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.

Collaborative positioning for swarms:A brief survey of vision,LiDAR and wireless sensors based methods

As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other agents.By receiving spatial information from both its environment and other agents,an agent can use various methods and sensor types to localize itself.With its high flexibility and robustness,collaborative positioning has become a widely used method in both military and civilian applications.This paper introduces the basic fundamental concepts and applications of collaborative positioning,and reviews recent progress in the field based on camera,LiDAR(Light Detection and Ranging),wireless sensor,and their integration.The paper compares the current methods with respect to their sensor type,summarizes their main paradigms,and analyzes their evaluation experiments.Finally,the paper discusses the main challenges and open issues that require further research.

Isomeric fluorescence sensors for wide range detection of ionizing radiations

In order to achieve a wider range of ionizing radiations detection,novel fluorescence sensing materials have been developed that utilize the fluorescence enhancement phenomenon caused by the intramolecular photoinduced electron transfer(PET)effect.Two perylene diimide isomers PDI-P and PDI-B were designed and synthesized,and their molecular structures were characterized by high-resolution Fourier transform mass spectrometry(HRMS),nuclear magnetic resonance hydrogen and carbon spectroscopy(~1H and~(13)C NMR).The interaction between ionizing radiation and fluorescent molecules was simulated by HCl titration.The results show that combining PDIs and HCl can improve fluorescence through the retro-PET process.Despite the similarities in chemical structures,the fluorescent enhancement multiple of PDI-B with aromatic amine as electron donor is much higher than that of PDI-P with alkyl amine.In the direct irradiation experiments of ionizing radiation,the emission enhancement multiples of PDI-P and PDI-B are 2.01 and 45.4,respectively.Furthermore,density functional theory(DFT)and time-dependent density functional theory(TDDFT)calculations indicate that the HOMO and HOMO-1 energy ranges of PDI-P and PDI-B are 0.54 e V and 1.13 e V,respectively.A wider energy range has a stronger driving force on electrons,which is conducive to fluorescence quenching.Both femtosecond transient absorption spectroscopy(fs-TAS)and transient fluorescence spectroscopy(TFS)tests show that PDI-B has shorter charge separation lifetime and higher electron transfer rate constant.Although both isomers can significantly reduce LOD during PET process,PDI-B with aromatic amine has a wider detection range of 0.118—240 Gy due to its larger emission enhancement,which is a leap of three orders of magnitude.It breaks through the detection range of gamma radiation reported in existing studies,and provides theoretical support for the further study of sensitive and effective new materials for ionizing radiation detection.

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments.To address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed analysis.This analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

Hybrid pedestrian positioning system using wearable inertial sensors and ultrasonic ranging

Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration structure.This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint model.In addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial sensors.Finally,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall system.Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.

A blockchain-empowered authentication scheme for worm detection in wireless sensor network

Wireless Sensor Network(WSN)is a distributed sensor network composed a large number of nodes with low cost,low performance and self-management.The special structure of WSN brings both convenience and vulnerability.For example,a malicious participant can launch attacks by capturing a physical device.Therefore,node authentication that can resist malicious attacks is very important to network security.Recently,blockchain technology has shown the potential to enhance the security of the Internet of Things(IoT).In this paper,we propose a Blockchain-empowered Authentication Scheme(BAS)for WSN.In our scheme,all nodes are managed by utilizing the identity information stored on the blockchain.Besides,the simulation experiment about worm detection is executed on BAS,and the security is evaluated from detection and infection rate.The experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.

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 Novel Fall Detection Framework Using Skip-DSCGAN Based on Inertial Sensor Data

With the widespread use of Internet of Things(IoT)technology in daily life and the considerable safety risks of falls for elderly individuals,research on IoT-based fall detection systems has gainedmuch attention.This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection(Skip-DSCGAN)for fall detection.The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data.A semisupervised learning approach is adopted to train the model using only activities of daily living(ADL)data,which can avoid data imbalance problems.Furthermore,a quantile-based approach is employed to determine the fall threshold,which makes the fall detection frameworkmore robust.This proposed fall detection framework is evaluated against four other generative adversarial network(GAN)models with superior anomaly detection performance using two fall public datasets(SisFall&MobiAct).The test results show that the proposed method achieves better results,reaching 96.93% and 92.75% accuracy on the above two test datasets,respectively.At the same time,the proposed method also achieves satisfactory results in terms ofmodel size and inference delay time,making it suitable for deployment on wearable devices with limited resources.In addition,this paper also compares GAN-based semisupervised learning methods with supervised learning methods commonly used in fall detection.It clarifies the advantages of GAN-based semisupervised learning methods in fall detection.

A Double Sensitive Fault Detection Filter for Positive Markovian Jump Systems With A Hybrid Event-Triggered Mechanism

This paper is concerned with the double sensitive fault detection filter for positive Markovian jump systems. A new hybrid adaptive event-triggered mechanism is proposed by introducing a non-monotonic adaptive law. A linear adaptive event-triggered threshold is established by virtue of 1-norm inequality.Under such a triggering strategy, the original system can be transformed into an interval uncertain system. By using a stochastic copositive Lyapunov function, an asynchronous fault detection filter is designed for positive Markovian jump systems(PMJSs) in terms of linear programming. The presented filter satisfies both L_-gain(?_-gain) fault sensitivity and L_1(?_1)internal differential privacy sensitivity. The proposed approach is also extended to the discrete-time case. Finally, two examples are provided to illustrate the effectiveness of the proposed design.

Parametric study on contact sensors for MASW measurement-based interfacial debonding detection for SCCS

Steel-concrete composite structures(SCCS)have been widely used as primary load-bearing components in large-scale civil infrastructures.As the basis of the co-working ability of steel plate and concrete,the bonding status plays an essential role in guaranteeing the structural performance of SCCS.Accordingly,efficient non-destructive testing(NDT)on interfacial debondings in SCCS has become a prominent research area.Multi-channel analysis of surface waves(MASW)has been validated as an effective NDT technique for interfacial debonding detection for SCCS.However,the feasibility of MASW must be validated using experimental measurements.This study establishes a high-frequency data synchronous acquisition system with 32 channels to perform comparative verification experiments in depth.First,the current sensing approaches for high-frequency vibration and stress waves are summarized.Secondly,three types of contact sensors,namely,piezoelectric lead-zirconate-titanate(PZT)patches,accelerometers,and ultrasonic transducers,are selected for MASW measurement.Then,the selection and optimization of the force hammer head are performed.Comparative experiments are carried out for the optimal selection of ultrasonic transducers,PZT patches,and accelerometers for MASW measurement.In addition,the influence of different pasting methods on the output signal of the sensor array is discussed.Experimental results indicate that optimized PZT patches,acceleration sensors,and ultrasonic transducers can provide efficient data acquisition for MASW-based non-destructive experiments.The research findings in this study lay a solid foundation for analyzing the recognition accuracy of contact MASW measurement using different sensor arrays.

High-resolution tumor marker detection based on microwave photonics demodulated dual wavelength fiber laser sensor

The specific detection of tumor markers is crucial in early tumor screening and subsequent treatment processes.To ac-curately distinguish the signal response caused by trace markers,the high demodulation resolution of the sensor is nec-essary.In this paper,we propose a dual-wavelength fiber laser sensing system enhanced with microwave photonics de-modulation technology to achieve high-resolution tumor marker detection.This sensing system can simultaneously per-form spectral wavelength-domain and frequency-domain analyses.Experimental results demonstrate that this system's refractive index(RI)sensitivity reaches 1083 nm/RIU by wavelength analysis and-1902 GHz/RIU by frequency analysis,with ideal detection resolutions of 1.85×10^(-5)RIU and 5.26×10^(-8)RIU,respectively.Compared with traditional wavelength domain analysis,the demodulation resolution is improved by three orders of magnitude,based on the same sensing structure.To validate its biosensing performance,carcinoembryonic antigen-related cell adhesion molecule 5(CEACAM5)is selected as the detection target.Experimental results show that the improved sensing system has a limit of detection(LOD)of 0.076 ng/mL and a detection resolution of 0.008 ng/mL.Experimental results obtained from human serum samples are consistent with clinical data,highlighting the strong clinical application potential of the proposed sens-ing system and analysis method.

Flexible Tactile Electronic Skin Sensor with 3D Force Detection Based on Porous CNTs/PDMS Nanocomposites

Flexible tactile sensors have broad applications in human physiological monitoring,robotic operation and human-machine interaction.However,the research of wearable and flexible tactile sensors with high sensitivity,wide sensing range and ability to detect three-dimensional(3D)force is still very challenging.Herein,a flexible tactile electronic skin sensor based on carbon nanotubes(CNTs)/polydimethylsiloxane(PDMS)nanocomposites is presented for 3D contact force detection.The 3D forces were acquired from combination of four specially designed cells in a sensing element.Contributed from the double-sided rough porous structure and specific surface morphology of nanocomposites,the piezoresistive sensor possesses high sensitivity of 12.1 kPa?1 within the range of 600 Pa and 0.68 kPa?1 in the regime exceeding 1 kPa for normal pressure,as well as 59.9 N?1 in the scope of<0.05 N and>2.3 N?1 in the region of<0.6 N for tangential force with ultra-low response time of 3.1 ms.In addition,multi-functional detection in human body monitoring was employed with single sensing cell and the sensor array was integrated into a robotic arm for objects grasping control,indicating the capacities in intelligent robot applications.

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.

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。

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.

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.

Detection of rotor position by harmonic back EMF of hybrid step motor

The magnhc network model of a hybrid step motor is established by the air gap rate permeance method,and the expression of harmonic back EMF is deduced, and from the analysis above, a vovel use of harmonic backEMF sed to extract rotor peition is proposed and a new position sensor integral with the motoris designed .Experi-ments verified the correctness of the theorecal analysis. Ths type of rotor position sensor lays a foundation for closed-loop conrol of step motor.

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.