Position Detection Research Articles

Measurements of photoneutron dose rate for 15 MV photon beam from medical linear accelerator using neutron survey meter

High-energy medical linear accelerators (>10MV) are increasingly used in the medical field to treat cancer patients depending on the treatment organ and patient physic. Medical linear accelerators (Linacs) operating above 10 MV photon beam produce unwanted neutrons (photoneutron) by means of photonuclear reaction (γ, n) between bremsstrahlung photon beams and constituent materials of Linac head. This study involved the measurement of the neutron dose rate from photon beam of medical linear accelerator (Elekta Synergy) operated at 15 MV photon. For the measurement of dose rate, a neutron survey meter LB 6411 probe was used. Neutron dose rate was obtained as a function of delivered dose, field size and detector position. In the study neutron dose rate was found 13.14 mSv/h at a field size of 10 × 10 cm2 at position (0,90,0) cm for 350 cGy and 9.26 mSv/h at 5 × 5 cm2 field size at position (0,80,0) cm for 200 cGy photon dose delivery. The dose rate varied with respect to detector position in an irregular manner; 2.95 mSv/h at position (-100,0,0) cm -on the side of maze entrance and 4.85 mSv/h at position (100,0,0) cm -off side of maze entrance.

Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks

The study of material hardness is crucial for determining its quality, potential failures, and appropriate applications, as well as minimizing losses incurred during the production process. To achieve this, certain criteria must be met to ensure high quality. This process is typically performed manually or using techniques based on analyzing indentation image patterns produced through the Vickers hardness technique. However, these techniques require that the indentation pattern is not aligned with the image edges. Therefore, this paper presents a technique based on convolutional neural networks (CNNs), specifically, a YOLO v3 network connected to a Dense Darknet-53 network. This technique enables the detection of indentation corner positions, measurement of diagonals, and calculation of the Vickers hardness value of D2 steel treated thermally and coated with Titanium Niobium Nitride (TiNbN), regardless of their position within the image. By implementing this architecture, an accuracy of 92% was achieved in accurately detecting the corner positions, with an average execution time of 6 seconds. The developed technique utilizes the network to detect the regions containing the corners and subsequently accurately determines the pixel coordinates of these corners, achieving an approximate relative percentage error between 0.17% to 5.98% in the hardness results.

Using Artificial Intelligence to Diagnose Osteoporotic Vertebral Fractures on Plain Radiographs.

Osteoporotic vertebral fracture (OVF) is a risk factor for morbidity and mortality in elderly population, and accurate diagnosis is important for improving treatment outcomes. OVF diagnosis suffers from high misdiagnosis and underdiagnosis rates, as well as high workload. Deep learning methods applied to plain radiographs, a simple, fast, and inexpensive examination, might solve this problem. We developed and validated a deep-learning-based vertebral fracture diagnostic system using area loss ratio, which assisted a multitasking network to perform skeletal position detection and segmentation and identify and grade vertebral fractures. As the training set and internal validation set, we used 11,397 plain radiographs from six community centers in Shanghai. For the external validation set, 1276 participants were recruited from the outpatient clinic of the Shanghai Sixth People's Hospital (1276 plain radiographs). Radiologists performed all X-ray images and used the Genant semiquantitative tool for fracture diagnosis and grading as the ground truth data. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to evaluate diagnostic performance. The AI_OVF_SH system demonstrated high accuracy and computational speed in skeletal position detection and segmentation. In the internal validation set, the accuracy, sensitivity, and specificity with the AI_OVF_SH model were 97.41%, 84.08%, and 97.25%, respectively, for all fractures. The sensitivity and specificity for moderate fractures were 88.55% and 99.74%, respectively, and for severe fractures, they were 92.30% and 99.92%. In the external validation set, the accuracy, sensitivity, and specificity for all fractures were 96.85%, 83.35%, and 94.70%, respectively. For moderate fractures, the sensitivity and specificity were 85.61% and 99.85%, respectively, and 93.46% and 99.92% for severe fractures. Therefore, the AI_OVF_SH system is an efficient tool to assist radiologists and clinicians to improve the diagnosing of vertebral fractures. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

High-resolution feature based central venous catheter tip detection network in X-ray images.

Hospital patients can have catheters and lines inserted during the course of their admission to give medicines for the treatment of medical issues, especially the central venous catheter (CVC). However, malposition of CVC will lead to many complications, even death. Clinicians always detect the malposition based on position detection of CVC tip via X-ray images. To reduce the workload of the clinicians and the percentage of malposition occurrence, we propose an automatic catheter tip detection framework based on a convolutional neural network (CNN). The proposed framework contains three essential components which are modified HRNet, segmentation supervision module, and deconvolution module. The modified HRNet can retain high-resolution features from start to end, ensuring the maintenance of precise information from the X-ray images. The segmentation supervision module can alleviate the presence of other line-like structures such as the skeleton as well as other tubes and catheters used for treatment. In addition, the deconvolution module can further increase the feature resolution on the top of the highest-resolution feature maps in the modified HRNet to get a higher-resolution heatmap of the catheter tip. A public CVC Dataset is utilized to evaluate the performance of the proposed framework. The results show that the proposed algorithm offering a mean Pixel Error of 4.11 outperforms three comparative methods (Ma's method, SRPE method, and LCM method). It is demonstrated to be a promising solution to precisely detect the tip position of the catheter in X-ray images.

Development of an HTS-SQUID Based Nondestructive Evaluation System for Boiler Tubes On-Site Inspection in Thermal Power Plant

In ultra-supercritical (USC) thermal power plants, Super304H (KA-SUS304J1HTB, ASME code: 2328) is widely used in the superheater and reheater tubes in boilers because of its excellent creep rupture strength under high-temperature and -pressure conditions. Thus, a degradation diagnosis of these tubes after long-term operation should be conducted using a quick and easy inspection. In this study, in order to discover the degradation of the boiler tubes for early diagnose before they rupture, a high-temperature superconductor (HTS)–superconducting quantum interference device (SQUID) with high sensitivity was used. A SQUID-based nondestructive evaluation system for the on-site inspection of boiler tubes in an USC thermal power plant was developed. Our system comprises a movable eddy current testing (ECT) probe and HTS-SQUID gradiometer. Five LED markers were set on a manually operated ECT probe for position detection using a stereo camera. Customized software was used for data acquisition through a lock-in amplifier and data visualization on a PC with a connected smart glass. Thus, the system can be used for quick inspection by time-sequence signals and detailed scanning through a two-dimensional contour map projected on the smart glass. Therefore, this study successfully introduced an HTS-SQUID-based nondestructive evaluation system for the on-site inspection of boiler tubes.

Analysis and Elimination of Speed Measurement Ripple in Sinusoidal Coded Gearwheel Encoder for Spindle Motor Drive

The gearwheel encoder with a magnetoresistive (MR) sensor is robust, simple, and widely used in high-speed industrial motor drives. Moreover, a high-precision rotor position detection is possible with the gearwheel encoder due to its large number of output pulses per revolution. The MR sensor outputs two sinusoidal signals that are 90 degrees apart, the period of which matches with one tooth of the gearwheel. However, these output signals are not perfectly sinusoidal because the MR sensor is manually mounted, and the magnitudes of these signals depend on the distance between the MR material and gearwheel. This slight imperfection in the output signals provokes large speed ripples in the speed measurement. This study analyzed the speed measurement ripples, and an effective ripple-elimination method was devised based on the results of the analysis. Ripple components caused by each signal error were calculated from the error signal models, and exact ripple frequencies were extracted. From the calculation results, a frequency adaptive series notch filter algorithm was designed. The simulation and experimental results demonstrated the effectiveness of the analysis and proposed approach. It is obviously shown through the experimental results that the speed measurement ripple is almost eliminated with the proposed method, and, thus, the analysis is valid.

Application of Graphene-Combined Rare-Earth Oxide (Sm2O3) in Solar-Blind Ultraviolet Photodetection.

Rare-earth oxide Sm2O3 is theoretically expected to be used in the preparation of ultraviolet (UV) detectors with low dark currents and high radiation resistance due to its characteristics of a wide bandgap, a high dielectric constant, and high chemical stability. However, certain features that rare-earth oxides possess, such as high resistivity and weak photoelectric response currents, have hindered relevant research on these kinds of materials in the field of UV detection. In this work, a p-Gr/i-Sm2O3/n-SiC heterojunction photovoltaic solar-blind UV sensor was constructed for the first time. Because of the high mobility of graphene (Gr) and the contribution of double built-in electric fields in the heterojunction, the collection efficiency of photogenerated carriers has been greatly improved, with the typical shortcomings of high resistivity and poor photoelectric response performance of rare-earth oxides having been overcome. This detector has exhibited outstanding performance at 0 V, including a responsivity of 19.8 mA/W and an open-circuit voltage of 0.68 V. Additionally, this detector has a detectivity as high as 1.2 × 1011 jones, which is at the front position of most ultraviolet detectors. The fabrication of this high-performance Sm2O3-based photovoltaic UV detector has broadened the application fields of rare-earth oxide semiconductors. Therefore, this project has important value for future research in relevant fields.

Effects of loop detector position on the macroscopic fundamental diagram

Loop detectors are probably the widest-used technology for traffic state estimation. Previous research has shown that loop detector positions within the link significantly affect the estimation of the macroscopic fundamental diagram (MFD) of a given network. This paper examines the biases produced by the positioning of loop detectors on the MFD, using both analytical and simulation methods, as well as empirical data from UTD19. We confirm earlier results that a uniform distribution of loop detector positions reduces the bias. We discovered that: (i) subsets of the MFD determined by the loop detector position can help estimate whether the loop detector MFD will have a bias; (ii) non-uniform distribution of loop detectors is more likely to cause a discrepancy in the position subsets of the MFD, particularly if detectors in the network are positioned more downstream with a greater variation; and (iii) a lower ratio of link length to green signal time increases the possibility of bias in loop detector MFD, while the impact of the aggregation interval was found to be negligible. This research opens the possibility for the bias of MFD induced by the loop detector data to be corrected by only using itself.

Complex amplitude field recovery of a scattering media obstructed object with multi-captured images.

An iterative-based method for recovering the complex amplitude field behind scattering media is presented in this Letter. This method compensates the random phase modulation of scattering media by using multiple captured scattered light fields. Complex amplitude reconstruction with local iterative averaging of scattered light fields, and double weighted feedback is efficiently applied. Two feasible types of system setups, with varying detector positions and wavelength, are proposed. Simulations and proof-of-concept experiments are employed to demonstrate the effectiveness of the proposed method in reconstructing complex amplitude of a hidden target.

Wearable Skin Resistance-based Tomographic Sensor for Imaging Contact Pressure Distribution on the Human Body.

This study aims to develop a flexible and thin tactile sensor that can capture the contact pressure distribution on the human body. We, therefore, propose a contact resistance-based tomographic tactile sensor that uses the skin as part of the detector. We first evaluated force sensitivity to show that using the skin as a probing layer is possible. We then developed a flexible detector that is 40 mm × 80 mm in size, 200 μm thickness and uses 16 electrodes. As a result, we successfully demonstrated that the proposed method enabled the detection of the contact position within an error of 12.5 % by using frequencies higher than 1 kHz.

Three-dimensional echo light field analysis for dual-band laser active detection of a cat-eye optical system.

Laser active detection technology utilizing the cat-eye effect provides rapid response, precise positioning, and long detection distances. However, current research mainly focuses on active detection within a single visible or near-infrared band, lacking quantitative analyses of the echo spot. In this paper, a four-interval theoretical model for dual band cat-eye target echo detection was constructed using matrix optics theory and Collins diffraction integration method. Dual-band echo detection experiments were conducted using 10.6 um far-infrared waves and 532 nm visible light waves, also the power, radius, and target-missing quantities of the echo spots were collected and quantitatively compared with the theoretical results. Results indicate that, due to the diffraction limit's effect on the distribution of the echo field, the echo power of far-infrared band detection is smaller than that of visible light band detection. The impact on the light spot caused by the positive and negative defocus values is asymmetric, with positive defocus having a lower impact on the echo spot than negative defocus at the same value. A weak positive defocus value that minimizes the radius of the echo spot and maximizes the echo power exists, with the value of weak positive defocus varying between detection bands. A linear relationship exists between the incident angle of the detection laser and the deviation of the echo spot. These findings provide a foundation for extracting working band details, predicting the motion trajectory of moving cat-eye targets, and achieving real-time tracking and detection recognition during laser active detection.

Predicting positions and orientations of individual kiwifruit flowers and clusters in natural environments

The positional and orientational detection of kiwifruit flower clusters in the natural environment is essential for the development of automated mechanical pollination systems. To ensure the timeliness and accuracy of this process, we propose a deep-learning-based method that predicts kiwifruit flower positions and orientations. Based on a single flower, this method effectively predicts the position and orientation of the entire cluster of flowers. First, the SOLOv2 instance segmentation model is used to locate and segment the location of the target kiwifruit flowers. Subsequently, the MobileNetV2 key point detection model is employed to detect the key points of the centre of gravity of the petal contour and the stamen part of the kiwifruit flower, and draw the orientation prediction line of the corresponding clusters. Finally, flower clusters are determined by calculating the Intersection over Union (IoU) of kiwi flowers. By taking the average of the orientation prediction line of each kiwi flower in a cluster as the entire cluster’s orientation, the method effectively generates a prediction of the said orientation. To verify the effectiveness of the proposed method, we compared and analysed the effects of SOLOv2 with the YOLOv5s, YOLOv4, and Faster-RCNN models with respect to the segmentation effect. The comparison results show that the SOLOv2 model exhibits a fast segmentation speed while maintaining a high segmentation accuracy. Verification results indicate that the SOLOv2 model has an accuracy rate of 74.2% for the detection of multi-target flowers of clustered kiwifruit from a wide perspective angle. It is therefore feasible to apply the proposed method to detect the positions and orientations of kiwi flower clusters in real time. This study can provide a technical reference for the development of robotic arms for the purpose of kiwifruit pollination.

Study of Alternative Imaging Methods for In Vivo Boron Neutron Capture Therapy.

Boron Neutron Capture Therapy (BNCT) is an innovative and highly selective treatment against cancer. Nowadays, in vivo boron dosimetry is an important method to carry out such therapy in clinical environments. In this work, different imaging methods were tested for dosimetry and tumor monitoring in BNCT based on a Compton camera detector. A dedicated dataset was generated through Monte Carlo tools to study the imaging capabilities. We first applied the Maximum Likelihood Expectation Maximization (MLEM) iterative method to study dosimetry tomography. As well, two methods based on morphological filtering and deep learning techniques with Convolutional Neural Networks (CNN), respectively, were studied for tumor monitoring. Furthermore, clinical aspects such as the dependence on the boron concentration ratio in image reconstruction and the stretching effect along the detector position axis were analyzed. A simulated spherical gamma source was studied in several conditions (different detector distances and boron concentration ratios) using MLEM. This approach proved the possibility of monitoring the boron dose. Tumor monitoring using the CNN method shows promising results that could be enhanced by increasing the training dataset.

Novel method to detect the speed and position of the HTS Maglev train by using the TMR sensor and the magnet arrays

Magnetic levitation (Maglev) train’s position and speed detection system facilitates the control of the Maglev train. Yet nearly all previous schemes are unsuitable for the high temperature superconducting (HTS) Maglev train. New sensors and signal sources are expected to improve the system. Here the tunnel magneto resistance (TMR) sensor and the magnetic arrays are used to realize the speed and position detection and the improved automatic multiscale-based peak detection (AMPD) algorithm is applied. Furthermore, the algorithm’s adaptability to various operating conditions is verified and the system can be applied in different working conditions. Finally, a preliminary experimental test is carried out. Such a system, accompanied by the equipment and the algorithm, offers results of accuracies of 95%–97% for speed measurement and 1 cm–3 cm for positioning, providing an avenue to improve the performance of HTS Maglev train’s operating control system.

A self-powered, high-precision and minimum-channel touch panel coupling triboelectrification and uniform resistance film

Touch panels utilizing surface-capacitive mechanism are ubiquitous in our daily life. However, the need for an additional probe signal generator and receiver module to detect the resistance distribution and variation caused by finger touching adds complexity and energy consumption to the system. Herein, we propose a self-powered and high-precision tribo-touch-position sensor (TPS) that can operate without an external probe signal generator and receiver module. By detecting the contact electrification signal and extracting the signal ratio between the edges of a uniform resistance film, TPS exhibits precise touch position detection with minimal channels. The mechanism and feasibility of this sensor is systematically analyzed from theory and experiment. Moreover, the resistance range and parameters that affect the sensing performance are discussed in depth. Compared to traditional capacitive touch panels, TPS can function in high moisture conditions and is not limited to grounded objects. Finally, the designed TPS achieves spatial resolutions of 0.001 and 1 mm in touching space and size, and a prototype 2D touch panel is fabricated experimentally. The signals ratio of 2D touch panel changes more obviously in the middle than on the sides while the fingers touch various positions. This work provides an alternative platform for simple and diverse contact localization using triboelectric effect.

Initial Rotor Position Detection of SPMSM with High-frequency Signal Injection

The accuracy of initial rotor position detection is important for senseless control, meanwhile, high frequency signal injection takes its advantages for its simple principle, small calculation amount and good real-time performance. In this paper, the principle of high frequency signal injection is analysed and systematically studied from position information error reading and the direction of d-axis. Besides, factors which may lead to the error of position information error reading is also studied, the second-order notch filter is added to get higher accuracy. Finally, the model of high frequency signal injection is set up to verify the effectiveness of the method, the result shows that method can run well at 0 speed and effectively estimate the actual rotor position.

System Based on Artificial Intelligence Edge Computing for Detecting Bedside Falls and Sleep Posture.

Bedside falls and pressure ulcers are crucial issues in geriatric care. Although many bedside monitoring systems have been proposed, they are limited by the computational complexity of their algorithms. Moreover, most of the data collected by the sensors of these systems must be transmitted to a back-end server for calculation. With an increase in the demand for the Internet of Things, problems such as higher cost of bandwidth and overload of server computing are faced when using the aforementioned systems. To reduce the server workload, certain computing tasks must be offloaded from cloud servers to edge computing platforms. In this study, a bedside monitoring system based on neuromorphic computing hardware was developed to detect bedside falls and sleeping posture. The artificial intelligence neural network executed on the back-end server was simplified and used on an edge computing platform. An integer 8-bit-precision neural network model was deployed on the edge computing platform to process the thermal image captured by the thermopile array sensing element to conduct sleep posture classification and bed position detection. The bounding box of the bed was then converted into the features for posture classification correction to correct the posture. In an experimental evaluation, the accuracy rate, inferencing speed, and power consumption of the developed system were 94.56%, 5.28 frames per second, and 1.5 W, respectively. All the calculations of the developed system are conducted on an edge computing platform, and the developed system only transmits fall events to the back-end server through Wi-Fi and protects user privacy.

Magnetic Poles Position Detection of Permanent Magnet Linear Synchronous Motor Using Four Linear Hall Effect Sensors

Magnetic pole position detection is the core of the closed-loop control system of the permanent magnet linear synchronous motor (PMLSM), and its position estimation accuracy directly affects control performance and dynamic response speed. In order to solve the problem of the increased estimation error of magnetic pole position caused by magnetic field distortion at the end of PMLSM while also considering the cost of control hardware, the paper uses four linear Hall sensors as magnetic pole position detection components and adopts an optimized estimation algorithm to improve the dynamic performance of the motor. Firstly, a numerical simulation of the magnetic field of poles was conducted using Ansoft Maxwell software, and combined with theoretical analysis, the optimal installation position range of four linear Hall orthogonal placements relative to the motor was obtained. Meanwhile, based on the existing vector tracking position observer, an improved observer detection model is proposed. The Matlab/Simulink software was used to compare the Hall-based detection model with the Hall-based improved observer detection model, verifying the feasibility of the improved detection algorithm. Finally, the rationality of the spatial layout design of linear Hall and the feasibility of improving the estimation algorithm were verified through experiments.

Research on the Effects of Detector Shape on Spectral Calibration of Infrared Hyperspectral Interferometer Based on Simulation Data

ABSTRACT Infrared hyperspectral instrument is a complex optical instrument. The requirement for the application of the infrared hyperspectral interferometer is fine calibration accuracy. The accuracy of the spectral frequency of the infrared hyperspectral interferometer is a key influencing factor of the radiometric calibration accuracy. In order to meet the quantitative application, the spectral calibration accuracy is generally within 10ppm. For infrared hyperspectral instruments in geostationary orbit, the focal plane has the characteristics of large array and small detector size. When the detector size of an infrared hyperspectral interferometer is in the geostationary orbit whether the detector as a point detector will affect the spectral calibration accuracy. In this paper, we simulate and analyze the influence of the shape of the detector (square and point detector) on the spectral calibration accuracy. This paper first builds an infrared hyperspectral observation and calibration simulation model. The brightness temperature deviation between the calibration spectrum and the input spectrum is close to 0 K, which shows the rationality of building the simulation model. Then, in the process of spectral calibration, two methods, square detectors and point detectors, are adopted, respectively. The spectral calibration of the square detector needs to calculate the line shape function of the instrument according to the detector position. Regarding point detector spectral calibration, this paper proposes a spectral calibration parameter optimization algorithm based on the optimization algorithm, which does not depend on the physical parameters of the instrument, but only uses the observed simulated spectrum and the reference spectrum. The results show that the spectral calibration accuracy of the point detector based on the optimization method can be close to 0ppm, which is consistent with the spectral calibration accuracy of the square detector.

Determination of the statistical distribution of drag and lift coefficients of refuse derived fuel by computer vision

This research investigates the flight behavior of refuse-derived fuel (RDF) in a drop shaft using Computer Vision to obtain statistical data on the aerodynamic properties of the particles.Methods to determine 3D geometry models of complex-shaped particles by photogrammetry and to obtain time resolved particle positions and velocities are described. Furthermore, an approach to obtain the frequency distribution of drag and lift coefficients from photogrammetric analysis and drop shaft experiments is presented. The image evaluation is based on algorithms of the open-source libraries OpenCV, COLMAP as well as MeshLab and Open3D. The precision of the system is validated employing model particles with known geometry. The 3D particle models overestimate the particle surface area by 4.58 %, the position detection works with a mean deviation of 2.73 %. The average sink rate is calculated with an accuracy of 4.87 % and the drag coefficient with an accuracy of 2.08 %. Finally, the frequency distribution of four RDF fractions, namely, textiles, cardboard, 3D plastic particles and 2D plastic foils are presented.