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A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot

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A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot

International Conference on Robotics and Automation Engineering (ICRAE)

作者： Li Chengbo Wang Xiaoming Huang Zixi Pang Guangyao Han Xinyu Lin Yaguang Dalian Free Trade Zone No. 1 High School Dalian China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China School of Computer Science Shaanxi Normal University Xi'an China Shenzhen Middle School Shenzhen China School of Data Science and Software Engineering Wuzhou University Wuzhou China Advanced Robotics and Intelligent Control Centre Singapore Polytechnic Singapore

ISBN: (纸本)9781665406987

The application of robots to assist humans in earthquake rescue has always been an important topic that researchers are constantly studying. CoSpace Rescue is an abstract modeling and simulation of earthquake rescue and a simulation platform for studying robot rescue search algorithms. Its mathematical model is a multi-objective optimization problem. In this paper, we propose a search algorithm design framework for the CoSpace Rescue robot. Based On this framework and the idea of the heuristic search algorithm, we design and implement the “Multi-objective Optimal Dynamic Adaptive On-Demand Search Algorithm”. The results of the experiment show that the algorithm can dynamically adapt to the changes in the environment and significantly improve the search efficiency of the rescue robot.

关键词： Automation Heuristic algorithms Earthquakes Mathematical models Scheduling Optimization Rescue robots

Waste heat harness in a thermal energy system using TEGs, and SCO 2 brayton cycle driven by renewable sources for electricity and liquid hydrogen Production: Thermo-Economic optimization using ANNs

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Case Studies in Thermal Engineering 2025年 72卷

作者： Amr S. Abouzied Sarminah Samad Pradeep Kumar Singh Ahmed H. Janabi Mohamed Shaban Asma Ahmed A. Mohammed Shoira Formanova H. Elhosiny Ali Samah G. Babiker Abdulrahman M. Alansari Department of Pharmaceutical Chemistry College of Pharmacy University of Hail Hail 81442 Saudi Arabia Department of Management College of Business Administration Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia Department of Mechanical Engineering Institute of Engineering & Technology GLA University Mathura U.P. 281406 India Computer Techniques Engineering Department College of Engineering & Technology Al-Mustaqbal University Babylon Iraq Department of Physics Faculty of Science Islamic University of Madinah Madinah 42351 Saudi Arabia Department of Computer Science University of Tabuk Saudi Arabia Department of Chemistry and Its Teaching Methods Tashkent State Pedagogical University Tashkent Uzbekistan Physics Department Faculty of Science King Khalid University P.O. Box 9004 Abha Saudi Arabia Department of Electronic Physics Faculty of Applied Science Red Sea University Port Sudan Sudan Department of Mechanical Engineering College of Engineering University of Business and Technology Jeddah 21361 Saudi Arabia

This research introduces an innovative thermal energy system that combines solar and wind energy to produce electricity, generate hydrogen, and facilitate liquefaction. This system includes a parabolic trough solar collector (PTSC) that heats nitrate salts, transferring the thermal energy to a supercritical carbon dioxide Brayton cycle (SCO 2 -BC). Furthermore, thermoelectric generators (TEG) are integrated to capture energy from waste heat sources. Additionally, this study breaks new ground by incorporating solar and wind power with a supercritical CO 2 cycle alongside hydrogen liquefaction, a field that is still relatively uncharted. A detailed techno-economic and environmental model is utilized to assess the system's performance, concentrating on critical indicators such as second law efficiency, total cost rate, hydrogen production rate, net power output, levelized costs, and the rate of CO 2 emission reduction. Following this, an optimization process is carried out using a genetic algorithm to investigate two different scenarios. Finally, the LINMAP method is applied to identify optimal solutions for each scenario. The study reveals that the system generated a grid power output of 461.2 kW and produced 8.3 kg of liquid hydrogen per hour. The overall cost of operation was established at 103.8 $/h with an exergy efficiency of 16.2 %. Further refinements resulted in values of 19.33 % for second-law efficiency, 124.80 $/h for cost rate, and 1021.64 kW for grid power.

关键词： Waste heat recovery Thermal energy utilization Parabolic trough solar collectors Thermoelectric generators Claude liquid hydrogen production Artificial neural networks and genetic algorithm

sbi reloaded: a toolkit for simulation-based inference workflows

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arXiv 2024年

作者： Boelts, Jan Deistler, Michael Gloeckler, Manuel Tejero-Cantero, Álvaro Lueckmann, Jan-Matthis Moss, Guy Steinbach, Peter Moreau, Thomas Muratore, Fabio Linhart, Julia Durkan, Conor Vetter, Julius Miller, Benjamin Kurt Herold, Maternus Ziaeemehr, Abolfazl Pals, Matthijs Gruner, Theo Bischoff, Sebastian Krouglova, Anastasia N. Gao, Richard Lappalainen, Janne K. Mucsányi, Bálint Pei, Felix Schulz, Auguste Stefanidi, Zinovia Rodrigues, Pedro L.C. Schröder, Cornelius Zaid, Faried Abu Beck, Jonas Kapoor, Jaivardhan Greenberg, David S. Gonçalves, Pedro J. Macke, Jakob H. Machine Learning in Science University of Tübingen Germany Tübingen AI Center Germany TransferLab AppliedAI Institute for Europe Germany ML Colab Cluster ML in Science University of Tübingen Germany Google Research United States Helmholtz-Zentrum Dresden-Rossendorf Germany Université Paris-Saclay INRIA CEA Palaiseau France Robert Bosch GmbH Germany School of Informatics University of Edinburgh United Kingdom University of Amsterdam Netherlands Research and Innovation Center BMW Group Germany Institute for Applied Mathematics and Scientific Computing University of the Bundeswehr Munich Germany Aix Marseille INSERM INS France TU Darmstadt Hessian.AI Germany University Hospital Tübingen M3 Research Center Germany Faculty of Science KU Leuven B-3000 Belgium Imec Belgium Methods of Machine Learning University of Tübingen Germany Neuroscience Institute Carnegie Mellon University United States Université Grenoble Alpes INRIA CNRS Grenoble INP LJK France Hertie Institute for AI in Brain Health University of Tübingen Germany Institute of Coastal Systems - Analysis and Modeling Helmholtz AI Germany Departments of Computer Science Electrical Engineering KU Leuven Belgium Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended sbi, a PyTorch-based package4 that implements Bayesian SBI algorithms based on neural networks. The sbi toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the sbi toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data. © 2024, CC BY-SA.

关键词： Neural networks

Guidance of development,validation,and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD)

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Military Medical Research 2025年第5期 788-798页

作者： Wen Wang Ying-Hui Jin Mei Liu Qiao He Jia-Yue Xu Ming-Qi Wang Guo-Wei Li Bo Fu Si-Yu Yan Kang Zou Xin Sun Institute of Integrated Traditional Chinese and Western Medicine Chinese Evidence-Based Medicine and Cochrane China CenterWest China HospitalSichuan University NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan Sichuan Center of Technology Innovation for Real World Data Center for Evidence-Based and Translational Medicine Zhongnan Hospital of Wuhan University Department of Health Research Methods Evidence and ImpactMcMaster University Center for Clinical Epidemiology and Methodology Guangdong Second Provincial General Hospital Biostatistics Unit Research Institute at St.Joseph's Healthcare Hamilton School of Computer Science and Engineering University of Electronic Science and Technology of China West China School of Public Health and West China Fourth Hospital Sichuan University

Background:In recent years,there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data (RCD).These studies rely on algorithms to identify specific health conditions (e.g.,diabetes or sepsis) for statistical ***,there has been substantial variation in the algorithm development and validation,leading to frequently suboptimal performance and posing a significant threat to the validity of study ***,these issues are often ***:We systematically developed guidance for the development,validation,and evaluation of algorithms designed to identify health status (DEVELOP-RCD).Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development,validation,and ***,we conducted an empirical study on an algorithm for identifying *** on these findings,we formulated specific workflow and recommendations for algorithm development,validation,and evaluation within the ***,the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize ***:A standardized workflow for algorithm development,validation,and evaluation was *** by specific health status considerations,the workflow comprises four integrated steps:assessing an existing algorithm’s suitability for the target health status;developing a new algorithm using recommended methods;validating the algorithm using prescribed performance measures;and evaluating the impact of the algorithm on study ***,13 good practice recommendations were formulated with detailed ***,a practical study on sepsis identification was included to demonstrate the application of this ***:The establishment of guidance is intended to aid researchers and clinician

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Human activity recognition based on dynamic Spatio-Temporal relations

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arXiv 2020年

作者： Liu, Zhenyu Liu, Yan Tao, Zhenchao Yao, Yaqiang Zhu, Yuening Wang, Lei Feng, Yuhong Department of Science and Technology Teaching China University of Political Science and Law Beijing100088 China School of Computer and Science Technology University of Science and Technique of China Hefei Anhui230027 China First Affiliated Hospital University of Science and Technique of China Hefei Anhui230027 China School of Materials and Engineering Shanghai University Shanghai China Third People’s Hospital of Hefei Hefei Anhui230000 China School of Computer and Software Shenzhen University Shenzhen China

Human activity, which usually consists of several actions (sub-activities), generally covers interactions among persons and/or objects. In particular, human actions involve certain spatial and temporal relationships, are the components of more complicated activity, and evolve dynamically over time. Therefore, the description of a single human action and the modeling of the evolution of successive human actions are two major issues in human activity recognition. In this paper, we develop a method for human activity recognition that tackles these two issues. In the proposed method, an activity is divided into several successive actions represented by spatiotemporal patterns, and the evolution of these actions are captured by a sequential model. A refined comprehensive spatiotemporal graph is utilized to represent a single action, which is a qualitative representation of a human action incorporating both the spatial and temporal relations of the participant objects. Next, a discrete hidden Markov model is applied to model the evolution of action sequences. Moreover, a fully automatic partition method is proposed to divide a long-term human activity video into several human actions based on variational objects and qualitative spatial relations. Finally, a hierarchical decomposition of the human body is introduced to obtain a discriminative representation for a single action. Experimental results on the Cornell Activity Dataset demonstrate the efficiency and effectiveness of the proposed approach, which will enable long videos of human activity to be better recognized. Copyright © 2020, The Authors. All rights reserved.

关键词： Hidden Markov models

Glucose Quantification from Absorption Spectroscopy: A Comprehensive Benchmark of Machine Learning and Filtering Chemometrics

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Heliyon 2024年第10期10卷

作者： Heydar Khadem Hoda Nemat Jackie Elliott Mohammed Benaissa Department of Electronic and Electrical Engineering University of Sheffield UK Department of Computer Science University of Manchester Manchester UK Engineering and Development Department KultraLab London UK Department of Oncology and Metabolism University of Sheffield UK Sheffield Teaching Hospitals Diabetes and Endocrine Centre Northern General Hospital Sheffield UK

The quantitative analysis of glucose using spectroscopy is a topic of great significance and interest in science and industry. One conundrum in this area is deploying appropriate preprocessing and regression tools. To contribute to addressing this challenge, in this study, we conducted a comprehensive and novel comparative analysis of various machine learning and preprocessing filtering techniques applied to near-infrared, mid-infrared, and a combination of near-infrared and mid-infrared spectroscopy for glucose assay. Our objective was to evaluate the effectiveness of these techniques in accurately predicting glucose levels and to determine which approach was most optimal. Our investigation involved the acquisition of spectral data from samples of glucose solutions using the three aforementioned spectroscopy techniques. The data was subjected to several preprocessing filtering methods, including convolutional moving average, Savitzky-Golay, multiplicative scatter correction, and normalisation. We then applied representative machine learning algorithms from three categories: linear modelling, traditional nonlinear modelling, and artificial neural networks. The evaluation results revealed that linear models exhibited higher predictive accuracy than nonlinear models, whereas artificial neural network models demonstrated comparable performance. Additionally, the comparative analysis of various filtering methods demonstrated that the convolutional moving average and Savitzky-Golay filters yielded the most precise outcomes overall. In conclusion, our study provides valuable insights into the efficacy of different machine learning techniques for glucose measurement and highlights the importance of applying appropriate filtering methods in enhancing predictive accuracy. These findings have important implications for the development of new and improved glucose quantification technologies.

关键词： near-infrared mid-infrared spectroscopy machine learning artificial intelligence glucose signal processing

Navigating Ethical Challenges in Generative AI-Enhanced Research: The ETHICAL Framework for Responsible Generative AI Use

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arXiv 2024年

作者： Eacersall, Douglas Pretorius, Lynette Smirnov, Ivan Spray, Erika Illingworth, Sam Chugh, Ritesh Strydom, Sonja Stratton-Maher, Dianne Simmons, Jonathan Jennings, Isaac Roux, Rian Kamrowski, Ruth Downie, Abigail Thong, Chee Ling Howell, Katharine A. Library Services University of Southern Queensland Australia University of the Philippines Open University Philippines School of Curriculum Teaching and Inclusive Education Faculty of Education Monash University Melbourne Australia Graduate Research School University of Technology Sydney Australia School of Education University of Newcastle Australia Department for Learning and Teaching Enhancement Edinburgh Napier University Edinburgh United Kingdom School of Engineering & Technology CML-NET Research Centre Central Queensland University Australia Centre for Learning Technologies Stellenbosch University South Africa School of Nursing and Midwifery University of Southern Queensland Australia Writing Services University of Alberta Canada eResearch and Specialised Platforms Griffith University Australia UniSQ College University of Southern Queensland Australia Griffith Graduate Research School Griffith University Australia Institute of Computer Science and Digital Innovation UCSI University Malaysia Graduate Research Edith Cowan University Perth Australia

The rapid adoption of generative artificial intelligence (GenAI) in research presents both opportunities and ethical challenges that should be carefully navigated. Although GenAI tools can enhance research efficiency through automation of tasks such as literature review and data analysis, their use raises concerns about aspects such as data accuracy, privacy, bias, and research integrity. This paper develops the ETHICAL framework, which is a practical guide for responsible GenAI use in research. Employing a constructivist case study examining multiple GenAI tools in real research contexts, the framework consists of seven key principles: ‘Examine policies and guidelines’, ‘Think about social impacts’, ‘Harness understanding of the technology’, ‘Indicate use’, ‘Critically engage with outputs’, ‘Access secure versions’, and ‘Look at user agreements’. Applying these principles will enable researchers to uphold research integrity while leveraging GenAI’s benefits. The framework addresses a critical gap between awareness of ethical issues and practical action steps, providing researchers with concrete guidance for ethical GenAI integration. This work has implications for research practice, institutional policy development, and the broader academic community while adapting to an AI-enhanced research landscape. The ETHICAL framework can serve as a foundation for developing AI literacy in academic settings and promoting responsible innovation in research methodologies. © 2024, CC BY.

关键词： Generative adversarial networks

Segmentation of shoulder muscle MRI using a new Region and Edge based Deep Auto-Encoder

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arXiv 2021年

作者： Khan, Saddam Hussain Khan, Asifullah Lee, Yeon Soo Hassan, Mehdi Jeong, Woong Kyo Pattern Recognition Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Center for Mathematical Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Department of Biomedical Engineering College of Medical Science Catholic University of Daegu South Korea Medical Teaching Institute Peshawar Pakistan Department of Computer Science Air University Islamabad Pakistan Department of Orthopaedic Surgery Korea University College of Medicine Seoul Korea Republic of

Automatic segmentation of shoulder muscle MRI is challenging due to the high variation in muscle size, shape, texture, and spatial position of tears. Manual segmentation of tear and muscle portion is hard, time-consuming, and subjective to pathological expertise. This work proposes a new Region and Edge-based Deep Auto-Encoder (RE-DAE) for shoulder muscle MRI segmentation. The proposed RE-DAE harmoniously employs average and max-pooling operation in the encoder and decoder blocks of the Convolutional Neural Network (CNN). Region-based segmentation incorporated in the Deep Auto-Encoder (DAE) encourages the network to extract smooth and homogenous regions. In contrast, edge-based segmentation tries to learn the boundary and anatomical information. These two concepts, systematically combined in a DAE, generate a discriminative and sparse hybrid feature space (exploiting both region homogeneity and boundaries). Moreover, the concept of static attention is exploited in the proposed RE-DAE that helps in effectively learning the tear region. The performances of the proposed MRI segmentation based DAE architectures have been tested using a 3D MRI shoulder muscle dataset using the hold-out cross-validation technique. The MRI data has been collected from the Korea University Anam Hospital, Seoul, South Korea. Experimental comparisons have been conducted by employing innovative custom-made and existing pre-trained CNN architectures both using transfer learning and fine-tuning. Objective evaluation on the muscle datasets using the proposed SA-RE-DAE showed a dice similarity of 85.58% and 87.07%, an accuracy of 81.57% and 95.58% for tear and muscle regions, respectively. The high visual quality and the objective result suggest that the proposed SA-RE-DAE is able to correctly segment tear and muscle regions in shoulder muscle MRI for better clinical decisions. Copyright © 2021, The Authors. All rights reserved.

关键词： Muscle

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction

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arXiv 2022年

作者： Wang, Xiaozhi Chen, Yulin Ding, Ning Peng, Hao Wang, Zimu Lin, Yankai Han, Xu Hou, Lei Li, Juanzi Liu, Zhiyuan Li, Peng Zhou, Jie Department of Computer Science and Technology BNRist China Shenzhen International Graduate School China THU-Siemens Ltd. China Joint Research Center for Industrial Intelligence and IoT China Tsinghua University Beijing China Xi’an Jiaotong-Liverpool University Suzhou China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Pattern Recognition Center WeChat AI Tencent Inc China

The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit event relation extraction (ERE) tasks: (1) Small scale. Due to the annotation complexity, the data scale of existing datasets is limited, which cannot well train and evaluate data-hungry models. (2) Absence of unified annotation. Different types of event relations naturally interact with each other, but existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. To address these issues, we construct a unified large-scale human-annotated ERE dataset MAVEN-ERE with improved annotation schemes. It contains 103, 193 event coreference chains, 1, 216, 217 temporal relations, 57, 992 causal relations, and 15, 841 subevent relations, which is larger than existing datasets of all the ERE tasks by at least an order of magnitude. Experiments show that ERE on MAVEN-ERE is quite challenging, and considering relation interactions with joint learning can improve performances. The dataset and source codes can be obtained from https://***/THU-KEG/MAVEN-ERE. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction