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neural network based soft sensor for pilot-plant distillation column 22

Neural network based soft sensor for pilot-plant distillatio...

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22nd Topical conference on Refinery processing 2019 - Topical conference at the 2019 AIChE Spring Meeting and 15th Global Congress on Process Safety

作者： Abdulla, Thaer A. Osman, Mohanad Edgar, Thomas F. University of Texas at Austin AustinTX United States Canadian Natural Resources Limited CalgaryAB Canada

ISBN: (纸本)9781510886773

The development of an inferential soft sensor for a pilot-plant distillation column separating an ethanol-water mixture using neural network (NN) method has been investigated in this work. Inferential sensors are increasingly used in the process industries to infer the value of the main quality variable while utilizing much easier to measure secondary variables of the process. The lags between the input variables and the output variable vary due to changes in operating conditions. Previous studies have introduced different methods to estimate lags for input and output variables, but all of them have assumed these lags to be constant regardless of the changes in the operating conditions. In this work, an inferential sensor that can infer the composition of ethanol at the top product using time lags for the input variables and varied first-order time constant lags with the output variable is developed. The developed inferential sensor is based on a neural network (NN) model. This NN model is built in MATLAB/Simulink. An OPC link is established in order to communicate between MATLAB/Simulink and the Emerson DetlaV distributed Control System (DCS) of the pilot-plant distillation column. Principal Component Analysis (PCA) and Projection to Latent Structures (PLS) methods are used in this work to remove the outliers from the input variables set and to determine the most correlated values of the input variables and their lags with the output variable Xa (ethanol mole fraction of distillate product) respectively. The model adaptively selects the correct first-order time constant lags of an output variable according to the instantaneous operating condition (the composition of ethanol is increased or decreased) and assigns a best value for each case. The prediction performance of the proposed NN models (with and without time lags for input variables) is illustrated using experimental data from the pilot-scale ethanol-water distillation column. More than 400 samples were colle

关键词： neural network models

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Smart Approach to Build A Deep neural network Based IDS for Cloud Environment Using an Optimized Genetic Algorithm 19

Smart Approach to Build A Deep Neural Network Based IDS for ...

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2nd International conference on networking, Information Systems and Security (NISS)

作者： Chiba, Zouhair Abghour, Noreddine Moussaid, Khalid El Omri, Amina Rida, Mohamed Hassan II Univ Casablanca Fac Sci LIMSAD Labs Casablanca 20100 Morocco

ISBN: (纸本)9781450366458

In recent years cloud computing has revolutionized the IT world with rapidly emerging and widely accepted paradigm for computing systems, and it has become popular among users in organizations and companies. Nowadays, numerous organizations have begun to upload their massive amount of prominent data into public cloud. Nevertheless, uploading sensitive data to open and distributed public cloud storage services poses security risks such as availability, confidentiality and integrity to organizations. Moreover, non-stop cloud services have caused high levels of intrusion and abuse. Thereby, protecting network accessible Cloud resources and services from various threats and attacks is of great concern. To address this issue, it is imperative to develop a powerful network Intrusion System (NIDS) to detect both outsider and insider intruders with high detection precision in the cloud environment. In this work, we propose a smart approach using an Improved Genetic Algorithm (IGA) to build a Deep neural network (DNN) based anomaly NIDS. Genetic Algorithm (GA) is improved through optimization strategies, namely Parallel processing and Fitness Value Hashing, which reduce execution time, convergence time and save processing power. Our approach consists to use IGA in order to search the optimal or near optimal combination of most relevant values of the parameters included in construction of DNN based IDS or impacting its performance, like feature selection, data normalization, architecture of DNN, activation function, learning rate and Momentum term, which ensure high detection rate, high accuracy and low false alarm rate. CloudSim 4.0 simulator platform and CICIDS 2017 benchmark dataset were used for simulation and validation of the proposed system. The experimental results obtained demonstrate that in comparison to several traditional and recent approaches, our proposed IDS achieves higher detection rate and lower false positive rate.

关键词： Cloud computing network intrusion detection system Anomaly detection Deep neural network Back propagation learning algorithm Optimization Genetic algorithm CICIDS2017 dataset

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Multi-agent solution for a distributed intelligent photo surveying 33

Multi-agent solution for a distributed intelligent photo sur...

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33rd Annual European Simulation and Modelling conference, ESM 2019

作者： Ivaschenko, Anton Krivosheev, Arkady Sitnikov, Pavel Samara State Technical University 244 Molodogvardeyskaya Samara Russia ITMO University 49 Kronverksky Pr Saint Peterburg Russia

ISBN: (纸本)9789492859099

In this paper it is proposed to solve a problem of stream processing of photo images produced by a number of distributed cameras using a multi-agent software solution. Specifically designed intelligent system implementing a neural network is introduced for image recognition. The proposed solution was required to process and analyze the results of electrical meters that are manually monitored by a group of patrol personnel inspectors using hand held devices. This example illustrates a necessity to provide autonomous pre-processing of photo images by an intelligent mobile application with a multi-agent architecture. This paper presents a multi-agent model for this solution and results of its implementation in practice. Copyright © 2019 EUROSIS-ETI.

关键词： Image recognition

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2019 11th International conference on Wireless Communications and Signal processing, WCSP 2019

2019 11th International Conference on Wireless Communication...

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11th International conference on Wireless Communications and Signal processing, WCSP 2019

ISBN: (纸本)9781728135557

The proceedings contain 298 papers. The topics discussed include: handover procedure design and performance optimization strategy in LEO-HAP system;secure transmission for UAV-Aided NOMA networks with SWIPT via precoding optimization;efficient virtualized resources allocation in network virtualization environment: a service oriented perspective;secure MISO beamforming optimization with randomly distributed eavesdroppers and interferers;BP list decoding of polar codes with adaptive bit splitting over critical set;study of a traffic model based on adaptive chaotic mapping for mMTC services;investigation of convolution neural network-based wavefront correction for FSO systems;hybrid precoding for massive MIMO systems using partially-connected phase shifter network;and near optimal decoding of polar-based turbo product codes.

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The Implementation of Asynchronous Advantage Actor-Critic with Stigmergy in network-assisted Multi-agent System

The Implementation of Asynchronous Advantage Actor-Critic wi...

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International conference on Wireless Communications and Signal processing (WCSP)

作者： Kun Chen Rongpeng Li Zhifeng Zhao Honggang Zhang Zhejiang University Zhejiang Lab

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

Multi-agent system (MAS) needs to mobilize multiple simple agents to complete complex tasks. However, it is difficult to coherently coordinate distributed agents by means of limited local information. In this paper, we propose a decentralized collaboration method named as "stigmergy" in network-assisted MAS, by exploiting digital pheromones (DP) as an indirect medium of communication and utilizing deep reinforcement learning (DRL) on top. Correspondingly, we implement an experimental platform, where KHEPERA IV robots form targeted specific shapes in a decentralized manner. Experimental results demonstrate the effectiveness and efficiency of the proposed method. Our platform could be conveniently extended to investigate the impact of network factors (e.g., latency, data rate, etc) on the level of collective intelligence.

关键词： Task analysis Decentralized control Solid modeling Robot kinematics Reinforcement learning neural networks Collaboration

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A Novel Approach for Traffic Anomaly Detection in Power distributed Control System and Substation System 13th

A Novel Approach for Traffic Anomaly Detection in Power Dist...

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13th International conference on network and System Security (NSS)

作者： Zhang, Li Lv, Zhuo Zhang, Xuesong Chen, Cen Li, Nuannuan Li, Yidong Wang, Wei Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing 100044 Peoples R China State Grid Henan Elect Power Res Inst Zhengzhou 450052 Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China

ISBN: (纸本)9783030369385;9783030369378

Industrial Control Systems (ICS) are the critical infrastructures of power grids. It is very important to monitor and control industrial equipment through the networks. Most ICSs currently used in smart grid contain IT key equipment and communication technologies to implement the communication and logic control functions. However, unlike traditional IT network traffic, these power-related industrial control systems have variety of proprietary protocols. Obviously, in typical complex systems the manually intensive processing of data is costly and sub-optimal. In this paper, we propose an novel traffic anomaly detection model for power ICS based on Multi-Head attention (MHA) method, in which the collected raw traffic was converted into the form of matrix. The MHA and Convolutional neural network (CNN) model were used to classify traffic data. We replace the traditional feature extraction and rule making process with an acceptable computational cost. The effectiveness of our approach is demonstrated by experiments on two real world power ICS testbeds: a power generation simulation platform based on a distributed control system (DCS) and a substation slave station. Comparing with some classical machine learning algorithms and Convolutional neural networks, the experimental results show that our MHA model outperforms the CNN-based and classical machine learning detection models with an accuracy rate of 99.86%.

关键词： Power distributed control system Anomaly detection Traffic classification

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App-Net: A Hybrid neural network for Encrypted Mobile Traffic Classification

App-Net: A Hybrid Neural Network for Encrypted Mobile Traffi...

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IEEE conference on Computer Communications Workshops, INFOCOM Wksps

作者： Xin Wang Shuhui Chen Jinshu Su College of Computer Science and Technology National University of Defense Technology National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

With the exponential growth of mobile traffic data, mobile traffic classification is in a great need. It is an essential step to improve the performance of network services such as QoS and security monitoring. However, the widespread use of encrypted protocols, especially the TLS protocol, has posed great challenges to traditional traffic classification techniques. As the rule-based deep packet inspection approaches are ineffective for encrypted traffic classification, various machine learning methods have been studied and used. Recently, deep learning solutions which enable automatic feature extraction are also proposed to classify encrypted traffic. In this paper we propose App-Net, an end-to-end hybrid neural network, to learn effective features from raw TLS flows for mobile app identification. App-Net is designed by combining RNN and CNN in a parallel way. So that it can learn a joint flow-app embedding to characterize both flow sequence patterns and unique app signatures. We evaluate App-Net on a real-world dataset that covering 80 apps. The results show that our method can achieve an excellent performance and outperform the state-of-the-art methods.

关键词： Cryptography Feature extraction Payloads Recurrent neural networks Machine learning Convolutional neural networks

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The artificial power system networks stability control using the technology of neural network

The artificial power system networks stability control using...

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2019 International Scientific and Technical conference Smart Energy Systems, SES 2019

作者： Alzakkar, A.M.-N. Valeev, I.M. Mestnikov, N.P. Nurullin, E.G. Institute of Electrical Engineering KSPEU Krasnoselskaya Street 51 Kazan Russia Institute of Heat Power Engineering KSPEU Krasnoselskaya Street 51 Kazan Russia

In the present work, the electric voltage stability at Muharda station in Syria was studied during the normal and up to normal loading states. The results were obtained using artificial neural network, which consists of three layers (input-hidden-output). This network is characterized by the speed and accuracy in processing before failure and supply turn-off, which may lead to economical problems. This study was carried out using two different generating schemes in this station (single - double). The performance of this network consists of two stages: training stage (off-line) and testing stage (on-line), and a comparison between these stages is carried out, which leads to optimization the load in testing cases depending on the training data. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://***/licenses/by/4.0/).

关键词： Multilayer neural networks

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Word-level BERT-CNN-RNN Model for Chinese Punctuation Restoration

Word-level BERT-CNN-RNN Model for Chinese Punctuation Restor...

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International conference on Computer and Communications (ICCC)

作者： Zhe Zhang Jie Liu Lihua Chi Xinhai Chen Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution neural network (CNN) and Recurrent neural network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%.

关键词： Recurrent neural networks Computational modeling Bit error rate Semantics Speech recognition Feature extraction Task analysis

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A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI 33

A state-space model for inferring effective connectivity of ...

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33rd conference on neural Information processing Systems (NeurIPS)

作者： Tu, Tao Paisley, John Haufe, Stefan Sajda, Paul Columbia Univ New York NY 10027 USA Charite Univ Med Berlin Berlin Germany

Inferring effective connectivity between spatially segregated brain regions is important for understanding human brain dynamics in health and disease. Non-invasive neuroimaging modalities, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), are often used to make measurements and infer connectivity. However most studies do not consider integrating the two modalities even though each is an indirect measure of the latent neural dynamics and each has its own spatial and/or temporal limitations. In this study, we develop a linear state-space model to infer the effective connectivity in a distributed brain network based on simultaneously recorded EEG and fMRI data. Our method first identifies task-dependent and subject-dependent regions of interest (ROI) based on the analysis of fMRI data. Directed influences between the latent neural states at these ROIs are then modeled as a multivariate autogressive (MVAR) process driven by various exogenous inputs. The latent neural dynamics give rise to the observed scalp EEG measurements via a biophysically informed linear EEG forward model. We use a mean-field variational Bayesian approach to infer the posterior distribution of latent states and model parameters. The performance of the model was evaluated on two sets of simulations. Our results emphasize the importance of obtaining accurate spatial localization of ROls from fMRI. Finally, we applied the model to simultaneously recorded EEG-fMRI data from 10 subjects during a Face-Car-House visual categorization task and compared the change in connectivity induced by different stimulus categories.

关键词： Electroencephalography

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