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10th IEEE Smart World Congress, SWC 2024

作者： You, Feiran Du, Hongyang Kang, Jiawen Ni, Wei Niyato, Dusit Jamalipour, Abbas The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia SydneyNSW2122 Australia University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Guangdong University of Technology School of Automation China Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798331520861

The rapid growth of Artificial Intelligence-Generated Content (AIGC) services has led to increased mobile user participation in related computations and interactions. This development has enabled AI-generated characters to interact with Virtual Reality (VR) users in real time, making the VR experience more interactive and personalized. In this paper, we consider an MEC system where VR users engage in AIGC services, focusing on the Generative Diffusion Model (GDM)based image generation tasks. Specifically, VR users initiate requests for computing resources, while computation offloading distributes the processing load across the MEC system. To manage AIGC edge computation offloading and cloudlet-VR user connections jointly, a Data Center Operator (DCO) employs a centralized Proximal Policy Optimization (PPO) algorithm. To protect VR users' privacy while preserving PPO functionality, we employ the Generative Diffusion Model (GDM), specifically the Denoising Diffusion Implicit Model (DDIM), which first introduces noise to the PPO state, then conducts a denoising process to recover the state information. We further employ Inverse Reinforcement Learning (IRL) to infer rewards for the recovered states, using expert demonstrations trained by the PPO. The similarity between PPO-generated rewards and IRL-inferred rewards is then computed. Simulation results demonstrate that our proposed approach successfully achieves computation offloading while protecting VR users' privacy within the PPO centralized management framework. © 2024 IEEE.

关键词： Virtual environments

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Load curve based on modeling a photovoltaic system, load and weather data - a case study in Brazil 15

Load curve based on modeling a photovoltaic system, load and...

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15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： Guaitolini, Teylor M. Nascimento, Ernani B. Breder, Rafaela P. Gobbi, Renan E. e Silva, Danilo P. Silva, Flavio B.B. Camargo, Renner S. Espaço Energia Laboratory Federal Institute of Espirito Serra Santo Brazil Graduate Department of Applied Computing Federal Institute of Espirito Serra Santo Brazil Electrical Engineering Department Federal Institute of Espirito Santo Vitoria Brazil Automation Engineering Dept Federal Institute of Espirito Serra Santo Brazil

ISBN: (纸本)9798350314182

This study presents a comprehensive case study centered around the modeling of a load curve for a public education institution in Brazil that harnesses energy from distributed sources, specifically photovoltaic panels. Actual demand data, sourced from an energy analyzer, and weather data collected through an automated weather station, were integrated into the photovoltaic generation model. The model's efficacy was meticulously assessed, juxtaposing the actual and simulated photovoltaic generation data, which showcased a remarkable alignment with a percentage error of merely 6.88%. Furthermore, the study delved into weekly load curve simulations spanning the spring and summer seasons. These simulations yielded a compelling percentage error of 8.01%. These outcomes underscore the potential of the proposed load curve model, not only in strategic solar energy production planning but also in the strategic optimization of energy costs. This work not only underscores the feasibility of accurate load curve prediction but also illuminates the capacity to explore solar energy production potential, thereby opening avenues for future research in revenue generation and energy credit evaluation. © 2023 IEEE.

关键词： Power and Energy Systems Renewable Energy Smart Grids

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Experimental Investigation of a High-Fidelity Transient Model for an Interior Permanent Magnet Machine with Arbitrary Stator Turn Fault

Experimental Investigation of a High-Fidelity Transient Mode...

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IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)

作者： Stjepan Stipetić Marinko Kovačić Damir Žarko Department of Electrical Machines Drives and Automation University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

An accurate transient model of the machine operating under fault conditions is essential to save time and resources for experimental testing when developing fault-tolerant control strategies. This paper describes the LUT based simulation and experimental verification of the high-fidelity interior permanent magnet machine model with stator turn fault available in the literature. The approach involves the extraction of flux linkage maps of the machine under turn fault conditions using offline static finite element (FE) analysis,and combining it with voltage equations of the machine. The method is not limited to IPM machines and can be used to model stator turn faults in other machines such as surface PM machines, synchronous reluctance machines, switched reluctance machines, wound field synchronous machines, etc. The proposed approach provides a complete representation of the spatial harmonics and magnetic saturation in the presence of inter-turn faults and under all load conditions, making it the most accurate representation of the faulty motor behaviour apart from a time stepping FE coupled analysis.

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Analog One-Cycle Control Based Scheme for Battery Charging in Photovoltaic Systems 8

Analog One-Cycle Control Based Scheme for Battery Charging i...

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8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023

作者： De Carvalho Neto, João T. Salazar, Andrés O. Federal Institute of Education Science and Technology of Rio Grande Do Norte Natal-East Side Campus RN Natal Brazil Federal University of Rio Grande Do Norte Department of Computing Engineering and Automation RN Natal Brazil

ISBN: (纸本)9798350321128

In recent years, the use of photovoltaic systems has getting more attention because radiation is converted directly into electricity without the use of inverters. A major scientific challenge in this theme consists of clean energy storage systems, widely researched for charging electric vehicles (EVs). Batteries are one of the most common ways to store the generated energy directly from photovoltaic systems. Current literature proposes several challenges in charging batteries using photovoltaic systems, especially when it comes to increasing their lifetime, since sudden changes in irradiation cause voltage overshoots in the batteries. This article presents a control strategy based in One-Cycle Control to prevent damages in the batteries adapting its voltage to varying input voltages. The main purpose of this paper is to present applicability of One-Cycle Control based control scheme used for battery charging. The experimental results present the power and controller signal analysis in steady state conditions. © 2023 IEEE.

关键词： Battery Charging One-Cycle Control Photovoltaic Energy

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An Efficient Classification Techniques for Brain Tumor Using Features Extraction and Statistic Methods, with Machine Learning Algorithms

An Efficient Classification Techniques for Brain Tumor Using...

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作者： Badshah, Shah Hussain Farhatullah khan, Gul Zaman Hassan, Muhammad Abul Junaid, Hazrat Sohail, Muhammad Mahbob, Muhammad Awais Ahamad, Izaz Ullah, Nadeem Department of Computing and Technology Abasyn University Peshawar Pakistan School of Automation China University of Geosciences Wuhan430074 China Department of Computer Software Engineering University of Engineering and Technology Mardan Pakistan Department of Computer Science and Information Technology University of Malakand Dir Lower Pakistan Department of Computer Science Bahria University Islamabad Pakistan Department of Computer Science University of Engineering and Technology Taxila Pakistan Department of Information Engineering and Computer Science University of Trento Trento38122 Italy

Today, brain tumor is a very dangerous disease that can even cause death. There are many ways to classify Brain MRI images of tumors. Various aspects of current research have limitations;some methods are accurate but take a long time to compute while some algorithms are fast, but with low accuracy. Consequently, a lot of effort is needed in this area. In this paper, our major goal is to reduce computation time and improve the classification accuracy for brain MRI images. This research proposes four phases: preprocessing, feature extraction, feature selection, and classification. During pre-processing, the median filter was used to reduce noise in the images, and the grayscale image size was reduced to further limit the image for efficient use. After that the Gray images are then given as inputs to the Feature extraction phase to select a limited feature from the image namely, texture features, shape features, smoothing features. The images features obtained in the feature’s extraction phase are still large, and we cannot simply feed them to machine learning models for processing due to computational constraints. So, we need to analyze it and pick out some interesting elements from the images. The statistical features namely, variances, mean, correlation are used in the feature selection phase to select more significant features to reduce the size of the features and reduce computational time. These features are combined and labeled in a file to train Machine Learning (ML) algorithms. We used two types of machine learning algorithms in the classification phase: classifier and evolutionary algorithm. The selected classifier algorithms results are combined in a file and we named it S-K-E-L Classifier and it gives us acceptable results in terms of accuracy and time. Similarly, an evolutionary algorithm named P-S-D-G Classifier also gives us acceptable results in terms of accuracy and time. Lastly, we combine both S-K-E-L Classifier and P-S-D-G Classifier in a file with

关键词： Classification (of information)

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Text Recognition for Station Equipment Troubleshooting Based on BERT-BiLSTM-CRF Models

Text Recognition for Station Equipment Troubleshooting Based...

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2023 China automation Congress, CAC 2023

作者： Bai, Wei Wang, Xiaosu Liang, Bo Yang, Guoyuan Zhi, Hanyu Qiao, Yinwei Institute of Computing Technology China Academy of Railway Sciences Co. Ltd. Beijing China Postgraduate Department China Academy of Railway Sciences Beijing China School of Automation and Software Engineering Shanxi University Taiyuan China

ISBN: (纸本)9798350303759

The excessive length and high semantic complexity of station equipment fault handling report texts are addressed to the extent that effective text recognition is difficult to achieve. A station equipment fault handling dataset is formulated and a text recognition system for station fault reports based on the BERT-BiLSTM-CRF model is designed. The method first preprocesses the data using the BERT pre-trained language model to generate word vectors, which are then fed into BiLSTM to obtain the embedded contextual information, which is passed through the CRF model to obtain the global optimal solution, and finally outputs the textual results of the station equipment fault handling. The experimental results show that the model outperforms the other five comparative models by more than 2% in terms of precision, recall, and F1 value (checking accuracy) for intelligent recognition over the five entities predicted by the other five comparative models. This brings a new direction of thinking for the analysis of equipment troubleshooting texts in railway stations. © 2023 IEEE.

关键词： Semantics

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Probabilistic Subgoal Representations for Hierarchical Reinforcement learning

arXiv

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arXiv 2024年

作者： Wang, Vivienne Huiling Wang, Tinghuai Yang, Wenyan Kämäräinen, Joni-Kristian Pajarinen, Joni Department of Electrical Engineering and Automation Aalto University Finland Huawei Helsinki Research Center Finland Computing Sciences Tampere University Finland

In goal-conditioned hierarchical reinforcement learning (HRL), a high-level policy specifies a subgoal for the low-level policy to reach. Effective HRL hinges on a suitable subgoal representation function, abstracting state space into latent subgoal space and inducing varied low-level behaviors. Existing methods adopt a subgoal representation that provides a deterministic mapping from state space to latent subgoal space. Instead, this paper utilizes Gaussian Processes (GPs) for the first probabilistic subgoal representation. Our method employs a GP prior on the latent subgoal space to learn a posterior distribution over the subgoal representation functions while exploiting the long-range correlation in the state space through learnable kernels. This enables an adaptive memory that integrates long-range subgoal information from prior planning steps allowing to cope with stochastic uncertainties. Furthermore, we propose a novel learning objective to facilitate the simultaneous learning of probabilistic subgoal representations and policies within a unified framework. In experiments, our approach outperforms state-of-the-art baselines in standard benchmarks but also in environments with stochastic elements and under diverse reward conditions. Additionally, our model shows promising capabilities in transferring low-level policies across different tasks. © 2024, CC BY.

关键词： Reinforcement learning

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Probabilistic subgoal representations for hierarchical reinforcement learning 24

Probabilistic subgoal representations for hierarchical reinf...

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Proceedings of the 41st International Conference on Machine Learning

作者： Vivienne Huiling Wang Tinghuai Wang Wenyan Yang Joni-Kristian Kämäräinen Joni Pajarinen Department of Electrical Engineering and Automation Aalto University Finland Huawei Helsinki Research Center Finland Computing Sciences Tampere University Finland

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PeanutNeRF: 3D Radiance Field for Peanuts

PeanutNeRF: 3D Radiance Field for Peanuts

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Saeed, Farah Sun, Jin Ozias-Akins, Peggy Chu, Ye Juliet Li, Changying Charlie University of Georgia School of Computing United States University of Georgia Institute of Plant Breeding Genetics and Genomics Department of Horticulture United States University of Florida Department of Agricultural and Biological Engineering United States University of Florida Biosensing Automation and Intelligence Laboratory United States

ISBN: (纸本)9798350302493

Accurate phenotypic analysis can help plant breeders efficiently identify and analyze suitable plant traits to enhance crop yield. While 2D images from RGB cameras are easily accessible, their trait estimation performance is limited due to occlusion and the absence of depth information. On the other hand, 3D data from LiDAR sensors are noisy and limited in their ability to capture very thin plant parts such as peanut plant pegs. To combine the merits of both the 2D and 3D data analysis, the 2D images were used to capture thin parts in peanut plants, and deep learning-based 3D reconstruction using captured 2D images was performed to obtain 3D point clouds with information about the scene from different angles. The neural radiance fields were optimized for implicit 3D representation of the plants. The trained radiance fields were queried for 3D reconstruction to achieve point clouds for a 360-degree view and frontal view of the plant. With frontal-view reconstruction and the corresponding 2D images, we used Frustum PVCNN to perform 3D detection of peanut pods. We showed the effectiveness of PeanutNeRF on peanut plants with and without foliage: it showed negligible noise and a chamfer distance of less than 4 × 10-4 from a manually cleaned version. The pod detection showed a precision of around 0.7 at the IoU threshold of 0.5 on the validation set. This method can assist in accurate plant phenotypic studies of peanuts and other important crops. © 2023 IEEE.

关键词： Crops

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Numerical Analysis of Internal Defect Detection in Aluminum Alloy Pipes Aims to Provid Data Support for AI Models

Numerical Analysis of Internal Defect Detection in Aluminum ...

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2024 lEEE International Conference on Advanced Information, Mechanical engineering, Robotics and automation, AIMERA 2024

作者： Bai, Pengshihan Wang, Shang Long, Jianqiao Zheng, Kexin Ma, Jing Li, Nandu Bruce, Steve University of Science and Technology Beijing School of Metallurgical and Ecological Engineering Beijing China Beijing Polytechnic School of Automotive Engineering Beijing China Newcastle University School of Computing United Kingdom Dezhou Natural Resources Bureau Shandong China Information China University of Geosciences School of Mechanical Engineering and Electronic Wuhan China SEH Automation Pte Ltd Department of Research & Development Singapore

ISBN: (纸本)9798350343335

The online effective detection of internal defects in metals has long faced many challenges. The rapid development of Artificial Intelligence (AI) technology has provided possibilities for improving defect detection techniques. However, AI algorithms rely heavily on large amounts of data, which are difficult to obtain through traditional experimental methods. To address this issue, this study proposes a method for the internal defect detection of aluminum alloy pipes and provides data support for AI models and algorithms. We established a "pinch roller-pressure roller-aluminum alloy pipes"model and obtained the variation rules of Mises stress, constraint reaction force caused by the change of internal defect position through numerical calculation. The results show that the position change of the internal defect has a clear functional relationship with the constraint reaction force of roller-A. When the defect is located near the lower part of roller-B, the constraint reaction force of roller-A reaches the maximum value (978.8 N). This study provides empirical evidence for establishing an online defectdetection AI models. It clarifies the evolution pattern of constraint forces related to crack location. These findings provide data support for developing efficient and accurate defect online detection systems based on AI to address the limitations of traditional methods. © 2024 IEEE.

关键词： Aluminum pipe

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