An improved initial rotor position estimation method using high-frequency pulsating voltage injection for PMSM

High frequency pulsating voltage injection method is a good candidate for detecting the initial rotor position of permanent magnet synchronous motor.However,traditional methods require a large number of filters,which leads to the deterioration of system stability and dynamic performance.In order to solve these problems,a new signal demodulation method is proposed in this paper.The proposed new method can directly obtain the amplitude of high-frequency current,thus eliminating the use of filters,improving system stability and dynamic performance and saving the work of adjusting filter parameters.In addition,a new magnetic polarity detection method is proposed,which is robust to current measurement noise.Finally,experiments verify the effectiveness of the method.

Clinical impact of endoscopy position detecting unit(UPD-3) for a non-sedated colonoscopy

AIM:To evaluate whether an endoscopy position detecting unit(UPD-3) can improve cecal intubation rates, cecal intubation times and visual analog scale(VAS) pain scores, regardless of the colonoscopist's level of experience.METHODS:A total of 260 patients(170 men and 90women)who underwent a colonoscopy were divided into the UPD-3-guided group or the conventional group(no UPD-3 guidance).Colonoscopies were performed by experts(experience of more than 1000colonoscopies)or trainees(experience of less than 100colonoscopies).Cecal intubation rates,cecal intubation times,insertion methods(straight insertion:shortening the colonic fold through the bending technique;roping insertion:right turn shortening technique)and patient discomfort were assessed.Patient discomfort during the endoscope insertion was scored by the VAS that was divided into 6 degrees of pain.RESULTS:The cecum intubation rates,cecal intubation times,number of cecal intubations that were performed in<15 min and insertion methods were not significantly different between the conventional group and the UPD-3-guided group.The number of patients who experienced pain during the insertion was markedly less in the UPD-3-guided group than in the conventional group.Univariate and multivariate analysis showed that the following factors were associated with lower VAS pain scores during endoscope insertion:insertion method(straight insertion)and UPD-3guidance in the trainee group.For the experts group,univariate analysis showed that only the insertion method(straight insertion)was associated with lower VAS pain scores.CONCLUSION:Although UPD-3 guidance did not shorten intubation times,it resulted in less patient painduring endoscope insertion compared with conventional endoscopy for the procedures performed by trainees.

Detecting the Position of the Moving-iron Solenoid by Non-displacement Sensor Based on Parameter Identification of Flux Linkage Characteristics

Currently, most researches use signals, such as the coil current or voltage of solenoid, to identify parameters; typically, parameter identification method based on variation rate of coil current is applied for position estimation. The problem exists in these researches that the detected signals are prone to interference and difficult to obtain. This paper proposes a new method for detecting the core position by using flux characteristic quantity, which adds a new group of secondary winding to the coil of the ordinary switching electromagnet. On the basis of electromagnetic coupling theory analysis and simulation research of the magnetic field regarding the primary and secondary winding coils, and in accordance with the fact that under PWM control mode varying core position and operating current of windings produce different characteristic of flux increment of the secondary winding. The flux increment of the electromagnet winding can be obtained by conducting time domain integration for the induced voltage signal of the extracted secondary winding, and the core position from the two-dimensional fitting curve of the operating winding current and flux-linkage characteristic quantity of solenoid are calculated. The detecting and testing system of solenoid core position is developed based on the theoretical research. The testing results show that the flux characteristic quantity of switching electromagnet magnetic circuit is able to effectively show the core position and thus to accomplish the non-displacement transducer detection of the said core position of the switching electromagnet. This paper proposes a new method for detecting the core position by using flux characteristic quantity, which provides a new theory and method for switch solenoid to control the proportional valve.

An artificial neural network for detecting weld position in arc welding process

A kind of self organizing artificial neural network used for weld detection is presented in this paper, and its concepts and issues are discussed. The network can transform the weld visual information into typical patterns and match with the weld data collected on line, and so realize the accurate detection of the weld position in arc welding process.

Vehicle detection based on information fusion of vehicle symmetrical contour and license plate position

An efficient vehicle detection approach is proposed for traffic surveillance images, which is based on information fusion of vehicle symmetrical contour and license plate position. The vertical symmetry axis of the vehicle contour in an image is. first detected, and then the vertical and the horizontal symmetry axes of the license plate are detected using the symmetry axis of the vehicle contour as a reference. The vehicle location in an image is determined using license plate symmetry axes and the vertical and the horizontal projection maps of the vehicle edge image. A dataset consisting of 450 images （15 classes of vehicles） is used to test the proposed method. The experimental results indicate that compared with the vehicle contour-based, the license plate location-based, the vehicle texture-based and the Gabor feature-based methods, the proposed method is the best with a detection accuracy of 90.7% and an elapsed time of 125 ms.

A Hybrid Intrusion Detection Method Based on Convolutional Neural Network and AdaBoost

To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.

A DETECTION METHOD OF HIGH RESOLUTION RADAR TARGETS BASED ON POSITION CORRELATION

This paper studies a detection method of targets of high resolution radar operating at the band of millimeter-wave(32-38GHz) under the background of the clutters, and proposes a new nonparametric detection method, which not only does less computation, but also is able to detect multiple extended targets radially distributed along distance "corridor", based on the position (range) correlation information of one-dimensional range images(or called range profiles) of high resolution radar targets. The experimental results, on the real echo data of tank illuminated by the millimeter-wave stepped frequency high resolution radar, have certified that such a method presented in this paper is a very effective detection method for multiple extended targets.

In-pit coal mine personnel uniqueness detection technology based on personnel positioning and face recognition

Since the coal mine in-pit personnel positioning system neither can effectively achieve the function to detect the uniqueness of in-pit coal-mine personnel nor can identify and eliminate violations in attendance management such as multiple cards for one person, and swiping one's cards by others in China at present. Therefore, the research introduces a uniqueness detection system and method for in-pit coal-mine personnel integrated into the in-pit coal mine personnel positioning system, establishing a system mode based on face recognition + recognition of personnel positioning card + release by automatic detection. Aiming at the facts that the in-pit personnel are wearing helmets and faces are prone to be stained during the face recognition, the study proposes the ideas that pre-process face images using the 2D-wavelet-transformation-based Mallat algorithm and extracts three face features: miner light, eyes and mouths, using the generalized symmetry transformation-based algorithm. This research carried out test with 40 clean face images with no helmets and 40 lightly-stained face images, and then compared with results with the one using the face feature extraction method based on grey-scale transformation and edge detection. The results show that the method described in the paper can detect accurately face features in the above-mentioned two cases, and the accuracy to detect face features is 97.5% in the case of wearing helmets and lightly-stained faces.

False positive detection of serum cryptococcal antigens due to insufficient sample dilution:A case series

At present,with the development of technology,the detection of cryptococcal antigen(CRAG)plays an increasingly important role in the diagnosis of cryptococcosis.However,the three major CRAG detection technologies,latex agglutination test(LA),lateral flow assay(LFA)and Enzyme-linked Immunosorbent Assay,have certain limitations.Although these techniques do not often lead to false-positive results,once this result occurs in a particular group of patients(such as human immunodeficiency virus patients),it might lead to severe consequences.

Initial Rotor Position Detection of 12/10 Flux-switching Permanent Magnet Motors

电机转子初始位置检测的精度直接决定其启动运行时的性能。针对12/10极永磁磁通切换型电机,在验证其凸极性的基础上,采用注入脉振高频信号实现转子初始位置估计。通过对传统高频脉振正弦电压信号注入的分析,研究了基于高频方波或三角波的注入方法,再利用电流差值调制或直接信号调制方法得到位置误差信号,同时根据磁路的饱和效应,通过对d轴电流过零点时间差值的累加,实现了磁极方向辨识。分析了位置误差信号的系数与初始位置检测收敛性的关系。通过实验验证了所提方法的正确性和可行性。

How crystal configuration affects the position detection accuracy in pixelated molecular SPECT imaging systems?

It is well known that inter-crystal scattering and penetration(ICS-P) are major spatial resolution limiting parameters in dedicated SPECT scanners with pixelated crystal.In this study,the effect of ICS-P on crystal identification in different crystal configurations was evaluated using GATE Monte Carlo simulation.A ^(99m)Tc pencil-beam toward central crystal element was utilized.Beam incident angle was assumed to vary from 0° to 45° in 5° steps.The effects of various crystal configurations such as pixel-size,pixel-gap,and crystal material were studied.The influence of photon energy on the crystal identification(CI) was also investigated.Position detection accuracy(PDA) was defined as a factor indicating performance of the crystal.Furthermore,a set of ^(99m)Tc point-source simulations was performed in order to calculate peak-to-valley(PVR) ratio for each configuration.The results show that the CsI(Na)manifests higher PDA than NaI(TI) and YAP(Ce).In addition,as the incident angle increases,the crystal becomes less accurate in positioning of the events.Beyond a crystal-dependent critical angle,the PDA monotonically reduces.The PDA reaches 0.44 for the CsI(Na) at 45° beam angle.The PDAs obtained by the point-source evaluation also behave the same as for the pencil-beam irradiations.In addition,the PVRs derived from flood images linearly correlate their corresponding PDAs.In conclusion,quantitative assessment of ICS-P is mandatory for scanner design and modeling the system matrix during iterative reconstruction algorithms for the purpose of resolution modeling in ultra-high-resolution SPECT.

A simplified method for local tool interference detection and tool position modification during five-axis ball-end milling process

Five-axis ball-end milling is commonly used to machine the complex surfaces. Local tool interference phenomenon which often occurs in five-axis milling should be urgently solved. In this paper, a simplified method to detect the occurrence of local tool interference and modify tool position is proposed. First, the detection matrix is established to detect local tool interference at all the cutter location points on tool path simultaneously in five-axis ball-end milling of complex surfaces. The algorithm of detection matrix based on point arithmetic is simple. Secondly, the new coordinates of the modified interfering-free points are obtained precisely by using the genetic algorithm. The feasibility of the method is validated by simulation in Matlab. This research is benefit to simplify the calculation of local tool interference detection and tool position modification.

INVESTIGATION ON FAULT DETECTION & DIAGNOSIS FOR POSITION SERVO SYSTEM OF AIRCRAFT ACTUATOR

A new approach to fault dignosis dealing with nonlinear system Hopfieldneural networks (HNN) is presented. The model parameters of the nonlinear systemtreated as functions of measured operating points and faults are estimated by HNN. Boththe nominal model of the healthy system and HNN training models corresponding to everyoperating point are recognized. In addition, the anticipated fault models corresponding toevery kind of fault and every operating point are obtaind in advance. The real systemmodel parameters of the system estimated by generalization process of HNN are matchedwith the nominal models of the healthy system and anticipated fault models. Consequent-ly, the final result of fault detection and diagnosis is acquired. The approach to fault diag-nosis is used in an aircraft actuating poisition servo system and the simulation resu1t is re-ported.

A Study on the Sensor Applications for Position Detection and Guideway Monitoring in High Speed Maglev

The high speed maglev is mainly characterized by propulsion using linear synchronous motor (LSM) and vehicle levitation from the guideway surface. In LSM propulsion control, the position detection sensor is used to detect running vehicle position for synchronized current generation. To maintain the stable levitating condition during vehicle running, the irregularity of guideway surface should be monitored by sensors measuring the displacement and acceleration between vehicle and guideway. In this study, the application methods of these sensors in the high speed maglev are investigated and through the experiments by using the small-scale test bed, the validity of examined methods is confirmed.

Automatic Detection of Electric Meter Position in Automated Assembly Line Turnover Box

In the process of metering and automatic production system in the measurement and verification of the loading robot,if there is a positional shift of the electric meter,the gripper of the loading robot will mechanically collide with the electric meter.Due to the large force of the loading robot,the appearance of the electric meter is damaged,and the mechanical arm of the loading robot is broken,which reduces the service life of the measuring and verifying equipment,which is not conducive to the continuous and stable operation of the automatic verification system.In this paper,an automatic detection system of electric meter position in the turnaround box is proposed,which can reduce the failure rate of the automatic verification system,improve the verification efficiency,reduce the loss of metering assets and the occurrence of safety accidents by realizing the offset detection and correction of the electric meter position in the turnover box.

Improving the target position detection in the crossed array trackers seeker using the pulses number distribution in the FOV

Seeking in a field of view(FOV) is influenced by the existence of jammers,noise,shine background or flying perturbations.All these factors may push the target out of the FOV and cause missing the target.In all the seekers the FOV is not fully exploited which means the target can be missed before becoming out of the FOV,this results of the nonlinearity of the reticle structure.In this paper,a novel method of the target position detection a crossed four slits or crossed array trackers(CAT) seeker will be designed,simulated and evaluated.The idea of this method depends on dividing the FOV into main regions up to a certain parameter,which is the pulses number;then,each main region will be divided into sub-regions up to a second parameter which will be the pulses distribution a spin period.The errors sources will be discussed and evaluated.Other new idea will be applied which is exploiting some area of the FOV where a part of the position data is missed in the information signal by pushing the target to the region where the information signal carries the total position data.

Effects of laser beam divergence angle on airborne LIDAR positioning errors

The influence of laser beam divergence angle on the positioning accuracy of scanning airborne light detection and ranging （LIDAR） is analyzed and simulated. Based on the data process and positioning principle of airborne LIDAR, the errors from pulse broadening induced by laser beam di vergence angle are modeled and qualitatively analyzed for different terrain surfaces. Simulated results of positioning errors and suggestions to reduce them are given for the flat surface, the downhill of slope surface, and the uphill surface.

Visual positioning of rectangular lead components based on Harris corners and Zernike moments

With the increasing necessities for reliable printed circuit board(PCB) product, there has been a considerable demand for high speed and high precision vision positioning system. To locate a rectangular lead component with high accuracy and reliability, a new visual positioning method was introduced. Considering the limitations of Ghosal sub-pixel edge detection algorithm, an improved algorithm was proposed, in which Harris corner features were used to coarsely detect the edge points and Zernike moments were adopted to accurately detect the edge points. Besides, two formulas were developed to determine the edge intersections whose sub-pixel coordinates were calculated with bilinear interpolation and conjugate gradient method. The last experimental results show that the proposed method can detect the deflection and offset, and the detection errors are less than 0.04° and 0.02 pixels.

S-Detect在乳腺肿块诊断中出现假阴性和假阳性的影响因素

目的:研究S-Detect技术在乳腺肿块诊断中出现假阳性和假阴性结果的影响因素。方法:回顾并分析2019年5月—2022年3月南京医科大学第一附属医院613例女性患者的697个乳腺病灶,患者均进行了常规超声检查和S-Detect检查并取得手术后病理学检查结果。以病理学检查结果为标准,绘制受试者工作特征(receiver operating characteristic,ROC)曲线评估S-Detect诊断效能。将患者年龄、肿块最大径、形状、边缘、生长方向、钙化情况、后方回声、血流分级纳入分析,采用t检验或Mann-Whitney U检验比较假阴性与真阴性组、假阳性与真阳性组的连续变量,采用χ^(2)检验或Fisher精确概率检验比较分类变量,采用多因素二元logistic回归法分析独立影响因素。结果:患者的平均年龄为(47.1±13.6)岁,697个病灶中,良性病灶350个,恶性病灶347个,S-Detect诊断曲线下面积(area under curve,AUC)为0.835,Kappa值为0.670。患者年龄≥45岁(OR=2.898,P=0.002)、肿块边缘不光整(OR=4.778,P<0.001)、血流分级为2或3级(OR=2.447,P=0.009)与假阴性结果相关。患者年龄<45岁(OR=9.735,P<0.001)、肿块最大径<20 mm(OR=2.480,P=0.015)、肿块形状规则(OR=4.097,P=0.003)、边缘光整(OR=8.175,P<0.001)、血流分级为0或1级(OR=3.351,P=0.001)与假阳性结果显著相关。结论:当S-Detect诊断结果为良性时,患者年龄较大、肿块边缘不光整、血流分级较高是出现假阴性的影响因素。当S-Detect诊断结果为恶性时,患者较年轻、肿块较小、形状规则、边缘光整、血流分级较低是出现假阳性的影响因素。

Ship Detection and Recognition Based on Improved YOLOv7

In this paper,an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks,such as the irregular shapes and varying sizes of ships.The improved model replaces the fixed anchor boxes utilized in conventional YOLOv7 models with a set of more suitable anchor boxes specifically designed based on the size distribution of ships in the dataset.This paper also introduces a novel multi-scale feature fusion module,which comprises Path Aggregation Network(PAN)modules,enabling the efficient capture of ship features across different scales.Furthermore,data preprocessing is enhanced through the application of data augmentation techniques,including random rotation,scaling,and cropping,which serve to bolster data diversity and robustness.The distribution of positive and negative samples in the dataset is balanced using random sampling,ensuring a more accurate representation of real-world scenarios.Comprehensive experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches in terms of both detection accuracy and robustness,highlighting the potential of the improved YOLOv7 model for practical applications in the maritime domain.