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Prediction of Motion Simulator Signals Using Time-Series Neural Networks

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2021年第5期57卷 3383-3392页

作者： Qazani, Mohammad Reza Chalak Asadi, Houshyar Lim, Chee Peng Mohamed, Shady Nahavandi, Saeid Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3216 Australia

A motion cueing algorithm (MCA) is employed to transform the linear and angular motion signals generated from a motion simulator without violating the physical and dynamical boundaries of the motion platform. In this respect, the accurate prediction of the motion scenarios is essential to enhance the efficiency of the MCA using prepositioning or time-varying reference model predictive control. While a recent approach that utilizes a feedforward neural network (NN) to forecast the motion scenarios is useful, the feedforward NN model has only forward dynamics relating to the signals without any feedback loop. In this article, a time-delay feedforward NN, a recurrent NN, and a nonlinear autoregressive (NAR) models with three different training procedures, i.e., Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient, are exploited to predict the motion scenarios. As the NAR model employs the historical signals as the inputs, it can predict the motion scenarios with higher accuracy rates. Based on the series of empirical evaluations, NAR trained with Levenberg-Marquardt is able to outperform the other two counterparts in producing more accurate predictions of the motion signals. The NAR method has a lower computational load as compared with that of the recurrent NN, facilitating its real-time application. In addition to the MCA, the NAR method can be employed in other areas, including autonomous vehicles and motion sickness studies. It can also be easily implemented for air, sea, and/or land vehicle simulators for training purposes in virtual reality environments.

关键词： Artificial neural networks Feedforward systems Predictive models filtering theory Low-pass filters Training filtering algorithms Bayesian regularization (BR) Levenberg-Marquardt (LM) motion cueing algorithm (MCA) motion signal prediction motion simulator nonlinear autoregressive (NAR) scaled conjugate gradient (SCG)

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CrowdSJ: Skyline-Join Query Processing of Incomplete Datasets With Crowdsourcing

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IEEE ACCESS 2021年 9卷 73216-73229页

作者： Ding, Linlin Zhang, Xiao Zhang, Hanlin Liu, Liang Song, Baoyan Liaoning Univ Sch Informat Shenyang 110036 Peoples R China Mine Dynam Disaster Monitoring & Warning Big Data Shenyang 110036 Peoples R China

Skyline query is very useful in decision-making systems, WSN and so on. As a variation of skyline query, skyline-join query can return the results from multiple datasets. However, incomplete datasets are a frequent phenomenon due to the widespread use of automated information extraction and aggregation. Existing methods for dealing with incomplete data, such as probability, data padding can solve the problem, but cannot effectively reflect the real situation and are lack of integrality. Therefore, in this paper, in order to reflect the situation more accuracy and more user-centric, we research the problem of skyline-join query over incomplete datasets with crowdsourcing, named CrowdSJ. The crowdsourcing-based skyline-join query processing problem over incomplete datasets is divided into two situations. One is the skyline-join query only involves the unknown crowdsourcing attribute and the join attribute, named Partial Skyline-Join with Crowdsourcing (PSJCrowd). The other one is the skyline-join query involves all the attributes, named All Skyline-Join with Crowdsourcing (ASJCrowd). For PSJCrowd, first, we filter the known dataset. Then, we present the level-preference-tree-index, and propose the partial skyline-join with crowdsourcing algorithm. For ASJCrowd, first, we filter the known dataset too. Second, we build a level-preference-tree-index based on the known attributes of the incomplete dataset. Third, we propose the skyline-join with crowdsourcing on single dataset algorithm, CrowdSJ-single, to filter the dataset containing unknown attributes. Then, we build a global level-preference-tree-index based on the known attributes of the incomplete dataset and the complete dataset. We propose the skyline-join with crowdsourcing on multiple datasets algorithm, CrowdSJ-multiple. We filter the linked tuples based on the global level-preference-tree-index and the results of each round of crowdsourcing. Numerous experiments on synthetic and real datasets demonstrate that

关键词： Crowdsourcing Indexes filtering algorithms Query processing Wireless sensor networks Task analysis Licenses Skyline-join query incomplete data crowdsourcing index structure

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Properties of the RFI Environment at 1400-1427 MHz as Observed by the Soil Moisture Active/Passive Mission Microwave Radiometer

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 7259-7267页

作者： Bringer, A. Johnson, J. T. Soldo, Y. Le Vine, D. M. Mohammed, P. Misra, S. de Matthaeis, P. Piepmeier, J. R. Ohio State Univ ElectroSci Lab Columbus OH 43212 USA European Spatial Agcy NL-2201 AZ Noordwijk Netherlands NASA Goddard Space Flight Ctr Code 916 Greenbelt MD 20771 USA Morgan State Univ Baltimore MD 21251 USA NASA Jet Prop Lab Pasadena CA 91109 USA Univ Space Res Assoc Columbia MD 21046 USA

The soil moisture active/passive (SMAP) satellite microwave radiometer has been providing global measurements of L-band thermal emission from Earth since April 2015. Although the radiometer operates in the protected 1400-1427 MHz portion of the radio spectrum, its measurements are still corrupted by either radio frequency interference (RFI) from out-of-band emissions via legal sources or by sources operating in-band illegally. The SMAP radiometer includes a digital backend that enables implementation of multiple ground-based RFI detection and filtering algorithms. This data is used to collect statistics and trends of Earth's RFI environment. This article examines properties of the global RFI environment as observed by SMAP, including information on RFI source properties (obtained from analysis of SMAP multiple detector outputs) and the evolution of the RFI environment in time. Residual RFI contributions after the application of SMAP RFI processing are also examined as preliminary information for the development of future methods to address their effect.

关键词： Detectors Spectrogram Microwave radiometry filtering algorithms NASA Antennas Time-frequency analysis L-band radiometry radio frequency interference (RFI) RFI detection SMAP

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Apperception of Plant Disease with avail of algorithm 2

Apperception of Plant Disease with avail of algorithm

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2nd International Conference on Power, Control and Computing Technologies, ICPC2T 2022

作者： Gutte, Vitthal S Mundhe, Pramod Bhandari, Mahesh Dr. Vishwanath Karad MIT World Peace University School of Computer Engineering and Technology Pune India

ISBN: (纸本)9781665458580

In today's world, agriculture is the most important source of growth. It is an essential component of both economic and social life. Plant disease is becoming an important field in India, in the literal sense of the word. In the past, disease detection systems were designed for monocot or dicot plant families. Gradually, as scientific and special to some science or trade progress, more safe, good, ready, and working well methods are offered and have undergone growth for early detection of plant disease through the smallest wide space for turning time. We have instrumented a careful way to show monocot and dicot disease in this paper. Our methodical approach aids in the treatment of the disease by reducing the number of errors. To detect plant leaf disease, we took three steps. It primarily takes three forms: first, breaking down the leaf into parts, then removing the points, and finally, ordering. It has come to be part of the acted-on part of plant leaf using the k-means quince into group's expert way in the breaking down into parts process. The material's feel, appearance, and color are revealed after it is broken down into primarily form parts. Finally, the information gleaned from the features is applied to the order using the Support Guide Machine to detect plant disease. © 2022 IEEE.

关键词： Image color analysis Instruments Power control Clustering algorithms filtering algorithms Turning Information filters

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Multiple Filter-Based Rankers to Guide Hybrid Grasshopper Optimization Algorithm and Simulated Annealing for Feature Selection With High Dimensional Multi-Class Imbalanced Datasets

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IEEE ACCESS 2021年 9卷 74127-74142页

作者： Sharifai, Abdulrauf Garba Zainol, Zurinahni Binti Univ Sains Malaysia Sch Comp Sci Gelugor 11800 Malaysia Yusuf Maitama Sule Univ Dept Comp Sci Kano 700252 Nigeria

DNA microarray data analysis is infamous due to a massive number of features, imbalanced class distribution, and limited available samples. In this paper, we focus on high-dimensional multi-class imbalanced problems. The high dimensional and multi-class imbalanced problem has posed acute challenges for the conventional classifiers to effectively perform classification tasks on both the minority and majority classes. Numerous efforts have been devoted to addressing either high dimensionality dataset or class imbalance problems. Nonetheless, few methods have been proposed to address the intersection of multi-class imbalanced and high-dimensional problems concurrently due to their intricate interactions. This paper presents novel hybrid algorithms for feature selection with the high dimensional multi-class imbalanced problem using multiple filter-based rankers (MFR) and hybrid Grasshopper optimization algorithm (GOA). The Simulated Annealing (SA) algorithm is incorporated into GOA. SA is used to enhance the best solution found by the GOA algorithm. The aim of using the SA here is to tackle the slow convergence and improve the exploitation by searching the high-quality regions found by the GOA. The experimental results confirm the effectiveness of the proposed methods in improving the classification performance in terms of area under the curve (AUC) compared to other well-known methods, which guarantees the ability of the proposed methods in searching the feature space and identifying very robust and discriminative features that best predict the minority class.

关键词： Optimization Feature extraction filtering algorithms Classification algorithms Search problems Task analysis Simulated annealing Grasshopper optimization algorithm simulated annealing tournament selection multiple filter-based rankers feature selection hybrid filter-wrapper high-dimensionality imbalanced class distribution

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Improved Particle Filter-Based Matching Method With Gravity Sample Vector for Underwater Gravity-Aided Navigation

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第6期68卷 5206-5216页

作者： Wang, Bo Zhu, Jingwei Ma, Zixuan Deng, Zhihong Fu, Mengyin Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

Gravity-matching algorithm is a key to the gravity-aided inertial navigation system (INS). The traditional particle filter-based matching algorithm with a gravity sample vector is efficient. However, the range of particle filter and the probability model of the actual location are not specified in the algorithm. An improved particle filter-based matching algorithm with a gravity sample vector is proposed. Because of the high short-term accuracy of INS, the error range of INS in a short period is analyzed in a polar coordinate system in this algorithm. First, the attitude error angle model of INS is established. Relative angle error is proposed to calculate latitudes and longitudes of particles in the fan area at any position. Then a particle filter embedded in a particle filter is proposed to calculate the error range of the real position and establish the probability model of this position. Finally, in order to reduce the matching error, the relative displacements of the positions of the particles and the upper matching positions are added to the weights of the particles. Simulation results show that the proposed method has higher accuracy and better robustness.

关键词： Matched filters Simulation filtering algorithms Particle filters Robustness Probability distribution Gravity Gravity-aided navigation particle filter matching algorithm

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Multiple Object Tracking for Occluded Particles

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IEEE ACCESS 2021年 9卷 1524-1532页

作者： Qian, Yifei Ji, Ru Duan, Yuping Yang, Runhuai Anhui Med Univ Dept Biomed Engn Hefei 230022 Peoples R China

The precise detection and tracking of multiple particles under a microscope are of significance in the research of the individual and cluster behavior of dynamic bacteria and subcellular structures. However, the existing detection algorithms cannot separate occluded particles from each other, and most of the tracking algorithms aimed to address the occlusion involve several uncertainties. In this paper, a two-step detection algorithm based on the threshold segmentation and morphological open operation has been developed for identify non-fluorescent labeled particles under microscope, which could separate micro-contact targets. Moreover, we have proposed a novel correlation algorithm that can exploit the strengths of the global shortest path algorithm and Hungarian algorithm, which updating online in real time and considering the occlusion among particles. The proposed approach could achieve the temporal optimal match and spatial optimal solution by utilizing the multi-frame information. Moreover, the proposed method could realize the tracking of occluded particles tracking, and outperform the single global shortest path algorithm and Hungarian algorithm. The proposed method was successfully applied to six real image sequences with the maximum number of particles per frame ranging from 23 to 55, as well as a synthetic and fluorescent labeled sequence. The results of the contrast experiments demonstrated that the proposed algorithm is practical and can realize real-time tracking.

关键词： Target tracking filtering algorithms Detection algorithms Image edge detection Fluorescence Uncertainty Morphological operations Microscopic image multiple particles tracking target occlusion global data association

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An Improved AKAZE Algorithm for Feature Matching of Moving Objects 7

An Improved AKAZE Algorithm for Feature Matching of Moving O...

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7th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Yongsheng, Lin Wenqiang, Fan Yuan, Zhao Peng, Liu School of Optoelectronic Information Yantai University Yantai China

ISBN: (纸本)9781665487115

Aiming at the problems of fast feature extraction and low matching accuracy of moving target detection features for moving targets in the existing image processing feature matching, an improved method is proposed based on the AKAZE algorithm. Using the nonlinear method of the AKAZE algorithm to construct the calculated feature points in the scale space, obtain the displacement of the same feature point between adjacent frames, and filter out the unsuitable feature points according to the rules of the improved algorithm. The experimental results show that although the improved algorithm has the disadvantage of increasing the matching time, by reducing the number of mismatched point pairs, the accuracy of feature matching can be improved relatively, and it is suitable for the capture of accurate objects in a small range. © 2022 IEEE.

关键词： Matched filters Fault tolerance Cloud computing Fault tolerant systems Object detection Streaming media filtering algorithms

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Fixed-Point Maximum Total Complex Correntropy Algorithm for Adaptive Filter

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 2188-2202页

作者： Qian, Guobing Mei, Jiaojiao Iu, Herbert H. C. Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Univ Western Australia Sch Elect Elect & Comp Engn Crawley WA 6009 Australia

Adaptive filtering for complex-valued data plays a key role in the field of signal processing. So far, there has been very little research for the adaptive filtering in complex-valued errors-in-variables (EIV) model. Compared with the complex correntropy, the total complex correntropy has shown superior performance in the EIV model. However, the current gradient based maximum total complex correntropy (MTCC) adaptive filtering algorithm has suffered from the tradeoff between fast convergence rate and low weight error power. In order to improve the performance of MTCC, we develop a fixed point maximum total complex correntropy (FP-MTCC) adaptive filtering algorithm in this study. The convergence analysis of the FP-MTCC is also provided in the paper. Furthermore, we develop two recursive FP-MTCC (RFP-MTCC) algorithms for the online adaptive filtering and provide the transient analysis of RFP-MTCC. Finally, the validity of the convergence and the superiority of the proposed algorithms are verified by simulations.

关键词： Convergence Adaptive filters Signal processing algorithms filtering Adaptation models Kernel filtering algorithms Complex domain errors-in-variables adaptive filtering fixed point

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Optimal Online Resonance Suppression in a Drive System Based on a Multifrequency Fast Search Algorithm

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IEEE ACCESS 2021年 9卷 55373-55387页

作者： Xia, Jiakuan Guo, Zhiyan Li, Zexing Shenyang Univ Technol Sch Elect Engn Shenyang 110870 Peoples R China

The resonant frequency drift caused by the change in mechanical parameters will cause the servo mechanism of CNC machine tools, robots and other equipment to produce mechanical resonance again. To address this problem, we propose a novel identification method of mechanical resonance based on the Fibonacci principle and a novel design method of notch filters based on optimization theory. First, by using a multifrequency sinusoidal signal, we design a fast search algorithm to search for the drift resonance frequency online;then, we optimize the notch filter to achieve fast and real-time resonance suppression by optimization theory. Compared with commonly employed passive resonance suppression methods, the proposed method can rapidly identify the parameters, more accurately suppress the resonance, minimize the phase angle loss and maintain the stability of the system. Finally, we apply the method to a 2-mass system experimental platform. The experimental results show that the proposed method is superior to the conventional passive resonant suppression method under the resonant frequency drift and other states and has strong robust performance.

关键词： Resonant frequency filtering algorithms Real-time systems filtering theory Servomotors Notch filters Optimization Servo mechanism mechanical resonance frequency drifts Fibonacci principle optimization theory notch filter phase angle loss

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