Position Detection System Research Articles

The design of the position detection system for drilling and stealing oil in oil pipeline

Oil theft by drilling refers to the theft of oil by drilling and installing branch pipes and valves on the oil pipeline, which can easily cause leakage and lead to the risk of pipeline failure. Drilling and oil theft is a problem that has troubled the oil pipeline enterprises for a long time, and it causes a series of serious consequences every year in China. This paper presents a design method of detecting system for locating the location of drilling and oil theft, which is accurate and reliable in performance. It can be operated under harsh conditions, and is very suitable for high pressure and harsh application environment. It has a good application prospect.

Implementation of a 3D position detection system for a medical simulator

BACKGOROUND AND OBJECTIVE: Cardiovascular disorders are increasing because of poor eating habits, excessive drinking, and lack of exercise. Some of the typical cardiovascular surgical procedures utilize catheters. Catheter-based procedures require the surgeons to have extensive experience and high proficiency at performing vascular interventions. However, the learning period to acquire such proficiency is lengthy and the opportunities for practical training and mastery are insufficient. Therefore, due to insufficient skill, dangerous situations with damage or rupture of the patient’s blood vessels may occur, thereby increasing the risk of medical accidents. Hence, it is necessary to have experience and proficiency for performing vascular interventions. Thus, it is necessary to develop a simulator to shorten learning time and reduce medical accidents.METHODS: In this study, we developed a position detection system for the simulator to use physical models to learn cardiovascular surgical intervention techniques. The developed system uses changes in the output values of a Hall sensor based on the position of a permanent magnet.RESULTS AND CONCLUSIONS: From the changing output values, the distance calculation equation is derived, and the position of the permanent magnet is effectively estimated from the calculations. The performance of the position detecting system was tested, and the results proved that the system could be sufficiently used as a simulator.

Sistem Pelacakan Posisi Aset Laboratorium Melalui Sensor Tanpa Kontak Fisik Menggunakan Metode K – Nearest Neighbor (K- NN)

The purpose of this research is to build a laboratory asset position tracking system automatically by utilizing data acquisition from contactless based sensors, namely UHF RFID. In addition, this study also identifies the location of the asset position using the K-Nearest Neighbor method. This case study of tracking the position of assets was conducted at the Manufacturing Automation Engineering Department and Mechatronics, Bandung Manufacturing Polytechnic. The identification of the position of laboratory assets in this study uses the RSSI RFID asset tag as a predictor which will then be compared with 240 training data samples (training data). Testing of the RFID tag position detection system was carried out by means of three RFID readers detecting RFID tags simultaneously and producing an average classification accuracy of 64%.

Adaptive data fusion method based on Kalman filter and its application in piston shape and position detection

In order to meet the needs of production, this paper studies the detection data processing algorithm for the angle of the piston shape and position, the groove width and the runout of the groove bottom. An adaptive data fusion method based on Kalman filtering is proposed. This algorithm can use the adaptive method to calculate the different weight coefficients of each data point according to the standardized sampling data and the smoothed data points obtained by the cubic B-spline approximate fitting algorithm. This paper obtain the fusion data; then calculate the groove angle, groove width and groove bottom runout of the piston according to the conversion formula. Finally, experimental data verification is carried out, which shows the effectiveness and good robustness of the data processing of this algorithm. At the same time, a piston shape and position detection system was developed. The system rotates a circle at 90 degrees per second to obtain the measurement data. After the system software and hardware processing, the groove angle detection accuracy can reach ±2 minutes, the groove width and the groove bottom. The detection accuracy of the runout amount is within ±2 microns, and the measurement items in the measurement time and accuracy side meet the customer’s technical document requirements and achieve satisfactory results.

A New Position Detection and Status Monitoring System for Joint of SCARA

High-precision position detection of joint and status monitoring of speed reduction gearbox (SRG) are two key technologies in the research and development of selective compliant assembly robot arm (SCARA). The existing schemes have some problems, such as the large occupation of space, high cost, complex implementation, lack of status monitoring, etc. Therefore, first, in order to get the accurate position information of joint when the SCARA is powered up at the first time, a new combined incremental encoder with unevenly distributed multiple indexing signals and initial position detection method is proposed. Second, in order to get the accurate position information and monitor the operation status of the SRG during the positioning operation, a real-time position detection and status monitoring method based on the transmission error using dual incremental encoder is proposed. The general design principle has been given, and various experimental results have confirmed the effectiveness of the proposed solution. The proposed new combined incremental encoder has the advantage of small occupied space, easy realization, and low cost. The proposed position detection and status monitoring method not only realizes the high-precision detection of the joint position but also provides the key data for the monitoring of the running state of the SRG.

Design and Implementation of a High-Speed Real-Time Position Detection System in Electromagnetic Coil Launcher

Electromagnetic coil launcher (ECL) is a specific application of electromagnetic launching technology and has broad prospects in the fields of new weapon systems and launching microsatellites. The acceleration force of the projectile in the ECL depends on the current of the driving coil and the projectile together with the mutual inductance between them, and to maximize the launch coefficient, each drive coil should be energized according to the projectile’s movement position. In order to realize the closed-loop control of the ECL and improve the energy utilization, a new position detection system (PDS) based on fiber optic sensor (FOS) is designed in this article, which can obtain the armature displacement and velocity in real time during the launch process. Then, the main factors influencing the detection results are analyzed and studied. Finally, after experimental verification, the PDS designed in this article has an error of 4.2% and 1.41% in the measurement of the armature position and velocity, respectively. The results are of great significance to the realization of ECL engineering.

Apparatus to Measure Subnanometer Fluctuation of Giant Unilamellar Vesicle Membranes.

The design, calibration, and performance of an apparatus are described to study the nanometer-scale thermal or driven fluctuations of free-standing vesicle membranes using a design resembling the position detection system of optical tweezers except for the laser power lower by orders of magnitude to avoid trapping. Over four decades of frequency, 1-10,000 Hz, it reports membrane fluctuation amplitudes 0.01-100 nm by measuring scattering of a laser beam as it passes membranes (∼1 μm cross-section) suspended in the aqueous medium. The low-power laser beam, <100 μW, is sharply focused on the edge of a giant unilamellar vesicle, and fluctuations of position are measured using a position-sensitive photodetector. The central result of this approach is the capability to reach small fluctuations otherwise inaccessible using other techniques. The typical obtained data are fit to the standard Helfrich mechanical model. The applications and limitations of the device are discussed, as well as other potential uses to which the apparatus may be applied by rational extension of the approach presented.

Neural network control system of cooperative robot based on genetic algorithms

Attitude detection of cooperative robots can help robots recognize environment, understand tasks, and improve the safety, accuracy and efficiency of robot work. For robots with unknown configuration parameters, their configuration can be estimated; it is convenient for further kinematics and dynamics analysis. For an economical cooperative robot, its kinematic parameters can be corrected by attitude detection. An independent robot attitude detection system can be used as a secondary auxiliary system, and the fault diagnosis system of the robot is composed of the high-precision encoder system of the robot body. This paper studies the neural network control system of cooperative robot based on genetic algorithm. In this paper, the robot is taken as the research object. Aiming at its strong conjunction, non-linearity and multivariable characteristics, the problem of robot motion control based on neural network is mainly discussed. On this basis, the basic genetic algorithm and an improved genetic algorithm called messy are used to optimize the structure of the neural network, so as to better realize the motion control problem of the robot. The results of this study show that: After optimization with messy genetic algorithm, not only the tracking effect is better, but also the number of hidden layer nodes of the network is reduced from 12 to 7. This greatly simplifies the structure of the network and makes the design and training of the network relatively simple.

Analysis and Adjustment of Positioning Error of PSD System for Mobile SOF-FTIR.

A PSD-based solar spot position detection system is developed for solar tracking closed-loop control of mobile SOF-FTIR (Solar Occultation Flux method based on Fourier Transform Infrared spectrometer). The positioning error factors of PSD (position sensitive detector) are analyzed in detail. A voltage model for PSD signal conditioning circuit has been established to investigate the noise factors. The model shows that the positioning error is mainly related to PSD dark current and circuit gain. A static voltage deduction calibration method based on genetic algorithm is proposed to eliminate the effect of dark current. The gain ratio between channels is calculated based on the fitting curve slope of discrete position data of PSD center point with different light intensity for circuit gain calibration. The positioning accuracy and precision are greatly enhanced, especially when the light intensity is weak, compared with uncalibrated results. The positioning accuracy of center, middle and edge areas of PSD can reach 0.14%, 0.49%, and 1.09%, respectively, after correction in the range of light intensity voltage from 40 mV to 20 V. The corresponding standard deviations of each region are 0.005, 0.009, and 0.014, respectively. The adjustment methods proposed in this paper improve both measurement accuracy and detection limit. The results demonstrate that the calibrated PSD positioning accuracy can meet the requirements of SOF-FTIR for solar tracking.

The Prospect of a New Smart Healthcare System: A Wearable Device-Based Complex Structure of Position Detecting and Location Recognition System.

In upcoming fourth industrial revolution era, it is inevitable to address smart healthcare as not only scientist but also clinician. We have the task to plan and realize this through human imagination, creativity, and applicability for the clarification of the direction of the development and utilization of this technology. One thing that is clear is that it is important to understand what information is needed, how to interpret it, what will be the outcomes, and how to respond in artificial intelligence and Internet of Things era. Therefore, we would like to briefly discuss the characteristics of smart healthcare, and, suggest one approach that is easily applicable in the current situation.

A Simple Discrete-Time Tracking Differentiator and Its Application to Speed and Position Detection System for a Maglev Train

In this brief, a novel tracking differentiator (TD) based on discrete time optimal control (DTOC) is presented. In particular, using the state back-stepping method, a DTOC law for a discrete-time, double-integral system is determined by linearized criterion, which equips the TD with a simple structure. The analysis of the proposed TD reveals its filtering mechanism. Simulation results show that it performs well in signal tracking and differentiation acquisition, and reduces the computational resources needed. Experiments conducted on the speed and position detection system for a maglev train demonstrate that the proposed TD group, with moving average algorithm, can filter noises, amend distortion signals effectively, and compensate for phase delays when the train is passing over track joints.

Obstacle avoidance path generation method for mobile robot using varying search range

In this study, an obstacle avoidance algorithm, with variable range searching, is proposed for a mobile robot. In the case of the distance transform method, the mobile robot cannot avoid the obstacle when it is narrow. We prepare a grid map with the obstacle and distance levels from a destination. A path with both the least number of turns, and the shortest distance, is generated. The search range depends on the distance between the current position and the obstacle. The algorithm can generate the avoidance path without regard to the shape of the obstacle. In the computer simulation, the proposed algorithm can generate a path that avoids the obstacle. As an experiment, a position detection system used contrast differences, by means of a web camera, to get the coordinates of the mobile robot. It is confirmed that, in the experiment, the mobile robot reached the goal grids without hitting the obstacle.

1P2-9 Position and angle detection system using photo-acoustic transmitter for catheter devices

1P2-9 Position and angle detection system using photo-acoustic transmitter for catheter devices

A miniaturized ultra‐high frequency microstrip traveling wave antenna Array for cm‐level two‐dimensional identification and position detection systems

AbstractIn this article, a miniaturized ultra‐high frequency (UHF) microstrip traveling wave antenna (TWA) array for cm‐level 2D identification (ID) and position detection systems is proposed. The proposed miniaturized TWA array with nine reading zones for ID and position detection systems has been reduced in size by utilizing LC‐baluns and a microstrip TWA array. It covers the authorized universal UHF radio frequency ID band (840‐960 MHz), has low insertion loss (0.8 dB), and high isolations between TWAs are ≤30 dB. The sensing precision of the proposed TWA array is approximately 1.5 cm and its physical size is 95 × 95 mm2 (~1/4 λ0 at 915 MHz).

Improving attitude detection performance for spherical motors using a MEMS inertial measurement sensor

A spherical motor has multiple degrees of freedom. Its motion control requires an accurate attitude detection system. This study proposes a low-cost microelectromechanical system (MEMS) inertial measurement sensor to detect the attitude of the spherical motor. The proposed sensor combines a three-axis gyroscope and a three-axis accelerometer to obtain raw position data of spherical motors to estimate rotor's attitude. To improve the accuracy of the estimated attitude from the raw position data, the authors reduce the inclination angle error between the motor's rotor and MEMS sensor's coordinate systems by compensating axial angle error. A high-speed camera that generally provides high accuracy of the attitude is used as a calibration device for comparison and validation. Experimental results show that the MEMS inertial measurement sensor can achieve accurate attitude estimation that is suitable for motion control of a spherical motor.

Analytical Development of a Four-Axis Magnetic Multimedia Control Element

Magnetic position and orientation detection systems have been penetrating the market in the last years due to their excellent properties. Specifically, the development of the cost effective 3-D Hall sensor brings new possibilities for consumer applications. In this paper, we propose a new magnetic design of a four-axis multimedia control element, which requires only a single magnet and a single 3-D Hall sensor. The development is based on an analytical model, which enables an efficient multivariate geometric optimization procedure. Optimal parameters are evaluated and discussed with respect to magnetic state separation up to several millitesla using a reasonable set of discrete rotational and tilt states. Experiments to prove the concept are presented.

Robust additive Gaussian process models using reference priors and cut-off-designs

Abstract When powerful numerical tools like the finite element method encounter their limits for the evaluation of physical systems it is very common to use surrogate models as an approximation. There are many possible choices concerning the model approach, among which Gaussian process models are the most popular ones due to their clear statistical basis. A very desirable attribute of such surrogates is a high flexibility for making them applicable to a great class of underlying problems while obtaining interpretable results. To achieve this Gaussian processes are used as basis functions of an additive model in this work. Another important property of a surrogate is stability, which can be especially challenging when it comes to the estimation of the correlation parameters. To solve this we use a Bayesian approach where a reference prior is assigned to each component of the additive model assuring robust correlation matrices. Due to the additive structure of the model a simplified parameter estimation process is proposed that reduces the usually high-dimensional optimization problem to a few sub-routines of low dimension. Finally, we demonstrate this concept by modeling the magnetic field of a magnetic linear position detection system.

Low-Frequency Noise Reduction in Dual-Fiber Optical Trap Using Normalized Differential Signal of Transmission Lights

The accuracy of long-term position detection in dual-fiber optical trap is limited by low-frequency noise. We propose a technique that can reduce such noise. A position detection system for dual-fiber optical trap using a quadrant photodiode is built. The transmission light from the trapped particle is detected by two photodiodes. The normalized differential signal of transmission lights is demonstrated to carry the noise caused by asymmetric laser power fluctuation irradiating the microsphere, which is proved to dominate low-frequency noise. By subtracting this normalized difference from the position signal, the noise-reduction capability of this technique is remarkably 67% depending on detection bandwidth. It is expected to be applied to integrated and microfluidic fiber-optic trap system.

Finding trap stiffness of optical tweezers using digital filters.

Obtaining trap stiffness and calibration of the position detection system is the basis of a force measurement using optical tweezers. Both calibration quantities can be calculated using several experimental methods available in the literature. In most cases, stiffness determination and detection system calibration are performed separately, often requiring procedures in very different conditions, and thus confidence of calibration methods is not assured due to possible changes in the environment. In this work, a new method to simultaneously obtain both the detection system calibration and trap stiffness is presented. The method is based on the calculation of the power spectral density of positions through digital filters to obtain the harmonic contributions of the position signal. This method has the advantage of calculating both trap stiffness and photodetector calibration factor from the same dataset in situ. It also provides a direct method to avoid unwanted frequencies that could greatly affect calibration procedure, such as electric noise, for example.

Weed location and recognition based on UAV imaging and deep learning

Abstract: To locate and identify weeds in a wheat field efficiently, an unmanned aerial vehicle (UAV) based imaging method was developed in this study.  A weed detection model based on image data through deep learning was developed and implemented.  The model uses the YOLOV3-tiny network to detect the pixel coordinates of weeds in images.  It acquires the position of weeds by converting the pixel coordinates to the geodetic coordinates.  The identified weeds were marked on the prescription map.  The algorithm was implemented and tested using a commercial DJI Phantom 3 UAV.  This study tested the performance of YOLOV3 and YOLOV3-tiny and found that YOLOV3-tiny was more suitable for mobile devices.  The performance of YOLOV3-tiny at different thresholds was tested.  The test results show that the model performs optimally when the threshold of the YOLOV3-tiny network is 0.5, under this condition, the mean Average Precision (mAP) is 72.5%, the Intersection-over-Union (IOU) is 80.12%, and the mobile device processing speed is 2FPS.  After testing and analyzing weed positioning, results show the average positioning error is10.31 cm, which is extremely small in agricultural operations.  The UAV-based weed position detection system can locate and identify weeds in the crop field at a high speed, efficiently and effectively. Keywords: UAV, deep learning, weed location, weed recognition, imaging method, target detection, Android APP DOI:  10.33440/j.ijpaa.20200301.63  Citation: Zhang R F, Wang C, Hu X P, Liu Y X, Chen S, Su B F. Weed location and recognition based on UAV imaging and deep learning. Int J Precis Agric Aviat, 2020; 3(1): 23–29.