Due to the complexity of modern industrial processes, there may be both linear and nonlinear relationships exist among process variables. In addition, the dynamic behavior of the process also brings challenges to proc...
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Due to the complexity of modern industrial processes, there may be both linear and nonlinear relationships exist among process variables. In addition, the dynamic behavior of the process also brings challenges to process ***, some linear monitoring methods have been developed for dynamic processes. However, the existing methods can not precisely extract the dynamic characteristics of nonlinear processes. What is more, purely linear or nonlinear methods can hardly tackle the hybrid linear and nonlinear relationships among process variables. To address the above issue, a novel method, termed slow feature networks(SFNet) is proposed and applied for dynamic process monitoring. On the one hand, a slowly varying constraint of hidden features is added to the autoencoder, so that the static and dynamic characteristics of nonlinear processes can be extracted concurrently. On the other hand, a linear mapping is incorporated into the nonlinear neural network structure,thereby providing parallel analysis of linear and nonlinear monitoring information. Five statistics are constructed for comprehensive process monitoring from both static and dynamic, linear and nonlinear perspectives. In this way, alarms corresponding to different statistical information are used to indicate different operating statuses with meaningful interpretation and enhanced process understanding. A real industrial example is adopted to validate the performance of the proposed method.
An average of 8000 forest wildfires occurs each year in Canada burning an average of 2.5M ha/year as reported by the Government of Canada. Given the current rate of climate change, this number is expected to increase ...
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An average of 8000 forest wildfires occurs each year in Canada burning an average of 2.5M ha/year as reported by the Government of Canada. Given the current rate of climate change, this number is expected to increase each year. Being able to predict how the fires spread would play a critical role in fire risk management. However, given the complexity of the natural processes that influence a fire system, most of the models used for simulating wildfires are computationally expensive and need a high variety of information about the environmental parameters to be able to give good performances. Deep learning algorithms allow computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined in terms of its relation to simpler concepts. We propose a deep learning predictor that uses a Deep Convolutional Auto-Encoder to learn the key structures of a forest wildfire spread from images and a Long Short Term Memory to predict the next phase of the fire. We divided the predictor problem in three phases: find a dataset of wildfires, learning the essential structure of forest fire, and predict the next image. We first present the simulated wildfires dataset and the algorithm we applied on it to make it more suitable to the model. Then we present the Deep Forest Wildfire Auto-Encoder and its implementation using the Caffe framework. Particular attention is given to the design considerations and to the best practice used to implement the model. We also present the design of the Deep Forest Wildfire Predictor, and some possible future variations of it.
Machine learning relies on developing models which represent data in informative and simple ways. Taking inspiration from the subfield of multitask learning, we in- vestigate the possibility of enhancing data represen...
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Machine learning relies on developing models which represent data in informative and simple ways. Taking inspiration from the subfield of multitask learning, we in- vestigate the possibility of enhancing data representations at intermediate layers in a neural network. Specifically, we add a decoder layer whose task is to reconstruct the model's input from the intermediate representation. Along with this contribu- tion, we introduce a number of algorithms for anomaly detection and supervised classification based on this framework and assess their performance. We find that anomaly detection works best in this framework when formulated as a classification problem between in-distribution and out-of-distribution data, and that supervised classification works best when using the simplest formulation with a linear classifier.
It is almost seventy years after the publication of Claude Shannon's "A Mathematical Theory of Communication" [1] and Norbert Wiener's "Extrapolation, Interpolation and Smoothing of Stationary T...
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ISBN:
(纸本)9781509041176
It is almost seventy years after the publication of Claude Shannon's "A Mathematical Theory of Communication" [1] and Norbert Wiener's "Extrapolation, Interpolation and Smoothing of Stationary Time Series" [2]. The pioneering works of Shannon and Wiener lay the foundation of communication, data storage, control, and other information technologies. This paper briefly reviews Shannon and Wiener's perspectives on the problem of message transmission over noisy channel and also experimentally evaluates the feasibility of integrating these two perspectives to train autoencoders close to the information limit. To this end, the principle of relevant information (PRI) is used and validated to optimally encode input imagery in the presence of noise.
We demonstrate a novel method for the automatic modulation classification based on a deep learning autoencoder network, trained by a nonnegativity constraint algorithm. The learning algorithm aims to constrain the neg...
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We demonstrate a novel method for the automatic modulation classification based on a deep learning autoencoder network, trained by a nonnegativity constraint algorithm. The learning algorithm aims to constrain the negative weights, learns features that amount to a part-based representation of data, and disentangles a more meaningful hidden structure. The performance of this algorithm is tested on the fourth-order cumulants of the modulated signals. The results indicate that the autoencoder with nonnegativity constraint (ANC) improves the sparsity and minimizes the reconstruction error in comparison with the conventional sparse autoencoder. The classification accuracy of an ANC based deep network shows improved accuracy under limited signal length and fading channel.
Deep learning algorithms have recently been applied to solving challenging problems in medicine such as medical image classification and analysis. In some areas, those algorithms have outperformed the human medical ex...
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Deep learning algorithms have recently been applied to solving challenging problems in medicine such as medical image classification and analysis. In some areas, those algorithms have outperformed the human medical experts experience in diagnosis. Thus, in this paper we apply three different deep networks to solve the problem of brain hemorrhage identification in CT images. The motivation behind this work is the difficulty that radiologists encounter when diagnosing a hemorrhagic brain CT image, in particularly in the early stages of the brain bleeding. autoencoder (AE), stacked autoencoder (SAE), and convolutional neural network (CNN) are employed and trained to classify the CT images into hemorrhagic or non-hemorrhagic. Experimentally, it was found that all employed networks performed differently in terms of accuracy, error reached, and training time. However, stacked autoencoder has achieved a higher accuracy and lesser error compared to other used networks.
A vision-based obstacle detection system is a key enabler for the development of autonomous robots and vehicles and intelligent transportation systems. This paper addresses the problem of urban scene monitoring and tr...
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A vision-based obstacle detection system is a key enabler for the development of autonomous robots and vehicles and intelligent transportation systems. This paper addresses the problem of urban scene monitoring and tracking of obstacles based on unsupervised, deep-learning approaches. Here, we design an innovative hybrid encoder that integrates deep Boltzmann machines (DBM) and auto-encoders (AE). This hybrid auto-encode (HAE) model combines the greedy learning features of DBM with the dimensionality reduction capacity of AE to accurately and reliably detect the presence of obstacles. We combine the proposed hybrid model with the one-class support vector machines (OCSVM) to visually monitor an urban scene. We also propose an efficient approach to estimating obstacles location and track their positions via scene densities. Specifically, we address obstacle detection as an anomaly detection problem. If an obstacle is detected by the OCSVM algorithm, then localization and tracking algorithm is executed. We validated the effectiveness of our approach by using experimental data from two publicly available dataset, the Malaga stereovision urban dataset (MSVUD) and the Daimler urban segmentation dataset (DUSD). Results show the capacity of the proposed approach to reliably detect obstacles. (C) 2017 Elsevier B.V. All rights reserved.
Anomaly detection refers to finding observations which do not conform to expected behavior. It is widely applied in many domains such as image processing, fraud detection, intrusion detection, medical health, etc. How...
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Anomaly detection refers to finding observations which do not conform to expected behavior. It is widely applied in many domains such as image processing, fraud detection, intrusion detection, medical health, etc. However, most of the anomaly detection techniques focus on detecting a single anomalous instance. Such techniques fail when there is only a slight difference between the anomalous instance and a non-anomalous instance. Various collective anomaly detection techniques (based on clustering, deep learning, etc) have been developed that determine whether a group of records form an anomaly even though they are only slightly anomalous instances. However, they do not provide any information about the attributes that make the group anomalous. In other words, they are focused only on detecting records that are collectively anomalous and are not able to detect anomalous patterns in general. FGSS is a scalable anomalous pattern detection technique that searches over both records and attributes. However, FGSS has several limitations preventing it from functioning on continuous, unstructured and high dimensional data such as images, etc. We propose a general framework called DeepFGSS, which uses autoencoder, enabling it to operate on any kind of data. We evaluate its performance using four experiments on both structured and unstructured data to determine its accuracy of detecting anomalies and efficiency of distinguishing between datasets containing anomalies and ones that do not.
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