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第三十八届中国控制会议

作者： Xiang Ai Yang-Yang Chen School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

This paper studies three-dimensional(3 D) formation flight problem of networked UAVs in non-holonomic kinematics under the bidirectional communication links with time delays, which makes an identity distinct from the literatures without communication delay. A novel Lyapunov function is built base on neighbors' delay information, which results in the algorithms for the surge velocity, pitch velocity and yaw velocity associated with neighbors' delay information. The system stability is proven by the Barbalat lemma. The numerical simulation result has been presented to support the theoretical results.

关键词： Sphere landing orbit tracking lateral formation forming communication delay

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3D Facial Landmarking with Denoising Auto-Encoder Networks

3D Facial Landmarking with Denoising Auto-Encoder Networks

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2019第十一届数字图像处理国际会议

作者： Liang Wang Shaoyan Gai Shuai Guo School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Cardiff University

3D Facial landmarking plays an important role on 3D face recognition and face expression ***,the most of methods underperform when faces have occluded region such as hair,glasses or *** solve this problem,a coarseto-fine method is proposed,containing several denoising auto-encoder networks(denoted as DANs).DANs not only can recover the lost information but improve the accuracy of *** based on Bosphorus dataset show a 100% of good landmarking under 6 mm precision of mean error,which demonstrates that our algorithm achieves the state-of-theart performance.

关键词： 3D facial landmarking occlusion denoising auto-encoder network shape-index map coarse-to-fine

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Moving Vehicle Detection with Shadow Elimination Based on Improved ViBe Algorithm 4

Moving Vehicle Detection with Shadow Elimination Based on Im...

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2019 4th Annual International Conference on Information System and Artificial Intelligence, ISAI 2019

作者： Xia, Xue Lu, Xiaobo Cao, Yichao Xia, Shudong Fu, Changqing School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Beijing PKU Trans Info Technology Co. Ltd Beijing100193 China

Moving vehicle detection based on video processing has been widely used in intelligent transportation system recently. However there are also many problems, such as dynamic background, ghost region, and shadow of moving objects. This paper proposes an improved ViBe object detection algorithm. First, an accurate background image is obtained by using the multi-frame averaging method, and then the background model is initialized by this accurate background image, thus effectively reducing the generation of ghost region. Whenever there is no moving object for a fixed number of consecutive frames in the video, this frame is updated to the background image. Conservative update strategy and foreground point counting method are adopted to update the background and reduce the impact of dynamic background on the foreground detection. Next, the foreground image detected by improved ViBe algorithm is input into the shadow elimination method proposed in this paper. Shadows in foreground pixels are detected in RGB color space, and then the pixels determined as shadows are eliminated. Finally, accurate moving vehicles are obtained. Our algorithm can effectively eliminate the shadows of moving vehicles, quickly adapt to background changes and illumination mutation, and get accurate moving objects, which is helpful for vehicle contour extraction and subsequent image processing. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Pixels

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Cloud Classification of Ground-based Cloud Images based on Convolutional Neural Network

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Journal of Physics: Conference Series 2021年第1期2035卷

作者： Tingting Zhu Liang Wei Yiren Guo College of Mechanical and Electronic Engineering Nanjing Forestry University Nanjing 210037 China Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University) Ministry of Education Nanjing 210096 China

Cloud is a crucial meteorological factor, and also a key factor affecting solar radiation variables for solar energy, especially the attenuation of different cloud types on solar radiation. However, there are few researches on the cloud classification based on the ground-based cloud image in order to predict solar energy. In this paper, the ground-based cloud images were collected, and then they were classified into five classes based on colour, texture and the attenuation on the theoretical clear-sky solar radiation. The ResNet18 model was trained to classify the ground-based cloud images automatically. The experimental results show that the classification accuracy of the ResNet18 is up to 91%, which is higher than the other two convolutional neural networks (AlexNet and VGG16) and is also much higher than the traditional machine learning classifiers. This work provides a new perspective and important supporting data for solar radiation forecast.

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Artificial Intelligence without Restriction Surpassing Human Intelligence with Probability One: Theoretical Insight into Secrets of the Brain with AI Twins of the Brain

arXiv

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arXiv 2024年

作者： Huang, Guang-Bin Westover, M. Brandon Tan, Eng-King Wang, Haibo Cui, Dongshun Ma, Wei-Ying Wang, Tiantong He, Qi Wei, Haikun Wang, Ning Tian, Qiyuan Lam, Kwok-Yan Yao, Xin Wong, Tien Yin School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Department of Neurology National Neuroscience Institute Singapore Research Centre of Big Data and Artificial Intelligence for Medicine First Affiliated Hospital of Sun Yat-Sen University Guangzhou China Duke-NUS Medical School National University of Singapore Singapore Mind PointEye Singapore Institute for AI Industry Research Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore School of Biomedical Engineering Tsinghua University Beijing China Singapore AI Safety Institute Nanyang Technological University Singapore School of Data Science Lingnan University Hong Kong School of Computer Science University of Birmingham United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Tsinghua Medicine Tsinghua University Beijing China

Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to surpass human intelligence in the future? This paper shows that in theory new AI twins with fresh cellular level of AI techniques for neuroscience could approximate the brain and its functioning systems (e.g. perception and cognition functions) with any expected small error and AI without restrictions could surpass human intelligence with probability one in the end. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of AI, especially in the realm of artificial neural networks. This paper also gives the answer to the two widely discussed fundamental questions: 1) whether AI could have potentials of discovering new principles in nature;2) whether error backpropagation (BP) algorithm commonly and efficiently used in tuning parameters in AI applications is also adopted in the brain. Intelligence is just one of fortuitous but sophisticated creations of the nature which has not been fully discovered. Like mathematics and physics, with no restrictions artificial intelligence would lead to a new subject with its self-contained systems and principles. We anticipate that this paper opens new doors for 1) AI twins and other AI techniques to be used in cellular level of efficient neuroscience dynamic analysis, functioning analysis of the brain and brain illness solutions;2) new worldwide collaborative scheme for interdisciplinary teams concurrently working on and modelling different types of neurons and synapses and different level of functioning subsystems of the brain with AI techniques;3) development of low energy of AI techniques with the aid of fundament

关键词： Neurology

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Optimizing PV-battery energy storage for netzero emission traction power system through artificial rabbit optimization method

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Journal of Energy Storage 2025年 126卷

作者： Li, Zhijian Hu, Yihua Ollerenshaw, Richard Li, Xinian He, Zengxiang Esfahani, Mohammad Nasr School of Physics Engineering and Technology University of York YorkYO10 5DD United Kingdom Department of Engineering King's College London LondonWC2R 2LS United Kingdom Siemens Rail Electrification Siemens Mobility York United Kingdom School of Information and Electronics Engineering Shandong Technology and Business University Yantai264005 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China School of Automation Southeast University Nanjing210096 China

The transition from traditional fossil fuels to renewable energy is becoming increasingly feasible and is essential for addressing energy and environmental challenges. In transportation electrification, the variability of power demand and the intermittent nature of renewable energy generation present challenges to achieving high renewable energy utilization. Battery energy storage systems (BESS) integrated to renewable resources offer a viable solution to these intermittency issues, though their costs require careful optimization. This paper explores the cost implications of using on-site renewable energy to power a railway traction system. It proposes a photovoltaic (PV) energy storage strategy optimized with the Artificial Rabbit Algorithm (ARO). By applying railway load and regional PV data, we optimize the capacity of BESS and the rated power of the PV site to meet railway electricity demands while minimizing investment and operational costs. The study shows that the capacity of BESS and the size of the PV installation depend on different self-sufficiency ratio (SSR) requirements, leading to an optimized configuration for highly renewable systems. Additionally, the study highlights that considering battery aging is crucial for reducing costs in traction power systems using renewable energy and remains economically viable under time-of-use pricing policies. © 2025 Elsevier Ltd

关键词： Battery storage

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Deep Attentive Generative Adversarial Network for Photo-Realistic Image De-Quantization

arXiv

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arXiv 2020年

作者： Zhang, Yang Hu, Changhui Lu, Xiaobo School of Automation Southeast University Nanjing210096 China Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China School of Automation Southeast University Nanjing210096 China

Most of current display devices are with eight or higher bit-depth. However, the quality of most multimedia tools cannot achieve this bit-depth standard for the generating images. De-quantization can improve the visual quality of low bit-depth image to display on high bit-depth screen. This paper proposes DAGAN algorithm to perform super-resolution on image intensity resolution, which is orthogonal to the spatial resolution, realizing photo-realistic de-quantization via an end-to-end learning pattern. Until now, this is the first attempt to apply Generative Adversarial Network (GAN) framework for image de-quantization. Specifically, we propose the Dense Residual Self-attention (DenseResAtt) module, which is consisted of dense residual blocks armed with self-attention mechanism, to pay more attention on high-frequency information. Moreover, the series connection of sequential DenseResAtt modules forms deep attentive network with superior discriminative learning ability in image de-quantization, modeling representative feature maps to recover as much useful information as possible. In addition, due to the adversarial learning framework can reliably produce high quality natural images, the specified content loss as well as the adversarial loss are back-propagated to optimize the training of model. Above all, DAGAN is able to generate the photo-realistic high bit-depth image without banding artifacts. Experiment results on several public benchmarks prove that the DAGAN algorithm possesses ability to achieve excellent visual effect and satisfied quantitative performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Consensus algorithms for a class of disturbed nonlinear multi-agent systems via integral sliding-mode control

Consensus algorithms for a class of disturbed nonlinear mult...

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Asian control Conference

作者： Guodong Wang Xiangyu Wang Shihua Li Jun Yang Dan Niu Xisong Chen Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education PR China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing PR China

ISBN: (数字)9784888983006

ISBN: (纸本)9781728102634

This paper studies the velocity and altitude consensus problems of multi-hypersonic vehicle systems with disturbances. By the combination of integral sliding-mode control method and high-order disturbance observer technique, consensus protocols for a class of leaderless multi-hypersonic vehicle systems are proposed to achieve the velocity and altitude consensus. Firstly, high-order disturbance observers are designed to estimate the disturbances affecting each hypersonic vehicle. Then, based on integral sliding-mode control and disturbance estimates, a consensus scheme is presented to achieve the velocity and altitude asymptotic consensus of the multi-hypersonic vehicle systems. Moreover, numerical simulations are given to validate the efficiency of the proposed consensus algorithms.

关键词： Sliding mode control Disturbance observers Protocols Vehicle dynamics Eigenvalues and eigenfunctions Consensus algorithm Multi-agent systems

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Distributed Adaptive Observer for Discrete-Time LTI SISO System with Completely Unknown Periodic Input

Distributed Adaptive Observer for Discrete-Time LTI SISO Sys...

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第三十八届中国控制会议

作者： Yaoyao Wen Donghua Chen Ya Zhang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Architecture Engineering Nanjing Institute of Technology

This paper focuses on designing a distributed observer for multiple sensors to estimate the input and state of a linear-time-invariant(LTI) single-input-single-output(SISO) discrete-time system with completely unknown periodic input. A consensus based adaptive observer to estimate the unknown input. The observer assumes the form of an adaptive finite impulse response(FIR) filter, which requires the agent to get the model and output of the target. In this paper, an adaptive distributed estimation algorithm is designed for some agents that cannot get the target model and output to estimate the target state, output and unknown input simply by relying on the information of its neighbors. Experimental results are provided to further verify and testify the effectiveness of the algorithm.

关键词： distributed estimation adaptive FIR unknown input sensor network

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Herding Behavior of Electricity Retailers in the Monthly Electricity Market: Model and Simulation

Herding Behavior of Electricity Retailers in the Monthly Ele...

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

作者： Fen Lin Junjie Wei Xiaohui Ren Yue Yao Ying Wang Kaifeng Zhang Fujian Power Exchange Center LLC Fuzhou China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

With the reformulation of the electricity market in China, the electricity retailers can buy the electricity from the electricity market. Recently, herding behaviors in the electricity retailers are commonly observed when they bid in the monthly electricity market. The herding behaviors may bring many unstable risks to the electricity market. This paper mainly studies the impacts from the herding behaviors of electricity retailers on the monthly electricity market. We model the herding behavior model mathematically based on the relationship network of electricity retailers. Then the herding behavior model is imbedded into an electricity market model with agent-based bidding model based on reinforcement learning. We carry on a systematic simulation on MATLAB, and the simulation results illustrate that the herding behaviors might bring positive or negative effects to electricity retailers depending on different bidding strategies.

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