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Chinese control and Decision Conference, CCDC

作者： Qiaobing Yao Hualong Yu Wankou Yang Changyin Sun Key Lab of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing China School of Autmation Southeast University Nanjing Jiangsu CN Key Laboratory of Child Development and Learning Science of Ministry of Education Southeast University Nanjing China

This paper presents an imaging method for 360-degree panoramic view based on four wide angle cameras. In order to complete the image mosaic, all the parameters such as focal length, principal point and distortion coefficients, etc are calibrated by our proposed calibration toolbox. Then, our approach does not adopt the scheme which stitching all the images to the surrounding view after distortion correction. The proposed method directly calculates the mapping relationship between the wide-angle lens images and cylindrical projection images to generate lookup tables which can greatly simplifies the computation and reduces the loss of information in each image. Finally, panoramic image is composed by image registration and image fusion. Experimental results show that this method is valid.

关键词： Machine vision Mobile robots

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Multi-Heterogeneity Public Goods Evolutionary Game on Lattice

SSRN

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SSRN 2022年

作者： Xie, Kai Liu, Xingwen Wang, Huazhang Jiang, Yulian Lab. of Complex Systems & Intelligent Control Key Lab. of Electronic Information of State Ethnic Affairs Commission College of Electrical Engineering Southwest Minzu University Sichuan Chengdu610225 China College of Electrical Engineering Southwest Minzu University Sichuan Chengdu610225 China

The topic of how cooperation emerges and develops in the real world has been intensively studied. Many mechanisms and models have been proposed to explain this complex phenomenon with the idealized assumption that all players are homogeneous or have little heterogeneity. This paper investigates the evolution of the public goods game with multiple heterogeneities. Roughly speaking, the quantity of investment (by a cooperator), punishment (received by a defector), or cost (paid by a punisher) is not fixed but depends on specific evolutionary situation. Moreover, heterogeneity is also introduced in expected payoff of each player for the strategy updating rule. Multi-heterogeneity makes the considered model more realistic. The simulation experiment shows that the combination of modified punishment mechanism and strategy updating rule can promote cooperation density in both the multi-heterogeneity and homogeneity models. © 2022, The Authors. All rights reserved.

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Evolution of Cooperation in Snowdrift Game with Multi-Memory Mechanism

Evolution of Cooperation in Snowdrift Game with Multi-Memory...

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Chinese control Conference (CCC)

作者： Yaojun Liu Xingwen Liu Nyima Tashi Lab. of Complex Systems & Intelligent Control Key Lab. of Electronic Information of State Ethnic Affairs Commission College of Electrical Engineering Southwest Minzu University Chengdu Sichuan China Engineering Research Center for Tibetan Information Processing School of Information Science and Technology Tibet University Lhasa China

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural evolution. Since memory can greatly influence the emergence of cooperative behavior, memory-based research on evolutionary games has proliferated in recent years. In the present paper, inspired by the well-known “Stanford marshmallow experiment” (SME), we proposed a multi-memory mechanism in Snowdrift game (SDG) on spatial lattice networks whose core lies in what follows: Players are divided into two groups according to individual self-control; different memory lengths are set by group and by time in a single realization. The simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary game. More specifically, as memory length increases to an intermediate length, the cooperation level increases as well. For mechanism with short memory length, the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative behavior. These findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation.

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A zigbee-based building energy and environment monitoring system integrated with campus GIS

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International Journal of Smart Home 2014年第2期8卷 107-114页

作者： Qian, Kun Ma, Xudong Peng, Changhai Ju, Qing Xu, Mengyuan Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education China School of Automation Southeast University Nanjing 210096 China School of Architecture Southeast University Nanjing 210096 China IIUSE Southeast University Nanjing 210096 China Key Laboratory of Urban and Architectural Heritage Conservation Ministry of Education China

In this paper, the design and development of a Building Energy and Environment Monitoring System (BEEMS) for smart campus applications is proposed. The system is implemented based on distributed sensor nodes using ZigBee technology, which empowers the collection and monitoring of various types of measurements that reflect the energy consumption and environmental status of buildings. These parameters include temperature, humidity, air velocity, sound, bubble globe temperature, TVOC, CO2 concentration, air quality level, et al. The application software system is developed using jQuery-based Ajax general interactive architecture, and further integrated with campus GIS, which offers rich analysis and report functions for monitoring both energy consumption and environment parameters. © 2014 SERSC.

关键词： Energy utilization

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Continuous Travel Time Prediction for Transit Signal Priority Based on a Deep Network

Continuous Travel Time Prediction for Transit Signal Priorit...

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International Conference on Intelligent Transportation

作者： Xiong Gang Wenwen Kang Feiyue Wang Fenghua Zhu Yisheng Lv Xisong Dong Jukka Riekki Susanna Pirttikangas State Key Lab. of Manage. & Control for Complex Syst. Institute of Automation Chinese Academy of Sciences Beijing Beijing CN The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Cloud Computing Center Chinese Academy of Sciences Dongguan CHINA Faculty of Information Technology and Electrical Engineering University of Oulu Finland

It has been recognized by many researchers that accurate bus travel time prediction is critical for successful deployment of traffic signal priority (TSP) systems. Although there exist a lot of studies on travel time prediction for Advanced Traveler Information systems (ATIS), this problem for TSP purpose is a little different and the amount of literature is limited. This paper proposes a deep learning based approach for continuous travel time prediction problem. Parameters of the deep network are fine-tuned following a layer-by-layer pre-training procedure on a dataset generated by traffic simulations. Variables that may affect continuous travel time are selected carefully. Experiments are conducted to validate the performance of the proposed model. The results indicate that the proposed model produces prediction with mean absolute error less than 4 seconds, which is accurate enough for TSP operations. This paper also reveals that, except for obvious factors like speed, travel distance and traffic density, the signal time when the prediction is made is also an important factor affecting travel time.

关键词： Vehicles Predictive models Object oriented modeling Training Artificial neural networks Data models

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High-level spatial modeling in convolutional neural network with application to pedestrian detection

High-level spatial modeling in convolutional neural network ...

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Canadian Conference on Electrical and Computer engineering (CCECE)

作者： Feng Liu Yongzhen Huang Wankou Yang Changyin Sun School of Automation. Southeast University Nanjing China Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Key Lab of Child Development and Learning Science of Ministry of Education. Southeast University Nanjing China

ISBN: (纸本)9781479958306

Convolutional neural network (CNN) has achieved great success in many vision tasks. A key to this success is its ability to powerful automatically learns both high-level and low-level features. In general, low-level features have a small size of receptive fields and appear multiple times in different locations of objects, while high-level semantic features have a relatively large size of receptive fields and only appear once in a specific location of objects. However, traditional CNN treats these two kinds of features in the same manner, i.e., learning them by the convolution operation, which can be approximately considered as cumulating the probabilities that a feature appears in different locations. This strategy is reasonable for low-level features but not for high-level semantic ones, especially in the case of pedestrian detection, where a local feature can be shared by different locations but a semantic part, e.g., a head, only appears once for a human. To jointly model the spatial structure and appearance of high-level semantic features, we propose a new module to learn spatially weighted max pooling in CNN. The proposed method is evaluated on several pedestrian detection databases and the experimental results show that it achieves much better performance than traditional CNN.

关键词： Convolution Semantics Kernel Deformable models Computational modeling Feature extraction Training

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Marker or Markerless? Mode-Switchable Optical Tactile Sensing for Diverse Robot Tasks

arXiv

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arXiv 2024年

作者： Ou, Ni Chen, Zhuo Luo, Shan The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China The Robot Perception Lab. Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom

Optical tactile sensors play a pivotal role in robot perception and manipulation tasks. The membrane of these sensors can be painted with markers or remain markerless, enabling them to function in either marker or markerless mode. However, this uni-modal selection means the sensor is only suitable for either manipulation or perception tasks. While markers are vital for manipulation, they can also obstruct the camera, thereby impeding perception. The dilemma of selecting between marker and markerless modes presents a significant obstacle. To address this issue, we propose a novel mode-switchable optical tactile sensing approach that facilitates transitions between the two modes. The marker-to-markerless transition is achieved through a generative model, whereas its inverse transition is realized using a sparsely supervised regressive model. Our approach allows a single-mode optical sensor to operate effectively in both marker and markerless modes without the need for additional hardware, making it well-suited for both perception and manipulation tasks. Extensive experiments validate the effectiveness of our method. For perception tasks, our approach decreases the number of categories that include misclassified samples by 2 and improves contact area segmentation IoU by 3.53%. For manipulation tasks, our method attains a high success rate of 92.59% in slip detection. Code, dataset and demo videos are availab.e at the project website https://***/Marker-Markerless-Transition/ Copyright © 2024, The Authors. All rights reserved.

关键词： Tactile sensors

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Adaptation Via Proxy: Building Instance-Aware Proxy for Unsupervised Domain Adaptive 3d Object Detection

SSRN

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SSRN 2023年

作者： Li, Ziyu Yao, Yuncong Quan, Zhibin Qi, Lei Feng, Zhenhua Yang, Wankou School of Automation Southeast University Nanjing210096 China Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China School of Computer Science and Engineering Southeast University Nanjing210096 China Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

LiDAR-based 3D detectors perform well on particular autonomous driving benchmarks, but may poorly generalize to other domains. Existing 3D domain adaptive detection methods usually require annotation-related statistics or continuous refinement of pseudo-lab.ls. The former is not always feasible for practical applications, while the latter lacks sufficient accurate supervision. In this work, we propose a novel unsupervised domain adaptive framework, namely Adaptation Via Proxy (AVP), that explicitly leverages cross-domain relationships to generate adequate high-quality samples without any target statistics, thus mitigating domain shifts for existing LiDAR-based 3D detectors. Specifically, we first train the detector on the source domain with curriculum example mining to enhance its generalization capability. Then, we fine-tune the pre-trained detector on the instance-aware proxy that is a data collection with stronger supervision. The instance-aware proxy utilizes the profitable instances from the source domain to provide more robust supervision for optimizing the detector to overcome domain gaps. Note that AVP is architecture-agnostic thus it can be easily injected with any LiDAR-based 3D detectors. Extensive experiments on Waymo, nuScenes, KITTI and Lyft demonstrate the superiority of the proposed method over the state-of-the-art approaches for different adaptation scenarios. © 2023, The Authors. All rights reserved.

关键词： Object detection

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A Projection-Based Algorithm for Constrained L1- Minimization Optimization with Application to Sparse Signal Reconstruction

A Projection-Based Algorithm for Constrained L1- Minimizatio...

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International Conference on Information Science and Technology (ICIST)

作者： Qingshan Liu Wei Zhang Jiang Xiong Bingrong Xu Long Cheng School of Mathematics Southeast University Nanjing China College of Computer Science and Engineering Chongqing Three Gorges University Chongqing China School of Automation Huazhong University of Science and Technolog< Wuhan China State Key Lab. of Management and Control for Complex Systems University of Chinese Academy of Sciences Beijing China

In this paper, a projection-base algorithm is proposed for solving the constrained L 1 -minimization problem. Furthermore, the algorithm is utilized to sparse signal reconstruction. The L 1 -minimization is first converted into some equations which are described by the projections onto a hyper box set and the nonnegative quadrant. Then a iterative algorithm is proposed for solving the L 1 -minimization problem. Next, the algorithm is applied to sparse signal reconstruction described as an L 1 -minimization problem subject to L ∞ -norm noise constraint, or equivalently bound constraint. Finally, several experiments are presented to show the performance of the proposed algorithm.

关键词： Signal processing algorithms Signal reconstruction Optimization Convergence Electronic mail Recurrent neural networks Lyapunov methods

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Haptic intelligent interfaces for NAO robot hand control

Haptic intelligent interfaces for NAO robot hand control

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International Conference on Advanced Mechatronic systems (ICAMechS)

作者： Octavian Melinte Luige Vladareanu Ligia Munteanu Hongnian Yu Shuang Cang Zeng-Guang Hou Gui-Bin Bian Hongbo Wang Dept. Robotics and Mechatronics Institute of Solid Mechanics of Romanian Academy Bucharest Romania Engineering and Computing Bournemouth University Dorset UK State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Parallel Robot and Mechatronic Sys. Lab. Yanshan University Qinhuangdao China

The paper presents a new approach on haptic interface control for NAO robotic hand. The haptic teleoperation of NAO robot hand raises some issues, mapping the haptic device dynamic being the most important. The mapping was achieved through Neural Network by determining a dynamic gain of robot haptic feedback. The results lead to the achievement of the haptic intelligent interfaces for the NAO robot hand control which can be integrated into an innovative haptic robot control system.

关键词： Decision support systems Humanoid robots Haptic interfaces Economic indicators Joints Mechatronics

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