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A framework for a practical nurse scheduling approach: A case of operating room of a hospital in thailand 1

AHFE Virtual Conference on the Human Side of Service engineering, 2020

作者： Rerkjirattikal, Pavinee Huynh, Van-Nam Olapiriyakul, Sun Supnithi, Thepchai School of Knowledge Science Japan Advanced Institute of Science and Technology Ishikawa923-1211 Japan School of Manufacturing Systems and Mechanical Engineering Sirindhorn International Institute of Technology Thammasat University Pathumthani12120 Thailand Language and Semantic Research Team National Electronics and Computer Technology Center Pathumthani12120 Thailand

ISBN: (数字)9783030510572

ISBN: (纸本)9783030510565

Nurse Schedule Problem (NSP) is the assignment of nurses to fulfill hospitals operational goal and regulations. The major aim of NSP is to minimize the total cost, maximizing nurse’s satisfaction, or maintain balanced distribution among preferable assignments and workloads. This paper is the preliminary research in development of NSP for a case of an operating room at a private hospital in Pathumthani, Thailand. An interview with the head nurse was conducted to capture insights and emerging issues that the head nurse wants to resolve. Then, a framework is proposed as a guideline for the development stochastic optimization model to tackle uncertainties in patients’ arrival and surgery duration which are the main concerns. This paper aims to serve as a fundamental stage for a further case based NSP development research. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

关键词： Job satisfaction

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Ferroelectrets:Heterogenous polymer electrets with high piezoelectric sensitivity for transducers

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Journal of advanced Dielectrics 2023年第4期13卷 3-26页

作者： Xunlin Qiu Peng Fang Axel Mellinger Ruy Alberto Pisani Altafim Werner Wirges Gunnar Gidion Dmitry Rychkov Shanghai Key Laboratory of Intelligent Sensing and Detection Technology School of Mechanical and Power Engineering East China University of Science and Technology Shanghai 200237P.R.China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen 518055P.R.China Department of Physics Central Michigan University 223 Dow Science ComplexMount PleasantMI 48859USA Computer Systems Department Informatics Center Federal University of ParáıbaJoão Pessoa-PBBrazil Department of Physics and Astronomy University of Potsdam PotsdamBrandenburgGermany Laboratory for Electrical Instrumentation Department of Microsystems Engineering-IMTEK University of FreiburgGeorges-Köhler-Allee 106Freiburg 79110Germany Technology and Study Center Weissenburg Deggendorf Institute of Technology WeissenburgBavariaGermany

Nowadays,the demand for advanced functional materials in transducer technology is growing *** materials transform mechanical variables(displacement or force)into electrical signals(charge or voltage)and vice *** are interesting from both fundamental and application points of ***(also called piezoelectrets)are a relatively young group of piezo-,pyro-and ferroelectric *** exhibit ferroic behavior phenomenologically undistinguishable from that of traditional ferroelectrics,although the materials per se are essentially non-polar space-charge electrets with artificial macroscopic dipoles(i.e.,internally charged cavities).A lot of work has been done on ferroelectrets and their applications up to *** this paper,we review and discuss mostly the work done at University of Potsdam on the research and development of *** will,however,also mention important results from other teams,and prospect the challenges and future progress trend of the field of ferroelectret research.

关键词： Ferroelectret piezoelectret space-charge electret transducer advanced functional materials

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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A Novel Scalable Framework For Constructing Dynamic Multi-objective Optimization Problems

A Novel Scalable Framework For Constructing Dynamic Multi-ob...

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Congress on Evolutionary Computation

作者： Qingshan Tan Changhe Li Hai Xia Sanyou Zeng Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Modeling dynamic multi-objective optimization problems (DMOPs) has been one of the most challenging tasks in the field of dynamic evolutionary optimization. Based on the analysis of the existing DMOPs, several features widely existed in real-world applications are not taken into account: different objectives may have different function models and variables to be optimized; and the number of conflicting variables should be independent from the number of objectives; the time-linkage property is not considered. In order to overcome the above issues, a novel framework for constructing DMOPs is proposed, where all objectives can be designed independently, and the number of the conflicting variables can be tuned by users. Moreover, it is easy to add new dynamic features to this framework. Several classical dynamic multi-objective optimization algorithms are tested on four scenarios, results show that these characteristics are challenging for the existing algorithms.

关键词： Schedules Analytical models Heuristic algorithms Object oriented modeling Evolutionary computation Benchmark testing Dynamic scheduling

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A Reinforcement-Learning-Based Evolutionary Algorithm Using Solution Space Clustering For Multimodal Optimization Problems

A Reinforcement-Learning-Based Evolutionary Algorithm Using ...

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Congress on Evolutionary Computation

作者： Hai Xia Changhe Li Sanyou Zeng Qingshan Tan Junchen Wang Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

In evolutionary algorithms, how to effectively select interactive solutions for generating offspring is a challenging problem. Though many operators are proposed, most of them select interactive solutions (parents) randomly, having no specificity for the features of landscapes in various problems. To address this issue, this paper proposes a reinforcement-learning-based evolutionary algorithm to select solutions within the approximated basin of attraction. In the algorithm, the solution space is partitioned by the k-dimensional tree, and features of subspaces are approximated with respect to two aspects: objective values and uncertainties. Accordingly, two reinforcement learning (RL) systems are constructed to determine where to search: the objective-based RL exploits basins of attraction (clustered subspaces) and the uncertainty-based RL explores subspaces that have been searched comparatively less. Experiments are conducted on widely used benchmark functions, demonstrating that the algorithm outperforms three other popular multimodal optimization algorithms.

关键词： Uncertainty Clustering algorithms Evolutionary computation Reinforcement learning Benchmark testing Search problems Feature extraction

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A Novel Multi-objective Evolutionary Algorithm Based on Space Partitioning 11th

A Novel Multi-objective Evolutionary Algorithm Based on Spac...

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11th International Symposium on Intelligence Computation and Applications, ISICA 2019

作者： Wu, Xiaofang Li, Changhe Zeng, Sanyou Yang, Shengxiang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

ISBN: (纸本)9789811555763

To design an effective multi-objective optimization evolutionary algorithms (MOEA), we need to address the following issues: 1) the sensitivity to the shape of true Pareto front (PF) on decomposition-based MOEAs;2) the loss of diversity due to paying so much attention to the convergence on domination-based MOEAs;3) the curse of dimensionality for many-objective optimization problems on grid-based MOEAs. This paper proposes an MOEA based on space partitioning (MOEA-SP) to address the above issues. In MOEA-SP, subspaces, partitioned by a k-dimensional tree (kd-tree), are sorted according to a bi-indicator criterion defined in this paper. Subspace-oriented and Max-Min selection methods are introduced to increase selection pressure and maintain diversity, respectively. Experimental studies show that MOEA-SP outperforms several compared algorithms on a set of benchmarks. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Computational and experimental design of fast and versatile magnetic soft robotic low Re swimmers

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Extreme Mechanics Letters 2025年 78卷

作者： R. Pramanik M. Park Z. Ren M. Sitti R.W.C.P. Verstappen P.R. Onck Computational & Numerical Mathematics Group Bernoulli Institute for Mathematics Computer Science & Artificial Intelligence University of Groningen Netherlands Micromechanics Group Zernike Institute for Advanced Materials University of Groningen Netherlands Physical Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart Germany School of Mechanical Engineering and Automation Beihang University Beijing China Institute for Biomedical Engineering ETH Zurich Switzerland School of Medicine and College of Engineering Koc University Istanbul Turkey

Miniaturized magnetic soft robots have shown extraordinary capabilities of contactless manipulation, complex path maneuvering, precise localization, and rapid actuation, enabling them to cater to challenging biomedical applications such as targeted drug delivery, internal wound healing, and laparoscopic surgery. However, despite their successful fabrication by several different research groups, a thorough design strategy encompassing the optimized kinematic performance of the three fundamental biomimetic swimming modes at miniaturized length scales has not been reported until now. Here, we resolve this by designing magnetic soft robotic swimmers (MSRSs) from the class of helical and undulatory low Reynolds number (Re) swimmers using a fully coupled, experimentally calibrated computational fluid dynamics model. We study (and compare) their swimming performance, and report their steady-state swimming speed for different non-dimensional numbers that capture the competition by magnetic loading, nonlinear elastic deformation, and viscous solid–fluid coupling. We investigated their stability for different initial spatial orientations to ensure robustness during real-life applications. Our results show that the helical ’finger-shaped’ swimmer is by far the fastest low Re swimmer in terms of body lengths per cycle, but that the undulatory ’carangiform-like’ swimmer proved to be the most versatile, bidirectional swimmer with maximum stability.

关键词： Soft robotics Magnetic swimmers Low Re Computational modeling Experimental design

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Cloud-based platform for intelligent healthcare monitoring and risk prevention in hazardous manufacturing contexts

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Procedia CIRP 2021年 99卷 50-56页

作者： Alessandro Simeone Alessandra Caggiano Lev Boun Rebecca Grant Intelligent Manufacturing Key Laboratory of Ministry of Education Shantou University 515063 Shantou Guangdong China Department of Industrial Engineering University of Naples Federico II Piazzale Tecchio 80 80125 Naples Italy Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh-J_LEAPT UniNaples) Naples Italy Dynamic Computer Systems PoB 1341 30300 Atlit Israel Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough LE11 3TU UK

This paper presents an intelligent cloud-based platform for workers healthcare monitoring and risk prevention in potentially hazardous manufacturing contexts. The platform is structured according to sequential modules dedicated to data acquisition, processing and decision-making support. Several sensors and data sources, including smart wearables, machine tool embedded sensors and environmental sensors, are employed for data collection, comprising information on offline clinical background, operational and environmental data. The cloud data processing module is responsible for extracting relevant features from the acquired data in order to feed a machine learning-based decision-making support system. The latter provides a classification of workers’ health status so that a prompt intervention can be performed in particularly challenging scenarios.

关键词： Industry 4.0 cloud manufacturing sustainable manufacturing smart wearables hazardous manufacturing context

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One to many: Adaptive instrument segmentation via meta learning and dynamic online adaptation in robotic surgical video

arXiv

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arXiv 2021年

作者： Zhao, Zixu Jin, Yueming Lu, Bo Ng, Chi-Fai Dou, Qi Liu, Yun-Hui Heng, Pheng-Ann The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The T Stone Robotics Institute The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China The Department of Surgery The Chinese University of Hong Kong Hong Kong

Surgical instrument segmentation in robot-assisted surgery (RAS) - especially that using learning-based models - relies on the assumption that training and testing videos are sampled from the same domain. However, it is impractical and expensive to collect and annotate sufficient data from every new domain. To greatly increase the label efficiency, we explore a new problem, i.e., adaptive instrument segmentation, which is to effectively adapt one source model to new robotic surgical videos from multiple target domains, only given the annotated instruments in the first frame. We propose MDAL, a meta-learning based dynamic online adaptive learning scheme with a two-stage framework to fast adapt the model parameters on the first frame and partial subsequent frames while predicting the results. MDAL learns the general knowledge of instruments and the fast adaptation ability through the video-specific meta-learning paradigm. The added gradient gate excludes the noisy supervision from pseudo masks for dynamic online adaptation on target videos. We demonstrate empirically that MDAL outperforms other state-of-the-art methods on two datasets (including a real-world RAS dataset). The promising performance on ex-vivo scenes also benefits the downstream tasks such as robot-assisted suturing and camera control. Copyright © 2021, The Authors. All rights reserved.

关键词： Surgical equipment

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Detecting and Counting Oysters

arXiv

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arXiv 2021年

作者： Sadrfaridpour, Behzad Aloimonos, Yiannis Yu, Miao Tao, Yang Webster, Donald University of Maryland Institute for Advanced Computer Studies University of Maryland College ParkMD United States Department of Mechanical Engineering and Institute for Systems Research University of Maryland College ParkMD United States Department of Bioengineering University of Maryland College ParkMD United States Wye Research and Education Center University of Maryland College ParkMD United States

— Oysters are an essential species in the Chesapeake Bay living ecosystem. Oysters are filter feeders and considered the vacuum cleaners of the Chesapeake Bay that can considerably improve the Bay’s water quality. Many oyster restoration programs have been initiated in the past decades and continued to date. Advancements in robotics and artificial intelligence have opened new opportunities for aquaculture. Drone-like ROVs with high maneuverability are getting more affordable and, if equipped with proper sensory devices, can monitor the oysters. This work presents our efforts for videography of the Chesapeake bay bottom using an ROV, constructing a database of oysters, implementing Mask R-CNN for detecting oysters, and counting their number in a video by tracking them. © 2021, CC BY-NC-ND.

关键词： Molluscs

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