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An optimal trajectory planning algorithm for autonomous trucks: Architecture, algorithm, and experiment

INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2020年第2期17卷

作者： Zhang, Feng Xia, Ranfei Chen, Xinxing Dongfeng Commercial Vehicle Tech Ctr Dept Adv Commod Dev Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Image Proc & Intelligent Control Key Lab Room 115South Bldg 1 Wuhan 430074 Peoples R China

Safe lane changing of the dynamic industrial park and port scenarios with autonomous trucks involves the problem of planning an effective and smooth trajectory. To solve this problem, we propose a new trajectory planning method based on the Dijkstra algorithm, which combines the Dijkstra algorithm with a cost function model and the Bezier curve. The cost function model is established to filter target trajectories to obtain the optimal target trajectory. The third-order Bezier curve is employed to smooth the optimal target trajectory. Road experiments are carried out with an autonomous truck to illustrate the effectiveness and smoothness of the proposed method. Compared with other conventional methods, the improved method can generate a more effective and smoother trajectory in the truck lane change.

关键词： Autonomous truck trajectory planning Dijkstra algorithm cost functional model Bezier curve

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Theoretical design and circuit implementation of novel digital chaotic systems via hybrid control

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CHAOS SOLITONS & FRACTALS 2020年 138卷

作者： Zheng, Jun Hu, Hanping Ming, Hao Liu, Xiaohui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518057 Peoples R China Minist Educ Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China

Chaos is a paradigm shift of all science, which provides a collection of concepts and methods to analyze a novel behavior that can arise in a wide range of disciplines. However, most of researches in simulations and applications of chaos are performed on finite-state automata, which inevitably causes chaos to collapse. Here we present a hybrid model by controlling digital system with continuous chaotic system to construct chaos on finite-state automata. A new concept and method named Generalized Symbolic Dynamics (GSD) is proposed to target the hybrid system. Based on GSD, a rigorous proof is given that the controlled digital system is chaotic in the sense of Devaney. Moreover, analog-digital hybrid circuit is built for the digital chaotic system. Finally, a simple pseudorandom number generator is designed as a proof of concept. Results show that the proposed generator has good performance for cryptography. Such digital chaotic systems, which are not subject to degradation, could pave the way for widespread applications of chaos. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Digital chaotic system Anti-control Generalized symbolic dynamics (GSD) Integrated circuit Pseudorandom number generator

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A gradient boosting decision tree algorithm combining synthetic minority oversampling technique for lithology identification

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GEOPHYSICS 2020年第4期85卷 WA147-WA158页

作者： Zhou, Kaibo Zhang, Jianyu Ren, Yusong Huang, Zhen Zhao, Luanxiao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Wuhan Polytech Univ Sch Elect & Elect Engn Wuhan 430023 Peoples R China Tongji Univ Sch Ocean & Earth Sci State Key Lab Marine Geol Shanghai 200092 Peoples R China

Lithology identification based on conventional well-logging data is of great importance for geologic features characterization and reservoir quality evaluation in the exploration and production development of petroleum reservoirs. However, there are some limitations in the traditional lithology identification process: (1) It is very time consuming to build a model so that it cannot realize real-time lithology identification during well drilling, (2) it must be modeled by experienced geologists, which consumes a lot of manpower and material resources, and (3) the imbalance of labeled data in well-log data may reduce the classification performance of the model. We have developed a gradient boosting decision tree (GBDT) algorithm combining synthetic minority oversampling technique (SMOTE) to realize fast and automatic lithology identification. First, the raw welllog data are normalized by maximum and minimum normalization algorithm. Then, SMOTE is adopted to balance the number of samples in each class in training process. Next, a lithology identification model is built by GBDT to fit the preprocessed training data set. Finally, the built model is verified with the testing data set. The experimental results indicate that the proposed approach improves the lithology identification performance compared with other machine-learning approaches.

关键词： lithology rock physics reservoir characterization

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Research on the detection method of alignment beacon based on the spatial structure feature of train bogies

Research on the detection method of alignment beacon based o...

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Chinese Automation Congress (CAC)

作者： Chong Cao Shaowei Wan Gang Peng Dingxin He Sheng Zhong Zhang Deng Wentao Guo School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China Beijing Railway Institute of Mechanical & Electrical Engineering Group Co. Ltd. Beijing China Wuhan Lisai Technology Co. Ltd. Wuhan China

The high-speed train inspection robot needs to perform alignment parking and image acquisition at the position of the train bogie, and reliable alignment identification marks are the basis for the inspection robot to achieve highprecision alignment parking. The existing alignment method is mainly based on the way of artificial beacons, in the inspection workshop through manual laying of additional identification marks such as QR codes, magnetic stripes, RFID electronic tags, auxiliary robot alignment parking, but this type of method in beacon maintenance, laying costs and other aspects have certain limitations. Aiming at the problem, this paper proposes a vision-based bogie beacon detection method, which combines the target detection technology with the unique bogie spatial structure feature of the train, and provides reliable identification marks for the alignment and parking of the inspection robot. This method improves the alignment accuracy, reduces the cost of laying and maintaining artificial beacons, and has high application value.

关键词： Shafts Costs Wheels Object detection Inspection Maintenance engineering Feature extraction

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Simplified and yet Turing universal spiking neural P systems with polarizations optimized by anti-spikes

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NEUROCOMPUTING 2020年 414卷 255-266页

作者： Wu, Tingfang Zhang, Taosheng Xu, Fei Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Inst Artificial IntelligenceEduc Minist China Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China

Spiking neural P systems with polarizations (PSN P systems) are a class of neural-inspired computation models, where the firing condition of rules is the neuron-associated polarization. It has previously been shown that PSN P systems are Turing universal by using tree types of polarizations, and 164 neurons are needed for constructing a Turing universal PSN P system as a function computing device. In this work, in order to answer the open problem whether this determination mechanism of polarizations can be simplified without the loss of computation power, one more type of object for information encoding, i.e., the anti-spike, is introduced into PSN P systems, thus, PSN P systems with anti-spikes are proposed, abbreviated as PASN P systems. It is proved that two types of polarizations are enough to guarantee the Turing universality of PASN P systems both as number generators and number acceptors. Furthermore, it is demonstrated that 121 neurons are sufficient for a PASN P system with two types of polarizations to achieve universality as a function computing device. These results manifest that anti-spikes are a powerful ingredient of PASN P systems to yield the improvement in computation performance and the reduction in the description complexity necessary to achieve Turing universality. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Natural computing Membrane computing Neural computation Spiking neural P system Computationally complete

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An Ultra-Sensitive Modular Hybrid EMG-FMG Sensor with Floating Electrodes

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SENSORS 2020年第17期20卷 4775页

作者： Ke, Ang Huang, Jian Chen, Luyao Gao, Zhaolong He, Jiping Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Image Proc & Intelligent Control Key Lab Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Opt & Elect Informat Wuhan 430074 Peoples R China Beijing Inst Technol Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China

To improve the reliability and safety of myoelectric prosthetic control, many researchers tend to use multi-modal signals. The combination of electromyography (EMG) and forcemyography (FMG) has been proved to be a practical choice. However, an integrative and compact design of this hybrid sensor is lacking. This paper presents a novel modular EMG-FMG sensor;the sensing module has a novel design that consists of floating electrodes, which act as the sensing probe of both the EMG and FMG. This design improves the integration of the sensor. The whole system contains one data acquisition unit and eight identical sensor modules. Experiments were conducted to evaluate the performance of the sensor system. The results show that the EMG and FMG signals have good consistency under standard conditions;the FMG signal shows a better and more robust performance than the EMG. The average accuracy is 99.07% while using both the EMG and FMG signals for recognition of six hand gestures under standard conditions. Even with two layers of gauze isolated between the sensor and the skin, the average accuracy reaches 90.9% while using only the EMG signal;if we use both the EMG and FMG signals for classification, the average accuracy is 99.42%.

关键词： EMG FMG hybrid sensor floating electrodes

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A Novel Cluster-Based Method for Single-channel Fetal Electrocardiogram Detection 43

A Novel Cluster-Based Method for Single-channel Fetal Electr...

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43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Hong, Yang Zhu, Hongling Yang, Xiaoyun Cheng, Cheng Yuan, Ye Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol MOE Key Lab Intelligent Control & Image Proc Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Tongji Hosp Tongji Med Coll Div CardiolDept Internal Med Wuhan Peoples R China

ISBN: (纸本)9781728111797

Fetal electrocardiography (FECG) is a promis- ing technology for non-invasive fetal monitoring. However, due to the low amplitude and non-stationary characteristics of the FECG signal, it is difficult to extract it from maternal abdominal signals. Moreover, most FECG extraction methods are based on multiple channels, which make it difficult to achieve fetal monitoring outside the clinic. This paper proposes an efficient cluster-based method for accurate FECG extraction and fetal QRS detection only using one channel signal. We designed min-max-min group as the basis for feature extraction. The extracted features are used to distinguish the different components of the abdominal signal, and finally extract the FECG signal. To verify the effectiveness of our algorithm, we conducted experiments on a public dataset and a dataset record from the Tongji Hospital. Experimental results show that our method can achieve an accuracy rate of more than 96% which is better than other algorithms.

关键词： Hospitals Clustering algorithms Electrocardiography Benchmark testing Feature extraction Hardware Biology

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Global-Local Discriminative Representation Learning Network for Viewpoint-Aware Vehicle Re-Identification in intelligent Transportation

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Chen, Xiaobo Yu, Haoze Zhao, Feng Hu, Yu Li, Zuoyong Shandong Technol & Business Univ Sch Comp Sci & Technol ogy Yantai 264005 Shandong Peoples R China Jiangsu Key Lab Image & Video Understanding Social Nanjing 210094 Peoples R China Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Shandong Peoples R China Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Jiangsu Peoples R China Minjiang Univ Coll Comp & Control Engn Fujian Prov Key Lab Informat Proc & Intelligent Co Fuzhou 350121 Peoples R China

Vehicle re-identification (Re-ID) that aims at matching vehicles across multiple nonoverlapping cameras is prevalently recognized as an important application of computer vision in intelligent transportation. One of the major challenges is to extract discriminative features that are resistant to viewpoint variations. To address this problem, this article proposes a novel vehicle Re-ID model from the perspectives of effective feature fusion and adaptive part attention (APA). First, we put forward a channel attention-based feature fusion (CAFF) module that can learn the significance of features from different layers of the backbone network. In such a way, our model can leverage complementary features for vehicle Re-ID. Then, to address the viewpoint variation problem, we present an APA module that evaluates the significance of local vehicle parts based on the visible areas and the extracted features. By doing so, our model can concentrate more on the vehicle parts with rich discriminative information while paying less attention to the parts with limited distinctive capability. Finally, the whole model is trained by simultaneous classification and metric learning. Experiments on two large-scale vehicle Re-ID datasets are carried out to evaluate the proposed model. The results show that our model achieves competing performance compared with other state-of-the-art approaches.

关键词： ~Attention mechanism feature fusion global and local features vehicle re-identification (Re-ID)

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Bayesian Learning-Based Harmonic State Estimation in Distribution Systems With Smart Meter and DPMU Data

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IEEE TRANSACTIONS ON SMART GRID 2020年第1期11卷 832-845页

作者： Zhou, Wei Ardakanian, Omid Zhang, Hai-Tao Yuan, Ye Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada

This paper studies the problem of locating harmonic sources and estimating the distribution of harmonic voltages in unbalanced three-phase power distribution systems. We develop an approach for harmonic state estimation utilizing two types of measurements from smart meters and distribution-level phasor measurement units (DPMUs). It involves regression analysis for power flow calculation, prediction of demands using recurrent neural networks, and sparse Bayesian learning for state estimation. The proposed approach requires fewer DPMUs than nodes, making it more applicable to existing distribution grids. We show the effectiveness of the proposed estimator through extensive numerical simulations on an IEEE test feeder. We also investigate how the increased penetration of distributed energy resources could affect the performance of our state estimator.

关键词： Harmonic analysis Load modeling Smart meters Admittance Integrated circuit modeling Phasor measurement units Current measurement Power system harmonics harmonic state estimation load forecasting supervised learning power distribution

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Charging stations expansion planning under government policy driven based on Bayesian regularization backpropagation learning

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NEUROCOMPUTING 2020年 416卷 47-58页

作者： Hu, Dandan Zhang, Jinsong Liu, Zhi-Wei South Cent Univ Nationalities Sch Management Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Coll Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

With the strong promotion of Chinese government, the number of electric vehicles is growing rapidly. Meanwhile, the development of corresponding charging infrastructure has to keep up. However, infrastructure construction is not a one-off investment, but a multi-period plan. A multi-period location and capacity expansion model of charging stations is built. A forecasting method is proposed to develop the model for estimating the parameters of electric vehicle quantities at each period. The forecasting tool is feed forward neural network based on Bayesian regularization backpropagation learning. The model also introduce the congestion rate to ensure the service quality. Then two kinds of greedy algorithms are proposed. A large number of experiments show that the two algorithms have some complementarity. Finally, the performance of our method is illustrated by a case study of Cixi city. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Electric vehicle Charging station Bayesian regularization back propagation learning Prediction Heuristic Congestion

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