Optimization of sequence of performing heterogeneous repair work for transport systems by criteria of timeliness

Kostogryzov, Andrey; Panov, Vitaly; Stepanov, Pavel V.; Nistratov, George; Nistratov, Andrey; Grigoriev, Leonid

doi:10.1109/ictis.2017.8047870

Cited by 7 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The models below help to implement concepts 1, 3 and 4 [6,9,[14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. In general, successful system operation is connected with counteractions against various system integrity loss hazards (of social, natural and technogenic origins) throughout system operation timeline.…”

Section: About "Black Box" Formalization To Predict "Failure" Risksmentioning

confidence: 99%

Probabilistic Predictive Modelling for Complex System Risk Assessments

Kostogryzov¹,

Махутов²,

Nistratov³

et al. 2023

Time Series Analysis - New Insights

View full text Add to dashboard Cite

The risks assessment is described by the action of estimating the probability distribution functions of possible successes or failures of a system during a given prediction period. Typical probabilistic predictive models and methods for solving risks prediction problems are described, and their classification is given. Priority development directions for risks prediction in standard system processes and their implementation procedures are proposed. The reported examples demonstrate the effects and interpretation of the predictive results obtained. Notes: 1. System is a combination of interacting elements organized to achieve one or more stated purposes (according to ISO/IEC/IEEE 15288 “Systems and software engineering—System life cycle processes”). 2. Risk is defined as the effect of uncertainty on objectives considering consequences. An effect is a deviation from the expected — positive and/or negative (according to ISO Guide 73).

show abstract

Section: About "Black Box" Formalization To Predict "Failure" Risksmentioning

confidence: 99%

Probabilistic Predictive Modelling for Complex System Risk Assessments

Kostogryzov¹,

Махутов²,

Nistratov³

et al. 2023

Time Series Analysis - New Insights

View full text Add to dashboard Cite

show abstract

“…As the results of selection, the author's models to estimate the probabilistic measures of a quality of used information and the probabilities of "success" and risks of "failure" for "black box" and for complex structures are proposed for AIS. The models are widely tested and approved in practice [15][16][17][18][19][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37].…”

Section: Selection Of the Modelsmentioning

confidence: 99%

“…A probabilistic space (Ω, B, P) for estimation of system operation processes is traditional [15][16][17][18][19][20][21], where Ω is a limited space of elementary events; B is a class of all subspace of Ω-space, satisfied to the properties of σ-algebra; and P is a probability measure on a space of elementary events Ω. Such space (Ω, B, P) is built and proposed for using [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37].…”

Section: Selection For "Black Box"mentioning

confidence: 99%

“…The spectrum of the explored systems by these methods includes systems (not only AIS) operated by government agencies, manufacturing structures (including power generation, coal enterprises, oil and gas systems), food storage, space industry, emergency services, municipal economy, etc. [15][16][17][18][19][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. The supporting software tools are original Russian creations registered by Rospatent [38][39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic Methods for Cognitive Solving of Some Problems in Artificial Intelligence Systems

Kostogryzov¹,

Korolev²

2020

Probability, Combinatorics and Control

Self Cite

View full text Add to dashboard Cite

As a result of the analysis of dispatcher intelligence centers and aerial, land, underground, underwater, universal, and functionally focused artificial intelligence robotics systems, the problems of rational control, due to be performed under specific conditions of uncertainties, are chosen for probabilistic study. The choice covers the problems of planning the possibilities of functions performance on the base of monitored information about events and conditions and the problem of robot route optimization under limitations on risk of "failure" in conditions of uncertainties. These problems are resolved with a use of the proposed probabilistic approach. The proposed methods are based on selected probabilistic models (for "black box" and complex systems), which are implemented effectively in wide application areas. The cognitive solving of problems consists in improvements, accumulation, analysis, and use of appearing knowledge. The described analytical solutions are demonstrated by practical examples.

show abstract

“…Early repair of road defects should be conducted to avoid further degradation (Ahmed et al, 2017;L. Gao et al, 2012;Gulotta et al, 2019; © 2021 Computer-Aided Civil and Infrastructure Engineering Kostogryzov et al, 2017;Riid et al, 2019), which can reduce maintenance costs from the lifecycle perspective (Herabat & Songchitruksa, 2003;Yao et al, 2020). Travel service providers (such as Baidu Maps and Google Maps) consider pavement condition information when providing path planning, thereby ensuring comfort and safety (Du, Liu et al, 2020;.…”

Section: Introductionmentioning

confidence: 99%

Cross‐scene pavement distress detection by a novel transfer learning framework

Che

Liu

et al. 2021

Computer aided Civil Eng

View full text Add to dashboard Cite

Deep learning has achieved promising results in pavement distress detection. However, the training model's effectiveness varies according to the data and scenarios acquired by different camera types and their installation positions. It is time consuming and labor intensive to recollect labeled data and retrain a new model every time the scene changes. In this paper, we propose a transfer learning pipeline to address this problem, which enables a distress detection model to be applied to other untrained scenarios. The framework consists of two main components: data transfer and model transfer. The former trains a generative adversarial network to transfer existing image data into a new scene style. Then, attentive CutMix and image melding are applied to insert distress annotations to synthesize the new scene's labeled data. After data expansion, the latter step transfers the feature extracted by the existing model to the detection application of the new scene through domain adaptation. The effects of varying degrees of knowledge transfer are also discussed. The proposed method is evaluated on two data sets from two different scenes with more than 40,000 images totally. This method can reduce the demand for training data by at least 25% when the model is applied in a new scene. With the same number of training images, the proposed method can improve the model accuracy by 26.55%.

show abstract

Optimization of sequence of performing heterogeneous repair work for transport systems by criteria of timeliness

Cited by 7 publications

References 4 publications

Probabilistic Predictive Modelling for Complex System Risk Assessments

Probabilistic Predictive Modelling for Complex System Risk Assessments

Probabilistic Methods for Cognitive Solving of Some Problems in Artificial Intelligence Systems

Cross‐scene pavement distress detection by a novel transfer learning framework

Contact Info

Product

Resources

About