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|
A survey on deep learning based face recognition
مروری بر شناخت چهره مبتنی بر یادگیری عمیق-2019
Deep learning, in particular the deep convolutional neural networks, has received increasing interests in face recognition recently, and a number of deep learning methods have been proposed. This paper summarizes about 330 contributions in this area. It reviews major deep learning concepts pertinent to face image analysis and face recognition, and provides a concise overview of studies on specific face recognition problems, such as handling variations in pose, age, illumination, expression, and heterogeneous face matching. A summary of databases used for deep face recognition is given as well. Finally, some open challenges and directions are discussed for future research.
Keywords: Deep learning | Face recognition | Artificial Neural Network | Convolutional Neural Networks | Autoencoder | Generative Adversarial Networks
Deep learning for waveform identification of resting needle electromyography signals
یادگیری عمیق برای شناسایی شکل موج سیگنالهای الکترومیوگرافی سوزن ساکن-2019
Objective: Given the recent advent in machine learning and artificial intelligence on medical data analysis, we hypothesized that the deep learning algorithm can classify resting needle electromyography (n- EMG) discharges. Methods: Six clinically observed resting n-EMG signals were used as a dataset. The data were converted to Mel-spectrogram. Data augmentation was then applied to the training data. Deep learning algorithms were applied to assess the accuracies of correct classification, with or without the use of pre-trained weights for deep-learning networks. Results: While the original data yielded the accuracy up to 0.86 on the test dataset, data-augmentation up to 200,000 training images showed significant increase in the accuracy to 1.0. The use of pre-trained weights (fine tuning) showed greater accuracy than ‘‘training from scratch”. Conclusions: Resting n-EMG signals were successfully classified by deep-learning algorithm, especially with the use of data augmentation and transfer learning techniques. Significance: Computer-aided signal identification of clinical n-EMG testing might be possible by deeplearning algorithms.
Keywords: Needle electromyography | Deep learning | Artificial neural network | Data augmentation | Resting discharge
Analysis of operating system identification via fingerprinting and machine learning
تجزیه و تحلیل شناسایی سیستم عامل از طریق اثر انگشت و یادگیری ماشین-2019
In operating system (OS) fingerprinting, the OS is identified using network packets and a rule-based matching method. However, this matching method has problems when the network packet information is insufficient or the OS is relatively new. This study com- pares the OS identification capabilities of several machine learning methods, specifically, K-nearest neighbors (K-NN), Decision Tree, and Artificial Neural Network (ANN), to that of a conventional commercial rule-based method. It is shown that the ANN correctly iden- tifies operating systems with 94% probability, which is higher than the accuracy of the conventional rule-based method.
Keywords: Operating system fingerprinting | Machine learning | Artificial Neural Network | NetworkMiner | K-nearest Neighbors | Decision Tree
Deep learning based winter wheat mapping using statistical data as ground references in Kansas and northern Texas, US
نقشه برداری گندم زمستانی مبتنی بر یادگیری عمیق با استفاده از داده های آماری به عنوان منابع زمینی در کانزاس و شمال تگزاس ، ایالات متحده-2019
Winter wheat is a major staple crop and it is critical to monitor winter wheat production using efficient and automated means. This study proposed a novel approach to produce winter wheat maps using statistics as the training targets of supervised classification. Deep neural network architectures were built to link remotely sensed image series to the harvested areas of individual administrative units. After training, the resultant maps were generated using the activations on a middle layer of the deep model. The direct use of statistical data to some extent alleviates the shortage of ground samples in classification tasks and provides an opportunity to utilize a wealth of statistical records to improve land use mapping. The experiments were carried out in Kansas and Northern Texas during 2001–2017. For each study area the goal was to create winter maps that are consistent with USDA county-level statistics of harvested areas. The trained deep models automatically identified the seasonal pattern of winter wheat pixels without using pixel-level reference data. The winter wheat maps were compared with the Cropland Data Layer (CDL) for years when the CDL is available. In Kansas where the winter wheat extent of the CDL has high reported accuracy and agrees well with county statistics, the maps produced from the deep model was evaluated using the CDL as an independent test set. Northern Texas was selected as an example where the winter wheat area of the CDL is very different from official statistics, and the maps by the deep model enabled a map-to-map comparison with the CDL to highlight the areas of discrepancy. Visual representation of the deep model behaviors and recognized patterns show that deep learning is an automated and robust means to handle the variability of winter wheat seasonality without the need of manual feature engineering and intensive ground data collection. Showing the possibility of generating maps solely from regional statistics, the proposed deep learning approach has great potential to fill the historical gaps of conventional sample-based classification and extend applications to areas where only regional statistics are available. The flexible deep network architecture can be fused with various statistical datasets to fully employ existing sources of data and knowledge.
Keywords: Crop classification | Deep learning | Artificial neural network | Convolutional neural network | MODIS | Winter wheat | USDA Quick Stats
Prediction of displacement in the equine third metacarpal bone using a neural network prediction algorithm
پیش بینی جابجایی در استخوان metacarpal سوم اسب با استفاده از الگوریتم پیش بینی شبکه عصبی-2019
Bone is a nonlinear, inhomogeneous and anisotropic material. To predict the behavior of bones expert systems are employed to reduce the computational cost and to enhance the accuracy of simulations. In this study, an artificial neural network (ANN) was used for the prediction of displacement in long bones followed by ex-vivo experiments. Three hydrated third metacarpal bones (MC3) from 3 thoroughbred horses were used in the experiments. A set of strain gauges were distributed around the midshaft of the bones. These bones were then loaded in compression in an MTS machine. The recordings of strains, load, Load exposure time, and displacement were used as ANN input parameters. The ANN which was trained using 3,250 experimental data points from two bones predicted the displacement of the third bone (R2 ≥ 0.98). It was suggested that the ANN should be trained using noisy data points. The proposed modification in the training algorithm makes the ANN very robust against noisy inputs measurements. The performance of the ANN was evaluated in response to changes in the number of input data points and then by assuming a lack of strain data. A finite element analysis (FEA) was conducted to replicate one cycle of force-displacement experimental data (to gain the same accuracy produced by the ANN). The comparison of FEA and ANN displacement predictions indicates that the ANN produced a satisfactory outcome within a couple of seconds, while FEA required more than 160 times as long to solve the same model (CPU time: 5 h and 30 min).
Keywords: Artificial neural network (ANN) | Displacement prediction | Finite element analysis (FEA) | Expert system | Long bones | Equine third metacarpal bone (MC3)
An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings
یک شبکه متخصص عصبی مصنوعی (ANN) با استفاده از الگوریتم کرم شب تاب مبتنی بر الکترومغناطیس (EFA) برای پیش بینی مصرف انرژی در ساختمان ها-2019
In this study, a new hybrid model, namely the Electromagnetism-based Firefly Algorithm - Artificial Neural Network (EFA-ANN), is proposed to forecast the energy consumption in buildings. The model is applied to evaluate the heating load (HL) and cooling load (CL) using two given datasets. Each dataset was obtained by monitoring the effect of the façade system and dimensions of the building, respectively, on energy consumption. The performance of EFA-ANN is validated by comparing the obtained results with other methods. It is shown that EFA-ANN provides a faster and more accurate prediction of HL and CL. A sensitivity analysis is performed to identify the impact of each input on the energy performance of the building. From the results of this study, it is evident that EFA-ANN can assist civil engineers and construction managers in the early designs of energy-efficient buildings.
Keywords: Electromagnetism-based firefly algorithm | Artificial neural network | Machine learning | Energy consumption
Screening and optimization of polymer flooding projects using artificial-neural-network (ANN) based proxies
غربالگری و بهینه سازی پروژه های سیلی پلیمری با استفاده از پروکسی مبتنی بر شبکه مصنوعی عصبی (ANN)-2019
Polymer flooding is one of the most broadly implemented chemical EOR processes due to its low injection cost and successes in oil production increments. This work develops artificial-neural-network based proxies by utilizing synthetic production histories generated from a high-fidelity numerical simulation model. Injectionpattern- based reservoir models are structured to establish the knowledgebase to train the proxies. A forward and an inverse-looking ANN models are structured in this study. The forward-looking expert system are employed as a forecasting and screening tool that is capable to predict time-based project responses. And the inverse-looking ANN predicts the project design schemes that fulfill the expected oil recoveries. The proxies are generalized considering reservoir rock and fluid properties and project design parameters. In this paper, we present results of extensive blind testing applications to confirm the validates of the proxy models. Afterwards, various applications of the expert systems are discussed. A project screening protocol that couples the expert system and particle swarm optimization (PSO) methodology is presented to maximize the polymer injection projects’ net present value (NPV). Moreover, we propose a robust computational workflow that coupled utilize the inverse and forward-looking proxies to find various polymer injection schemes to fulfill the expected oil production profile, which effectively addresses the issue associated with the existence of non-unique solutions in the inverse design problems. The expert ANN systems and the associated project design workflows provide versatile approaches for the field engineers to obtain quick techno-economical assessments of polymer injection projects.
Keywords: Artificial neural network | Polymer injection | Optimization | EOR screening | EOR project design
Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model
معیار ارزش پیش بینی یادگیری عمیق قبل از عمل برای اولیه آرتروپلاستی کامل زانو: توسعه و اعتبار مدل شبکه عصبی مصنوعی-2019
Background: The objective is to develop and validate an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition before primary total knee arthroplasty (TKA). The secondary objective applied the ANN to propose a risk-based, patient-specific payment model (PSPM) commensurate with case complexity. Methods: Using data from 175,042 primary TKAs from the National Inpatient Sample and an institutional database, an ANN was developed to predict LOS, charges, and disposition using 15 preoperative variables. Outcome metrics included accuracy and area under the curve for a receiver operating characteristic curve. Model uncertainty was stratified by All Patient Refined comorbidity indices in establishing a riskbased PSPM. Results: The dynamic model demonstrated “learning” in the first 30 training rounds with areas under the curve of 74.8%, 82.8%, and 76.1% for LOS, charges, and discharge disposition, respectively. The PSPM demonstrated that as patient comorbidity increased, risk increased by 2.0%, 21.8%, and 82.6% for moderate, major, and severe comorbidities, respectively. Conclusion: Our deep learning model demonstrated “learning” with acceptable validity, reliability, and responsiveness in predicting value metrics, offering the ability to preoperatively plan for TKA episodes of care. This model may be applied to a PSPM proposing tiered reimbursements reflecting case complexity.
Keywords: machine learning | total knee arthroplasty (TKA) | artificial neural network | deep learning | artificial intelligence
Processing big-data with Memristive Technologies: Splitting the Hyperplane Efficiently
پردازش داده های بزرگ با تکنولوژی Memristive: تقسیم Hyperplane به طور موثر-2018
An important cornerstone of data processing is the ability to efficiently capture structure in data. This entails treating the input space as a hyperplane that needs partitioning. We argue that several modern electronic systems can be understood as carrying out such partitionings: from standard logic gates to Artificial Neural Networks (ANNs). More recently, memristive technologies equipped such systems with the benefit of continuous tuneability directly in hardware, thus rendering these reconfigurable in a power and space efficient manner. Here, we demonstrate several proof-of-concept examples where memristors enable circuits optimised to carry out different flavours of the fundamental task of splitting the hyperplane. These include threshold logic and receptive field based classifiers that are presented within the context of a unified perspective.
Keywords: memristor, Metal Oxide RRAM, Artificial Neural Networks, Threshold Logic Gates, Template Pixel, Texel, Clusterer ,Fuzzy Gate