Real-time evaluation of compaction quality based on RF- ACGWO with high robustness and generalization ability

The effective evaluation of compaction quality is a key issue for the safety of earth-rock dams. However, existing prediction models of compaction quality are designed to improve prediction accuracy but generally ignore generalizability and robustness, resulting in deviations from practical evaluation results, making these models inapplicable to complex construction environments. To address these problems, a novel real-time evaluation model for construction unit compaction quality based on random forest optimized by adaptive chaos grey wolf algorithm (RF-ACGWO) is proposed in this article. In RF-ACGWO, RF predicts compaction quality, while ACGWO increases efficiency and accuracy for traditional RF parameter selection and improves the generalizability and robustness of the model. Also, meteorological factors at a project site are also considered to affect the model, thereby improving model accuracy. After embedding the proposed method in a Three-Dimensions (3D) rolling monitoring system, real-time evaluation, guidance and feedback on a project site can be obtained. Compared to the conventional evaluation methods, RF-ACGWO achieves the highest accuracy of 0.838, the best generalizability of 0.793 and the most stable robustness when applied to a large-scale, real-life hydraulic engineering project.

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Real-Time Public Transport Operations: Library of Control Strategies

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2647-04 ◽

2017 ◽

Vol 2647 (1) ◽

pp. 26-32 ◽

Cited By ~ 4

Author(s):

Mahmood Mahmoodi Nesheli ◽

Avishai (Avi) Ceder

Keyword(s):

Real Time ◽

Public Transport ◽

Large Scale ◽

Real Life ◽

Small Scale ◽

Control Procedure ◽

Service Reliability ◽

Real Time Control ◽

Time Control ◽

Control Actions

Modern public transport (PT) operations have evolved into a complex multimodal system in which small-scale disorder can propagate. Large-scale disruptions to passengers and PT agencies result. Various studies have been developed to model PT control at the operational level; however, the main downside of possible real-time control actions is the lack of intelligent modeling and a systematic process that can activate such actions immediately. This study presents a real-time control procedure to increase service reliability and to improve successful coordinated transfers in a complex PT system. The developed method aims at minimizing total travel time for passengers and reducing the uncertainty of meetings between PT vehicles. A library of operational tactics is first built to serve as a basis of the real-time decision-making process. The methodology developed is applied to a real-life case study in Auckland, New Zealand. The results showed improvements in system performance and confirmed the use of real-time control actions to maintain reliable PT service.

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Chained Analysis of Effective Prestress for Long-Span PC Bridge: Identification, Simulation and Prediction

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.255-260.891 ◽

2011 ◽

Vol 255-260 ◽

pp. 891-895

Author(s):

Qi Guo ◽

Shuan Hai He

Keyword(s):

Large Scale ◽

Evaluation Model ◽

Structural Safety ◽

Beam Model ◽

Long Span ◽

Prestressing Force ◽

Structural Deterioration ◽

Practical Evaluation ◽

Loss Method ◽

Prestressing Loss

In order to make much detail and more practical evaluation on structural safety and durability for long-span PC bridges, the condition for prestressing reservation in service stage turns out to be a necessary key index. For the sake of measuring it exactly, an invented instrument named Stretching Force Tester is applied to monitor the effective prestressing force of strand. The precision of this technology is guaranteed by means of amending analogous boundary condition and minimizing test errors through the tool of double-stage differences. Based on comparatively sophisticated prestressing loss method in current code, an evaluation model on attenuation of effective prestress is built up. By means of obtained stress datum on strand, recognition on two types of nominal coefficients for prestress losses is realized and regularities of practical distribution can be simulated simultaneously. The experimental results of a large-scale beam model show that the relative error for cable tension force between test value and standard one is able to meet the needs of engineering use and general principle for effective prestress can be demonstrated. Therefore, it laid the foundation for inspection and evaluation on structural deterioration of existing long-span PC bridges.

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Four dimensions characterize comprehensive trait judgments of faces

10.31234/osf.io/87nex ◽

2019 ◽

Cited By ~ 1

Author(s):

Chujun Lin ◽

Umit Keles ◽

Ralph Adolphs

Keyword(s):

Large Scale ◽

Real Life ◽

Three Dimensions ◽

Prior Work ◽

Four Dimensions ◽

Individual Level ◽

Level Data ◽

Trait Words ◽

Psychological Dimensions

People readily attribute many traits to faces: some look beautiful, some competent, some aggressive1. These snap judgments have important consequences in real life, ranging from success in political elections to decisions in courtroom sentencing2,3. Modern psychological theories argue that the hundreds of different words people use to describe others from their faces are well captured by only two or three dimensions, such as valence and dominance4, a highly influential framework that has been the basis for numerous studies in social and developmental psychology5–10, social neuroscience11,12, and in engineering applications13,14. However, all prior work has used only a small number of words (12 to 18) to derive underlying dimensions, limiting conclusions to date. Here we employed deep neural networks to select a comprehensive set of 100 words that are representative of the trait words people use to describe faces, and to select a set of 100 faces. In two large-scale, preregistered studies we asked participants to rate the 100 faces on the 100 words (obtaining 2,850,000 ratings from 1,710 participants), and discovered a novel set of four psychological dimensions that best explain trait judgments of faces: warmth, competence, femininity, and youth. We reproduced these four dimensions across different regions around the world, in both aggregated and individual-level data. These results provide a new and most comprehensive characterization of face judgments, and reconcile prior work on face perception with work in social cognition15 and personality psychology16.

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Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

10.1101/2021.04.12.439440 ◽

2021 ◽

Author(s):

Florian Krause ◽

Nikolaos Kogias ◽

Martin Krentz ◽

Michael Luehrs ◽

Rainer Goebel ◽

...

Keyword(s):

Real Time ◽

Executive Control ◽

Large Scale ◽

Acute Stress ◽

Self Regulation ◽

Real Life ◽

Brain Network ◽

Large Scale Network ◽

Novel Approach ◽

Scale Network

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.

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Real-Time Evaluation Model of Urban Rail Train Communication Network

Proceedings of the 2013 International Conference on Electrical and Information Technologies for Rail Transportation (EITRT2013)-Volume I - Lecture Notes in Electrical Engineering ◽

10.1007/978-3-642-53778-3_29 ◽

2014 ◽

pp. 303-310

Author(s):

Yin Tian ◽

Honghui Dong ◽

Limin Jia ◽

Yong Qin ◽

Shao huang Pang

Keyword(s):

Communication Network ◽

Real Time ◽

Evaluation Model ◽

Urban Rail ◽

Train Communication Network ◽

Time Evaluation

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Information Management Practice of Cold Rolling Progress Based on "High-Speed Management"

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.572.160 ◽

2012 ◽

Vol 572 ◽

pp. 160-164

Author(s):

Sheng Hui Jia ◽

Lei Guo ◽

Wei Xiang Wang

Keyword(s):

Process Control ◽

Cold Rolling ◽

Production Process ◽

Real Time ◽

Information Management ◽

High Speed ◽

Large Scale ◽

Management Practice ◽

Speed Management ◽

Time Evaluation

A unit of high-speed, large-scale, continuous, automated PCM, which is used for production process information management practice, is introduced in this paper. According to the practice of cold rolling equipment and "High-speed Management," advanced management methods and tools are embedded to process control, so as to achieve online control of "quality flow", real-time evaluation and improvement.

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FAFNet: A False Alarm Filter Algorithm for License Plate Detection Based on Deep Neural Network

Traitement du signal ◽

10.18280/ts.380525 ◽

2021 ◽

Vol 38 (5) ◽

pp. 1495-1501

Author(s):

Hui Huang ◽

Zhe Li

Keyword(s):

Neural Network ◽

False Alarm ◽

Real Time ◽

Large Scale ◽

Real Life ◽

Detection Algorithm ◽

False Alarms ◽

License Plate ◽

Embedded Devices ◽

License Plate Detection

The license plate detection technology has been widely applied in our daily life, but it encounters many challenges when performing license plate detection tasks in special scenarios. In this paper, a license plate detection algorithm is proposed for the problem of license plate detection, and an efficient false alarm filter algorithm, namely the FAFNet (False-Alarm Filter Network) is proposed for solving the problem of false alarms in license plate location scenarios in China. At first, this paper adopted the YOLOv5 target detection algorithm to detect license plates, and used the FAFNet to re-identify the images to avoid false detection. FAFNet is a lightweight convolutional neural network (CNN) that can solve the false alarm problem of real-time license plate recognition on embedded devices, and its performance is good. Next, this paper proposed a model generalization method for the purpose of making the proposed FAFNet be applicable to the license plate false alarm scenarios in other countries without the need to re-train the model. Then, this paper built a large-scale false alarm filter dataset, all samples in the dataset came from the industries and contained a variety of complex real-life scenarios. At last, experiments were conducted and the results showed that, the proposed FAFNet can achieve high-accuracy false alarm filtering and can run in real-time on embedded devices.

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Assessment of Reliability and Quality Performance using the Impact of Shortfall Generation Capacity Index on Power Systems

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.3190 ◽

2019 ◽

Vol 9 (6) ◽

pp. 4937-4941

Author(s):

B. M. Alshammari

Keyword(s):

Power Systems ◽

Large Scale ◽

Real Life ◽

Power Grids ◽

Three Dimensions ◽

Evaluation Methodology ◽

Practical Applications ◽

Generation Capacity ◽

Capacity Index ◽

The Impact

This paper presents a novel practical technique developed and applied for assessment of reliability and quality in real-life power systems. System-wide integrated performance indices are capable of addressing and revealing areas of deficiencies and bottlenecks as well as shortfalls in the composite generation-transmission-demand structure of large-scale power grids. The new evaluation methodology offers a general and comprehensive framework to assess the harmony and compatibility of generation capacities, transmission and required demand in a power system. The technique used in this paper is evaluated by the shortfall generation capacity index which is based on three dimensions introduced to represent the relationship between certain system generation capacity and demand. Also, practical applications to the Saudi power grid are presented for demonstration purposes.

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Real-Time Diagnostics, Prognostics and Health Management for Large-Scale Manufacturing Maintenance Systems

ASME 2008 International Manufacturing Science and Engineering Conference, Volume 2 ◽

10.1115/msec_icmp2008-72511 ◽

2008 ◽

Cited By ~ 7

Author(s):

Leandro G. Barajas ◽

Narayan Srinivasa

Keyword(s):

Real Time ◽

Large Scale ◽

Business Processes ◽

Health Management ◽

Real Life ◽

Lessons Learned ◽

Multidimensional Data ◽

Design Development ◽

Maintenance Systems ◽

Tactical Decisions

Traditional technologies emphasize either experience or model-based approaches to the Diagnostics, Prognostics & Health Management (DPHM) problem. However, most of these methodologies often apply only to the narrow type of machines that they were developed for, and only support strategic level assessments as opposed to real-time tactical decisions. By enabling widespread integration of diagnostics and prognostics into our manufacturing business processes, we have reduced spacio-temporal uncertainties associated with future states and system performance and therefore enabled more informed and effective decisions on manufacturing activities. For large-scale systems, the usual approach is to aggregate multidimensional data into a single-dimensional stream. These methods are generally adequate to extract key performance indicators. However, they only point to observable effects of a failure and not to their root causes. An integrated framework for DPHM requires the availability of bidirectional cause-effect relationships that enable system-wide health management rather than just predicting what its future state would be. This paper summarizes best practices, benchmarks, and lessons learned from the design, development, deployment, and execution of DPHM systems into real-life applications in the automotive industry.

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