DP-Stability During Heavy Lift Operations Using a Modified Kalman Filter

2018 ◽  
Author(s):  
Eelco Harmsen ◽  
Radboud van Dijk ◽  
Petter Stuberg
Keyword(s):  
Kalman Filter ◽  
Dynamic Positioning ◽  
Mooring System ◽  
Engineering Analysis ◽  
Single Object ◽  
Joint Effort ◽  
Wide Range ◽  
Floating Object ◽  
Heavy Lift ◽  
Modified Kalman Filter

During heavy lift operations, staying on position using a Dynamic Positioning (DP) system offers many advantages compared with a mooring system. However, when the vessel is connected to another fixed or floating object during the lifting operation through its hoist wires it may experience instabilities in the DP-system. These DP-instabilities are caused by the inability of the DP system to handle the relatively stiff external spring of the hoist wire correctly. This phenomenon is well known and mitigating measures such as Heavy Lift Mode have been developed over the years that work well for stationary vessels. However, when two vessels are lifting a single object together (e.g. QUAD lift), existing solutions to prevent this DP-instability are insufficient, as the nature of such lift requires a synchronous move on DP. During studies to the fundamental behavior of a DP system during heavy lift operations it is found that modifications to the Kalman filter can prevent these DP-instabilities. Heerema Marine Contractors presented the DP-stability challenges to Kongsberg Maritime, and a joint effort resulted in an implementation of a modified Kalman filter in the Kongsberg Maritime DP system. Also a dedicated engineering analysis to predict risk of DP-instabilities for specific lift configurations has been developed. The modified DP-system is tested in large number of simulations (both desktop and a full mission simulator) to test the ability of the updated DP-system to deal with a wide range of specific heavy lift conditions. Results were evaluated between Heerema office, Kongsberg and offshore personnel for developing the optimum Kalman filter parameters. Finally, the system is tested during a dedicated DP-trial program onboard Thialf. As the results of all these tests were very successful, the new High Kalman filter was made available onboard Thialf as a permanent option next to the original functionalities. The paper addresses the steps followed to define the new Kalman filter settings, the simulations performed to test the new filter as well as to show results of the offshore tests that were done to validate the numerical analysis.

scholarly journals Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Michael D. Paskett ◽  
Mark R. Brinton ◽  
Taylor C. Hansen ◽  
Jacob A. George ◽  
Tyler S. Davis ◽  
...  
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Activities Of Daily Living ◽  
Convolutional Neural Network ◽  
Daily Living ◽  
Complex Data ◽  
Multi Layer Perceptron ◽  
Experimental Conditions ◽  
Modified Kalman Filter ◽  
Prosthesis Control

Abstract Background Advanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not fully utilize the rich control information from residual nerves and musculature. Continuous decoders can provide more intuitive prosthesis control using multi-channel neural or electromyographic recordings. Three components influence continuous decoder performance: the data used to train the algorithm, the algorithm, and smoothing filters on the algorithm’s output. Individual groups often focus on a single decoder, so very few studies compare different decoders using otherwise similar experimental conditions. Methods We completed a two-phase, head-to-head comparison of 12 continuous decoders using activities of daily living. In phase one, we compared two training types and a smoothing filter with three algorithms (modified Kalman filter, multi-layer perceptron, and convolutional neural network) in a clothespin relocation task. We compared training types that included only individual digit and wrist movements vs. combination movements (e.g., simultaneous grasp and wrist flexion). We also compared raw vs. nonlinearly smoothed algorithm outputs. In phase two, we compared the three algorithms in fragile egg, zipping, pouring, and folding tasks using the combination training and smoothing found beneficial in phase one. In both phases, we collected objective, performance-based (e.g., success rate), and subjective, user-focused (e.g., preference) measures. Results Phase one showed that combination training improved prosthesis control accuracy and speed, and that the nonlinear smoothing improved accuracy but generally reduced speed. Phase one importantly showed simultaneous movements were used in the task, and that the modified Kalman filter and multi-layer perceptron predicted more simultaneous movements than the convolutional neural network. In phase two, user-focused metrics favored the convolutional neural network and modified Kalman filter, whereas performance-based metrics were generally similar among all algorithms. Conclusions These results confirm that state-of-the-art algorithms, whether linear or nonlinear in nature, functionally benefit from training on more complex data and from output smoothing. These studies will be used to select a decoder for a long-term take-home trial with implanted neuromyoelectric devices. Overall, clinical considerations may favor the mKF as it is similar in performance, faster to train, and computationally less expensive than neural networks.

Image noise smoothing using a modified Kalman filter

2016 ◽  
Vol 173 ◽  
pp. 1625-1629 ◽  
Author(s):  
Jian Pan ◽  
Xinhua Yang ◽  
Huafeng Cai ◽  
Bingxian Mu
Keyword(s):  
Kalman Filter ◽  
Image Noise ◽  
Modified Kalman Filter ◽  
Noise Smoothing

scholarly journals Towards the Development of Language Analysis Tools for the Written Latgalian Language

2020 ◽  
Author(s):  
Daiga Deksne ◽  
Anna Vulāne
Keyword(s):  
Web Service ◽  
Morphological Analysis ◽  
Written Language ◽  
Analysis Tool ◽  
Tool Development ◽  
Joint Effort ◽  
Analysis Tools ◽  
Language Analysis ◽  
Wide Range ◽  
Development Of Language

This paper reports on the development of spell checking and morphological analysis tools for Latgalian. The Latgalian written language is a historic variant of the Latvian language. There is a wide range of language analysis tools available for Latvian, whereas the Latgalian language lacks such tools. The work is done by the joint effort of linguists who work on morphologically marked lexicon creation and IT specialists who work on language tool development. For the creation of a morphological analysis tool, we reuse the FST technology used for the Latvian morphological analyzer. We create a spelling dictionary that can be used with the Hunspell engine. All tools are accessible via Web Service. For now, the Latgalian lexicon contains 13,139 lemmas marked by 105 inflection groups. The work of lexicon replenishment still continues.

scholarly journals Performance Analysis of Computational Intelligence Correction

2021 ◽  
Author(s):  
Nalini Arasavali ◽  
Sasibhushanarao Gottapu
Keyword(s):  
Kalman Filter ◽  
Performance Analysis ◽  
Computational Intelligence ◽  
A Priori ◽  
Covariance Matrices ◽  
System Model ◽  
Process Noise ◽  
Navigation Algorithm ◽  
Modified Kalman Filter ◽  
Measurement Noises

Abstract Kalman filter (KF) is a widely used navigation algorithm, especially for precise positioning applications. However, the exact filter parameters must be defined a priori to use standard Kalman filters for coping with low error values. But for the dynamic system model, the covariance of process noise is a priori entirely undefined, which results in difficulties and challenges in the implementation of the conventional Kalman filter. Kalman Filter with recursive covariance estimation applied to solve those complicated functional issues, which can also be used in many other applications involving Kalaman filtering technology, a modified Kalman filter called MKF-RCE. While this is a better approach, KF with SAR tuned covariance has been proposed to resolve the problem of estimation for the dynamic model. The data collected at (x: 706970.9093 m, y: 6035941.0226 m, z: 1930009.5821 m) used to illustrate the performance analysis of KF with recursive covariance and KF with computational intelligence correction by means of SAR (Search and Rescue) tuned covariance, when the covariance matrices of process and measurement noises are completely unknown in advance.

scholarly journals Channel Estimation for SCM-OFDM Systems by Modified Kalman Filter

2013 ◽  
Vol 05 (03) ◽  
pp. 666-670 ◽  
Author(s):  
Tao Peng ◽  
Yue Xiao ◽  
Shaoqian Li ◽  
Huaqiang Shu ◽  
Eric Pierre Simon
Keyword(s):  
Kalman Filter ◽  
Channel Estimation ◽  
Ofdm Systems ◽  
Modified Kalman Filter

scholarly journals Instantaneous Frequency Estimation Based on Modified Kalman Filter for Cone-Shaped Target

2020 ◽  
Vol 12 (17) ◽  
pp. 2766
Author(s):  
Ke Ren ◽  
Lan Du ◽  
Xiaofei Lu ◽  
Zhenyu Zhuo ◽  
Lu Li
Keyword(s):  
Kalman Filter ◽  
Survival Rate ◽  
Instantaneous Frequency ◽  
Estimation Method ◽  
Consensus Algorithm ◽  
Instantaneous Frequency Estimation ◽  
Time Frequency ◽  
Multicomponent Signals ◽  
Modified Kalman Filter ◽  
Trajectory Correction

The instantaneous frequency (IF) is a vital parameter for the analysis of non-stationary multicomponent signals, and plays an important role in space cone-shaped target recognition. For a cone-shaped target, IF estimation is not a trivial issue due to the proximity of the energy of the IF components, the intersections among different IF components, and the existence of noise. Compared with the general parameterized time-frequency (GPTF), the traditional Kalman filter can perform better when the energy of different signal components is close. Nevertheless, the traditional Kalman filter usually makes association mistakes at the intersections of IF components and is sensitive to the noise. In this paper, a novel IF estimation method based on modified Kalman filter (MKF) is proposed, in which the MKF is used to associate the intersecting IF trajectories obtained by the synchroextracting transform (SET). The core of MKF is the introduction of trajectory correction strategy in which a trajectory survival rate is defined to judge the occurrence of association mistakes. When the trajectory survival rate is below the predetermined threshold, it means that an association mistakes occurs, and then the new trajectories generated by the random sample consensus algorithm are used to correct the wrong associations timely. The trajectory correction strategy can effectively obviate the association mistakes caused by the intersections of IF components and the noise. The windowing technique is also used in the trajectory correction strategy to improve computational speed. The experimental results based on the electromagnetic computation data show that the proposed method is more robust and precise than the traditional Kalman filter. Moreover, the proposed method has great performance advantages compared with other methods (i.e., the multiridge detection, the ant colony optimization, and the GPTF methods) especially in the case of low signal noise ratio (SNR).

Research on the Optimization of the Offset and the Power Consumption for a DP Assisted Mooring System

2013 ◽  
Author(s):  
Hongchao Wang ◽  
Lei Wang ◽  
Hanlin Liu
Keyword(s):  
Power Consumption ◽  
The Other ◽  
Engineering Practice ◽  
Dynamic Positioning ◽  
Positioning System ◽  
Mooring System ◽  
Mooring Lines ◽  
Positioning Accuracy ◽  
Dynamic Positioning System ◽  
Two Measures

DP assisted mooring system is a new positioning system including mooring system and dynamic positioning system. In this paper, two measures are proposed to reduce the offset and the power consumption of a generic semi-submersible platform which is equipped with a DP assisted mooring system during operation. One is to tighten the windward mooring lines and slacken the leeward mooring lines, the other is to reduce or cancel the forbidden angles of the thrusters adjacent to the failed thrusters. Given that the two measures can both reduce the offset and the power consumption of the semi-submersible unit, it is suggested to combine the two measures together to obtain better positioning accuracy. This paper is helpful to engineering practice.

Optimal depth estimation using modified Kalman filter in the presence of non‐Gaussian jitter noise

2018 ◽  
Vol 82 (3) ◽  
pp. 224-231 ◽  
Author(s):  
Hoon‐Seok Jang ◽  
Mannan Saeed Muhammad ◽  
Tae‐Sun Choi
Keyword(s):  
Kalman Filter ◽  
Depth Estimation ◽  
Optimal Depth ◽  
Modified Kalman Filter ◽  
Non Gaussian
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