Position Detection Research Articles

Synthesis of an Amine Moiety‐Based Fluorescent Probe and the Relationship Between the Amino Substitution Position and Formaldehyde Detection Performance

AbstractA novel amine moiety‐based fluorescent probe (BA) was prepared using diphenylacetylene as fluorophore and amino group as acceptor. The probe achieves detection of formaldehyde through fluorescence enhancement by inhibition the photoinduced electron transfer (PET) between diphenylacetylene and amino group. And the different formaldehyde detection performance of 2‐(phenylethynyl)aniline (BA‐1), 3‐(phenylethynyl)aniline (BA‐2) and 4‐(phenylethynyl)aniline (BA‐3) indicates that the position of amino recognition group on the benzene ring of diphenylacetylene has a significant impact on the detection ability of this type fluorescent probe. When the amino group is in the ortho‐position of the benzene ring, probe BA‐1 can rapidly enhance the fluorescence intensity within 20 min with limit of detection as low as 0.75 μM; When the amino group is located in meta‐position of benzene ring, the solution of BA‐2 also exhibited a certain degree of fluorescence enhancement, but it is weaker than BA‐1; When the amino group is at the para‐position of benzene ring, BA‐3 has almost no response to formaldehyde.

MSSD: multi-scale self-distillation for object detection

Knowledge distillation techniques have been widely used in the field of deep learning, usually by extracting valid information from a neural network with a large number of parameters and a high learning capacity (the teacher model) to a neural network with a small number of parameters and a low learning capacity (the student model). However, there are inefficiencies in the transfer of knowledge between teacher and student. The student model does not fully learn all the knowledge of the teacher model. Therefore, we aim to achieve knowledge distillation of our network layer by a single model, i.e., self-distillation. We also apply the idea of self-distillation to the object detection task and propose a multi-scale self-distillation approach, where we argue that knowledge distillation of the information contained in feature maps at different scales can help the model better detect small targets. In addition, we propose a Gaussian mask based on the target region as an auxiliary detection method to improve the accuracy of target position detection in the distillation process. We then validate our approach on the KITTI dataset using a single-stage detector YOLO. The results demonstrate a 2.8% improvement in accuracy over the baseline model without the use of a teacher model.

A Novel Phase Current Difference Construction Based Initial Rotor Position Detection Method for Surface Mounted PMSM Without Injections of High-Frequency Voltage or Pulse Sequence

A Novel Phase Current Difference Construction Based Initial Rotor Position Detection Method for Surface Mounted PMSM Without Injections of High-Frequency Voltage or Pulse Sequence

Computational adaptive telescope imaging via self-interference digital holography

Conventional optical telescope only collect the amplitude of optical wavefront with incoherent light, while self-interference incoherent digital holography offers a unique method of coding both the optical wavefront amplitude and phase information by interference patterns. We developed an adaptive non-scanning three-dimensional (3D) telescopic imaging technique based on self-interference incoherent holography with a geometric phase lens (GP-SIDH). The phase recorded in the point spread hologram and the incoherent self-interference extended holograms are employed to detect spatial positions, correct optical aberration and extract edges by computational reconstruction. The 3D imaging and position detection performances of our proposed method are investigated. Both simulations and experimental results show that the proposed method exhibits better performances for different depth objects imaging and space position detection. Furthermore, this system can be easily modularization and implanted into any existing optical telescopic system owing to its simplicity and convenience. The proposed approach can be a powerful tool for the multi-objects long-working-distance imaging, which is more valuable for telescope application.

Comparing Video Analysis to Computerized Detection of Limb Position for the Diagnosis of Movement Control during Back Squat Exercise with Overload.

Incorrect limb position while lifting heavy weights might compromise athlete success during weightlifting performance, similar to the way that it increases the risk of muscle injuries during resistance exercises, regardless of the individual's level of experience. However, practitioners might not have the necessary background knowledge for self-supervision of limb position and adjustment of the lifting position when improper movement occurs. Therefore, the computerized analysis of movement patterns might assist people in detecting changes in limb position during exercises with different loads or enhance the analysis of an observer with expertise in weightlifting exercises. In this study, hidden Markov models (HMMs) were employed to automate the detection of joint position and barbell trajectory during back squat exercises. Ten volunteers performed three lift movements each with a 0, 50, and 75% load based on body weight. A smartphone was used to record the movements in the sagittal plane, providing information for the analysis of variance and identifying significant position changes by video analysis (p < 0.05). Data from individuals performing the same movements with no added weight load were used to train the HMMs to identify changes in the pattern. A comparison of HMMs and human experts revealed between 40% and 90% agreement, indicating the reliability of HMMs for identifying changes in the control of movements with added weight load. In addition, the results highlighted that HMMs can detect changes imperceptible to the human visual analysis.

Archaeology of archaeology at Heloros: Re-interpreting the urban layout of a complex Greek settlement in Sicily using proximal sensing and data fusion

Heloros lies on a low hill situated along Sicily’s Ionian shore. Archaeologists believe this city was the first subcolony of Syracuse. Despite its long history and prowess, Heloros is and therefore less known. This article answers crucial questions regarding the site’s chronology, architecture, and topography. This involves digitizing and verifying legacy data and fusing them in a Geographic Information System with newly acquired 3D and geospatial documentation that we collected using global positioning, digital photogrammetry, drones, terrestrial and airborne Light Detection and Ranging, and ground penetrating radar. Our results present new insights into Heloros' history, including information about its pre-Greek occupation and revisions to the interpretation of important buildings and fortifications. Our research demonstrated that the Archaeology of Archaeology investigation we carried out at Heloros, when enhanced by our ‘digital excavation’ approach can generate new knowledge on archaeological sites without requiring new excavations.

Dual-actuator mixed-mode bending tests on structural adhesive joints

In this article, a novel mixed-mode bending (MMB) test setup is presented and used to investigate the fracture behavior of the structural adhesive SikaPower®-498. The novelty lies in the implementation of two actuators that can be controlled independently of each other. This opens up investigation possibilities that go beyond those of the MMB standard configuration. For example, a varying mode-mixity can be prescribed during testing, allowing identification of an entire fracture envelope from a single experiment. Additionally, an optical fiber strain measurement (OFS) system is used to determine the curvatures of the substrates, allowing for a detection of the actual crack tip position and an analysis of the loading conditions behind the crack tip. The presented work focuses on the experimental calibration of the compliance change during crack growth, on tests performed at constant (external) mode-mixity using a mode partitioning approach based on the J-integral, as well as on tests carried out under varying mode-mixity. Additional aspects of the test setup such as stable crack growth regime or differences between the results of different evaluation methods are discussed. Finally, the obtained fracture envelopes are compared with references from literature, including for the first time a comparison between mixed-modes I+II and I+III.

Pinhole camera for electron beam size diagnostic at storage ring with an ultralow emittance

In this work, we propose to use a pinhole camera at high photon energies, specifically 200–300 keV, to measure ultra-small electron beam size by means of bending magnet radiation. We show that there is a sufficient photon flux at the detector position. Our theoretical analysis includes an examination of the applicability of the van Cittert-Zernike theorem for the bending magnet radiation generated by an ultralow emittance electron beam and a detailed analysis of the imaging properties of rectangular pinhole cameras. This led us to practical, universal formulas. We identify the optimal aperture size and resolution of the camera in the given geometry. The theoretical findings are further substantiated by wavefront propagation numerical simulations of partially coherent radiation. This study serves both as a practical guide for optical engineering and an educational resource for explaining the imaging properties of pinhole cameras. Published by the American Physical Society 2024

Development of techniques for gantryless associated-particle imaging

To move fast-neutron radiography using the associated-particle imaging technique from the laboratory to the field, the development of new analysis techniques is required. In particular, the relative positions of the source and detectors need to be determined when they have been placed by hand, the normalization for a particular source–detector geometry needs to be determined without a measurement in the same geometry with no object present, and accurate image stitching is required when multiple detector positions are necessary to image an object. The present work describes methods that employ transmission neutron data to localize a fast-neutron imaging panel with respect to the neutron source, calculate a normalization for a given source–detector geometry, stitch images together, and describes the required system calibrations. The reported techniques enable in-field neutron radiography for cases in which the source–detector geometry is not well known a priori and where operational constraints preclude a normalization measurement.

Bus Driver Head Position Detection Using Capsule Networks under Dynamic Driving Conditions

Monitoring bus driver behavior and posture in urban public transport’s dynamic and unpredictable environment requires robust real-time analytics systems. Traditional camera-based systems that use computer vision techniques for facial recognition are foundational. However, they often struggle with real-world challenges such as sudden driver movements, active driver–passenger interactions, variations in lighting, and physical obstructions. Our investigation covers four different neural network architectures, including two variations of convolutional neural networks (CNNs) that form the comparative baseline. The capsule network (CapsNet) developed by our team has been shown to be superior in terms of efficiency and speed in facial recognition tasks compared to traditional models. It offers a new approach for rapidly and accurately detecting a driver’s head position within the wide-angled view of the bus driver’s cabin. This research demonstrates the potential of CapsNets in driver head and face detection and lays the foundation for integrating CapsNet-based solutions into real-time monitoring systems to enhance public transportation safety protocols.

Novel Design of Multi-DOF Motor Position Estimation Based on Wireless Power Transmission Modeling

In this paper, a multi-coil wireless power transmission model is presented. The model is applied to the multi-degree-of-freedom (multi-DOF) motor rotor position detection system. The wireless power transmission (WPT) system is evenly distributed on the upper part of the rotor, and the receiving coil is placed on the rotor shaft. When the motor rotor rotates along the three motion states of pitch, spin and tilt, the receiving coil and multi-transmit coil will produce angle deviation. The precise mutual inductance estimation method can be used to obtain the rotor position quickly and avoid the complicated finite element calculation. The system can not only realize the pose estimation of the multi-DOF motor, but also provide flexible energy supplement for the manipulator. The WPT position measurement method is used for PID control of multi-DOF motor, and it is compared with the micro-electro-mechanical-system (MEMS) attitude measurement method. The mechanical sliding measurement method of the encoder is taken as a reference. The results show that the position detection method of WPT has high accuracy, and the estimation error is less than 2%.

Role of chest radiographs and electrocardiograms in predicting the hemodynamics of congenital heart disease

Aim: This study aimed to evaluate the role of chest radiographs and electrocardiograms in predicting the hemodynamics of congenital heart disease (CHD). Methods: This retrospective study included 50 patients with a diagnosis of CHD who had undergone any form of cardiac intervention, either surgical or nonsurgical between September 2019 and September 2020. Chest radiographs and electrocardiograms were evaluated and compared with the diagnostic gold standard echocardiography. Results: Chest radiographs had the highest sensitivity, specificity, and accuracy, with all being 100%, in detecting situs and cardiac position. There was a very good agreement between chest radiographs and echocardiography in the detection of both situs and cardiac position (κ = 1.00, P &lt; 0.001), while moderate agreement was observed for the detection of cardiomegaly, position of the aortic knuckle, main pulmonary artery dilation, and right pulmonary artery dilation. Electrocardiograms had a high sensitivity (100.00%), but modest specificity and accuracy for the detection of left ventricle pressure overload. For the detection of left atrial enlargement and left ventricle volume overload, electrocardiograms had high specificity (94.12% and 94.29%, respectively) but low sensitivity and modest accuracy. There was a moderate agreement between electrocardiograms and echocardiography in the detection of right ventricle pressure overload (κ = 0.43, P = 0.002) and left ventricle volume overload (κ = 0.46, P &lt; 0.001). Conclusions: The study findings indicate that chest radiographs and electrocardiograms alone are not adequate for the assessment of hemodynamics of CHD and reinstates the recommendation that in addition to routine chest radiographs and electrocardiograms, echocardiography should be performed.

Advanced exploitation of unmerged reflection data during processing and refinement with autoPROC and BUSTER.

The validation of structural models obtained by macromolecular X-ray crystallography against experimental diffraction data, whether before deposition into the PDB or after, is typically carried out exclusively against the merged data that are eventually archived along with the atomic coordinates. It is shown here that the availability of unmerged reflection data enables valuable additional analyses to be performed that yield improvements in the final models, and tools are presented to implement them, together with examples of the results to which they give access. The first example is the automatic identification and removal of image ranges affected by loss of crystal centering or by excessive decay of the diffraction pattern as a result of radiation damage. The second example is the `reflection-auditing' process, whereby individual merged data items showing especially poor agreement with model predictions during refinement are investigated thanks to the specific metadata (such as image number and detector position) that are available for the corresponding unmerged data, potentially revealing previously undiagnosed instrumental, experimental or processing problems. The third example is the calculation of so-called F(early) - F(late) maps from carefully selected subsets of unmerged amplitude data, which can not only highlight the location and extent of radiation damage but can also provide guidance towards suitable fine-grained parametrizations to model the localized effects of such damage.

Optimizing the Fixed Number Detector Placement for the Nuclear Reactor Core Using Reinforcement Learning

Monitoring three-dimensional flux distribution in a nuclear reactor core is essential for improving safety and economics, which requires strategically placed in-core detectors. However, the deployment of these sensors is often constrained by physical, industrial, and economic limitations. This study treats optimizing the location of in-core detectors as a Markov decision process and develops a reinforcement learning (RL)–based framework to provide a solution for detector placement given a fixed number of detectors and available detector positions. The RL-based framework contains an environment consisting of a Proper Orthogonal Decomposition–based power reconstruction function paired with a novel reward function based on the power reconstruction error and a well-educated agent that updates the detector placement. Four RL algorithms including Proximal Policy Optimization, Deep Q-Network, Advantage Actor-Critic, and Monte Carlo Tree Search are investigated to optimize the detector placement and are analyzed. Genetic Algorithm (GA), a traditional optimization approach, is applied for comparison. The findings reveal that RL outperforms GA in terms of the quality of optimal solutions, demonstrating an inclination toward locating a global solution. Moreover, the flexible nature of RL enables the integration of developed novel reward functions from a specific reactor core into other reactors, considering the particular engineering requirements within the RL-based framework, thereby enhancing the optimization of in-core detector configurations.

Research and manufacture of the mini gamma camera using multi-anode photo multiplier tube (Ma-PMT)

Gamma camera is an imaging technique that captures gamma radiation emitting from the radiation sources. In this paper, the authors built a Mini-Gamma camera system using a micro photomultiplier tube (µPMT) H12700, signal processing circuit, and control software as self-designed products. The Gamma camera system includes four parts: collimator, scintillator NaI(Tl), µPMT, processing, and controller boards. The electronic system had preamplifiers used to convert negative signals from the µPMT to positive signals, integrators used to sharpen the signal from the preamplifier, and an amplifier circuit to amplify the signal from the preamplifier, a position detection circuit to detect the location of radiation interaction in the crystal. All circuits in the Mini-Gamma camera system are optimized to achieve the best projection image quality. In this paper, the lab-built Mini-Gamma Camera has an energy resolution of 45% at 60 keV gamma rays, the affected field of the camera is 42×48.5 mm2, and the spatial resolution of the image is around 1 mm.

Enhanced Volts-per-Hertz Sensorless Starting of Permanent Magnet Motor with Heavy Loads in Long-Cable Subsea Applications

Permanent magnet (PM) motors are gaining prominence in subsea applications such as drilling, pumping, and boosting for oil and natural gas extraction. These motors are gradually replacing traditional induction motors. However, starting and operating PM motors at low speeds under heavy loads poses significant challenges. This is because of unknown initial rotor positions and resistive voltage drops due to the presence of a sinewave filter, transformer, and long cable. An unknown rotor position may result in temporary reverse speed, which may cause a loss of synchronism; therefore, initial rotor position estimation is preferable. Additionally, addressing the voltage drop issue requires careful voltage compensation to avoid transformer core saturation. In this paper, an enhanced V/Hz starting of a PM motor is proposed with initial position detection (IPD) and voltage compensation to start the motor reliably with a heavy load. The proposed control method is verified with controller hardware in the loop (C-HIL) real-time simulation using a Typhoon HIL-604 emulator and a Texas Instruments TMS320F28335 digital signal processor (DSP) control card.

Quantification of hardware effects of an on-line Dual Energy X-ray Absorptiometry scanner when predicting lamb carcass composition

An on-line Dual Energy X-ray Absorptiometry (DXA) scanner's tissue composition prediction precision and accuracy was tested across the entire height of the unit's detector, and the hardware was assessed for robustness by measuring X-ray photon intensity throughout production days. There was good precision when predicting the tissue composition of 5 different lamb fat and lean muscle mixtures across 3 different thicknesses (R2 = 0.93 to 0.98, RMSE = 3.18% to 5.83%), however was less precise at the greatest thickness of 200 mm (R2 = 0.59, RMSE = 11.4%). There was no significant difference in the prediction of tissue composition at 8 of the 9 detector positions, however the position at the perpendicular of the X-ray photon beam was significantly different, with a fat prediction error of −4%, although no lamb carcass is detected in this position during normal production. A significant upwards drift in X-ray photon intensity was found over the course of production, especially immediately after restarting the DXA scanner following a period of inactivity. This upwards drift may affect tissue composition predictions over the span of a production day if uncorrected.

INTEGRATION OF HBIM, XR AND BEACONS FOR CULTURAL MEDIATION OF HISTORICAL HERITAGE: THE CASE OF “AL-QUARAOUIYINE MOSQUE” IN FES

Abstract. The mediation of cultural heritage is a significant concern in the field of history and culture valorization. Technologies such as HBIM (Historic Building Information Modeling), XR (Extended Reality), and Beacons offer highly promising solutions that have demonstrated their value in various heritage-related applications, namely 3D reconstruction, documentation, restoration, preservation, and mediation. The primary objective of this paper is to propose an approach that combines HBIM () and XR (), integrating Beacon sensors to create a rich cultural mediation experience. Our approach is based on an HBIM model of the site, enriched with multi-source semantic information stored in a NoSQL database. It also leverages Beacon sensors to deliver historical content related to the study site (Mosque Al-Quaraouiyine) based on the user's position, thereby providing a cultural mediation experience within an XR environment. This study resulted in the creation of an HBIM model of the study site, the integration of a MongoDB database for storing semantic data, the utilization of the Unity SDK to retrieve this data, and the deployment of Beacons for user position detection within the XR environment. These advancements led to the display of contextual historical and multimedia information through interactive panels, offering a rich and interactive experience. The added value of this project lies in its innovative combination of several modern technologies to provide an educational and immersive experience to users, while also paving the way for future developments towards the dissemination of cultural information based on new technologies.

Comparative study on the results of orthodontic diagnostics by using algorithms generated by Artificial Intelligence and simple algorithms.

Artificial intelligence (AI) is computer-generated intelligence, as opposed to the natural intelligence of humans and some animals. Kaplan and Haenlein define AI as "the ability of a system to correctly interpret external data, to learn from such data and use what it has learned to achieve specific goals and tasks through a flexible adaptation". The term "artificial intelligence" is used colloquially to describe machines that mimic the "cognitive" functions that people associate with other human minds. One of the areas where technological advances have brought significant changes is orthodontics, especially in terms of diagnosis and orthodontic prediction.The aim of this study is to conduct a comparative analysis between the results obtained by using the complete algorithms that define Artificial Intelligence and the simple algorithms of classical medical software, used in the detection of the position and shape of teeth in various orthodontic anomalies. A group of 45 patients with maxillary-dento anomalies Angle Class I (DDM with crowding and deviation of the superior inter-incisive line) was studied. Two types of algorithms were used in the study group: modern type I algorithms and simple algorithms used in classical software to detect the position of the frontal teeth. Through the symmetrical points of the face the facial axes were determined, and after the detection of the contour of each tooth the incisional curve was calculated. The median line was analyzed against the vertical axis of the face, and the incisional curve towards the horizontal axis. The study shows that AI algorithms offer an increased level of tooth position detection, compared to traditional softwares. Complex algorithms, specific to Artificial Intelligence, showed superior detection, and more stability in the analysis. Technological evolution and the development of machine learning capabilities have opened new perspectives in guiding orthodontic treatments through artificial intelligence (AI).

Improvement of lithium battery corner detection accuracy based on image restoration method

Target detection technology has been widely used in the automatic production of lithium batteries. However, motion blur will lead to the reduction of the angular position detection accuracy of lithium batteries. To solve this problem, an improved fuzzy recovery model for angular position of lithium battery is proposed in this paper. Firstly, the improved lightweight neural network RepVGG was used as the main module of the backbone network, so that the network could improve the performance of network feature extraction while reducing the number of calculation parameters and improving the reasoning speed of fuzzy restoration. Secondly, we optimize the multi-Dconv head transposed attention (MDTA) module and reference it to the generator, which reduces the complexity of the model and strengthens the network’s attention to details and textures, and improves the visual effect of the restored image. Finally, we design a lightweight globally connectable residual network called SAC Block and use it to to improve the discriminator, which enhances the global receptive field of the model and improves the structural similarity between the restored image and the original image. In order to verify the effectiveness of the method, we verify it on the self-built dataset and GoPro dataset. The experiments show that our proposed lightweight model improves the peak signal-to-noise ratio (PSNR) index by 9.2% and 8.6% respectively compared with the original model. The visual effect of the restored image is better than that of other current similar algorithms, and it is confirmed that our model can better improve the accuracy of lithium battery angular position detection.