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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Extended NNs-Based Adaptive Event-Triggered Asymptotic Control for Nonlinear Systems Against Actuator Faults 7

Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Liu, Shanlin Zhang, Yingwei Zhao, Xudong Wang, Hesheng Northeastern University College of Information Science and Engineering Shenyang China Northeastern University State Laboratory of Synthesis Automation of Process Industry Shenyang China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350363173

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent. © 2024 IEEE.

关键词： Approximation algorithms

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RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Yichong Liu, Xiaojian Cheng, Fan Pan, Xiaoyong Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions. © 2024 IEEE.

关键词： Binding sites

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Artificial Intelligence System for Emotion Recognition and Text Analytics

Artificial Intelligence System for Emotion Recognition and T...

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2022 IEEE International Conference on Electro information Technology, eIT 2022

作者： Chaudhari, Namrata Agarwal, Reshu Narwade, Meghna Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

Harnessing the power of emotional intelligence by analyzing a person's behavioral and linguistic skills can help humans improve their approach to social interactions. In this paper, we propose an artificial intelligence-based stand-alone system that will allow us to classify and analyze facial expressions in real-time and perform sentiment analysis by examining the body of the text (extracted from audio) to understand the opinion expressed by it. This helps us provide a deeper understanding of how humans really feel at a given time. The proposed system uses a deep neural network (DNN) for classifying eight basic emotions based on features extracted from facial expressions and uses pretrained sentiment analysis tools to quantify text (extracted from audio) based on polarity. The system is implemented by extracting the audio and visual cues from real-time scenarios and using these extracted cues to perform facial expression recognition and text sentiment analysis. The integrated system extracts video and audio simultaneously with a frame rate of 4-5 fps. The facial emotion detection system successfully detects facial expressions of faces detected in real-time video with an accuracy of about 86.75%. The speech extracted is converted to text, cleaned, and processed to determine if the attitude of the speaker in a given situation is positive, negative, or neutral. © 2022 IEEE.

关键词： Deep neural networks

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Soccer Player Recognition using Artificial Intelligence and computer Vision

Soccer Player Recognition using Artificial Intelligence and ...

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2022 IEEE International Conference on Electro information Technology, eIT 2022

作者： Diop, Charles-Alexandre Pelloux, Baptiste Yu, Xinrui Yi, Won-Jae Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665480093

Groundbreaking features and functionalities are available in sports broadcasting programs, specifically in soccer games, such as post-game analysis, tracking of players, tracking of the ball, and associated teams' and players' statistical information. However, viewers don't have control to choose which features to view on-demand and aren't able to interact with any of these functionalities. Our system aims for the fundamental system of an on-demand viewer-driven application that would be able to track players on the field, identify formation changes, follow team strategy changes, and gather all statistical information of the player and the team on a single screen. In order to realize such an application, we focus on developing a method to track players on the field using multiple Artificial Intelligence (AI) and computer vision techniques where our application employs facial recognition and jersey number recognition algorithms. As a live soccer game broadcast would have various camera views of the players, our custom-made database contains captured images from different scenarios of camera views such as different angles and zooms of the player and is used to train the model using Convolutional Neural Network (CNN). Our system is scalable to different types of sports, such as basketball, baseball, volleyball, and more where players have their numbers on their jerseys and would be feasible to apply our system since soccer involves more players on the field than other sports games. © 2022 IEEE.

关键词： Cameras

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High Precision Analog Gauge Reader Using Optical Flow and computer Vision

High Precision Analog Gauge Reader Using Optical Flow and Co...

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2022 IEEE International Conference on Electro information Technology, eIT 2022

作者： Chavan, Shruti Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

This paper aims at developing algorithms to automate the process of reading analog gauges at different operational industries, healthcare sector and automobiles using Artificial Intelligence (AI) techniques. Proposed algorithms start with video stabilization using optical flow followed by image processing which performs segmentation using HSV color space and morphological operations. Next, Hough transform is applied to determine the dial region and pointer. Using the coordinates locating the pointer in the frame, angle made with minimum valued scale mark is calculated using trigonometry and evaluated reading is displayed in the form of text and a progress bar. The proposed algorithms for Analog Gauge Reader (AGR) is executed using open-source libraries such as OpenCV, NumPy, PIL with Python programming and it is developed in the PyCharm environment. The experimental results show significant decrease in the error with the introduction of image preprocessing and the generalizability of the algorithm to different types of analog gauges captured in different conditions of illuminance. The accuracy and precision of the algorithms is 99% to 100% in the experimental studies. © 2022 IEEE.

关键词： Hough transforms

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Automatic Detection of Obstructive Sleep Apnea Based on Multimodal Imaging System and Binary Code Alignment 18th

Automatic Detection of Obstructive Sleep Apnea Based on Mult...

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18th International Forum of Digital Multimedia Communication, IFTC 2021

作者： Yang, Ruoshu Zhang, Ludan Wang, Yunlu Hu, Menghan Li, Qingli Zhang, Xiao-Ping Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200062 China Department of Electrical Computer and Biomedical Engineering Ryerson University Toronto Canada

ISBN: (纸本)9789811922657

There are many patients with obstructive sleep apnea syndrome, which has caused concern. When it occurs, the nasal airflow disappears, and the breathing action of the chest and abdomen still exists. Therefore, we propose a multimodal imaging system in tandem with binary code alignment to remotely and automatically detect the obstructive sleep apnea. The RGB-thermal imaging module is applied to monitor the breathing conditions in the nose or mouth area. The depth camera is utilized to monitor the undulating state of the chest cavity. The obtained respiratory waveform is afterwards subjected to the limiting filter to suppress noisy signals that are not related to the mission objective. The signals derived from thermal camera and depth camera are pooled together for binary code alignment. When these two synchronously acquired bimodal signals do not match well, the obstructive sleep apnea is present. Our method achieves the satisfactory performance with accuracy, precision, recall and F1 of 91.38 %, 96.15 %, 86.21 %, and 90.91 %, respectively. This indicates that the proposed multimodal imaging system coupled with binary code alignment has a potential for non-contact and automatic detection of obstructive sleep apnea. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Infrared imaging

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Language Reasoning in Vision-Language-Action Model for Robotic Grasping

Language Reasoning in Vision-Language-Action Model for Robot...

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2024 China Automation Congress, CAC 2024

作者： Fan, Lingling Chen, Kang Xu, Zhezhuang Yuan, Meng Huang, Ping Huang, Weibing College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Evomotion Co. Ltd. Guangdong Shenzhen518000 China

ISBN: (纸本)9798350368604

Grasping tasks are crucial for the interaction between the robot and environment, requiring the robot to make autonomous decisions based on environmental conditions and instructions. Recently, multimodal Vision Language Models (VLMs) have demonstrated significant potential for robotic grasping. However, previous work has often been confined to high-level planning due to the gap between the text output of VLMs and the numerical predictions of robot actions, as well as the challenges of transferring rich semantic information from large models to the prediction of grasping actions. In this paper, we co-fine-tune a pre-trained VLM using the mix of large-scale visual language data and robotic grasping trajectory data, integrating model into an end-to-end robot control system to construct a Vision Language Action Model (VLA) capable of generating the robot’s underlying actions. Simultaneously, to fully leverage the prior knowledge and reasoning capabilities of the pre-trained model, we insert a language reasoning structure to bridge the mapping from high-level tasks to low-level actions. This method first deduces the relevant information regarding the target object and the end-effector motions, followed by predicting the grasping actions value with finer granularity based on the language reasoning, thereby enhancing the accuracy of action predictions. A series of grasping experiments demonstrate that our method achieves a high grasping success rate under flexible language instructions and exhibits strong generalization capabilities for unseen objects and scenes. © 2024 IEEE.

关键词： Visual languages

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Transformer-Based Sequence Modeling with Action Discretization for Robotic Grasping

Transformer-Based Sequence Modeling with Action Discretizati...

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2024 China Automation Congress, CAC 2024

作者： Hou, Yaokun Zheng, Song Xu, Zhezhuang Yuan, Meng Huang, Ping Huang, Weibing College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Evomotion Co. Ltd. Guangdong Shenzhen518000 China

ISBN: (纸本)9798350368604

The powerful representational and sequential modeling capabilities of Transformer have been validated in various fields such as natural language processing. However, how to better address sequential decision-making problems like robotic grasping through the Transformer architecture and achieve autonomous control of robotic grasping remains a longstanding challenge. Models that directly output continuous decision actions for robotic arms often rely on mean squared error (MSE) loss for training, which is unable to effectively handle the randomness and diversity of actions. This limitation leads to the generation of relatively singular actions in certain grasping scenarios, reducing the success rate of grasping. To address this issue, this paper proposes a Transformer-based method for discretizing robotic arm actions, where actions are discretized by dimension to enable autoregressive modeling. Experimental results demonstrate that this method effectively improves the grasping performance of robotic arms in simulation environments. © 2024 IEEE.

关键词： Mean square error

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Weighted Consistent Multi-view Discriminant Analysis in Unreliable Labeling Environment 1

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3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Li, Meihua Zhao, Yang Pei, Jihong Yang, Xuan College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Guangdong Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Multi-view discriminant analysis method (MvDA) is an effective supervised multi-view learning method. However, in practical multi-view learning application scenarios, there may be the problem that the sample label information from some viewpoints is unreliable. In the MvDA method, the information of all views is considered to have equal reliability. The data information in views containing unreliable label information may make it difficult for MvDA to learn accurate common features among data from different views. Therefore, in this paper, a weighted consistent multi-view discriminant analysis (WMvDA-VC) is proposed for the multi-view learning task in unreliable labeling environment. By considering the data characteristics under different views, the information from different views is assigned different reliability weights. Using the weighted scatter matrices, multiple projection matrices are learned. Then the data from different views are projected into the same common projection space. In the common subspace, dissimilar samples in different views are dispersed as much as possible and similar samples are aggregated as much as possible. The impact of data from views containing unreliable labels on the multi-view learning performance is reduced by adjusting the weights of the views with unreliable labels. The method is experimented on four widely used datasets. The experimental results show that the proposed WMvDA-VC exhibits excellent performance in the classification recognition task with unreliably labeled environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Learning systems

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