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9th Joint International Symposium on Atmospheric and Ocean Optics/Atmospheric Physics

作者： Kurakov, VA Komarov, VS Kreminskii, AV Lomakina, NY Popov, YB Popova, AI Suvorov, SS Tomsk State Univ Control Syst & Radioelect Tomsk 634055 Russia

ISBN: (纸本)0819448281

In the paper, the problem of spatial forecast of mesoscale fields at the point of space uncovered by meteorological information is discussed. The algorithms for estimating and forecasting the atmospheric parameters based on kalman filtering theory. The offered algorithm takes into account horizontal statistical structure of afield at separate atmospheric levels and its time dynamics. The atmospheric parameter in a point is defined on the basis of a second-order polynomial model. The offered algorithm of the spatial forecast is investigated on the data long-term balloon observations for layer-by-layer averaging of temperature, zonal and meridional 1 wind velocity components.

关键词： spatial forecast kalman filtering algorithm mesoscale fields of temperature and zonal and meridional wind velocity components

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State of charge prediction of supercapacitors via combination of kalman filtering and backpropagation neural network

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IET ELECTRIC POWER APPLICATIONS 2018年第4期12卷 588-594页

作者： Wang Houlian Zhou Gongbo China Univ Min & Technol Sch Mech & Elect Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Jiangsu Key Lab Mine Mech & Elect Equipment Xuzhou 221116 Jiangsu Peoples R China

Supercapacitors are increasingly applied to the field of electric vehicles. Although the state of charge (SOC) directly shows the remaining capacity of supercapacitors in an energy management system, vehicle drivers also require the changes of supercapacitors in the future for driving reference. How to predict SOC of supercapacitors has become a pressing problem. In order to solve the above problem, a method combining backpropagation (BP) neural network with the kalman filtering algorithm is proposed to predict SOC in the future. The BP neural network inputs SOC estimated by the kalman filtering algorithm as the training data to train network, and thereby being able to forecast the SOC in the future period. The algorithm is verified in simulations and experiments under the two conditions: NewYorkBus and NYCC, with the consideration of the influence of the length of training data and temperature. The results show the max absolute error during prediction at different lengths is under 6% in simulations and experiments. In addition, temperature has almost no effect on the prediction accuracy. The current research implies that this method can be applied to predict supercapacitors' SOC in the future.

关键词： supercapacitors electric vehicles kalman filters backpropagation neural nets power engineering computing state-of-charge prediction supercapacitors kalman filtering algorithm backpropagation neural network SOC prediction energy management system vehicle drivers driving reference BP neural network training data NewYorkBus NYCC

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Investigation of filtering algorithm for Noise Reduction in Displacement Sensing Signal

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IEEE SENSORS JOURNAL 2021年第6期21卷 7808-7812页

作者： Li, Changping Huang, Ruining Yi, Ying Bermak, Amine Chongqing Univ Posts & Telecommun Coll Commun & Informat Engn Chongqing 400065 Peoples R China Harbin Inst Technol Sch Mech Engn & Automat Shenzhen 518055 Peoples R China Hamad Bin Khalifa Univ Qatar Fdn Coll Sci & Engn Div Informat & Comp Technol Doha 34100 Qatar

With the emergence of new sensing technologies, soft substrate-based displacement sensing has gained popularity in the last decade. Nevertheless, performance degradation due to noise from unexpected vibrations of the substrate can affect the sensors accuracy and lead to poor measurements. Few studies have focused on noise reduction to improve the accuracy of the sensing data. Based on mathematical modeling, this paper compares the two most representative filtering algorithms based on minimum mean square error (MMSE), namely recursive least squares (RLS) and kalman algorithms for their predictive ability, accuracy, and reliability. Based on the modeling and comparison, we propose a kalman filtering algorithm to suppress the vibration noise in the displacement-based dosage sensor. The filter-processed data from the sensors measurement mainly contains the actual resistance change upon mechanical displacement of the sensor substrate and the undesired noise from its vibration. With the filtering algorithm, the noise can be reduced from the sensors measurement, and an accurate correlation between resistance change and displacement of the sensor can be established. This would provide a straightforward sensing signal that reflects the displacement, and in turn, achieves precise dosage monitoring in drug delivery devices, which can be used as a test-bed application for the sensor.

关键词： Sensors filtering algorithms kalman filters Noise measurement Resistance Noise reduction Estimation Displacement sensor kalman filtering algorithm noise reduction

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AN EFFECTIVE kalman filtering METHOD FOR ENHANCING SPEECH IN THE PRESENCE OF COLORED NOISE 5

AN EFFECTIVE KALMAN FILTERING METHOD FOR ENHANCING SPEECH IN...

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5th International Conference on Audio, Language and Image Processing (ICALIP)

作者： Xia, Youshen Wei, Qingquan Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China

ISBN: (纸本)9781509006540

The kalman filtering algorithm for speech enhancement is easily implemented and is efficient under white noise environments. This paper proposes an effective kalman filtering algorithm for enhancing speech corrupted by colored noise, based on a whitened matrix. Compared with the conventional kalman filtering algorithms to handle colored noise, the proposed kalman filtering algorithm has low computational complexity and overcomes the difficulty of estimating the covariance matrix of colored noise. Simulation results confirm that the proposed kalman filtering algorithm has better performance than several conventional algorithms in decreasing colored noise and speech distortion.

关键词： Single-channel speech enhancement colored noise kalman filtering algorithm

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A linear signal filtering smoothing algorithm for ship dynamic positioning

A linear signal filtering smoothing algorithm for ship dynam...

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31st Chinese Control Conference

作者： Xiong Jianbin Wang Qinruo Liu Yijun Ye Baoyu Wu Haoyi Guang Dong Univ Technol Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9789881563811

The algorithm based on extended kalman filter and in combination with linear smoothing was proposed in this paper, as there are some deviations larger abnormal values in measurement of the ship position system. And this method was used to process nonlinear continuous system. It was applied to ship dynamic positioning. First, we deep analyze nonlinear model of ship dynamic positioning, then turn it into a linear model. Second, discrete linear model of ship dynamic positioning, finally, filter using the extended kalman filter algorithm and smooth filtered signal using smoothing linear algorithm. The experimental results show that the method was simple, reasonable, effective and easy for real-time applications.

关键词： kalman filtering algorithm linear smoothing algorithm Ship dynamic positioning discretization

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RSSI Localization Method for Mine Underground Based on RSSI Hybrid filtering algorithm 9

RSSI Localization Method for Mine Underground Based on RSSI ...

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Liu Yi Liang Tao Sun Jun School of Mechanical Electronic and Information Engineering China University of Mining and Technology (Beijing)

ISBN: (纸本)9781509038237;9781509038220

Aiming at the coal mine personnel positioning method based on wireless sensor network technology, contrasting with advantage of now-existing feasible methods, consulting with traditional trilateral positioning, this paper proposed weighted trilateral localization method based on RSSI ranging. In order to ensure the precision of distance measurement, analysis the cause of error of RSSI, improved the performance of the linear discrete kalman filter, to propose hybrid filtering algorithm. The simulation shows that the improved method has effectively reduce the positioning error and improve the personnel positioning accuracy.

关键词： wireless sensor networks RSSI kalman filtering algorithm weighted trilateral localization algorithm

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A Study on Applying an Autoregressive Model with the kalman filtering in Accuracy Improvement of Dissolved Oxygen Measurement

A Study on Applying an Autoregressive Model with the Kalman ...

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International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

作者： Tanatipuknon, Asadang Karnjana, Jessada Thammasat Univ Sirindhorn Int Inst Technol Pathum Thani Thailand Natl Sci & Technol Dev Agcy NECTEC Pathum Thani Thailand

ISBN: (纸本)9781728101644

The dissolved oxygen (DO) measurement plays a crucial role in every automatic aerator-control system for shrimp farming because the DO affects both the animal survival rate and the growth rate. As a consequence, the accuracy degree of the DO sensor matters. The sensor with high accuracy is not economical. Therefore, we propose a framework for accuracy improvement in the DO measurement of the low-cost sensor. The proposed framework is based on the kalman filtering algorithm in which an autoregressive technique is used to model the state transition. Therefore, it does not require a complex DO dynamic model. Experimental results show that the framework can be used to improve measurement accuracy in most cases. In the best case, our proposed method increases the accuracy by 13.5%. However, the degree of improvement is on a small scale.

关键词： kalman filtering algorithm autoregressive model dissolved oxygen measurement shrimp farming

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Reduced-complexity tracking scheme based on adaptive weighting for location estimation

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IET COMMUNICATIONS 2013年第7期7卷 673-684页

作者： Wang, Chin-Liang Chiou, Yih-Shyh Tsai, Fuan Natl Tsing Hua Univ Inst Commun Engn Hsinchu 30013 Taiwan Natl Tsing Hua Univ Dept Elect Engn Hsinchu 30013 Taiwan Natl Cent Univ Ctr Space & Remote Sensing Res Jhongli 32001 Taiwan Chung Yuan Christian Univ Dept Elect Engn Chungli 32023 Taiwan

This study presents an efficient location tracking algorithm to reduce the computational complexity of the conventional kalman filtering algorithm. In the proposed location estimation and tracking approach, using the inherent message-passing nature of factor graphs, the data information is passed efficiently between the variable nodes and the factor nodes by taking weights based on the message reliability, thus simplifying implementation of the Bayesian filtering approach for location tracking. Numerical simulations and experimental results show that the proposed location tracking scheme not only can achieve the location accuracy close to that of the kalman filtering scheme, but also has lower computational complexity with decoupling approach.

关键词： computational complexity estimation theory graphs kalman filters message passing mobile radio reliability tracking decoupling approach Bayesian filtering approach message reliability factor nodes variable nodes data information factor graphs message-passing nature kalman filtering algorithm computational complexity location tracking algorithm location estimation adaptive weighting-based reduced-complexity tracking scheme computational complexity estimation theory graphs kalman filters message passing mobile radio reliability tracking decoupling approach Bayesian filtering approach message reliability factor nodes variable nodes data information factor graphs message-passing nature kalman filtering algorithm computational complexity location tracking algorithm location estimation adaptive weighting-based reduced-complexity tracking scheme

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Investigation of a DC OCT signal processing method based on an improved kalman algorithm

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OPTIK 2018年 171卷 809-820页

作者： Shen, Yan Ge, Jin-ming Liu, Rui-tong Fan, Wei Yang, Ying-xuan Harbin Inst Technol Dept Elect Engn Harbin 150001 Heilongjiang Peoples R China State Grid Liaoning Elect Power Res Inst Shenyang 110006 Liaoning Peoples R China

In this paper, we propose an improved kalman filtering algorithm, to solve problems associated with processing of direct current (DC) signals in optical current transformers (OCTs). The algorithm was realized as follows. Based on the characteristics of the OCT, mixed noise was included with the parameters and state information of the measurement model, to ensure that it could be processed. State predictions and update equations for the improved kalman algorithm were obtained, according to the minimum mean squared error criterion, using the established model. Finally, real-time noise estimation and correction was achieved by designing a recursive noise filter. Results from simulations and experiments demonstrate that the improved signal processing algorithm is effective in filtering mixed noise from DC signals measured by an OCT. Furthermore, the proposed algorithm is able to achieve real-time noise correction during the filtering process.

关键词： Optical current transformer Signal processing kalman filtering algorithm

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Intelligent ensemble T-S fuzzy neural networks with RCDPSO_DM optimization for effective handling of complex clinical pathway variances

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COMPUTERS IN BIOLOGY AND MEDICINE 2013年第6期43卷 613-634页

作者： Du, Gang Jiang, Zhibin Diao, Xiaodi Yao, Yang E China Normal Univ Sch Business Shanghai 200241 Peoples R China Shanghai Jiao Tong Univ Dept Ind Engn & Logist Management Shanghai 200240 Peoples R China Shanghai Putuo Dist Cent Hosp Shanghai 200062 Peoples R China Shanghai 6 Peoples Hosp Shanghai 200233 Peoples R China

Takagi-Sugeno (T-S) fuzzy neural networks (FNNs) can be used to handle complex, fuzzy, uncertain clinical pathway (CP) variances. However, there are many drawbacks, such as slow training rate, propensity to become trapped in a local minimum and poor ability to perform a global search. In order to improve overall performance of variance handling by T-S FNNs, a new CP variance handling method is proposed in this study. It is based on random cooperative decomposing particle swarm optimization with double mutation mechanism (RCDPSO_DM) for T-S FNNs. Moreover, the proposed integrated learning algorithm, combining the RCDPSO_DM algorithm with a kalman filtering algorithm, is applied to optimize antecedent and consequent parameters of constructed T-S FNNs. Then, a multi-swarm cooperative immigrating particle swarm algorithm ensemble method is used for intelligent ensemble T-S FNNs with RCDPSO_DM optimization to further improve stability and accuracy of CP variance handling. Finally, two case studies on liver and kidney poisoning variances in osteosarcoma preoperative chemotherapy are used to validate the proposed method. The result demonstrates that intelligent ensemble T-S FNNs based on the RCDPSO_DM achieves superior performances, in terms of stability, efficiency, precision and generalizability, over PSO ensemble of all T-S FNNs with RCDPSO_DM optimization, single T-S FNNs with RCDPSO_DM optimization, standard T-S FNNs, standard Mamdani FNNs and T-S FNNs based on other algorithms (cooperative particle swarm optimization and particle swarm optimization) for CP variance handling. Therefore, it makes CP variance handling more effective. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Intelligent ensemble FNNs RCDPSO_DM Clinical pathways Double mutation mechanism kalman filtering algorithm Variance

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