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2nd international conference on Mechanical engineering, Intelligent manufacturing and Automation Technology, MEMAT 2022

作者： Zou, Yujie Liu, Fei Qu, Jingjing Jing, Hui Kuang, Bing Wang, Gang Li, Hang hang School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541004 China Science and Technology Department Guilin University of Aerospace Technology Guangxi Guilin541004 China Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9783800757619

In this article, we give a brief overview of sensors and sensor fusion in autonomous vehicles field. We focus on sensor fusion of key sensors in autonomous vehicles: camera, radar and lidar. This article will introduce the latest sensor fusion algorithms developments in this field. The results show that adding more sensors to the sensor fusion system can improve the performance and robustness of the system. In addition, the multi-sensor fusion of GNSS and IMU data are vital for positioning and mapping, which is a solution to the problem of the real-time requirements of automatic driving. Sensor fusion plays a vital role in autonomous driving systems, so it is one of the fastest growing areas in the field of autonomous vehicles. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Autonomous vehicles

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Deep Reinforcement Learning for Volt/VAR Control in Distribution systems: A Review 5

Deep Reinforcement Learning for Volt/VAR Control in Distribu...

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5th international conference on Energy, Electrical and Power engineering, CEEPE 2022

作者： Hai, Di Zhu, Tao Duan, Shangqi Huang, Wei Li, Wenyu Yunnan Power Grid Co. Ltd Kunming Power Supply Bureau Kunming China Yunnan Power Grid Co. Ltd Power Dispatching Control Center Kunming China

ISBN: (纸本)9781665479059

An increasing number of distributed generators are integrated to distribution systems, which has a huge impact on the network power flow and leads to severe voltage fluctuation problems. Volt/VAR control is regarded as an effective approach to improve voltage quality and reduce power loss. Deep reinforcement learning is a data-driven approach to effectively solve decision-making problems. The application of deep reinforcement learning in volt/VAR control circumvent the accurate knowledge of network information, and is endowed with less computational burden. This paper provides a general review of the application of deep reinforcement learning in volt/VAR control in terms of basic notations, Markov decision process formulations, and control framework. Future directions are also discussed. © 2022 IEEE.

关键词： Power engineering Voltage fluctuations Process control Reinforcement learning Markov processes Control systems Robustness

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Towards View-invariant Vehicle Speed Detection from Driving Simulator Images 14

Towards View-invariant Vehicle Speed Detection from Driving ...

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14th international conference on knowledge Discovery and Information Retrieval, KDIR 2022 as part of 14th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2022

作者： Martínez, Antonio Hernández Llorca, David Fernández Daza, Iván García Computer Engineering Department University of Alcalá Alcalá de Henares Spain Seville Spain

ISBN: (纸本)9789897586149

The use of cameras for vehicle speed measurement is much more cost effective compared to other technologies such as inductive loops, radar or laser. However, accurate speed measurement remains a challenge due to the inherent limitations of cameras to provide accurate range estimates. In addition, classical vision-based methods are very sensitive to extrinsic calibration between the camera and the road. In this context, the use of data-driven approaches appears as an interesting alternative. However, data collection requires a complex and costly setup to record videos under real traffic conditions from the camera synchronized with a high-precision speed sensor to generate the ground truth speed values. It has recently been demonstrated (Martinez et al., 2021) that the use of driving simulators (e.g., CARLA) can serve as a robust alternative for generating large synthetic datasets to enable the application of deep learning techniques for vehicle speed estimation for a single camera. In this paper, we study the same problem using multiple cameras in different virtual locations and with different extrinsic parameters. We address the question of whether complex 3D-CNN architectures are capable of implicitly learning view-invariant speeds using a single model, or whether view-specific models are more appropriate. The results are very promising as they show that a single model with data from multiple views reports even better accuracy than camera-specific models, paving the way towards a view-invariant vehicle speed measurement system. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Speed

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I-Card: A Generative AI-Supported Intelligent Design Method Card Deck 25

I-Card: A Generative AI-Supported Intelligent Design Method ...

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2025 CHI conference on Human Factors in Computing systems, CHI 2025

作者： Chen, Liuqing Cheang, Wengteng Jiang, Zhaojun Xu, Yuan Cai, Zebin Sun, Lingyun Childs, Peter Han, Ji Hansen, Preben Zuo, Haoyu College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China School of Computer Science and Engineering Central South University Hunan Changsha China International Design Institute Zhejiang University Zhejiang Hangzhou China Dyson School of Design Engineering Imperial College London London United Kingdom Department of Innovation Technology and Entrepreneurship University of Exeter Exeter United Kingdom Dept of Computer and Systems Sciences Stockholm University Kista Sweden

ISBN: (纸本)9798400713941

A design method card deck helps designers understand and provoke thinking by presenting each method in a simple format and allow designers to switch between methods seamlessly by maintaining the same simple format across the deck. However, recent observations have shown designers hesitate to use a card deck due to the lack of support, while other tools have provided identified support with generative AI. Through a formative study, we identified the specific support designers need when applying the design method cards and intentions in integrating generative AI. Accordingly, we developed the intelligent design method card deck, I-Card, which integrates generative AI to provide applicable design methods, design knowledge and data support, and interactive and dynamic support. A user study demonstrates that I-Card improved the design efficiency and applicability by offering personalized guidance, enhanced decision-making with comprehensive data generation and provided more design inspiration via interactive support. © 2025 Copyright held by the owner/author(s).

关键词： design cards Design method design method cards design support tool generative AI

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conference News

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Fuzzy Information and engineering 2024年第2期1卷 229-230页

The 3rd international conference on Fuzzy Information and engineering (ICFIE'09) will be held in Chongqing, a beautiful mountain city in China in September 26–29, 2009. The conference, sponsored by Fuzzy Information and engineering Branch of ORSC, is to be organized by Chongqing University of Science & Technology, China. Based on the success of two previous conferences, the ICFIE'09 is a major symposium for scientists, engineers and practitioners in China as well as in the world to present their latest researches, results, ideas, developments and applications in fuzzy areas, such as fuzzy sets and systems, fuzzy information and knowledge engineering, operation researches and optimizations under fuzzy environments, fuzzy decision making and forecast, fuzzy data mining, artificial intelligence and machine learning, high performance computing and visualization, etc. It aims to bring together researchers, engineers and practitioners to exchange their topics on the latest achievements and innovations in a wide area from fuzzy information and engineering, to discuss thought-provoking developments and challenges, and to consider potential future directions.

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CYBER-PHYSICAL INTERNET LOCAL AREA NETWORK FOR AVIATION MAINTENANCE, REPAIR AND OVERHAUL (MRO) INTRALOGISTICS 50

CYBER-PHYSICAL INTERNET LOCAL AREA NETWORK FOR AVIATION MAIN...

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50th international conference on Computers and Industrial engineering: Sustainable Digital Transformation, CIE 2023

作者： Chen, Qiqi Zhao, Zhiheng Huang, George Q. Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781713886952

Aviation maintenance, repair, and overhaul intralogistics (MRO intralogistics) is essential to ensure regular maintenance procedures. MRO intralogistics remains challenging due to its complexity and variability. Many advanced technologies, such as physical Internet (PI) and Internet-of-Things, have been introduced to improve the efficiency of production intralogistics operations in e-commerce and manufacturing. However, MRO intralogistics has rarely been discussed. Motivated by a real-life case, this paper presents an innovative scheme, Cyber-physical Internet (CPI), for MRO intralogistics to address its operational challenges. First, this study introduces CPI, which integrates multiple technologies, including PI, Digital-Twin, Industrial Internet-of-Things and Robotic mobile fulfilment system, for re-engineering MRO intralogistics. CPI-enabled MRO intralogistics ensures the full-process real-time data collection from the MRO intralogistics physical environment. In addition, this work discusses new decision-making problems under CPI-enabled MRO intralogistics. Second, this paper proposes a detailed architecture model, CPI Local Area Network (CPI-LAN), to empower the interoperability of physical and virtual spaces of CPI-enabled MRO intralogistics. Third, this work designs a solution framework based on fuzzy logic and real-time data to coordinate the operational decisions between two participants in MRO intralogistics. The paper contributes to three aspects. First, CPI enhances the traceability and visibility of both operations and resources of MRO intralogistics. Second, the solution framework embedding real-time data synchronizes the operations under CPI-enabled MRO intralogistics. Third, this work conducts the proposed solution in a real-life case and shows the potential benefits of CPI-enabled MRO intralogistics. © 2023 Computers and Industrial engineering. All rights reserved.

关键词： Internet of things

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Application of knowledge Graph in the Association Model of Energy Consumption data for Steel Plant Furnaces

Application of Knowledge Graph in the Association Model of E...

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Electrical engineering and Control Science (IC2ECS), international conference on

作者： Shen Xia Ziyi Liu Jinming Huang He Song Yuan Cheng State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Company Limited Wuhan City China

ISBN: (数字)9798331510107

ISBN: (纸本)9798331510114

The energy consumption data of steelmaking furnaces comes from multiple different systems and equipment, including sensors, monitoring instruments, control systems, etc. The format, accuracy, and sampling frequency of these data may vary. Due to the diversity of energy consumption data sources for steel mills' furnaces, it is necessary to establish an effective data association mechanism to ensure the accuracy and reliability of energy consumption data for steel mills' furnaces. Therefore, this paper proposes a method for associating energy consumption data of steelmaking furnaces using knowledge graphs. By establishing a knowledge graph framework, extracting, cleaning, and converting energy consumption data for steel mills' furnaces, the preprocessing of energy consumption data for steel mills' furnaces is completed. On this basis, by classifying the energy consumption data of steel mills' furnaces into the same semantic document and integrating it, and then fusing the semantic information of different steel mills' furnace energy consumption data, the correlation of steel mills' furnace energy consumption data is achieved. The experimental results show that the proposed method can effectively improve the accuracy and computational efficiency of energy consumption data correlation in steelmaking furnaces.

关键词： Energy consumption Accuracy Correlation Furnaces Semantics knowledge graphs Cleaning Computational efficiency Steel data mining

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data-driven Process Parameter Optimisation for Laser Wire Metal Additive manufacturing 27

Data-driven Process Parameter Optimisation for Laser Wire Me...

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27th international conference on Automation and Computing, ICAC 2022

作者： Roberts, Matthew Xia, Min Kennedy, Andrew Lancaster University Engineering Department Lancaster United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Laser Wire Additive manufacturing (LWAM) requires a clear understanding of process parameters and their effects on the geometry and wider material properties of the parts produced to support the production of consistent, repeatable quality parts. Furthermore, its ability to capitalise on using novel alloys depends on efficient characterisation of optimum process parameters. In this work, a method for identifying the range of usable parameters is presented, which produces sufficient data to train Cascade Forward Neural Networks, which are capable of predicting process windows and basic LWAM track geometries for 316L stainless steel. The performance of these networks provides the foundation for further work to identify optimum process parameters and, through transfer learning, may reduce the experimental requirements for the process development of other alloys. © 2022 IEEE.

关键词： 3D printers

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A Methodological Approach for data-Intensive Web Application Design on Top of data Lakes 24th

A Methodological Approach for Data-Intensive Web Applicatio...

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24th international conference on Web Information systems engineering, WISE 2023

作者： Bianchini, Devis Garda, Massimiliano Department of Information Engineering University of Brescia Via Branze 38 Brescia25123 Italy

ISBN: (纸本)9789819972531

data exploration and decision making may benefit from the availability of data-intensive web applications, that enable domain experts to navigate across massive, dynamic and heterogeneous data sources, stored in the so-called data Lakes. However, traditional design strategies for this kind of applications require in the background well-defined and cleaned data structures. Conceptual modelling may be fruitfully employed to provide web developers with a comprehensive vision over data Lake sources, on which web applications are designed. Nevertheless, the cumbersome nature of data Lakes turns the conceptual model into a dynamic entity, which must be properly managed. In this paper, we propose a methodological approach to design data-intensive web applications on top of a data Lake. A conceptual data model, weaved over data Lake sources, is leveraged to identify the relevant information to be included in the web application. The methodology makes the model evolve both with new data sources content emerging from the data Lake, through a zone-based operations pipeline that prepares a curated version of the raw data (bottom-up), and with additional domain knowledge provided by web developers derived from the data-intensive web application design (top-down). The approach, independent from any specific implementation technology, is declined in the context of a real case study regarding an ongoing research project in the cultural heritage domain. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lakes

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Damage Identification on Aircraft Wing Using Convolutional Neural Network Based Pattern Recognition

Damage Identification on Aircraft Wing Using Convolutional N...

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international conference on Intelligent manufacturing and Energy Sustainability, ICIMES 2023

作者： Sucharitha, M. Sesha Talpa Sai, P.H.V. Thazha, Subeesh Kopiyil Thomas, Mathew Hunagund, Basavaraj Krishna Anand, V.G. School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Mechanical Engineering Malla Reddy College of Engineering and Technology Hyderabad India School of Mechanical Engineering Lincoln University College Kuala Lumpur Malaysia Department of Aeronautical Engineering Malla Reddy College of Engineering and Technology Hyderabad India

ISBN: (纸本)9789819967735

Damages to the aircraft body must be identified in order to avoid unforeseen events that could result in catastrophes if not examined properly. As most of these damages are provoked under normal working conditions, such as in this case during aircraft flight operations, rectifying these damages is nearly impractical. This work veils through a compiled report of Structural Health Monitoring (SHM) of an aircraft wing aiming at the deterioration caused throughout the entire structure of the wing which is subjected to numerous aerodynamics and mechanical constraints. The SHM report provides data which is used to study intramural Damages. Based on the investigation, the Convolutional Neural network (CNN), which is a typical algorithm of deep learning along with Recurrence graph, was chosen as the appropriate Methodology. The structural response recurrence graph can reveal details about the damage, similarity, and underlying structure. Wavelet packets are used to filter and recreate the initial structural response signal, considering the vibrational coupling between the aircraft and wing. Then, as a novel kind of damage feature, the recurrence graph of distinct damages is taken as input picture of the CNN. The findings show that this method provides added information about damage than traditional statistical pattern recognition techniques. These outcomes confirmed the proposed CNN-based model’s suitability for use with additional aircraft components. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Structural health monitoring

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