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An Artificial neural network for Solving distributed Optimal Control of the Poisson's Equation

neural processing LETTERS 2019年第1期49卷 159-175页

作者： Ghasemi, Shojaeddin Effati, Sohrab Ferdowsi Univ Mashhad Dept Appl Math Mashhad Iran Ferdowsi Univ Mashhad Ctr Excellence Soft Comp & Intelligent Informat P Mashhad Iran

This paper presents a simple and efficient method based on artificial neural network to solve distributed optimal control of Poisson's equation with Dirichlet boundary condition. The trial solutions are used to approximate the state and control variables. These trial solutions are considered by using a single layer neural network. By replacing the trial solutions in objective function and Poisson's equation, then using the weighted residual method, distributed optimal control of Poisson's equation is converted to a linear quadratic optimal control problem. The weights of the trial solutions are computed by solving the new problem. In order to solve the linear quadratic optimal control problem, the Pontryagin maximum principle is used. Finally we apply the proposed method on several examples that in computational experiments, the high efficiency of the presented method is illustrated.

关键词： Optimal control Poisson's equation Artificial neural network Weighted residual method Pontryagins maximum principle

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Intelligent Algorithm of Semantic Analysis Based on BP neural network

Intelligent Algorithm of Semantic Analysis Based on BP Neura...

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作者： Zhuang, Yan Zhang, Cheng Xu, Jie Dong, Liang Xu, Ning He, Liang Wang, Jie Guo, Feng Jin, Bo Yu, Zheng Ke, Wangsong Hu, Yaodong Zhang, Ping State Grid Hubei Information and Telecommunication Company Limited Hubei Wuhan China

With the growth of scientific and technological information technology and the rapid popularization of the Internet, network big data and information technology are also growing rapidly. Information technology provides people with more information, and it also significantly increases the operating and management costs of my country’s Internet companies. In order to solve this problem thoroughly, people propose a new type of it development, research and business model, namely BP neural network technology. At present, BP neural network technology has been widely used in various application fields such as network storage, search engines, distributed computers, e-commerce, social networks, and has achieved rapid growth. This article mainly adopts the method of organically combining theoretical exploration and empirical research, and systematically analyzes the data collected through research based on the views and research contents of some scholars in recent years. Combining with the analysis of the data of intelligent semantic analysis algorithm, some relevant characteristics of BP neural network are summarized. This article mainly focuses on the research of an intelligent algorithm for image semantic analysis for image processing. The semantic analysis intelligent algorithm can well change the situation of target detection difficulties. This article uses an intelligent algorithm based on BP neural network to automatically analyze and distinguish differences. The final results of the research show that this paper uses the attention model and proposes a semantic analysis algorithm combined with graphic target detection through a multi-scale segmentation network. The experiment shows that the three performances of attention are 71.6, 56.5 and 49.3, which can be learned this algorithm is better than the same comparison algorithm in terms of three performance evaluation indexes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： neural networks

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Machine Learning-based Fault Diagnosis for Distribution networks with distributed Renewable Energy Resources 6

Machine Learning-based Fault Diagnosis for Distribution Netw...

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6th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Li, Bin Zhao, Ruifeng Qiu, Junqi Guangdong Power Grid Co Ltd Zhongshan Power Supply Bur Zhongshan Peoples R China Guangdong Power Grid Co Ltd Power Dispatch & Control Ctr Guangzhou Peoples R China

ISBN: (纸本)9798350373486;9798350373479

With the growing capacity of distributed renewable energy resources (RERs) integrated into distribution grids, the power flow distribution becomes more complex. Traditional fault diagnosis solutions are proving inadequate for the demands of the evolving distribution network, thereby diminishing the reliability and sensitivity of the protection system. To address this challenge, this paper introduces an innovative fault diagnosis approach for distribution networks incorporating RERs, leveraging signal processing techniques and machine learning algorithms. Initially, effective features are obtained from the measured current signals by the Hilbert-Huang transform (HHT). Subsequently, these fault features serve as inputs for training feed-forward neural networks to build fault diagnosis models (including detection, classification, and segment identification). Simulation tests are conducted on a 13-node distribution network with three different types of RERs. Simulation results show that the method can accurately diagnose distribution network faults, and is robust to fault inception angle variations, transition resistance, and noise interference.

关键词： distribution network fault diagnosis Hilbert-Huang transform machine learning renewable energy resource

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EmbRace: Accelerating Sparse Communication for distributed Training of Deep neural networks 51

EmbRace: Accelerating Sparse Communication for Distributed T...

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51st International Conference on Parallel processing (ICPP)

作者： Li, Shengwei Lai, Zhiquan Li, Dongsheng Zhang, Yiming Ye, Xiangyu Duan, Yabo Univ Def Technol Natl Key Lab Parallel & Distributed Proc Comp Coll Changsha Peoples R China Xiamen Univ Xiamen Peoples R China

ISBN: (纸本)9781450397339

distributed data-parallel training has been widely adopted for deep neural network (DNN) models. Although current deep learning (DL) frameworks scale well for dense models like image classification models, we find that these DL frameworks have relatively low scalability for sparse models like natural language processing (NLP) models that have highly sparse embedding tables. Most existing works overlook the sparsity of model parameters thus suffering from significant but unnecessary communication overhead. In this paper, we propose EmbRace, an efficient communication framework to accelerate communications of distributed training for sparse models. EmbRace introduces Sparsity-aware Hybrid Communication, which integrates AlltoAll and model parallelism into data-parallel training, so as to reduce the communication overhead of highly sparse parameters. To effectively overlap sparse communication with both backward and forward computation, EmbRace further designs a 2D Communication Scheduling approach which optimizes the model computation procedure, relaxes the dependency of embeddings, and schedules the sparse communications of each embedding row with a priority queue. We have implemented a prototype of EmbRace based on PyTorch and Horovod, and conducted comprehensive evaluations with four representative NLP models. Experimental results show that EmbRace achieves up to 2.41x speedup compared to the state-of-the-art distributed training baselines.

关键词： distributed training deep learning sparsity of NLP models communication scheduling

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distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees 36

Distributed Methods with Compressed Communication for Solvin...

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36th Conference on neural Information processing Systems (NeurIPS)

作者： Beznosikov, Aleksandr Richtarik, Peter Diskin, Michael Ryabinin, Max Gasnikov, Alexander Innopolis Univ Innopolis Russia MIPT Dolgoprudnyi Russia HSE Univ Moscow Russia Yandex Moscow Russia KAUSTI Riyadh Saudi Arabia RAS IITP Moscow Russia Innopolis Univ Res Ctr Artificial Intelligence Innopolis Russia

ISBN: (纸本)9781713871088

Variational inequalities in general and saddle point problems in particular are increasingly relevant in machine learning applications, including adversarial learning, GANs, transport and robust optimization. With increasing data and problem sizes necessary to train high performing models across various applications, we need to rely on parallel and distributed computing. However, in distributed training, communication among the compute nodes is a key bottleneck during training, and this problem is exacerbated for high dimensional and over-parameterized models. Due to these considerations, it is important to equip existing methods with strategies that would allow to reduce the volume of transmitted information during training while obtaining a model of comparable quality. In this paper, we present the first theoretically grounded distributed methods for solving variational inequalities and saddle point problems using compressed communication: MASHA1 and MASHA2. Our theory and methods allow for the use of both unbiased (such as Randk;MASHA1) and contractive (such as Topk;MASHA2) compressors. New algorithms support bidirectional compressions, and also can be modified for stochastic setting with batches and for federated learning with partial participation of clients. We empirically validated our conclusions using two experimental setups: a standard bilinear min-max problem, and large-scale distributed adversarial training of transformers.

关键词： Optimization

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SUSTAINABLE DEVELOPMENT IN MEDICAL APPLICATIONS USING neural network ARCHITECTURE

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第2期25卷 782-791页

作者： Jiang, Shuyi Changchun Univ Sci & Technol Coll Comp Sci & Technol Changchun 130012 Peoples R China

The purpose of this research is to propose a methodology utilizing machine learning techniques to support medical organizations in effectively managing risks. Specifically, the study aims to connect social media data to identify and assess potential threats, ultimately enabling healthcare management to make informed decisions for their organizations and clients. The research employs machine learning algorithms to analyze user-generated content on social media platforms, generating comprehensive visual representations of various risk categories and their magnitudes. Additionally, the study utilizes data simplification techniques, including categorization, to streamline data processing and enhance overall efficiency. A computational framework is also developed, incorporating closed-form connections for threat assessment and evaluation. The study further empirically analyses the Consumer Value Stores (CVS) established for medical care in the United States. The findings reveal that prevalent threats within the lower quartile of client messages about CVS services include operational, financial, and technological risks. The severity of these risks is distributed among high risk (21.8%), moderate risk (78%), and minimal risk (0.2%). The research also presents several metrics to demonstrate the robustness of the proposed framework, confirming its effectiveness in effectively identifying and addressing potential threats. This research provides insights that can help healthcare management make informed decisions and foster a safer and more secure environment for their organizations and the people they serve.

关键词： Conversational Interpretation Artificial Intelligence Internet-based Information Mining Medical Analytics Risk Administration

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HSDP: Accelerating Large-scale Model Training via Efficient Sharded Data Parallelism 22

HSDP: Accelerating Large-scale Model Training via Efficient ...

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22nd IEEE International Symposium on Parallel and distributed processing with Applications, ISPA 2024

作者： Hao, Yanqi Lai, Zhiquan Wang, Wei Li, Shengwei Liu, Weijie Ge, Keshi Li, Dongsheng College of Computer Changsha China

ISBN: (纸本)9798331509712

Large deep neural network (DNN) models have demonstrated exceptional performance across diverse downstream tasks. Sharded data parallelism (SDP) has been widely used to reduce the memory footprint of model states. In a DNN training cluster, a device usually has multiple inter-device links that connect to other devices, like NVLink and InfiniBand. However, existing SDP approaches employ a single link at any given time, encountering challenges in efficient training due to significant communication overheads. We observe that the inter-device links can work independently without affecting each other. To reduce the fatal communication overhead of distributed training of large DNNs, this paper introduces HSDP, an efficient SDP training approach that enables the simultaneous utilization of multiple inter-device links. HSDP partitions models in a novel fine-grained manner and orchestrates the communication processes of partitioned parameters while considering inter-device links. This design enables concurrent communication execution and reduces communication overhead. To further optimize the training performance of HSDP, we propose a HSDP planner. The HSDP planner first abstracts the model partition and execution of HSDP into a communication parallel strategy, and builds a cost model to estimate the performance of each strategy. We then formulate the strategy searching as an optimization problem and solve it with an off-the-shelf solver. Evaluations on representative DNN workloads demonstrate that HSDP achieves up to 1.30× speedup compared to the state-of-the-art SDP training approaches. © 2024 IEEE.

关键词： Deep neural networks

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Convolutional Recurrent neural networks for the Classification of Cetacean Bioacoustic Patterns 48

Convolutional Recurrent Neural Networks for the Classificati...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Makropoulos, Dimitris N. Tsiami, Antigoni Prospathopoulos, Aristides Kassis, Dimitris Frantzis, Alexandros Skarsoulis, Emmanuel Piperakis, George Maragos, Petros National Technical University of Athens Greece Anavyssos Greece Pelagos Cetacean Research Institute Vouliagmeni 16671 Greece IACM Foundation of Research and Technology - Hellas Crete Heraklion Greece

ISBN: (纸本)9781728163277

In this paper we focus on the development of a convolutional recurrent neural network (CRNN) to categorize biosignals collected in the Hellenic Trench, generated by two cetacean species, sperm whales (Physeter macrocephalus) and striped dolphins (Stenella coeruleoalba). We convert audio signals into mel-spectrograms and forward the input into a deep residual network (ResNet), designed to capture spectral patterns. Next, ResNet's output is reshaped into a time-distributed layer and fed into recurrent network variants, Long Short-Term Memory (LSTMs) or Gated Recurrent Units (GRUs), able to recognize long-term time dependencies on extracted features. The hybrid network perfectly classifies audio signals into three categories (dolphins, sperm whales, ambient noise) while it also exhibits high learning ability on recognising intraclass representations of overlapping acoustic patterns (clicks vs whistles and clicks, both emitted by dolphins). The proposed scheme outperforms traditional Machine Learning (ML) techniques, baseline ResNet and LSTM architectures or their deep parallel combinations. © 2023 IEEE.

关键词： Long short-term memory

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Advances in Thermal Imaging: A Convolutional neural network Approach for Improved Breast Cancer Diagnosis 2

Advances in Thermal Imaging: A Convolutional Neural Network ...

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2nd International Conference on distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Agughasi, Victor Ikechukwu Bhimshetty, Sampoorna Deepu, R. Mala, M.V. Maharaja Institute of Technology Mysore Department of Computer Science & Engineering Karnataka India University of Visvesvaraya College of Engineering Department of Computer Science & Engineering Karnataka India Pes University Department of Computer Science & Engineering Karnataka Bangalore India

ISBN: (纸本)9798350382952

This study explores the application of thermal imaging in breast cancer diagnostics, presenting a novel methodology that integrates pre-processing techniques and Convolutional neural networks (CNN) for the classification of thermal breast images. A comprehensive dataset from the DMR Database is used, comprising balanced samples of normal and cancerous images. The images are augmented, standardized, and enhanced through data augmentation, resizing, and filtering, with crucial features extracted via the Histogram of Oriented Gradients (HOG). A CNN model, specifically constructed for this study, is then trained and tested on these processed images. The approach's effectiveness is benchmarked against other classifiers, displaying promising results with accuracy rates ranging from 95.7% to 98.5%. Future work suggests exploring the fusion of traditional and advanced imaging modalities, expanding datasets, and utilizing pre-trained models for improved diagnostic precision. This research signifies a step toward the development of real-time, efficient diagnostic tools for breast cancer, highlighting the potential impact on patient outcomes and the broader medical field. © 2024 IEEE.

关键词： Infrared imaging

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Optimal Gradient Sliding and its Application to distributed Optimization Under Similarity 36

Optimal Gradient Sliding and its Application to Distributed ...

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36th Conference on neural Information processing Systems (NeurIPS)

作者： Kovalev, Dmitry Beznosikov, Aleksandr Borodich, Ekaterina Gasnikov, Alexander Scutari, Gesualdo King Abdullah Univ Sci & Technol Thuwal Saudi Arabia MIPT Moscow Russia HSE Univ Yandex Russia Inst Informat Transmiss Problems RAS Moscow Russia Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781713871088

We study structured convex optimization problems, with additive objective r := p + q, where r is (mu-strongly) convex, q is L-q-smooth and convex, and p is L-p-smooth, possibly nonconvex. For such a class of problems, we proposed an inexact accelerated gradient sliding method that can skip the gradient computation for one of these components while still achieving optimal complexity of gradient calls of p and q, that is, O(root L-p/mu) and O(root L-q/mu), respectively. This result is much sharper than the classic black-box complexityO(root(L-p + L-q)/mu), especially when the difference between L-p and L-q is large. We then apply the proposed method to solve distributed optimization problems over master-worker architectures, under agents' function similarity, due to statistical data similarity or otherwise. The distributed algorithm achieves for the first time lower complexity bounds on both communication and local gradient calls, with the former having being a longstanding open problem. Finally the method is extended to distributed saddleproblems (under function similarity) by means of solving a class of variational inequalities, achieving lower communication and computation complexity bounds.

关键词： Convex optimization

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