Design of Online Vision Detection System for Stator Winding Coil

The quality of the stator winding coil directly affects the performance of the motor.A dual-camera online machine vision detection method to detect whether the coil leads and winding regions were qualified was designed.A vision detection platform was designed to capture individual winding images,and an image processing algorithm was used for image pre-processing,template matching and positioning of the coil lead area to set up a coordinate system.After eliminating image noise by Blob analysis,the improved Canny algorithm was used to detect the location of the coil lead paint stripped region,and the time was reduced by about half compared to the Canny algorithm.The coil winding region was trained with the ShuffleNet V2-YOLOv5s model for the dataset,and the detect file was converted to the Open Neural Network Exchange(ONNX)model for the detection of winding cross features with an average accuracy of 99.0%.The software interface of the detection system was designed to perform qualified discrimination tests on the workpieces,and the detection data were recorded and statistically analyzed.The results showed that the stator winding coil qualified discrimination accuracy reached 96.2%,and the average detection time of a single workpiece was about 300 ms,while YOLOv5s took less than 30 ms.

A Novel Chip-based Spectrophotometer for Online Detection

A chip-based spectrophotometer integrated with optical fiber is successfully demonstrated.Grade concentration of lactate solution flowed through the chip to perform an online detection.The response time (100s)and Limit of Detection (LOD, 50mg/L)of the device were measured.This device shows comparable performance with traditional commercial instrument, while greatly decreases the sample requirement per detection and reduces the size of total system,introducing a novel method for real-time detection.

A proposed NMR solution for multi-phase flow fluid detection

In the petroleum industry,detection of multi-phase fluid flow is very important in both surface and down-hole measurements.Accurate measurement of high rate of water or gas multi-phase flow has always been an academic and industrial focus.NMR is an efficient and accurate technique for the detection of fluids;it is widely used in the determination of fluid compositions and properties.This paper is aimed to quantitatively detect multi-phase flow in oil and gas wells and pipelines and to propose an innovative method for online nuclear magnetic resonance(NMR)detection.The online NMR data acquisition,processing and interpretation methods are proposed to fill the blank of traditional methods.A full-bore straight tube design without pressure drop,a Halbach magnet structure design with zero magnetic leakage outside the probe,a separate antenna structure design without flowing effects on NMR measurement and automatic control technology will achieve unattended operation.Through the innovation of this work,the application of NMR for the real-time and quantitative detection of multi-phase flow in oil and gas wells and pipelines can be implemented.

Rapid online analysis of trace elements in steel using a mobile fiber-optic laser-induced breakdown spectroscopy system

A mobile fiber-optic laser-induced breakdown spectrometer(FO-LIBS) prototype was developed to rapidly detect a large quantity of steel material online and quantitatively analyze the trace elements in a large-diameter steel tube.Twenty-four standard samples and a polynomial fitting method were used to establish calibration curve models.The R^2 factors of the calibration curves were all above 0.99,except for Cu,indicating the elements’ strong self-absorption effect.Five special steel materials were rapidly detected in the steel mill.The average absolute errors of Mn,Cr,Ni,V,Cu,and Mo in the special steel materials were 0.039,0.440,0.033,0.057,0.003,and0.07 wt%,respectively,and their average relative errors fluctuated from 2.9% to 15.7%.The results demonstrated that the performance of this mobile FO-LIBS prototype can be compared with that of most conventional LIBS systems,but the more robust and flexible characteristics of the FO-LIBS prototype provide a feasible approach for promoting LIBS from the laboratory to the industry.

Use of conductivity method for online measurement of alkalinity of caustic washing liquid

In this paper,the online measurement of the alkalinity of caustic washing liquid by the conductivity method was discussed,and a working curve of the conductivity vs.alkalinity was established,for which the correlation coefficient is 0.9979(n=7).The influence of the temperature of the caustic washing liquid and the presence of coexisting ions,such as iron and oil,on the accuracy of the conductivity method was discussed.The temperature is compensated for by establishing the correlation between conductivity and temperature.When the iron concentration is≤1000 mg/L,and the oil concentration is≤1000 mg/L,the deviation in the results obtained using the conductivity and titration methods is≤2.5 g/L.The t-test results show that there is no systematic devia-tion between the conductivity and titration methods.The conductivity method has the advantages of a fast response and good real-time performance,which meet the requirements for the online determination of the alkalinity of caustic washing liquid.

A Review on Prerequisites of a Set-Up for Particle Detection by Ultrasonic Waves in Aluminium Melts

In many applications in aluminium industry, the number of inclusion-critical products increases and the quality of those products depend on the inclusion concentration and size. In order to improve the quality of aluminium products and the effectiveness of the processes, a reliable and cheaper on-line detection method is needed. Ultrasonic detection has been used in steel industry, but relatively rare in aluminium industry, although it would theoretically allow for an online non-intrusive detection of the cleanliness of the melt. In this work, the current information on ultrasonic inclusion detection was gathered and recommendations were provided on the Prerequisites for a set-up for ultrasonic detection of non-metallic inclusions in aluminium as a contribution on previous works. It has been concluded that ultrasonic waves seem promising, and should be experimented more on an industrial level to have a clear view on the potentials of the method.

Design on Online Fault Diagnosis System of Stealmaking Gas Dedust Fan

The study introduces the meanings of the technology on dedust fan′s online detection and fault diagnosis,the ways of fault diagnosis,the common fault analysis and the design of stealmaking gas dedust fan′s online fault diagnosis.It shows the whole system′s design,establishment and functional test.XM series modules have been used to realize the online fault diagnosis.The system′s functional requirements are proved by experiment.

Research on modeling method of continuous spectrum water quality online detection based on random forest

It's common to use the method of continuous spectroscopy in water quality testing. But there're some problems with it. For example, the scanning results have a large number of nonlinear signals, and the covariance between variables is serious, which can lead to a decrease in the model prediction accuracy. In this paper, the standard solutions of nitrate nitrogen(NO_(3)-N) and nitrite nitrogen(NO_(2)-N) were used as the subject to be tested, and the data of the scanned waves and absorbance were obtained by use of spectral detector. The data were processed by noise reduction first and then the random forest(RF) algorithm was adopted to establish the regression relationship between concentration and absorbance. For comparison, partial least squares(PLS) and support vector machine(SVM) algorithm models were also established. For the same given data, the three reverse models can make the projection of the concentration respectively. The experimental results show that the RF algorithm predicts NO_(2)-N concentrations significantly better than the SVM algorithm and PLS algorithm. This proves that the RF algorithm has good prediction ability in spectral water quality detection because of its high model accuracy and better adaptability, which could be a reference for similar research on continuous spectral water quality online detection.

Pushing the frontiers: Chip-based detection based on micro-and nano-structures

Changes in trace substances in human metabolites, which are related to disease processes and health status, can serve as chemical markers for disease diagnosis and symptom monitoring. Real-time online detection is an inevitable trend for the future of health monitoring, and the construction of chips for detection faces major challenges. The response of sensors often fails to meet the requirements for chipbased detection of trace substances due to the low efficiency of interfacial heterogeneous reactions, necessitating a rational design approach for micro-and nano-structures to improve sensor performance with respect to sensitivity and detection limits. This review focuses on the influence of micro-and nanostructures that used in chip on sensing. Firstly, this review categorizes sensors into chemiresistors, electrochemical sensors, fluorescence sensors, and surface enhanced Raman scattering(SERS) sensors based on their sensing principle, which have significant applications in disease diagnosis. Subsequently, commencing from the application requirements in the field of sensing, this review focuses on the different structures of nanoparticle(NP) assemblies, including wire, layered, core-shell, hollow, concave and deformable structures. These structures change in the size, shape, and morphology of conventional structures to achieve characteristics such as ordered alignment, high specific surface area, space limitation,vertical diffusion, and swaying behavior with fluid, thereby addressing issues such as poor signal transmission efficiency, inadequate adsorption and capture capacity, and slow mass transfer speed during sensing. Finally, the design direction of micro-and nano-structures, and possible obstacles and solutions to promote chip-based detection have been discussed. It is hope that this article will inspire the exploration of interface micro-and nano-structures modulated sensing methods.

Design of Drilling and Riveting Multi-functional End Effector for CFRP and Aluminum Components in Robotic Aircraft Assembly

To fulfill the demands for higher quality,efficiency and flexibility in aviation industry,a multi-functional end effector is designed to automate the drilling and riveting processes in assembling carbon fiber reinforced polymer(CFRP)and aluminum components for a robotic aircraft assembly system.To meet the specific functional requirements for blind rivet installation on CFRP and aluminum materials,additional modules are incorporated on the end effector aside of the basic processing modules for drilling.And all of these processing modules allow for a onestep-drilling-countersinking process,hole inspection,automatic rivet feed,rivet geometry check,sealant application,rivet insertion and installation.Besides,to guarantee the better quality of the hole drilled and joints riveted,several online detection and adjustment measures are applied to this end effector,including the reference detection and perpendicular calibration,which could effectively ensure the positioning precision and perpendicular accuracy as demanded.Finally,the test result shows that this end effector is capable of producing each hole to a positioning precision within ±0.5 mm,aperpendicular accuracy within 0.3°,a diameter tolerance of H8,and a countersink depth tolerance of±0.01 mm.Moreover,it could drill and rivet up to three joints per minute,with acceptable shearing and tensile strength.

A strategy for structure-activity relationship study on antioxidants in Echinops latifolius Tausch extracts by online HPLC-radical scavenging detection coupled with ESI-IT-TOF-MS^(n)

Echinops latifolius Tausch(ELT)is the traditional Mongolian medicine for the treatment of osteoporosis,and the ambiguous composition of active ingredients is an important factor in restricting the modernization and globalization of this herb.Considering the traditional activity screening strategy is time-consuming and labor intensive,online HPLC active ingredient detection coupled with ESI-IT-TOF-MS^(n) strategy was employed in this study to isolate,identify and screen active compounds from the herbal medicines at the same time.The structure-activity relationship of these compounds was elucidated as well.Owing to the association of osteoporosis progression and oxidative stress,the antioxidants screening from ELT could be a good interpretive of the active substance in this herb.Meanwhile,DPPH equivalent method was an indicative of the most powerful antioxidant in ELT.Consequently,the screening and identification of the antioxidants in ELT was performed by using on-line HPLC-radical scavenging detection coupled with ESI-IT-TOF-MS^(n) strategy,and the structure-activity relationship was investigated based on DPPH equivalent method.Finally,20 constituents(including apigenin glucosides,caffeic acid,biscaffeoylquinic acids,biscaffeoylquinic acid methyl esters,ect.)were characterized in ELT extracts,and 18 components showed appreciable radical scavenging capacity.In addition,the structure-activity relationship study was carried out based on 14 compounds isolated from our laboratory,and the structural requirements of the compounds on antioxidant activity were obtained:(1)compounds with phenolic hydroxyl groups could have antioxidant activity;(2)the antioxidant activity could not be facilitated by the number of hydroxyl groups,but affected by the number of caffeoyl groups;(3)the substitution position of caffeoyl on quinic acid had a greater influence on DPPH activity;(4)methoxy groups could reduce the antioxidant activity.Collectively,this work provided the biochemical perspective to link active compounds and anti-osteoporosis action of ELT,and further explained how ELT worked in osteoporosis patients with bone loss.

Fast Image Segmentation Algorithm Based on Salient Features Model and Spatial-frequency Domain Adaptive Kernel

A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes of variable sample morphological characteristics,low contrast and complex background texture.Firstly,by analyzing the spectral component distribution and spatial contour feature of the image,a salient feature model is established in spatial-frequency domain.Then,the salient object detection method based on Gaussian band-pass filter and the design criterion of adaptive convolution kernel are proposed to extract the salient contour feature of the target in spatial and frequency domain.Finally,the selection and growth rules of seed points are improved by integrating the gray level and contour features of the target,and the target is segmented by seeded region growing.Experiments have been performed on Berkeley Segmentation Data Set,as well as sample images of online detection,to verify the effectiveness of the algorithm.The experimental results show that the Jaccard Similarity Coefficient of the segmentation is more than 90%,which indicates that the proposed algorithm can availably extract the target feature information,suppress the background texture and resist noise interference.Besides,the Hausdorff Distance of the segmentation is less than 10,which infers that the proposed algorithm obtains a high evaluation on the target contour preservation.The experimental results also show that the proposed algorithm significantly improves the operation efficiency while obtaining comparable segmentation performance over other algorithms.

An online anomaly detection method for stream data using isolation principle and statistic histogram

Online anomaly detection for stream data has been explored recently,where the detector is supposed to be able to perform an accurate and timely judgment for the upcoming observation.However,due to the inherent complex characteristics of stream data,such as quick generation,tremendous volume and dynamic evolution distribution,how to develop an effective online anomaly detection method is a challenge.The main objective of this paper is to propose an adaptive online anomaly detection method for stream data.This is achieved by combining isolation principle with online ensemble learning,which is then optimized by statistic histogram.Three main algorithms are developed,i.e.,online detector building algorithm,anomaly detecting algorithm and adaptive detector updating algorithm.To evaluate our proposed method,four massive datasets from the UCI machine learning repository recorded from real events were adopted.Extensive simulations based on these datasets show that our method is effective and robust against different scenarios.

Online detection of bursty events and their evolution in news streams

Online monitoring of temporally-sequenced news streams for interesting patterns and trends has gained popularity in the last decade.In this paper,we study a particular news stream monitoring task:timely detection of bursty events which have happened recently and discovery of their evolutionary patterns along the timeline.Here,a news stream is represented as feature streams of tens of thousands of features(i.e.,keyword.Each news story consists of a set of keywords.).A bursty event therefore is composed of a group of bursty features,which show bursty rises in frequency as the related event emerges.In this paper,we give a formal definition to the above problem and present a solution with the following steps:(1) applying an online multi-resolution burst detection method to identify bursty features with different bursty durations within a recent time period;(2) clustering bursty features to form bursty events and associating each event with a power value which reflects its bursty level;(3) applying an information retrieval method based on cosine similarity to discover the event's evolution(i.e.,highly related bursty events in history) along the timeline.We extensively evaluate the proposed methods on the Reuters Corpus Volume 1.Experimental results show that our methods can detect bursty events in a timely way and effectively discover their evolution.The power values used in our model not only measure event's bursty level or relative importance well at a certain time point but also show relative strengths of events along the same evolution.

Nondestructive perception of potato quality in actual online production based on cross-modal technology

Nowadays,China stands as the global leader in terms of potato planting area and total potato production.The rapid and nondestructive detection of the potato quality before processing is of great significance in promoting rural revitalization and augmenting farmers’income.However,existing potato quality sorting methods are primarily confined to theoretical research,and the market lacks an integrated intelligent detection system.Therefore,there is an urgent need for a post-harvest potato detection method adapted to the actual production needs.The study proposes a potato quality sorting method based on cross-modal technology.First,an industrial camera obtains image information for external quality detection.A model using the YOLOv5s algorithm to detect external green-skinned,germinated,rot and mechanical damage defects.VIS/NIR spectroscopy is used to obtain spectral information for internal quality detection.A convolutional neural network(CNN)algorithm is used to detect internal blackheart disease defects.The mean average precision(mAP)of the external detection model is 0.892 when intersection of union(IoU)=0.5.The accuracy of the internal detection model is 98.2%.The real-time dynamic defect detection rate for the final online detection system is 91.3%,and the average detection time is 350 ms per potato.In contrast to samples collected in an ideal laboratory setting for analysis,the dynamic detection results of this study are more applicable based on a real-time online working environment.It also provides a valuable reference for the subsequent online quality testing of similar agricultural products.

Automatic Determination of Water Hardness by Vector Colorimetry with Acid Chrome Blue K

Based on the Mg^(2+)complexation with acid chrome blue K(ACBK)at pH 10.2,an automatic system was designed to determine total hardness of water.The system consists of a vector colorimeter,a multi-channel sampling pump and both reagents A and B.Two kinds of reagent solutions were prepared and used in this system,i.e.,ammoniacal buffer and ACBK—disodium magnesium EDTA solutions.The experimental results of the standard solutions containing 2 and 3 mg/L of total hardness showed that the relative standard deviations(RSDs)were 1.9%and 2.2%,respectively,and the limit of detection(LOD)was only 0.035 mg/L.The detection of four natural water samples showed that the recoveries were between 85.0%and 108.6%,consistent with those obtained by ICP-AES method.

The Best Answers？ Think Twice： Identifying Commercial Campagins in the CQA Forums

In an emerging trend, more and more Internet users search for information from Community Question and Answer （CQA） websites, as interactive communication in such websites provides users with a rare feeling of trust. More often than not, end users look for instant help when they browse the CQA websites for the best answers. Hence, it is imperative that they should be warned of any potential commercial campaigns hidden behind the answers. Existing research focuses more on the quality of answers and does not meet the above need. Textual similarities between questions and answers are widely used in previous research. However, this feature will no longer be effective when facing commercial paid posters. More context information, such as writing templates and a user’s reputation track, needs to be combined together to form a new model to detect the potential campaign answers. In this paper, we develop a system that automatically analyzes the hidden patterns of commercial spam and raises alarms instantaneously to end users whenever a potential commercial campaign is detected. Our detection method integrates semantic analysis and posters’ track records and utilizes the special features of CQA websites largely different from those in other types of forums such as microblogs or news reports. Our system is adaptive and accommodates new evidence uncovered by the detection algorithms over time. Validated with real-world trace data from a popular Chinese CQA website over a period of three months, our system shows great potential towards adaptive detection of CQA spams.

Extracellular ascorbic acid fluctuation during the protective process of ischemic preconditioning in rabbit renal ischemia-reperfusion model measured

Background Ascorbic acid has important antioxidant ischemic preconditioning on later ischemia-reperfusion ascorbic acid in ischemic preconditioning in the kidney. properties, and may play a role in the protective effects of Herein, we examined the role of endogenous extracellular Methods We developed a solitary rabbit kidney model where animals received ischemia-reperfusion only （ischemia-reperfusion group, n=-15） or ischemic preconditioning followed by ischemia-reperfusion （ischemic preconditioning group, n=15）. Ischemia-reperfusion was induced by occluding and loosening of the renal pedicle. The process of ischemic preconditioning included 15-minute brief ischemia and 10-minute reperfusion. In vivo microdialysis coupled with online electrochemical detection was used to determine levels of endogenous extracellular ascorbic acid in both groups. The extent of tissue damage was determined in kidney sections stained with hematoxylin and eosin. Serum creatinine and urea nitrogen were also detected to assess renal function. Results During ischemia-reperfusion, the extracellular ascorbic acid concentration during ischemia increased rapidly to the peak level （（130.01±9.98）%）, and then decreased slowly to near basal levels. Similar changes were observed during reperfusion （peak level, （126.78±18.24）%）. In the ischemic preconditioning group there was a similar pattern of extracellular ascorbic acid concentration during ischemic preconditioning. However, the ascorbic acid level was significantly lower during the ischemia and early reperfusion stage compared to the ischemia-reperfusion group. Additionally, the extent of glomerular ischemic collapse, tubular dilation, tubular denudation, and loss of brush border were markedly attenuated in the ischemic preconditioning group. Levels of serum creatinine and urea nitrogen were also decreased significantly in the ischemic preconditioning group. Conclusions Ischemic preconditioning may protect renal tissue against ischemia-reperfusion injury via use of extracellular ascorbic acid. In vivo microdialysis coupled with online electrochemical detection is effective for continuous monitoring extracellular ascorbic acid in the renal cortex.