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A neuro-observer-based optimal control for nonaffine nonlinear systems with control input saturations

Control Theory and Technology 2021年第2期19卷 283-294页

作者： Behzad Farzanegan Mohsen Zamani Amir Abolfazl Suratgar Mohammad Bagher Menhaj Distributed Intelligent Optimization Research Lab Department of Electrical EngineeringAmirkabir University of TechnologyTehranIran Computational Intelligence Lab Department of Electrical EngineeringAmirkabir University of TechnologyTehranIran The School of Electrical Engineering and Computer Science The University of NewcastleNewcastleAustralia Department of Medical Physics and Engineering Shiraz University of Medical SciencesShirazIran

In this study,an adaptive neuro-observer-based optimal control(ANOPC)policy is introduced for unknown nonaffine nonlinear systems with control input ***–Jacobi–Bellman(HJB)framework is employed to minimize a non-quadratic cost function corresponding to the constrained control *** consists of both analytical and algebraic *** the analytical part,first,an observer-based neural network(NN)approximates uncertain system dynamics,and then another NN structure solves the HJB *** the algebraic part,the optimal control input that does not exceed the saturation bounds is *** weights of two NNs associated with observer and controller are simultaneously updated in an online *** ultimately uniformly boundedness(UUB)of all signals of the whole closed-loop system is ensured through Lyapunov’s direct ***,two numerical examples are provided to confirm the effectiveness of the proposed control strategy.

关键词： Input constraints Optimal control Neural networks Nonaffine nonlinear systems Reinforcement learning Unknown dynamics

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Development of LiFi System for Communication and Control in Hilly Regions

Development of LiFi System for Communication and Control in ...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Adarsh Dabral Raj Gusain Anurag Vidyarthi Rishi Prakash R. Gowri Department of Computer Science Engineering Propagation Research Lab Graphic Era (Deemed to be University) Dehradun India Department of Electronics & Communication Engineering Propagation Research Lab Graphic Era (Deemed to be University) Dehradun India Department of Electronics & Communication Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Science Engineering Graphic Era Hill University Dehradun India

In hilly regions where cable distribution is challenging and costly traditional communication systems often face difficulties. LiFi technology emerges as a promising solution in such contexts, leveraging communication through visible light. It uses light as a medium to provide data speeds that surpass those of Wi-Fi, This research paper presents an approach for a communication system specifically designed for hilly terrains using Light Fidelity (LiFi) technology. The proposed paper start with an introduction of LiFi technology and advantages of it over conventional communication methods in these regions. However, it also acknowledges the inherent limitations of Li-Fi, such as its low range, which is especially relevant in hilly topographies. The main body of the paper details the system's architecture, comprising a transmitter for sending data, a receiver to receive data, and dual operation: text for sending massage and operate devices, and audio for communication. To underscore its practicality, the system is showcased through two applications: an SOS emergency feature for hilly terrains and a device activation/deactivation application. In conclusion, the paper provides an in-depth examination of how this tailored system functions and its potential benefits for communities and operations in hilly landscapes.

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Effects of Social Conditions in a Virtual Exergame for Cognitive Rehabilitation in Multiple Sclerosis 13

Effects of Social Conditions in a Virtual Exergame for Cogni...

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13th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2023

作者： Ameri, Keivan Battegazzorre, Edoardo Galletti, Chiara Bottino, Andrea Podda, Jessica Tacchino, Andrea Brichetto, Giampaolo De Michieli, Lorenzo Barresi, Giacinto Politecnico di Torino Department of Control and Computer Engineering Turin Italy Istituto Italiano di Tecnologia Rehab Technologies Lab Genoa Italy Scientific Research Area Italian Multiple Sclerosis Foundation Genoa Italy

ISBN: (纸本)9798350324150

Rehabilitation is essential for individuals with multiple sclerosis (MS) to improve their quality of life and mitigate the progression of the disease. Cognitive deficits, which commonly affect MS patients, significantly impact daily functioning and well-being. This paper focuses on the development of a digital version of the Tangram puzzle using virtual reality (VR) to promote logical reasoning, attention, and social interactions to enhance engagement in cognitive rehabilitation for MS patients. The developed simulation integrates social presence in the form of a non-playable character (NPC) intending to improve player performance and motivation to ultimately promote engagement and adherence to treatment. A user study involving different game modes with the NPC in either a supporting or interfering role, was conducted to evaluate the effectiveness and usability of the Tangram VR exergame. After collecting and analyzing questionnaire scores and performance data, our results suggest that the application was well-received by users, and the introduction of an NPC had an impact in terms of execution times and sense of frustration, depending on its behaviour. Nonetheless, only a limited set of significant differences between modes was found, suggesting that further study is necessary to validate the results fully. © 2023 IEEE.

关键词： Virtual reality

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Hanging Around: Cognitive Inspired Reasoning for Reactive Robotics 14

Hanging Around: Cognitive Inspired Reasoning for Reactive Ro...

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14th International Conference on Formal Ontology in Information Systems, FOIS 2024

作者： Pomarlan, Mihai de Giorgis, Stefano Ringe, Rachel Hedblom, Maria M. Tsiogkas, Nikolaos Applied Linguistics Department University of Bremen Bremen Germany Institute of Cognitive Sciences and Technologies National Research Council Catania Italy Digital Media Lab University of Bremen Bremen Germany Department of Computing Jönköping School of Engineering Jönköping Sweden Department of Computer Science KU Leuven Leuven Belgium

ISBN: (纸本)9781643685618

Situationally-aware artificial agents operating with competence in natural environments face several challenges: spatial awareness, object affordance detection, dynamic changes and unpredictability. A critical challenge is the agent’s ability to identify and monitor environmental elements pertinent to its objectives. Our research introduces a neurosymbolic modular architecture for reactive robotics. Our system combines a neural component performing object recognition over the environment and image processing techniques such as optical flow, with symbolic representation and reasoning. The reasoning system is grounded in the embodied cognition paradigm, via integrating image schematic knowledge in an ontological structure. The ontology is operatively used to create queries for the perception system, decide on actions, and infer entities’ capabilities derived from perceptual data. The combination of reasoning and image processing allows the agent to focus its perception for normal operation as well as discover new concepts for parts of objects involved in particular interactions. The discovered concepts allow the robot to autonomously acquire training data and adjust its subsymbolic perception to recognize the parts, as well as making planning for more complex tasks feasible by focusing search on those relevant object parts. We demonstrate our approach in a simulated world, in which an agent learns to recognize parts of objects involved in support relations. While the agent has no concept of handle initially, by observing examples of supported objects hanging from a hook it learns to recognize the parts involved in establishing support and becomes able to plan the establishment/destruction of the support relation. This underscores the agent’s capability to expand its knowledge through observation in a systematic way, and illustrates the potential of combining deep reasoning with reactive robotics in dynamic settings. © 2024 The Authors.

关键词： Optical flows

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Modeling Model Predictive Control: A Category Theoretic Framework for Multistage Control Problems

Modeling Model Predictive Control: A Category Theoretic Fram...

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American Control Conference (ACC)

作者： Tyler Hanks Baike She Evan Patterson Matthew Hale Matthew Klawonn James Fairbanks Department of Computer Science University of Florida Department of Electrical and Computer Engineering Georgia Tech Topos Institute Information Directorate U.S. Air Force Research Lab

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Model predictive control (MPC) is an optimal control technique which involves solving a sequence of constrained optimization problems across a given time horizon. In this paper, we introduce a category theoretic framework for constructing complex MPC problem formulations by composing subproblems. Specifically, we construct a monoidal category - called Para(Conv) - whose objects are Euclidean spaces and whose morphisms represent constrained convex optimization problems. We then show that the multistage structure of typical MPC problems arises from sequential composition in Para(Conv), while parallel composition can be used to model constraints across multiple stages of the prediction horizon. This framework comes equipped with a rigorous, diagrammatic syntax, allowing for easy visualization and modification of complex problems. Finally, we show how this framework allows a simple software realization in the Julia programming language by integrating with existing mathematical programming libraries to provide high-level, graphical abstractions for MPC.

关键词： Visualization Optimal control Predictive models Syntactics Mathematical models Software Libraries

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FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair

arXiv

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arXiv 2023年

作者： Fatima, Sakina Hemmati, Hadi Briand, Lionel The School of EECS University of Ottawa Canada The Electrical Engineering and Computer Science Department York University Canada The Lero SFI centre for Software Research University of Limerick Ireland

Flaky tests are problematic because they non-deterministically pass or fail for the same software version under test, causing confusion and wasting development effort. While machine learning models have been used to predict flakiness and its root causes, there is much less work on providing support to fix the problem. To address this gap, in this paper, we focus on predicting the type of fix that is required to remove flakiness and then repair the test code on that basis. We do this for a subset of flaky tests where the root cause of flakiness is in the test itself and not in the production code. One key idea is to guide the repair process with additional knowledge about the test’s flakiness in the form of its predicted fix category. Thus, we first propose a framework that automatically generates labeled datasets for 13 fix categories and trains models to predict the fix category of a flaky test by analyzing the test code only. Our experimental results using code models and few-shot learning show that we can correctly predict most of the fix categories. To show the usefulness of such fix category labels for automatically repairing flakiness, we augment the prompts of GPT 3.5 Turbo, a Large Language Model (LLM), with such extra knowledge to request repair suggestions. The results show that our suggested fix category labels, complemented with in-context learning, significantly enhance the capability of GPT 3.5 Turbo in generating fixes for flaky tests. Based on the execution and analysis of a sample of GPT-repaired flaky tests, we estimate that a large percentage of such repairs, (roughly between 51% and 83%) can be expected to pass. For the failing repaired tests, on average, 16% of the test code needs to be further changed for them to pass. © 2023, CC BY.

关键词： Software testing

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Statistical Estimation in the Spiked Tensor Model via the Quantum Approximate Optimization Algorithm 38

Statistical Estimation in the Spiked Tensor Model via the Qu...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Leo Basso, Joao Mei, Song Walter Burke Institute for Theoretical Physics The IQIM California Institute of Technology PasadenaCA91125 United States Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Department of Mathematics University of California BerkeleyCA94720 United States NASA Ames Research Center Moffett FieldCA94035 United States USRA Mountain ViewCA94043 United States Department of Statistics Department of EECS University of California BerkeleyCA94720 United States

The quantum approximate optimization algorithm (QAOA) is a general-purpose algorithm for combinatorial optimization that has been a promising avenue for near-term quantum advantage. In this paper, we analyze the performance of the QAOA on the spiked tensor model, a statistical estimation problem that exhibits a large computational-statistical gap classically. We prove that the weak recovery threshold of 1-step QAOA matches that of 1-step tensor power iteration. Additional heuristic calculations suggest that the weak recovery threshold of p-step QAOA matches that of p-step tensor power iteration when p is a fixed constant. This further implies that multi-step QAOA with tensor unfolding could achieve, but not surpass, the asymptotic classical computation threshold Θ(n(q-2)/4) for spiked q-tensors. Meanwhile, we characterize the asymptotic overlap distribution for p-step QAOA, discovering an intriguing sine-Gaussian law verified through simulations. For some p and q, the QAOA has an effective recovery threshold that is a constant factor better than tensor power iteration. Of independent interest, our proof techniques employ the Fourier transform to handle difficult combinatorial sums, a novel approach differing from prior QAOA analyses on spin-glass models without planted structure. © 2024 Neural information processing systems foundation. All rights reserved.

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Fast exact leverage score sampling from khatri-rao products with applications to tensor decomposition 23

Fast exact leverage score sampling from khatri-rao products ...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Vivek Bharadwaj Osman Asif Malik Riley Murray Laura Grigori Aydin Buluç James Demmel Electrical Engineering and Computer Science Department UC Berkeley and Computational Research Division Lawrence Berkeley National Lab Computational Research Division Lawrence Berkeley National Lab International Computer Science Institute and Computational Research Division Lawrence Berkeley National Lab and Electrical Engineering and Computer Science Department UC Berkeley Institute of Mathematics EPFL & Lab for Simulation and Modelling Paul Scherrer Institute Computational Research Division Lawrence Berkeley National Lab and Electrical Engineering and Computer Science Department UC Berkeley Electrical Engineering and Computer Science Department UC Berkeley

We present a data structure to randomly sample rows from the Khatri-Rao product of several matrices according to the exact distribution of its leverage scores. Our proposed sampler draws each row in time logarithmic in the height of the Khatri-Rao product and quadratic in its column count, with persistent space overhead at most the size of the input matrices. As a result, it tractably draws samples even when the matrices forming the Khatri-Rao product have tens of millions of rows each. When used to sketch the linear least squares problems arising in CANDECOMP / PARAFAC tensor decomposition, our method achieves lower asymptotic complexity per solve than recent state-of-the-art methods. Experiments on billion-scale sparse tensors validate our claims, with our algorithm achieving higher accuracy than competing methods as the decomposition rank grows.

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Optimizing Post-Cancer Treatment Prognosis: A Study of Machine Learning and Ensemble Techniques

arXiv

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arXiv 2025年

作者： Chakraborty, Joyee Tohin, Mazahrul Islam Rashid, Danbir Tanmoy, Tanjil Ahmed Mony, Md. Jehadul Islam Department of Computer Science and Engineering Leading University Ragibnagar Sylhet3112 Bangladesh DeepNet Research and Development Lab Sylhet3100 Bangladesh

The aim is to create a method for accurately estimating the duration of post-cancer treatment, particularly focused on chemotherapy, to optimize patient care and recovery. This initiative seeks to improve the effectiveness of cancer treatment, emphasizing the significance of each patient’s journey and well-being. Our focus is to provide patients with valuable insight into their treatment timeline because we deeply believe that every life matters. We combined medical expertise with smart technology to create a model that accurately predicted each patient’s treatment timeline. By using machine learning, we personalized predictions based on individual patient details which were collected from a regional government hospital named Sylhet M.A.G. Osmani Medical College & Hospital, Sylhet, Bangladesh, improving cancer care effectively. We tackled the challenge by employing around 13 machine learning algorithms and analyzing 15 distinct features, including Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, etc we obtained a refined precision in predicting cancer patient’s treatment durations. Furthermore, we utilized ensemble techniques to reinforce the accuracy of our methods. Notably, our study revealed that our majority voting ensemble classifier displayed exceptional performance, achieving 77% accuracy, with LightGBM and Random Forest closely following at approximately 76% accuracy. Our research unveiled the inherent complexities of cancer datasets, as seen in the Decision Tree’s 59% accuracy. This emphasizes the need for improved algorithms to better predict outcomes and enhance patient care. Our comparison with other methods confirmed our promising accuracy rates, showing the potential impact of our approach in improving cancer treatment strategies. This study marks a significant step forward in optimizing post-cancer treatment prognosis using machine learning and ensemble techniques. © 2025, CC BY.

关键词： Chemotherapy

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MSPA-DLA++: A Multi-Scale Phase Attention Deep Layer Aggregation for Lesion Detection in Multi-Phase CT Images

MSPA-DLA++: A Multi-Scale Phase Attention Deep Layer Aggrega...

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19th World Congress on Medical and Health Informatics, MedInfo 2023

作者： Kitrungrotsakul, Titinunt Xu, Yingying Chen, Qingqing Liu, Jing Li, Yinhao Lin, Lanfen Hu, Hongjie Tong, Ruofeng Li, Jingsong Chen, Yen-Wei Research Center for Healthcare Data Science Zhejiang Lab China Department of Radiology Sir Run Run Shaw Hospital China Graduate School of Information Science and Engineering Ritsumeikan Univ. Japan College of Computer Science and Technology Zhejiang Univ. China

ISBN: (纸本)9781643684567

Object detection using convolutional neural networks (CNNs) has achieved high performance and achieved state-of-the-art results with natural images. Compared to natural images, medical images present several challenges for lesion detection. First, the sizes of lesions vary tremendously, from several millimeters to several centimeters. Scale variations significantly affect lesion detection accuracy, especially for the detection of small lesions. Moreover, the effective extraction of temporal and spatial features from multi-phase CT images is also an important issue. In this paper, we propose a group-based deep layer aggregation method with multiphase attention for liver lesion detection in multi-phase CT images. The method, which is called MSPA-DLA++, is a backbone feature extraction network for anchor-free liver lesion detection in multi-phase CT images that addresses scale variations and extracts hidden features from such images. The effectiveness of the proposed method is demonstrated on public datasets (LiTS2017) and our private multiphase dataset. The results of the experiments show that MSPA-DLA++ can improve upon the performance of state-of-the-art networks by approximately 3.7%. © 2024 International Medical Informatics Association (IMIA) and IOS Press.

关键词： Feature extraction

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