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automated, Nomenclature Based data Point Selection for Industrial Event Log Generation 19th

Automated, Nomenclature Based Data Point Selection for Indus...

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Koehler, Wolfgang Jing, Yanguo Coventry Univ Fac Engn Environm & Comp Sch Comp Elect & Math Priory St Coventry CV1 5FB W Midlands England

ISBN: (纸本)9783030034962;9783030034955

Within the automotive industry today, data collection, for legacy manufacturing equipment, largely relies on the data being pushed from the machine's PLCs to an upper system. Not only does this require programmers' efforts to collect and provide the data, but it is also prone to errors or even intentional manipulation. External monitoring, is available through Open Platform Communication (OPC), but it is time consuming to set up and requires expert knowledge of the system as well. A nomenclature based methodology has been devised for the external monitoring of unknown controls systems, adhering to a minimum set of rules regarding the naming and typing of the data points of interest, which can be deployed within minutes without human intervention. the validity of the concept will be demonstrated through implementation within an automotive body shop and the quality of the created log will be evaluated. the impact of such a fine grained monitoring effort on the communication infrastructure will also be measured within the manufacturing facility. It is concluded that, based on the methodology provided in this paper, it is possible to derive OPC groups and items from a PLC program without human intervention in order to obtain a detailed event log.

关键词： Terminology

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data Pre-processing to Apply Multiple Imputation Techniques: A Case Study on Real-World Census data 19th

Data Pre-processing to Apply Multiple Imputation Techniques:...

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Ruiz-Chavez, Zoila Salvador-Meneses, Jaime Garcia-Rodriguez, Jose Tallon-Ballesteros, Antonio J. Univ Cent Ecuador Ciudadela Univ Quito Ecuador Univ Alicante Ap 99 Alicante 03080 Spain Univ Seville Seville Spain

ISBN: (纸本)9783030034962;9783030034955

Improving accuracy or reducing computational cost are the main approaches of machine learning techniques, but it depends heavily on the test data used. Even more so when it comes to from real-world data such as censuses, surveys or tokens that contain a high level of missing values. the data absence or presence of outliers are problems that must be treated carefully prior to any process related to data analysis. the following work presents an overview of data pre-processing and aims at presenting the steps to follow prior to process large volumes of high-dimensionality data with categorical variables. As part of the dimensionality reduction process, when there is a high level of missing values present in one or more variables, we use the Pairwise and Listwise Deletion methods. thus, the generation of m-clusters using the Kohonen Self-Organizing Maps (SOM) algorithm with H2O over R is also considered as a division of data into similar groups, which are used as cluster to apply Multiple Imputation algorithms, creating different m-values to impute a missing value.

关键词： Machine learning data preparation data mining Imputation methods

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Robust plant segmentation from challenging background with a multiband acquisition and a supervised machine learning algorithm 5

Robust plant segmentation from challenging background with a...

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5th international conference on Bioimaging, BIOIMAGING 2018 - Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

作者： Jerbi, Taha Ramirez, Aaron Velez Straeten, Dominique Van Der Laboratory of Functional Plant Biology Dept. of Biology Ghent University K. L. Ledeganckstraat 35 Gent9000 Belgium

ISBN: (纸本)9789897582783

Remote sensing through imaging forms the basis for non-invasive plant phenotyping and has numerous applications in fundamental plant science as well as in agriculture. Plant segmentation is a challenging task especially when the image background reveals difficulties such as the presence of algae and moss or, more generally when the background contains a large colour variability. In this work, we present a method based on the use of multiband images to construct a machine learning model that separates between the plant and its background containing soil and algae/moss. Our experiment shows that we succeed to separate plant parts from the image background, as desired. the method presents improvements as compared to previous methods proposed in the literature especially with data containing a complex background. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Supervised learning

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intelligent Auto-grading System 5

Intelligent Auto-grading System

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5th IEEE international conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Wang, Zining Liu, Jianli Dong, Ruihai Beijing Univ Technol Beijing Dublin Int Coll Beijing Peoples R China Univ Coll Dublin Sch Comp Sci Insight Ctr Data Analyt Dublin Ireland

ISBN: (纸本)9781538660058

Teachers tend to set the free-text questions for testing the comprehensive ability of students. that leads to the increasing attention to the intelligent auto-grading system for easing the grading load on examiners. In this paper, we present a novel automatic essay scoring system based on Natural Language Processing and Deep learning technologies. In particular, the proposed system encodes an essay as sequential embeddings and harnesses a bi-directional LSTM to catch the semantic information. Meanwhile, the system constructs the attention for each essay so that the network can learn to focus on the valid information correctly in an article, which can also provide the reasonable evidence of the predictive result. the dataset for training and testing is the public essay set available in the automated Student Assessment Prize on Kaggle. the study shows that our system achieves state-of-the-art performance in grade prediction, and more importantly, our intelligent auto-grading system can focus on the critical words and sentences, analyze the logical semantic relationship of the context and predict the interpretable grades.

关键词： Automatic Grading System Natural Language Processing Deep learning Neural Network

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Reconstruction and Body Size Detection of 3D Sheep Body Model Based on Point Cloud data 11th

Reconstruction and Body Size Detection of 3D Sheep Body Mode...

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

作者： Zhou, Yanqing Xue, Heru Wang, Chunlan Jiang, Xinhua Gao, Xiaojing Bai, Jie Inner Mongolia Agr Univ Coll Comp & Informat Engn Hohhot Peoples R China

ISBN: (纸本)9783030061791;9783030061784

Aiming at the high workload, low precision, strong stress of the traditional manual measurement to obtain the sheep growth parameters, a novel measurement technology was proposed. the specimen of the Sunite sheep about 2-3 years old were chosen for study. By reverse engineering technology, point cloud data of sheep was captured by the 3D laser scanner. Because of noise point cloud data, the improved algorithm of k-nearest neighbor was used to process the data. To improve the subsequent processing time and efficiency, octree coding was employed to reduce data, which can get evenly distribution of point cloud data and retain sheep features. then, 3D surface model of sheep body was reconstructed using Delaunay triangulation. Some parameters were extracted, including sheep body length, body height, hip height, hip width and chest width. Compared actual parameters values with computing values of two ways, by Geomagic platform and the proposed algorithms on the Matlab, average relative errors of two ways were 1.23% and 1.01%, respectively. So results of the proposed algorithm were with small error range. Using the point clouds can reconstruct sheep surface for computing body size without stress.

关键词： Sheep body parameters Point clouds Octree coding three-dimensional reconstruction data pretreatment

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Design of Lifelong learning Content Using the Mobile E-Time Capsule System 11th

Design of Lifelong Learning Content Using the Mobile E-Time ...

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11th international conference on Interactive Mobile Communication Technologies and learning (IMCL)

作者： Takemata, Kazuya Minamide, Akiyuki Wakayama, Shintarou Nishiyama, Takumi Kanazawa Tech Coll Kanazawa Ishikawa Japan Kurashiki Printing Co Ltd Tokyo Japan

ISBN: (纸本)9783319751757;9783319751740

In this study, we develop a system called an "e-time capsule," where a "time capsule" event for students to put their mementos into a container, bury it in the school yard, and dig it out in a certain time later and open it is achieved on the cloud. the students' image data of the daily scenes in their classrooms and their products in classes, which are collected using a mobile device, are stored in a server located in the classrooms. the schedule of opening the time capsule is determined based on an agreement by all students of the class at the time when the capsule is buried. In Japan, a time capsule is often opened at the time of coming-of-age ceremonies when the participants attend a coming-of-age ceremony at the age of 20, also as a way of a reunion. In the system which is developed through this study, the opening event is achieved by distributing the data to the smart phones of the students who come to the reunion venue. this paper addresses the data collection and storage using mobile devices of the students.

关键词： Time capsule Portfolio learning activity

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Merging ELMs with Satellite data and Clear-Sky Models for Effective Solar Radiation Estimation 19th

Merging ELMs with Satellite Data and Clear-Sky Models for Ef...

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Cornejo-Bueno, L. Casanova-Mateo, C. Sanz-Justo, J. Salcedo-Sanz, S. Univ Alcala Dept Signal Proc & Commun Madrid Spain Univ Politecn Madrid Dept Civil Engn Construct Infrastruct & Transport Madrid Spain Univ Valladolid LATUV Lab Teledetecc Valladolid Spain

ISBN: (纸本)9783030034962;9783030034955

this paper proposes a new approach to estimate Global Solar Radiation based on the use of the Extreme learning Machine (ELM) technique combined with satellite data and a clear-sky model. Our study area is the radiometric station of Toledo, Spain. In order to train the Neural Network proposed, one complete year of hourly global solar radiation data (from the 1st of May 2013 to the 30th of April 2014) is used as the target of the experiments, and different input variables are considered: a cloud index, a clear-sky solar radiation model and several reflectivity values from Meteosat visible images. To assess the results obtained by the ELM we have selected as a reference a physical-based method which considers the relation between a clear-sky index and a cloud cover index. then a measure of the Root Mean Square Error (RMSE) and the Pearson's Correlation Coefficient (r(2)) is obtained to evaluate the performance of the suggested methodology against the reference model. We show the improvement of the results obtained by the ELM with respect to those obtained by the physical-based method considered.

关键词： Solar radiation estimation Extreme learning machines Meteosat data

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Machine learning for Drugs Prescription 19th

Machine Learning for Drugs Prescription

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Silva, P. Rivolli, A. Rocha, P. Correia, F. Soares, C. Univ Porto Sch Engn Porto Portugal Fed Univ Technol Apucarana Parana Brazil Glintt Healthcare Solut Sa Porto Portugal

ISBN: (纸本)9783030034931;9783030034924

In a medical appointment, patient information, including past exams, is analyzed in order to define a diagnosis. this process is prone to errors, since there may be many possible diagnoses. this analysis is very dependent on the experience of the doctor. Even with the correct diagnosis, prescribing medicines can be a problem, because there are multiple drugs for each disease and some may not be used due to allergies or high cost. therefore, it would be helpful, if the doctors were able to use a system that, for each diagnosis, provided a list of the most suitable medicines. Our approach is to support the physician in this process. Rather than trying to predict the medicine, we aim to, given the available information, predict the set of the most likely drugs. the prescription problem may be solved as a Multi-Label classification problem since, for each diagnosis, multiple drugs may be prescribed at the same time. Due to its complexity, some simplifications were performed for the problem to be treatable. So, multiple approaches were done with different assumptions. the data supplied was also complex, with important problems in its quality, that led to a strong investment in data preparation, in particular, feature engineering. Overall, the results in each scenario are good with performances almost twice the baseline, especially using Binary Relevance as transformation approach.

关键词： Machine learning Classification problems Multi-label classification Health systems Medicines prescriptions

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General Structure Preserving Network Embedding 1

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Zhu, Sinan Jia, Caiyan Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

Network embedding has attracted increasing attention in recent years since it represents large scale networks in low-dimensional space and provides an easier way to analysis networks. Existing embedding methods either focus on preserving the microscopic topology structure, or incorporate the mesoscopic community structure of a network. However, in the real world, a network may not only contain community structure, but have bipartite-structure, star-structure or other general structures, where nodes in each cluster have similar patterns of connections to other nodes. Empirically, general structure is important for describing the features of networks. In this paper, based on nonnegative matrix factorization framework, we propose GS-NMF which is capable of integrating topology structure and general structure into embedding process. the experimental results show that GS-NMF overcomes the limitation of previous methods and achieves obvious improvement on node clustering, node classification, and visualization.

关键词： Network embedding General structure Nonnegative matrix factorization

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Performance Evaluation of Autoencoder for Coding and Modulation in Wireless Communications

Performance Evaluation of Autoencoder for Coding and Modulat...

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international conference on Wireless Communications and Signal Processing (WCSP)

作者： Jialong Xu Wei Chen Bo Ai Ruisi He Yujian Li Junhong Wang Tutun Juhana Adit Kurniawan School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Electrical Engineering and Informatics Institute of Technology Bandung Bandung Indonesia

the end-to-end autoencoder is a novel and attracting concept to innovate communication system architecture. In its training stage, the end-to-end autoencoder needs differentiable channel models to execute back-propagation algorithm. this shortage impedes the further development of the end-to-end autoencoder. Estimating the channel model by reinforcement learning is a good way to alleviate this problem. However, these training methods increase the difficulty in the training stage. In this paper, we set up a general channel model and employ the measured channel data as input data to alleviate this problem. We compare the performance of the end-to-end autoencoder and Hamming code modulated by Binary Phase Shift Keying under additive white Gaussian noise channel and Rayleigh channel and explore the performance of the end-to-end encoder under the real scenarios. Furthermore, we investigate the performance of the end-to-end autoencoder under mismatched channels. the results demonstrate that the performance of the end-to-end autoencoder is similar with Hamming code and has a significant performance improvement under mismatched channels.

关键词： Encoding Training Receivers Wireless communication Decoding Channel models AWGN channels

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