Review of the state of the art in experimental studies and mathematical modeling of tire performance on ice

2014 ◽  
Vol 53 ◽  
pp. 19-35 ◽  
Author(s):  
Anudeep Kishore Bhoopalam ◽  
Corina Sandu
Keyword(s):  
Mathematical Modeling ◽  
State Of The Art ◽  
Experimental Studies ◽  
The State

Network Cleansing

2009 ◽  
pp. 80-97 ◽  
Author(s):  
Paolo Marcatili ◽  
Anna Tramontano
Keyword(s):  
Experimental Data ◽  
Computational Methods ◽  
Gold Standard ◽  
State Of The Art ◽  
Experimental Studies ◽  
The State ◽  
Biological Information ◽  
Ppi Network

This chapter provides an overview of the current computational methods for PPI network cleansing. The authors first present the issue of identifying reliable PPIs from noisy and incomplete experimental data. Next, they address the questions of which are the expected results of the different experimental studies, of what can be defined as true interactions, of which kind of data are to be integrated in assigning reliability levels to PPIs and which gold standard should the authors use in training and testing PPI filtering methods. Finally, Marcatili and Tramontano describe the state of the art in the field, presenting the different classes of algorithms and comparing their results. The aim of the chapter is to guide the reader in the choice of the most convenient methods, experiments and integrative data and to underline the most common biases and errors to obtain a portrait of PINs which is not only reliable but as well able to correctly retrieve the biological information contained in such data.

Nonlaminar Behavior in Bearings: A Critical Review of the Literature

1974 ◽  
Vol 96 (1) ◽  
pp. 174-181 ◽  
Author(s):  
E. A. Saibel ◽  
N. A. Macken
Keyword(s):  
Flow Field ◽  
Critical Review ◽  
State Of The Art ◽  
Experimental Studies ◽  
The State ◽  
Basic Flow ◽  
Review Of The Literature ◽  
Design Data

The state-of-the-art of nonlaminar behavior in bearings is presented. Analytical and experimental studies are discussed. It is pointed out that the basic flow field is still not clearly understood, and that there is much more information needed before design data can be accurately predicted.

scholarly journals Electroresistive method for monitoring the technical condition, quality, reliability and safety the supports of rolling of electrical machines

2022 ◽  
Vol 1211 (1) ◽  
pp. 012021
Author(s):  
K V Podmasreryev ◽  
V V Markov ◽  
V V Mishin ◽  
A V Selikhov ◽  
N V Uglova
Keyword(s):  
Mathematical Modeling ◽  
Electrical Resistance ◽  
Experimental Studies ◽  
Rolling Bearing ◽  
Technical Condition ◽  
Electric Machines ◽  
The State ◽  
Electrical Machines ◽  
Technical State ◽  
Internal Environment

Abstract The necessity of monitoring the technical condition of the rolling supports of electric machines has been substantiated. It is proposed to use the electrical resistance of the bearing as an indicator of the technical condition the rolling support. The results of mathematical modeling of electrical resistance in the form of a function of resistance from factors of the internal environment of the bearing and modes of its assembly and operation in a rolling bearing are presented. An electroresistive method for monitoring the technical state the rolling support is proposed, which differs from the known methods by original algorithms for collecting information about the state of bearing parts, experimental studies have been carried out to confirm the efficiency of this method.

scholarly journals Deep residual network framework for structural health monitoring

2020 ◽  
pp. 147592172091837 ◽  
Author(s):  
Ruhua Wang ◽  
Chencho ◽  
Senjian An ◽  
Jun Li ◽  
Ling Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Health Monitoring ◽  
Damage Identification ◽  
State Of The Art ◽  
Experimental Studies ◽  
The State ◽  
Residual Network ◽  
Structural Health

Convolutional neural networks have been widely employed for structural health monitoring and damage identification. The convolutional neural network is currently considered as the state-of-the-art method for structural damage identification due to its capabilities of efficient and robust feature learning in a hierarchical manner. It is a tendency to develop a convolutional neural network with a deeper architecture to gain a better performance. However, when the depth of the network increases to a certain level, the performance will degrade due to the gradient vanishing issue. Residual neural networks can avoid the problem of vanishing gradients by utilizing skip connections, which allows the information flowing to the next layer through identity mappings. In this article, a deep residual network framework is proposed for structural health monitoring of civil engineering structures. This framework is composed of purely residual blocks which operate as feature extractors and a fully connected layer as a regressor. It learns the damage-related features from the vibration characteristics such as mode shapes and maps them into the damage index labels, for example, stiffness reductions of structures. To evaluate the efficacy and robustness of the proposed framework, an intensive evaluation is conducted with both numerical and experimental studies. The comparison between the proposed approach and the state-of-the-art models, including a sparse autoencoder neural network, a shallow convolutional neural network and a convolutional neural network with the same structure but without skip connections, is conducted. In the numerical studies, a 7-storey steel frame is investigated. Four scenarios with considering measurement noise and finite element modelling errors in the data sets are studied. The proposed framework consistently outperforms the state-of-the-art models in all the scenarios, especially for the most challenging scenario, which includes both measurement noise and uncertainties. Experimental studies on a prestressed concrete bridge in the laboratory are conducted. The proposed framework demonstrates consistent damage prediction results on this beam with the state-of-the-art models.

scholarly journals Residual Flexural Capacity of Corroded Prestressed Reinforced Concrete Beams

Metals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 442
Author(s):  
Mahdi Kioumarsi ◽  
Armando Benenato ◽  
Barbara Ferracuti ◽  
Stefania Imperatore
Keyword(s):  
Reinforced Concrete ◽  
State Of The Art ◽  
Experimental Studies ◽  
Experimental Tests ◽  
The State ◽  
Reinforced Concrete Beams ◽  
Flexural Behavior ◽  
Industrial Buildings ◽  
Residual Capacity ◽  
Art Research

Infrastructures and industrial buildings are commonly exposed to aggressive environments and damaged by corrosion. In prestressed reinforced concrete structures, the potential risks of corrosion could be severe since reinforcements are already subjected to high amounts of stress and, consequently, their load-bearing capacity could abruptly decrease. In recent years, some experimental studies have been conducted to explore the flexural behavior of corroded pretensioned reinforced concrete (PRC) beams, investigating several aspects of residual structural performance. Although many studies have been done in this area, there is no concise paper reviewing the state-of-the-art research. Accordingly, the main objective of this paper is to provide a review of the available experimental tests for residual capacity assessment of corroded PRC beams. Based on the state-of-the-art review, a degradation law for the flexural strength of corroded PRC beams is suggested.

scholarly journals SAS+ Planning as Satisfiability

2012 ◽  
Vol 43 ◽  
pp. 293-328 ◽  
Author(s):  
R. Huang ◽  
Y. Chen ◽  
W. Zhang
Keyword(s):  
State Of The Art ◽  
Structural Information ◽  
Experimental Studies ◽  
The State ◽  
Experimental Results ◽  
Sat Solving ◽  
Encoding Scheme ◽  
Memory Efficiency ◽  
Sat Encoding ◽  
Statistical Results

Planning as satisfiability is a principal approach to planning with many eminent advantages. The existing planning as satisfiability techniques usually use encodings compiled from STRIPS. We introduce a novel SAT encoding scheme (SASE) based on the SAS+ formalism. The new scheme exploits the structural information in SAS+, resulting in an encoding that is both more compact and efficient for planning. We prove the correctness of the new encoding by establishing an isomorphism between the solution plans of SASE and that of STRIPS based encodings. We further analyze the transition variables newly introduced in SASE to explain why it accommodates modern SAT solving algorithms and improves performance. We give empirical statistical results to support our analysis. We also develop a number of techniques to further reduce the encoding size of SASE, and conduct experimental studies to show the strength of each individual technique. Finally, we report extensive experimental results to demonstrate significant improvements of SASE over the state-of-the-art STRIPS based encoding schemes in terms of both time and memory efficiency.

scholarly journals Gamification in the Educational Context: A Systematic Mapping of Literature with a Focus on the Evaluation of Gamification

RENOTE ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 398-407
Author(s):  
Wendel Souto Reinheimer ◽  
Roseclea Duarte Medina
Keyword(s):  
State Of The Art ◽  
Experimental Studies ◽  
Evaluation Process ◽  
The State ◽  
Learning Performance ◽  
Educational Context ◽  
Systematic Mapping ◽  
Educational Field

Gamification has been a strategy widely used in the educational field to promote learning, engage and motivate students. Despite this, studies point to some issues related to the evaluation process in educational contexts. Thus, this work aims to identify the state of the art of evaluation in educational contexts. To this end, a systematic mapping of the literature was conducted. In total, 106 (one hundred and six) works were analyzed. As a result, a very heterogeneous scenario was found in the gamification evaluation process. Most authors carry out the evaluation of gamification in non-experimental studies; among the most used instruments are questionnaires. Regarding the observed metrics, most studies investigate metrics related to learning/performance, participation/interaction and metrics collected based on the opinion/perception of the participants.

scholarly journals Recent Advances in Mathematical Aspects of Engineering

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 811
Author(s):  
Rahmat Ellahi ◽  
Sadiq M. Sait ◽  
Huijin Xu
Keyword(s):  
State Of The Art ◽  
Experimental Studies ◽  
The State ◽  
Original Research ◽  
Theoretical Studies ◽  
Special Issue ◽  
Mathematical Methods ◽  
Recent Advances ◽  
Engineering Problems ◽  
Research Findings

This special issue took this opportunity to invite researchers to contribute their latest original research findings, review articles, and short communications on advances in the state of the art of mathematical methods, theoretical studies, or experimental studies that extend the bounds of existing methodologies to new contributions addressing current challenges and engineering problems on “Recent Advances in Mathematical Aspects of Engineering” to be published in Symmetry.

scholarly journals Diversity-Driven Extensible Hierarchical Reinforcement Learning

2019 ◽  
Vol 33 ◽  
pp. 4992-4999 ◽  
Author(s):  
Yuhang Song ◽  
Jianyi Wang ◽  
Thomas Lukasiewicz ◽  
Zhenghua Xu ◽  
Mai Xu
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
State Of The Art ◽  
Experimental Studies ◽  
The State ◽  
Transferable Skills ◽  
Hierarchical Reinforcement Learning ◽  
Multiple Levels

Hierarchical reinforcement learning (HRL) has recently shown promising advances on speeding up learning, improving the exploration, and discovering intertask transferable skills. Most recent works focus on HRL with two levels, i.e., a master policy manipulates subpolicies, which in turn manipulate primitive actions. However, HRL with multiple levels is usually needed in many real-world scenarios, whose ultimate goals are highly abstract, while their actions are very primitive. Therefore, in this paper, we propose a diversitydriven extensible HRL (DEHRL), where an extensible and scalable framework is built and learned levelwise to realize HRL with multiple levels. DEHRL follows a popular assumption: diverse subpolicies are useful, i.e., subpolicies are believed to be more useful if they are more diverse. However, existing implementations of this diversity assumption usually have their own drawbacks, which makes them inapplicable to HRL with multiple levels. Consequently, we further propose a novel diversity-driven solution to achieve this assumption in DEHRL. Experimental studies evaluate DEHRL with nine baselines from four perspectives in two domains; the results show that DEHRL outperforms the state-of-the-art baselines in all four aspects.

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