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Neural machine translation by jointly learning to align and translate 3

Neural machine translation by jointly learning to align and ...

引用

3rd International conference on learning Representations, ICLR 2015

作者： Bahdanau, Dzmitry Cho, KyungHyun Bengio, Yoshua Jacobs University Bremen Germany Université de Montréal Canada

Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder–decoders and encode a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder–decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition. © 2015 International conference on learning Representations, ICLR. All rights reserved.

关键词： Neural machine translation

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Optimizing MEMS Optical Arrays with Silicon Micro Mirrors using Machine learning Techniques 3

Optimizing MEMS Optical Arrays with Silicon Micro Mirrors us...

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3rd International conference on Computational Modelling, Simulation and optimization, ICCMSO 2024

作者： Joshi, Vilas Ramrao Sharma, Surender Kumar Galande, Vijay Agatrao Sachin Patil, Pallavi Patil, Ushadevi Suttarwala, Hussain Sharma, Deepak K. Savitribai Phule Pune University Department of Computer Engineering Pune India Poornima University Department of Electrical Engineering Jaipur India KJEI's Trinity Academy of Engineering Department of Engineering Science Pune India Genba Sopanrao Moze College of Engineering Department of Civil Engineering Pune India American Express Senior Software Engineer PhoenixAZ85054 United States University of Technology Jaipur India

ISBN: (纸本)9798350361391

In the rapidly evolving landscape of optical microsystems, the infusion of Machine learning (ML) techniques stands out as a catalyst for unprecedented progress. This research paper delves into the intricate interplay between Micro-Electro-Mechanical Systems (MEMS), optical arrays, and silicon micro mirrors, shedding light on the transformative prowess unleashed by Machine learning in optimizing these intricate components. Focusing on the limitations posed by conventional designs, the paper not only illuminates the challenges encountered but also presents a compelling narrative of how ML algorithms emerge as formidable allies, poised to surmount these hurdles with ingenuity and precision. The exploration extends beyond a mere technical discourse;it unfolds as a narrative of innovation, where the convergence of MEMS, optical arrays, and silicon micro mirrors, guided by the intelligence of Machine learning, transcends traditional boundaries, propelling optical microsystems into an era of unparalleled efficiency and adaptability. This research beckons a new epoch, where the intricate dance of silicon micro mirrors and the intricate mechanics of MEMS find harmony through the symphony of Machine learning, reshaping the landscape of optical microsystems and inspiring future possibilities that were once deemed beyond reach. © 2024 IEEE.

关键词： Microchannels

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Improving 3rd Grade student's writing ability via asynchronous discussions: A case study

Improving 3rd Grade student's writing ability via asynchrono...

引用

International Workshop on Computational Intelligence in Networks and Systems

作者： Bratitsis, Tharrenos Kandroudi, Marina Early Childhood Education Department University of Western Macedonia Florina Greece

ISBN: (纸本)9780769542782

The aim of the research, presented in the current paper, was to examine the impact of asynchronous discussions' usage in students' writing ability. It is proposed that critical thinking skills could be cultivated through electronic discussions. In fact, through dialogues students have the opportunity to construct meanings, interact with others and thus acquire knowledge through meaningful communication. In support of this view, the present study was conducted with the participation of 27 3rd Grade students in a primary school in Athens, who participated in the creation of an electronic, interactive newspaper through online discussions. The activity was implemented using the DIAS system, an asynchronous discussion platform with integrated Interaction Analysis tools, exploited in this case to facilitate the researcher's analysis. The results indicate that students made significantly fewer mistakes in the online environment, both quantitatively and qualitatively. Additionally, they took initiatives, worked individually and were engaged into a purposeful interaction with others, therefore reinforcing autonomous learning. © 2010 IEEE.

关键词： Students

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A Fused Deep learning Expert System for Road Damage Detection and Size Analysis Using YOLO9tr and Depth Estimation 3

A Fused Deep Learning Expert System for Road Damage Detectio...

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3rd International conference on intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Youwai, Sompote Chaiyaphat, Achitaphon Chaipetch, Pawarotorn AI Research Group Department of Civil Engineering Faculty of Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand Infraplus Co. Ltd. Bangkok Thailand

ISBN: (纸本)9798331529222

This paper presents an innovative fusion of deep learning models for automated road damage detection and measurement. By combining the YOLO9tr object detection algorithm with a grayscale-based depth estimation technique, our system addresses key limitations in traditional 2D assessment methods. The YOLO9tr component enables real-time detection of various road damages, including potholes, cracks, and surface deterioration, while our depth estimation module extracts dimensional information from standard grayscale images using a novel calibration approach. Through extensive testing on diverse road conditions, our system achieved a Mean Absolute Percentage Error (MAPE) of 19.4% in damage size estimation, with consistent performance across various damage types. The system's ability to operate with standard imaging equipment, coupled with its comprehensive calibration methodology spanning 3.5 to 7.0 meters, makes it a practical solution for large-scale infrastructure monitoring. Our approach demonstrates that accurate road damage assessment can be achieved without specialized depth sensors, contributing significantly to the field of automated transportation infrastructure maintenance. ©2024 IEEE.

关键词： Damage detection

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Dynamic Region Detection and Removal Based on Deep learning and Optical Flow 3

Dynamic Region Detection and Removal Based on Deep Learning ...

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3rd conference on Fully Actuated System Theory and Applications (FASTA)

作者： Fan, Shixiong Qian, Mengmeng Mao, Songheng Liu, Aoqiang Fan, Yuan Anhui Univ Sch Elect Engn & Automat Hefei 230601 Anhui Peoples R China

ISBN: (纸本)9798350373707;9798350373691

Simultaneous Localization And Mapping (SLAM) refers to the process in which a robot interacts with its surrounding environment to achieve self-positioning and construct an environmental map simultaneously. However, traditional SLAM algorithms assume scene rigidity, often leading to inaccurate positioning and mapping in dynamic environments. To address this limitation, we propose a visual RGB-D SLAM system that integrates ORB features, YOLOv5, Lucas-Kanade (LK) optical flow, and geometric information. During the ORB feature extraction stage, dynamic points are identified by utilizing LK optical flow to assess whether the Region of Interest (ROI) generated by the neural network is a dynamic area. Subsequently, dynamic feature points within the dynamic ROI are removed using a combination of optical flow and depth information. For dynamic points that are either missed or unrecognized by the neural network, we employ multi-view geometry for their removal. We tested the system's performance on the public TUM dataset, demonstrating its capability to achieve SLAM functionality in dynamic environments.

关键词： Neural Network Geometry ORB LK Optical Flow

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2023 3rd International conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2023

2023 3rd International Conference on Electrical Engineering ...

引用

3rd International conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2023

ISBN: (纸本)9798350303698

The proceedings contain 172 papers. The topics discussed include: a novel distributed generation injection signal line fault detection technology;capacity estimation of lithium-ion batteries based on transformer model;park oriented energy event linkage management strategy;research on optimal scheduling of multi-source power system with complementary hydro and photovoltaic generation;integrated monitoring technology for tension and inclination of overhead transmission lines based on fiber Bragg grating;research on obstacle avoidance of a 7-DOF manipulator;insulator defect detection based on deep learning;stator modal analysis of permanent magnet motors for electric vehicles;study on fast stability of new power system based on the method of gradient dynamic deviation and PMU data;research on ADRC based on speed loop control of permanent magnet synchronous motor;and optimization design of flat wire permanent magnet synchronous motor for electric vehicles.

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Machine learning-Based Digital Twin for Monitoring Fruit Quality Evolution 3

Machine Learning-Based Digital Twin for Monitoring Fruit Qua...

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3rd International conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Melesse, Tsega Y. Bollo, Matteo Di Pasquale, Valentina Centro, Francesco Riemma, Stefano Univ Salerno Dept Ind Engn Via G Paolo II Fisciano SA Italy SAP Italia SpA Via Amsterdam 125 I-00144 Rome Italy

A technological gap to monitor fruit quality evolution in the food supply chain is causing a huge waste of fruits. A digital twin is a promising tool to minimize fruit waste by monitoring and predicting the status of fresh produce throughout its life. In post-harvest engineering, the digital twin could be defined as a virtual representation of real produce. The objective of this work is to present a new approach to create a machine learning-based digital twin of banana fruit to monitor its quality changes throughout storage. The thermal camera has been used as a data acquisition tool due to its capability to detect the surface and physiological changes of fruits throughout the storage. In this study, after constructing the dataset of thermal data belonging to four classes, the training of the model has been performed using intelligent technologies from SAP. The solution has applied a deep convolutional neural network to monitor the fruit status based on the thermal information, and the training process has shown higher accuracy. Thus, 99% of prediction accuracy has been achieved which is proved to be a promising technique for the development of fruit digital twins. The application of thermal imaging techniques can be used as a data source to create a machine learning-based digital twin of fruit that can minimize waste in the food supply chain. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： digital twin thermal images machine learning image classification food waste

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Reinforced Evolutionary Algorithms for Game Difficulty Control 3

Reinforced Evolutionary Algorithms for Game Difficulty Contr...

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3rd International conference on Algorithms, Computing and Artificial Intelligence, ACAI 2020

作者： Cui, Guangwu Shen, Ruimin Chen, Yingfeng Zou, Juan Yang, Shengxiang Fan, Changjie Zheng, Jinghua Xiangtan University Xiangtan Hunan China Net Ease Shenzhen China

ISBN: (纸本)9781450388115

In the field of game designing, artificial intelligence is used to generate responsive, adaptive, or intelligent behaviors primarily in Non-Player-Characters (NPCs). There is a large demand for controlling game AI since a variety of players expect to be provided NPC opponents with appropriate difficulties to improve their game experience. However, to the best of our knowledge, a few works are focusing on this problem. In this paper, we firstly present a Reinforced Evolutionary Algorithm based on the Difficulty-Difference objective (REA-DD) to the DLAI problem, which combines reinforcement learning and evolutionary algorithms. REA-DD is able to generate the desired difficulty level of game AI accurately. Nonetheless, REA can only obtain a kind of game AI in each run. To improve efficiency, another algorithm based on Multi-objective optimization is proposed, regarded as RMOEA-DD, which obtains DLAI after one run. Experiments on the game Pong from ALE and apply on a commercial game named The Ghost Story to show that our algorithms provide valid methods to the DLAI problem both in the term of controlling accuracy and efficiency. © 2020 ACM.

关键词： Reinforcement learning

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Neural Network Algorithms and Big Data Technology in the Construction of intelligent Data Models 3

Neural Network Algorithms and Big Data Technology in the Con...

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3rd IEEE International conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Ma, Lijie He, Yong Wuhan Donghu University Foundational Courses Department Hubei Wuhan China

ISBN: (纸本)9798350318609

It is a very difficult question to process and store large-scale data. The purpose of this article is to explore the application of neural network algorithms and big data technologies in the construction of intelligent data models. The LSTM model used in the article extracts features from the input data through learning, and classifies, predicts, and makes decisions based on these features;secondly, data mining techniques are selected to discover potential patterns and correlation laws, and provide more comprehensive information support for the construction of intelligent data models. The article summarizes the application fields of neural network algorithms and big data technologies in the construction of intelligent data models, and shows the great potential of neural network algorithms and big data technologies in improving data analysis and decision-making capabilities. The research results show that the F1 value of the method in this paper is between O. 85-0. 95, and the combination of neural network algorithm and big data technology has achieved remarkable results in the construction of intelligent data models. © 2024 IEEE.

关键词： Long short-term memory

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Proceedings of the 3rd Artificial Intelligence and Interactive Digital Entertainment conference, AIIDE 2007

Proceedings of the 3rd Artificial Intelligence and Interacti...

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3rd Artificial Intelligence and Interactive Digital Entertainment conference, AIIDE 2007

ISBN: (纸本)9781577353256

The proceedings contain 27 papers. The topics discussed include: dynamic generation of dilemma-based interactive narratives;a lightweight intelligent virtual cinematography system for machinima production;SquadSmart: hierarchical planning and coordinated plan execution for squads of characters;automatic design of balanced board games;personality-based adaptation for teamwork in game agents;interactive storytelling: a player modelling approach;automatic rule ordering for dynamic scripting;sorts: a human-level approach to real-time strategy AI;learning a table soccer robot a new action sequence by observing and imitating;a believable agent for first-person shooter games;motivational ambient and latent behaviors in computer RPGS;level annotation and test by autonomous exploration: abbreviated version;from synthetic characters to virtual actors;and a comparative analysis of story representations for interactive narrative systems.

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