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.

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.

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.

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.

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.

基于改进LightGBM的电力通信数据流量异常检测方法

针对电力通信网络攻击模式多变、检测泛化能力不足的问题,提出基于改进轻量级梯度提升机(Light Gradient Boosting Machine,LightGBM)的电力通信数据流量异常检测方法。结合最近邻规则(Edited Nearest Neighbor,ENN)算法、小波包分解技术和信息熵分析方法,提取电力通信数据流量异常特征,引入直方图算法和带深度限制的Leaf-wise生长策略,通过改进LightGBM算法建立电力通信数据流量异常检测模型,在模型中找到最优超参数配置,提高电力通信数据流量异常检测的准确率和效率。实验结果表明,设计方法在准确识别不同网络攻击类型和增强检测泛化能力方面具有显著优势,能够更好地应对电力通信网络中复杂多变的网络威胁,为电力通信系统的安全稳定运行提供有力保障。

基于Light GBM的僵尸网络检测方法

针对僵尸网络难以有效检测的问题,构建了一种基于Light GBM的僵尸网络检测模型。首先,构建了一种基于流的多变量网络流特征,该特征能够全面反映网络流量性质,并编写Zeek插件实现了高速流量下的流特征数据采集。接着,基于Light GBM构建僵尸网络检测模型,该模型的检测准确率为99.986%。最后,将该模型与基于随机森林和半监督算法的检测模型进行比较;结果表明该模型检测准确率更高,能够有效检测僵尸网络。

Image Analysis of Fabric Pilling Based on Light Projection

The objective assessment of fabric pilling based on light projection and image analysis has been exploited recently.The device for capturing the cross-sectional images of the pilled fabrics with light projection is elaborated.The detection of the profile line and integration of the sequential cross-sectional pilled image are discussed.The threshold based on Gaussian model is recommended for pill segmentation.The results show that the installed system is capable of eliminating the interference with pill information from the fabric color and pattern.

Spatio-temporal Dynamics of Urbanization in China Using DMSP/OLS Nighttime Light Data from 1992–2013

Understanding the dynamics of urbanization is essential to the sustainable development of cities. Meanwhile the analysis of urban development can also provide scientifically and effective information for decision-making. With the long-term Defense Meteorological Satellite Program’s Operational Linescan System(DMSP/OLS) nighttime light images, a pixel level assessment of urbanization of China from 1992 to 2013 was conducted in this study, and the spatio-temporal dynamics and future trends of urban development were fully detected. The results showed that the urbanization and urban dynamics of China experienced drastic fluctuations from 1992 to 2013, especially for those in the coastal and metropolitan areas. From a regional perspective, it was found that the urban dynamics and increasing trends in North Coast China, East Coast China and South Coast China were much more stable and significant than that in other regions. Moreover, with the sustainability estimating of nighttime light dynamics, the regional agglomeration trends of urban regions were also detected. The light intensity in nearly 50% of lighted pixels may continuously decrease in the future, indicating a severe situation of urbanization within these regions. In this study, The results revealed in this study can provided a new insight in long time urbanization detecting and is thus beneficial to the better understanding of trends and dynamics of urban development.

A compact Einstein–Podolsky–Rosen entangled light source

We present a stable entangled light source that integrates the pump laser, entanglement generator, detectors, and electronic control systems. By optimizing the design of the mechanical elements and the optical path, the size of the source is minimized, and the quantum correlations over 6 d B can be directly provided by the entangled source. The compact and stable entangled light source is suitable for practical applications in quantum information science and technology. The presented protocol provides a useful reference for manufacturing products of bright entangled light sources.