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distributed Photovoltaic Scenario Generation Based on Edge-Cloud Collaborative Architecture

Distributed Photovoltaic Scenario Generation Based on Edge-C...

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58th IEEE/IAS Industrial and Commercial Power Systems Technical conference Asia (IEEE I and CPS Asia)

作者： Huang, Jinju Mao, Zhihang Xie, Chenzheng Sun, Yingyun North China Elect Power Univ Dept Elect & Elect Engn Beijing Peoples R China

ISBN: (数字)9781665450669

ISBN: (纸本)9781665450669

With the expansion of the power system and the growth in renewable energy penetration, the installed capacity of distributed photovoltaics in the power system will increase significantly in the future, and the amount of related data will increase sharply in the future. Traditional centralized cloud management is difficult to afford the massive data processing business to guarantee service quality. This paper proposes an edge-cloud collaboration architecture based on Kubernetes. At the edge, we cluster the distributed photovoltaics to the nearby centralized photovoltaic power plants with strong correlation and synthesize as a virtual station. Subsequently, we use historical meteorological and power data to predict the next day power curve. At the cloud, we generate the scenarios for the centralized station based on the day-ahead power data uploaded by the edge, then we obtain the scenarios for distributed photovoltaic cluster by converting the scenarios of centralized station with the capacity conversion formula.

关键词： scenario generation edge-cloud collaboration distributed photovoltaic power forecast neural network

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Deep Unrolling-Based Joint Active User Detection and Channel Estimation in distributed Antenna Systems

Deep Unrolling-Based Joint Active User Detection and Channel...

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International conference on Wireless Communications and Signal processing (WCSP)

作者： Junhui Liu Qirui Xu Shihao Zhu Ming Zhao Wuyang Zhou CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Hefei Anhui China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

This paper addresses the challenge of joint active user detection (AUD) and channel estimation (CE) for grant-free random access within massive machine-type communications (mMTC) enabled distributed antenna systems (DAS). Sparse Bayesian learning (SBL) integrated with variational Bayesian inference (VBI) methods exploit channel sparsity effectively and are commonly employed for AUD and CE. Nevertheless, these techniques generally require matrix inversion operations and numerous iterations, leading to high computational complexity. To mitigate these issues, we propose a deep unrolling neural network based on a fast inversion algorithm, introducing learnable parameters to accelerate convergence. Additionally, we consider the pilot collision in grant-free random access, which is often overlooked in most literature, and presents a pilot collision handling method that leverages the spatial sparsity of distributed antennas. Simulation results validate the reliability of the proposed network and the method for pilot collision processing.

关键词： Wireless communication Simulation neural networks Signal processing algorithms Channel estimation Signal processing Bayes methods Sparse matrices Antenna arrays Convergence

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A Lightweight CNN-based Mechanism to Efficiently Identify Electric Meters under Stealthy False Data Injection Attacks in Smart Power Grid 29

A Lightweight CNN-based Mechanism to Efficiently Identify El...

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29th IEEE International conference on Parallel and distributed Systems, ICPADS 2023

作者： Ye, Tong Zhou, Boyang Nie, Yinghui Zhu, Zhen Intelligent Networks Institute Zhejiang Lab Hangzhou311100 China Hangzhou Institute for Advanced Study Ucas School of Intelligent Science and Technology Hangzhou310024 China

ISBN: (纸本)9798350330717

In the era of the power grid digitization, the smart electric meters faces the stealthy false data injection attack (SFDI), which compromises the state estimation (SE) in the supervisory control and data acquisition system (SCADA). Especially, the state-of-the-art identification mechanism in the intrusion detection systems (IDSes) cannot meet the timely response demand, despite its high accuracy as it adopts a deep CNN to capture the injections via fully exploiting the wide-area measurement statistics. To solve the above challenge, we propose an efficient and accurate identification mechanism (EACI) designed in the following aspects. To improve the efficiency, EACI adopts a lightweight CNN with reduced neural layers to avoid the unnecessary exploitation. To maintain the accuracy, EACI extracts the major correlation features in the adjacent statistics, and employs the CNN to accurately discriminate the injections. We evaluate EACI under extensive SFDI scenarios, in both the low and medium voltages power grids constructed according to the IEEE 30-bus and 57-bus datasets, respectively. The numerical results demonstrate that EACI reduces the response time to hundreds of microseconds, while keeping a high detection accuracy. © 2023 IEEE.

关键词： convolutional neural network Intrusion detection system smart power grid stealthy false data injection attack

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Indian Currency Recognition:A Lightweight Transfer Learning Approach for Visually Impaired 7

Indian Currency Recognition:A Lightweight Transfer Learning ...

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7th International conference on Parallel, distributed and Grid Computing, PDGC 2022

作者： Saini, Poonam Goyal, Ankit Pokhrel, Bishwash Singh, Jaspreet Kumar, Neetish Punjab Engineering College Dept. of Computer Science and Engg. Chandigarh India

ISBN: (纸本)9781665454018

The knowledge of artificial intelligence, especially, machine learning has been widely used to handle various real-world applications and related societal challenges. One of the crucial issues faced by visually impaired people is to correctly recognize paper currency and its denomination easily. The paper proposes a convolutional neural network-based currency recognition system applied to the Indian Currency. The basic features include data pre-processing to filter out the old currency notes and incorrect labels, followed by a pre-trained model selection. The proposed model is trained using transfer learning, and afterward, a custom layer is added to recognize currency notes. Further, a fully connected customized neural network architecture has been designed. The model undergoes fine tuning of essential hyperparameters. The proposed model classifies Indian banknotes with more reliable accuracy (99.5%), which is even flexible to different image orientations (flipped, folded, out of frame, etc.) as well as empty backgrounds for a real-time application. © 2022 IEEE.

关键词： network architecture

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Online Meta-Learning via Learning with Layer-distributed Memory 35

Online Meta-Learning via Learning with Layer-Distributed Mem...

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35th Annual conference on neural Information processing Systems (NeurIPS)

作者： Babu, Sudarshan Savarese, Pedro Maire, Michael TTI C Chicago IL 60637 USA Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9781713845393

We demonstrate that efficient meta-learning can be achieved via end-to-end training of deep neural networks with memory distributed across layers. The persistent state of this memory assumes the entire burden of guiding task adaptation. Moreover, its distributed nature is instrumental in orchestrating adaptation. Ablation experiments demonstrate that providing relevant feedback to memory units distributed across the depth of the network enables them to guide adaptation throughout the entire network. Our results show that this is a successful strategy for simplifying meta-learning - often cast as a bi-level optimization problem - to standard end-to-end training, while outperforming gradient-based, prototype-based, and other memory-based meta-learning strategies. Additionally, our adaptation strategy naturally handles online learning scenarios with a significant delay between observing a sample and its corresponding label - a setting in which other approaches struggle. Adaptation via distributed memory is effective across a wide range of learning tasks, ranging from classification to online few-shot semantic segmentation.

关键词： Memory architecture

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UNVEILING THE TIMBRE LANDSCAPE: A LAYERED ANALYSIS OF TENOR SAXOPHONE IN RAVE MODELS 21

UNVEILING THE TIMBRE LANDSCAPE: A LAYERED ANALYSIS OF TENOR ...

引用

21st Sound and Music Computing conference, SMC 2024

作者： Carvalho, Nádia Sousa, Jorge Bernardes, Gilberto Portovedo, Henrique University of Porto Faculty of Engineering INESC TEC Porto Portugal University of Aveiro INET-md Aveiro Portugal

ISBN: (纸本)9789893520758

This paper presents a comprehensive investigation into the explainability and creative affordances derived from navigating a latent space generated by Realtime Audio Variational AutoEncoder (RAVE) models. We delve into the intricate layers of the RAVE model's encoder and decoder outputs by leveraging a novel timbre latent space that captures micro-timbral variations from a wide range of saxophone extended techniques. Our analysis dissects each layer's output independently, shedding light on the distinct transformations and representations occurring at different stages of the encoding and decoding processes and their sensitivity to a spectrum of low-to-high-level musical attributes. Remarkably, our findings reveal consistent patterns across various models, with the first layer consistently capturing changes in dynamics while remaining insensitive to pitch or register alterations. By meticulously examining and comparing layer outputs, we elucidate the underlying mechanisms governing saxophone timbre representation within the RAVE framework. These insights not only deepen our understanding of neural network behavior but also offer valuable contributions to the broader fields of music informatics and audio signal processing, ultimately enhancing the degree of transparency and control in co-creative practices within deep learning music frameworks. © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original.

关键词： Deep neural networks

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Simulation of Industrial Economic Development Model Based on BP neural network 22

Simulation of Industrial Economic Development Model Based on...

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3rd Asia-Pacific conference on Image processing, Electronics and Computers, IPEC 2022

作者： Wu, Haiyan Yang, Guangli Guangdong Dejiu New Energy Co. Ltd Guangdong Foshan528000 China

ISBN: (纸本)9781450395786

neural network is a new subject based on bionics principle. Linear model has high requirements for data. Generally, the data is required to be normally distributed. If the original data deviates from the normal distribution, it is necessary to standardize the original data, otherwise the regression results will be affected. With the globalization of finance and the complication of financial derivatives, the function of optimizing the allocation of financial resources has been continuously expanded and promoted, and its role in promoting finance and economy has been continuously strengthened, and its position in the national economy has been gradually promoted. The realization of the function of the financial industry depends on the real economy and macroeconomic policies. The role of the financial industry in promoting the economy will be affected by the external socio-economic environment, and the new normal is the most important economic environment in China. Combined with BP neural network, this paper further studies and analyzes the development of financial industry economic development model. © 2022 Association for Computing Machinery. All rights reserved.

关键词： Finance

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Industrial Vision Optimization distributed Strategy based on Edge Intelligence Collaboration 17

Industrial Vision Optimization Distributed Strategy based on...

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17th IEEE International Wireless Communications and Mobile Computing conference (IEEE IWCMC)

作者： Song, Yaqi Shen, Yun Ding, Peng Zhang, Xuezhi Xue, Yuying China Telecom Corp Ltd Dept Serv & Applicat Innovat Res Inst Beijing Peoples R China

ISBN: (纸本)9781728186160

In this paper, it is proposed that a novel strategy of based hierarchical data distribution and deep neural networks distribution over edge and end devices. In the Industrial Internet of Things environment, deep learning tasks such as smoke and fire classification based on convolutional neural network usually need to be performed on edge servers and end devices, which have limited computing resources, while edge servers have abundant computing resources. While being able to accommodate inference of a deep neural network (DNN) at the edge server, a distributed deep neural network (DDNN) also allows localized inference using a portion of the neural network at the end sensing devices. Therefore, this article proposed the distributed strategy can dynamically adjust network layers and data allocation proportion of end devices and edge servers according to different tasks to shorten the data processing time. A joint optimization problem is proposed to minimize the total delay, which is affected by the complexity of the DL model, the inference error rate, the computing power of the end devices and the edge servers. An analytical solution of a closed solution is derived and an optimal distributed data allocation and neural network allocation algorithm is proposed

关键词： Deep Learning (DL) distributed Deep neural network distributed Data Allocation Strategy IIoT

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37th International conference on Architecture of Computing Systems, ARCS 2024

37th International Conference on Architecture of Computing S...

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37th International conference on Architecture of Computing Systems, ARCS 2024

ISBN: (纸本)9783031661457

The proceedings contain 23 papers. The special focus in this conference is on Architecture of Computing Systems. The topics include: The Power of Training: How Different neural network Setups Influence the Energy Demand;an Efficient Multi Quantile Regression network with Ad Hoc Prevention of Quantile Crossing;Modifiable Artificial DNA - Change Your System’s ADNA at Any Time;From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in Solving Mesh-Based PDEs;enhancing Maritime Behaviour Analysis Through Novel Feature Engineering and Digital Shadow Modelling: A Case Study in the Kiel Fjord;Synthesizing Training Data for Intelligent Weed Control Systems Using Generative AI;towards the Online Reconfiguration of a Dependable distributed On-Board Computer;An Organic Computing Approach for CARLA Simulator;Idle is the New Sleep: Configuration-Aware Alternative to Powering Off FPGA-Based DL Accelerators During Inactivity;On-the-Fly CT Image Pre-processing on MPSoC-FPGAs;AccProf: Increasing the Accuracy of Embedded Application Profiling Using FPGAs;accelerating WebAssembly Interpreters in Embedded Systems Through Hardware-Assisted Dispatching;Exploring the ARM Coherent Mesh network Topology;comparison of a Binary Signed-Digit Adder with Conventional Binary Adder Circuits on Layout Level;Case Studies on the Impact and Challenges of Heterogeneous NUMA Architectures for HPC;a Hierarchical Modeling Approach for Assessing the Reliability and Performability of Burst Buffers;Generative-Based Algorithm for Data Clustering on Hybrid Classical-Quantum NISQ Architecture;improving Memory Dependence Prediction with Static Analysis;Atalanta: Open-Source RISC-V Microcontroller for Rust-Based Hard Real-Time Systems;software-Based Erasure-Tolerant Coding with Buffering and Compression;determination of Optimal H-Matrices for 2-Bit Error Correcting Codes.

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distributed and Parallel Sparse Computing for Very Large Graph neural networks

Distributed and Parallel Sparse Computing for Very Large Gra...

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2022 IEEE International conference on Big Data, Big Data 2022

作者： Petit, Quentin R. Li, Chong Emad, Nahid Huawei Paris Research Center Université Paris-Saclay Boulogne-Billancourt France Huawei Paris Research Center Distributed and Parallel Software Lab Boulogne-Billancourt France Université Versailles Saint-Quentin Maison de la Simulation LI-PaRAD Saclay France

ISBN: (纸本)9781665480451

Deep learning (DL) requires high-performance processing on big data. Graph neural networks, a challenging topic in DL using linear algebra methods, need algorithmic solutions to efficiently assign and process graph data on modern distributed and parallel machines, which are considered with mixed arithmetic and various types of tensor/matrix accelerators. Determining compression techniques for the graph's sparse data structures is one of the key *** first objective is to design and implement a reusable parallel numerical library to resolve large neural network graphs. Our design strategy is drawn on a component-based approach and targets maximum code reuse in various parallel contexts while allowing for performance optimization. The solution could be later integrated into a DL framework like MindSpore. © 2022 IEEE.

关键词： Matrix algebra

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