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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

Building Simulation 2024年第2期17卷 319-334页

作者： Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao School of Architecture and Urban Planning Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical *** learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind ***,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow *** study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow *** the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial *** information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency *** spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced *** results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,*** also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind *** reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding *** enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind *** proposed spatial-frequen

关键词： pedestrian wind flow prediction generative adversarial network Gaussian kernel wavelet transform objective function

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Electromagnetic Churning of Laser Melt-Pool in Metal 3D Printing

Electromagnetic Churning of Laser Melt-Pool in Metal 3D Prin...

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2024 NDIA Michigan Chapter Ground Vehicle Systems engineering and Technology Symposium, GVSETS 2024

作者： Karpenko, Oleksii Udpa, Satish Udpa, Lalita Haq, Mahmood Department of Electrical and Computer Engineering MI United States Department of Civil and Environmental Engineering MI United States

Manufacturing flaws and microstructure irregularities pose challenges for the widespread adoption of metal additive manufacturing (MAM) in the US Army. These issues stem from the influence of melt-pool dynamics on the properties of 3D-printed metal parts, which are highly dependent on multiple process parameters. This paper investigates the potential of using electromagnetic fields (EM) to control the melt-pool dynamics in MAM, aiming to eliminate flaws and irregularities. A novel technique is proposed, involving a coil and strategically positioned permanent magnets to actively churn the melt pool. Initial validation of this approach was conducted using COMSOL Multiphysics® through simulation modeling, with ongoing efforts for experimental verification. The findings indicate promising opportunities for enhancing the consistency of 3D printed parts. © Rights reserved by the National Defense Industrial Association (NDIA) Michigan Chapter, authors and their respected organizations.

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Joint Biomedical Entity and Relation Extraction Based on Multi-Granularity Convolutional Tokens Pairs of Labeling

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computers, Materials & Continua 2024年第9期80卷 4325-4340页

作者： Zhaojie Sun Linlin Xing Longbo Zhang Hongzhen Cai Maozu Guo Department of Computer Science and Technology Shandong University of TechnologyZibo255000China Department of Agricultural Engineering and Food Science Shandong University of TechnologyZibo255000China Department of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China

Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of *** biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this *** a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or ***,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification *** the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word ***,we use a biaffine predictor to assist in predicting the labels of word pairs for relation *** model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous ***,we evaluated our model on two publicly accessible *** experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal *** the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal *** model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.

关键词： Deep learning biomedical joint extraction triple classification multi-granularity 2D convolution

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Reinforcement learning-driven local transactive energy market fordistributed energy resources

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Energy and AI 2022年第2期8卷 162-176页

作者： Steven Zhang Daniel May Mustafa Gül Petr Musilek Electrical and Computer Engineering University of AlbertaCanada Civil and Environmental Engineering University of AlbertaCanada

Local energy markets are emerging as a tool for coordinating generation, storage, and consumption of energyfrom distributed resources. In combination with automation, they promise to provide an effective energymanagement framework that is fair and brings system-level savings. The cooperative–competitive natureof energy markets calls for multi-agent based automation with learning energy trading agents. However,depending on the dynamics of the agent–environment interaction, this approach may yield unintended behaviorof market participants. Thus, the design of market mechanisms suitable for reinforcement learning agentsmust take into account this interplay. This article introduces autonomous local energy exchange (ALEX) asan experimental framework that combines multi-agent learning and double auction mechanism. Participantsdetermine their internal price signals and make energy management decisions through market interactions,rather than relying on predetermined external price signals. The main contribution of this article is examinationof compatibility between specific market elements and independent learning agents. Effects of different marketproperties are evaluated through simulation experiments, and the results are used for determine a suitablemarket design. The results show that market truthfulness maintains demand-response functionality, while weakbudget balancing provides a strong reinforcement signal for the learning agents. The resulting agent behavioris compared with two baselines: net billing and time-of-use rates. The ALEX-based pricing is more responsiveto fluctuations in the community net load compared to the time-of-use. The more accurate accounting ofrenewable energy usage reduced bills by a median 38.8% compared to net billing, confirming the ability tobetter facilitate demand response.

关键词： Transactive energy Demand response Distributed energy resources(DER) DER integration Local energy market Reinforcement learning

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Course construction based on FD-QM standards and analysis and mining of course data 24

Course construction based on FD-QM standards and analysis an...

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2024 International Conference on Intelligent Education and computer Technology, IECT 2024

作者： Lv, Cheng Department of Computer Science School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9798400709920

In response to the shortcomings of the SPOC course "Introduction to Computational Thinking"at Beijing University of civil engineering and Architecture, the teaching team has transformed and improved the course design and practice based on the existing High quality undergraduate courses in Beijing universities and Beijing Curriculum Ideological and Political Demonstration Course. We aim to maintain consistency and linkage in learning objectives, learning assessments, teaching materials, learning activities, and course technology under the principle of consistent construction. Learning objectives are clearer and more measurable;learning assessment is more scientific, diverse, and reliable;Curriculum resources can meet the needs of students at different levels and majors;learning activities can fully mobilize students' subjective initiative;Curriculum technology helps to achieve learning objectives, and can reflect the characteristics of the discipline, with universality and progressiveness. Through the construction and practice of the FD-QM standard, the course team has established the principles of course standardization, clarified the direction of course material construction, drawn a knowledge graph of course knowledge points, accurately grasped the degree of learning objectives, upgraded the "characteristic"teaching resource library, enhanced student interest, and integrated ideological and political education into the curriculum, ultimately achieving a comprehensive improvement in the quality of course teaching. © 2024 ACM.

关键词： Knowledge graph

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Analysis and Practice of Ideological and Political Education in computer Public Basic Courses in Architecture Universities Based on Machine Learning and Deep Learning 24

Analysis and Practice of Ideological and Political Education...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Education Digitalization and computer Science, EDCS 2024

作者： Lv, Cheng Department of Computer Science School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9798400710360

In response to the shortcomings of ideological and political education in the computer basic course of our school, the teaching team has conducted in-depth research on the connotation of ideological and political education in the course, accurately grasped the direction of ideological and political education in the course, innovatively proposed the"progressive method of ideological and political education", and constructed an integrated curriculum system of ideological and political education in the course;Using Orange3 software and machine learning algorithms to deeply explore the ideological and political elements of the curriculum, constructing an analytical model, formulating personalized teaching and learning strategies, and constructing a double-layer teaching evaluation system that emphasizes both morality and ability. Through the analysis of course data, it is shown that the effectiveness of ideological and political education in the curriculum has been comprehensively improved, and the quality of teaching has been comprehensively enhanced. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Just wing it: near-optimal estimation of missing mass in a Markovian sequence

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 15100-15142页

作者： Ashwin Pananjady Vidya Muthukumar Andrew Thangaraj Schools of industrial and systems engineering and electrical and computer engineering georgia institute of technology atlanta Schools of Electrical and Computer Engineering and Industrial and Systems Engineering Georgia Institute of Technology Atlanta Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

We study the problem of estimating the stationary mass--also called the unigram mass-- that is missing from a single trajectory of a discrete-time, ergodic Markov chain. This problem has several applications--for example, estimating the stationary missing mass is critical for accurately smoothing probability estimates in sequence models. While the classical Good-Turing estimator from the 1950s has appealing properties for i.i.d. data, it is known to be biased in the Markovian setting, and other heuristic estimators do not come equipped with guarantees. Operating in the general setting in which the size of the state space may be much larger than the length n of the trajectory, we develop a linear-runtime estimator called Windowed Good-Turing (WINGIT) and show that its risk decays as Õ(Tmix/n), where Tmix denotes the mixing time of the chain in total variation distance. Notably, this rate is independent of the size of the state space and minimax-optimal up to a logarithmic factor in n/Tmix. We also present an upper bound on the variance of the missing mass random variable, which may be of independent interest. We extend our estimator to approximate the stationary mass placed on elements occurring with small frequency in the trajectory. Finally, we demonstrate the efficacy of our estimators both in simulations on canonical chains and on sequences constructed from natural language text.

关键词： missing mass good-turing Markov chains minimax optimal

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An Integrated Control Framework for Safe and Ergonomic Human-Drone Interaction in Industrial Warehouses

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3570-3585页

作者： Proia, Silvia Cavone, Graziana Scarabaggio, Paolo Carli, Raffaele Dotoli, Mariagrazia University of Modena and Reggio Emilia Department of Sciences and Methods for Engineering Modena41121 Italy University Roma Tre Department of Civil Computer Science and Aeronautical Technologies Engineering Rome00154 Italy Polytechnic of Bari Department of Electrical and Information Engineering Bari70126 Italy

This study introduces a novel control framework for human-drone interaction (HDI) in industrial warehouses, targeting pick-and-delivery operations. The goals are to enhance operator safety as well as well-being and, at the same time, to improve efficiency and reduce production costs. To these aims, the speed and separation monitoring (SSM) operation method is employed for the first time in HDI, drawing an analogy to the safety requirements outlined in collaborative robots' ISO standards. The so-called protective separation distance is used to ensure the safety of operators engaged in collaborative tasks with drones. In addition, we employ the rapid upper limb assessment (RULA) method to evaluate the ergonomic posture of operators during interactions with drones. To validate the proposed approach in a realistic industrial setting, a quadrotor is deployed for pick-and-delivery tasks along a predefined trajectory from the picking bay to the palletizing area, where the interaction between the drone and a moving operator takes place. The drone navigates toward the interaction space while avoiding collisions with shelves and other drones in motion. The control strategy for the drone cruise navigation integrates simultaneously the time-variant artificial potential field (APF) technique for trajectory planning and the iterative linear quadratic regulator (LQR) controller for trajectory tracking. Differently, in the descent phase, the receding horizon LQR algorithm is employed to follow a trajectory planned in accordance with the SSM, which starts from the approach point at the border of the interaction space and ends in the volume with the operator's minimum RULA. The presented control strategy facilitates drone management by adapting the drone's position to changes in the operator's position while satisfying HDI safety requirements. The results of the proposed HDI framework simulations for the case study demonstrate the effectiveness of the method in ensuring a safe and er

关键词： ISO Standards

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Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms

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Biomimetic Intelligence & Robotics 2024年第4期4卷 76-91页

作者： Timothy Sellers Tingjun Lei Chaomin Luo Zhuming Bi Gene Eu Jan Department of Electrical and Computer Engineering Mississippi State UniversityStarkville 39762USA Department of Civil and Mechanical Engineering Purdue University Fort WayneFort Wayne 46805USA Department of Computer Science Asia UniversityTaichung 413305TaiwanChina

In the field of autonomous robots,achieving complete precision is challenging,underscoring the need for human intervention,particularly in ensuring *** Autonomy Teaming(HAT)is crucial for promoting safe and efficient human-robot collaboration in dynamic indoor *** paper introduces a framework designed to address these precision gaps,enhancing safety and robotic interactions within such *** to our approach is a hybrid graph system that integrates the Generalized Voronoi Diagram(GVD)with spatio-temporal graphs,effectively combining human feedback,environmental factors,and key *** integral component of this system is the improved Node Selection Algorithm(iNSA),which utilizes the revised Grey Wolf Optimization(rGWO)for better adaptability and ***,an obstacle tracking model is employed to provide predictive data,enhancing the efficiency of the *** insights play a critical role,from supplying initial environmental data and determining key waypoints to intervening during unexpected challenges or dynamic environmental *** simulation and comparison tests confirm the reliability and effectiveness of our proposed model,highlighting its unique advantages in the domain of *** comprehensive approach ensures that the system remains robust and responsive to the complexities of real-world applications.

关键词： Human autonomy teaming(HAT) Robot path planning Generalized Voronoi diagram(GVD) Spatio-temporal graphs Bio-inspired algorithms

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