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|
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
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
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
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
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
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
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
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
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