Cycle slip detection approach based on time-relative positioning theory

An efficient cycle slip detection method is proposed for high precision positioning and navigation results with global positioning system （GPS）,which is based on the assumption of a high sampling interval, measurement errors are so small that they can be ignored in the temporal single difference observables. And ambiguities are ordinarily equal to zero,but could be the number of cycles that have ＂slipped＂ if loss-of-lock has occurred.Therefore,cycle slips are estimated as parameters of time-relative positioning observation equations.Because the temporal single difference observables are taken at different epochs and different stations with a single GPS receiver,if time-relative positioning observation equations are linearized as that of conventional relative positioning,the design matrix will be rank defective.To obtain a stable linearization scheme,time-relative positioning observation equations are further analyzed,and the concept of virtual measurement is applied.A sample of data collected on a vehicle test shows that a cycle slip detection approach based on time-relative positioning theory can detect slips at the value of one cycle.The results also indicate if two satellites are so near to each other that they have the same equivalent to satellite-receiver geometry,cycle slip detection will be difficult and may get wrong results.Cycle slips of different satellites also affect detection by satellite-receiver geometry.

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.

基于改进Detection Transformer的棉花幼苗与杂草检测模型研究

基于深度学习的目标检测技术在棉花幼苗与杂草检测领域已取得一定进展。论文提出了基于改进Detection Transformer的棉花幼苗与杂草检测模型,以提高杂草目标检测的准确率和效率。首先,引入了可变形注意力模块替代原始模型中的Transformer注意力模块,提高模型对特征图目标形变的处理能力。提出新的降噪训练机制,解决了二分图匹配不稳定问题。提出混合查询选择策略,提高解码器对目标类别和位置信息的利用效率。使用Swin Transformer作为网络主干,提高模型特征提取能力。通过对比原网络,论文提出的模型方法在训练过程中表现出更快的收敛速度,并且在准确率方面提高了6.7%。

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.

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.

Improved plasma position detection method in EAST Tokamak using fast CCD camera

To control the steady-state operation of Tokamak plasma, it is crucial to accurately obtain its shape and position. This paper presents a method for use in rapidly detecting plasma configuration during discharge of the Experimental Advanced Superconducting Tokamak device. First, a visible/infrared integrated endoscopy diagnostic system with a large field of view is introduced,and the PCO.edge5.5 camera in this system is used to acquire a plasma discharge image. Based on the analysis of various traditional edge detection algorithms, an improved wavelet edge detection algorithm is then introduced to identify the edge of the plasma. In this method, the local maximum of the modulus of wavelet transform is searched along four gradient directions, and the adaptive threshold is adopted. Finally, the detected boundary is fitted using the least square iterative method to accurately obtain the position of the plasma. Experimental results obtained using the EAST device show that the method presented in this paper can realize expected goals and produce ideal effects;this method thus has significant potential for application in further feedback control of plasma.

FAST IMPLEMENTATION OF ORTHOGONAL EMPIRICAL MODE DECOMPOSITION AND ITS APPLIGA-TION INTO HARMONIC DETECTION

Since the empirical mode decomposition （EMD） lacks strict orthogonality, the method of orthogonal empirical mode decomposition （OEMD） is innovationally proposed. The primary thought of this method is to obtain the intrinsic mode function （IMF） and the residual function by auto-adaptive band-pass filtering. OEMD is proved to preserve strict orthogonality and completeness theoretically, and the orthogonal basis function of OEMD is generated, then an algorithm to implement OEMD fast, IMF binary searching algorithm is built based on the point that the analytical band-pass filtering preserves perfect band-pass feature in the frequency domain. The application into harmonic detection shows that OEMD successfully conquers mode aliasing, avoids the occurrence of false mode, and is featured by fast computing speed. Furthermore, it can achieve harmonic detection accurately combined with the least square method.

Effects of excitation frequency on detection accuracy of orthogonal wavelet decomposition for structural health monitoring

Accurate estimation of stiffness loss is a challenging problem in structural health monitoring.In this studyorthogonal wavelet decomposition is used for identifying the stiffness loss in a single degree of freedom spring-mass-dampersystem.The effects of excitation frequency on accuracy of damage detection is investigated.Results show that pseudo-aliaseffects caused by the orthogonal wavelet decomposition(OWD),affect damage detectability.It is demonstrated that theproposed approach is sunable for damage detection when the excitation frequency is relatively low.This study shows how apriori knowledge about the signal and ability to control the sampling frequency can enhance damage detectability.

Structural Damage Detection Method Based on Decomposition of the Operating Deflection Shapes

Full-field measurement techniques such as the scanning laser Doppler vibrometer （LDV） and the electronic speckle pattern interferometry systems can provide a dense and accurate vibration measurement on structural operating deflection shape （ODS） on a relatively short period of time.The possibility of structural damage detection and localization using the ODS looks likely more attractive than when using traditional measurement techniques which address only a small number of discrete points.This paper discusses the decomposition method of the structural ODSs in the time history using principal component analysis to provide a novel approach to the structural health monitoring and damage detection.The damage indicator is proposed through comparison of structural singular vectors of the ODS variation matrixes between the healthy and damaged stages.A plate piece with a fix-free configuration is used as an example to demonstrate the effectiveness of the damage detection and localization using the proposed method.The simulation results show that：（1） the dominated principal components and the corresponding singular vectors obtained from the decomposition of the structural ODSs maintain most of all vibration information of the plate,especially in the case of harmonic force excitations that the 1st principal component and its vectors mostly dominated in the system;（2） the damage indicator can apparently flag out the damage localization in the case of the different sinusoidal excitation frequencies that may not be close to any of structural natural frequencies.The successful simulation indicates that the proposed method for structural damage detection is novel and robust.It also indicates the potentially practical applications in industries.

Misbehavior Detection Method by Time Series Change of Vehicle Position in Vehicle-to-Everything Communication

In recent years, research has been conducted on connected vehicles (CVs) that are equipped with communication devices and can be connected to networks. CVs share their own position information and surrounding information with other vehicles using Vehicle-to-Everything (V2X) communication. CVs can recognize obstacles on non-line-of-sight (NLoS), which cannot be recognized by autonomous vehicles, and reduce travel time to a destination by cooperative driving. Therefore, CVs are expected to provide safe and efficient transportation. On the other hand, problems of security of V2X communication by CVs have been discussed. Safe and efficient transportation by CVs is on the basis of the assumption that correct vehicle information is shared. If fake vehicle information is shared, it will affect the driving of CVs. In particular, vehicle position faking has been shown that it can induce traffic congestion and accidents, which is a serious problem. In this study, we define position faking by CV as misbehavior and propose a method to detect misbehavior on the basis of changes in vehicle position time series data composed of vehicle position information. We evaluated the proposed method using four different misbehavior models. F-measure of misbehavior models that CV sends random position information detected by the proposed method is higher than one by a related method. Therefore, the proposed method is suitable for detecting misbehavior in which the position information changes over time.

Ultra-low power anti-crosstalk collision avoidance light detection and ranging using chaotic pulse position modulation approach

A novel concept of collision avoidance single-photon light detection and ranging（LIDAR） for vehicles has been demonstrated, in which chaotic pulse position modulation is applied on the transmitted laser pulses for robust anti-crosstalk purposes. Besides, single-photon detectors（SPD） and time correlated single photon counting techniques are adapted, to sense the ultra-low power used for the consideration of compact structure and eye safety. Parameters including pulse rate, discrimination threshold, and number of accumulated pulses have been thoroughly analyzed based on the detection requirements, resulting in specified receiver operating characteristics curves. Both simulation and indoor experiments were performed to verify the excellent anti-crosstalk capability of the presented collision avoidance LIDAR despite ultra-low transmitting power.

SAR Image Change Detection Algorithm Based on Different Empirical Mode Decomposition

Empirical mode decomposition (EMD) is a data-driven and multi-scale transform theory, and it is a nonlinear and non-stationary signal processing theory. But each EMD decomposition theory has its advantages and disadvantages. Synthetic aperture radar (SAR) imaging is an important remote sensing technique to obtain the change information, and SAR image data belongs to non-stationary signal. So EMD is very suitable for SAR image processing. There are two kinds of typical EMD theories, which are the ensemble empirical mode decomposition (EEMD) and bidimensional empirical mode decomposition (BEMD). Based on the deep study of the two methods, this paper proposed a new SAR image change detection algorithm, which is called the FCD-EMD algorithm, i.e. fusion change detection based on EMD. So FCD-EMD algorithm can obtain more accurate information, which not only includes the directional information obtained by EEMD, but also can contain the spatial information got by BEMD. The main contribution of the FCD-EMD algorithm is to fuse the detail information in different directions, so that the results obtained are more accurate than the individual method. On the other hand, it can reduce the influence of speckle noise in SAR images by feature selections. The actual SAR image data verify the algorithm proposed in this paper and good experimental results are obtained, which show that the new method is feasible.

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。

Position detection of component aperture based on integral imaging technology

Aiming at the problem that it is difficult to locate all the aperture positions of the large size component using Houghcircle detection method,this article presents a non-contact measurement method combining the integral imaging technology withHough circle detection algorithm.Firstly,a set of integral imaging information acquisition algorithms were proposed accordingto the classical imaging theory.Secondly,the camera array experiment device was built by using two-dimensional translationstage and charge coupled device(CCD)camera.When the system is operating,element image array captured with the camera isused to achieve the positioning of the component aperture using Hough circle detection and coordinate acquisition algorithm.Based on the above theory,a verification experiment was carried out.The results show that the detection error of the componentaperture position is within0.3mm,which provides effective theoretical support for the application of integral imagingtechnology in high precision detection

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 novel oil spill detection method from synthetic aperture radar imageries via a bidimensional empirical mode decomposition

Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar（SAR） sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena. It is aimed to distinguishing oil spills or look-alike objects. A novel method based on a bidimensional empirical mode decomposition is proposed. The selected dark formations are first decomposed into several bidimensional intrinsic mode functions and the residue. Subsequently, 64 dimension feature sets are calculated using the Hilbert spectral analysis and five new features are extracted with a relief algorithm. Mahalanobis distances are then used for classification. Three data sets containing oil spills or look-alikes are used to test the accuracy rate of the method. The accuracy rate is more than 90%. The experimental results demonstrate that the novel method can detect oil spills validly and accurately.

Support and Positioning Mechanism of a Detection Robot inside a Spherical Tank

Large pressure equipment needs to be tested regularly to ensure safe operation;wall-climbing robots can carry the necessary tools to inspect spherical tanks,such as cameras and non-destructive testing equipment.However,a wall-climbing robot inside a spherical tank cannot be accurately positioned owing to the particularity of the spherical tank structure.This paper proposes a passive support and positioning mechanism fixed in a spherical tank to improve the adsorption capacity and positioning accuracy of the inspection robot.The main body of the mechanism was designed as a truss composed of carbon fiber telescopic rods and can work in spherical tanks with diameters of 4.6-15.7 m.The structural strength,stiffness,and stability of the mechanism are analyzed via force and deformation simulations.By constructing a mathematical model of the support and positioning mechanism,the influence of structural deformation on the supporting capacity is analyzed and calculated.The robot positioning method based on the support and positioning mechanism can effectively locate the robot inside a spherical tank.Experiments verified the support performance and robot positioning accuracy of the mechanism.This research proposes an auxiliary support and positioning mechanism for a detection robot inside a spherical tank,which can effectively improve the positioning accuracy of the robot and meet the robotic inspection requirements.

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.

Piecewise linear detection for direct superposition modulation

Considering high-order digital modulation schemes, the bottleneck in consumer products is the detector rather than the modulator. The complexity of the optimal a posteriori probability （APP） detector increases exponentially with respect to the number of modulated bits per data symbol. Thus, it is necessary to develop low-complexity detection algorithms with an APP-like performance, especially when performing iterative detection, for example in conjunction with bit interleaved coded modulation. We show that a special case of superposition modulation, dubbed Direct Superposition Modulation （DSM）, is particularly suitable for complexity reduction at the receiver side. As opposed to square QAM, DSM achieves capacity without active signal shaping. The main contribution is a low-cost detection algorithm for DSM, which enables iterative detection by taking a priori information into account. This algorithm exploits the approximate piecewise linear behavior of the soft outputs of an APP detector over the entire range of detector input values. A theoretical analysis and simulation results demonstrate that at least max-log APP performance can be reached, while the complexity is significantly reduced compared to classical APP detection.

Speech-stream detection in short-wave channel based on empirical mode decomposition and higher-order statistics

To capture the presence of speech embedded in nonspeech events and background noise in shortwave non-cooperative communication, an algorithm for speech-stream detection in noisy environments is presented based on Empirical Mode Decomposition （EMD） and statistical properties of higher-order cumulants of speech signals. With the EMD, the noise signals can be decomposed into different numbers of IMFs. Then, the fourth-order cumulant （ FOC ） can be used to extract the desired feature of statistical properties for IMF components. Since the higher-order eumulants are blind for Gaussian signals, the proposed method is especially effective regarding the problem of speech-stream detection, where the speech signal is distorted by Gaussian noise. With the self-adaptive decomposition by EMD, the proposed method can also work well for non-Gaussian noise. The experiments show that the proposed algorithm can suppress different noise types with different SNRs, and the algorithm is robust in real signal tests.