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Ultimate Boundedness and Output Convergence of Prioritized Output Tracking Control Under Nonsmooth and Imperfect Feedback Linearization

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IEEE Transactions on Automatic Control 2025年

作者： An, Sang-Ik Lee, Dongheui Park, Gyunghoon University of Seoul School of Electrical and Computer Engineering Korea Republic of Tu Wien Autonomous Systems at Institute of Computer Technology Faculty of Electrical Engineering and Information Technology Austria Institute of Robotics and Mechatronics Germany

The concept of priority has been introduced to the robotic systems in 1980 s as an effort to overcome problems caused by singularity and it has attracted considerable attention from both the robotics and control societies. However, none of the previous works have successfully addressed two fundamental degenerate properties of singularity: nonsmoothness and imperfect inversion. This technical note proposes a prioritized output tracking control method that guarantees the ultimate boundedness and the convergence of higher priority outputs when the input-output feedback linearization becomes nonsmooth and imperfect by singularity. For that purpose, we firstly prioritize the input-output feedback linearization and discuss how two degenerate properties can occur when the system becomes singular. Then, we introduce the differential inclusion to deal with the nonsmooth internal dynamics and establish a condition using the upper Dini derivative for asymptotically stable zero dynamics. Also, we propose a strictly positive realness-based feedback gain design and find a condition related to an M-matrix in order to handle nonlinearities that appear in the imperfect feedback linearization. Finally, we combine these two results and show the ultimate boundedness and the output convergence. Additionally, we provide a motivational example with a planar four-link manipulator to illustrate the effectiveness and limitations of the proposed method. © 1963-2012 IEEE.

关键词： Feedback linearization

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MSMA: Multi-Agent Trajectory Prediction in Connected and Autonomous Vehicle Environment with Multi-Source Data Integration 24

MSMA: Multi-Agent Trajectory Prediction in Connected and Aut...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Chen, Xi Bhadani, Rahul Sun, Zhanbo Head, Larry Dept. of Systems and Industrial Engineering Univ. of Arizona TucsonAZ United States Dept. of Electrical and Computer Engineering Univ. of Alabama in Huntsville HuntsvilleAL United States School of Transportation and Logistics Southwest Jiaotong Univ. Chengdu China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

The prediction of surrounding vehicle trajectories is crucial for collision-free path planning. In this study, we focus on a scenario where a connected and autonomous vehicle (CAV) serves as the central agent, utilizing both sensors and communication technologies to perceive its surrounding traffics consisting of autonomous vehicles (AVs), connected vehicles (CVs), and human-driven vehicles (HDVs). Our trajectory prediction task is aimed at all the detected surrounding vehicles. To effectively integrate the multi-source data from both sensor and communication technologies, we propose a deep learning framework called MSMA utilizing a cross-attention module for multi-source data fusion. Vector map data is utilized to provide contextual information. The trajectory data set is collected in CARLA simulator with synthesized data errors introduced. Numerical experiments demonstrate that in a mixed traffic flow scenario, the integration of data from different sources enhances our understanding of the environment. This notably improves trajectory prediction accuracy, particularly in situations with a high CV market penetration rate. © ASCE.

关键词： Data fusion

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Sample complexity of neural policy mirror descent for policy optimization on low-dimensional manifolds

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 11019-11085页

作者： Zhenghao Xu Xiang Ji Minshuo Chen Mengdi Wang Tuo Zhao H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta GA Department of Electrical and Computer Engineering Princeton University Princeton NJ

Policy gradient methods equipped with deep neural networks have achieved great success in solving high-dimensional reinforcement learning (RL) problems. However, current analyses cannot explain why they are resistant to the curse of dimensionality. In this work, we study the sample complexity of the neural policy mirror descent (NPMD) algorithm with deep convolutional neural networks (CNN). Motivated by the empirical observation that many high-dimensional environments have state spaces possessing low-dimensional structures, such as those taking images as states, we consider the state space to be a d-dimensional manifold embedded in the D-dimensional Euclidean space with intrinsic dimension d ≪ D. We show that in each iteration of NPMD, both the value function and the policy can be well approximated by CNNs. The approximation errors are controlled by the size of the networks, and the smoothness of the previous networks can be inherited. As a result, by properly choosing the network size and hyperparameters, NPMD can find an ε-optimal policy with Õ(ε-d/α-2) samples in expectation, where α ∈ (0, 1] indicates the smoothness of environment. Compared to previous work, our result exhibits that NPMD can leverage the low-dimensional structure of state space to escape from the curse of dimensionality, explaining the efficacy of deep policy gradient algorithms.

关键词： deep reinforcement learning policy optimization function approximation convolutional neural network Riemannian manifold

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Single Block Encoder-Decoder Transformer Model for Multi-Step Traffic Flow Forecasting 6

Single Block Encoder-Decoder Transformer Model for Multi-Ste...

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6th IEEE Symposium on computers and Informatics, ISCI 2024

作者： Omar, Mas Yakub, Fitri Talip, Mohamad Sofian Abu Maslan, Mohd Nazmin Sinha, Vijay Kumar Muljono, Muljono Universiti Teknologi Malaysia Wee Laboratory Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia Universiti Teknologi Malaysia Malaysia-Japan International Institute of Technology Department of Electronic System Engineering Kuala Lumpur Malaysia University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Universiti Teknikal Malaysia Faculty of Industrial and Manufacturing Technology and Engineering Melaka Malaysia Chitkara University School of Engineering & Technology Department of Computer Science & Engineering Himachal Pradesh India Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350353853

Accurate traffic flow forecasting is crucial for managing and planning urban transportation systems. Despite the widespread use of sequence modelling models like Long Short-Term Memory (LSTM) for this purpose, the potential of Transformer models remains underexplored. This is particularly true for the simplest form of a single block encoder-decoder Transformer model, which can be finely tuned through optimised hyperparameters. This paper examines the performance of a singular horizon-step forecasting method for multi-step traffic flow forecasting using a proposed Single Block Encoder-Decoder Transformer model optimised with a Grid Search algorithm. Results demonstrate that this model can enhance forecasting accuracy compared to the state-of-the-art LSTM model typically used for multi-step forecasting. The model effectively captures long-range temporal dependencies within a single road traffic flow dataset. It was tested on hourly traffic flow data to forecast the next 24 hours for the I5-North freeway in California, sourced from the Caltrans Performance Measurement System. The optimal configuration included an embedding dimension of 32, a feed-forward dimension of 128, and 8 attention heads. Results show a significant improvement, with a 4.7% reduction in Root Mean Squared Error compared to an LSTM model with two hidden layers of 100 neurons each, showcasing the potential of Single Block Encoder-Decoder Transformer models for real-world traffic prediction applications. © 2024 IEEE.

关键词： Mean square error

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Proposed Privacy Preservation Technique for Color Medical Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 719-732页

作者： Walid El-Shafai Hayam A.Abd El-Hameed Noha A.El-Hag Ashraf A.M.Khalaf Naglaa F.Soliman Hussah Nasser AlEisa Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Nowadays,the security of images or information is very *** paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image ***,the secret medical image is encrypted using Advanced Encryption Standard(AES)***,the secret report of the patient is embedded into the encrypted secret medical image with the Least Signiﬁcant Bit(LSB)watermarking *** that,the encrypted secret medical image with the secret report is concealed in a cover medical image,using Kekre’s Median Codebook Generation(KMCG)***,the stego-image obtained is split into 16 ***,it is sent to the *** adopt this strategy to send the secret medical image and report over a network *** proposed technique is assessed with different encryption quality metrics including Peak Signal-to-Noise Ratio(PSNR),Correlation Coefﬁcient(Cr),Fea-ture Similarity Index Metric(FSIM),and Structural Similarity Index Metric(SSIM).Histogram estimation is used to conﬁrm the matching between the secret medical image before and after *** results demonstrate that the proposed technique achieves good performance with high quality of the received medical image and clear image details in a very short processing time.

关键词： LSB steganography AES algorithm KMCG algorithm

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Realizing Global Citizen Solutions for Sustainable Development Goals: A Collaborative engineering Course Between Thai and Japanese Students 23

Realizing Global Citizen Solutions for Sustainable Developme...

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23rd International Symposium on Communications and Information Technologies, ISCIT 2024

作者： Sae-Jia, Thanachot Leelawat, Natt Tang, Jing Murakami, Rie Taoka, Yuki Ota, Eri Punyabukkana, Proadpran Chulalonkorn University Faculty of Engineering Department of Environmental Engineering Bangkok Thailand Chulalongkorn University Faculty of Engineering Department of Industrial Engineering Bangkok Thailand Chulalongkorn University Disaster and Risk Management Information Systems Research Unit Bangkok Thailand Graduate School of System Design and Management Keio University Yokohama Japan International School of Engineering Chulakongkorn University Faculty of Engineering Bangkok Thailand Global Education Section Center for Entrepreneurship Education Tokyo Institute of Technology Tokyo Japan School of Environment and Society Tokyo Institute of Technology Tokyo Japan Chulalongkorn University Faculty of Engineering Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350353952

One of the problems faced by engineering and science students is focusing solely on technical knowledge while needing more cultural context. In today's interconnected world, addressing global challenges necessitates collaborative efforts from individuals across nations. Accordingly, a joint course between Chulalongkorn University in Thailand and the Tokyo Institute of Technology in Japan has been established. The recent theme of the course, 'Gamification towards Sustainable Development Goals (SDGs),' aimed to foster global citizenship and address SDGs through multicultural collaboration. This study examines the similarities and differences in problem identification between Thai and Japanese student groups participating in the course. Data was collected from discussions, presentations, and group assignments throughout the course. The findings indicate that while both groups share common concerns, such as waste management, language barriers, and transportation issues, their perspectives and proposed solutions are influenced by cultural characteristics. Thai students emphasized infrastructural inadequacies and local traditions, whereas Japanese students focused on efficiency and the impacts of tourism. The study highlights the importance of understanding cultural nuances in collaborative international projects to achieve effective and innovative solutions to global challenges. © 2024 IEEE.

关键词： Sustainable development goals

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Water-harvesting polymer coatings for plant leaves

RSC Applied Polymers

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RSC Applied Polymers 2024年第1期3卷 173-180页

作者： Milatz, Roland Reink, Carmen van den Berg, Tomas E. Duvigneau, Joost Vancso, G. Julius Wurm, Frederik R. Sustainable Polymer Chemistry Department of Molecules and Materials MESA+ Institute for Nanotechnology Faculty of Science and Technology University of Twente Enschede Netherlands Department of Integrated Devices and Systems Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede 7522 NB Netherlands

Climate change-induced water scarcity threatens global plant life and agricultural productivity. Here, we present a novel atmospheric water harvesting (AWH) coating designed to alleviate heat and dry stress potentially. This polymer coating utilizes block copolymers carrying catechol-anchoring groups, specifically poly(dopamine methacrylamide) (PDOMA), to adhere to plant leaves. As a hydrophilic block, either poly((oligoethylene glycol) methacrylate) (POEGMA) or the thermoresponsive block poly(N-isopropylacrylamide) (PNIPAM) was used, which can adsorb water from the air during cooler periods in its hydrophilic state. As the temperature increases above the lower critical solution temperature (LCST) of PNIPAM, the polymer transitions to a hydrophobic state, releasing the captured water to the leaf surface. We synthesized PNIPAM-b-PDOMA copolymers via RAFT polymerization and confirmed their composition (IR, ¹H NMR and ¹H DOSY NMR spectroscopy) with a cloud point temperature of 33 ± 1 °C. The coatings were applied to model substrates (SiO₂, polyethylene) and corn leaves. Compared to uncoated controls, coated substrates demonstrated a substantial increase in water uptake from humid air, absorbing up to 50 wt% of the coating's weight. The coating's adherence and thermoresponsive behavior were confirmed on corn leaves through contact angle measurements, showing a shift from hydrophilic (29 ± 3°) below the LCST to hydrophobic (80 ± 2°) above the LCST, closer to the native, hydrophobic leaf (110 ± 10°). Crucially, photosynthesis induction experiments revealed that the coating did not negatively impact the plant's natural photosynthetic processes. This study establishes a promising copolymer platform for developing AWH coatings to support plants in the face of increasing drought conditions. © 2025 RSC.

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Sustainable Smart Road: A Unified Cost Estimation Model for Renewable-Powered Smart Roads 4

Sustainable Smart Road: A Unified Cost Estimation Model for ...

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4th IEEE International Conference on electrical, computer, and Energy Technologies, ICECET 2024

作者： Colombo, Cristian Giovanni Longo, Michela Nocera, Leonardo Ota, Ryosuke Zaninelli, Dario Politecnico di Milano Department of Energy Milan Italy Politecnico di Milano Milan Italy Tokyo Metropolitan University Faculty of Systems Design Department of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350395914

With new stringent policies affecting the transport and generation sectors, new technologies have emerged to drive the electric and technological transition. One of these is the smart road, a highly sensorised system to enable the integration of new vehicles and safety technologies. In this paper, the implementation of a smart road is simulated, first from an energy point of view and then from an economic point of view, by identifying the power demand of components and their cost. Next, the energetic core of the smart road is implemented. The energy core consists of a PV plant capable of powering the infrastructure. In this paper a simulation to test the proposed methodology is implemented. In the case study, a 280.05 kW smart road is powered by an optimally dimensioned PV plant. The production guaranteed by the 283.8 kWp plant will be 389.49 MW h/year and requires 57232. Furthermore, the 234.57 x20AC investment for the plant will represent less than 10% of the total investment, highlighting how renewable integration can be an economic sustainable purpose. Finally, another method to return the investment as quickly as possible will be proposed. © 2024 IEEE.

关键词： PV' Renewable Integration Road Infrastructure Smart Road

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The group robustness is in the details: revisiting finetuning under spurious correlations 24

The group robustness is in the details: revisiting finetunin...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Tyler LaBonte John C. Hill Xinchen Zhang Vidya Muthukumar Abhishek Kumar H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology and H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Google DeepMind

ISBN: (纸本)9798331314385

Modern machine learning models are prone to over-reliance on spurious correlations, which can often lead to poor performance on minority groups. In this paper, we identify surprising and nuanced behavior of finetuned models on worst-group accuracy via comprehensive experiments on four well-established benchmarks across vision and language tasks. We first show that the commonly used class-balancing techniques of mini-batch upsampling and loss upweighting can induce a decrease in worst-group accuracy (WGA) with training epochs, leading to performance no better than without class-balancing. While in some scenarios, removing data to create a class-balanced subset is more effective, we show this depends on group structure and propose a mixture method which can outperform both techniques. Next, we show that scaling pretrained models is generally beneficial for worst-group accuracy, but only in conjunction with appropriate class-balancing. Finally, we identify spectral imbalance in finetuning features as a potential source of group disparities — minority group covariance matrices incur a larger spectral norm than majority groups once conditioned on the classes. Our results show more nuanced interactions of modern finetuned models with group robustness than was previously known. Our code is available at https://***/tmlabonte/revisiting-finetuning.

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Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter

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computers, Materials & Continua 2023年第7期76卷 665-686页

作者： Wadhah Mohammed M.Aqlan Ghassan Ahmed Ali Khairan Rajab Adel Rajab Asadullah Shaikh Fekry Olayah Shehab Abdulhabib Saeed Alzaeemi Kim Gaik Tay Mohd Adib Omar Ernest Mangantig School of Computer Sciences Universiti Sains MalaysiaUSM11800PenangMalaysia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia86400JohorMalaysia IPPT Universiti Sains MalaysiaUSM11800PenangMalaysia

Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood *** severe forms,it can lead to *** genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for ***,controlling thalassemia is extremely important and is made by promoting screening to the general population,particularly among thalassemia *** Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health *** individuals’sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the *** effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning(VADER).In this study applied twitter intelligence tool(TWINT),Natural Language Toolkit(NLTK),and VADER constitute the three main *** represents a gold-standard sentiment lexicon,which is basically tailored to attitudes that are communicated by using social *** contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier calledVADERto analyze the sentiment of the general population,particularly among thalassemia carriers on the social media platform *** this study,the results showed that the proposed approach achieved 0.829,0.816,and 0.818 regarding precision,recall,together with F-score,*** tweets were crawled using the search keywords,“thalassemia screening,”thalassemia test,“and thalassemia diagnosis”.Finally,results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets,respectively.

关键词： Social media platform Twitter screening thalassemia lexicon-based VADER

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