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26th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2022

作者： Zhou, Yushan Cao, Yunqi Sepúlveda, Nelson Zhang, Hongjian State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310027 China Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States

ISBN: (纸本)9781733419048

A lab-on-a-chip (LOC) thermal mass flow sensor based on microelectromechanical systems (MEMS) technology is designed, fabricated, and characterized. Vanadium dioxide (VO2), a nonlinear phase-change material with 3-order of change in resistance is placed in the down streaming direction of the heat source as the sensing element. The resistance of VO2 is monitored under gas flow rates ranging from 0 to 37.8 μL·s-1 with a maximum sensitivity of 368.56 ppm/(μL·min-1). The studies presented in this research may enable the application of utilizing nonlinear metamaterial in microfluidic LOC sensors with orders of magnitude improvement in sensitivity. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

关键词： Phase change materials

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Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach 25

Real-Time Object Detection and Robotic Manipulation for Agri...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Zhao, Hongyu Tang, Zezhi Li, Zhenhong Dong, Yi Si, Yuancheng Lu, Mingyang Panoutsos, George Imperial College The Department Of Physics London United Kingdom University Of Sheffield The Department Of Automatic Control And Systems Engineering SheffieldS1 3JD United Kingdom University Of Manchester The Department Of Electrical And Electronic Engineering Manchester United Kingdom University Of Southampton The Department Of Electronics And Computer Science Southampton United Kingdom Fudan University The Department Of Economics Shanghai200433 China AmesIA50011 United States

ISBN: (纸本)9798350340266

The optimization of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialization. Nowadays, the utilization of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of Convolutional Neural Networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The You Only Look Once(YOLO) algorithmic framework is employed with traditional Rectangular Bounding Boxes (R-Bbox) for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation. © 2024 IEEE.

关键词： Crops

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Collaborative Satisfaction of Long-Term Spatial Constraints in Multi-Agent Systems: A Distributed Optimization Approach (extended version)

arXiv

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arXiv 2025年

作者： Mehdifar, Farhad Dhullipalla, Mani H. Bechlioulis, Charalampos P. Dimarogonas, Dimos V. The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden The Division of Systems and Control The Department of Electrical and Computer Engineering University of Patras Patra Greece

This paper addresses the problem of collaboratively satisfying long-term spatial constraints in multi-agent systems. Each agent is subject to spatial constraints, expressed as inequalities, which may depend on the positions of other agents with whom they may or may not have direct communication. These constraints need to be satisfied asymptotically or after an unknown finite time. The agents’ objective is to collectively achieve a formation that fulfills all constraints. The problem is initially framed as a centralized unconstrained optimization, where the solution yields the optimal configuration by maximizing an objective function that reflects the degree of constraint satisfaction. This function encourages collaboration, ensuring agents help each other meet their constraints while fulfilling their own. When the constraints are infeasible, agents converge to a least-violating solution. A distributed consensus-based optimization scheme is then introduced, which approximates the centralized solution, leading to the development of distributed controllers for single-integrator agents. Finally, simulations validate the effectiveness of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Multi agent systems

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Cardiac Clarity: Harnessing Machine Learning for Accurate Heart-Disease Prediction

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IEEE Access 2025年 13卷 97529-97544页

作者： Sujith Santhosh Krishnaraj Chadaga R.Vijaya Arjunan Sandra D'Souza Department of Instrumentation and Control Engineering Manipal Academy of Higher Education Manipal Institute of Technology Manipal Karnataka India Department of Computer Science and Engineering Manipal Academy of Higher Education Manipal Manipal Institute of Technology Manipal Karnataka India

The major contributor to global mortality is cardiovascular disease, posing a formidable challenge to the global healthcare system. Heart disease often develops and progresses without noticeable symptoms, emphasizing the need for earlier detection to prevent severe outcomes. AI models provide tools for accurate diagnosis, data analysis, and predictive diagnosis of people who may be suffering from heart disease. But increasingly, solutions like Explainable AI (XAI) are gaining traction. XAI demystifies the decisions of AI models, making them transparent and understandable to the point where they boost confidence in the context of AI diagnostics by healthcare professionals; the AI then provides support to professionals to decide the best course of action for each patient based on the analysis. XAI techniques such as SHAP, LIME, QLattice and Anchor are popular, and have been used in this study. We analysed a heart disease dataset collected from a multispecialty hospital in India, which is publicly available on Mendeley. We used ML models, including RF, Logistic Regression, KNN, XGBoost, DT and SVM. These models were optimized and stack-ensembled to form the ‘CARDIACX’ model to predict the risk of heart disease. The models were fine-tuned using Grid Search, Random Search, and Bayesian Optimization methods, achieving promising results with Random Forest achieving an AUC =0.99 and accuracy of 98.5%, outperforming all the other models and demonstrating robust performance on various metrics such as AUC, PR Curve, Log Loss, Jaccard Score, and MCC. This study provides a reliable and transparent framework for early detection of heart disease, providing useful information and enabling patients to receive timely and personalized care.

关键词： Heart Diseases Accuracy Machine learning Predictive models Medical services Explainable AI Tuning Support vector machines Random forests

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Multi-agent Deep Reinforcement Learning Based Multi-objective Charging control for Electric Vehicle Charging Station 7th

Multi-agent Deep Reinforcement Learning Based Multi-objectiv...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Gong, Jiabao Fu, Weiming Kang, Yu Qin, Jiahu Xiao, Feng Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819733279

This paper proposes a multi-agent deep reinforcement learning (MADRL) based algorithm for charging control of multiple electric vehicles (EVs) in an electric vehicle charging station (EVCS) with dynamic operations. By taking the uncertainties of EV users’ behaviors and charging prices into consideration, a Markov game with multiple objectives is first established, where the individual objective of reducing EV charging costs and the collective objective of mitigating charging load fluctuations are both studied. Then, a multi-agent actor-double-critic (MAADC) algorithm with a unified advantage actor-critic framework is proposed, where two advantage functions are designed as the local and global critics, and the integrated actor is employed to balance the learning towards the individual and collective objectives. The simulation results obtained in a delicately built EVCS environment show the effectiveness of MAADC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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Efficient photoelectrochemical conversion of CO_(2)to syngas by photocathode engineering

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Green Energy & Environment 2022年第3期7卷 545-553页

作者： Sheng Chu Pengfei Ou Roksana Tonny Rashid Yuyang Pan Daolun Liang Huiyan Zhang Jun Song Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Electrical and Computer Engineering McGill UniversityMontrealQC H3A 0E9Canada Department of Mining and Materials Engineering McGill UniversityMontrealQC H3A 0C5Canada

The synthesis of renewable chemical fuels from CO_(2) and H_(2)O via photoelectrochemical(PEC)route reprensents a promising room-temperature approach for transforming greenhouse gas into value-added chemicals(e.g.,syngas),but to date it has been hampered by the lack of efficient photocathode for CO_(2) ***,we report efficient PEC CO_(2) reduction into syngas by photocathode *** photocathode is consisting of a planar p-n Si junction for strong light harvesting,GaN nanowires for efficient electron extraction and transfer,and Au/TiO_(2)for rapid electrocatalytic syngas *** photocathode yields a record-high solar energy conversion efficiency of 2.3%.Furthermore,desirable syngas compositions with CO/H_(2)ratios such as 1:2 and 1:1 can be produced by simply varying the size of Au *** calculations reveal that the active sites for CO and H_(2)generation are the facet and undercoordinated sites of Au particles,respectively.

关键词： syngas photocathode photoelectrochemical

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Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels

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IEEE Internet of Things Journal 2025年第11期12卷 17123-17136页

作者： Ma, Shuai Sun, Zhiye Shen, Bin Wu, Youlong Li, Hang Shi, Guangming Li, Shiyin Al-Dhahir, Naofal Peng Cheng Laboratory Shenzhen518066 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Shenzhen Research Institute of Big Data DIIS Guangdong Shenzhen518172 China Xidian University School of Artificial Intelligence Shaanxi Xi’an710071 China The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

In this article, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multiuser broadcast (broadcast communication (BC)) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multiuser semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in the same time-frequency resource. Specifically, for inference tasks, we design a SFDMA digital BC network based on robust information bottleneck (RIB), which can achieve a tradeoff between inference performance, data compression and multiuser interference. Moreover, for image reconstruction tasks, we develop a SFDMA digital BC network by utilizing a Swin Transformer, which significantly reduces multiuser interference. More importantly, SFDMA can protect the privacy of users’ semantic information, in which each receiver can only decode its own semantic information. Furthermore, we establish a relationship between performance and signal to interference plus noise ratio (SINR), which is fitted by an Alpha-Beta–Gamma (ABG) function. Furthermore, an optimal power allocation method is developed for the inference and reconstruction tasks. Extensive simulations verify the effectiveness and superiority of our proposed SFDMA scheme. © 2014 IEEE.

关键词： Image reconstruction

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Safe Multi-Robotic Arm Interaction via 3D Convex Shapes

arXiv

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arXiv 2025年

作者： Kaypak, Ali Umut Wei, Shiqing Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical and Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

Inter-robot collisions pose a significant safety risk when multiple robotic arms operate in close proximity. We present an online collision avoidance methodology leveraging 3D convex shape-based High-Order control Barrier Functions (HOCBFs) to address this issue. While prior works focused on using control Barrier Functions (CBFs) for human–robotic arm and single-arm collision avoidance, we explore the problem of collision avoidance between multiple robotic arms operating in a shared space. In our methodology, we utilize the proposed HOCBFs as centralized and decentralized safety filters. These safety filters are compatible with any nominal controller and ensure safety without significantly restricting the robots’ workspace. A key challenge in implementing these filters is the computational overhead caused by the large number of safety constraints and the computation of a Hessian matrix per constraint. We address this challenge by employing numerical differentiation methods to approximate computationally intensive terms. The effectiveness of our method is demonstrated through extensive simulation studies and real-world experiments with Franka Research 3 robotic arms. © 2025, CC BY.

关键词： Robotic arms

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Uncertainty-Informed Model Selection Method for Nonlinear System Identification and Interpretable Machine Learning 32

Uncertainty-Informed Model Selection Method for Nonlinear Sy...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Gu, Yuanlin Wei, Hua-Liang University of Stirling Division of Computer Science and Maths StirlingFK9 4LA United Kingdom University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom

ISBN: (纸本)9798350395440

Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify the optimal models among many potential candidates. This article proposes an uncertainty-informed method to address the model selection problem. The performance of the proposed method is evaluated on a dataset generated from a complex system model. The experimental results demonstrate the effectiveness of the proposed method and its superiority over conventional approaches. This method has minimal requirements for the length of training data and model types, making it applicable for various modeling frameworks. © 2024 IEEE.

关键词： Machine learning

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