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Chinese control and Decision Conference, CCDC

作者： Fei Gong Shuo Shan Haikun Wei Kanjian Zhang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In renewable energy grids, solar irradiance prediction is significant for electric operation planning. The emphasis of our work is forecasting irradiance changes over multiple days ahead using hourly irradiance data in association with weather parameters such as temperature, humidity, barometric pressure, wind speed, and cloud cover. Specifically, a framework based on TimesNet was proposed for 4-day global horizontal irradiance (GHI) forecasting and a comparative experiment was conduct with three well-performing models in long-term forecasting tasks. The experimental results indicate that the suggested method outperformed the accuracy when compared to other models, with MSE 0.198, MAE 0.259, RMSE 0.445 and R 2 0.787.

关键词： Solar irradiance Temperature distribution Computational modeling Wind speed Time series analysis Weather forecasting Predictive models

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Aperiodically Intermittent control for Prescribed-Time Synchronization of Stochastic complex Networks

Aperiodically Intermittent Control for Prescribed-Time Synch...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Haoyu Zhou Lei Xue Jian Liu Tong Wu Yongbao Wu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This paper tackles the challenge of achieving prescribed-time synchronization (PTS) for stochastic complex networks (SCNs) under the framework of aperiodically intermittent control (AIC). The adoption of a time-varying function ensures all states of the SCNs reach synchronization at the preset time. Compared with the PTS under the framework of deterministic systems, the introduction of stochastic disturbances has more practical significance. Introducing an auxiliary function makes the Lyapunov method mentioned in this paper valid under the AIC framework. Moreover, the AIC proposed in this paper relies on the average control rate instead of the infimum control rate, leading to less conservative results. At last, the viability of the theoretical results is confirmed by a simulation of a strongly connected digraph.

关键词： Automation Delay effects Directed graphs complex networks Synchronization Lyapunov methods Convergence

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Guided Transformative GAN: Joint Face Image Super-Resolution and Frontalization

Guided Transformative GAN: Joint Face Image Super-Resolution...

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International Conference on systems and Informatics (ICSAI)

作者： Minghao Mu Wei Wang Xiaobo Lu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

Joint face super-resolution and frontalization of non-frontal low-resolution faces is of significant importance for many face analysis applications. However, at the same time, it is a challenging task. In this paper, a novel Guided Transformative Generative Adversarial Network (GT-GAN) is proposed to address the challenge of generating frontal high-resolution face images from non-frontal low-resolution face images. Existing methods usually treat super-resolution and frontalization as two separate tasks, leading to unsatisfactory processing results. GTG AN innovatively achieves both goals within a unified frame-work. Specifically, we propose Profile Attention Guidance (PAG) to generate landmark heatmaps that provide a priori pose information to the generative network and direct its attention to critical facial regions. In addition, an Attention Fusion Transformation Module (AFTM) is proposed to significantly improve the hallucination performance of the network by utilizing the attention mechanism and STNs, combined with sequential feature enhancement strategies. The experimental results show that GT-GAN exhibits superior performance in the joint face super-resolution and frontalization task on the public face dataset Multi-PIE, which provides new ideas and references for this challenging topic.

关键词： Heating systems Attention mechanisms Superresolution Generative adversarial networks Informatics Faces

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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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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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A Image steganography Technique based on Denoising Diffusion Probabilistic Models

A Image steganography Technique based on Denoising Diffusion...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Enzhi Xu Yang Cao Le Hu Chenxing Wang School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Image steganography is the process of hiding secret information into an image while still keeping its original appearance intact. It plays a crucial role in secure communication and data storage. While deep learning-based techniques have shown notable progress in the field of image steganography, they still face challenges in extraction accuracy and robust security. This paper introduces an innovative image steganography method utilizing the diffusion model, which is the most powerful technique of image generation. Our method utilizes the sampling process of the diffusion model to hide the secret image in the generated image, and the receiver uses the secret key to extract the secret data accurately. The results of our experiments validate the effectiveness of our approach, outperforming existing methods in security, capacity, and extraction precision, which shows the potential for practical applications.

关键词： Resistance Steganography Visualization Accuracy Noise reduction Receivers Diffusion models

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Development and control of Underwater Gliding Robots:A Review

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1543-1560页

作者： Jian Wang Zhengxing Wu Huijie Dong Min Tan Junzhi Yu the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering ScienceBIC-ESATCollege of EngineeringPeking UniversityBeijing 100871China

As one of the most effective vehicles for ocean development and exploration,underwater gliding robots(UGRs)have the unique characteristics of low energy consumption and strong ***,by borrowing the motion principles of current underwater robots,a variety of novel UGRs have emerged with improving their maneuverability,concealment,and environmental friendliness,which significantly broadens the ocean *** this paper,we provide a comprehensive review of underwater gliding robots,including prototype design and their key *** the perspective of motion characteristics,we categorize the underwater gliding robots in terms of traditional underwater gliders(UGs),hybrid-driven UGs,bio-inspired UGs,thermal UGs,and ***,their buoyancy driven system,dynamic and energy model,and motion control are concluded with detailed ***,we have discussed the current critical issues and future *** review offers valuable insight into the development of next-generation underwater robots well-suited for various oceanic applications,and aims to gain more attention of researchers and engineers to this growing field.

关键词： Buoyancy driven motion control oceanic applications system development underwater gliding robots

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PVTimesNet: A Hybrid Deep Learning Method for Enhancing Ultra-short-term PV Power Forecasting

PVTimesNet: A Hybrid Deep Learning Method for Enhancing Ultr...

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2024 China Automation Congress, CAC 2024

作者： Wu, Ji Wang, Kai Ma, Wenwen Zhang, Jingxin Wang, Shibo China Electric Power Research Institute Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (纸本)9798350368604

Accurate ultra-short-term photovoltaic (PV) power forecasting is crucial for the real-time scheduling of grid systems. However, the inherent variability of solar energy makes this task extremely challenging. To enhance PV power forecasting, this paper introduces a novel hybrid model named PVTimesNet, designed to harness the strengths of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) for effective feature extraction. PVTimesNet consists of two parallel branches: the CNN1d branch, which captures correlations between multiple variables and adjacent time steps, and the LSTM branch, which learns the temporal dependencies within the PV power sequences. The features extracted by both branches are then concatenated and passed through a fully connected layer to generate multi-step PV power forecasts. Experimental results demonstrate that for forecast horizons of 1 to 4 hours, the proposed model significantly outperforms individual models and other CNN-LSTM hybrid structures. Additionally, it exhibits superior performance compared to related methods for longer forecast horizons. © 2024 IEEE.

关键词： Hybrid power

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Enabling Few-Shot Learning with PID control: A Layer Adaptive Optimizer 41

Enabling Few-Shot Learning with PID Control: A Layer Adaptiv...

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41st International Conference on Machine Learning, ICML 2024

作者： Yu, Le Li, Xinde Zhang, Pengfei Zhang, Zhentong Dunkin, Fir School of Automation Southeast University Nanjing China Nanjing Center for Applied Mathematics Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Southeast University Shenzhen Research Institute Shenzhen China

Model-Agnostic Meta-Learning (MAML) and its variants have shown remarkable performance in scenarios characterized by a scarcity of labeled data during the training phase of machine learning models. Despite these successes, MAML-based approaches encounter significant challenges when there is a substantial discrepancy in the distribution of training and testing tasks, resulting in inefficient learning and limited generalization across domains. Inspired by classical proportional-integral-derivative (PID) control theory, this study introduces a Layer-Adaptive PID (LA-PID) Optimizer, a MAML-based optimizer that employs efficient parameter optimization methods to dynamically adjust task-specific PID control gains at each layer of the network, conducting a first-principles analysis of optimal convergence conditions. A series of experiments conducted on four standard benchmark datasets demonstrate the efficacy of the LA-PID optimizer, indicating that LA-PID achieves state-of-the-art performance in few-shot classification and cross-domain tasks, accomplishing these objectives with fewer training steps. Code is available on https://***/yuguopin/LA-PID. Copyright 2024 by the author(s)

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