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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Li, Chenming Wang, Jiankun Meng, Max Q.-H. The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Hong Kong Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen China Southern University of Science and Technology Jiaxing Research Institute Jiaxing China University of Alberta Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350327182

Robot path planning in high-dimensional continuous space is of great significance in robotics. The conventional bidirectional fast marching method utilizes brute-force search to explore the space, which is computationally expensive. Though improving the planning efficiency, heuristic-based bidirectional fast marching search results in a suboptimal path under a predetermined number of random samples. To address these issues, we propose Focused Bidirectional Fast Marching Trees (FBFMT*), developing a novel vertex exploring sequence to constrain the forward and backward searches within the brute-force searched regions. It enables the search trees to meet in the middle of the solution path, which guarantees the optimality under the current set of samples and searches faster than the bidirectional brute-force strategy. Extensive simulations in different scenarios validate the superiority of the proposed method, showing that FBFMT∗ can speed up the planning process and find a better solution. We also extend the proposed method to general conditions so that the forward and backward searches of FBFMT∗ can meet at any portion of the path. FBFMT∗ reveals that the heuristic-based bidirectional fast marching search strategy can be a general and efficient technique for robot path planning in high-dimensional continuous spaces. © 2023 IEEE.

关键词： Robot programming

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Analysis and Characterization of Defects in FeFETs

Analysis and Characterization of Defects in FeFETs

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2023 IEEE International Test Conference, ITC 2023

作者： Thapar, Dhruv Thomann, Simon Chaudhuri, Arjun Amrouch, Hussam Chakrabarty, Krishnendu Duke University Department of Electrical and Computer Engineering North Carolina United States Ai Processor Design Germany Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Arizona United States

ISBN: (纸本)9798350343250

Emerging devices are susceptible to manufacturing defects due to immature fabrication processes. Ferroelectric field-effect transistors, referred to as FeFETs, are promising emerging devices, but the impact of manufacturing imperfections on these devices has yet to be studied. Thus, we combine a technology CAD (TCAD) model with a fault-injection technique to represent fabrication defects in a FeFET. The TCAD model is calibrated against a fabricated metal-ferroelectric-metal capacitor and uses a multi-domain ferroelectric-layer structure. We address two classes of defects in the ferroelectric layer and map them to stuck-at-fault models referred to as neutral faults (SAP°) and stuck-at-plus and stuck-at-minus (SAP+ and SAP-) faults. We also develop a machine-learning (ML) framework to characterize these fault-injected FeFET devices. The ML framework provides a significant speedup in predicting the health of the FE layer as compared to computationally heavy TCAD simulations. Our study of defects in ferroelectric FET (FeFET), which is done for the first time, and the insights gained thereof can provide valuable feedback for the fabrication and yield learning of FeFET-based circuits. © 2023 IEEE.

关键词： Defects

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Dynamic Setpoint Optimization Using Metaheuristic Algorithms for Wastewater Treatment Plants 48

Dynamic Setpoint Optimization Using Metaheuristic Algorithms...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Salles, Rodrigo Mendes, Jerome Henggeler Antunes, Carlos Moura, Pedro Dias, Joana University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Pólo II Coimbra3030-290 Portugal University of Coimbra Inesc Coimbra Department of Electrical and Computer Engineering Pólo II Coimbra3030-290 Portugal Ctga Park no 70 Taveiro Coimbra3045-487 Portugal

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Wastewater Treatment Plants (WWTPs) are complex structures, with nonlinear characteristics, that have strict quality criteria, and carry out the treatment using various resources, with special emphasis on electricity. The optimization of WWTPs is a necessity to achieve sustainability. This work proposes the use of metaheuristic algorithms to optimize the functioning of a WWTP. Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing are used to minimize the aeration energy consumption prediction by an artificial neural network. The optimization framework aims dynamically adjusts the reference value for the proportional-integral (PI) controller. The proposed approach was able to dynamically adjust the oxygen pumping requirement and thus reduce the electricity consumption compared to the default PI controller adopted by the Benchmark Simulation Model no. 2 (BSM2). © 2022 IEEE.

关键词： Genetic algorithms

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Joint Multi-stage Planning of Renewable Generation, HESS, and AESS for Deeply Decarbonizing Power systems with High-penetration Renewables

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IEEE Transactions on Sustainable Energy 2024年第3期16卷 1613-1628页

作者： Yu, Zhipeng Chi, Yingtian Lin, Jin Liu, Feng Li, Jiarong Song, Yonghua Ren, Zhengwei Lu, Chengcheng Ji, Mengbo Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Beijing100087 China Tsinghua University Sichuan Energy Internet Research Institute Chengdu610213 China Harvard University Harvard John A. Paulson School of Engineering and Applied Sciences Cambridge United States University of Macau Department of Electrical and Computer Engineering Macau999078 China Tsinghua University Department of Electrical Engineering Beijing100087 China China Electric Power Planning & Engineering Institute National Electric Power Planning and Research Center Beijing100087 China Co. Ltd. Beijing100087 China Ordos City Hanxia Renewable Energy Co. Ltd. Ordos014300 China

The further decarbonization of power systems with high renewable energy penetration faces the problem of inter-day intermittence of renewable energy sources (RES) and the seasonal imbalance between RES and load demand, due to the limited regulation ability of conventional units such as thermal generation. Regular solutions based on battery energy storage system (BESS) are too costly to be practical. To address issues above, hydrogen energy storage system (HESS) and ammonia energy storage system (AESS) are introduced to gradually replace thermal generation. Specifically, first, HESS and AESS are incorporated into the multi-stage capacity expansion planning (MSCEP) model with carbon emission reduction constraints. Yearly data with hourly time resolution are utilized for each stage to accurately describe the intermittence of RES. Then, an improved column generation (CG) with Dantzig-Wolfe decomposition (DWD) embedded solution approach is used to efficiently solve the large-scale MSCEP model. Finally, a real-life system in China is studied. The results indicate that the proposed method can guarantee high power supply reliability (PSR) under different renewable energy penetration levels, avoiding the low PSR problem that may be caused by the existing typical scenario-based method (TSM) under high penetration (≥30%). Moreover, HESS and AESS are essential to reduce the cost of decarbonization. Especially under the goal of carbon neutrality, the contribution of HESS and AESS in reducing levelized cost of energy (LCOE) reaches 12.28% and 14.59%, respectively, leading to a levelized cost of carbon reduction (LCOCr) of 998 RMB/t. © 2010-2012 IEEE.

关键词： Hydrogen storage

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Localization of Dengue Virus Likelihood Using Image Processing Technique 6

Localization of Dengue Virus Likelihood Using Image Processi...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Hosen, Md. Mahbub Islam, Md. Rahatul Tabassum, Faria Tonni, Syeda Thamina Noor, Malik Mohammad Nafiur Rahman, Shahid Northern University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Kyushu Institute of Technology Dept. of Human Intelligence Systems Wakamatsu Kitakyushu Japan Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh Advance Robotics Research Center Human Intelligence Lab Dhaka Bangladesh Canadian University of Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350306613

The Aedes mosquito, found in tropical areas, transmits, and causes dengue fever. The spread of the dengue fever virus from an infected Aedes mosquito bite typically takes between three and fifteen days to manifest itself clinically as dengue fever symptoms. Early and automated detection of this illness will reduce the occurrence of outbreaks and facilitate the digitization of the system. Our primary objective is to identify regions at high risk for dengue fever using limited image data through the utilization of sophisticated machine learning and image processing methods. This work showcases the application of transfer learning algorithms for the timely identification of regions at risk for dengue mosquito infestation, particularly in situations where obtaining early data on dengue outbreaks is challenging. The experiments included images of 2150 dengue-positive data sets and 1440 dengue-negative data sets. We used AI and image classifiers Yolo V4 Tin, Tensor Flow, Google Colab, etc. for the training data set, and the system makes predictions with a 99.63% accuracy rate. Our system has some unique attributes;it not only detects the percentage of dengue probability but also the specific dengue-prone location on the submitted image. © 2024 IEEE.

关键词： aedes mosquito dengue-prone image processing yolo v4 tiny

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TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition

arXiv

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arXiv 2023年

作者： Barros, Tiago Garrote, Luís Aleksandrov, Martin Premebida, Cristiano Nunes, Urbano J. The University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal Dahlem Center for Machine Learning and Robotics Freie Universität Berlin Berlin Germany

Autonomous driving systems often require reliable loop closure detection to guarantee reduced localization drift. Recently, 3D LiDAR-based localization methods have used retrieval-based place recognition to find revisited places efficiently. However, when deployed in challenging real-world scenarios, the place recognition models become more complex, which comes at the cost of high computational demand. This work tackles this problem from an information-retrieval perspective, adopting a first-retrieve-then-re-ranking paradigm, where an initial loop candidate ranking, generated from a 3D place recognition model, is re-ordered by a proposed lightweight transformer-based re-ranking approach (TReR). The proposed approach relies on global descriptors only, being agnostic to the place recognition model. The experimental evaluation, conducted on the KITTI Odometry dataset, where we compared TReR with s.o.t.a. re-ranking approaches such as αQE and SGV, indicate the robustness and efficiency when compared to αQE while offering a good trade-off between robustness and efficiency when compared to SGV. © 2023, CC BY.

关键词： Efficiency

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AGVs vs AMRs: A Comparative Study of Fleet Performance and Flexibility

AGVs vs AMRs: A Comparative Study of Fleet Performance and F...

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Iberian robotics Conference (ROBOT)

作者： Rita Tomé Silva Marina Brilhante Héber Sobreira Diogo Matos Pedro Costa Department of Electrical and Computer Engineering Faculty of Engineering of the University of Porto Porto Portugal Centre of Robotics in Industry and Intelligent Systems INESC TEC Porto Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

Autonomous Mobile Robots (AMRs) and Automated Guided Vehicles (AGVs) have emerged as key innovations in the industry world, with AMRs offering flexibility a nd adaptability for dynamic environments, while AGVs provide high accuracy for repetitive tasks; thus, this research proposes a study of fleets of both AGVs and AMRs to enhance productivity and efficiency in industrial settings. Several tests were performed where the duration of a mission, the success and collision rate, and the average number of disputes per mission were analyzed in order to obtain results. In conclusion, while AGVs tend to be more reliable and consistent in task completion, AMRs offer greater flexibility a nd speed.

关键词： Productivity Technological innovation Remotely guided vehicles Navigation Scalability Path planning Mobile robots Reliability Vehicle dynamics Autonomous robots

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Analysis and Design Optimization of Surface Permanent Magnet Motor to Improve Torque Density and Ripple 11

Analysis and Design Optimization of Surface Permanent Magnet...

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11th International Conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Shakhin, Yussuf Talapiden, Kulash Minh Thao, Nguyen Gia Bagheri, Mehdi Duc Do, Ton Department of Robotics and Mechatronics AstanaZ05H0P9 Kazakhstan Toyota Technological Institute Graduate School of Engineering Japan Department of Electrical and Computer Engineering AstanaZ05H0P9 Kazakhstan

ISBN: (纸本)9788957083505

Small-scale permanent magnet motor to reach high torque density and low torque ripple is discussed in this research. To increase the torque density, meanwhile making the motor size smaller and lighter, the motor axial length within a constrained diameter is optimized in view of various pole-slot configurations. Different slot-poles are selected and various combinations are studied, and the stator windings are optimized. To compare the torque density and ripple of new designs, precise models are created and simulated through ANSYS Maxwell 2D FEA under constant operational conditions, such as rated speed, torque, and power. Over the new designs, the influence of loss and temperature distribution variations in motor elements are analyzed. The simulation results show that the proposed 20-pole 18-slot motor design will obtain superior torque density and lower torque ripple compared to the baseline design. The final design provides a 30.7 % improvement of the torque density within the same rated torque condition. © 2023 The Korean institute of Power Electronics.

关键词： Finite element method

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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Blind Asynchronous Goal-Oriented Detection for Massive Connectivity 21

Blind Asynchronous Goal-Oriented Detection for Massive Conne...

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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Daei, Sajad Razavikia, Saeed Kountouris, Marios Skoglund, Mikael Fodor, Gabor Fischione, Carlo School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Communication Systems Department EURECOM Sophia Antipolis06410 France Ericsson Research Sweden

ISBN: (纸本)9783903176553

Resource allocation and multiple access schemes are instrumental for the success of communication networks, which facilitate seamless wireless connectivity among a growing population of uncoordinated and non-synchronized users. In this paper, we present a novel random access scheme that addresses one of the most severe barriers of current strategies to achieve massive connectivity and ultra reliable and low latency communications for 6G. The proposed scheme utilizes wireless channels’ angular continuous group-sparsity feature to provide low latency, high reliability, and massive access features in the face of limited time-bandwidth resources, asynchronous transmissions, and preamble errors. Specifically, a reconstruction-free goal oriented optimization problem is proposed which preserves the angular information of active devices and is then complemented by a clustering algorithm to assign active users to specific groups. This allows to identify active stationary devices according to their line of sight angles. Additionally, for mobile devices, an alternating minimization algorithm is proposed to recover their preamble, data, and channel gains simultaneously, enabling the identification of active mobile users. Simulation results show that the proposed algorithm provides excellent performance and supports a massive number of devices. Moreover, the performance of the proposed scheme is independent of the total number of devices, distinguishing it from other random access schemes. The proposed method provides a unified solution to meet the requirements of machine-type communications and ultra reliable and low latency communications, making it an important contribution to the emerging 6G networks. © 2023 IFIP.

关键词： Clustering algorithms

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