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 c...
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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 ...
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The aim of the research, presented in the current paper, was to examine the impact of asynchronous discussions39; usage in students39; writing ability. It is proposed that critical thinking skills could be cultiva...
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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 techniq...
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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, tr...
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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.
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...
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.
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 ...
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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.
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 sin...
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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 intelligen...
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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: ...
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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