scholarly journals Deep neural networks in hydrology: the new generation of universal and efficient models

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Georgy V. Ayzel ◽  
◽  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Hydrological Modeling ◽  
Recent Change ◽  
Model Parameters ◽  
Learning Technology ◽  
Water Balance Components ◽  
Wide Range ◽  
New Generation

For around a decade, deep learning – the sub-field of machine learning that refers to artificial neural networks comprised of many computational layers – modifies the landscape of statistical model development in many research areas, such as image classification, machine translation, and speech recognition. Geoscientific disciplines in general and the field of hydrology in particular, also do not stand aside from this movement. Recently, the proliferation of modern deep learning-based techniques and methods has been actively gaining popularity for solving a wide range of hydrological problems: modeling and forecasting of river runoff, hydrological model parameters regionalization, assessment of available water resources, identification of the main drivers of the recent change in water balance components. This growing popularity of deep neural networks is primarily due to their high universality and efficiency. The presented qualities, together with the rapidly growing amount of accumulated environmental information, as well as increasing availability of computing facilities and resources, allow us to speak about deep neural networks as a new generation of mathematical models designed to, if not to replace existing solutions, but significantly enrich the field of geophysical processes modeling. This paper provides a brief overview of the current state of the field of development and application of deep neural networks in hydrology. Also in the following study, the qualitative long-term forecast regarding the development of deep learning technology for managing the corresponding hydrological modeling challenges is provided based on the use of “Gartner Hype Curve”, which in the general details describes a life cycle of modern technologies.

scholarly journals Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1579
Author(s):  
Dongqi Wang ◽  
Qinghua Meng ◽  
Dongming Chen ◽  
Hupo Zhang ◽  
Lisheng Xu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Channel Model ◽  
Expert Knowledge ◽  
Automatic Detection ◽  
Data Representation ◽  
Learning Technology ◽  
Arrhythmia Detection ◽  
Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

Bearing fault diagnosis using deep learning techniques coupled with handcrafted feature extraction: A comparative study

2020 ◽  
pp. 107754632092914
Author(s):  
Mohammed Alabsi ◽  
Yabin Liao ◽  
Ala-Addin Nabulsi
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Deep Learning ◽  
Comparative Study ◽  
Domain Knowledge ◽  
Deep Neural Networks ◽  
Performance Limits ◽  
Data Repositories ◽  
Learning Techniques ◽  
Wide Range

Deep learning has seen tremendous growth over the past decade. It has set new performance limits for a wide range of applications, including computer vision, speech recognition, and machinery health monitoring. With the abundance of instrumentation data and the availability of high computational power, deep learning continues to prove itself as an efficient tool for the extraction of micropatterns from machinery big data repositories. This study presents a comparative study for feature extraction capabilities using stacked autoencoders considering the use of expert domain knowledge. Case Western Reserve University bearing dataset was used for the study, and a classifier was trained and tested to extract and visualize features from 12 different failure classes. Based on the raw data preprocessing, four different deep neural network structures were studied. Results indicated that integrating domain knowledge with deep learning techniques improved feature extraction capabilities and reduced the deep neural networks size and computational requirements without the need for exhaustive deep neural networks architecture tuning and modification.

Network attributes describe a similarity between deep neural networks and large scale brain networks

2019 ◽  
Author(s):  
Kosuke Takagi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Human Brain ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Distribution Model ◽  
Common Mechanism ◽  
Learning Models ◽  
Connection Weight ◽  
Wide Range

Abstract Despite the recent success of deep learning models in solving various problems, their ability is still limited compared with human intelligence, which has the flexibility to adapt to a changing environment. To obtain a model which achieves adaptability to a wide range of problems and tasks is a challenging problem. To achieve this, an issue that must be addressed is identification of the similarities and differences between the human brain and deep neural networks. In this article, inspired by the human flexibility which might suggest the existence of a common mechanism allowing solution of different kinds of tasks, we consider a general learning process in neural networks, on which no specific conditions and constraints are imposed. Subsequently, we theoretically show that, according to the learning progress, the network structure converges to the state, which is characterized by a unique distribution model with respect to network quantities such as the connection weight and node strength. Noting that the empirical data indicate that this state emerges in the large scale network in the human brain, we show that the same state can be reproduced in a simple example of deep learning models. Although further research is needed, our findings provide an insight into the common inherent mechanism underlying the human brain and deep learning. Thus, our findings provide suggestions for designing efficient learning algorithms for solving a wide variety of tasks in the future.

scholarly journals SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networks

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-24
Author(s):  
Gokul Krishnan ◽  
Sumit K. Mandal ◽  
Manvitha Pannala ◽  
Chaitali Chakrabarti ◽  
Jae-Sun Seo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Design Space Exploration ◽  
Deep Neural Networks ◽  
Feasible Solution ◽  
Computing System ◽  
Efficient Design ◽  
Simulation Speed ◽  
Wide Range ◽  
On Chip

In-memory computing (IMC) on a monolithic chip for deep learning faces dramatic challenges on area, yield, and on-chip interconnection cost due to the ever-increasing model sizes. 2.5D integration or chiplet-based architectures interconnect multiple small chips (i.e., chiplets) to form a large computing system, presenting a feasible solution beyond a monolithic IMC architecture to accelerate large deep learning models. This paper presents a new benchmarking simulator, SIAM, to evaluate the performance of chiplet-based IMC architectures and explore the potential of such a paradigm shift in IMC architecture design. SIAM integrates device, circuit, architecture, network-on-chip (NoC), network-on-package (NoP), and DRAM access models to realize an end-to-end system. SIAM is scalable in its support of a wide range of deep neural networks (DNNs), customizable to various network structures and configurations, and capable of efficient design space exploration. We demonstrate the flexibility, scalability, and simulation speed of SIAM by benchmarking different state-of-the-art DNNs with CIFAR-10, CIFAR-100, and ImageNet datasets. We further calibrate the simulation results with a published silicon result, SIMBA. The chiplet-based IMC architecture obtained through SIAM shows 130 and 72 improvement in energy-efficiency for ResNet-50 on the ImageNet dataset compared to Nvidia V100 and T4 GPUs.

scholarly journals Deep Learning and Medical Diagnosis: A Review of Literature

2018 ◽  
Vol 2 (3) ◽  
pp. 47 ◽  
Author(s):  
Mihalj Bakator ◽  
Dragica Radosav
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Medical Diagnosis ◽  
Medical Image ◽  
Deep Neural Networks ◽  
Medical Image Analysis ◽  
Research Articles ◽  
Learning Technology ◽  
Review Of Literature ◽  
Medical Field

In this review the application of deep learning for medical diagnosis is addressed. A thorough analysis of various scientific articles in the domain of deep neural networks application in the medical field has been conducted. More than 300 research articles were obtained, and after several selection steps, 46 articles were presented in more detail. The results indicate that convolutional neural networks (CNN) are the most widely represented when it comes to deep learning and medical image analysis. Furthermore, based on the findings of this article, it can be noted that the application of deep learning technology is widespread, but the majority of applications are focused on bioinformatics, medical diagnosis and other similar fields.

scholarly journals Measuring the Uncertainty of Predictions in Deep Neural Networks with Variational Inference

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6011 ◽  
Author(s):  
Jan Steinbrener ◽  
Konstantin Posch ◽  
Jürgen Pilz
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Network Architecture ◽  
Deep Neural Networks ◽  
Variational Inference ◽  
Model Parameters ◽  
A Posteriori ◽  
Novel Approach ◽  
Credible Intervals ◽  
Posteriori Distribution

We present a novel approach for training deep neural networks in a Bayesian way. Compared to other Bayesian deep learning formulations, our approach allows for quantifying the uncertainty in model parameters while only adding very few additional parameters to be optimized. The proposed approach uses variational inference to approximate the intractable a posteriori distribution on basis of a normal prior. By representing the a posteriori uncertainty of the network parameters per network layer and depending on the estimated parameter expectation values, only very few additional parameters need to be optimized compared to a non-Bayesian network. We compare our approach to classical deep learning, Bernoulli dropout and Bayes by Backprop using the MNIST dataset. Compared to classical deep learning, the test error is reduced by 15%. We also show that the uncertainty information obtained can be used to calculate credible intervals for the network prediction and to optimize network architecture for the dataset at hand. To illustrate that our approach also scales to large networks and input vector sizes, we apply it to the GoogLeNet architecture on a custom dataset, achieving an average accuracy of 0.92. Using 95% credible intervals, all but one wrong classification result can be detected.

scholarly journals Deep Learning: Implications for Human Learning and Memory

2020 ◽  
Author(s):  
James Lloyd McClelland ◽  
Matthew M. Botvinick
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Learning And Memory ◽  
Deep Neural Networks ◽  
Human Learning ◽  
Machine Intelligence ◽  
Learning Approaches ◽  
Human Capabilities ◽  
Wide Range ◽  
Learning Research

Recent years have seen an explosion of interest in deep learning and deep neural networks. Deep learning lies at the heart of unprecedented feats of machine intelligence as well as software people use every day. Systems built on deep learning have surpassed human capabilities in complex strategy games like go and chess, and we use them for speech recognition, image captioning, and a wide range of other applications. A consideration of deep learning is crucial for a Handbook of Human Memory, since human brains are deep neural networks, and an understanding of artificial deep learning systems may contribute to our understanding of how humans and animals learn and remember. Deep neural networks are complex, structured systems that process information in a parallel, distributed, and context sensitive fashion, and deep learning is the effort to use these systems to acquire capabilities we associate with intelligence through an experience dependent learning process. Within the field of Artificial Intelligence, work in deep learning is typically directed toward the goal of creating and understanding intelligence using all available tools and resources without consideration of their biological plausibility. Many of the ideas, however, at the heart of deep learning draw their inspiration from the brain and from characteristics of human intelligence we believe are best captured by these brain-inspired systems (Rumelhart, McClelland, and the PDP Research Group, 1986). Furthermore, ideas emerging from deep learning research can help inform us about memory and learning in humans and animals. Thus, deep learning research can be seen as fertile ground for engagement between researchers who work on related issues with implications for both biological and machine intelligence.We begin by introducing the basic constructs employed in deep learning and then consider several of the widely used learning paradigms and architectures used in these systems. We then turn to a consideration of how the constructs of deep learning relate to traditional constructs in the psychological literature on learning and memory. Next, we consider recent developments in the field of reinforcement learning that have broad implications for human learning and memory. We conclude with a consideration of areas where human capabilities still far exceed current deep learning approaches, and describe possible future directions toward understanding how these abilities might best be captured.

scholarly journals Adv-Plate Attack: Adversarially Perturbed Plate for License Plate Recognition System

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hyun Kwon ◽  
Jang-Woon Baek
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Recognition System ◽  
License Plate ◽  
Learning Technology ◽  
License Plate Recognition ◽  
Adversarial Examples ◽  
Adversarial Example

Deep learning technology has been used to develop improved license plate recognition (LPR) systems. In particular, deep neural networks have brought significant improvements in the LPR system. However, deep neural networks are vulnerable to adversarial examples. In the existing LPR system, adversarial examples study specific spots that are easily identifiable by humans or require human feedback. In this paper, we propose a method of generating adversarial examples in the license plate, which has no human feedback and is difficult to identify by humans. In the proposed method, adversarial noise is added only to the license plate among the entire image to create an adversarial example that is erroneously recognized by the LPR system without being identified by humans. Experiments were performed using the baza silka dataset, and TensorFlow was used as the machine learning library. When epsilon is 0.6 for the first type, and alpha and the iteration of the second type are 0.4 and 1000, respectively, the adversarial examples generated by the first and second type generation methods are reduced to 20% and 15% accuracy in the LPR system.

scholarly journals Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Malte Seemann ◽  
Lennart Bargsten ◽  
Alexander Schlaefer
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Computed Tomography Angiography ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Synthetic Image ◽  
Wide Range ◽  
The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

scholarly journals Trigonometric Inference Providing Learning in Deep Neural Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6704
Author(s):  
Jingyong Cai ◽  
Masashi Takemoto ◽  
Yuming Qiu ◽  
Hironori Nakajo
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Trigonometric Approximation ◽  
Model Parameters ◽  
Training Algorithms ◽  
Activation Functions ◽  
Classical Training ◽  
Sum Formula

Despite being heavily used in the training of deep neural networks (DNNs), multipliers are resource-intensive and insufficient in many different scenarios. Previous discoveries have revealed the superiority when activation functions, such as the sigmoid, are calculated by shift-and-add operations, although they fail to remove multiplications in training altogether. In this paper, we propose an innovative approach that can convert all multiplications in the forward and backward inferences of DNNs into shift-and-add operations. Because the model parameters and backpropagated errors of a large DNN model are typically clustered around zero, these values can be approximated by their sine values. Multiplications between the weights and error signals are transferred to multiplications of their sine values, which are replaceable with simpler operations with the help of the product to sum formula. In addition, a rectified sine activation function is utilized for further converting layer inputs into sine values. In this way, the original multiplication-intensive operations can be computed through simple add-and-shift operations. This trigonometric approximation method provides an efficient training and inference alternative for devices with insufficient hardware multipliers. Experimental results demonstrate that this method is able to obtain a performance close to that of classical training algorithms. The approach we propose sheds new light on future hardware customization research for machine learning.

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