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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： G. Satish Chitaranjan Dalai Vinjamuri S. N. Ch. Dattu K. Rayudu T. Sathish Kamlesh Chandra Purohit Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Telangana India Department of Civil Engineering Odisha University Technology and Research Bhubaneswar Odisha Department of: Mechanical Engineering Aditya Engineering College Surampalem Andhra Pradesh India Department of Electrical and Electronics Engineering B. V. Raju Institute of Technology Narsapur Telangana India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

This article presents an optimization model for energy management (EM) in a microgrid composed of renewable energy sources. The microgrid in question, corresponding to an electric vehicle (EVs) charging station, has solar panels, wind turbines, and a bank of batteries. The loads considered are EVs that enter the charging station requesting the charging of their batteries and equipment from a small convenience store, located in the charging station itself. This work maximize the supply of active power to EVs and, simultaneously, minimize the interruptions in the energy supply. An algorithm (with the aid of computational tools) will be used to deal with the decision-making process related to turbines, panels and loads. The implementations were made in MATLAB, for modeling the energy sources, and AMPL, for applying the optimization algorithm.

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NMPC-based UAV 3D Target Tracking in the Presence of Obstacles and Visibility Constraints

NMPC-based UAV 3D Target Tracking in the Presence of Obstacl...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Tyagi, Prakrit Kumar, Yogesh Sujit, P.B. Delhi Technological University Department of Mechanical Engineering New Delhi India IIIT-Delhi Department of Electronics and Communications Engineering New Delhi India Indian Institute of Science Education and Research Bhopal Department of Electrical Engineering and Computer Science Bhopal India

ISBN: (纸本)9780738131153

Persistent target tracking using fixed-wing unmanned aerial vehicles (UAVs) in urban areas is an important application. However, due to the kinematic constraints of the UAV coupled with the visibility obstruction due to terrain impose hard constraints on the UAV motion for persistent tracking. In this paper, we propose a nonlinear model predictive control (NMPC) based controller with a gimballed camera to persistently track a target on the ground. The controller determines the control commands for the UAV and the gimbal azimuth and elevation angles. Simulations results show that the proposed approach can efficiently track the target compared to the NMPC framework without gimbal. © 2021 IEEE.

关键词： Controllers

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Predicting Interaction Shape of Soft Continuum Robots using Deep Visual Models

Predicting Interaction Shape of Soft Continuum Robots using ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yunqi Huang Abdulaziz Y. Alkayas Jialei Shi Federico Renda Helge Wurdemann Thomas George Thuruthel Department of Computer Science University College London London UK Department of Mechanical and Nuclear Engineering Khalifa University Abu Dhabi UAE Department of Mechanical Engineering University College London London UK Department of Mechanical Engineering Mechatronics in Medicine The Hamlyn Centre for Robotic Surgery Imperial College London UK Khalifa University Center for Autonomous Robotic Systems (KUCARS) Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Soft continuum robots, characterized by their inherent compliance and dexterity, are increasingly pivotal in applications requiring delicate interactions with the environment such as the medical field. Despite their advantages, challenges persist in accurately modeling and controlling their shape during interactions with surrounding objects. This is because of the difficulty in modeling the large degrees of freedom in soft-bodied objects that become more active during interactions. In this study, we present a deep visual model to predict the interaction shapes of a soft continuum robot in contact with surrounding objects. By formulating this task as a forward-statics problem, the model uses the initial state images containing the object configuration and future actuation values to predict interactive state images of the robot under this actuation condition. We developed and tested the model in both simulated and physical environments, explored the model’s predictive capabilities using monocular and binocular views, and tested the model’s generalization ability on different datasets. Our results show that deep learning methods are a promising tool for solving the complex problem of predicting the shape of a soft continuum robot interacting with the environment, requiring no prior knowledge about the system dynamics and explicit mapping of the environment. This study paves the way for future explorations in robot-environment interaction modeling and the development of more adaptable interaction shape control strategies.

关键词： Adaptation models Visualization Accuracy Uncertainty Shape Robot kinematics Predictive models Data collection Data models Continuum robots

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Error Bound for Hill-Function Approximations in a Class of Stochastic Transcriptional Network Models

Error Bound for Hill-Function Approximations in a Class of S...

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IEEE Conference on Decision and Control

作者： Dylan Hirsch Theodore W. Grunberg Domitilla Del Vecchio Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA

Hill functions are often used in stochastic models of gene regulation to approximate the dependence of gene activity on the concentration of the transcription factor (TF) that regulates the gene. However, it is generally unknown how much error one may incur from this approximation. We investigate this question in the context of transcriptional networks (TNs). Under the assumption of rapid binding and unbinding of TFs with their gene targets, we bound the approximation error (in terms of the total variation distance) between a mass-action stochastic model and a corresponding model with Hill function propensities. To do so, we use a combination of singular perturbation theory and moment analysis for stochastic chemical reaction networks. We assume throughout that TFs regulate genes in a one-to-one fashion, each regulated gene produces a single TF, TFs do not multimerize, and each gene only has a single TF binding site. These results are pertinent for the modeling of TNs and may also carry relevance for more general biological processes.

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Risk Assessment for Nonlinear Cyber-Physical Systems under Stealth Attacks

arXiv

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arXiv 2024年

作者： Chen, Guang Sun, Zhicong Ding, Yulong Yang, Shuang-Hua Department of Mechanical and Energy Engineering Southern University of Science and Technology China Department of Civil and Environmental Engineering Hong Kong Polytechnic University Hong Kong Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet SUSTech Shenzhen518055 China Department of Computer Science and Engineering SUSTech Shenzhen518055 China Department of Computer Science University of Reading United Kingdom

Stealth attacks pose potential risks to cyber-physical systems because they are difficult to detect. Assessing the risk of systems under stealth attacks remains an open challenge, especially in nonlinear systems. To comprehensively quantify these risks, we propose a framework that considers both the reachability of a system and the risk distribution of a scenario. We propose an algorithm to approximate the reachability of a nonlinear system under stealth attacks with a union of standard sets. Meanwhile, we present a method to construct a risk field to formally describe the risk distribution in a given scenario. The intersection relationships of system reachability and risk regions in the risk field indicate that attackers can cause corresponding risks without being detected. Based on this, we introduce a metric to dynamically quantify the risk. Compared to traditional methods, our framework predicts the risk value in an explainable way and provides early warnings for safety control. We demonstrate the effectiveness of our framework through a case study of an automated warehouse. Copyright © 2024, The Authors. All rights reserved.

关键词： Cyber Physical System

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Improving human essential protein prediction using only protein sequences via ensemble learning

Improving human essential protein prediction using only prot...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zeng, Min Wang, Nian Wu, Yifan Li, Yiming Wu, Fang-Xiang Li, Min Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha410083 China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering SaskatoonSKS7N 5A9 Canada

ISBN: (纸本)9781665401265

Accurate prediction of essential proteins by using computational methods can effectively reduce the cost of wet-lab experiments. Existing computational methods usually rely on constructed protein-protein interaction (PPI) networks with different kinds of biological data. However, high-quality PPI networks and other biological data are not available for all proteins. Thus, it is very necessary and valuable to develop accurate methods for fast and effective prediction of essential proteins by using only protein sequences. We propose EPGBDT, a machine learning ensemble model, to improve the performance of essential protein prediction by using only protein sequences. EP-GBDT has an ensemble structure that combines multiple Gradient Boosting Decision Tree (GBDT) base classifiers. In addition, to reduce the effects of imbalanced dataset, EP-GBDT uses a sampling technique. The results show that EP-GBDT outperforms state-of-the-art sequence-based methods and network-based centrality measures. The source code and datasets can be downloaded from https://***/CSUBioGroup/EP-GBDT. © 2021 IEEE.

关键词： Decision trees

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Precise Identification of Anomalies in Brats MR Brain Images using Metaheuristic Optimization Techniques

Precise Identification of Anomalies in Brats MR Brain Images...

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International Conference on Inventive Computation Technologies (ICICT)

作者： S. Rosaline K. Maheswari B. Dhanasakkaravarthi Vaishali M Sathish Kannan Nandakumar Selvaraju Department of Electronics and Communication Engineering R.M.K. Engineering College Kavaraipettai Thiruvallur Tamil Nadu India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Mechanical Engineering Agni College of Technology Thazhambur. Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Automobile Engineering Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

A condition known as an anomaly occurs when some brain features behave improperly and cause either minor or significant harm to brain cells. Brain tumors, vascular malformations, and inflammatory lesions are among the most well-known abnormalities. A variety of Machine Learning (ML) and Deep Learning (DL) methods are employed in the analysis and detection of brain irregularities. The use of metaheuristic optimization is one such method. Three optimization processes have been chosen for this investigation. The study examines and documents anomaly detection efficiency. A public dataset comprising brain scan images from disparate institutions is employed in the research to analyze the effectiveness of the detection procedure. The three algorithms used are Sequential Minimal Optimization (SMO), Krill Herd Optimization (KHO), and Particle Swarm Optimization (PSO). The three algorithms are fed the preprocessed images for training and testing. From the simulation result, it is established that the KHO algorithm is the best method for identifying brain abnormalities. The KHO achieved an accuracy of 0.96, whereas the competitive model like PSO scored an accuracy value of 0.93 and SMO attained 0.95. The KHO method is quite accurate in detecting the presence of any kind of abnormality.

关键词： Training Magnetic resonance imaging Simulation Metaheuristics Software algorithms Medical services Software

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Exploring the Role of AI in Enhancing Remote Sensing Technologies for Climate Change Studies

Exploring the Role of AI in Enhancing Remote Sensing Technol...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Ahmed Alkhayyat Raman Kumar Hassan M. Al-Jawahry Bhagwati Lal Pareek Jaspreet Kaur Jayant Jagtap College of Technical Engineering The Islamic University Najaf Iraq School of Mechanical Engineering Rayat Bahra University Mohali India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Sangam University Bhilwara Rajasthan India Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India NIMS School of Computing Science and Artificial Intelligence NIMS University Rajasthan Jaipur India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

The current study focuses on the application of Intelligent Systems in the improvement of data analysis for climate changevariables using remote sensing systems. The idea is to enhance greatly interpretability of sophisticated remote sensing data, which is essential for analyzing the effects of the climate change. The use of the AI means that the climate ascertaining process of big data patterns can be expedited and consequently augment the knowledge in climate science. This research will further explore on the approaches and uses of these technologies so as to demonstrate how they can be used to enhance knowledge on climate change. the study will use advanced models from the AI classification, including CNNs and RNNs. They were used where the climate work is most sensitive: for land cover classification, vegetation health assessment, and the identification of climatic abuses such as glaciers” melting and heat island effect. CNN models gave much better accuracy in classification with accuracies as high as 92. 7 percent for actual experiment results compared to conventionally developed methods. The research also managed to assess the vegetation health using the NDVI analysis and identify severe climatic fluctuations. They stress the positive impact of the key ideas of artificial intelligence on enhancing the accuracy and frequency of the identification of environmental changes for improving climate change mitigation and adaptation. Future work will concern the further development of the AI models, the comprehensive integration of multi-source data as well as the further improvement of the models explainability to increase the AI solution's contribution to the actual scientific analysis of the global climate.

关键词： Climate change Accuracy Vegetation mapping Land surface Glaciers Data models Thermal pollution Artificial intelligence Remote sensing Meteorology

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Enhancing Human-Robot Collaborative Assembly in Manufacturing Systems Using Large Language Models

arXiv

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arXiv 2024年

作者： Lim, Jonghan Patel, Sujani Evans, Alex Pimley, John Li, Yifei Kovalenko, Ilya Department of Industrial and Manufacturing Pennsylvania State University State College United States Department of Mechanical Engineering Pennsylvania State University State College United States Department of Computer Science and Engineering Pennsylvania State University State College United States Department of Industrial and Manufacturing Department of Mechanical Engineering Pennsylvania State University State College United States

The development of human-robot collaboration has the ability to improve manufacturing system performance by leveraging the unique strengths of both humans and robots. On the shop floor, human operators contribute with their adaptability and flexibility in dynamic situations, while robots provide precision and the ability to perform repetitive tasks. However, the communication gap between human operators and robots limits the collaboration and coordination of human-robot teams in manufacturing systems. Our research presents a human-robot collaborative assembly framework that utilizes a large language model for enhancing communication in manufacturing environments. The framework facilitates human-robot communication by integrating voice commands through natural language for task management. A case study for an assembly task demonstrates the framework’s ability to process natural language inputs and address real-time assembly challenges, emphasizing adaptability to language variation and efficiency in error resolution. The results suggest that large language models have the potential to improve human-robot interaction for collaborative manufacturing assembly applications. © 2024, CC BY.

关键词： Human robot interaction

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Handwashing Action Detection System for an Autonomous Social Robot

Handwashing Action Detection System for an Autonomous Social...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Sasidharan, Sreejith Prabha, Pranav Pasupuleti, Devasena Das, Anand M Kapoor, Chaitanya Manikutty, Gayathri Pankajakshan, Praveen Rao, Bhavani Amrita Vishwa Vidyapeetham Ammachi Labs Amritapuri India Amrita Vishwa Vidyapeetham Department of Mechanical Engineering Amritapuri India Amrita Vishwa Vidyapeetham Department of Computer Science Amritapuri India VP Data Science and Ai Cropin Ai Labs Bengaluru India

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

Young children are at an increased risk of contracting contagious diseases such as COVID-19 due to improper hand hygiene. An autonomous social agent that observes children while handwashing and encourages good hand washing practices could provide an opportunity for handwashing behavior to become a habit. In this article, we present a human action recognition system, which is part of the vision system of a social robot platform, to assist children in developing a correct handwashing technique. A modified convolution neural network (CNN) architecture with Channel Spatial Attention Bilinear Pooling (CSAB) frame, with a VGG-16 architecture as the backbone is trained and validated on an augmented dataset. The modified architecture generalizes well with an accuracy of 90% for the WHO-prescribed handwashing steps even in an unseen environment. Our findings indicate that the approach can recognize even subtle hand movements in the video and can be used for gesture detection and classification in social robotics. © 2022 IEEE.

关键词： Deep learning

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