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检索条件"机构=Key Laboratory of Measurement and Control of Complex System of Engineering"

共 1227 条记录，以下是271-280 订阅

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Linguistic dynamic analysis for single lane's traffic status based on FCM

Linguistic dynamic analysis for single lane's traffic status...

引用

2014 International Conference on Informative and Cybernetics for Computational Social systems, ICCSS 2014

作者： Jie, Wang Hong, Mo Fenghua, Zhu College of Electrical AndInformation Engineering Changsha University of Science and Technology Changsha China State Key Laboratory of Management and Control for Complex System Beijing China

ISBN: (纸本)9781479947522

The results of traditional traffic status analysis are mostly single values, whose accuracy can't be determined;fuzzy c-means clustering (FCM) algorithm based on fuzzy theory can calculate the clustering center of plenty data quickly and easily;linguistic dynamic systems could describe the dynamic rules of complex systems in the language level. In this paper, membership functions are decided by FCM;result of a specific time period taken as one example is obtained;it's discussed that linguistic dynamic analysis of traffic status in different period within a day by the same method. © 2014 IEEE.

关键词： Linguistics

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A self-learning algorithm based on support vector machine for scheduling a job-shop-like knowledgeable manufacturing cell

A self-learning algorithm based on support vector machine fo...

引用

2011 International Conference on Mechanical engineering, Materials and Energy, ICMEME 2011

作者： Li, Wen-Chao Yan, Hong-Sen Key Laboratory of Measurement and Control of Complex Systems Engineering Southeast University Ministry of Education Nanjing 210096 China Department of Transportation Jiangsu University Zhenjiang 212013 China

ISBN: (纸本)9783037853405

The job-shop-like knowledgeable manufacturing cell scheduling is a NP-complete problem and there has not been a completely valid algorithm for it until now. An algorithm with self -learning ability is proposed through the addition of precedence constraint of operations on the basis of directed graph. A method based on support vector machine is constructed to choose accurately interchangeable operations by small samples earning to obtain the better scheduling. The classification accuracy can be improved by the continuous addition of new instances to the sample library. The results of simulation show that the algorithm performs well for the job-shop-like knowledgeable manufacturing cell. © (2012) Trans Tech Publications, Switzerland.

关键词： Computational complexity

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DOUBLE WAVELET THRESHOLD FILTER DESIGN FOR REAL-TIME ADAPTIVE DENOISING IN OA-ICOS

DOUBLE WAVELET THRESHOLD FILTER DESIGN FOR REAL-TIME ADAPTIV...

引用

2024 Jiangsu Annual Conference on Automation, JACA 2024

作者： Deng, Yixin Wang, Zairan Chen, Yang-Yang Liu, Qin Wang, Xiangyu School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781837242689

In the Off-Axis Integrated Cavity Output Spectroscopy (OA-ICOS) technique, single-stage filters are commonly used to reduce noise and improve the signal-to-noise ratio, enabling more precise CO2 concentration detection. However, these filters often underperform under real-time conditions. To enhance real-time performance, this paper proposes a novel framework based on Double Wavelet Threshold Filter (DWTF) that adaptively suppresses noise while preserving key features of CO2 concentration signal. Firstly, one wavelet threshold filter (WT) is introduced to calculate the mean of the current sequence, providing an initial denoising step. Secondly, another WT is employed to extract features from a longer sequence for adaptive computation of the discount factor. Finally, an exponential moving average filter (EMA) is applied to combine both current and historical data to produce a smoother aggregated output. To validate the effectiveness, DWTF was compared with other conventional algorithms for a CO2 dataset with three distinct states: smooth, ship and mutant, obtained by OA-ICOS. The experimental results demonstrate that DWTF performs effectively in all three states. © The Institution of engineering & Technology 2024.

关键词： Adaptive filters

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Improved particle filter for object tracking

Improved particle filter for object tracking

引用

作者： Zhang, Tao Fei, Shu-Min College of Automation Electronic Engineering Qingdao University of Sciences and Technology Qingdao China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9781424487363

Robust real-time tracking of non-rigid objects is a challenging task. Color is a powerful feature for tracking deformable objects in image sequences with complex backgrounds. Color distribution is applied, as it is robust to partial occlusion, is rotation and scale invariant and computationally efficient. Particle filter has been proven very successful for non-linear and non-Gaussian estimation tracking problems. The article presents the integration of color distributions into particle filtering. A target is tracked with a particle filter by comparing its histogram with the histograms of the sample positions using the Bhattacharyya distance. Additionally, to solve the sample impoverishment (all particles collapse to a single point within a few iterations) in the particle-filter algorithm, a new resampling algorithm is proposed to tackle sample impoverishment. The performance of the proposed filter is evaluated qualitatively on various real-world video sequences. The experimental results show that the improved color-based particle filter algorithm can reduce sample impoverishment effectively and track the moving object very well. © 2011 IEEE.

关键词： Color

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Adaptive Augmented Kalman Filter-Based control for Electronic Throttle systems with Disturbances and Noises 42

Adaptive Augmented Kalman Filter-Based Control for Electroni...

引用

42nd Chinese control Conference, CCC 2023

作者： Li, Wen Su, Jinya Li, Shihua School of Automation Southeast University Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing210096 China

ISBN: (纸本)9789887581543

In this study, we investigate the control problem of electronic throttle systems in the presence of practical challenges such as disturbances and measurement noises. To address these challenges, we propose an adaptive augmented Kalman filter (AAKF) based control approach that combines the strengths of extended state observer in disturbance estimation and adaptive Kalman fil-ter in adaptive noise filtering. The outputs of AAKF are integrated into the Backstepping control design, resulting in a composite control that concurrently achieves fast disturbance rejection and noise suppression. We conduct a comparative simulation study against conventional methods without adaptive filtering to validate the effectiveness of the proposed AAKF-based control strat-egy, which exhibits superior position control accuracy and disturbance attenuation performance. We envision that our proposed control strategy will contribute to improving vehicle power, fuel economy, and emission performance. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Adaptive filtering

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Research on key Technologies of Cross-domain Guidance 8

Research on Key Technologies of Cross-domain Guidance

引用

8th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Guo, Xiaoyun Wu, Chengfeng Fang, Xueli Xu, Cheng Li, Hui Beijing Institute of Mechanical and Electrical Engineering Beijing China Beijing Institute of Mechanical and Electrical Engineering Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China Beijing Institute of Mechanical and Electrical Engineering Key Laboratory of Complex Aviation Simulation System Beijing China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Cross-domain synergy and intelligence will become important features of future warfare. Cross-domain guidance is one of the typical forms of cross-domain synergy. It can significantly improve the ability to respond quickly to random targets and time-sensitive targets and enhance the anti-jamming ability in the case of confrontation. This article introduces the concept and winning mechanism of cross-domain guidance, analyzes the research progress of cross-domain guidance at home and abroad, combs the key technologies of cross-domain guidance, summarizes the current challenges and problems in cross-domain guidance research, and proposes development suggestions. © 2022 SPIE

关键词： Optical engineering

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A Hierarchical Formation-Containment control Scheme for Second-Order Multi-agent systems 7th

A Hierarchical Formation-Containment Control Scheme for Sec...

引用

7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Cao, Yue Wang, Xiangyu Chen, Yangyang 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

ISBN: (纸本)9789819733231

In this paper, a hierarchical formation-containment control scheme is proposed for second-order multi-agent systems. The proposed hierarchical scheme decouples the control task into two layers, i.e., reference signal generator and agents’ tracking controllers. First, a reference signal generator is designed in Layer 1 for leader agents and follower agents, respectively, to produce the formation reference signals and containment reference signals, whose outputs "achieve" formation-containment asymptotically. Second, tracking controllers are designed in Layer 2 for each agent to track the reference signal. The proposed hierarchical formation-containment scheme realizes formation-containment asymptotically. The simulations are performed to validate the effectiveness and practicality of the scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multi agent systems

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Smoke vehicle detection based on multi-feature fusion and hidden Markov model

Smoke vehicle detection based on multi-feature fusion and hi...

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作者： Tao, Huanjie Lu, Xiaobo 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

Existing smoke vehicle detection methods and vision-based smoke detection methods are vulnerable to false alarms. This paper presents an automatic smoke vehicle detection method based on multi-feature fusion and hidden Markov model (HMM). In this method, we first detect moving objects using an improved visual background extractor (ViBe) algorithm and obtain smoke-colored blocks using color histogram features in the HSI (hue, saturation, and intensity) color space. The adaptive scale local binary pattern (AS-LBP) and the discriminative edge orientation histogram (disEOH) are proposed and combined to characterize the smoke-colored blocks. More specifically, the proposed AS-LBP, a texture feature descriptor, is based on the quadratic fitting of our labelled data to obtain the best scale. The proposed disEOH, a gradient-based feature descriptor, is robust to noise by extracting discriminative edge information using Gaussian filters and principal component analysis (PCA). The discrete cosine transform (DCT) is employed to extract frequency domain information from the region fused by smoke blocks. To utilize the dynamic features, the HMMs are employed to analyze and classify the smoke-colored block sequences and region sequences in continuous frames. The experimental results show that the proposed method achieves better performances than existing smoke detection methods, especially achieves lower false alarms. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Smoke

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Pylon Reconstruction Based on NeRF 14

Pylon Reconstruction Based on NeRF

引用

14th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2024

作者： Hu, Zijian Wang, Chenxing Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9798350361889

In this paper, we propose a pylon reconstruction method based on Neural Radiance Fields (NeRF) technology. The advantage of this method lies in its ability to reconstruct pylons from images, with a multi-view model maintaining good performance even under complex lighting and occlusion conditions. We demonstrate the process of using NeRF to reconstruct electric power towers through experiments. During the experimental process, we collect images of electric power towers from different environments and angles, then apply NeRF to process these images, using deep learning to learn and generate a network model capable of expressing the scene. The experimental results show that our method can complete the reconstruction of scenes with only image inputs, and the reconstructed models have a certain accuracy in terms of geometric details and textures. © 2024 IEEE.

关键词： Deep learning

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Driving behaviour recognition from still images by using multi-stream fusion CNN

Driving behaviour recognition from still images by using mul...

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作者： Hu, Yaocong Lu, Mingqi Lu, Xiaobo 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

Abnormal driving behaviour is a leading cause of serious traffic accidents threatening human life and public property globally. In this paper, we investigate the use of a deep learning approach to automatically recognize driving behaviour (such as normal driving, driving with hands off the wheel, calling, playing mobile phone, smoking and talking with passengers) in a single image. The task of driving behaviour recognition can be regarded as a multi-class classification problem, and we resolve this problem from two aspects in our study: (1) Employ multi-stream CNN to extract multi-scale features by filtering images with receptive fields of different kernel sizes and (2) investigate different fusion strategies to combine the multi-scale information and generate the final decision for driving behaviour recognition. The effectiveness of our proposed method is validated by extensive experiments carried out on our self-created simulated driving behaviour dataset, as well as a real driving behaviour dataset, and the experiment results demonstrate that the proposed multi-stream CNN-based method achieves the significant performance improvements compared to the state of the art. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Deep learning

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