Adaptive Measurement Model of Navigation by Stellar Refraction based on Multiple Models Switching

2014 ◽  
Vol 67 (4) ◽  
pp. 673-685 ◽  
Author(s):  
Bo Yang ◽  
Fan Si ◽  
Fan Xu ◽  
Wenlan Zhou
Keyword(s):  
Measurement Method ◽  
Control Algorithm ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Measurement Model ◽  
High Accuracy ◽  
Mode Switching ◽  
Atmospheric Density ◽  
Simulation Based ◽  
Adaptive Measurement

In recent years, navigation by stellar refraction has received considerable attention, having advantages of high accuracy, simple construction, and low cost. Nevertheless, there are many limitations to the precision and application of this method using a traditional measurement model. This article studies the changing pattern of atmospheric density, the disturbed atmospheric density model and measurement model of stellar refraction ranging from 20 km to 50 km. Furthermore, a control algorithm of multiple mode switching and an adaptive measurement model are proposed. With this method, any refracted starlight from the scope of between 20 km and 50 km can be captured and the measurement model at the appropriate height can be automatically established. Due to this, the reliability and practicality of navigation have been raised considerably. Accuracy of navigation using the adaptive measurement method is observed to improve by about 14%, using computer simulation based on an Unscented Kalman Filter (UKF).

scholarly journals Low cost, high accuracy 1.5m2 heliostat

2020 ◽  
Author(s):  
Derek Schulte ◽  
Kyam Krieger ◽  
Carl W. Chin ◽  
Alexander Sonn
Keyword(s):  
Low Cost ◽  
High Accuracy

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

Ultrasonic measurement of clamping force for injection molding machine

2019 ◽  
Vol 39 (4) ◽  
pp. 388-396 ◽  
Author(s):  
Peng Zhao ◽  
Yao Zhao ◽  
Jianfeng Zhang ◽  
Junye Huang ◽  
Neng Xia ◽  
...  
Keyword(s):  
Injection Molding ◽  
Measurement Method ◽  
Large Scale ◽  
Force Measurement ◽  
Low Cost ◽  
Ultrasonic Method ◽  
Injection Molding Process ◽  
Propagation Time ◽  
Clamping Force ◽  
Molding Process

AbstractAn online and feasible clamping force measurement method is important in the injection molding process and equipment. Based on the sono-elasticity theory, anin situclamping force measurement method using ultrasonic technology is proposed in this paper. A mathematical model is established to describe the relationship between the ultrasonic propagation time, mold thickness, and clamping force. A series of experiments are performed to verify the proposed method. Experimental findings show that the measurement results of the proposed method agree well with those of the magnetic enclosed-type clamping force tester method, with difference squares less than 2 (MPa)2and errors bars less than 0.7 MPa. The ultrasonic method can be applied in molds of different thickness, injection molding machines of different clamping scales, and large-scale injection cycles. The proposed method offers advantages of being highly accurate, highly stable, simple, feasible, non-destructive, and low-cost, providing significant application prospects in the injection molding industry.

Dynamic Simulation Control Algorithm Based on the Time Domain

2014 ◽  
Vol 568-570 ◽  
pp. 1020-1025
Author(s):  
Zhuo Wei Jiang ◽  
Chun Ming Gao
Keyword(s):  
Dynamic Simulation ◽  
Time Domain ◽  
Control Algorithm ◽  
Low Cost ◽  
Analog Filter ◽  
Cost Performance ◽  
Dynamic Simulator ◽  
Simulation Control ◽  
The Time Domain ◽  
Computation Speed

In view of badly transplanting of analog filter and low cost performance of digital filter for the washing out signal methods used by dynamic simulator, this paper proposed a computer intelligent time domain method. We decompose signal with the computer intelligence in the time domain, and convert the signal into the corresponding movement form respectively, then get the final result by overlaying them. The experimental results show that this method not only can achieve the effect of the traditional methods, better portability and faster computation speed, but also can be achieved directly on general computers.

High accuracy low-cost videogrammetric system: an application to 6DOF estimation of ship models

2013 ◽  
Author(s):  
Erica Nocerino ◽  
Fabio Menna ◽  
Salvatore Troisi
Keyword(s):  
Low Cost ◽  
High Accuracy

Design and Implement of High-Accuracy On-Line Metal Thickness Measuring Instrument Based on NiosII Processor

2013 ◽  
Vol 834-836 ◽  
pp. 930-934
Author(s):  
Shou Liang Yang ◽  
Bao Liang Yang
Keyword(s):  
Eddy Current ◽  
Low Cost ◽  
High Accuracy ◽  
Resolving Power ◽  
Current Sensor ◽  
Measuring Instrument ◽  
Precision Data ◽  
Eddy Current Sensor ◽  
Metal Thickness ◽  
On Line

The paper proposes a new design of high-accuracy On-line Metal Thickness Measuring Instrument, which was based on EP2C20 series FPGA chip, through adding NiosII soft processor and other interfaces to FPGA, equipped with high precision data collection system and TFT LCD module and so on. The key hardware blocks schematics and components of the RC Oscillation Circuit,eddy current sensor Circuit,rectifier and filter Circuit,A/D converting circuit,FPGA Circuit are described,software flow charts and sample codes are given. According to practice, The measurement range of this system is 1~100 mm and the resolving power is 0.1 μm. degree of linearity is 1%, The system has many features including small volume of hardware, low cost, high detecting precision, convenient operating, high intelligent and so on, leading to broad and bright future. Key words: NiosII processor; eddy current sensor; metal thickness

scholarly journals Vehicle Running State Estimation by Adaptive Soft-Sensing Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Liang Hao ◽  
Lixin Guo ◽  
Shuwei Liu
Keyword(s):  
Working Condition ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Lateral Acceleration ◽  
Measurement Information ◽  
Tire Model ◽  
Steering Angle ◽  
Soft Sensing ◽  
Adaptive Unscented Kalman Filter ◽  
Cost Measurement

Vehicle running state adaptive unscented Kalman filter soft-sensing algorithm is put forward in this paper based on traditional UKF which can estimate vehicle running state parameters and suboptimal Sage-Husa noise estimator which can effectively solve the problem of noises varying with time. Meanwhile 3-DOF dynamic model of vehicle and HSRI tire model are established. So vehicle running state can be accurately estimated by fusing the low-cost measurement information of longitudinal and lateral acceleration and handwheel steering angle. Under the typical working condition, AUKF soft-sensing algorithm is verified with substantial vehicle tests. Comparing with UKF soft-sensing algorithm, the result indicates AUKF soft-sensing algorithm has a good performance in robustness and is able to realize the effective estimation of vehicle running state more precisely than UKF soft-sensing algorithm.

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