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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Xinxing Chen Jian Huang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

In this paper, we propose a bio-inspired Fuzzy Lévy Taxis algorithm to solve the robotic odor source localization problem in dynamic odor plumes. According to the proposed algorithm, the robot is programmed to move for a length with a turning angle at each step until reaching the odor source. The movement length and the turning angle follow two specific probability distribution, of which the parameters are adaptive through a fuzzy logic system. The proposed algorithm was compared with the Adaptive Lévy Taxis algorithm in simulated pseudo-Gaussian plumes. Our proposed algorithm shows a higher success rate and efficiency. The algorithm has also been systematically evaluated in simulated filament-based odor plumes under various environmental conditions. The results revealed that the performance of the proposed algorithm is consistently good in various environmental conditions in terms of success rate, number of steps and distance overhead to find the odor source.

关键词： Public transportation Heuristic algorithms Turning Robot sensing systems Adaptation models Fuzzy systems

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keypoint-Based Feature Matching For Partial Person Re-Identification

Keypoint-Based Feature Matching For Partial Person Re-Identi...

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IEEE International Conference on Image processing

作者： Chuchu Han Changxin Gao Nong Sang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

As a derivative of person re-identification (re-ID), partial re- ID aims to retrieve a partial pedestrian across holistic person images captured by non-overlapping cameras. This task is more challenging and closer to real-world applications. Since we cannot locate the part of the partial image, the misaligned region compromises the performance greatly when directly (a) compare a partial pedestrian with a holistic one. To alleviate this issue, we propose a keypoint-Based Feature Matching (KBFM) network, which constructs a simple and effective framework for partial re-ID. Specifically, our architecture explicitly leverages the keypoints generated by pose estimation. Based on the visible keypoints, coordinates of the corresponding visible region can be computed. And the keypoint-based feature embeddings can be generated by bilinear sampling. When matching two images, we extract their features on the basis of shared visible keypoints, avoiding the misalignment and disturbance. Moreover, considering the triplet loss cannot be flexibly built in the partial re-ID pipeline, we improve the original sampling method and achieve significant performance. Extensive experimental results on two widely used benchmarks demonstrate significant performance improvements of our method over most state-of-the-art methods.

关键词： Feature extraction Cameras Task analysis Pose estimation Training Pipelines Generative adversarial networks

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A New control System Based on An Improved PID Neural Network controller for Landslide System control

A New Control System Based on An Improved PID Neural Network...

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Chinese control Conference (CCC)

作者： Ying Zhang Shu Sun Cheng Lian Xiaoping Wang School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In this paper, a new control system based on the improved PID neural network controller (PIDNNC) is designed to control the landslide displacement. In the control system, the fuzzy comprehensive judgement model of landslide system is established to judge whether the system needs to be controlled and give the expected displacement. To solve the problem of random initialization in PIDNNC, This paper proposed an improved PIDNNC based on fuzzy PID controller (F-PIDNNC). The fuzzy PID controller is used to obtain the proportional, integral and differential coefficients k p , k i and k d . Then the obtained parameters are taken as the initial weights of PIDNN. The effectiveness of the proposed control system is simulated on Baishuihe and Shiliushubao landslides. The results show that the proposed control system based on F-PIDNNC is much accurate and faster than the traditional one-dimensional fuzzy PID controller and PIDNNC.

关键词： Terrain factors control systems Neural networks Neurons Predictive models Stability criteria

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Battery Parameter Identification Using Recursive Least Squares with Variable Directional Forgetting

Battery Parameter Identification Using Recursive Least Squar...

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International Conference on control and Automation (ICCA)

作者： Kun Zhu Yiming Wan Chenou Li Xin Luo Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

An accurate battery model is important to perform various tasks of battery management. Battery model parameters change with working conditions, thus requiring continuous update for parameter estimation. The recursive least-squares algorithm with a forgetting strategy is widely used to estimate time-varying parameters online. However, the online data vary with battery operations, so the data may fail to contain sufficient excitation. When the online data is not persistently exciting, most existing forgetting strategies in battery literature suffer from covariance blowup, which means the algorithm cannot identify parameters reliably. In order to cope with non-persistent excitation, a recursive least-squares algorithm with variable direction forgetting is presented in this paper for battery parameter estimation. Instead of discounting all old information, it performs forgetting only in the direction of sufficient excitation. The forgetting factor can adaptively adjust based on excitation, which enhances trade-off between stability and tracking capability of the algorithm. A battery simulation model with time-varying parameters is used to illustrate the effectiveness of our proposed algorithm.

关键词： Batteries State of charge Electronic countermeasures Computational modeling Parameter estimation Load modeling Integrated circuit modeling

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A novel TSK fuzzy system incorporating multiview collaborative transfer learning for personalized epileptic EEG detection

arXiv

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arXiv 2021年

作者： Li, Andong Deng, Zhaohong Lou, Qiongdan Choi, Kup-Sze Shen, Hongbin Wang, Shitong School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Digital Design and Software Technology Wuxi214122 China Centre for Smart Health Hong Kong Polytechnic University Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

—In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can greatly improve the accuracy of epilepsy detection while reducing the workload of physicians. However, there are many challenges in practical applications for personalized epileptic EEG detection (i.e., training of detection model for a specific person), including the difficulty in extracting effective features from one single view, the undesirable but common scenario of lacking sufficient training data in practice, and the no guarantee of identically distributed training and test data. To solve these problems, we propose a TSK fuzzy system-based epilepsy detection algorithm that integrates multi-view collaborative transfer learning. To address the challenge due to the limitation of single-view features, multi-view learning ensures the diversity of features by extracting them from different views. The lack of training data for building a personalized detection model is tackled by leveraging the knowledge from the source domain (reference scene) to enhance the performance of the target domain (current scene of interest), where mismatch of data distributions between the two domains is resolved with adaption technique based on maximum mean discrepancy. Notably, the transfer learning and multi-view feature extraction are performed at the same time. Furthermore, the fuzzy rules of the TSK fuzzy system equip the model with strong fuzzy logic inference capability. Hence, the proposed method has the potential to detect epileptic EEG signals effectively, which is demonstrated with the positive results from a large number of experiments on the CHB-MIT dataset. © 2021, CC BY-NC-ND.

关键词： Electrophysiology

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Unsupervised Time Series Anomaly Detection under Data Contamination

Unsupervised Time Series Anomaly Detection under Data Contam...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Xiaohui Lin Zuoyong Li Xunhua Huang Xinwei Chen Haoyi Fan College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China School of Computer Science and Technology Harbin University of Science and Technology Harbin China Fujian Engineering and Research Center of New Chinese Lacquer Materials Minjiang University Fuzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Unsupervised learning utilizes unlabeled data to alleviate the reliance on large amounts of labeled data, and it has made great progress in time series anomaly detection. However, there are still some thorny issues unsolved, especially the incapability to maintain the robustness of the model when the time series dataset is contaminated. In this work, we propose an unsupervised time series anomaly detection method on data contamination, named distribution alignment autoencoder (DAAE), in which a temporal sliding reward function combines the adversarial distribution alignment to mine the contaminated data. Specifically, the dimensionality reduction and reconstruction of the time series are implemented by an autoencoder. Next, using two discriminators, the latent features obtained by dimensionality reduction are mapped to the Gaussian distribution, and the high-confidence normal time series are generated by sampling noise from the Gaussian distribution. Then, we compute the mean square error between high-confidence normal samples and the training data. Finally, we feed the error values to the temporal sliding reward function to mine the contaminated training samples. Experimental results compared with baselines on public datasets demonstrate the effectiveness of DAAE.

关键词： Training Dimensionality reduction Time series analysis Training data Mean square error methods Gaussian distribution Robustness

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Global Ultimate Mittag-Leffler Lag Quasi-Synchronization of Delayed Fractional-Order Memristive Neural Networks with Switching Jumps Mismatch via Pinning control

Global Ultimate Mittag-Leffler Lag Quasi-Synchronization of ...

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第三十九届中国控制会议

作者： Jia Jia Zhigang Zeng School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology School of Control Science and Engineering Shandong University

In this paper, a novel pinning controller, which is applied to the selected partial neurons with partial errors needed, is designed for global ultimate Mittag-Leffler lag quasi-synchronization(GUMLQS) of delayed fractional-order memristive neural networks(FMNNs) with switching jumps mismatch. Since the right-hand sides of the equations of FMNNs are discontinuous,FMNNs have no solution in the ordinary sense, and Filippov solutions are adopted for FMNNs. With the assistance of fractional Halanay inequality, a Lyapunov function in quadratic form and the theory of fractional derivative, an LMI-based GUMLQS criterion is derived. For a prescribed GUMLQS error bound, the control gain matrices can be calculated out via a group of ***, the sufficient condition for the feasibility of the LMIs is obtained, which is an important basis for the selection of controlled neurons. Finally, a numerical example shows that the designed pinning controller can achieve GUMLQS between master-slave FMNNs with the prescribed ultimate error bound.

关键词： Memristive Neural Networks(MNNs) Fractional-Order Pinning control Global Ultimate Mittag-Leffler Lag Quasi-Synchronization(GUMLQS)

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Nonlinear Correction Approach for FMCW Radar based on Spectrum Recovery

Nonlinear Correction Approach for FMCW Radar based on Spectr...

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IEEE International Conference on Signal and Image processing (ICSIP)

作者： Yanqi Liu Lili Gao Tianyue Shi Xinhua Mao Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China R&D Center for Artificial Intelligence and Detection Technology Beijing institute of control and electronic technology Beijing China

ISBN: (纸本)9781665446006

There are two basic approaches for LFM (linear frequency modulation) signal pulse compression – matched filtering and Dechrip. For high-resolution imaging of small scenes, Dechirp is much more popular because it requires a lower sampling rate. However, the transmission signal of demodulation needs to be an ideal chirped signal for Dechirp. When linear modulation cannot be guaranteed accurately, the compression result will be spatially defocused, which causes the compensation complicated. This paper first derives the equivalence of matched filtering and Dechirp processing. Then, for the Dechirp received signal, this paper proposes a pulse compression method for nonlinear FM signals based on matched filtering without upsampling. Finally, the feasibility of the method was verified through experiments.

关键词： Pulse compression methods Image coding Frequency modulation Filtering imaging High-resolution imaging Radar

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Real Aperture and Synthetic Aperture Joint imaging Approach for Vehicle-mounted Forward Look Radar

Real Aperture and Synthetic Aperture Joint Imaging Approach ...

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IEEE International Conference on Signal and Image processing (ICSIP)

作者： Huixin Liu Lili Gao Tianyue Shi Xinhua Mao Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics And Astronautics Nanjing China R&D Center for Artificial Intelligence and Detection Technology Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (纸本)9781665446006

The vehicle-mounted millimeter-wave forward-looking imaging is a research hotspot in the radar field recently. If imaging is done only through the real aperture, the azimuth resolution of the radar will be still greatly restricted for the limitation of the aperture size. Considering the movement of the car itself, the synthetic aperture principle can be used to improve the azimuth resolution. However, synthetic aperture radar cannot completely solve the problem due to the blind area and left-right blurring of forward-looking area imaging (a small angular range directly in front). To this end, a new real aperture synthetic aperture joint imaging method is proposed, in which the real aperture can solve the problem of radar left and right blur, and can image at the forward-looking area. For the squint area, the length of the synthetic aperture is often longer than the real aperture, so higher resolution can be obtained by using synthetic aperture processing. Finally, the simulation results demonstrate the correctness of the back projection algorithm used in this vehicle-mounted forward look radar.

关键词： Image resolution Azimuth Simulation Conferences imaging Apertures Radar imaging

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Visual SLAM Based on Dynamic Object Detection

Visual SLAM Based on Dynamic Object Detection

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第33届中国控制与决策会议

作者： Bocheng Chen Gang Peng Dingxin He Cheng Zhou Bin Hu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Shantui Construction Machinery Co. Ltd.

On the one hand,traditional visual SLAM does not consider dynamic objects in the scene,on the other hand,deep learning technology has been widely used in computer *** paper combines the two organically,and proposes an algorithm that uses dynamic object detection to improve the robustness of visual SLAM in a dynamic ***,we use the object detection network integrated into the attention mechanism to detect the dynamic target in the key ***,we follow the optical flow detection to further determine the dynamic feature points in the scene and eliminate ***,we use the static feature points for camera tracking to achieve highly robust monocular visual *** method described in this paper can not only eliminate dynamic feature points,but also retain as many static feature points as *** method described in this paper is compared with the original ORB-SLAM2 algorithm and DS-SLAM algorithm,and tested with public data *** results show that the method described in this paper can effectively eliminate the influence of dynamic objects on the visual SLAM algorithm.

关键词： Visual SLAM dynamic object detection deep learning optical flow detection attention mechanism

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