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Characterization of the velocity and concentration of pneumatically conveyed particles in the upstream of an acoustic emission waveguide through CFD-DEM modelling and electrostatic sensing

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Measurement: Sensors 2024年 38卷

作者： Zeng, Xingxing Yan, Yong Qian, Xiangchen Lu, Yunlong School of Control and Computer Engineering North China Electric Power University Beijing China Hangzhou International Innovation Institute Beihang University Hangzhou China

Characterization of the velocity and concentration of pneumatically conveyed particles in the upstream of the waveguide protruded into the flow is essential for measuring the mass flow rate and size distribution of particles using acoustic emission methods. Computational fluid dynamics-discrete element method (CFD-DEM) is employed in this paper to simulate the collisions between particles and waveguides with a varying protrusion depth in a vertical pipe. Then an instrumentation system incorporating electrostatic sensors is developed to measure the particle velocity and concentration in the upstream of the waveguide. Experimental tests were carried out on a particle flow test rig. The experimental and modelling results show that the waveguide protruded into the flow between 2 and 10 mm results in a reduction in particle velocity of approximately 30%–35 %, and an increase in particle concentration of approximately 3%–20 %. The experimental results show a close agreement with the modelling data. © 2024

关键词： Computational fluid dynamics

Modular Redesign of Mechatronic Systems: Formulation of Module Specifications Guaranteeing System Dynamics Specifications

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arXiv 2024年

作者： Janssen, Lars A.L. Fey, Rob H.B. Besselink, Bart van de Wouw, Nathan Dynamics & Control Department of Mechanical Engineering Eindhoven University of Technology Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Netherlands

Complex mechatronic systems are typically composed of interconnected modules, often developed by independent teams. This development process challenges the verification of system specifications before all modules are integrated. To address this challenge, a modular redesign framework is proposed in this paper. Herein, first, allowed changes in the dynamics (represented by frequency response functions (FRFs)) of the redesigned system are defined with respect to the original system model, which already satisfies system specifications. Second, these allowed changes in the overall system dynamics (or system redesign specifications) are automatically translated to dynamics (FRF) specifications on module level that, when satisfied, guarantee overall system dynamics (FRF) specifications. This modularity in specification management supports local analysis and verification of module design changes, enabling design teams to work in parallel without the need to iteratively rebuild the system model to check fulfilment of system FRF specifications. A modular redesign process results that shortens time-to-market and decreases redesign costs. The framework’s effectiveness is demonstrated through three examples of increasing complexity, highlighting its potential to enable modular mechatronic system (re)design. © 2024, CC BY-NC-ND.

关键词： Specifications

MSR: A Multifaceted Self-Retrieval Framework for Microscopic Cascade Prediction 39

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MSR: A Multifaceted Self-Retrieval Framework for Microscopic...

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Hong, Dongsheng Chen, Chao Li, Xujia Wang, Shuhui Lin, Wen Liao, Xiangwen College of Computer and Data Science Fuzhou University China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Hong Kong University of Science and Technology Hong Kong College of Computer and Control Engineering Minjiang University China

ISBN: (纸本)157735897X

The microscopic cascade prediction task has wide applications in downstream areas like "rumor detection". Its goal is to forecast the diffusion routines of information cascade within networks. Existing works typically formulate it as a classification task, which fails to well align with the Social Homophily assumption, as it just use the features of "infected" users while neglecting those of "uninfected" users in representation learning. Moreover, these methods focus primarily on social relationships, thereby dismissing other vital dimensions like users' historical behavior and the underlying preferences behind it. To address these challenges, we introduce the MSR (Multifaceted Self-Retrieval) framework. During encoding, in addition to the existing social graph, we construct a preference graph to represent "behavioral preferences" and further propose a modified multi-channel GRAU for multi-view analysis of cascade phenomenon. For decoding, our approach diverges from classification-based methods by reformulating the task as an information retrieval problem that predicts the target user with similarity measures. Empirical evaluations on public datasets demonstrate that this framework significantly outperforms baselines on Hits@κ and MAP@κ, affirming its enhanced ability. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Taxonomies

Coco-LIC: Continuous-Time Tightly-Coupled LiDAR-Inertial-Camera Odometry using Non-Uniform B-spline

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arXiv 2023年

作者： Lang, Xiaolei Chen, Chao Tang, Kai Ma, Yukai Lv, Jiajun Liu, Yong Zuo, Xingxing The Institute of Cyber-Systems and Control Zhejiang University Hangzhou China The Department of Computer Engineering Technical University of Munich Germany

In this paper, we propose an efficient continuous-time LiDAR-Inertial-Camera Odometry, utilizing non-uniform B-splines to tightly couple measurements from the LiDAR, IMU, and camera. In contrast to uniform B-spline-based continuous-time methods, our non-uniform B-spline approach offers significant advantages in terms of achieving real-time efficiency and high accuracy. This is accomplished by dynamically and adaptively placing control points, taking into account the varying dynamics of the motion. To enable efficient fusion of heterogeneous LiDAR-Inertial-Camera data within a short sliding-window optimization, we assign depth to visual pixels using corresponding map points from a global LiDAR map, and formulate frame-to-map reprojection factors for the associated pixels in the current image frame. This way circumvents the necessity for depth optimization of visual pixels, which typically entails a lengthy sliding window with numerous control points for continuous-time trajectory estimation. We conduct dedicated experiments on real-world datasets to demonstrate the advantage and efficacy of adopting non-uniform continuous-time trajectory representation. Our LiDAR-Inertial-Camera odometry system is also extensively evaluated on both challenging scenarios with sensor degenerations and large-scale scenarios, and has shown comparable or higher accuracy than the state-of-the-art methods. The codebase of this paper will also be open-sourced at https://***/APRIL-ZJU/Coco-LIC. Copyright © 2023, The Authors. All rights reserved.

关键词： Pixels

Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models

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arXiv 2024年

作者： Dong, Yihong Jiang, Xue Liu, Huanyu Jin, Zhi Gu, Bin Yang, Mengfei Li, Ge Ministry of Education School of Computer Science Peking University Beijing China Beijing Institute of Control Engineering China China Academy of Space Technology China

Recent statements about the impressive capabilities of large language models (LLMs) are usually supported by evaluating on open-access benchmarks. Considering the vast size and wide-ranging sources of LLMs’ training data, it could explicitly or implicitly include test data, leading to LLMs being more susceptible to data contamination. However, due to the opacity of training data, the black-box access of models, and the rapid growth of synthetic training data, detecting and mitigating data contamination for LLMs faces significant challenges. In this paper, we propose CDD, which stands for Contamination Detection via output Distribution for LLMs. CDD necessitates only the sampled texts to detect data contamination, by identifying the peakedness of LLM’s output distribution. To mitigate the impact of data contamination in evaluation, we also present TED: Trustworthy Evaluation via output Distribution, based on the correction of LLM’s output distribution. To facilitate this study, we introduce two benchmarks, i.e., DET-CON and COMIEVAL, for data contamination detection and contamination mitigation evaluation tasks. Extensive experimental results show that CDD achieves the average relative improvements of 21.8%-30.2% over other contamination detection approaches in terms of Accuracy, F1 Score, and AUC metrics, and can effectively detect implicit contamination. TED substantially mitigates performance improvements up to 66.9% attributed to data contamination across various contamination setups. In real-world applications, we reveal that ChatGPT exhibits a high potential to suffer from data contamination on HumanEval benchmark.1 Copyright © 2024, The Authors. All rights reserved.

关键词： Contamination

SSIE-Diffusion: Personalized Generative Model for Subject-Specific Image Editing

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SSIE-Diffusion: Personalized Generative Model for Subject-Sp...

International Joint Conference on Neural Networks (IJCNN)

作者： Lin Guo Meiling Liu Kaiqun Fu Jiyun Zhou College of Computer and Control Engineering Northeast Forestry University Harbin China Department of Electrical Engineering and Computer Science South Dakota State University South Dakota USA d Lieber Institute Johns Hopkins University Baltimore USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Large-scale Text-to-Image (T2I) models can generate high-quality images by controlling the synthesis based on textual prompts. However, concepts related to specific subjects are often challenging to accurately describe through text, leading to the model's inability to generate diverse images containing these specific concepts. Current methods exhibit shortcomings, including stringent requirements for input images, poor model generalization, uncontrollable image generation quality, lack of editing flexibility and unbalanced semantic consistency. In this paper, we propose a personalized generation method for targeted subject image editing, referred to as SSIE-Diffusion. We address these challenges by training an image encoder to invert original images into text embeddings, ensuring the controllability and accuracy of concept generation. To mitigate overfitting issues during training, we devise a mask loss. Additionally, building upon the T2I model, we propose fine-tuning the U-Net architecture in the model using a regularized dataset. This involves improving the projection matrices mapping to key and value features in the cross-attention layers, enhancing the visual fidelity of personalized images of the subject, and simultaneously addressing language drift issues to balance semantic consistency. Experimental results demonstrate the effectiveness of our proposed method in editing specific subjects within individual images, surpassing current SOTA methods. Across various scenarios, the generated images exhibit superior visual fidelity and editing flexibility, while maintaining high computational efficiency.

关键词： Training Visualization Image synthesis Computational modeling Semantics Neural networks Text to image

Designing a Secure Smart Remote Patient Monitoring and Warning System using Big Data and Internet of Things Ecosystems 10

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Designing a Secure Smart Remote Patient Monitoring and Warni...

17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Ramesh, S.P. Abdulwahid, Ali Hadi Anjum, Afsana Venkatesh, N. Singh, Rajesh Chakravarthi, M. Kalyan Amet University Department of Computer Science and Engineering Tamil Nadu Kanathur India Southern Technical University Engineering Technical College Department of Control and Automation Basra Iraq Jazan University Department of Information Technology and Security Jazan Saudi Arabia School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India Uttaranchal Institute of Technology Uttaranchal University India School of Electronics Engineering VIT-AP University Amaravathi India

ISBN: (纸本)9789380544472

The demand on healthcare is growing as a result of the expanding global population, rising demands for successful treatment, and an underlying improvement in life quality. Consequently, healthcare remains one of the most significant social and economic problems in the world, necessitating the development of new and more sophisticated scientific and technological treatments.E-health services are provided via personalised healthcare systems to meet the medical and support needs of the ageing population. An important development in the Big Data age is the Internet of Things, which facilitates several real-time engineering solutions through improved services. Healthcare systems are now using analytics over IoT data streams as a stream of user data to find new information, forecast rapid recognition, and make decisions on life-threatening situations. In this study, researchers have carried out a thorough review of the hottest recent revolutionary technologies in bespoke healthcare systems, with a focus on hypervisor, cloud infrastructure, big data analytic, the Web of Things, and smartphone apps. In order to make early disease detection and diagnosis possible, it is important to develop a better healthcare system. We have examined these problems and suggested potential solutions while offering secure e-health services. The purpose of this study is to design a smart and secure remote monitoring and warning system using the latest emerging technologies in 21stcentury such as big data analytics and Internet of Things (IoT) ecosystems. In this study the researchers discussed a detailed research methodology to meet the outcomes of the reerach. All the relevant findings and analysis are explained in descriptive manner. Moreover, this study highlights the constantly expanding requirements for better medical systems in the present and offers potential directions for future research. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

Online convex optimization for robust control of constrained dynamical systems

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arXiv 2024年

作者： Nonhoff, Marko Dall’Anese, Emiliano Müller, Matthias A. The Institute of Automatic Control Leibniz University Hannover Hannover Germany The Department of Electrical and Computer Engineering Boston University BostonMA02215 United States

This article investigates the problem of controlling linear time-invariant systems subject to time-varying and a priori unknown cost functions, state and input constraints, and exogenous disturbances. We combine the online convex optimization framework with tools from robust model predictive control to propose an algorithm that is able to guarantee robust constraint satisfaction. The performance of the closed loop emerging from application of our framework is studied in terms of its dynamic regret, which is proven to be bounded linearly by the variation of the cost functions and the magnitude of the disturbances. We corroborate our theoretical findings and illustrate implementational aspects of the proposed algorithm by a numerical case study on a tracking control problem of an autonomous vehicle. © 2024, CC BY-NC-ND.

关键词： Cost functions

Temperature Measurement of Circulating Fluidized Bed Based on Cloud Model Spatiotemporal Fusion 43

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Temperature Measurement of Circulating Fluidized Bed Based o...

43rd Chinese control Conference, CCC 2024

作者： Mo, Yu Hou, Yajie Wang, Wenjun Han, Xiaojuan Guoneng Zhishen Control Technology Co. Ltd. Beijing102211 China State Nuclear Power Planning and Design Institute Co. Ltd. Beijing100095 China Ping An District Power Supply Company of Haidong State Grid Qinghai Electric Power Company Qinghai810610 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China

ISBN: (纸本)9789887581581

Accurate measurement of bed temperature is a prerequisite for ensuring the safe and stable operation of circulating fluidized beds. A temperature measurement method of circulating fluidized beds based on cloud model spatiotemporal fusion is proposed, according to the layout characteristics of measurement points in the circulating fluidized bed temperature measurement system. The one-dimensional forward cloud generator is used to generate normal cloud droplet distribution curves for the bed temperature of the front and rear walls, after the gross errors are eliminated by data consistency testing, the bed temperature data of the front and rear walls in time and space is fused by the cloud model theory. The effectiveness of the method proposed in this paper is verified through simulation analysis of actual operating data of a circulating fluidized bed power plant. The simulation results show that the method proposed in this paper significantly improves the accuracy of circulating fluidized bed temperature measurement, which can effectively reduce the safety hazards caused by boiler bed temperature being too high or too low. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Fluidized beds