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
Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images
Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images-2019
Abstract Background: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25e26% of discordance for classifying a benign nevus versus malignant melanoma. A recent study indicated the potential of deep learning to lower these discordances. However, the performance of deep learning in classifying histopathologic melanoma images was never compared directly to human experts. The aim of this study is to perform such a first direct comparison. Methods: A total of 695 lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi/345 melanoma). Only the haematoxylin & eosin (H&E) slides of these lesions were digitalised via a slide scanner and then randomly cropped. A total of 595 of the resulting images were used to train a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison to 11 histopathologists. Three combined McNemar tests comparing the results of the CNNs test runs in terms of sensitivity, specificity and accuracy were predefined to test for significance (p < 0.05). Findings: The CNN achieved a mean sensitivity/specificity/accuracy of 76%/60%/68% over 11 test runs. In comparison, the 11 pathologists achieved a mean sensitivity/specificity/accuracy of 51.8%/66.5%/59.2%. Thus, the CNN was significantly (p Z 0.016) superior in classifying the cropped images. Interpretation: With limited image information available, a CNN was able to outperform 11 histopathologists in the classification of histopathological melanoma images and thus shows promise to assist human melanoma diagnoses.
KEYWORDS : Melanoma | Pathology | Histopathology | Deep learning | Artificial intelligence
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
Temporal and spatial deep learning network for infrared thermal defect detection
شبکه یادگیری عمیق زمانی و مکانی برای تشخیص نقص حرارتی مادون قرمز-2019
Most common types of defects for composite are debond and delamination. It is difficult to detect the inner defects on a complex shaped specimen by using conventional optical thermography nondestructive testing (NDT) methods. In this paper, a hybrid of spatial and temporal deep learning architecture for automatic thermography defects detection is proposed. The integration of cross network learning strategy has the capability to significantly minimize the uneven illumination and enhance the detection rate. The probability of detection (POD) has been derived to measure the detection results and this is coupled with comparison studies to verify the efficacy of the proposed method. The results show that visual geometry group-Unet (VGG-Unet) cross learning structure can significantly improve the contrast between the defective and non-defective regions. In addition, investigation of different feature extraction methods in which embedded in deep learning is conducted to optimize the learning structure. To investigate the efficacy and robustness of the proposed method, experimental studies have been carried out for inner debond defects on both regular and irregular shaped carbon fiber reinforced polymer (CFRP) specimens.
Keywords: Deep learning | Segmentation | Thermography defect detection | Nondestructive testing
Deep learning enabled cutting tool selection for special-shaped machining features of complex products
یادگیری عمیق انتخاب ابزار برش را برای ویژگی های ماشینکاری خاص محصولات پیچیده امکان پذیر می کند-2019
Each complex product contains many special-shaped machining features required to be machined by the specific customized cutting tools. In this context, we propose a deep learning based cutting tool selection approach, which contributes to make it effective and efficiency for and also improves the intelligence of the process of cutting tool selection for special-shaped machining features of complex products. In this approach, one-to-one correspondence between each special-shaped machining feature and each cutting tool is first analyzed and established. Then, the problem of cutting tool selection could be transformed into a feature recognition problem. To this end, each special-shaped machining feature is represented by its multiple drawing views that contain rich information for differentiating each of these features. With numbers of these views as training set, a deep residual network (ResNet) is trained successfully for feature recognition, where the recognized features cutting tool could also be automatically selected based on the one-to-one correspondence. With the learned ResNet, engineers could use an engineering drawing to select cutting tools intelligently. Finally, the proposed approach is applied to the special-shaped machining features of a vortex shell workpiece to demonstrate its feasibility. The presented approach provides a valuable insight into the intelligent cutting tool selection for special-shaped machining features of complex products.
Keywords: Cutting tool selection | Special-shaped machining features | Complex products | Residual networks | Deep learning
Enhancing transportation systems via deep learning: A survey
تقویت سیستم های حمل و نقل از طریق یادگیری عمیق: یک مرور-2019
Machine learning (ML) plays the core function to intellectualize the transportation systems. Recent years have witnessed the advent and prevalence of deep learning which has provoked a storm in ITS (Intelligent Transportation Systems). Consequently, traditional ML models in many applications have been replaced by the new learning techniques and the landscape of ITS is being reshaped. Under such perspective, we provide a comprehensive survey that focuses on the utilization of deep learning models to enhance the intelligence level of transportation systems. By organizing multiple dozens of relevant works that were originally scattered here and there, this survey attempts to provide a clear picture of how various deep learning models have been applied in multiple transportation applications.
Keywords: Deep learning | Transportation systems | Survey
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
Automatic detection, localization and segmentation of nano-particles with deep learning in microscopy images
تشخیص خودکار ، بومی سازی و تقسیم نانو ذرات با یادگیری عمیق در تصاویر میکروسکوپی-2019
With the growing amount of high resolution microscopy images automatic nano-particle detection, shape analysis and size determination have gained importance for providing quantitative support that gives important information for the evaluation of the material. In this paper, we present a new method for detection of nanoparticles and determination of their shapes and sizes simultaneously with deep learning. The proposed method employs multiple output convolutional neural networks (MO-CNN) and has two outputs: first is the detection output that gives the locations of the particles and the other one is the segmentation output for providing the boundaries of the nano-particles. The final sizes of particles are determined with the modified Hough algorithm that runs on the segmentation output. The proposed method is tested and evaluated on a dataset containing 17 TEM images of Fe3O4 and silica coated nano-particles. Also, we compared these results with U-net algorithm which is a popular deep learning method. The experiments showed that the proposed method has 98.23% accuracy for detection and 96.59% accuracy for segmentation of nano-particles.
Keywords: Nano-particle | Deep learning | Object detection | MO-CNN | Hough transform
Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning
تشخیص خطای دنده نیمه نظارت شده با استفاده از سیگنال لرزش خام بر اساس یادگیری عمیق-2019
In aerospace industry, gears are the most common parts of a mechanical transmission system. Gear pitting faults could cause the transmission system to crash and give rise to safety disaster. It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration. In this paper, a novel method named augmented deep sparse autoencoder (ADSAE) is proposed. The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data. This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear. The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions. The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy. This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults. The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods. This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.
KEYWORDS : Deep learning | Gear pitting diagnosis | Gear teeth | Raw vibration signal | Semi-supervised learning | Sparse autoencoder