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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Al-Khshali, Hasan H. Ilyas, Muhammad Ucan, Osman N. Altinbas Univ Elect & Comp Engn Istanbul Turkey

ISBN: (纸本)9781728125473

Malware programmers look for ways to attack computers and networks. They try to find entry points that bypass security and enable them to slip into the system. One of these ways is through Portable Executable (PE) files. On the other hand, methods are devised to discover this danger and take action against it. Artificial intelligence (AI) can play an important role in the process of discovering maiwares inside PE files. Using AI as a tool, this work aims to study the features of PE file headers as a means of detecting malware and assess the effect of these features on the level of accuracy. The study uses various numbers of PE features. Two different algorithms are used, each with two options, in order to discover their relative effectiveness. Tests are carried out using a specified control data set so that relative performance can be assessed. The criterion used is the level of accuracy obtained with a large number and variation of groups of studies. Each study starts with a collection of features, then features are progressively added to study the impact of these features on accuracy. This was important in showing that not all the features have a positive impact on accuracy. Also, there were some indications that using a large number of features will not always improve the accuracy. Using graphs, it was shown that accuracy will be enhanced after adding a certain number of features. Graphs also show that, along the way of adding the features, accuracy sometimes improves and, in some other times, it goes down, so not all added features are useful. More than 100 runs were made, using a total of 29 features. The highest accuracy obtained with Decision Tree was 0.987, and 0.979 in Neural Networks-Multi-layer Perceptron (NN_MLPC).

关键词： Artificial intelligence (AI) Decision Tree (DT) Neural Network Multi-Layer Perceptron Classifier (NN MLPC) Portable Executable (PE)

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Intelligent Factory System Design Based on Single Chip Microcomputer 6

Intelligent Factory System Design Based on Single Chip Micro...

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6th International symposium on Computer and Information Processing Technology, ISCIPT 2021

作者： Li, Aoqi Wang, Zhe Liu, Yizhi Beijing Normal University School of Artificial Intelligence Beijing China Hefei University of Technology School of Mechanical Engineering Hefei China Changsha University of Science Technology School of Electrical Information Engineering Changsha China

ISBN: (纸本)9781665441377

With the advent of the 'Internet plus' era, intelligent integration technology based on the Internet of Things technology has attracted widespread attention in industrial enterprises because of the large scale of large factories, management difficulties, fire, dangerous gas leakage, unknown personnel invasion, and other accidents that cannot be timely responded. The smart factory control system was designed based on a single-chip microcomputer. It is based on the CPS system, and it monitors environmental information through the close coordination of computational resources and physical resources and uses the wireless sensor network deployed in the monitoring plant area. Then the information is processed and transmitted to the host computer through the single-chip microcomputer so that the host computer can block the accident area in time. Thus, the goal of comprehensive and effective safety management of the whole factory is realized. At last, through the demonstration and Proteus software simulation results show that the system can effectively improve the response speed of factory personnel in emergencies, improve the management quality, but also save human resources, assist the safety supervision personnel in ensuring the safety of the factory to provide convenience, to promote the construction of intelligent society further to provide new ideas. © 2021 ieee.

关键词： Computer software

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Compressing Interpretable Representations of Piecewise Linear Neural Networks using Neuro-Fuzzy Models

Compressing Interpretable Representations of Piecewise Linea...

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ieee symposium Series on computational intelligence (SSCI)

作者： Leon Glass Wael Hilali Oliver Nelles Corporate Research Robert Bosch GmbH Renningen Germany Automatic Control - Mechatronics University of Siegen Siegen Germany

ISBN: (纸本)9781728190495

We present Rectified Linear Unit based Local Linear Model Tree (ReLUMoT). A model that bridges the gap between Piecewise Linear Neural Networks (PLNN) and Local Model Networks (LMN) like those resulting from the LoLiMoT algorithm. Essentially, we perform the input space partitioning of LoLiMoT by training a PLNN and extracting its linear regions. These become the input space partitions of ReLUMoT. From the perspective of PLNNs our approach compresses and smoothens low-dimensional models, while making them interpretable. From the perspective of LoLiMoT, our approach replaces the incremental and heuristic input space partitioning scheme with gradient-based training of a neural network, which is considerably more flexible.

关键词： Training computational modeling Atmospheric modeling Neural networks Pipelines Merging Feature extraction

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Optimization of air flowrate under different control strategies focus on biological process in wastewater treatment plant

Optimization of air flowrate under different control strateg...

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ieee symposium Series on computational intelligence (SSCI)

作者： Lili Lorensia Mallu Wu–Yang Sean Maria Pacheco Chung Yuan Christian University Chung-Li Taiwan

ISBN: (纸本)9781728190495

To optimize energy consumption is one of the major issues in wastewater treatment while maintaining effluent water quality standards. Aeration in biological processes requires more energy in wastewater treatment plants than other processes. In this study, simulations were conducted by using the ASM2d module of GPS-X. Several control strategies-namely, on/off dissolved oxygen control, proportional (P) / integral (I) dissolved oxygen control, and cascade ammonia control-are performed in biological processes to confirm the effects of ammonia reduction and optimize energy cost. According to the results of cascade control, it is 23 % smaller than the original energy cost. Sensitivity analysis was further proceeded to confirm the influence of tuning parameters, namely, proportional and integral gains. Consequently, proportional gain improves response speed and controls system accuracy. Integral gain is effective in decreasing the steady-state error of the system. The optimized values of proportional and integral gains lead to the best performance of aeration and provides a reference for first-phase plant operation.

关键词： Ammonia Energy consumption Costs Sensitivity analysis Plants (biology) Biological system modeling Atmospheric modeling

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Detecting Anomalous Multivariate Time-Series via Hybrid Machine Learning

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ieee TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷

作者： Terbuch, Anika O'Leary, Paul Khalili-Motlagh-Kasmaei, Negin Auer, Peter Zohrer, Alexander Winter, Vincent Univ Leoben Chair Automation Dept Prod Engn A-8700 Leoben Austria Univ Leoben Chair Informat Technol Dept Math & Informat Technol A-8700 Leoben Austria Keller Grundbau Ges mbH A-1110 Vienna Austria

This article investigates the use of hybrid machine learning (HML) for the detection of anomalous multivariate time-series (MVTS). Focusing on a specific industrial use-case from geotechnical engineering, where hundreds of MVTS need to be analyzed and classified, has permitted extensive testing of the proposed methods with real measurement data. The novel hybrid anomaly detector combines two means for detection, creating redundancy and reducing the risk of missing defective elements in a safety relevant application. The two parts are: 1) anomaly detection based on approximately 50 physics-motivated key performance indicators (KPIs) and 2) an unsupervised variational autoencoder (VAE) with long short-term memory layers. The KPI captures expert knowledge on the properties of the data that infer the quality of produced elements;these are used as a type of auto-labeling. The goal of the extension using machine learning (ML) is to detect anomalies that the experts may not have foreseen. In contrast to anomaly detection in streaming data, where the goal is to locate an anomaly, each MVTS is complete in itself at the time of evaluation and is categorized as anomalous or nonanomalous. The article compares the performance of different VAE architectures [e.g., long short-term memory (LSTM-VAE) and bidirectional LSTM (BiLSTM-VAE)]. The results of using a genetic algorithm to optimize the hyperparameters of the different architectures are also presented. It is shown that modeling the industrial process as an assemblage of subprocesses yields a better discriminating power and permits the identification of interdependencies between the subprocesses. Interestingly, different autoencoder architectures may be optimal for different subprocesses;here two different architectures are combined to achieve superior performance. Extensive results are presented based on a very large set of real-time measurement data.

关键词： Machine learning Anomaly detection Key performance indicator Instruments Buildings Real-time systems Quality control Artificial intelligence in measurement and instrumentation hybrid learning key performance indicator (KPI) long short-term memory (LSTM)-variational autoencoder (VAE) outlier detection time-series

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A Hierarchical Bidirectional LSTM Sequence Model for Extractive Text Summarization in Electric Power Systems 13

A Hierarchical Bidirectional LSTM Sequence Model for Extract...

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13th International symposium on computational intelligence and Design (ISCID)

作者： Jiang, Wei Zou, Yunfeng Zhao, Ting Zhang, Qiang Ma, Yinglong State Grid Corp China Beijing 100031 Peoples R China State Grid Jiangsu Elect Power Co Ltd Mkt Serv Ctr Nanjing Peoples R China Global Energy Interconnect Res Inst Co Ltd Artificial Intelligence Elect Power Syst State Gr Beijing 102200 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

ISBN: (纸本)9781728184463

With the increasing volume of documents in electric power systems, it is urgent and necessary for electric power systems managers to efficiently analyze the massive documents and make reasonable decisions by capturing the main points of the document as quickly as possible. The text summarization technique provides a feasible way to efficiently analyze and obtain the main contents residing in the document. In this paper, we present a Hierarchical Bidirectional Long Term Short Memory Sequence model for extractive text summarization in electric power systems in order to efficiently and accurately summarize electric power documents and obtain a summary of the document. Our model is divided into four layers including the embedding layer, the word layer, the sentence layer, and the classification layer in a hierarchical manner. The related experiments were made based on the electric power data set that contains more than 2000 electrical papers, in comparison with the existing approaches based on the CRF, CNN, and RNN models. The experimental results show that the performance based on our approach is superior to the three approaches against the three performance indexes ROUGE-1, ROUGE-2, and ROUGE-L.

关键词： Text Summarization Extractive Summarization BiLSTM Electric Power Documents

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Multi-agent system for dynamic scheduling

Multi-agent system for dynamic scheduling

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International Joint Conference on Neural Networks (IJCNN) held as part of the ieee World Congress on computational intelligence (ieee WCCI)

作者： Firme, Bernardo Lopes, Guilherme Martins, Miguel S. E. Coito, Tiago Viegas, Joaquim Sousa, Joao M. C. Reis, Joao C. P. Figueiredo, Joao Vieira, Susana Univ Lisbon Inst Super Tecn IDMEC Lisbon Portugal Univ Lisbon Inst Super Tecn Lisbon Portugal Univ Evora Dept Phys Evora Portugal

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169262

This paper proposes a flexible manufacturing system based on intelligent computational agents. A Multi-Agent System composed of 4 types of reactive agents was designed to control the operation of a real implementation in the Intelligent automation Lab at Instituto Superior Tecnico. This implementation was based and constructed analogously to a known benchmark, AIP-PRIMECA. The agents were modelled using Petri nets and agent communications were defined through the combination of FIPA Interaction Protocols. The system was tested under the conditions of static and dynamic scenarios, having its performance validated whenever possible by comparison with results from a Potential Fields Approach in the same benchmark. Overall, the performance exhibited by the proposed MAS was slightly better and it is worth highlighting the simple behaviour of each agent and ability to respond in real-time to all the dynamic scenarios tested.

关键词： Multi-Agent Systems Agent Communication Manufacturing control Flexible Manufacturing System

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Testbed for Mobile Robot Platoon control System Evaluation 19

Testbed for Mobile Robot Platoon Control System Evaluation

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ieee Joint 19th International symposium on computational intelligence and Informatics / 7th International Conference on Recent Achievements in Mechatronics, automation, Computer Sciences and Robotics

作者： Feher, Aron Nagy, Lehel Marton, Lorinc Univ Pannonia Fac Informat Technol Veszprem Hungary Sapientia Hungarian Univ Transylvania Dept Elect Engn Targu Mures Romania

ISBN: (纸本)9781728156255

This paper presents the development of a testbed for mobile robot platoons based on modern microcontroller units. The sensor/actuator interfacing capacity, the general-purpose communication module and the simple programming environment of the applied controller enable the rapid and flexible development of the robot platoon control system. In spite of the low implementation costs, the testbed allows the implementation and testing of advanced control strategies and communication protocols. The paper presents both the software and hardware development of the testbed. Real-time experimental measurements are also presented to show the applicability of the developed system.Y

关键词： Mobile robots

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On Image Processing System for Robot control using DSK 6713 DSP Kit 26

On Image Processing System for Robot Control using DSK 6713 ...

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ieee 26th International symposium for Design and Technology in Electronic Packaging (SIITME)

作者： Petrea, G. Nicolau, V Andrei, M. Dunarea de Jos Univ Galati Dept Elect & Telecommun Galati Romania

ISBN: (纸本)9781728175065

Real-time image processing techniques are used in many automation applications like in autonomous vehicle (AV) and robot control. In this paper, an image processing system for real-time video signal processing is studied, using DSK 6713 kit from Texas Instruments. The DSK kit is used to acquire frames from a camera module through HPI port and store them into external SDRAM memory. The DSP is used to extract the frames from memory through EMIF bus, and to run complex processing algorithms on 1D and 2D signals, like spectral analysis and artificial intelligence. Studying the system performances, three possible bottlenecks were identified and analyzed regarding image processing algorithms.

关键词： image processing DSP real time FFT

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Cooperative Optimization Strategy for Distributed Energy Resource System using Multi-Agent Reinforcement Learning

Cooperative Optimization Strategy for Distributed Energy Res...

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ieee symposium Series on computational intelligence (SSCI)

作者： Zhaoyang Liu Tianchun Xiang Tianhao Wang Chaoxu Mu School of Electrical and Information Engineering Tianjin University Tianjin China State Grid Tianjin Electrical Power Company Tianjin China Electric Power Research Institute State Grid Tianjin Electrical Power Company Tianjin China

ISBN: (纸本)9781728190495

In this paper, a consensus multi-agent deep reinforcement learning algorithm is introduced for distributed cooperative secondary voltage control of microgrids. To reduce dependence on the system model and enhance communication efficiency, we propose a fully decentralized multi-agent advantage actor critic (A2C) algorithm with local communication networks, which considers each distributed energy resource (DER) as an agent. Both local state and the messages received from neighbors are employed by each agent to learn a control strategy. Moreover, the maximum entropy reinforcement learning framework is applied to improve exploration of agents. The proposed algorithm is verified in two different scale microgrid setups, which are microgrid-6 and microgrid-20. Experiment results show the effectiveness and superiority of our proposed algorithm.

关键词： Reinforcement learning Microgrids Entropy Distributed power generation Communication networks Voltage control Optimization

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