Expressing uncertainty in neural networks for production systems

2021 ◽  
Vol 69 (3) ◽  
pp. 221-230
Author(s):  
Samim Ahmad Multaheb ◽  
Bernd Zimmering ◽  
Oliver Niggemann
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Production Systems ◽  
Control Loops ◽  
Automation Systems ◽  
High Level

Abstract The application of machine learning, especially of trained neural networks, requires a high level of trust in their results. A key to this trust is the network’s ability to assess the uncertainty of the computed results. This is a prerequisite for the use of such networks in closed-control loops and in automation systems. This paper describes approaches for enabling neural networks to automatically learn the uncertainties of their results.

scholarly journals Deep learning based DNA:RNA triplex forming potential prediction

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yu Zhang ◽  
Yahui Long ◽  
Chee Keong Kwoh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Cross Validation ◽  
Roc Curves ◽  
Triplex Dna ◽  
Triplex Formation ◽  
Machine Learning Model ◽  
Non Coding Rnas ◽  
High Level ◽  
Integrated Program

Abstract Background Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: (1) they identify a large number of triplex-forming lncRNAs, but the limited number of experimentally verified triplex-forming lncRNA indicates that maybe not all of them can form triplex in practice, and (2) their predictions only consider the theoretical relationship while lacking the features from the experimentally verified data. Results In this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex-forming lncRNAs and DNA sites based on the experimentally verified data, where the high-level features are learned by the convolutional neural networks. In the fivefold cross validation, the average values of Area Under the ROC curves and PRC curves for removed redundancy triplex-forming lncRNA dataset with threshold 0.8 are 0.9649 and 0.9996, and these two values for triplex DNA sites prediction are 0.8705 and 0.9671, respectively. Besides, we also briefly summarize the cis and trans targeting of triplexes lncRNAs. Conclusions The TriplexFPP is able to predict the most likely triplex-forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities and the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.

Creep Rupture Forecasting

2014 ◽  
Vol 2 (2) ◽  
pp. 1-25 ◽  
Author(s):  
Stylianos Chatzidakis ◽  
Miltiadis Alamaniotis ◽  
Lefteri H. Tsoukalas
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Process Model ◽  
Production Systems ◽  
Learning Algorithms ◽  
Difficult Problem ◽  
Network Size ◽  
Creep Rupture ◽  
Machine Learning Algorithms ◽  
Training Data

Creep rupture is becoming increasingly one of the most important problems affecting behavior and performance of power production systems operating in high temperature environments and potentially under irradiation as is the case of nuclear reactors. Creep rupture forecasting and estimation of the useful life is required to avoid unanticipated component failure and cost ineffective operation. Despite the rigorous investigations of creep mechanisms and their effect on component lifetime, experimental data are sparse rendering the time to rupture prediction a rather difficult problem. An approach for performing creep rupture forecasting that exploits the unique characteristics of machine learning algorithms is proposed herein. The approach seeks to introduce a mechanism that will synergistically exploit recent findings in creep rupture with the state-of-the-art computational paradigm of machine learning. In this study, three machine learning algorithms, namely General Regression Neural Networks, Artificial Neural Networks and Gaussian Processes, were employed to capture the underlying trends and provide creep rupture forecasting. The current implementation is demonstrated and evaluated on actual experimental creep rupture data. Results show that the Gaussian process model based on the Matérn kernel achieved the best overall prediction performance (56.38%). Significant dependencies exist on the number of training data, neural network size, kernel selection and whether interpolation or extrapolation is performed.

scholarly journals CNN2Gate: An Implementation of Convolutional Neural Networks Inference on FPGAs with Automated Design Space Exploration

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2200
Author(s):  
Alireza Ghaffari ◽  
Yvon Savaria
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Automatic Synthesis ◽  
Software Libraries ◽  
Learning Software ◽  
High Level

Convolutional Neural Networks (CNNs) have a major impact on our society, because of the numerous services they provide. These services include, but are not limited to image classification, video analysis, and speech recognition. Recently, the number of researches that utilize FPGAs to implement CNNs are increasing rapidly. This is due to the lower power consumption and easy reconfigurability that are offered by these platforms. Because of the research efforts put into topics, such as architecture, synthesis, and optimization, some new challenges are arising for integrating suitable hardware solutions to high-level machine learning software libraries. This paper introduces an integrated framework (CNN2Gate), which supports compilation of a CNN model for an FPGA target. CNN2Gate is capable of parsing CNN models from several popular high-level machine learning libraries, such as Keras, Pytorch, Caffe2, etc. CNN2Gate extracts computation flow of layers, in addition to weights and biases, and applies a “given” fixed-point quantization. Furthermore, it writes this information in the proper format for the FPGA vendor’s OpenCL synthesis tools that are then used to build and run the project on FPGA. CNN2Gate performs design-space exploration and fits the design on different FPGAs with limited logic resources automatically. This paper reports results of automatic synthesis and design-space exploration of AlexNet and VGG-16 on various Intel FPGA platforms.

Deep Learning

2016 ◽  
Vol 10 (03) ◽  
pp. 417-439 ◽  
Author(s):  
Xing Hao ◽  
Guigang Zhang ◽  
Shang Ma
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Complex Structures ◽  
Training Algorithms ◽  
High Level ◽  
And Training

Deep learning is a branch of machine learning that tries to model high-level abstractions of data using multiple layers of neurons consisting of complex structures or non-liner transformations. With the increase of the amount of data and the power of computation, neural networks with more complex structures have attracted widespread attention and been applied to various fields. This paper provides an overview of deep learning in neural networks including popular architecture models and training algorithms.

scholarly journals CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1495
Author(s):  
Hyun Kwon ◽  
Hyunsoo Yoon ◽  
Ki-Woong Park
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Web Services ◽  
Mobile Devices ◽  
Deep Neural Networks ◽  
The Internet ◽  
Image Generation ◽  
Style Transfer ◽  
Transfer Method ◽  
High Level

Mobile devices such as sensors are used to connect to the Internet and provide services to users. Web services are vulnerable to automated attacks, which can restrict mobile devices from accessing websites. To prevent such automated attacks, CAPTCHAs are widely used as a security solution. However, when a high level of distortion has been applied to a CAPTCHA to make it resistant to automated attacks, the CAPTCHA becomes difficult for a human to recognize. In this work, we propose a method for generating a CAPTCHA image that will resist recognition by machines while maintaining its recognizability to humans. The method utilizes the style transfer method, and creates a new image, called a style-plugged-CAPTCHA image, by incorporating the styles of other images while keeping the content of the original CAPTCHA. In our experiment, we used the TensorFlow machine learning library and six CAPTCHA datasets in use on actual websites. The experimental results show that the proposed scheme reduces the rate of recognition by the DeCAPTCHA system to 3.5% and 3.2% using one style image and two style images, respectively, while maintaining recognizability by humans.

scholarly journals A Review on Deep Learning Algorithms for Speech and Facial Emotion Recognition

2016 ◽  
Vol 1 (3) ◽  
pp. 92-108 ◽  
Author(s):  
Charlyn Pushpa Latha ◽  
Mohana Priya
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Structured Learning ◽  
Hierarchical Learning ◽  
Learning Techniques ◽  
Deep Boltzmann Machine ◽  
Learning Technique ◽  
High Level ◽  
Multiple Processing

Deep Learning is the recent machine learning technique that tries to model high level abstractions in data by using multiple processing layers with complex structures. It is also known as deep structured learning, hierarchical learning or deep machine learning. The term “deep learning" indicates the method used in training multi-layered neural networks. Deep Learning technique has obtained remarkable success in the field of face recognition with 97.5% accuracy. Facial Electromyogram (FEMG) signals are used to detect the different emotions of humans. Some of the deep learning techniques discussed in this paper are Deep Boltzmann Machine (DBM), Deep Belief Networks (DBN), Convolutional Neural Networks (CNN) and Stacked Auto Encoders respectively. This paper focuses on the review of some of the deep learning techniques used by various researchers which paved the way to improve the classification accuracy of the FEMG signals as well as the speech signals.

scholarly journals Malware Detection on Byte Streams of PDF Files Using Convolutional Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Young-Seob Jeong ◽  
Jiyoung Woo ◽  
Ah Reum Kang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Input Data ◽  
Malware Detection ◽  
Learning Models ◽  
High Level ◽  
The Given ◽  
Machine Learning Models

With increasing amount of data, the threat of malware keeps growing recently. The malicious actions embedded in nonexecutable documents especially (e.g., PDF files) can be more dangerous, because it is difficult to detect and most users are not aware of such type of malicious attacks. In this paper, we design a convolutional neural network to tackle the malware detection on the PDF files. We collect malicious and benign PDF files and manually label the byte sequences within the files. We intensively examine the structure of the input data and illustrate how we design the proposed network based on the characteristics of data. The proposed network is designed to interpret high-level patterns among collectable spatial clues, thereby predicting whether the given byte sequence has malicious actions or not. By experimental results, we demonstrate that the proposed network outperform several representative machine-learning models as well as other networks with different settings.

scholarly journals The Use of Neural Networks and Genetic Algorithms to Control Low Rigidity Shafts Machining

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4683
Author(s):  
Antoni Świć ◽  
Dariusz Wołos ◽  
Arkadiusz Gola ◽  
Grzegorz Kłosowski
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Short Term Memory ◽  
Real Life ◽  
Control Process ◽  
Lstm Network ◽  
High Level

The article presents an original machine-learning-based automated approach for controlling the process of machining of low-rigidity shafts using artificial intelligence methods. Three models of hybrid controllers based on different types of neural networks and genetic algorithms were developed. In this study, an objective function optimized by a genetic algorithm was replaced with a neural network trained on real-life data. The task of the genetic algorithm is to select the optimal values of the input parameters of a neural network to ensure minimum deviation. Both input vector values and the neural network’s output values are real numbers, which means the problem under consideration is regressive. The performance of three types of neural networks was analyzed: a classic multilayer perceptron network, a nonlinear autoregressive network with exogenous input (NARX) prediction network, and a deep recurrent long short-term memory (LSTM) network. Algorithmic machine learning methods were used to achieve a high level of automation of the control process. By training the network on data from real measurements, we were able to control the reliability of the turning process, taking into account many factors that are usually overlooked during mathematical modelling. Positive results of the experiments confirm the effectiveness of the proposed method for controlling low-rigidity shaft turning.

scholarly journals Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

Challenges ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 2
Author(s):  
Tilman Klaeger ◽  
Sebastian Gottschall ◽  
Lukas Oehm
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Data Science ◽  
Production Systems ◽  
Ground Truth ◽  
Data Sets ◽  
Human Machine Interaction ◽  
Automation Systems ◽  
Machine Learning Applications ◽  
Cyber Physical Production Systems

Much research is done on data analytics and machine learning for data coming from industrial processes. In practical approaches, one finds many pitfalls restraining the application of these modern technologies especially in brownfield applications. With this paper, we want to show state of the art and what to expect when working with stock machines in the field. The paper is a review of literature found to cover challenges for cyber-physical production systems (CPPS) in brownfield applications. This review is combined with our own personal experience and findings gained while setting up such systems in processing and packaging machines as well as in other areas. A major focus in this paper is on data collection, which tends be more cumbersome than most people might expect. In addition, data quality for machine learning applications is a challenge once leaving the laboratory and its academic data sets. Topics here include missing ground truth or the lack of semantic description of the data. A last challenge covered is IT security and passing data through firewalls to allow for the cyber part in CPPS. However, all of these findings show that potentials of data driven production systems are strongly depending on data collection to build proclaimed new automation systems with more flexibility, improved human–machine interaction and better process-stability and thus less waste during manufacturing.

scholarly journals A Review on Deep Learning Algorithms for Speech and Facial Emotion Recognition

2020 ◽  
Vol 1 (3) ◽  
pp. 92-108
Author(s):  
Charlyn Pushpa Latha ◽  
Mohana Priya
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Structured Learning ◽  
Hierarchical Learning ◽  
Learning Techniques ◽  
Deep Boltzmann Machine ◽  
Learning Technique ◽  
High Level ◽  
Multiple Processing

Deep Learning is the recent machine learning technique that tries to model high level abstractions in databy using multiple processing layers with complex structures. It is also known as deep structured learning,hierarchical learning or deep machine learning. The term “deep learning" indicates the method used in trainingmulti-layered neural networks. Deep Learning technique has obtained remarkable success in the field of facerecognition with 97.5% accuracy. Facial Electromyogram (FEMG) signals are used to detect the different emotionsof humans. Some of the deep learning techniques discussed in this paper are Deep Boltzmann Machine (DBM), DeepBelief Networks (DBN), Convolutional Neural Networks (CNN) and Stacked Auto Encoders respectively. This paperfocuses on the review of some of the deep learning techniques used by various researchers which paved the way toimprove the classification accuracy of the FEMG signals as well as the speech signals

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