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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Predictive Maintenance of Oil and Gas Equipment using Recurrent Neural Network 11

Predictive Maintenance of Oil and Gas Equipment using Recurr...

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11th Curtin University Technology, Science and engineering international conference, CUTSE 2018

作者： Abbasi, Tayaba Lim, King Hann Yam, Ke San CDT 250 Sarawak Miri98009 Malaysia

Oil and gas industry projects involving equipment acquisition and installation are usually capital intensive. the recent crude oil price fall has tightened the expenditure and therefore reinforced the importance of effective maintenance management across the oil and gas industry. Rotating mechanical equipment such as induction motor, compressors and pumps, are essential elements in industrial processes. Effective maintenance of these equipment is crucial to avoid several damage and downtime for repair. Predictive maintenance has attracted huge attention in this industry driven by sensors and data acquisition. this paper focuses on developing machine learning algorithm based on recurrent neural network (RNN) using long short-term memory (LSTM) to carry out predictive maintenance of Air booster compressor (ABC) motor. the resulting experiment demonstrates the performance of RNN-LSTM algorithm implemented for fault prognosis model of rotating equipment predictive maintenance. the application of these algorithms could mitigate risk and reduce cost in the oil and gas operation. © Published under licence by IOP Publishing Ltd.

关键词： data acquisition

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A Routing Algorithm Based on Weighted Graph for Power Distribution Network 1

引用

11th EAI international conference on Simulation Tools and Techniques, SIMUTools 2019

作者： Huang, Renxiang Jia, Huibin Huang, Xing Sichuan University of Arts and Science Dazhou635000 China School of Electrical and Electronic Engineering North China Electric Power University Baoding China State Grid Liaoning Electric Power Company Shenyang China

ISBN: (数字)9783030322168

ISBN: (纸本)9783030322151

Smart power distribution network refers to the network that realizes information transmission among the power generation, transmission, transformation, consumption. With the rapid development of the power distribution network, the network topology becomes more and more complex. the scheduling of measurement, protection and control information can be realized by routing selection. However, the traditional routing algorithm cannot be applied due to its poor adaptability to the structural of the modern intelligent power system. In order to meet the requirements of low latency and high reliability in data communication of power distribution network, this paper utilize the weighted graph theory to describe the power distribution network. then, an intelligent routing algorithm is proposed based on the analysis of the connectivity, delay, reliability and other parameters. Simulation results show that the proposed routing scheme is feasible and effective, which can also realize the load balancing of the power distribution network. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2019.

关键词： Graph theory

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Investigating the use of Machine learning for Smart Parking Applications

Investigating the use of Machine Learning for Smart Parking ...

引用

international conference on Knowledge and Systems engineering

作者： Jonathan Barker Sabih ur Rehman School of Computing and Mathematics Charles Sturt University Australia

Traffic congestion caused by greater competition for limited parking spaces in the world's major cities is a growing problem. To overcome this challenge, a study has been carried out to use a smart parking application that utilises machine learning algorithms to help predict future car parking occupancy rates at Port Macquarie campus of Charles Sturt University (CSU), Australia. Parking data was collected over a five-week period and the WEKA Machine learning Workbench was used to identify high-performing algorithms for predicting future parking occupancy rates. In the initial phase, some well known algorithms were used to investigate occupancy rates. In the next phase of the study, student class timetable data was used to enhance prediction accuracy and investigate parking occupancy trends. While most algorithms proved to be accurate in stable conditions, the KStar algorithm appeared to produce better results during highly variable conditions.

关键词： Machine learning Prediction algorithms Machine learning algorithms Automobiles intelligent sensors Real-time systems

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Multi-layered learning for Information Extraction from Adverse Drug Event Narratives 11th

Multi-layered Learning for Information Extraction from Adver...

引用

11th international Joint conference on Biomedical engineering Systems and Technologies (BIOSTEC)

作者： Wunnava, Susmitha Qin, Xiao Kakar, Tabassum Tlachac, M. L. Kong, Xiangnan Rundensteiner, Elke A. Sahoo, Sanjay K. De, Suranjan Worcester Polytech Inst Worcester MA 01609 USA US FDA Ctr Drug Evaluat & Res Silver Spring MD 20993 USA

ISBN: (纸本)9783030291969;9783030291952

Recognizing named entities in Adverse Drug Reactions narratives is a crucial step towards extracting valuable patient information from unstructured text and transforming the information into an easily processable structured format. this motivates using advanced data analytics to support data-driven pharmacovigilance. Yet existing biomedical named entity recognition (NER) tools are limited in their ability to identify certain entity types from these domain-specific narratives, resulting in poor accuracy. To address this shortcoming, we propose our novel methodology called Tiered Ensemble learning System with Diversity (TELS-D), an ensemble approach that integrates a rich variety of named entity recognizers to procure the final result. there are two specific challenges faced by biomedical NER: the classes are imbalanced and the lack of a single best performing method. the first challenge is addressed through a balanced, under-sampled bagging strategy that depends on the imbalance level to overcome this highly skewed data problem. To address the second challenge, we design an ensemble of heterogeneous entity recognizers that leverages a novel ensemble combiner. Our experimental results demonstrate that for biomedical text datasets: (i) a balanced learning environment combined with an ensemble of heterogeneous classifiers consistently improves the performance over individual base learners and (ii) stacking-based ensemble combiner methods outperform simple majority voting based solutions by 0.3 in F1-score.

关键词： Pharmacovigilance Adverse Drug Reaction Class imbalance Ensemble learning

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Analog Circuit Incipient Fault Diagnosis from Raw Signals using Multi-Layer Extreme learning Machine

Analog Circuit Incipient Fault Diagnosis from Raw Signals us...

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Prognostics and System Health Management (PHM-Jinan), international conference on

作者： Guangquan Zhao Yongmei Liu Jun Zhou Kankan Wu School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Shanghai Institute of Satellite Engineering Shanghai China

ISBN: (数字)9781728151816

ISBN: (纸本)9781728151823

Analog circuits play important roles in modern electronic systems. Incipient fault diagnosis of analog circuits is a recognized challenging research direction due to the difficulty of fault feature extraction and identification. this paper proposes an early fault diagnosis algorithm for analog circuits based on multilayer extreme learning machine (ML-ELM).Its basic idea comes from the Auto-encoder(AE) and the Extreme learning Machine (ELM). the proposed method which combines the characteristics of both, so it has the ability of feature extraction and fast training speed. In this method, the time domain sampling signal of the circuit can be directly used as the fault sample, and the unsupervised feature extraction is carried out layer by layer through the deep network, the diagnosis results are obtained after the supervised classification by the traditional ELM algorithm. the whole process does not rely on multi-step iterative and reverses fine-tuning. the experimental results of the Sallen-Key band-pass filter circuit and Leapfrog low-pass filter circuit show that the method has not only high diagnosis accuracy, but also fast diagnosis speed. Because this method gets rid of the tedious manual feature extraction and complex signal processing, it improves the training efficiency and makes the method more universal.

关键词： Circuit faults Feature extraction Fault diagnosis Analog circuits Training Signal processing algorithms data models

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Research on the Key Techniques of Semantic Mining of Information Digest in the Field of Agricultural Major Crops Based on Deep learning 11th

Research on the Key Techniques of Semantic Mining of Informa...

引用

11th IFIP WG 5.14 international conference on Computer and Computing Technologies in Agriculture (CCTA)

作者： Gong, Hao G. J. M. Cui, Yunpeng Qian, Ping CAAS Agr Informat Inst Beijing Peoples R China

ISBN: (纸本)9783030061791;9783030061784

Nowadays application scopes of deep learning research in the machine learning subfield have been gradually expanded, mainly in the field of computer vision and natural language processing. However, in the latter NLP field, there is very little semantic excavation research on agricultural literature data. this paper bases on the attempting to combine relevant paradigms of semantic mining techniques and characteristics of agricultural digest data, for the service of providing new methods and technologies of information acquisition and analysis in the agricultural information domain. data cleaning methods and data mining experiment are mainly based on deep learning algorithms, which are Seq2Seq and attention mechanism. Finally, through qualitative evaluation and quantitative evaluation of the experimental results, which based on the ROUGE evaluation index system, the experiment shows that the semantic mining model has reached the optimal level of model evaluation in the certain range.

关键词： NLP Semantic mining Deep learning ROUGE

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Posture tracking using a machine learning algorithm for a home AAL environment 11th

Posture tracking using a machine learning algorithm for a ho...

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11th KES international conference on intelligent Decision Technologies, KES-IDT 2019

作者： Sandybekov, Maksim Grabow, Clemens Gaiduk, Maksym Seepold, Ralf HTWG Konstanz Alfred-Wachtel-Str. 8 Konstanz78462 Germany Universidad de Sevilla Avda. Reina Mercedes s/n Seville41012 Spain I.M. Sechenov First Moscow State Medical University 2-4 Bolshaya Pirogovskaya st. Moscow119991 Russia

ISBN: (纸本)9789811383021

the number of home office workers sitting for many hours is increasing. the sensor chair is tracking users’ sitting behavior which the help of pressure sensors and tries to avoid wrong postures which may cause diseases. the system provides live monitoring of the pressure distribution via web interface, as well as sitting posture prediction in real time. Posture analysis is realized through machine learning algorithm using a decision tree classifier that is compared to a random forest. data acquisition and aggregation for the learning process happens with a mobile app adding users biometrical data and the taken sitting posture as label. the sensor chair is able to differentiate between an arched back, a neutral posture or a laid back position taken on the chair. the classifier achieves an accuracy of 97.4% on our test set and is comparable to the performance of the random forest with 98.9%. © Springer Nature Singapore Pte Ltd. 2019.

关键词： data acquisition

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Neural model of projecting flexural strength of cement concrete intended for airfield pavements 11

Neural model of projecting flexural strength of cement concr...

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11th international Scientific conference on Aeronautics, Automotive and Railway engineering and Technologies, BulTrans 2019

作者： Linek, Malgorzata Kielce University of Technology Faculty of Civil Engineering and Architecture Department of Transportation Engineering Aleja Tysiaclecia Państwa Polskiego Street 7 Kielce25-314 Poland

this work present to the mathematical model in the form of Artificial Neural Network (ANN), intended for projecting concrete flexural strength. Input data was classified according to the type of component material and its content in concrete mix (cement contents, coarse aggregate, fine aggregate, water and admixtures). In order to determine mathematical model, a multilayer, one-way perceptron network was used, recursion network with sigmoidal neurons. the model assumes that neurons are gathered in some layers (one input layer, hidden layers and one output layer). the conducted cross-section of the impact of variable parameters values (learning constant α and momentum values η) on the accuracy of representation of flexural strength was analyses. Assessment criterion was assumed as consideration the lowest mistake level and 100% compliance. According to the obtained results ANN was assumed to be the best representing network for constant value of momentum 0.3, learning constant of 0.04 and 9 neurons in a hidden layer. Very good coincidence of component models with experiment results was achieved. At testing stage, the coincidence was achieved at the level of 99.25%, in case of the assumed network structure. During model verification by means of experimental results, the average coincidence was 99.68%. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Neurons

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Modeling and analysis of crash severity for electric bicycle 11th

Modeling and analysis of crash severity for electric bicycle

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11th international KES conference on intelligent Interactive Multimedia: Systems and Services, KES-IIMSS 2018

作者： Xu, Cheng Yu, Xiaonan Department of Traffic Management Engineering Zhejiang Police College Hangzhou310053 China College of Civil Engineering and Architecture Zhejiang University Hangzhou310058 China

ISBN: (纸本)9783319922300

Electric bicycle (E-bike) traffic crashes have become an important traffic safety problem in many Chinese cities. Based on the traffic crash data of E-bikes from Xintang region in Hangzhou, China, the time distribution, spatial distribution, and influencing factors for electric bicycles-related traffic crashes were analyzed, and the main factors that affect the traffic crash of electric bicycles were obtained. On this basis, a logistic model of the influencing factors on the severity of traffic crashes for electric bicycles was set up. the key factors affecting the severity of traffic crashes on electric bicycles were obtained, which provided the basis for the prevention and safety management of traffic crashes on electric bicycles. © Springer international Publishing AG, part of Springer Nature 2019.

关键词： Bicycles

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A Multi-Layer Feed Forward Neural Network Approach for Diagnosing Diabetes 11

A Multi-Layer Feed Forward Neural Network Approach for Diagn...

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11th international conference on Developments in eSystems engineering (DeSE)

作者： Dutt, Micheal Nunavath, Vimala Goodwin, Morten Univ Agder CAIR Grimstad Norway

ISBN: (纸本)9781538667125

Diabetes is one of the worlds major health problems according to the World Health Organization. Recent surveys indicate that there is an increase in the number of diabetic patients resulting in an increase in serious complications such as heart attacks and deaths. Early diagnosis of diabetes, particularly of type 2 diabetes, is critical since it is vital for patients to get insulin treatments. However, diagnoses could be difficult especially in areas with few medical doctors. It is, therefore, a need for practical methods for the public for early detection and prevention with minimal intervention from medical professionals. A promising method for automated diagnosis is the use of artificial intelligence and in particular artificial neural networks. this paper presents an application of Multi-Layer Feed Forward Neural Networks (MLFNN) in diagnosing diabetes on publicly available Pima Indian Diabetes (PID) data set. A series of experiments are conducted on this data set with variation in learning algorithms, activation units, techniques to handle missing data and their impact on diagnosis accuracy is discussed. Finally, the results are compared with other states of art methods reported in the literature review. the achieved accuracy is 82.5% best of all related studies.

关键词： Diabetes Training Neurons Testing Biological neural networks Insulin Machine learning algorithms

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