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Brain Functional Networks with Dynamic Hypergraph Manifold Regularization for Classification of End-Stage Renal Disease Associated with Mild Cognitive Impairment

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computer Modeling in engineering & Sciences 2023年第6期135卷 2243-2266页

作者： Zhengtao Xi Chaofan Song Jiahui Zheng Haifeng Shi Zhuqing Jiao School of Microelectronics and Control Engineering Changzhou UniversityChangzhou213164China School of Computer Science and Artificial Intelligence Changzhou UniversityChangzhou213164China Department of Radiology Changzhou Second People’s Hospital Affiliated to Nanjing Medical UniversityChangzhou213003China

The structure and function of brain networks have been altered in patients with end-stage renal disease(ESRD).Manifold regularization(MR)only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain *** solve this issue,we developed a method to construct a dynamic brain functional network(DBFN)based on dynamic hypergraph MR(DHMR)and applied it to the classification of ESRD associated with mild cognitive impairment(ESRDaMCI).The construction of DBFN with Pearson’s correlation(PC)was transformed into an optimization *** convolution and hyperedge convolution superposition were adopted to dynamically modify the hypergraph structure,and then got the dynamic hypergraph to form the manifold regular terms of the dynamic *** DHMR and L_(1) norm regularization were introduced into the PC-based optimization model to obtain the final DHMR-based DBFN(DDBFN).Experiment results demonstrated the validity of the DDBFN method by comparing the classification results with several related brain functional network construction *** work not only improves better classification performance but also reveals the discriminative regions of ESRDaMCI,providing a reference for clinical research and auxiliary diagnosis of concomitant cognitive impairments.

关键词： End-stage renal disease mild cognitive impairment brain functional network dynamic hypergraph manifold regularization classification

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A NLP and YOLOv8-Integrated Approach for Enabling Visually Impaired Individuals to Interpret Their Environment 9

A NLP and YOLOv8-Integrated Approach for Enabling Visually I...

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Avanzato, Roberta Mandelli, Lorenzo Randieri, Cristian Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy ficonTEC Service GmbH Im Finigen 3 Achim 28832 Germany 22060 Italy

Image captioning represents a significant challenge within the field of computer Vision. This task involves processing an image as input, identifying objects within it, comprehending the relationships between these objects, including their implicit characteristics, and generating a concise description as output. Given the vast number of potential interactions, acquiring sufficient training examples is a formidable task. Prior research has demonstrated that objects and predicates, when involved in less common relationships, occur more frequently when considered independently. Consequently, the proposed solution involves training two distinct visual models for objects and predicates, which are subsequently combined to capture as many relationships as possible. This study aims to leverage the information obtained regarding detected objects and their relationships to generate a comprehensive description of the image. By facilitating user interaction through Visual Question Answering, a task that bridges computer Vision and Natural Language Processing, we can create an interactive approach to image captioning. Given a question and an image, the system is designed to reason based on both the image content and general knowledge, and generate an accurate answer. We believe that such a system provides an effective method for visually impaired individuals to understand the content of an image. © 2023 Copyright for this paper by its authors.

关键词： Natural language processing systems

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A Foreground Feature Embedding Network for Object Detection in Remote Sensing Images 18th

A Foreground Feature Embedding Network for Object Detection...

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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Wu, Jiahui Cai, Yuanzheng Wang, Tao Luo, Zhiming Shan, Senhua Li, Zuoyong College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350108 China Department of Artificial Intelligence Xiamen University Xiamen361005 China Istrong Technology Limited Company Fuzhou350108 China

ISBN: (纸本)9789819996360

Compared with traditional natural images, remote sensing images (RSIs) typically have high resolution. The objects in the images are densely distributed, with heterogeneous orientation and large scale variation, even among objects of the same class. In recent years, object detection algorithms have made great strides in general images, but they are still difficult to meet the challenges that exist in RSIs. Therefore, we propose a foreground feature embedding network (FFE-Net) for object detection in RSIs. To better grasp the object features in RSIs, we design a foreground feature embedding module (FFEM) to learn the foreground features of the object. This is achieved by introducing an additional semantic segmentation branch and embedding the features in the classification and regression branches. Simultaneously, we propose a modified Gaussian function with focal loss (MGFFL) as a way to eliminate the extra background noise from soft labels, making the learned foreground features more robust. Our experimental results on two publicly available remote sensing image datasets, DOTA-v1.0 and HRSC2016, validate the effectiveness of FFE-Net. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Performing Effective Generative Learning from a Single Image Only 32

Performing Effective Generative Learning from a Single Image...

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32nd Wireless and Optical Communications Conference, WOCC 2023

作者： Xu, Qihui Chen, Jinshu Tang, Jiacheng Kang, Qi Zhou, MengChu Tongji University Department of Control Science and Engineering Shanghai201804 China New Jersey Institute of Technology The Helen and John C. Hartmann Department of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9798350337150

Generative adversarial networks (GANs) can be well used for image generation. Yet their training typically requires large amounts of data, which may not be available. This paper proposes a new algorithm for effective generative learning given a single image only. The proposed method involves building GAN models with a hierarchical pyramid structure and a parallel-branch design that enables independent learning of the foreground and background areas. This work conducts a set of well-designed experiments. The results well demonstrate that the proposed method produces the images of higher quality and better diversity than existing methods do. Thus, this work advances the field of generative learning for image generation. © 2023 IEEE.

关键词： Generative adversarial networks

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A Multi-Layer Long Short-Term Memory Neural Network on Battery State-of-Health Estimation with Transfer Learning 3

A Multi-Layer Long Short-Term Memory Neural Network on Batte...

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3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

作者： Tian, Jilun Zhang, Jiusi Jiang, Yuchen Luo, Hao Huang, Congsheng Chow, Mo-Yuen Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan North Carolina State University Department of Electrical and Computer Engineering RaleighNC27606 United States

ISBN: (纸本)9798350324754

Due to the important part of batteries in industrial systems, its safety analysis has causes widespread attention from researchers, and its effective maintenance decision-making is needed. Data-driven state-of-health (SOH) estimation can provide useful information by monitoring historical data during the aging process, but it can be failed in the cross-domain scenarios due to the different data distributions. To tackle this issue, we propose a long short-term memory (LSTM) neural network with an additional fully connected dense as a basic predictor, and apply the pretrain and fine-tuning training algorithm to realize the high-performance prediction. By validated in two real-world datasets, we find that blindly expanding the training set may have a negative impact on model accuracy, and the proposed TL-LSTM can achieve a great performance under the cross-domain tasks. © 2023 IEEE.

关键词： Long short-term memory

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Dynamic Topology Optimization for Efficient and Decentralised Federated Learning

Dynamic Topology Optimization for Efficient and Decentralise...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Menegatti, Danilo Giuseppi, Alessandro Poli, Cecilia Pietrabissa, Antonio Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy Istituto Superiore di Sanità National Centre for Innovative Technologies in Public Health Department Rome Italy

ISBN: (纸本)9798350362480

Topology optimization in decentralised federated learning settings enables the design of policies aimed at minimizing the number of communication rounds needed to reach algorithmic convergence. Given a federation of autonomous agents, finding the optimal topology which guarantees that the underlying graph is connected is still an open issue. This paper proposes a novel energy-aware topology optimization algorithm with the goal to derive an optimal topology which maximizes the algebraic connectivity of the corresponding graph in presence of energy and communication constraints. The effectiveness of the proposed approach is validated in the context of a consensus-based federated learning algorithm over an e-Health scenario. © 2024 IEEE.

关键词： Federated learning

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Dubins Vehicle Intercept of a Brownian Target on a Sphere 63

Dubins Vehicle Intercept of a Brownian Target on a Sphere

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Milutinovic, Dejan Casbeer, David W. University of California Department of Electrical and Computer Engineering Santa CruzCA United States Air Force Research Laboratory Control Science Center Wright-Patterson AFBOH United States

ISBN: (纸本)9798350316339

This paper focuses on a navigation of a Dubins vehicle (DV) to intercept a moving target on a sphere. The uncertainty in the target motion is described by a Brownian motion model. An Itô-type stochastic differential equation for the relative motion between DV and the Brownian moving target is derived. This stochastic differential equation is used to formulate an intercept controller as a minimum time optimal controller. The properties of the controller are incorporated into the numerical computations of the controller, which by its construction can intercept a Brownian target. Our numerical simulation results show that the computed controller can intercept both stationary and moving targets on the sphere. © 2024 IEEE.

关键词： Stochastic systems

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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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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Multi-task lane-free driving strategy for Connected and Automated Vehicles: A multi-agent deep reinforcement learning approach

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engineering Applications of Artificial Intelligence 2025年 154卷

作者： Berahman, Mehran Karalakou, Athanasia Rostami-Shahrbabaki, Majid Bogenberger, Klaus Department of Electrical and Computer Engineering Shiraz University Shiraz Iran Chair of Traffic Engineering and Control Technical University of Munich Munich Germany

Deep reinforcement learning has shown promise in various engineering applications, including vehicular traffic control. The non-stationary nature of traffic, especially in the lane-free environment with more degrees of freedom in vehicle behaviors, poses challenges for decision-making since a wrong action might lead to a catastrophic failure. In this paper, we propose a novel driving strategy for Connected and Automated Vehicles (CAVs) based on a competitive Multi-Agent Deep Deterministic Policy Gradient approach. The developed multi-agent deep reinforcement learning algorithm creates a dynamic and non-stationary scenario, mirroring real-world traffic complexities and making trained agents more robust. The algorithm's reward function is strategically and uniquely formulated to cover multiple vehicle control tasks, including maintaining desired speeds, overtaking, collision avoidance, and merging and diverging maneuvers. Moreover, additional considerations for both lateral and longitudinal passenger comfort and safety criteria are taken into account. We employed inter-vehicle forces, known as nudging and repulsive forces, to manage the maneuvers of CAVs in a lane-free traffic environment. The proposed driving algorithm is trained and evaluated on lane-free roads using the Simulation of Urban Mobility platform. Experimental results demonstrate the algorithm's efficacy in achieving various defined objectives. These include laterally sorting vehicles based on their desired speeds with minimal deviation from those speeds. Additionally, the jerk and acceleration values remain within acceptable ranges. Overall, these promising results highlight the potential of the proposed approach to enhance safety and efficiency in autonomous driving within lane-free traffic environments. © 2025 The Authors

关键词： Deep reinforcement learning

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Cross-domain Analog Fault Injection for Designing Robust Smart Systems

Cross-domain Analog Fault Injection for Designing Robust Sma...

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Forum on Specification, Verification and Design Languages, FDL

作者： Francesco Tosoni Nicola Dall’Ora Enrico Fraccaroli Sara Vinco Franco Fummi Department of Engineering IM University of Verona (Italy) Department of Control and Computer Engineering Politecnico di Torino (Italy)

ISBN: (数字)9798331504571

ISBN: (纸本)9798331504588

Under the pressure of the Industry 4.0 revolution, and now with the European Chips Act, smart systems are becoming omnipresent in all industrial sectors, e.g., automotive and aerospace. Such systems contain digital and analog components belonging to several physical domains, e.g., electrical and mechanical. To ensure robustness, the whole system must be validated as early as possible in the development cycle, by taking into account all such domains, as recommended by the ISO 26262 standard in the case, e.g., of automotive systems. Unfortunately, validation techniques, including fault injection and simulation are not as advanced on the analog side as the digital counterpart: i) they are not fully standardized ii) they are highly domain-dependent, and iii) they are performed separately from the digital flow. This article proposes to improve the design of smart systems by generating faulty scenarios through analog fault injection across several physical domains. By exploiting these faulty scenarios, it is possible to improve the robustness of the analog part and, at the same time, to improve the quality of the digital part that controls the system functionality. A multi-domain case study containing a microcontroller and a three-axis accelerometer is presented to demonstrate the validity of the proposed approach in many industrial contexts.

关键词： Design automation Microcontrollers ISO Standards Europe control systems Robustness Fourth Industrial Revolution

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