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General Simultaneous Localization and Mapping Scheme for mmWave Communication Systems

IEEE Internet of Things Journal 2024年第12期11卷 22521-22536页

作者： Du, Tao Yang, Jie Wen, Chao-Kai Xia, Shuqiang Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University Key Laboratory Of Measurement And Control Of Complex Systems Of Engineering Ministry Of Education The Frontiers Science Center For Mobile Information Communication And Security Nanjing210096 China Institute Of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan Zte Corporation The State Key Laboratory Of Mobile Network And Mobile Multimedia Technology Shenzhen518055 China Southeast University National Mobile Communications Research Laboratory The Frontiers Science Center For Mobile Information Communication And Security Nanjing210096 China

Utilizing high-resolution antenna arrays and wide bandwidth of the millimeter-wave (mmWave) spectrum in 5G new radio (NR) mmWave communication systems holds the potential for high-throughput data transmission while enabling user localization and environmental mapping. However, the majority of existing simultaneous localization and mapping (SLAM) algorithms rely on methods akin to the extended Kalman filter for generating initial map features. These methods prove ineffective when the measurement dimension is insufficient. Furthermore, there is a notable absence of research exploring mmWave prototype systems to evaluate and compare the performance and viability of various SLAM algorithms. To address these challenges, we propose an innovative probability hypothesis density (PHD) generation scheme for birth events and have developed a prototype system. Our approach, referred to as PHD-SLAM, exhibits remarkable effectiveness even in scenarios where the measurement dimension falls short of map features. This means it can function seamlessly with only delay or angle information available. Additionally, we have designed a 28GHz mmWave beam scanning prototype system that leverages the 5G NR frame for accomplishing SLAM algorithms. Following this, we conducted extensive simulations and experimental evaluations to gauge the performance of several leading-edge SLAM algorithms under diverse mmWave circumstances, encompassing PHD-based and belief propagation (BP) SLAM algorithms. Our analysis reveals that both PHD and BP SLAM can achieve agent localization precision within a decimeter and mapping precision within a meter, capitalizing on the angle parameters of mmWave signals. While BP SLAM showcases reduced computational demand, its estimation precision is marginally inferior to that of PHD-SLAM. © 2014 IEEE.

关键词： Belief propagation

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GraspDiff: Grasping Generation for Hand-Object Interaction With Multimodal Guided Diffusion

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IEEE Transactions on Visualization and Computer Graphics 2024年 PP卷 PP-PP页

作者： Zuo, Binghui Zhao, Zimeng Sun, Wenqian Yuan, Xiaohan Yu, Zhipeng Wang, Yangang Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

Grasping generation holds significant importance in both robotics and AI-generated content. While pure network paradigms based on VAEs or GANs ensure diversity in outcomes, they often fall short of achieving plausibility. Additionally, although those two-step paradigms that first predict contact and then optimize distance yield plausible results, they are always known to be time-consuming. This paper introduces a novel paradigm powered by DDPM, accommodating diverse modalities with varying interaction granularities as its generating conditions, including 3D object, contact affordance, and image content. Our key idea is that the iterative steps inherent to diffusion models can supplant the iterative optimization routines in existing optimization methods, thereby endowing the generated results from our method with both diversity and plausibility. Using the same training data, our paradigm achieves superior generation performance and competitive generation speed compared to optimization-based paradigms. Extensive experiments on both in-domain and out-of-domain objects demonstrate that our method receives significant improvement over the SOTA method. We will release the code for research purposes. © 1995-2012 IEEE.

关键词： 3D modeling

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Mining Better Samples and Semantic Consistency for Contrast Learning in Forest Semantic Segmentation 41

Mining Better Samples and Semantic Consistency for Contrast ...

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第41届中国控制会议

作者： Chongjin Chen Yichao Cao Qingfei Tang Xiaobo Lu School of Software Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing Enbo Technology Co. Ltd.

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Image segmentation has impressive progress in the past several *** good segmentation usually follows pixelwise well-annotated labels which is ***,the robustness would not be guaranteed due to lack-ofdiversity *** work usually focuses on pixels individually and pay less attention to the neighbor *** local context would be scarce and the global context is not utilized following these *** proposal a method,named Forest Semantic Segmentation Network(FSSNet) to address these *** organizes original version and augmented version of images,as two inputs into student branch and teacher branch,and force the two outputs being consistent to strengthen the robustness of our ***,we not only consider pixel itself and also the neighbor pixels because the context of neighbor pixels helps understanding the *** utilizes contrastive loss with memory bank to involve global context in training which will make pixels closer to others in same category and far away from pixels of different categories.A bank filter is suggested to improve the quality of features in the memory *** also proposal a new sample strategy to improve the effect of contrastive loss and reduce the *** method can improve accuracy and strengthen the robustness with affordable extra computation during training process,and no additional computation during inference toward *** to benchmark,the proposed approach can improve the mIoU by 3.1% on our challenging dataset.

关键词： Semantic segmentation Contrastive learning Deep learning

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Sensorless control of SPMSM Based on Generalized Super-twisting Observer and Non-smooth controller

Sensorless Control of SPMSM Based on Generalized Super-twist...

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第42届中国控制会议

作者： Dejin Chen Ke Yu Shihong Ding Xiangyu Wang Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

To enhance the estimation accuracy and dynamic performance of sensorless surface-mounted permanent magnet synchronous motor(SPMSM) drives,a sensorless control scheme based on generalized super-twisting observer(GSTO) and nonsmooth controller is ***,a GSTO for back electromotive force(back-EMF) estimation is *** with the conventional super-twisting observer,the GSTO has a faster convergence rate and stronger robustness due to the additional *** a linear extended state observer(LESO) is adopted to estimate the position,speed,and lumped disturbance at the same ***,a non-smooth composite speed controller is designed by combining the disturbance feed-forward *** with the conventional PI speed controller,the non-smooth controller has a shorter settling time and a better disturbance rejection ***,the effectiveness of the proposed method is verified by simulation results.

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Fall-LSTM:A Fall Detection Network Based on Spatio-Temporal Location Module

Fall-LSTM:A Fall Detection Network Based on Spatio-Temporal ...

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第42届中国控制会议

作者： Dapeng Xu Xiaobo Lu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University

Fall events have unique dynamic features,which are not fully utilized by existing fall detection *** on video understanding,we propose Fall-LSTM to learn such features pertinently without additional ***-LSTM is composed of CNN-LSTM framework and two excitation modules i.e.,Spatial Attention Module(SAM) and Temporal Location Module(TLM).SAM provides spatial constraints on motion for feature layers through foreground extraction and spatial *** emphasizes frames with high probability of fall events to LSTM by inferring the rate and trend of motion in *** results show that our proposed modules significantly improve the performance of LSTM model,outperforming the state-of-theart methods on two public Fall Detection Datasets i.e.,Le2 i and UR.

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Hmdo: Markerless Multi-View Hand Manipulation Capture with Deformable Objects

SSRN

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SSRN 2023年

作者： Xie, Wei Yu, Zhipeng Zhao, Zimeng Zuo, Binghui Wang, Yangang Southeast University The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

We construct the first markerless deformable interaction dataset recording interactive motions of the hands and deformable objects, called HMDO (Hand Manipulation with Deformable Objects). Our motivation is the current lack of hand and deformable object interaction datasets, as 3D hand and deformable object reconstruction is challenging. Mainly due to mutual occlusion, the interaction area is difficult to observe, and the visual features between hands and objects are entangled. To tackle this challenge, we propose a method to annotate our captured data. Our key idea is to collaborate with estimated hand features to guide the object pose estimation, and then optimize the deformation process of the object by analyzing the relationship between the hand and the object. Through comprehensive evaluation, the proposed method can reconstruct interactive motions of hands and deformable objects with high quality. In the future, this dataset could boost the research of learning-based reconstruction of deformable interaction scenes. © 2023, The Authors. All rights reserved.

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SegSdetr: Harnessing supervisions in SAM with detection label for efficient smoke detection

SegSdetr: Harnessing supervisions in SAM with detection labe...

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2023 Jiangsu Annual Conference on Automation, JACA 2023

作者： Li, Dan Lu, Xiaobo College of Software Engineering Southeast University Suzhou215123 China School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Ministry of Education Nanjing210096 China

ISBN: (纸本)9781839539848

In recent years, forest fires often result in significant environmental and economic losses, spurred researchers to investigate early forest fire detection methods. Embedded devices with the object detection task can efficiently alarm early fire including smoke from a large field of view along with providing additional information such as location of the fire. But it's difficult to deploy large DNN in the chipsets in embedded devices. To satisfied the requirements for lightweight, this paper contributes to the existing research by proposing an efficient smoke detection model (SegSdetr) harnessing supervisions in Segment Anything Model (SAM) combined detection label. We lightweight the structure of model's backbone by solving linear redundancy observed in feature maps within models. And to compensate the drop accuracy from the lightweight, we have proposed a method harnessing the reference of SAM model. SegSdetr exhibits significantly superior precision and a 30% reduction in parameter efficiency compared to RT-DETR with the original backbone model. After incorporating our method, there is a further enhancement in precision. Additionally, our model outperforms the YOLOv8 family where a 3% increase in mAP50 with yolov8-n with the same Flops. © The Institution of Engineering & Technology 2023.

关键词： Smoke

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BPH-YOLOv5: Improved yolov5 based on biformer prediction head for small target cigatette detection

BPH-YOLOv5: Improved yolov5 based on biformer prediction hea...

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2023 Jiangsu Annual Conference on Automation, JACA 2023

作者： Zheng, Xinyi Lu, Xiaobo College of Software Engineering Southeast University Suzhou215123 China 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)9781839539848

Small object detection in the cigarette detection field is a recent popular task. As cigarette targets in surveillance are tiny, the object scale leads to a great challenge of object detection. To solve the issue mentioned above, we propose BPH-YOLOv5 based on YOLOv5. We add one more prediction head to detect different-scale objects, which is more sensitive to tiny objects using multi-scale features. Then we replace the original prediction heads with BiFormer Prediction Heads (BPH) to explore the prediction potential with an attention mechanism. Experiments showed that the mean average precision (mAP) of BPHYOLOv5 in the cigarette dataset reached 90.34%, which was 6.94% higher than that of YOLOv5-s prototype, 7.20% higher than that of YOLOv7, which are encouraging and competitive. Tests on the actual scenarios verified that the accuracy of smallscale target detection was significantly improved. © The Institution of Engineering & Technology 2023.

关键词： Forecasting

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Non-smooth Speed control of Switched Reluctance Motor Based on Generalized Proportional Integral Observer

Non-smooth Speed Control of Switched Reluctance Motor Based ...

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第42届中国控制会议

作者： Qingzhao Yin Zheng Tian Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

This paper investigates the speed regulation control of switched reluctance motor(SRM) *** improve the antidisturbance performance of SRM,a composite non-smooth control strategy is ***,the structure of SRM is analyzed,and a simplified nonlinear model is obtained based on a segmented representation of the varying phase inductance.A virtual control function is introduced to represent the nonlinear part of the torque equation,whose inverse function is cascaded to linearized the nonlinear ***,a generalized proportional integral observer(GPIO) is constructed to estimate the lumped disturbance of the system,which is used for feedforward compensation ***,a composite speed controller is designed based on a combination of finite time proportional feedback and feed-forward compensation based on GPIO(FTP+GPIO).The speed error closed-loop system can be regarded as a first-order finite time control system with bounded *** analysis shows that the proposed scheme can improve the anti-disturbance performance of the closed-loop *** effectiveness of the proposed method is verified by simulation ***,it is compared with proportional feedback combining feed-forward compensation(P+GPIO) method and proportional integral(PI) control method.

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A Nonsmooth Observer Based on Friction Model Compensation for Sensorless Permanent Magnet Synchronous Motor 42

A Nonsmooth Observer Based on Friction Model Compensation fo...

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42nd Chinese control Conference, CCC 2023

作者： Zhang, Zaishan Zhu, Wenwu Yu, Ke Wang, Xiangyu Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China School of Electrical Engineering and Automation Hefei University of Technology Hefei230009 China

ISBN: (纸本)9789887581543

In this paper, a nonsmooth observer considering friction compensation is proposed for the sensorless permanent magnet synchronous motor (PMSM) to estimate the rotor position and speed. Friction is one of the main factors affecting the tracking accuracy of the motor at low speed, especially in the back and forth motion of the motor. Firstly, based on the Stribeck friction model, a nonlinear friction model of the motor is established. Secondly, a nonsmooth observer with friction compensation is designed to estimate the position and speed of the rotor, and the lumped disturbance of the system at the same time. Further, the stability analysis of this nonsmooth observer is given. Finally, the effectiveness of the proposed method is verified by the simulation results. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Sensorless control

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