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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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Battery Panel Defect Detection Method Based on Deep Convolutional Neural Network

Battery Panel Defect Detection Method Based on Deep Convolut...

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international conference on Wireless Communications and Signal Processing (WCSP)

作者： Shibao Jiang Taotao Wang Shengli Zhang Wei Wang Hui Wang College of Electronical and Information Engineering Shenzhen University Shenzhen China Shenzhen Toplab Artificial Intelligence Technology Co. Ltd China Kashi University Kashgar Xinjiang China

Defect detection of product surface can be made manually or automatically utilizing pattern recognition. Traditional methods usually suffer from low efficiency, high cost, sensitivity to environmental changes, and low detection accuracy. this paper proposes a deep convolutional neural network (DCNN) approach for battery panel defect detection. the training data can be collected by taking images for battery panels from an actual production line. Usually, this training data suffers from many problems such as poor image quality, underrepresentation of defective samples, and different types of defects. We use data enhancement techniques to preprocess the acquired images to obtain as many different samples as possible. Furthermore, we utilized the improved Focal Loss function to deal with prominent problems such as small samples and non-uniform sample data sets. the method achieved good results in the experiment.

关键词： Feature extraction Convolutional neural networks Machine learning Training Classification algorithms data models Entropy

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Cooperative Multi-agent on Head Yaw of Humanoid Robot Using Consensus

Cooperative Multi-agent on Head Yaw of Humanoid Robot Using ...

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Joint international conference on Computer engineering, Network and intelligent Multimedia (CENIM) / 11th AUN/SEED-Net Regional conference on Computer and Information engineering (RCCIE)

作者： Nugroho, Arif Yuniarno, Eko M. Purnomo, Mauridhi H. Inst Teknol Sepuluh Nopember Dept Elect Engn Surabaya Indonesia Inst Teknol Sepuluh Nopember Dept Elect Engn Dept Comp Engn Surabaya Indonesia

ISBN: (纸本)9781538675090

this paper presents the comparison between 2-proposed graph topologies. In this concern, each proposed graph topology consists of 3-nodes that represent a number of humanoid robots and 3-edges representing information exchange among robots. Two proposed graph topologies have been implemented on head yaw of humanoid robots. From the experimental results, two proposed graph topologies have been successfully applied to reach consensus. But, each proposed graph topology has different characteristic. the cyclic graph topology is similar to unsupervised learning characteristic because this topology cannot ensure where the consensus value will be achieved. Meanwhile, non-cyclic graph topology has the same characteristic as supervised learning where consensus value can be reached based on the predefined target value.

关键词： Consensus cyclic graph topology head yaw non-cyclic graph topology supervised learning unsupervised learning

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A Two-Tiers Framework for Cloud data Storage (CDS) Security Based on Agent 11th

A Two-Tiers Framework for Cloud Data Storage (CDS) Security ...

引用

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

作者： Arki, Oussama Zitouni, Abdelhafid Abdelhamid Mehri Constantine 2 Univ LIRE Labs Constantine 25000 Algeria

ISBN: (纸本)9783319751757;9783319751740

Cloud Computing is a new model for delivering resources such as computing and storage on demand through a network. It has many advantages like reducing the cost, however, it remains the problem of security, which is the major brake for the migration towards Cloud Computing. Cloud data Storage (CDS) is an important service of Cloud, it provides the way to store and manage data remotely in the Cloud, but data security remains the problem, which makes users worried about the confidentiality and the integrity of their data. In this paper, we propose a framework of security to ensure the CDS, which is based on agents. It contains two layers: Cloud Provider layer and the User layer.

关键词： Cloud Computing Cloud data Storage Multi-Agent System Integrity Confidentiality

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Analysis of Driver's Steering Behavior for Lane Change Prediction

Analysis of Driver's Steering Behavior for Lane Change Predi...

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international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Lixing Li Penghui Li Intelligent Vehicle Test & Evaluation Center China Automotive Engineering Research Institute Co. Ltd. Beijing China Tsinghua University High School International Beijing China Intelligent Vehicle Test and Evaluation Center China Automotive Engineering Research Institute Co. Ltd. Beijing China State Key Laboratory of Automotive Safety & Energy School of Vehicle and Mobility Tsinghua University Beijing China

With the development of intelligent transportation systems and advanced driver assistance systems (ADAS), early prediction of driver's intentions is required. Among that, lane change is a complex driver conducted maneuver, thus, improper execution could lead to severe traffic accidents. therefore, it is urgent to develop lane change prediction systems, especially which could work in the initial phase of lane change. Many previous studies have developed lane change detection algorithms, however, they use different criterions as onset of lane change: (1) the onset of lane change maneuver, (2) the time that vehicle crosses the lane line. this makes it hard to compare the earliest time where these systems could detect lane change reliably. therefore, a systematically comparison of the predicting time in these algorithms is required, as well, parameters which could be used for early lane change detection need to be highlighted. In the present study, a driving simulator study of lane change was conducted, with 18 drivers involved. data of vehicle's lateral position, steering wheel angle and velocity during both lane change and lane keep phase were collected. the results showed that, the time gap between the two criterions is approximately 3 s, thus, the earliest predicting time of these algorithms ranged from the onset of lane change maneuver to the first peak of steering wheel angle. In addition, a further analysis of drivers' steering behavior of lane changing showed that, means and variances of steering wheel velocity, in the phase up to 0.55 s before the onset of lane change maneuver, were significantly different from that in lane keep phase. this suggests steering wheel velocity a potential parameter for early lane change detection.

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HEALthINF 2018 - 11th international conference on Health Informatics, Proceedings;Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

HEALTHINF 2018 - 11th International Conference on Health Inf...

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11th international conference on Health Informatics, HEALthINF 2018 - Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

ISBN: (纸本)9789897582813

the proceedings contain 88 papers. the topics discussed include: temporal conformance analysis and explanation on comorbid patients;the privacy management platform - an enabler for device interoperability and information security in mHealth applications;stair climb power measurements via inertial measurement units - towards an unsupervised assessment of strength in domestic environments;interoperability in pervasive health: is it tackled as a priority?;cross-cohort evaluation of machine learning approaches to fall detection from accelerometer data;a remote home monitoring system to support informal caregivers of people with dementia;predicting future antibiotic susceptibility using regression-based methods on longitudinal Massachusetts antibiogram data;introduction of a Bayesian network builder algorithm - personalized infectious disease risk prediction;a methodology to perform semi-automatic distributed EHR database queries;a rule-based method applied to the imbalanced classification of radiation toxicity;early prediction of MRSA infections using electronic health records;mobile-based risk assessment of diabetic retinopathy using a smartphone and adapted ophtalmoscope;one size does not fit all: an ensemble approach towards information extraction from adverse drug event narratives;and identifying characteristic physiological patterns of Parkinson's disease sufferers using sample entropy of pulse waves.

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Inferring Temporal Structure from Predictability in Bumblebee learning Flight 19th

Inferring Temporal Structure from Predictability in Bumblebe...

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

作者： Meyer, Stefan Bertrand, Olivier J. N. Egelhaaf, Martin Hammer, Barbara Univ Bielefeld Dept Neurobiol Univ Str 25 D-33615 Bielefeld Germany Univ Bielefeld Dept Machine Learning Inspirat 1 D-33619 Bielefeld Germany

ISBN: (纸本)9783030034931;9783030034924

Insects are succeeding in remarkable navigational tasks. Bumblebees, for example, are capable of learning their nest location with sophisticated flight manoeuvres, forming a so-called learning flight. the learning flights - thought to be partially pre-programmed - enable the bumblebee to memorise spatial relations between its inconspicuous nest entrance and environmental cues. To date, environmental features (e.g. object positions on the eyes) and learning experience of the insect were used to describe the flights, but its structure, thought to facilitated learning, has not been investigated systematically. In this work, we present a novel approach, to examine whether and in which time span flight behaviour is predictable based on intrinsic properties only rather than external sensory information. We study the temporal composition of learning flights by estimating the smoothness of the underlying process. We then use echo state networks (ESN) and linear models (ARIMA) to predict the bumblebee trajectory from its past motion and identify different time-scales in learning flights using their prediction-power. We found that direct visual information is not necessary within a 200ms time-window to explain the bumblebee behaviour during its learning flight.

关键词： Time series prediction Modelling Echo state network learning flight Bumblebee

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A Game-Based learning Platform for Vocational Education and Training 11th

A Game-Based Learning Platform for Vocational Education and ...

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

作者： Kotsifakos, Dimitrios Zinoviou, Xenofon Monachos, Stefanos Douligeris, Christos Univ Piraeus Dept Informat Piraeus Greece

ISBN: (纸本)9783319751757;9783319751740

this article records our contribution to the international debate on the use of modern technologies in the learning process. Our theme is the development of online games that support the learning processes of Vocational Education and Training. the medium we used for this implementation concerns an online platform that includes polymorphic games. the learning content of the platform refers to topics related to web-based algorithms, techniques and data flow methods, such as the Bellman-Ford, Dijkstra, Floyd, and Johnson algorithms. Each digital scenario has a specific goal. the completion of each scenario will familiarize the student/ user with the process of the particular algorithm and how each algorithm is implemented in a networking environment. Our main goal of developing the platform is to use all the available modern technologies to create a strong overall learning experience through gaming that will also broaden the students' knowledge of network algorithms.

关键词： Game-based-learning Vocational education training Network flow algorithms Education Web technologies

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Incremental adaptive semi-supervised fuzzy clustering for data stream classification 11

Incremental adaptive semi-supervised fuzzy clustering for da...

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11th IEEE international conference on Evolving and Adaptive intelligent Systems (EAIS)

作者： Casalino, Gabriella Castellano, Giovanna Mencar, Corrado Univ Bari Aldo Moro Dept Comp Sci INdAM Res Grp GNCS Bari Italy

ISBN: (纸本)9781538613764

data stream mining refers to methods able to mine continuously arriving and evolving data sequences or even large scale static databases. Most of data stream classification methods are supervised, hence they require labeled samples that are more difficult and expensive to obtain than unlabeled ones. Semi-supervised learning algorithms can solve this problem by using unlabeled samples together with a few labeled ones to build classification models. Recently we introduced a method for data stream classification based on an incremental semi-supervised fuzzy clustering algorithm. this method processes data belonging to different classes assuming that they are available during time as chunks. It creates a fixed number of clusters that is set equal to the number of classes. In real-world contexts a fixed number of clusters may not capture adequately the evolving structure of streaming data. To overcome this limitation in this work we extend our method by introducing a dynamic component that is able to adapt dynamically the number of clusters. Preliminary experimental results on a real-world benchmark dataset show the effectiveness of the dynamic mechanism introduced in the method.

关键词： Prototypes Clustering algorithms data mining data models Linear programming Supervised learning Benchmark testing

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An Analysis on Use of Deep learning and Lexical-Semantic Based Sentiment Analysis Method on Twitter data to Understand the Demographic Trend of Telemedicine

An Analysis on Use of Deep Learning and Lexical-Semantic Bas...

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international conference on Knowledge and Systems engineering

作者： Harshvadan Talpada Malka N. Halgamuge Nguyen Tran Quoc Vinh School of Computing and Mathematics Charles Sturt University Melbourne Victoria Australia Dep. of Electrical and Electronic Engineering The University of Melbourne Victoria Australia Faculty of Information Technology The University of Da Nang - University of Science and Education Vietnam

Technology has turned into a fundamental piece of everybody's life. Social media technology is already used widely by the public to speak out once mind openly. this data can be leveraged to have a better understanding of the current state of decision making. However, Twitter data is highly unstructured. Sentiment analysis can be applied to such health-related data to extract useful information regarding public opinion. the aim of the research is to understand (i) the correlation between Deep learning versus lexical and semantic-based sentiment prediction methods, (ii) the sentiment prediction accuracy of these methods on manually annotated sentiment dataset (iii) domain-specific knowledge on accuracy of the sentiment prediction methods, and (iv) to utilize Twitter-based sentiment to understand the influence of telemedicine in regards to heart attack and epilepsy. Four sentiment prediction methods are utilized for the research;Lexical and Semantic-based (Valence Aware Dictionary and Sentiment Reasoner (VADER) and TextBlob) and Deep learning based (Long Short Term Memory (LSTM) and sentiment model from Stanford CoreNLP). the dataset that we retrieved consists of 1.84 million old health-related tweets. Our finding suggests that lexical and semantic-based methods for sentiment prediction offer better accuracy than Deep learning methods;when a large enough and evenly distributed training dataset is not available. We observed that domain-specific knowledge affects the prediction accuracy of sentiment, mainly when the target text contains more domain-specific words. Sentiment prediction on Twitter data can be utilized to understand the demographic distribution of sentiment. In our case, we observed that telemedicine has a high number of positive sentiment. It is still in its infancy and has not spread to a broader demographic.

关键词： Training Machine learning Telemedicine Twitter Sentiment analysis Prediction methods

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Automatic Detection of ECG Abnormalities by Using an Ensemble of Deep Residual Networks with Attention 1st

Automatic Detection of ECG Abnormalities by Using an Ensembl...

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1st international Workshop on Machine learning and Medical engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th international Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Liu, Yang He, Runnan Wang, Kuanquan Li, Qince Sun, Qiang Zhao, Na Zhang, Henggui Harbin150001 China School of Physics and Astronomy The University of Manchester ManchesterM13 9PL United Kingdom SPACEnter Space Science and Technology Institute Shenzhen518117 China International Laboratory for Smart Systems and Key Laboratory of Intelligent of Computing in Medical Image Ministry of Education Northeastern University Shenyang110004 China The Department of Pharmacology Beijing Electric Power Hospital Beijing100073 China

ISBN: (纸本)9783030333263

Heart disease is one of the most common diseases causing morbidity and mortality. Electrocardiogram (ECG) has been widely used for diagnosing heart diseases for its simplicity and non-invasive property. Automatic ECG analyzing technologies are expected to reduce human working load and increase diagnostic efficacy. However, there are still some challenges to be addressed for achieving this goal. In this study, we develop an algorithm to identify multiple abnormalities from 12-lead ECG recordings. In the algorithm pipeline, several preprocessing methods are firstly applied on the ECG data for denoising, augmentation and balancing recording numbers of variant classes. In consideration of efficiency and consistency of data length, the recordings are padded or truncated into a medium length, where the padding/truncating time windows are selected randomly to suppress overfitting. then, the ECGs are used to train deep neural network (DNN) models with a novel structure that combines a deep residual network with an attention mechanism. Finally, an ensemble model is built based on these trained models to make predictions on the test data set. Our method is evaluated based on the test set of the First China ECG intelligent Competition dataset by using the F1 metric that is regarded as the harmonic mean between the precision and recall. the resultant overall F1 score of the algorithm is 0.875, showing a promising performance and potential for practical use. © 2019, Springer Nature Switzerland AG.

关键词： Electrocardiograms

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