有效的解决方案.