发现、检测和预测物理对象属性的智能计算技术:全面调查

IF 13.3 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shaili Mishra, Anuja Arora
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引用次数: 0

摘要

物理对象属性的爆炸性应用极大地促进了实时应用,如机器人技术,使其能够准确地感知物体的存在。各种实时系统性质和属性的变化与环境因素导致的物理属性改变有关。这些基于物理的物体属性特征吸引了研究人员的关注,同时也为现实生活中的问题提供了解决方案。但是,物理性质变化的检测和预测非常多样化,涉及许多物理定律和物体属性(材料、形状、引力、颜色、状态变化),这些都增加了这些任务的复杂性。要阐明物理定律,需要借助标准化方程和相关因素进行大量手动建模,而不是理解物理定律。为了采用这些物理定律来获得直观有效的结果,研究人员开始应用计算模型来学习不断变化的属性行为,以替代使用手工制作和设备生成的变量状态。如果没有预见到物理性质检测方面的挑战,不预先采取必要的措施,即将推出的计算模型驱动的物理对象变化将无法发挥应有的作用。因此,本调查报告旨在全面展示物理对象属性检测和预测的理论和实证研究。此外,本文还提出了一个通用范式,以及众多物理对象属性的表征参数。文中简要总结了适用的机器学习、深度学习和元启发式方法。此外,还为研究人员提供了一份广泛的已发布和公开的数据集清单,其中包含不同的物体和参数。此外,还介绍了物理性质发现和检测计算技术的性能指标,以便对结果进行定量评估。最后,还具体说明了未来需要探索的几个开放研究课题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent computational techniques for physical object properties discovery, detection, and prediction: A comprehensive survey

The exploding usage of physical object properties has greatly facilitated real-time applications such as robotics to perceive exactly as it appears in existence. Changes in the nature and properties of diverse real-time systems are associated with physical properties modification due to environmental factors. These physics-based object properties features attract the researchers’ attention while developing solutions to real-life problems. But, the detection and prediction of physical properties change are very diverse, covering many physics laws and object properties (material, shape, gravitational force, color, state change) which append complexity to these tasks. Instead of well-understood physics laws, elucidating physics laws requires substantial manual modeling with the help of standardized equations and associated factors. To adopt these physical laws to get instinctive and effective outcomes, researchers started applying computational models to learn changing property behavior as a substitute for using handcrafted and equipment-generated variable states. If physical properties detection challenges are not anticipated and required measures are not precluded, the upcoming computational model-driven physical object changing will not be able to serve appropriately. Therefore, this survey paper is drafted to demonstrate comprehensive theoretical and empirical studies of physical object properties detection and prediction. Furthermore, a generic paradigm is proposed to work in this direction along with characterization parameters of numerous physical object properties. A brief summarization of applicable machine learning, deep learning, and metaheuristic approaches is presented. An extensive list of released and openly available datasets for varying objects and parameters rendered for researchers. Additionally, performance measures regarding computational techniques for physical properties discovery and detection for quantitative evaluation of outcomes are also entailed. Finally, a few open research issues that need to be explored in the future are specified.

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来源期刊
Computer Science Review
Computer Science Review Computer Science-General Computer Science
CiteScore
32.70
自引率
0.00%
发文量
26
审稿时长
51 days
期刊介绍: Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.
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