Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives
استفاده از یادگیری ماشین مبتنی بر شبکه عصبی برای تولید افزودنی: برنامه های فعلی ، چالش ها و دیدگاه های آینده-2019
Additive manufacturing (AM), also known as three-dimensional printing, is gaining increasing attention from academia and industry due to the unique advantages it has in comparison with traditional subtractive manufacturing. However, AM processing parameters are difficult to tune, since they can exert a huge impact on the printed microstructure and on the performance of the subsequent products. It is a difficult task to build a process–structure–property–performance (PSPP) relationship for AM using traditional numerical and analytical models. Today, the machine learning (ML) method has been demonstrated to be a valid way to perform complex pattern recognition and regression analysis without an explicit need to construct and solve the underlying physical models. Among ML algorithms, the neural network (NN) is the most widely used model due to the large dataset that is currently available, strong computational power, and sophisticated algorithm architecture. This paper overviews the progress of applying the NN algorithm to several aspects of the AM whole chain, including model design, in situ monitoring, and quality evaluation. Current challenges in applying NNs to AM and potential solutions for these problems are then outlined. Finally, future trends are proposed in order to provide an overall discussion of this interdisciplinary area.
Keywords: Additive manufacturing | 3D printing | Neural network | Machine learning | Algorithm
Emergent Schrödinger equation in an introspective machine learning architecture
معادله شرودینگر اضطراری در یک معماری یادگیری ماشین درون نگر-2019
Can physical concepts and laws emerge in a neural network as it learns to predict the observation data of physical systems? As a benchmark and a proof-of-principle study of this possibility, here we show an introspective learning architecture that can automatically develop the concept of the quantum wave function and discover the Schrödinger equation from simulated experimental data of the potential-todensity mappings of a quantum particle. This introspective learning architecture contains a machine translator to perform the potential to density mapping, and a knowledge distiller auto-encoder to extract the essential information and its update law from the hidden states of the translator, which turns out to be the quantum wave function and the Schrödinger equation. We envision that our introspective learning architecture can enable machine learning to discover new physics in the future.
Keywords: Quantum physics | Machine learning | Potential-to-density mapping | Neural network | Recurrent autoencoder
Radiological images and machine learning: Trends, perspectives, and prospects
تصاویر رادیولوژی و یادگیری ماشین: روند، دیدگاه ها، و چشم انداز-2019
The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities such as x-rays, computed tomography, magnetic resonance imaging and positron emission tomography imaging. In many applications, machine learning based systems have shown comparable performance to human decision-making. The applications of machine learning are the key ingredients of future clinical decision making and monitoring systems. This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computer-aided systems, image registration, and content-based image retrieval systems. Synchronistically, we will briefly discuss current challenges and future directions regarding the application of machine learning in radiological imaging. By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.
Keywords: Deep learning | Machine learning | Imaging modalities | Deep neural networ
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
Computing interface curvature from volume fractions: A machine learning approach
محاسبه انحنای رابط از کسری حجم: یک روش یادگیری ماشین-2019
The volume of fluid (VOF) method is widely used to simulate the flow of immiscible fluids. It uses a discrete and sharp volume fractions field to represent the fluid-fluid interface on a Eulerian grid. The most challenging part of the VOF method is the accurate computation of the local interface curvature which is essential for evaluation of the surface tension force at the interface. In this paper, a machine learning approach is used to develop a model which predicts the local interface curvature from neigh- bouring volume fractions. A novel data generation methodology is devised which generates well-balanced randomized data sets comprising of spherical interface patches of different configurations/orientations. A two-layer feed-forward neural network with different network parameters is trained on these data sets and the developed models are tested for different shapes i.e . ellipsoid, 3D wave and Gaussian. The best model is selected on the basis of specific criteria and subsequently compared with conventional curva- ture computation methods (convolution and height function) to check the nature and grid convergence of the model. The model is also coupled with a multiphase flow solver to evaluate its performance using standard test cases: i) stationary bubble, ii) oscillating bubble and iii) rising bubble under gravity. Our results demonstrate that machine learning is a feasible approach for fairly accurate curvature computa- tion. It easily outperforms the convolution method and even matches the accuracy of the height function method for some test cases.
Keywords: Volume of fluid | Curvature computation | Machine learning | Neural network | Multiphase flow | Grace diagram
Automated crater detection algorithms from a machine learning perspective in the convolutional neural network era
الگوریتم های تشخیص خودکار دهانه از دیدگاه یادگیری ماشین در دوره شبکه عصبی حلقوی-2019
Convolutional Neural Networks (CNN) offer promising opportunities to automatically glean scientifically relevant information directly from annotated images, without needing to handcraft features for detection. Crater counting started with hand counting hundreds, thousands, or even millions of craters in order to determine the age of geological units on planetary bodies of the solar system. Automated crater detection algorithms have attempted to speed up this process. Previous research has employed computer vision techniques with handcrafted features such as light and shadow patterns, circle finding, or edge detection. This research continues, but now some researchers use techniques like convolutional neural networks that enable the algorithm to develop its own features. As the field of machine learning undergoes exponential growth in terms of paper count and research methods, the crater counting application can benefit from the new research, especially when conducting joint interdisciplinary projects. Despite these advancements, the crater counting community has not yet adopted standard methods for automating the process despite decades of research. This survey enumerates challenges for both planetary geologists and machine learning researchers, looks at the recent automatic crater detection advancements using machine learning techniques (primarily in methods using CNNs), and makes recommendations for the path toward greater automation.
Keywords: Crater detection | Feature extraction | Automation | Machine learning | Convolutional neural networks | Mars
Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements
یادگیری ماشین کاربردی برای بازیابی توزیع دما و غلظت اندازه گیری انتشار مادون قرمز-2019
Inversion of temperature and species concentration distributions from radiometric measurements involves solving nonlinear, ill-posed and high-dimensional problems. Machine Learning approaches allow solving such highly nonlinear problems, offering an alternative way to deal with complex and dynamic systems with good flexibility. In this study, we present a machine learning approach for retrieving temperatures and species concentrations from spectral infrared emission measurements in combustion systems. The training spectra for the machine learning model were synthesized through calculations from HITEMP 2010 for gas mixtures of CO2, H2O, and CO. The method was tested for different line-of-sight temperature and concentration distributions, different gas path lengths and different spectral intervals. Experimental validation was carried out by measuring spectral emission from a Hencken flat flame burner with a Fourier-transform infrared spectrometer with different spectral resolutions. The temperature fields above the burner for combustion with equivalence ratios of ϕ=1, ϕ=0.8, and ϕ=1.4 were retrieved and were in excellent agreement with temperatures deduced from Rayleigh scattering thermometry.
Keywords: Inverse radiation | Temperature | Concentration Machine learning | Neural network
ReviewModus: Text classification and sentiment prediction of unstructured reviews using a hybrid combination of machine learning and evaluation models
ReviewModus: طبقه بندی متن و پیش بینی احساسات بررسی های بدون ساختار با استفاده از ترکیب هیبریدی از مدل های یادگیری و ارزیابی ماشین-2019
While research interest on product and service evaluation from unstructured text reviews is increasing, investigating the effectiveness of predictive analytical models in this context is still under-explored. With the advancement in machine learning research, an opportu- nity exists to bridge this gap using a model-based product and service evaluation. We pro- pose in this article ReviewModus , a text mining and processing framework that (1) relies on the model structure and its corresponding assessment questions to train a machine learning algorithm to predict the classification of reviews around the model dimensions; (2) predicts the sentiments within the reviews based on external review training datasets; and (3) transforms the extracted measures from the reviews for further analysis. Our ap- proach is evaluated in the context of 11 e-government services where the performance of the framework is compared to the manual processing of unstructured reviews cross- checked by three independent evaluators. Our study shows promising classification results with a micro-average F-score reaching 85.16%, and a high sentiment prediction correlation (71.44%) with the manually performed sentiment assessment.
Keywords: Machine learning | Text mining | Neural network | Logistic regression | e-government
تحلیل احساسات مبتنی بر یادگیری عمیق در متن رومی اردو
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 9
آنالیز احساسات با توجه به رویکرد همه جانبه در آنالیز احساسات کاربران شبکه های اجتماعی مختلف، انجمن ها، سایت های بازاریابی الکترونیکی و وبلاگ ها، اهمیت زیادی دارد. داده های مربوط به احساسات در وب اهمیت زیادی دارد و بر مشتریان، خوانندگان و شرکت های تجاری تأثیر می گذارد. شبکه عصبی مکرر به طور گسترده ای در انجام وظایف پردازش زبان طبیعی مورد استفاده قرار گرفته است، زیرا برای مدل سازی داده های متوالی به صورت موثر طراحی شده است.
در این مقاله از مدل عصبی عمیق حافظه کوتاه-طولانی مدت (LSTM) استفاده شده است. توانایی فوق العاده ای در ضبط اطلاعات دور برد و حل مشکل کاهش گرادیان و همچنین ارائه اطلاعات متنی آتی، معناشناسی توالی لغات با شکوه دارد. این مقاله پایه و اساس تطبیق روش های یادگیری عمیق در آنالیز رومن اردو است. نتایج تجربی نشان داد که مدل ما دقت قابل توجهی دارد و دقت بیشتری از روش های یادگیری ماشین دارد.
کليدواژه: شبکه عصبی مکرر (RNN)| حافظه کوتاه-بلند مدت (LSTM) | آنالیز معنایی رومن اردو | تعبیه لغت
|مقاله ترجمه شده|
A unique feature extraction using MRDWT for automatic classification of abnormal heartbeat from ECG big data with Multilayered Probabilistic Neural Network classifier
استخراج ویژگی منحصر به فرد با استفاده از MRDWT برای طبقه بندی خودکارضربان قلب غیر طبیعی از داده های بزرگ ECG با چند لایه طبقه بندی احتمالی شبکه عصبی-2018
This paper employs a novel adaptive feature extraction techniques of electrocardiogram (ECG) signal for detection of cardiac arrhythmias using multiresolution discrete wavelet transform from ECG big data. In this paper, five types ECG arrhythmias including normal beats have been classified. The MIT-BIH database of 48 patient records is utilized for detection and analysis of cardiac arrhythmias. Proposed feature extraction utilizes Daubechies as wavelet function and extracts 21 feature points which include the QRS complex of ECG signal. The Multilayered Probabilistic Neural Network (MPNN) classifier is pro posed as the best-suited classifier for the proposed feature. Total 1700 ECG betas were tested using MPNN classifier and compared with other three classifiers Back Propagation (BPNN), Multilayered Perceptron (MLP) and Support Vector Machine (SVM). The system efficiency and performance have been evaluated using seven types of evaluation criteria: precision (PR), F-Score, positive predictivity (PP), sensitivity (SE), classification error rate (CER) and specificity (SP). The overall system accuracy, using MPNN technique utilizing the proposed feature, obtained is 99.53% whereas using BPNN, MLP and SVM provide 97.94%, 98.53%, and 99%. The processing time using MPNN classifier is only 3 s which show that the proposed techniques not only very accurate and efficient but also very quick.
Keywords: Signal processing ، Artificial intelligence ، Pattern recognition ، Soft computing ، Wavelet transform