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Robust Beamforming Based on Covariance Matrix Reconstruction and ADMM

Robust Beamforming Based on Covariance Matrix Reconstruction...

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2021 CIE International Conference on Radar, Radar 2021

作者： Gong, Pengcheng Wang, Zhaobin Xu, Kaiyan School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China Hubei University of Technology Hubei Key Lab. for High-efficiency Utiliz. of Solar Ener. and Oper. Control of Energy Storage System Wuhan430068 China

ISBN: (纸本)9781665498142

Considering at the issue that the performance of the traditional beamforming algorithm will decrease sharply when the disturbance occurs at the disturbed position and the steering vector mismatch occurs, a new robust beamforming method based on covariance matrix reconstruction and Alternating Direction Method of Multipliers (ADMM) is proposed. Firstly, based on the maximum output power criterion of beamformers, an optimization model is designed to solve the optimal steering vector. Then, the covariance matrix is reconstructed by using the defined interference range to widen the nullity and enhance the anti-motion interference ability of the system. Then, for the quadratic inequality constraint problem of the steering vector, this paper uses ADMM to solve the model iteratively, and obtains the specific solution of the steering vector in each iteration. In addition, the complexity of the algorithm is also analyzed. The experimental results show that, compared with the existing beamforming algorithms, the proposed method Widen the width of zero notch in the interference area, improves the anti-interference performance of the beam, and can well correct the mismatched steering vector. © 2021 IEEE.

关键词： Alternating Direction Method of Multipliers Beamforming Covariance matrix reconstruction Null widening Quadratic constraint

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AUV dead-reckoning navigation based on neural network using a single accelerometer 11

AUV dead-reckoning navigation based on neural network using ...

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11th ACM International Conference on Underwater Networks and systems, WUWNet 2016

作者： Xie, Yan-Xin Liu, Jun Hu, Cheng-Quan Cui, Jun-Hong Xu, Hongli State Key Laboratory of Robotics CAS No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University Changchun China Underwater Sensor Network Lab. University of Connecticut StorrsCT06269 United States Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9781450346375

The accuracy of the Autonomous Underwater Vehicles (AUVs) navigation system determines whether they can safely operate and return. Traditional Dead-reckoning (DR) relies on the inertial sensors such as gyroscope and accelerometer. A major challenge for DR navigation is from measurement error of the inertial sensors (gyroscope, accelerometer, etc.), especially when the AUV is near or at the ocean surface. The AUV's motion is affected by ocean waves, and its pitch angle changes rapidly with the waves. This rapid change and the measurement errors will cause great noise to the direction measured by gyroscopes, and then lead to a large error to the DR navigation. To address this problem, a novel DR method based on neural network (DR-N) is proposed to explore the time-varying relationship between acceleration measurement and orientation measurement, which leverages acoustic localization and neural network estimate timely pitch angle through the explored time-varying relationship. This method enables AUV's DR navigation with a single acceleration, without relying on both acceleration and gyroscope. Most importantly, we can improve the accuracy of AUV navigation through avoiding DR errors caused by gyroscope noise at the sea surface. Simulations show DR-N significantly improves navigation accuracy.

关键词： Accelerometers

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Implementation and test of human-operated and human-like adaptive impedance controls on Baxter robot

Implementation and test of human-operated and human-like ada...

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15th Annual Conference on Advances in Autonomous Robotics systems, TAROS 2014

作者： Liang, Peidong Yang, Chenguang Wang, Ning Li, Zhijun Li, Ruifeng Burdet, Etienne Centre for Robotics and Neural Systems School of Computing and Mathematics Plymouth University United Kingdom MOE Key Lab. of Autonomous System and Network Control College of Automation Science and Engineering South China University of Technology China State Key Laboratory of Robotics and System Harbin Institute of Technology China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Bioengineering Imperial College of Science Technology and Medicine London United Kingdom

ISBN: (纸本)9783319104003

This paper presents an improved method to teleoperate impedance of a robot based on surface electromyography (EMG) and test it experimentally. Based on a linear mapping between EMG amplitude and stiffness, an incremental stiffness extraction method is developed, which uses instantaneous amplitude identified from EMG in a high frequency band, compensating for non-linear residual error in the linear mapping and preventing muscle fatigue from affecting the control. Experiments on one joint of the Baxter robot are carried out to test the approach in a disturbance attenuation task, and to compare it with automatic human-like impedance adaptation. The experimental results demonstrate that the new human operated impedance method is successful at attenuating disturbance, and results similarly to as automatic disturbance attenuation, thus demonstrating its efficiency. © 2014 Springer International Publishing.

关键词： Stiffness

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Support Vector Machine with Nonlinear-Kernel Optimization for Lateralization of Epileptogenic Hippocampus in MR Images

Support Vector Machine with Nonlinear-Kernel Optimization fo...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Mohammad-Parsa Hosseini Mohammad R. Nazem-Zadeh Fariborz Mahmoudi Hao Ying Hamid Soltanian-Zadeh Medical Image Analysis Lab. Radiology Department Henry Ford Health System Department of Electrical and Computer Engineering Wayne State University Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran

ISBN: (纸本)9781424479276

Surgical treatment is suggested for seizure control in medically intractable epilepsy patients. Detailed pre-surgical evaluation and lateralization using Magnetic Resonance Images (MRI) is expected to result in a successful surgical outcome. In this study, an optimized pattern recognition approach is proposed for lateralization of mesial Temporal Lobe Epilepsy (mTLE) patients using asymmetry of imaging indices of hippocampus. T1-weighted and Fluid-Attenuated Inversion Recovery (FLAIR) images of 76 symptomatic mTLE patients are considered. First, hippocampus is segmented using automatic and manual segmentation methods;then, volumetric and intensity features are extracted from the MR images. A nonlinear Support Vector Machine (SVM) with optimized Gaussian Radial Basis Function (GRBF) kernel is used to classify the imaging features. Using leave-one-out cross validation, this method results in a correct lateralization rate of 82%, a probability of detection for the left side of 0.90 (with false alarm probability of 0.04) and a probability of detection for the right side of 0.69 (with zero false alarm probability). The lateralization results are compared to linear SVM, multi-layer perceptron Artificial Neural Network (ANN), and volumetry and FLAIR asymmetry analysis. This lateralization method is suggested for pre-surgical evaluation using MRI before surgical treatment in mTLE patients. It achieves a more correct lateralization rate and fewer false positives.

关键词： Magnetic Resonance Imaging (MRI) Hippocampus Epilepsy Lateralization Support Vector Machine (SVM)

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Multi-objective optimal control algorithm for HVAC based on particle swarm optimization

Multi-objective optimal control algorithm for HVAC based on ...

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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Yanyu Zhang Peng Zeng Chuanzhi Zang School of Computer and Information Engineering Henan University Kaifeng China University of Chinese Academy of Sciences Beijing China Key Lab. of Networked Control System Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781479936519

Residential sector is the biggest potential field of reducing peak demand through demand response (DR) in smart grid. Heating, ventilating, and air conditioning (HVAC) is the largest residential electricity user in house. Therefore, controlling the operation of HVAC is an effective method to implement DR in residential sector. The algorithms proposed in literature are single objective optimization algorithms that only minimize the electricity cost and could not quantify the user's comfort level. To tackle this problem, this paper proposes a comfort level indicator, builds a multi-objective scheduling model, and presents a multi-objective optimal control algorithm for HVAC based on particle swarm optimization (PSO). The algorithm controls the operation of HVAC according to electricity price, outdoor temperature forecast, and user preferences to minimize the electricity cost and maximize the user comfort level simultaneously. The proposed algorithm is verified by simulations, and the results demonstrate that it can decrease the electricity cost significantly and maintain the user comfort level effectively.

关键词： Electricity Vectors Smart grids Pareto optimization Optimal control Temperature control

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function lab.ls are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific lab.ls, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function lab.ls at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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A control-design-based solution to robotic ecology: Autonomy of achieving cooperative behavior from a high-level astronaut command

A control-design-based solution to robotic ecology: Autonomy...

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作者： Wang, Jing Qu, Zhihua Ihlefeld, Curtis M. Hull, Richard A. Department of Electrical and Computer Engineering University of Central Florida Orlando FL 32816 United States Kennedy Space Center FL 32899 United States Lockheed Martin Missiles and Fire Control 5600 Sand Lake Rd. MP-450 Orlando FL 32819 United States School of Electrical and Computer Science University of Central Florida IEEE AIAA University of Central Florida Department of Electrical and Computer Engineering National Aeronautics and Space Administration Kennedy Space CenterKennedy Space Center Applied Physics Lab. Advanced Concepts Business Unit Science Applications International Corporation SAIC IEEE Control System Society

In this paper, we propose a cooperative control strategy for a group of robotic vehicles to achieve the specified task issued from a high-level astronaut command. The problem is mathematically formulated as designing the cooperative control for a general class of multiple-input-multiple-output (MIMO) dynamical systems in canonical form with arbitrary but finite relative degrees such that the outputs of the overall system converge to the explicitly given steady state. The proposed cooperative control for individual vehicle only need to use the sensed and communicated outputs information from its local neighboring vehicles. No fixed leader and time-invariant communication networks are assumed among vehicles. Particularly, a set of less-restrictive conditions on the connectivity of the sensor/communication networks are established, under which it is rigorously proven by using the newly found nice properties of the convergence of sequences of row stochastic matrices that the cooperative objective of the overall system can be achieved. Simulation results for a group of vehicles achieving a target and surrounding a specified object in formation are provided to support the proposed approach in this paper.

关键词： Robotics

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Percentile-based contention window design for random access MIMO interference networks

Percentile-based contention window design for random access ...

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IEEE International Conference on Communications Workshops, ICC

作者： Zhenzhe Sun Chengnian Long Mary Ann Weitnauer Shanghai Jiao Tong University Shanghai CN Dept. of Automation Key Lab. of System Control and Information Processing Ministry of Education School of Electronic and Computer Engineering Georgia Institute of Technology Atlanta Georgia

This paper describes a new method for how interfering MIMO links might transmit more effectively in a random access network. We assume perfect transmitter-side channel state information, zero-forcing pre-coding to guarantee no interference on links that have already won access, and zero-forcing receiver processing at the still-contending links to suppress interference from the links that have already won. We define an effectiveness metric, the Instantaneous Equivalent SNR Percentile (IESP), in which the Equivalent SNR is the SNR of a single-input-single-output (SISO) link that would have the same capacity of a MIMO link after its interference constraints have been met and the IESP is the percentile of the Equivalent SNR, assuming independent Rayleigh fading. We propose that fairness among heterogeneous contending links be realized by giving the shorter contention window to the link with the higher IESP based on its own distribution, enabling links with few antennas to compete with links that have many antennas. Through simulation of the sum capacity of the winning set of links, the proposed contention window design is shown to provide a higher sum capacity than contention based on equal-sized windows.

关键词： Signal to noise ratio Receivers MIMO Interference Vectors Transmitters Frequency modulation

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM Image Processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China

ISBN: (纸本)9781728162157

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-lab.ls and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-lab.ls are updated iteratively to enhance the reliability of lab.ls. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools. © 2020 IEEE.

关键词： Image reconstruction

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An integrative study of home energy management for residential energy consumers

An integrative study of home energy management for residenti...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Dong Zhang Peng Zeng Yanyu Zhang Shuhui Li State Grid Tianjin Electric Power Research Institute Tianjin China Key Lab. of Networked Control System Chinese Academy of Sciences Shenyang China Department of Electrical & Computer Engineering University of Alabama Tuscaloosa AL USA

This paper studies how to optimize the energy usage of home appliances in the demand response framework from the consumer's perspective. The loads of major home appliances are divided into three categories: fixed loads, regulate-able loads, and deferrable loads. For efficient usage of the home appliances, an integrative optimization of the three category loads is needed. The paper investigates the relation of the integrative optimization and individual optimization of each category load. A regression-based learning strategy is employed to learn HAVC energy consumption model for development of more efficient DR policy. The study is conducted through an integrative computational experiment approach that combines a home energy simulator and MATlab.together for demand response development and evaluation. The paper examines how the integrative demand response of the residential home appliances are affected by dynamic pricing tariffs, seasons, and weather. Case studies are conducted by considering home energy consumption, dynamic electricity pricing schemes, and demand response methods.

关键词： Home appliances Energy consumption Load modeling Optimization Load management Water heating Thermostats

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