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Predictive Error Switching Strategy for Multilevel Matrix converter

Predictive Error Switching Strategy for Multilevel Matrix co...

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India Council international Subsections conference (INDISCON), IEEE

作者： M. Senthil Kumaran K. Murugesan S.Solai Manohar A.Darwin Jose Raju Department of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of Electrical and Electronics Engineering CMR Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering St.Xavier’s Catholic College of Engineering Nagercoil India

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

A predictive error control switching strategy (PESS) is proposed in this paper for a three-level direct matrix converter (TLDMC). In the proposed PESS, to control the input current and output voltage indirect-space vectors of the matrix converter are used. During every sampling period, using the principle of least square method the switching vectors are selected by computing the minimum sum of the squared errors of both voltage and current. the envelope of the minimum error of the space vectors over a sampling period directs the overall error in the PESS technique. the PESS technique experiences considerably high input current harmonics for lower modulation indices. the PESS technique is modified to improve the performance of TLDMC during lower modulation indices. the modified PESS for TLDMC is evaluated using MATLAB model and the same is validated by experimentation.

关键词： Space vector pulse width modulation Switching loss Modulation Switches parallel processing Harmonic analysis Vectors Real-time systems Matrix converters Voltage control

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Unpaired thick Cloud Removal with Multiscale SAR-optical Data Fusion

Unpaired Thick Cloud Removal with Multiscale SAR-optical Dat...

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Computer Vision, Image and Deep Learning (CVIDL), international conference on

作者： Xuanyu Xiang Yihua Tan National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Cloud removal is vital for the analysis of optical satellite images. To alleviate the impact of thick clouds, recent advances integrate deep learning withmultimodal data. To relax the requirements of paired training samples, existing methods utilized cycle-consistent architecture to learn the relation between cloudy and cloud-free images based on unpaired training samples. When considering thick cloud removal based on unpaired training data, the relevant studies are insufficient and they face two challenges: 1) the information in non-cloudy areas is not preserved well after cloud removal; 2) the recovered information lacks consistency with the global textures and structures. Based on optical-SAR fusion and cycle-consistent training, this paper proposes Multiscale Cycle-consistent Fusion (MCF) model. MCF designs preservation loss to overcome the first challenge, and proposes global-local discriminator combined with parallel Dilated Channel-weighted Module (PDCM) for the second challenge. MCF is evaluated both on a simulated dataset and a real dataset, which demonstrates its effectiveness.

关键词： Training Deep learning Optical losses Clouds Training data Optical imaging Data models

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Offline Imitation Learning Using Reward-free Exploratory Data 22

Offline Imitation Learning Using Reward-free Exploratory Dat...

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Proceedings of the 2022 5th international conference on algorithms, Computing and Artificial Intelligence

作者： Hao Wang Dawei Feng Bo Ding Wei Li College of Computer National University of Defense Technology China National Laboratory for Parallel and Distributed Processing National University of Defense Technology China Independent Researcher China

ISBN: (纸本)9781450398336

Offline imitative learning(OIL) is often used to solve complex continuous decision-making tasks. For these tasks such as robot control, automatic driving and etc., it is either difficult to design an effective reward for learning or very expensive and time-consuming for agents to collect data interactively with the environment. However, the data used in previous OIL methods are all gathered by reinforcement learning algorithms guided by task-specific rewards, which is not a true reward-free premise and still suffers from the problem of designing an effective reward function in real tasks. To this end, we propose the reward-free exploratory data driven offline imitation learning (ExDOIL) framework. ExDOIL first trains an unsupervised reinforcement learning agent by interacting with the environment, and collects enough unsupervised exploration data during training; then, a task independent yet simple and efficient reward function is used to relabel the collected data; Finally, an agent is trained to imitate the expert to complete the task through a conventional RL algorithm such as TD3. Extensive experiments on continuous control tasks demonstrate that the proposed framework can achieve better imitation performance(28% higher episode returns on average) comparing with previous SOTA method(ORIL) without any task-specific rewards.

关键词： dataset

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Scheduling of parallel Tasks in Cloud Environment Using DAG MODEL 5th

Scheduling of Parallel Tasks in Cloud Environment Using DAG ...

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5th international conference on Intelligent Computing and Applications, ICICA 2019

作者： Kapoor, Sakshi Panda, Surya Narayan Chitkara Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9789811555657

the scheduling of tasks in a heterogeneous multiprocessor system in the cloud is still a demanding problem that is being explored by many researchers. parallel computing which itself is a research area is now integrated with cloud computing. In this paper, we present a systematic study of a directed acyclic graph (DAG) model in a parallel multiprocessor system. Basic concepts of parallel computing along with issues like task scheduling have been discussed in detail. Computational solutions can be demonstrated as directed acyclic graphs (DAG) model having edges and nodes with weights. Each task or job in the DAG has its own execution time, which incorporates into various processors. this paper gives the basic idea of parallel processing using DAG. the main objective behind scheduling using the DAG model is to reduce the finish time or completion time of parallel applications through proper assignment of tasks among multiprocessors. Most of these schemes consider the precedence constraints among tasks. Up to now, Direct Acyclic Graph (DAG) is considered a prominent approach used for modeling the precedence constraints between the nodes or tasks. there are various scheduling algorithms that use DAG for scheduling jobs. the most popular heterogeneous earliest finish time (HEFT) is one of them. A literature review on various task scheduling schemes in combination with artificial intelligence concepts is also presented. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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Approximation algorithms for Two parallel Dedicated Machine Scheduling with Conflict Constraints 1

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15th Annual international conference on Combinatorial Optimization and Applications (COCOA)

作者： Zhang, An Zhang, Liang Chen, Yong Chen, Guangting Wang, Xing Hangzhou Dianzi Univ Dept Math Hangzhou 310018 Peoples R China Taizhou Univ Linhai 317000 Peoples R China

ISBN: (数字)9783030926816

ISBN: (纸本)9783030926816;9783030926809

We investigate two parallel dedicated machine scheduling with conflict constraints. the problem of minimizing the makespan has been shown to be NP-hard in the strong sense under the assumption that the processing sequence of jobs on one machine is given and fixed a priori. the problem without any fixed sequence was previously recognized as weakly NP-hard. In this paper, we first present a 9/5-approximation algorithm for the problem with a fixed sequence. then we show that the tight approximation ratios of the algorithm are 7/4 and 5/3 for two subproblems which remain strongly NP-hard. We also send an improved algorithm with approximation ratio 3 - root 2 approximate to 1.586 for one subproblem. Finally, we prove that the problem without any fixed sequence is actually strongly NP-hard, and design a 5/3-approximation algorithm to solve it.

关键词： parallel dedicated machines Conflict graph Approximation algorithm NP-hard

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Session-Recommendation Based on Gated Neural Network to Extract Item Structure Feature Graph 22

Session-Recommendation Based on Gated Neural Network to Extr...

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5th international conference on Data Storage and Data Engineering, DSDE 2022

作者： Zhang, Wei Yang, Yan Zhong, Yingli College of Computer Science and Technology Heilongjiang University China Heilongjiang Key Laboratory of Database and Parallel Computing Heilongjiang University China School of Computer Science and Technology Heilongjiang University China

ISBN: (纸本)9781450395724

the issue of inaccurate user portrait recommendations for short-Term conversations is solved. the classical recommendation is based on an assumption: the user's historical behavior can represent the user's stable long-Term preferences, and the result is generated based on the user's historical behavior. However, there is sparseness in the user's historical behavior information record in the actual data set, and the obtained data cannot accurately represent the user's preferences. Research on a session recommendation focuses on user behavior data in the short term, and aims at predict anonymous user behavior based on in-session behavior information. It plays a vital role in application scenarios where user behavior information is scarce. Feature extraction is performed on the user's anonymous behavior information graph generated in a fixed period of time as the embedding representation of user's interest preference;the graph structure of the same item in the global click interaction graph is combined with the input a gated graph neural network as the item attribute embedding representation. the prediction part adopts simulated interactive calculation to produce results. With the help of gated graph neural network, features extraction and analysis of data in short-Term sessions: session-based user behavior data and in-session item attributes are obtained from the graph data structure, which reduces errors caused by inconsistent data processing methods. the user's current interest is captured based on the behavior information in the session, the global inter-item conversion graph is generated based on the sequence of click moments, and the feature vector is obtained through graph neural network processing. the generation of recommendation results is more time-sensitive, complete the anonymity problem due to the scarcity of user's information, and improves the accuracy of recommendations. © 2022 ACM.

关键词： Graph neural networks

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Predicting IMDb movie rating using deep learning 5

Predicting IMDb movie rating using deep learning

引用

5th international conference on Communication and Electronics Systems, ICCES 2020

作者： Gogineni, Saikiran Pimpalshende, Anjusha CSE CMR College Of Engineering & Technology Hyderabad India

ISBN: (纸本)9781728153711

Automatic text classification is considered as a significant task in the current digital world where a huge volume of data is present in the form of text. S o, it has been necessary to come up with efficient algorithms that could classify reviews and emotions provided by the user without human intervention. this will help others in choosing a better movie to watch. S everal metrics such as accuracy, Recall, and area under the ROC curve are used to determine the efficiency of algorithms and to suggest accurate classifiers. In our paper, the efficiency of classical machine learning algorithms and deep learning architectures on the IMDB movie review dataset has been contrasted and proposed the most efficient architecture. the former part of the paper focuses on pre-processing the text documents and the latter part of the paper experiments with advanced deep learning techniques on the text. © 2020 IEEE.

关键词： Text processing

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HCPipe: Hidden Computation Pipeline Model parallelism for Large-scale DCNN Model Training

HCPipe: Hidden Computation Pipeline Model Parallelism for La...

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international conference on Computer Engineering and Applications (ICCEA)

作者： Bin Liu Yongyao Ma Zhonghao Zhang Zeyu Ji Guoqiang Hu Yijie Chen College of Information Engineering Northwest A&F University Yangling China Network and Educational Technology Center Northwest A&F University Yangling China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

the complexity of Deep Convolutional Neural Networks (DCNNs) with big datasets significantly prolongs the time of model training. Pipeline Model parallelism (PMP) is a commonly used method in distributed training for shortening model time. However, traditional PMP methods struggle to maximize hidden computational, resulting in significant idle time. this paper introduces an innovative method, HCPipe, which employs a hidden computation method to overlap computation times further, effectively reducing idle time in pipeline training. the effectiveness of this method is firstly theoretically demonstrated. Subsequently, a model partitioning method based on dynamic programming is designed, aimed at rapidly dividing the model. To evaluate the performance of HCPipe, using the ImageNet dataset extensive experiments were conducted with ResNet and VGG models on a 4-GPU server. the experimental results indicate that the proposed HCPipe achieves significant performance improvements. Compared to GPipe, DAPPLE, and GEMS, the average throughput for the VGG series is increased by 13.3%, 4.6%, and 128%, respectively, while the average throughput for the ResNet series is increased by 27.8%, 14.4%, and 157%, respectively.

关键词： Training Computational modeling Pipelines Graphics processing units parallel processing throughput Dynamic programming

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11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2022

11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putt...

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11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2022

ISBN: (纸本)9783031083402

the proceedings contain 35 papers. the special focus in this conference is on Mining Humanistic Data. the topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Cancer Patients;CAIPI in Practice: Towards Explainable Interactive Medical Image Classification;a Deep Q Network-Based Multi-connectivity Algorithm for Heterogeneous 4G/5G Cellular Systems;simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of Stake;Maximum Likelihood Estimators on MCMC Sampling algorithms for Decision Making;employing Natural Language processing Techniques for Online Job Vacancies Classification;Probabilistic Quantile Multi-step Forecasting of Energy Market Prices: A UK Case Study;proactive Buildings: A Prescriptive Maintenance Approach;performance Meta-analysis for Big-Data Univariate Auto-Imputation in the Building Sector;non-intrusive Diagnostics for Legacy Heat-Pump Performance Degradation;a 5G-Based Architecture for Localization Accuracy;anomaly Detection in Small-Scale Industrial and Household Appliances;an Innovative Software Platform for Efficient Energy, Environmental and Cost Planning in Buildings Retrofitting;deep Learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic Images;An Intelligent Grammar-Based Platform for RNA H-type Pseudoknot Prediction;An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI Images;the Use of Robotics in Critical Use Cases: the 5G-ERA Project Solution;fundamental Features of the Smart5Grid Platform Towards Realizing 5G Implementation;experimentation Scenarios for Machine Learning-Based Resource Management;efficient Data Management and Interoperability Middleware in Business-Oriented Smart Port Use Cases;5G for the Support of Smart Power Grids: Millisecond Level Precise Distributed Generation Monitoring and Real-Time Wide Area Monitoring;monitoring Neurological Disorder Patients via Deep Learning Based Facial Expressions An

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Towards Russian Text Generation Problem Using OpenAI's GPT-2 5

Towards Russian Text Generation Problem Using OpenAI's GPT-2

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5th international conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Shatalov, Oleksii Ryabova, Nataliya Natl Univ Radio Elect Nauky Av 14 UA-61000 Kharkiv Ukraine

this work is devoted to Natural Language Generation (NLG) problem. the modern approaches in this area based on deep neural networks are considered. the most famous and promising deep neural network architectures that are related to this problem are considered, in particular, the most popular free software solutions for NLG based on Transformers architecture with pre- trained deep neural network models GPT-2 and BERT. the main problem is that the main part of already existing solutions is devoted to the English language. But there are few models that are able to generate text in Russian. Moreover, the text they generate often belongs to a general topic and not about a specific subject area. the object of the study is the generation of a contextually coherent narrow-profile text in Russian. Within the framework of the study, a model was trained for generating coherent articles of a given subject area in Russian, as well as a software application for interacting with it.

关键词： Natural Language Generation Natural Language processing Transformers Architecture Deep Learning Transfer Learning GPT-2

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