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arXiv 2020年

作者： Xue, Fei Wang, Xin Wang, Junqiu Zha, Hongbin School of Electrical Engineering and Computer Sciences Peking University Beijing100871 China Beijing Changcheng Aeronautical Measurement and Control Technology Research Institute Beijing10081 China

We propose a novel deep visual odometry (VO) method that considers global information by selecting memory and refining poses. Existing learning-based methods take the VO task as a pure tracking problem via recovering camera poses from image snippets, leading to severe error accumulation. Global information is crucial for alleviating accumulated errors. However, it is challenging to effectively preserve such information for end-to-end systems. To deal with this challenge, we design an adaptive memory module, which progressively and adaptively saves the information from local to global in a neural analogue of memory, enabling our system to process long-term dependency. Benefiting from global information in the memory, previous results are further refined by an additional refining module. With the guidance of previous outputs, we adopt a spatial-temporal attention to select features for each view based on the co-visibility in feature domain. Specifically, our architecture consisting of Tracking, Remembering and Refining modules works beyond tracking. Experiments on the KITTI and TUM-RGBD datasets demonstrate that our approach outperforms state-of-the-art methods by large margins and produces competitive results against classic approaches in regular scenes. Moreover, our model achieves outstanding performance in challenging scenarios such as texture-less regions and abrupt motions, where classic algorithms tend to fail. Copyright © 2020, The Authors. All rights reserved.

关键词： computer vision

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Nonlinear Curvature Modeling for MPC of Autonomous Vehicles*

Nonlinear Curvature Modeling for MPC of Autonomous Vehicles*

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International Conference on Intelligent Transportation

作者： Gonçalo Collares Pereira Pedro F. Lima Bo Wahlberg Henrik Pettersson Jonas Mårtensson Research and Development Scania CV AB Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

This paper investigates how to compensate for curvature response mismatch in lateral Model Predictive control (MPC) of an autonomous vehicle. The standard kinematic bicycle model does not describe accurately the vehicle yaw-rate dynamics, leading to inaccurate motion prediction when used in MPC. Therefore, the standard model is extended with a nonlinear function that maps the curvature response of the vehicle to a given request. Experimental data shows that a two Gaussian functions approximation gives an accurate description of this mapping. Both simulation and experimental results show that the corresponding modified model significantly improves the control performance when using Reference Aware MPC for autonomous driving of a Scania heavy-duty construction truck.

关键词： Kinematics Bicycles Autonomous vehicles Computational modeling Vehicle dynamics Predictive models Standards

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Faithful geometric measures for genuine tripartite entanglement

arXiv

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arXiv 2023年

作者： Ge, Xiaozhen Liu, Lijun Wang, Yong Xiang, Yu Zhang, Guofeng Li, Li Cheng, Shuming The Department of Control Science and Engineering Tongji University Shanghai201804 China Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong Department of Mathematics and Computer Science Shanxi Normal University Taiyuan030006 China State Key Laboratory for Mesoscopic Physics School of Physics Frontiers Science Center for Nano-optoelectronics Collaborative Innovation Center of Quantum Matter Peking University Beijing100871 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai201804 China Institute for Advanced Study Tongji University Shanghai200092 China

We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems. We first show that the triangle relation (Formula Presented) holds for all subadditive bipartite entanglement measure E, all permutations under parties i, j, k, all α [0, 1], and all pure tripartite states. It provides a geometric interpretation that bipartition entanglement, measured by α, corresponds to the side of a triangle, of which the area with α (0, 1) is nonzero if and only if the underlying state is genuinely entangled. Then, we rigorously prove the non-obtuse triangle area with 0 1, and the triangle area is not a measure for any α > 1/2. Hence, our results are expected to aid significant progress in studying both discrete and continuous multipartite entanglement. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum entanglement

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Research on tomography algorithm of furnace flame temperature field based on acoustic method in thermal power station

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Journal of Physics: Conference Series 2021年第1期2121卷

作者： Zhe Kan Shuqiang Ding Xiaolei Wang School of Information and Control Engineering Liaoning Petrochemical University Fushun China School of Computer and Communication Engineering Liaoning Petrochemical University Fushun China

An improved Tikhonov regularization algorithm MCTR based on Markov radial basis function is proposed to obtain the feedback of closed-loop control for pulverized coal injection process in thermal power plant, so as to solve the ill posed problem in temperature field reconstruction in furnace. The algorithm constructs a new regularization matrix to replace the standard Tikhonov regularization element matrix, so as to achieve the correction effect of the new algorithm. MCTR algorithm determines the boundary singular value of small singular value defined in this paper by determining the proportion between the standard deviation component corresponding to small singular value and the sum of standard deviation components corresponding to all singular values after singular value decomposition of coefficient matrix. After determining the boundary, a new regularization matrix is constructed according to the eigenvector corresponding to small singular value. Compared with the MTR algorithm using the identity matrix as the regularization matrix, MCTR algorithm only selectively modifies the parameters corresponding to small singular values after determining the regularization parameters, which improves the stability of the parameter solution and the reconstruction accuracy of the temperature field, and is helpful to the automatic control of thermal power plants and the efficiency of coal combustion.

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Hybrid Outliers Identification of High-dimensional Wind Power Operation Data

Hybrid Outliers Identification of High-dimensional Wind Powe...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Wenchang Hou Lei Li Yang Hu School of Control and Computer Engineering North China Electric Power University Beijing China Inner Mongolia Power(Group) CO. LTD Inner Mongolia China

ISBN: (数字)9781728157481

ISBN: (纸本)9781728157498

Aiming at a large number of abnormal data in wind turbine operation data, considering the wind turbine operation characteristics and data clustering, this paper proposes a method for cleaning abnormal power data of wind turbines based on high-dimensional space clustering. First, based on the control strategy differences and operating mechanisms of wind turbines, the mechanism cleaning of abnormal wind power data is performed; Second, consider the wind speed, rotor speed, and power, and use Gaussian mixture model (GMM) to cluster the data in three-dimensional space to achieve the preliminary cleaning of abnormal power data of wind turbines; Finally, on the basis of the preliminary cleaned data, an optimized multi-directional quartile method is used in the Copula space for refined cleaning. The actual data of a certain type of wind turbine in North China is selected for example analysis. The simulation results show that the method proposed in this paper is reasonable and effective.

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Decoupled self attention for accurate one stage object detection

arXiv

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arXiv 2020年

作者： Wu, Kehe Chen, Zuge Ma, Qi Zhang, Xiaoliang Li, Wei School of Control and Computer Engineering North China Electric Power University Beijing102206 China Information Technology Co. Ltd. Hangzhou311121 China

As the scale of object detection dataset is smaller than that of image recognition dataset ImageNet, transfer learning has become a basic training method for deep learning object detection models, which will pretrain the backbone network of object detection model on ImageNet dataset to extract features for classification and localization subtasks. However, the classification task focuses on the salient region features of object, while the location task focuses on the edge features of object, so there is certain deviation between the features extracted by pretrained backbone network and the features used for localization task. In order to solve this problem, a decoupled self attention(DSA) module is proposed for one stage object detection models in this paper. DSA includes two decoupled self-attention branches, so it can extract appropriate features for different tasks. It is located between FPN and head networks of subtasks, so it is used to extract global features based on FPN fused features for different tasks independently. Although the network of DSA module is simple, but it can effectively improve the performance of object detection, also it can be easily embedded in many detection models. Our experiments are based on the representative one-stage detection model RetinaNet. In COCO dataset, when ResNet50 and ResNet101 are used as backbone networks, the detection performances can be increased by 0.4% AP and 0.5% AP respectively. When DSA module and object confidence task are applied in RetinaNet together, the detection performances based on ResNet50 and ResNet101 can be increased by 1.0% AP and 1.4% AP respectively. The experiment results show the effectiveness of DSA module. Code is at: https://***/chenzuge1/***. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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Dynamic modeling and system identification of user engagement in mHealth interventions using a Bayesian approach for missing data imputation

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control engineering Practice 2025年 164卷

作者： Mohamed El Mistiri Steven De La Torre Benjamin M. Marlin Misha Pavel Predrag Klasnja Donna Spruijt-Metz Daniel E. Rivera Control Systems Engineering Laboratory in the Chemical Engineering Department School for Engineering of Matter Transport at Arizona State University Tempe 85282 AZ USA Herbert Wertheim School of Public Health & Human Longevity Science University of California San Diego La Jolla 92093 CA USA Manning College of Information and Computer Sciences University of Massachusetts Amherst Amherst 01003 MA USA Khoury College of Computer Sciences Northeastern University Boston 02115 MA USA Division of Biomedical and Health Informatics School of Information University of Michigan Ann Arbor 48109 MI USA Dornsife Center for Economic and Social Research University of Southern California Los Angeles 90089 CA USA

Digital behavior change interventions (DBCIs) have been found to positively impact health behaviors and are becoming increasingly important as an emerging topic for control systems applications. However, their effectiveness is heavily dependent upon user engagement with both the digital tool (e.g., mHealth app, wearable activity tracker) and the behavior change intervention (e.g., exercise activity planning). In this paper, engagement refers to the unique interactions of a participant with either of these components resulting in digital traces (e.g., app page views). Furthermore, engagement in DBCIs will change over the course of the intervention in response to an individual’s environment, context, and psychological state. Intensive data collection enables modeling engagement in DBCIs as a dynamical system using fluid analogies, and applying prediction-error methods from system identification to estimate models. Missingness represents both a fundamental and practical concern in this application domain. This work addresses missingness using a novel Bayesian imputation method applied to data from the HeartSteps II physical activity intervention study. The benefits of this approach include the ability to impute missing data points more accurately than traditional methods and quantify uncertainty resulting from imputation and data scarcity; the latter is essential to the implementation of robust closed-loop interventions. The methods presented in this work provide insights into critical factors that impact engagement behavior over time and in context, ultimately benefiting the development of digital behavior change interventions relying on control engineering approaches.

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Performance evaluation of CFB unit combustion system based on comprehensive economics and entropy weight method

Performance evaluation of CFB unit combustion system based o...

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2019 International Conference on Sensing, Diagnostics, Prognostics, and control, SDPC 2019

作者： Qin, Ce Gao, Mingming Zhang, Hongfu North China Electric Power University State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China

ISBN: (纸本)9781728101996

performance evaluation method for the combustion system in the variable load process of circulating fluidized bed (CFB) unit based on the minimum variance control is proposed. Each operating parameter are initially weighted according to the dynamic economic index of the operating parameters, and entropy weight method is used to adjust the weights of operating parameter under different operating conditions. Then, the minimum variance index based on comprehensive economic and entropy weight method is proposed to evaluate the performance of the combustion system of CFB unit, which reduces the polarization effect generated by the single weighting performance evaluation method and improves the objective scientificality of the operating parameter weighting. Finally, a case study on the combustion system of a 300MW CFB unit reveals that the minimum variance index based on the comprehensive economy and entropy weight method can better evaluate the performance of the combustion system in the dynamic process of the unit, compared with the traditional performance evaluation method. It is of great significance to the operational safety and economy of CFB boiler units. © 2019 IEEE.

关键词： Economics

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On Solar Photovoltaic Parameter Estimation: Global Optimality Analysis and a Simple Efficient Differential Evolution Method

arXiv

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arXiv 2020年

作者： Gao, Shuhua Zhao, Yunyi Xiang, Cheng Yu, Ming Tan, Kuan Tak Lee, Tong Heng School of Control Science and Engineering Shandong University Shandong Jinan China Electrical & Computer Engineering National University of Singapore Singapore Power Automation Pte Ltd. Singapore Engineering Cluster Singapore Institute of Technology Singapore

A large variety of sophisticated metaheuristic methods have been proposed for photovoltaic parameter extraction. Our aim is not to develop another metaheuristic method but to investigate two practically important yet rarely studied issues: (i) whether existing results are already globally optimal;(ii) whether a significantly simpler metaheuristic can achieve equally good performance. We take the two widely used I-V curve datasets for case studies. The first issue is addressed using a branch and bound algorithm, which certifies the global minimum rigorously or locates a fairly tight upper bound, despite its intolerable slowness. These values are useful references for fair evaluation and further development of metaheuristics. Next, extensive examination and comparison reveal that, perhaps surprisingly, an elementary differential evolution (DE) algorithm can either attain the global minimum certified above or obtain the best-known result. More attractively, the simple DE algorithm takes only a fraction of the runtime of state-of-the-art metaheuristic methods and is particularly preferable in time-sensitive applications. This novel, unusual, and notable finding also indicates that the employment of increasingly complicated metaheuristics might be somewhat overkilling for regular PV parameter estimation. Finally, we discuss the implications of these results for future research and suggest the simple DE method as the first choice for industrial applications. © 2020, CC BY.

关键词： Global optimization

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Relation between Functional Brain Networks and Fluid Intelligence Using Information Theory

Relation between Functional Brain Networks and Fluid Intelli...

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Iranian Conference on Electrical engineering (ICEE)

作者： Mahnaz Ashrafi Nayereh Ghazi Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Henry Ford Health System Detroit Michigan USA

ISBN: (数字)9781728172965

ISBN: (纸本)9781728172972

Human intelligence is a mental capability that can describe the level of individual behavioral and cognitive abilities. Recent works suggest that the resting state graph metrics and similarity measures that capture the nonlinear interactions between regions are better than other conventional approaches to link neuroimaging data to intelligence metrics. In this study, for the first time, we investigated the capacity of mutual information (MI) in predicting the intelligence. To this end, the network edges are defined based on MI and the relationship between graph metrics extracted from resting state brain networks and intelligence is explored. Results show that graphs constructed based on MI have stronger relationship with intelligence in comparison to those constructed based on Pearson correlation.

关键词： Correlation Measurement Mutual information Integrated circuits Functional magnetic resonance imaging Time series analysis Fluids

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