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Modeling intent and destination prediction within a Bayesian framework: Predictive touch as a usecase 贝叶斯框架下的意图和目的地预测建模:预测触摸作为一个用例
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2020-10-27 DOI: 10.1017/dce.2020.11
Runze Gan, Jiaming Liang, B. I. Ahmad, S. Godsill
Abstract In various scenarios, the motion of a tracked object, for example, a pointing apparatus, pedestrian, animal, vehicle, and others, is driven by achieving a premeditated goal such as reaching a destination. This is albeit the various possible trajectories to this endpoint. This paper presents a generic Bayesian framework that utilizes stochastic models that can capture the influence of intent (viz., destination) on the object behavior. It leads to simple algorithms to infer, as early as possible, the intended endpoint from noisy sensory observations, with relatively low computational and training data requirements. This framework is introduced in the context of the novel predictive touch technology for intelligent user interfaces and touchless interactions. It can determine, early in the interaction task or pointing gesture, the interface item the user intends to select on the display (e.g., touchscreen) and accordingly simplify as well as expedite the selection task. This is shown to significantly improve the usability of displays in vehicles, especially under the influence of perturbations due to road and driving conditions, and enable intuitive contact-free interactions. Data collected in instrumented vehicles are shown to demonstrate the effectiveness of the proposed intent prediction approach. Impact Statement The presented Bayesian framework facilitates automated decision-making, resource allocation and future action planning with applications in various fields, such as in human–computer interaction (HCI), surveillance, robotics, to name a few. It led to the introduction of the patented HCI technology predictive touch, developed as part of a collaboration with Jaguar Land Rover and is set for commercialization; it won a Jaguar Land Rover TATA Innovista Award 2020 (“Dare To Try” category). Predictive touch does not only offer an intuitive approach to touchless interactions (i.e., no physical contact with the display is required), but also it can significantly improve the usability of interactive displays in vehicles or any moving platform, reduce the attention they require and enhance the input accuracy, including under the influence of perturbations due to road and driving conditions. This has been demonstrated in various on-road trials. This touchless interaction technology can have widespread applications in a post COVID-19 world by minimizing the risk of transmission of pathogens via touch surfaces, for instance, when using ticketing or self checkout machines, control panels, and interactive displays in public spaces, kiosks, or workplaces, and so on. It also offers a means to easily interact with emerging display technologies that do not have a physical surface, such as 2D/3D projections and in virtual or augmented reality, and offers additional design flexibility to support inclusive design practices.
摘要在各种场景中,被跟踪物体的运动,例如指向装置、行人、动物、车辆和其他物体,是通过实现预定目标(如到达目的地)来驱动的。尽管这是到达这个终点的各种可能的轨迹。本文提出了一个通用的贝叶斯框架,该框架利用随机模型来捕捉意图(即目的地)对对象行为的影响。它导致了一种简单的算法,可以从嘈杂的感官观察中尽早推断出预期的终点,并且对计算和训练数据的要求相对较低。该框架是在用于智能用户界面和无接触交互的新型预测触摸技术的背景下引入的。它可以在交互任务或指示手势的早期确定用户打算在显示器(例如,触摸屏)上选择的界面项目,并相应地简化和加快选择任务。这被证明显著提高了车辆中显示器的可用性,特别是在道路和驾驶条件引起的扰动的影响下,并实现了直观的无接触交互。在装有仪器的车辆中收集的数据表明了所提出的意图预测方法的有效性。影响声明所提出的贝叶斯框架有助于自动化决策、资源分配和未来行动规划,应用于各个领域,如人机交互(HCI)、监控、机器人等。这导致了HCI专利技术预测触摸的引入,该技术是与捷豹路虎合作开发的,并将商业化;它获得了2020年捷豹路虎TATA Innovista奖(“敢于尝试”类别)。预测触摸不仅为无触摸交互提供了一种直观的方法(即不需要与显示器进行物理接触),而且可以显著提高车辆或任何移动平台中交互式显示器的可用性,减少它们所需的注意力,提高输入精度,包括在由于道路和驾驶条件引起的扰动的影响下。这已在各种道路试验中得到证明。这种无接触互动技术可以通过最大限度地降低病原体通过触摸表面传播的风险,在新冠肺炎后的世界中得到广泛应用,例如,在公共场所、信息亭或工作场所使用售票机或自助收银机、控制面板和交互式显示器等。它还提供了一种与新兴的没有物理表面的显示技术(如2D/3D投影和虚拟或增强现实)轻松交互的方式,并提供了额外的设计灵活性,以支持包容性的设计实践。
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引用次数: 7
Learning socio-organizational network structure in buildings with ambient sensing data 利用环境传感数据学习建筑物中的社会组织网络结构
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2020-10-02 DOI: 10.1017/dce.2020.9
A. Sonta, Rishee K. Jain
Abstract We develop a model that successfully learns social and organizational human network structure using ambient sensing data from distributed plug load energy sensors in commercial buildings. A key goal for the design and operation of commercial buildings is to support the success of organizations within them. In modern workspaces, a particularly important goal is collaboration, which relies on physical interactions among individuals. Learning the true socio-organizational relational ties among workers can therefore help managers of buildings and organizations make decisions that improve collaboration. In this paper, we introduce the Interaction Model, a method for inferring human network structure that leverages data from distributed plug load energy sensors. In a case study, we benchmark our method against network data obtained through a survey and compare its performance to other data-driven tools. We find that unlike previous methods, our method infers a network that is correlated with the survey network to a statistically significant degree (graph correlation of 0.46, significant at the 0.01 confidence level). We additionally find that our method requires only 10 weeks of sensing data, enabling dynamic network measurement. Learning human network structure through data-driven means can enable the design and operation of spaces that encourage, rather than inhibit, the success of organizations. Impact Statement The structure of social and organizational relationships in commercial building workplaces is a key component of work processes. Understanding this structure—typically described as a network of relational ties—can help designers of workspaces and managers of workplaces make decisions that promote the success of organizations. These networks are complex, and as a result, our traditional means of measuring them are time and cost intensive. In this paper, we present a novel method, the Interaction Model, for learning these network structures automatically through sensing data. When we compare the learned network to network data obtained through a survey, we find statistically significant correlation, demonstrating the success of our method. Two key strengths of our proposed method are, first, that it uncovers network patterns quickly, requiring just 10 weeks of data, and, second, that it is interpretable, relying on intuitive opportunities for social interaction. Data-driven inference of the structure of human systems within our built environment will enable the design and operation of engineered built spaces that promote our human-centered objectives.
摘要我们开发了一个模型,该模型使用商业建筑中分布式插塞式能量传感器的环境传感数据成功地学习了社会和组织人类网络结构。商业建筑设计和运营的一个关键目标是支持其内部组织的成功。在现代工作空间中,一个特别重要的目标是协作,它依赖于个人之间的物理交互。因此,了解工人之间真正的社会组织关系可以帮助建筑物和组织的管理者做出改善协作的决策。在本文中,我们介绍了交互模型,这是一种利用分布式插头负载能量传感器的数据来推断人类网络结构的方法。在一个案例研究中,我们将我们的方法与通过调查获得的网络数据进行比较,并将其性能与其他数据驱动工具进行比较。我们发现,与以前的方法不同,我们的方法推断出的网络与调查网络的相关性在统计学上具有显著性(图相关性为0.46,在0.01置信水平下具有显著性)。我们还发现,我们的方法只需要10周的传感数据,就可以实现动态网络测量。通过数据驱动的方式学习人际网络结构可以实现空间的设计和运营,从而鼓励而不是抑制组织的成功。影响声明商业建筑工作场所的社会和组织关系结构是工作流程的关键组成部分。了解这种结构——通常被描述为关系纽带网络——可以帮助工作区的设计者和工作区的管理者做出促进组织成功的决策。这些网络是复杂的,因此,我们传统的测量方法是时间和成本密集型的。在本文中,我们提出了一种新的方法,交互模型,用于通过传感数据自动学习这些网络结构。当我们将学习到的网络与通过调查获得的网络数据进行比较时,我们发现了统计学上显著的相关性,证明了我们方法的成功。我们提出的方法的两个关键优势是,首先,它可以快速发现网络模式,只需要10周的数据;其次,它是可解释的,依赖于直观的社交机会。在我们的建筑环境中,对人类系统结构的数据驱动推理将使工程建筑空间的设计和运营成为可能,从而促进我们以人为中心的目标。
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引用次数: 5
Machine learning approaches to identify and design low thermal conductivity oxides for thermoelectric applications 识别和设计用于热电应用的低热导率氧化物的机器学习方法
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2020-09-09 DOI: 10.1017/dce.2020.7
A. Tewari, Siddharth Dixit, Niteesh Sahni, S. Bordas
Abstract The search space for new thermoelectric oxides has been limited to the alloys of a few known systems, such as ZnO, SrTiO3, and CaMnO3. Notwithstanding the high power factor, their high thermal conductivity is a roadblock in achieving higher efficiency. In this paper, we apply machine learning (ML) models for discovering novel transition metal oxides with low lattice thermal conductivity ( $ {k}_L $ ). A two-step process is proposed to address the problem of small datasets frequently encountered in material informatics. First, a gradient-boosted tree classifier is learnt to categorize unknown compounds into three categories of $ {k}_L $ : low, medium, and high. In the second step, we fit regression models on the targeted class (i.e., low $ {k}_L $ ) to estimate $ {k}_L $ with an $ {R}^2>0.9 $ . Gradient boosted tree model was also used to identify key material properties influencing classification of $ {k}_L $ , namely lattice energy per atom, atom density, band gap, mass density, and ratio of oxygen by transition metal atoms. Only fundamental materials properties describing the crystal symmetry, compound chemistry, and interatomic bonding were used in the classification process, which can be readily used in the initial phases of materials design. The proposed two-step process addresses the problem of small datasets and improves the predictive accuracy. The ML approach adopted in the present work is generic in nature and can be combined with high-throughput computing for the rapid discovery of new materials for specific applications. Impact Statement Discovery of new materials is a complex and challenging task. Sequential nature of experimental route of investigating new materials makes it tedious and resource expensive. Application of data centric methods have shown a lot of promise in the recent past in the rapid discovery of new materials. Machine learning (ML) algorithms do not only predict the properties of interest, but also provide insight into the complex correlations between properties of materials. But the availability of large materials database is a challenge, which are usually required for these methods to attain high levels of predictive accuracy. In this work, a two-step ML process has been proposed to overcome the aforementioned challenge. The proposed method has been demonstrated using a dataset of transition metal oxides to predict their lattice thermal conductivity. Low thermal conductivity transition metal oxides are specially attractive for high temperature thermoelectric application because they exhibit excellent high temperature stability and have tunable electrical properties. The proposed method was able to provide most influencing fundamental materials properties, which can be readily used as design parameters in the early stages of materials selection. The method can be combined with high throughput computations to discover novel materials for specific applications.
摘要新型热电氧化物的搜索空间仅限于几种已知系统的合金,如ZnO、SrTiO3和CaMnO3。尽管功率因数高,但它们的高导热性是实现更高效率的障碍。在本文中,我们应用机器学习(ML)模型来发现具有低晶格热导率的新型过渡金属氧化物(${k}_L$)。为了解决材料信息学中经常遇到的小数据集问题,提出了一个两步过程。首先,学习梯度增强树分类器将未知化合物分类为三类${k}_L$:低、中、高。在第二步中,我们在目标类上拟合回归模型(即低${k}_L$)估计为${k}_L$,其中${R}^2>0.9$。梯度增强树模型也被用于识别影响$分类的关键材料特性{k}_L$,即每个原子的晶格能、原子密度、带隙、质量密度和氧与过渡金属原子的比率。在分类过程中,只使用了描述晶体对称性、化合物化学和原子间键合的基本材料特性,这可以很容易地用于材料设计的初始阶段。所提出的两步过程解决了小数据集的问题,并提高了预测精度。本工作中采用的ML方法本质上是通用的,可以与高通量计算相结合,用于快速发现特定应用的新材料。影响声明发现新材料是一项复杂而富有挑战性的任务。研究新材料的实验路线的顺序性使其乏味且资源昂贵。最近,以数据为中心的方法在快速发现新材料方面显示出了很大的前景。机器学习(ML)算法不仅可以预测感兴趣的特性,还可以深入了解材料特性之间的复杂相关性。但是,大型材料数据库的可用性是一个挑战,这些方法通常需要这些数据库才能达到高水平的预测精度。在这项工作中,提出了一个两步ML过程来克服上述挑战。所提出的方法已经使用过渡金属氧化物的数据集来预测其晶格热导率。低热导率过渡金属氧化物对高温热电应用特别有吸引力,因为它们表现出优异的高温稳定性并具有可调的电学性质。所提出的方法能够提供最具影响的基本材料特性,这些特性可以很容易地用作材料选择早期阶段的设计参数。该方法可以与高通量计算相结合,以发现适用于特定应用的新型材料。
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引用次数: 12
A Parallel World Framework for scenario analysis in knowledge graphs 知识图中场景分析的并行世界框架
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2020-07-14 DOI: 10.1017/dce.2020.6
A. Eibeck, A. Chadzynski, Mei Qi Lim, K. Aditya, Laura Ong, A. Devanand, G. Karmakar, S. Mosbach, Raymond Lau, I. Karimi, Eddy Y. S. Foo, M. Kraft
Abstract This paper presents Parallel World Framework as a solution for simulations of complex systems within a time-varying knowledge graph and its application to the electric grid of Jurong Island in Singapore. The underlying modeling system is based on the Semantic Web Stack. Its linked data layer is described by means of ontologies, which span multiple domains. The framework is designed to allow what-if scenarios to be simulated generically, even for complex, inter-linked, cross-domain applications, as well as conducting multi-scale optimizations of complex superstructures within the system. Parallel world containers, introduced by the framework, ensure data separation and versioning of structures crossing various domain boundaries. Separation of operations, belonging to a particular version of the world, is taken care of by a scenario agent. It encapsulates functionality of operations on data and acts as a parallel world proxy to all of the other agents operating on the knowledge graph. Electric network optimization for carbon tax is demonstrated as a use case. The framework allows to model and evaluate electrical networks corresponding to set carbon tax values by retrofitting different types of power generators and optimizing the grid accordingly. The use case shows the possibility of using this solution as a tool for CO2 reduction modeling and planning at scale due to its distributed architecture. Impact Statement The methodology developed in this paper allows simulation of complex systems that consist of many interdependent parts, such as an industrial park, as well as variations thereof, referred to as parallel worlds. In addition to the ability to consider different scenarios, a key distinguishing feature of our approach, which is based on a generic all-purpose design that enables interoperability between heterogeneous software and, as a consequence, cross-domain applications, is its employment of knowledge graphs and autonomous software agents. As such, the methodology presented here allows city planners and policy makers to ask what-if questions or explore alternatives—a process that can play an important role in decision-making. As an example, optimizing the electrical grid of Jurong Island in Singapore is considered, for two different levels of carbon tax, thus demonstrating how the methodology can assist planning for carbon footprint reduction.
摘要本文提出了一种在时变知识图中模拟复杂系统的并行世界框架,并将其应用于新加坡裕廊岛的电网。底层建模系统基于语义Web堆栈。它的链接数据层是通过本体论来描述的,本体论跨越多个领域。该框架旨在允许对假设场景进行通用模拟,即使是针对复杂、互连、跨域的应用程序,也可以对系统内的复杂上层结构进行多尺度优化。该框架引入的并行世界容器确保了跨各种域边界的结构的数据分离和版本控制。属于世界特定版本的操作的分离由场景代理负责。它封装了对数据的操作功能,并充当对知识图上操作的所有其他代理的并行世界代理。以碳税的电网优化为例进行了论证。该框架允许通过改造不同类型的发电机并相应地优化电网,对与设定的碳税值相对应的电网进行建模和评估。该用例显示了使用该解决方案作为大规模CO2减排建模和规划工具的可能性,因为其具有分布式架构。影响陈述本文中开发的方法允许模拟由许多相互依存的部分组成的复杂系统,如工业园区及其变体,称为平行世界。除了考虑不同场景的能力外,我们的方法的一个关键区别特征是采用了知识图和自主软件代理,该方法基于通用通用设计,能够实现异构软件之间的互操作性,从而实现跨领域应用程序之间的互互操作性。因此,这里提出的方法允许城市规划者和政策制定者提出假设问题或探索替代方案——这一过程可以在决策中发挥重要作用。例如,针对两个不同级别的碳税,考虑优化新加坡裕廊岛的电网,从而展示了该方法如何有助于减少碳足迹的规划。
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引用次数: 22
Numerical simulation, clustering, and prediction of multicomponent polymer precipitation 多组分聚合物沉淀的数值模拟、聚类和预测
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2020-07-10 DOI: 10.1017/dce.2020.14
Pavan K Inguva, L. Mason, Indranil Pan, Miselle Hengardi, O. Matar
Abstract Multicomponent polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved understanding of morphology classification, driven by composition-informed prediction tools, will aid polymer engineering practice. We use a modified Cahn–Hilliard model to simulate polymer precipitation. Such physics-based models require high-performance computations that prevent rapid prototyping and iteration in engineering settings. To reduce the required computational costs, we apply machine learning (ML) techniques for clustering and consequent prediction of the simulated polymer-blend images in conjunction with simulations. Integrating ML and simulations in such a manner reduces the number of simulations needed to map out the morphology of polymer blends as a function of input parameters and also generates a data set which can be used by others to this end. We explore dimensionality reduction, via principal component analysis and autoencoder techniques, and analyze the resulting morphology clusters. Supervised ML using Gaussian process classification was subsequently used to predict morphology clusters according to species molar fraction and interaction parameter inputs. Manual pattern clustering yielded the best results, but ML techniques were able to predict the morphology of polymer blends with ≥90% accuracy.
摘要多组分聚合物系统在有机光伏和药物递送应用中备受关注,其中不同的形态影响性能。在成分知情预测工具的推动下,对形态分类的更好理解将有助于聚合物工程实践。我们使用改进的Cahn–Hilliard模型来模拟聚合物沉淀。这种基于物理的模型需要高性能的计算,以防止在工程环境中进行快速原型设计和迭代。为了降低所需的计算成本,我们将机器学习(ML)技术应用于模拟聚合物共混物图像的聚类和后续预测,并结合模拟。以这种方式集成ML和模拟减少了绘制作为输入参数函数的聚合物共混物形态所需的模拟次数,并且还生成了可供其他人用于此目的的数据集。我们通过主成分分析和自动编码器技术探索降维,并分析由此产生的形态聚类。随后,根据物种摩尔分数和相互作用参数输入,使用高斯过程分类的监督ML来预测形态聚类。人工模式聚类产生了最好的结果,但ML技术能够预测聚合物共混物的形态,准确率≥90%。
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引用次数: 4
Introducing Data-Centric Engineering: An open access journal dedicated to the transformation of engineering design and practice 介绍以数据为中心的工程:一个开放获取的期刊,致力于工程设计和实践的转变
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2020-06-18 DOI: 10.1017/dce.2020.5
M. Girolami
Data-Centric Engineering isapeer-reviewed,openaccess journalforworkthatpromotestheuseofexperimental and observational data — and new methods of sensing, measurement, and data capture — in all areas of engineering in order to design systems and products that are more reliable, resilient, efficient and safe. For more details see cambridge.org/dce.
《以数据为中心的工程》是一份经过论文评审的开放获取期刊,旨在促进实验和观测数据的使用——以及传感、测量和数据捕获的新方法——在所有工程领域,以设计更可靠、更有弹性、更高效和更安全的系统和产品。欲了解更多详情,请参阅cambridge.org/dce。
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引用次数: 3
Data-Centric Engineering in modern science from the perspective of a statistician, an engineer, and a software developer 从统计学家、工程师和软件开发人员的角度看现代科学中的以数据为中心的工程
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2020-06-18 DOI: 10.1017/dce.2020.2
Christophe Ley, Mike Tibolt, Dirk Fromme
Abstract Data-Centric Engineering is an emerging branch of science that certainly will take on a leading role in data-driven research. We live in the Big Data era with huge amounts of available data and unseen computing power, and therefore a crafty combination of Statistics (or, in more modern terms, Data Science), Computer Science and Engineering is required to filter out the most important information, master the ever more difficult challenges of a changing world and open new paths. In this paper, we will highlight some of these aspects from a combined perspective of a statistician, an engineer and a software developer. In particular, we will focus on sound data handling and analysis, computational science in Structural Engineering, data care, security and monitoring, and conclude with an outlook on future developments.
摘要数据中心工程是一个新兴的科学分支,它肯定会在数据驱动的研究中发挥主导作用。我们生活在大数据时代,拥有大量可用数据和看不见的计算能力,因此需要将统计学(或者更现代的说法是数据科学)、计算机科学和工程巧妙地结合起来,以过滤出最重要的信息,掌握不断变化的世界中越来越困难的挑战,并开辟新的道路。在本文中,我们将从统计学家、工程师和软件开发人员的角度来强调其中的一些方面。特别是,我们将专注于健全的数据处理和分析、结构工程中的计算科学、数据护理、安全和监测,并对未来发展进行展望。
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引用次数: 3
A manifesto for increasing access to data in engineering 增加工程数据访问的宣言
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2020-06-18 DOI: 10.1017/dce.2020.3
L. Dodds, Pauline L'Henaff, James Maddison, D. Yates
Abstract This paper introduces a set of principles that articulate a shared vision for increasing access to data in the engineering and related sectors. The principles are intended to help guide progress toward a data ecosystem that provides sustainable access to data, in ways that will help a variety of stakeholders in maximizing its value while mitigating potential harms. In addition to being a manifesto for change, the principles can also be viewed as a means for understanding the alignment, overlaps and gaps between a range of existing research programs, policy initiatives, and related work on data governance and sharing. After providing background on the growing data economy and relevant recent policy initiatives in the United Kingdom and European Union, we then introduce the nine key principles of the manifesto. For each principle, we provide some additional rationale and links to related work. We invite feedback on the manifesto and endorsements from a range of stakeholders.
摘要本文介绍了一套原则,阐明了在工程和相关部门增加数据访问的共同愿景。这些原则旨在帮助指导建立一个提供可持续数据访问的数据生态系统,以帮助各种利益相关者最大限度地实现其价值,同时减轻潜在危害。除了作为变革宣言外,这些原则还可以被视为理解一系列现有研究计划、政策举措以及数据治理和共享相关工作之间的一致性、重叠和差距的一种手段。在提供了英国和欧盟不断增长的数据经济和最近相关政策举措的背景后,我们介绍了宣言的九项关键原则。对于每一项原则,我们都提供了一些额外的基本原理和相关工作的链接。我们邀请一系列利益相关者对宣言进行反馈和支持。
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引用次数: 2
Developing and evaluating predictive conveyor belt wear models 开发和评估预测输送带磨损模型
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2020-06-18 DOI: 10.1017/dce.2020.1
Callum Webb, J. Sikorska, R. N. Khan, M. Hodkiewicz
Abstract Conveyor belt wear is an important consideration in the bulk materials handling industry. We define four belt wear rate metrics and develop a model to predict wear rates of new conveyor configurations using an industry dataset that includes ultrasonic thickness measurements, conveyor attributes, and conveyor throughput. All variables are expected to contribute in some way to explaining wear rate and are included in modeling. One specific metric, the maximum throughput-based wear rate, is selected as the prediction target, and cross-validation is used to evaluate the out-of-sample performance of random forest and linear regression algorithms. The random forest approach achieves a lower error of 0.152 mm/megatons (standard deviation [SD] = 0.0648). Permutation importance and partial dependence plots are computed to provide insights into the relationship between conveyor parameters and wear rate. This work demonstrates how belt wear rate can be quantified from imprecise thickness testing methods and provides a transparent modeling framework applicable to other supervised learning problems in risk and reliability.
摘要输送带磨损是散装物料处理行业的一个重要考虑因素。我们定义了四个皮带磨损率指标,并使用行业数据集开发了一个模型来预测新输送机配置的磨损率,该数据集包括超声波厚度测量、输送机属性和输送机吞吐量。所有变量都有望以某种方式解释磨损率,并包含在建模中。选择一个特定的度量,即基于最大吞吐量的磨损率,作为预测目标,并使用交叉验证来评估随机森林和线性回归算法的样本外性能。随机森林方法实现了0.152 mm/兆吨的较低误差(标准偏差[SD]=0.0648)。计算了排列重要性和部分依赖图,以深入了解输送机参数和磨损率之间的关系。这项工作展示了如何通过不精确的厚度测试方法量化皮带磨损率,并提供了一个透明的建模框架,适用于风险和可靠性方面的其他监督学习问题。
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引用次数: 11
Deep kernel learning approach to engine emissions modeling 发动机排放建模的深度核学习方法
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2020-06-18 DOI: 10.1017/dce.2020.4
Changmin Yu, M. Seslija, George Brownbridge, S. Mosbach, M. Kraft, M. Parsi, Mark Davis, Vivian J. Page, A. Bhave
Abstract We apply deep kernel learning (DKL), which can be viewed as a combination of a Gaussian process (GP) and a deep neural network (DNN), to compression ignition engine emissions and compare its performance to a selection of other surrogate models on the same dataset. Surrogate models are a class of computationally cheaper alternatives to physics-based models. High-dimensional model representation (HDMR) is also briefly discussed and acts as a benchmark model for comparison. We apply the considered methods to a dataset, which was obtained from a compression ignition engine and includes as outputs soot and NOx emissions as functions of 14 engine operating condition variables. We combine a quasi-random global search with a conventional grid-optimization method in order to identify suitable values for several DKL hyperparameters, which include network architecture, kernel, and learning parameters. The performance of DKL, HDMR, plain GPs, and plain DNNs is compared in terms of the root mean squared error (RMSE) of the predictions as well as computational expense of training and evaluation. It is shown that DKL performs best in terms of RMSE in the predictions whilst maintaining the computational cost at a reasonable level, and DKL predictions are in good agreement with the experimental emissions data.
摘要我们将深度核学习(DKL)应用于压燃式发动机排放,并将其性能与同一数据集上的其他替代模型进行比较。深度核学习可以被视为高斯过程(GP)和深度神经网络(DNN)的组合。代孕模型是基于物理模型的一类计算成本较低的替代品。还简要讨论了高维模型表示(HDMR),并将其作为比较的基准模型。我们将所考虑的方法应用于数据集,该数据集是从压燃式发动机获得的,包括作为14个发动机工况变量函数的烟灰和NOx排放量作为输出。我们将准随机全局搜索与传统的网格优化方法相结合,以确定几个DKL超参数的合适值,这些参数包括网络架构、内核和学习参数。根据预测的均方根误差(RMSE)以及训练和评估的计算费用,比较了DKL、HDMR、纯GP和纯DNN的性能。结果表明,在预测中,DKL在RMSE方面表现最好,同时将计算成本保持在合理水平,并且DKL预测与实验排放数据非常一致。
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引用次数: 12
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DataCentric Engineering
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