نتیجه جستجو - Prediction

ردیف | عنوان | نوع |
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الگوریتم تکاملی چند هدفی مبتنی بر شبکه عصبی برای زمانبندی گردش کار پویا در محاسبات ابری
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 16 - تعداد صفحات فایل doc فارسی: 45 زمانبندی گردشکار یک موضوع پژوهشی است که به طور گسترده در محاسبات ابری مورد مطالعه قرار گرفته است و از منابع ابری برای کارهای گردش کار استفاده می¬شود و برای این منظور اهداف مشخص شده در QoS را لحاظ می¬کند. در این مقاله، مسئله زمانبندی گردش کار پویا را به عنوان یک مسئله بهینه سازی چند هدفه پویا (DMOP) مدل می¬کنیم که در آن منبع پویایی سازی بر اساس خرابی منابع و تعداد اهداف است که ممکن است با گذر زمان تغییر کنند. خطاهای نرم افزاری و یا نقص سخت افزاری ممکن است باعث ایجاد پویایی نوع اول شوند. از سوی دیگر مواجهه با سناریوهای زندگی واقعی در محاسبات ابری ممکن است تعداد اهداف را در طی اجرای گردش کار تغییر دهد. در این مطالعه یک الگوریتم تکاملی چند هدفه پویا مبتنی بر پیش بینی را به نام الگوریتم NN-DNSGA-II ارائه می¬دهیم و برای این منظور شبکه عصبی مصنوعی را با الگوریتم NGSA-II ترکیب می¬کنیم. علاوه بر این پنج الگوریتم پویای مبتنی بر غیرپیش بینی از ادبیات موضوعی برای مسئله زمانبندی گردش کار پویا ارائه می¬شوند. راه¬حل¬های زمانبندی با در نظر گرفتن شش هدف یافت می¬شوند: حداقل سازی هزینه ساخت، انرژی و درجه عدم تعادل و حداکثر سازی قابلیت اطمینان و کاربرد. مطالعات تجربی مبتنی بر کاربردهای دنیای واقعی از سیستم مدیریت گردش کار Pegasus نشان می¬دهد که الگوریتم NN-DNSGA-II ما به طور قابل توجهی از الگوریتم¬های جایگزین خود در بیشتر موارد بهتر کار می¬کند با توجه به معیارهایی که برای DMOP با مورد واقعی پارتو بهینه در نظر گرفته می¬شود از جمله تعداد راه¬حل¬های غیرغالب، فاصله¬گذاری Schott و شاخص Hypervolume. |
مقاله ترجمه شده |

2 |
Deep Learning-Driven Particle Swarm Optimisation for Additive Manufacturing Energy Optimisation
بهینه سازی ازدحام ذرات با محوریت یادگیری عمیق برای بهینه سازی انرژی تولید افزودنی-2019 The additive manufacturing (AM) process is characterised as a high energy-consuming process, which
has a significant impact on the environment and sustainability. The topic of AM energy consumption
modelling, prediction, and optimisation has then become a research focus in both industry and academia.
This issue involves many relevant features, such as material condition, process operation, part and
process design, working environment, and so on. While existing studies reveal that AM energy
consumption modelling largely depends on the design-relevant features in practice, it has not been given
sufficient attention. Therefore, in this study, design-relevant features are firstly examined with respect
to energy modelling. These features are typically determined by part designers and process operators
before production. The AM energy consumption knowledge, hidden in the design-relevant features, is
exploited for prediction modelling through a design-relevant data analytics approach. Based on the new
modelling approach, a novel deep learning-driven particle swarm optimisation (DLD-PSO) method is
proposed to optimise the energy utility. Deep learning is introduced to address several issues, in terms
of increasing the search speed and enhancing the global best of PSO. Finally, using the design-relevant
data collected from a real-world AM system in production, a case study is presented to validate the
proposed modelling approach, and the results reveal its merits. Meanwhile, optimisation has also been
carried out to guide part designers and process operators to revise their designs and decisions in order
to reduce the energy consumption of the designated AM system under study. Keywords: Additive Manufacturing | Energy Consumption Modelling | Prediction and Optimisation | Deep Learning | Particle Swarm Optimisation |
مقاله انگلیسی |

3 |
First-principles and Machine Learning Predictions of Elasticity in Severely Lattice-distorted High-Entropy Alloys with Experimental Validation
اصول اول و پیش بینی یادگیری ماشین از الاستیسیته در آلیاژهای آنتروپی با تحریف شدید شبکه با استفاده از اعتبار سنجی تجربی-2019 Stiffness usually increases with the lattice-distortion-induced strain, as observed in many nanostructures.
Partly due to the size differences in the component elements, severe lattice distortion naturally exists in
high entropy alloys (HEAs). The single-phase face-centered-cubic (FCC) Al0.3CoCrFeNi HEA, which has
large size differences among its constituent elements, is an ideal system to study the relationship between
the elastic properties and lattice distortion using a combined experimental and computational approach
based on in-situ neutron-diffraction (ND) characterizations, and first-principles calculations. Analysis of
the interatomic distance distributions from calculations of optimized special quasi random structure (SQS)
found that the HEA has a high degree of lattice distortion. When the lattice distortion is explicitly
considered, elastic properties calculated using SQS are in excellent agreement with experimental
measurements for the HEA. The calculated elastic constant values are within 5% of the ND
measurements. A comparison of calculations from the optimized SQS and the SQS with ideal lattice sites
indicate that the lattice distortion results in the reduced stiffness. The optimized SQS has a bulk modulus
of 177 GPa compared to the ideal lattice SQS with a bulk modulus of 194 GPa. Machine learning (ML)
modeling is also implemented to explore the use of fast, and computationally efficient models for
predicting the elastic moduli of HEAs. ML models trained on a large dataset of inorganic structures are
shown to make accurate predictions of elastic properties for the HEA. The ML models also demonstrate
the dependence of bulk and shear moduli on several material features which can act as guides for tuning
elastic properties in HEAs. Keywords: First-principles calculation | Elastic constants | in situ tension test | Neutron diffraction | Machine learning |
مقاله انگلیسی |

4 |
Cryptocurrency forecasting with deep learning chaotic neural networks
پیش بینی cryptocurrency با یادگیری عمیق شبکه های عصبی پر هرج و مرج-2019 We implement deep learning techniques to forecast the price of the three most widely traded digital currencies i.e., Bitcoin, Digital Cash and Ripple. To the best of our knowledge, this is the first work to make use of deep learning in cryptocurrency prediction. The results from testing the existence of non- linearity revealed that the time series of all digital currencies exhibit fractal dynamics, long memory and self-similarity. The predictability of long-short term memory neural network topologies (LSTM) is signif- icantly higher when compared to the generalized regression neural architecture, set forth as our bench- mark system. The latter failed to approximate global nonlinear hidden patterns regardless of the degree of contamination with noise, as they are based on Gaussian kernels suitable only for local approximation of non-stationary signals. Although the computational burden of the LSTM model is higher as opposed to brute force in nonlinear pattern recognition, eventually deep learning was found to be highly efficient in forecasting the inherent chaotic dynamics of cryptocurrency markets. Keywords: Digital currencies | Deep learning | Fractality | Neural networks | Chaos | Forecasting |
مقاله انگلیسی |

5 |
Data-based structure selection for unified discrete grey prediction model
Data-based structure selection for unified discrete grey prediction model-2019 Grey models have been reported to be promising for time series prediction with small samples, but the diversity kinds of model structures and modelling assumptions restrains their further applications and developments. In this paper, a novel grey prediction model, named discrete grey polynomial model, is proposed to unify a family of univariate discrete grey models. The proposed model has the capacity to represent most popular homogeneous and non-homogeneous discrete grey models and furthermore, it can induce some other novel models, thereby highlighting the relationship between the models and their structures and assumptions. Based on the proposed model, a data-based algorithm is put forward to se- lect the model structure adaptively. It reduces the requirement for modeler’s knowledge from an expert system perspective. Two numerical experiments with large-scale simulations are conducted and the re- sults show its effectiveness. In the end, two real case tests show that the proposed model benefits from its adaptive structure and produces reliable multi-step ahead predictions. Keywords: Grey system theory | Discrete grey model | Structure selection | Matrix decomposition |
مقاله انگلیسی |

6 |
Selective sampling and inductive inference: Drawing inferences based on observed and missing evidence
نمونه گیری انتخابی و استنتاج استقرایی: طراحی استنتاج مبتنی بر شواهد مشاهده شده و از دست رفته-2019 We propose and test a Bayesian model of property induction with evidence that has been selectively
sampled leading to “censoring” or exclusion of potentially relevant data. A core model
prediction is that identical evidence samples can lead to different patterns of inductive inference
depending on the censoring mechanisms that cause some instances to be excluded. This prediction
was confirmed in four experiments examining property induction following exposure to
identical samples that were subject to different sampling frames. Each experiment found narrower
generalization of a novel property when the sample instances were selected because they
shared a common property (property sampling) than when they were selected because they
belonged to the same category (category sampling). In line with model predictions, sampling
frame effects were moderated by the addition of explicit negative evidence (Experiment 1),
sample size (Experiment 2) and category base rates (Experiments 3–4). These data show that
reasoners are sensitive to constraints on the sampling process when making property inferences;
they consider both the observed evidence and the reasons why certain types of evidence has not
been observed. Keywords: Inductive reasoning | Property inference | Categorization | Bayesian models |
مقاله انگلیسی |

7 |
Machine learning estimates of plug-in hybrid electric vehicle utility factors
تخمین یادگیری ماشین فاکتورهای وسیله نقلیه الکتریکی هیبریدی توکار-2019 Plug-in hybrid electric vehicles (PHEV) combine an electric drive train with a conventional one
and are able to drive on gasoline when the battery is fully depleted. They can thus electrify many
vehicle miles travelled (VMT) without fundamental range limits. The most important variable for
the electrification potential is the ratio of electric VMT to total VMT, the so-called utility factor
(UF). However, the empirical assessment of UFs is difficult since important factors such as daily
driving, re-charging behaviour and frequency of long-distance travel vary noteworthy between
drivers and large data collections are required. Here, we apply machine learning techniques
(regression tree, random forest, support vector machine, and neural nets) to estimate real-world
UF and compare the estimates to actual long-term average UF of 1768 individual Chevrolet Volt
PHEV. Our results show that UFs can be predicted with high accuracy from individual summary
statistics to noteworthy accuracy with a mean absolute error of five percentage points. The accuracy
of these methods is higher than a simple simulation with electric driving until the battery
is discharged and one full daily recharge. The most important variables in estimating UF according
to a linear regression model are the variance and skewness of the daily VMT distributions
as well as the frequency of long-distance driving. Thus, our findings make UF predictions from
existing data sets for driving of conventional vehicles more accurate. Keywords: Electric vehicles | Plug-in hybrid electric vehicle | Utility factor | Machine learning |
مقاله انگلیسی |

8 |
Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma
یادگیری ماشین برای پیش بینی متاستاز گره غشایی در کارسینوم سلول سنگفرشی اولیه دهان-2019 Objectives: To develop and validate an algorithm to predict occult nodal metastasis in clinically node negative
oral cavity squamous cell carcinoma (OCSCC) using machine learning. To compare algorithm performance to a
model based on tumor depth of invasion (DOI).
Materials and methods: Patients who underwent primary tumor extirpation and elective neck dissection from
2007 to 2013 for clinical T1-2N0 OCSCC were identified from the National Cancer Database (NCDB). Multiple
machine learning algorithms were developed to predict pathologic nodal metastasis using clinicopathologic data
from 782 patients. The algorithm was internally validated using test data from 654 patients in NCDB and was
then externally validated using data from 71 patients treated at a single academic institution. Performance was
measured using area under the receiver operating characteristic (ROC) curve (AUC). Machine learning and DOI
model performance were compared using Delong’s test for two correlated ROC curves.
Results: The best classification performance was achieved with a decision forest algorithm (AUC=0.840). When
applied to the single-institution data, the predictive performance of machine learning exceeded that of the DOI
model (AUC=0.657, p=0.007). Compared to the DOI model, machine learning reduced the number of neck
dissections recommended while simultaneously improving sensitivity and specificity.
Conclusion: Machine learning improves prediction of pathologic nodal metastasis in patients with clinical T1-
2N0 OCSCC compared to methods based on DOI. Improved predictive algorithms are needed to ensure that
patients with occult nodal disease are adequately treated while avoiding the cost and morbidity of neck dissection
in patients without pathologic nodal disease. Keywords: Oral cancer | Squamous cell carcinoma | Machine learning | Artificial intelligence |
مقاله انگلیسی |

9 |
DeepPF: A deep learning based architecture for metro passenger flow prediction
DeepPF: معماری مبتنی بر یادگیری عمیق برای پیش بینی جریان مسافر مترو-2019 This study aims to combine the modeling skills of deep learning and the domain knowledge in
transportation into prediction of metro passenger flow. We present an end-to-end deep learning
architecture, termed as Deep Passenger Flow (DeepPF), to forecast the metro inbound/outbound
passenger flow. The architecture of the model is highly flexible and extendable; thus, enabling
the integration and modeling of external environmental factors, temporal dependencies, spatial
characteristics, and metro operational properties in short-term metro passenger flow prediction.
Furthermore, the proposed framework achieves a high prediction accuracy due to the ease of
integrating multi-source data. Numerical experiments demonstrate that the proposed DeepPF
model can be extended to general conditions to fit the diverse constraints that exist in the
transportation domain. Keywords: Passenger flow prediction | Deep learning architecture | Domain knowledge |
مقاله انگلیسی |

10 |
Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways
یادگیری عمیق یکپارچه و مدل تعقیب خودرو تصادفی برای پویایی ترافیک در بزرگراه های چند خطه-2019 The current paper proposes a novel stochastic procedure for modelling car-following behaviours
on a multi-lane motorway. We develop an integrated multi-lane stochastic continuous car-following
model where a deep learning architecture is used to estimate a probability of lanechanging
(LC) manoeuvres. To the best of our knowledge, this work is among the very few papers
which exploit deep learning to model driving behaviour on a multi-lane road. The objective of
this study is to establish a coupled stochastic continuous multi-lane car-following model using
Langevin equations to cope with probabilistic characteristics of LC manoeuvres. In particular, a
stochastic volatility, derived from LC manoeuvres is introduced in a multi-lane stochastic optimal
velocity model (SOVM). In additions, Convolutional Neural Network (CNN) is applied to estimate
a probability of LC manoeuvres in the integrated multi-lane car-following model. Furthermore,
imaged second-based trajectories of the lane-changer and surrounding vehicles are used to
identify whether LC manoeuvres occur by using the CNN. Finally, the proposed method is validated
using a real-world high-resolution vehicle trajectory dataset. The results indicate that the
prediction of the integrated SOVM is almost identical to the observed trajectories of the lanechangers
and the following vehicles in the initial and the target lane. It has been found that the
proposed multi-lane SOVM can tackle the unpredictable fluctuations in the velocity of the vehicles
in the acceleration/deceleration zone. Keywords: Stochastic car-following model | Deep learning | Lane-changing behaviour |
مقاله انگلیسی |