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A Short Review of Truck and Drone Collaborative Delivery Problem 10

A Short Review of Truck and Drone Collaborative Delivery Pro...

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10th IEEE international conference on Cyber Security and Cloud Computing and 9th IEEE international conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhu, Yena Liu, Min Jin, Dongyao Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan China

ISBN: (纸本)9798350312461

With the emergence of efficient logistics models and the development of drone technology, the last-mile delivery problem has become a focus of attention for scholars in various fields. the advantages of drones, such as low cost, energy saving, and independence from road conditions, have made them increasingly popular in the delivery industry. However, drones have shortcomings such as short endurance, small payload capacity, and susceptibility to interference. therefore, the cooperative delivery model of vehicles and drones has important theoretical research significance and practical application value. Existing literature reviews focus on static truck-drone delivery problems (TDDPs) and methods such as exact algorithms, heuristics, and metaheuristics. However, real-life truck-drone delivery problems are easily affected by dynamic environmental changes. Accordingly, this article introduces some new dynamic variants of TDDPs proposed in recent years, which consider variable drone flight endurance, urban crowded road conditions, stochastic customer demands, and the influence of weather changes. In recent years, some new methods for solving TDDPs have been introduced, such as the increasingly popular machine learning and reinforcement learning methods. Finally, the possible future research directions and development trends of TDDPs are presented. © 2023 IEEE.

关键词： Reinforcement learning

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Scheduling of the Battery for Peak Shaving Using Model predictive Control (MPC) 9

Scheduling of the Battery for Peak Shaving Using Model Predi...

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9th IEEE international conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2023

作者： Abidin, Siti Aisyah Zainal Yusoff, Siti Hajar Mansor, Hasmah Sapihie, Siti Nadiah Mohd Fahmi, Muhammad Aqil Muqri Muhamad Department of Electrical and Computer Engineering Kuala Lumpur Malaysia Petronas Research Sdn. Bhd. Selangor Malaysia

ISBN: (纸本)9798350343380

In this age of technology, the higher demand of renewable energy has become the main factor that contributes to the increasing power consumption. the electrical power generated needs to match with the load demand or the power consumption of the consumers, or else it will lead to the mismatch of supply and demand problem. However, the spike in the load demand will impact the electricity cost of the consumers. therefore, the purpose of this research is to find an optimal solution to shave the peak load to reduce the electricity bills of the consumers. there are a lot of controllers that can be used to control the scheduling of the battery, and this research will focus on battery scheduling by using Model predictive Controller (MPC). the MPC technique is able to control the battery energy storage system (BESS) to discharge during the peak hours and also charge during the off-peak hours. However, the MPC has a complex and high optimization algorithm due to its dynamic model, but MPC technique ensures that it is environment-friendly compared to the traditional method of peak-shaving. this research proposed the battery logic control to schedule the charge-discharge of the battery and for the MPC to optimize and get the best output for the peak shaving. the MPC design will be done by using MATLAB as the main software as it is very ideal to simulate the peak shaving by using MPC optimization © 2023 IEEE.

关键词： Model predictive control

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Contact parameter estimation using a constrained extended Kalman filter 30

Contact parameter estimation using a constrained extended Ka...

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30th international conference on Noise and Vibration engineering, ISMA 2022 and 9th international conference on Uncertainty in Structural Dynamics, USD 2022

作者： Mazzanti, L. Vivet, M. De Gregoriis, D. Tamarozzi, T. Blockmans, B. Siemens Digital Industries Software Interleuvenlaan 68 LeuvenB-3001 Belgium KU Leuven Department of Mechanical Engineering Celestijnenlaan 300 HeverleeB-3001 Belgium DMMS Core Lab Flanders Make Belgium

ISBN: (纸本)9789082893151

In structural dynamics simulations, sophisticated contact models are required to accurately describe contact interactions. these contact models typically depend on several, possibly uncertain, parameters. In recent years, significant efforts have been spent in developing hybrid techniques that enhance the predictive performance of numerical models with real-world measured data. the Virtual Sensing (VS) estimation framework uses a Kalman Filter-based approach to combine experimental measurement data with numerical models. In this work, an extension of VS for contact model parameter estimation is presented. the developed VS formulation is numerically validated using a lumped-parameter model of a mass-spring-damper system to demonstrate the potential of the methodology. the objective of the experiment is accurate joint state-contact parameter estimation, starting from an inaccurate system model. Results show that the inclusion of contact parameter in the estimation improves the accuracy of the overall estimation. © 2022 proceedings of ISMA 2022 - international conference on Noise and Vibration engineering and USD 2022 - international conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Parameter estimation

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Enhancing Urban Building Energy Simulations: Advanced Evaluation of Stochastic Occupancy models with Real Occupancy Data 9th

Enhancing Urban Building Energy Simulations: Advanced Evalua...

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9th international Building Physics conference

作者： Dabirian, Sanam Alamatsaz, Kayhan Eicker, Ursula Concordia Univ Montreal PQ H3H 2L9 Canada Canada Excellence Res Chair Montreal PQ Canada

ISBN: (纸本)9789819783113;9789819783090;9789819783083

the precision of energy simulations in buildings is crucial for efficiently managing and sustaining energy resources, particularly in intricate settings like university campuses. this study investigates how to enhance energy simulation models by integrating detailed occupancy data, evaluating stochastic occupancy models, and comparing the actual data of urban building archetypes. Traditional energy models, which often rely on standardized occupancy schedules, can lead to significant discrepancies between predicted and actual energy usage. this study introduces an approach incorporating electricity consumption data from campus facility management into building simulation models. this data reflects actual occupancy patterns, including peak and off-peak usage periods, transient populations, and space-specific activities, providing a more accurate simulation basis. the proposed model was applied to a group of buildings at Concordia University, Montreal, Canada. the results indicate a significant improvement in the model predictive accuracy, with the mean-variance reduced by 11%, highlighting the critical impact of accurately modeling occupancy patterns on energy consumption. this research underscores the importance of considering occupancy dynamics in building energy simulations. By aligning simulation parameters with real-world occupancy behaviors and applying them to urban building archetypes, it is possible to achieve more accurate energy conservation measures and sustainable practices. the findings demonstrate the potential for using existing infrastructure as scalable, cost-effective tools for collecting occupancy data, offering a novel, practical approach to enhancing simulation accuracy in various urban environments and promoting more sustainable, energy-efficient operations in campus settings.

关键词： Urban building energy modeling Occupancy modeling Stochastic modeling

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Urban Traffic Accident Factor Analysis, Risk Prediction and Early Warning Based on Big Data Technology 23

Urban Traffic Accident Factor Analysis, Risk Prediction and ...

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9th international conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Hou, Xuehui Faculty of Information Security and Engineering Xinjiang Police College China

ISBN: (纸本)9781450399029

Urban road traffic safety has become a social problem related to people’s life safety, the main factors affecting the occurrence and severity of road traffic accidents are human factors, time, month, vehicle conditions, lighting, road conditions, weather conditions, visibility, etc. Using descriptive and predictive modeling, descriptive modeling is based on Hive database software to analyze the full sample data or Pyechart to visualize the data, the model studies and charts on historical data to obtain corresponding data regularity. predictive models use machine learning algorithms, accident severity is taken as the target variable, through data cleaning, PCA was used for dimension reduction, RobustScaler data normalization and other processing, select the feature variables, Random forest algorithm or Xgboost algorithm was used to analyze the importance of characteristic variables that affect the severity of accidents. So as to find out the rules, in order to take preventive measures, reduce traffic accidents, to ensure people’s safety. © 2023 Copyright held by the owner/author(s).

关键词： Risk analysis

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Traffic flow prediction based on dynamic spatial enhancement 9

Traffic flow prediction based on dynamic spatial enhancement

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9th international conference on Electromechanical Control Technology and Transportation, ICECTT 2024

作者： Bao, Shumei Chen, Cheng Huo, Boyan Lv, Xiaoyi Chen, Chen Software College Xinjiang University 448 Northwest Road Xinjiang Uygur Autonomous Region Urumqi830006 China

ISBN: (纸本)9781510682399

Traffic flow prediction is a spatiotemporal prediction task. the biggest difference between traffic flow and ordinary time series prediction problems is that it is restricted by the topology of the road network. therefore, it is necessary to accurately mine the spatial information in the road network. this paper introduces a spatial block designed to obtain spatial correlation, incorporating both Graph Convolutional Networks (GCN) and a novel spatial transformer module for handling spatial properties. By leveraging spatial transformers, the model can extract dynamic spatial correlations among individual nodes. the dynamic spatial information and static spatial information are integrated through the gating mechanism. In addition, this chapter also adds the Informer module based on GRU to mine long-term sequence information to improve the forgetting problem of using the GRU module. the final experimental results were verified on the London M25 highway data set and compared with other benchmark methods and found to achieve good results. © 2024 SPIE.

关键词： Prediction models

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Predicting COVID-19 Transmission in Southern California with Machine Learning Methods 9

Predicting COVID-19 Transmission in Southern California with...

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9th international conference on Big Data Analytics, ICBDA 2024

作者： Li, Han Wei, Ran Wang, Wenyu Yu, Nanpeng School of Computer Science and Engineering University of California Riverside Riverside United States School of Public Policy University of California Riverside Riverside United States University of California Riverside Department of Electrical and Computer Engineering Riverside United States

ISBN: (纸本)9798350352962

the COVID-19 pandemic has posed significant chal-lenges to global health, economies, and social structures since its emergence in late 2019. Southern California, a region with unique characteristics and diverse communities, has experienced difficul-ties in controlling the virus's spread. Understanding the factors that influence COVID-19 transmission in Southern California is crucial for informing public health policies and mitigating the virus's impact on the communities. In this study, we collected weekly zip code level data from March to December 2020 in Southern California and applied various machine learning algorithms, such as graph neural networks (GNN), multi-layer perceptrons (MLP), and gradient boosting trees (XGBoost), to model the spread of COVID-19. Our main contributions include the development of various COVID-19 forecasting models that incorporate socioeconomically significant variables and zip code level mobility data, coupled with variable impact assessments to provide valuable insights for informing future public health policies and strategies. Our predictive model not only attains high levels of accuracy in forecasting but also facilitates the understanding of how each variable contributes to the final outcome. these findings have the potential to enhance our understanding of COVID-19 transmission patterns and inform targeted mitigation strategies in the region. © 2024 IEEE.

关键词： COVID-19

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Computational Simulation of Adolescent Idiopathic Scoliosis in the Spine 31

Computational Simulation of Adolescent Idiopathic Scoliosis ...

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31st National and 9th international Iranian conference on Biomedical engineering, ICBME 2024

作者： Zadeh-Posti, Mohammad Hosein Kamal, Zeinab Rajaeirad, Mohadese Einafshar, Mohammadjavad Matin Jamshidi, Nima University of Guilan Faculty of Mechanical Engineering Rasht Iran Amirkabir University of Technology Department of Biomedical Engineering Tehran Iran University of Isfahan Faculty of Engineering Department of Biomedical Engineering Isfahan Iran

ISBN: (纸本)9798331529710

Adolescent Idiopathic Scoliosis (AIS) is a condition marked by an abnormal curvature of the spine. this study aims to model AIS using computational techniques to analyze varying bone densities within vertebrae under different muscle paralysis conditions. the aim is to investigate how unilateral muscle paralysis affects bone density and to model conditions similar to AIS. A three-dimensional model of the L2 vertebra was created using Mimics and 3-matic software for segmentation and meshing. the model was imported into ABAQUS for further integration and assignment of properties. the bone remodeling algorithm was applied over 12 months, simulating the intact scoliosis (IS) condition. Loading conditions specific to IS were applied, and three scenarios of muscle paralysis were modeled: longissimus pars thoracic (LGPT) muscle paralysis, multifidus lumborum (MFL) muscle paralysis, and both muscles' paralysis. the current study found variable bone density and Young's modulus distributions for cancellous and cortical bones in the AIS model. Trabecular bone density varied between 0.297 and 0.317g/cm3, with Young's modulus ranging from 99.61 to 115.83 MPa. Cortical bone density ranged from 1.466 to 1.665 g/cm3, while Young's modulus was between 11,794 and 16,110 MPa. the highest von Mises stress was observed in the cortical bone, particularly in the concave region, which is under higher compression in AIS patients than healthy spines. the results of this study support Frost's mechanostat theory, suggesting that uneven loading leads to higher bone density on the concave side of scoliotic vertebrae. the study demonstrates the feasibility of using computational models to simulate AIS and highlights the significance of muscle conditions on bone remodeling and stress distribution in vertebrae. © 2024 IEEE.

关键词： ABAQUS

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An Experimental and Numerical Study on Omnidirectional Wind Turbine Prototypes 9th

An Experimental and Numerical Study on Omnidirectional Wind ...

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9th international and 49th National conference on Fluid Mechanics and Fluid Power, FMFP 2022

作者： Perka, Rishikaran Sabareesh, V. Vijayan, Sujith Kuntikana, Pramod Department of Mechanical Engineering IIT Palakkad Palakkad678557 India

ISBN: (纸本)9789819710324

Omnidirectional wind turbines are versatile in their kind of design such that they can capture wind from multiple directions to generate power. the aim of the present work is to experiment with three prototypes of such turbine rotors at three different wind speeds and to study the results with the computational analysis of such models. the general type of rotor model analysed is cuboctahedron type and the computational fluid flow analysis is performed using ANSYS Fluent software with appropriate boundary conditions to study the velocity variation inside each of them. From the experimental results, it is seen that out of three configurations of the rotor models, the third one has higher power output at different wind speeds. the computational analysis also suggests that this kind of modified rotor types ensure higher power output than conventional HAWT types. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wind turbines

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9th IFIP WG 13.2 international Working conference on Human-Centered software engineering, HCSE 2022

9th IFIP WG 13.2 International Working Conference on Human-C...

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9th IFIP WG 13.2 international Working conference on Human-Centered software engineering, HCSE 2022

ISBN: (纸本)9783031147845

the proceedings contain 13 papers. the special focus in this conference is on Human-Centered software engineering. the topics include: Meeting Strangers Online: Feature models for Trustworthiness Assessment;requirements-Based Composition of Tailored Decision Support Systems;towards Collaborative Vision Video Production: How to Integrate Different Stakeholders;development of a Conversational Dietary Assessment Tool for Cardiovascular Patients;emoti-Office, Using Emotional and Contextual Data to Improve Employees’ Working Needs in the Office;i Feel You;challenges with Traditional Human-Centered Design for Developing Neurorehabilitation software;dissecting the Security and Usability Alignment in the Industry;models-Based Analysis of Both User and Attacker Tasks: Application to EEVEHAC;on the Potentials of Realtime Sentiment Analysis on Text-Based Communication in software Projects;little Stakeholder Communication in Distributed Scrum Projects During the Covid-19 Period.

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