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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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An Effective Personnel Detection System Based on Radio Frequency Identification 11

An Effective Personnel Detection System Based on Radio Frequ...

引用

11th IEEE international conference on Advanced Infocomm Technology, ICAIT 2019

作者： Wang, Zhijun Zhang, Qian Jia, Hao Duan, Xueming Huo, Xinyu Zhang, Zhaoxin Lan, Peng Sun, Fenggang Shandong Agricultural University College of Information Science and Engineering Tai'an China

ISBN: (纸本)9781728147789

In privacy scenarios, the camera based personnel detection is usually not feasible. To this end, we present a radio frequency identification (RFID) based personnel detection approach in this paper, which can not only avoid the privacy violation problem, but also have the advantage of low cost, strong practicability and easy implementation. Specifically, the passive RFID tags are placed on the walls with a fixed distance, which can return the received signal strength indication (RSSI). Signals are received through the RFID antenna, and data are processed preliminarily by the RFID reader. With the collected data, a machine learning model is trained, and then used to detect the existence of people in a stable and reliable approach. Our experiments show that the detection accuracy of the proposed approach is approximately 99%. © 2019 IEEE.

关键词： Antennas

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Modeling a Knowledge-Based System for Cyber-physical Systems: Applications in the Context of learning Analytics 11th

Modeling a Knowledge-Based System for Cyber-physical Systems...

引用

11th international conference on Computational Collective Intelligence (ICCCI)

作者： Gueye, Mamadou Lamine Univ Pau & Pays Adour LIUPPA Pau France

ISBN: (纸本)9783030283742;9783030283735

Knowledge-based systems are major concerns in the field of artificial intelligence for the development of cyber-physical systems capable of self-management and adaptation to their context. the representation and knowledge management of these cyber-physical systems integrating heterogeneous actors must ensure the empowerment and optimization of these systems, as well as their ability to adapt to dynamic and unpredictable changes in their environment. In this document we show how a knowledge-based system based on semantic web technologies and IBM's reference model of Autonomic Computing (AC) can offer intelligent collaboration and coordination between people, data, services, robots and connected objects in the implementation of self-management processes in cyber-physical systems. Our solution consists to design a knowledge base in the field of learning Analytics (LAs) involving a complex range of knowledge and heterogeneous components. this ontological knowledge base is guided by a functional decomposition approach based on the operating principle of the MAPE-K (Monitor-Analyze-Plan-Execute and Knowledge) autonomous control loop to provide the system with self-management capabilities.

关键词： Knowledge base Ontology Autonomic computing learning Analytics

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A Novel Bearing Fault Diagnosis Method Based on Multi-scale Transfer Sample Entropy

A Novel Bearing Fault Diagnosis Method Based on Multi-scale ...

引用

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

作者： Yu Ren Yongbo Li Xianzhi Wang Shun Wang Shubin Si School of Mechanical Engineering Northwestern Polytechnical University Xi’an China School of Aeronautics Northwestern Polytechnical University Xi’an China

ISBN: (数字)9781728151816

ISBN: (纸本)9781728151823

To solve the defects that the generalization ability of the traditional data-driven fault diagnosis model reduces or even fails in mechanical system diagnosis, a multi-scale transfer sample entropy is proposed based on the idea of transfer learning. First, the multi-scale transfer sample entropy method is proposed to extract fault features based on multi-scale sample entropy and feature transfer learning. Second, the parameters of the multiscale transfer sample entropy method are optimized to further improve the fault identification accuracy. Finally, the experiment results of rolling bearing show that the proposed multi-scale transfer sample entropy can effectively improve the generalization ability of the data-driven model and realize the accurate identification of different fault locations of the rolling bearing under a small number of samples.

关键词： Entropy Fault diagnosis Feature extraction Time series analysis Support vector machines Classification algorithms Vibrations

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A learning Environment for Geography and History Using Mixed Reality, Tangible Interfaces and Educational Robotics 21st

A Learning Environment for Geography and History Using Mixed...

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21st international conference on Interactive Collaborative learning (ICL) / 47th IGIP international conference on engineering Pedagogy - Teaching and learning in a Digital World

作者： Xefteris, Stefanos Palaigeorgiou, George Tsorbari, Areti Univ Western Macedonia Florina Greece

ISBN: (纸本)9783030119355;9783030119348

Integrating ICT technologies in history and geography teaching may promote critical thinking and bridge the gap between unconstructive information accumulation and an explorative and critical learning approach. the aim of this study was to design, deploy and evaluate a low cost and easy-to-use mixed reality learning environment for interdisciplinary and embodied learning of geography, history and computational thinking the proposed learning environment is comprised of an augmented 3D-tangible model of southern Europe where students interacted using their fingers, and a second treasure hunt augmented interactive floor depicting historical sites, where students performed tasks with Mindstorms EV3 robots. Students swapped between finger-based and robot-based journeys in Europe in two pairs until the end of the game. In order to evaluate our proposal, six groups of four undergraduate participants played with the environment in 6 sessions and for approximately 45 min. data were collected with a pre and post knowledge test, an attitudes questionnaire and semi-formal group interviews. Students scored significantly higher in the posttests and their answers in the questionnaires revealed that the multimodal environment enhanced their engagement and motivation, helped them orient themselves better around Europe's geophysical features, while the robotics treasure hunt consolidated their computational thinking skills, inducing a highly entertaining dimension. this approach better conforms to students' interactive experiences and expectations, gamifies learning and exploits embodied learning opportunities. Students engaged in the two augmented spaces in a perceptually immersive experience which became a more authentic and meaningful educational space.

关键词： Mixed reality Tangible interfaces Educational robotics Geography learning History learning

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Approach to testing many state machine models in education 11

Approach to testing many state machine models in education

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11th international conference on Computer Supported Education, CSEDU 2019

作者： Ogata, Shinpei Kayama, Mizue Okano, Kozo Faculty of Engineering Shinshu University Wakasato 4-17-1 Nagano-shi Nagano380-8553 Japan

ISBN: (纸本)9789897583674

In state machine modeling education, the effort required by instructors to test a large number of learner-created models should be reduced to concentrate on the following feedback activity. Although there are several methods for validating and verifying a state machine model, a considerable problem for instructors is the lack of tools to test multiple models at once. this study proposes a preliminary approach and a support tool to efficiently and promptly test multiple state machine models. A basic approach to solving this problem is creating test cases and then testing multiple state machine models simultaneously using these test cases. To reduce the instructors’ testing effort, the proposed approach includes three new concepts: (1) a logger extension to capture simulated data generated by an existing state machine simulation tool called SMart-learning;(2) a method for creating test cases based on these logs;and (3) a feature to test many models using these test cases. As a result of a preliminary evaluation, it was confirmed that the proposed approach could be useful to test many answer models efficiently. © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Unified Modeling Language

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S-Parameters Based Channel Characterization Using Ultra-Wideband Pulse Signal

S-Parameters Based Channel Characterization Using Ultra-Wide...

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international conference on Electrical and Computer engineering (ICECE)

作者： Md. Kamal Hossain Mohammad R. Haider School of Engineering University of Alabama at Birmingham Birmingham AL USA

ISBN: (数字)9781665422543

ISBN: (纸本)9781665446617

Ultra-Wideband has recently become a powerful technology that involves wireless communication, digital signal processing, machine learning, and even hardware design for ubiquitous sensing applications. this paper presents a straight forward signal processing method to characterize the wireless communication channel using the UWB pulse signal. this proposed method uses a narrow width based modified Hermitian pulse (MHP) for calculating the output signal at the receiver end from measured network parameters. Magnitude attenuation, wave shape distortion, and phase shift of the output signal indicate the impact of the communication channel on the traveling wave. A neural network-based deep learning algorithm uses to classify the obstacle types from the measured output signal using the MATLAB tool. Measurement results demonstrate the real scenario of channel effects on the high-frequency UWB signal and select a suitable modulation scheme for data encoding. It also helps design the receiver compensation, reconstruct the distorted signal, and channel access to use the MHP pulse signal for high-density data communication. this method can also be used other sensing applications such as damage localization, detection, displacement measurement, material identification, etc.

关键词： Wireless communication Pulse measurements Displacement measurement Sensors Scattering parameters Distortion measurement Ultra wideband technology

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

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11th international conference on Computer and Automation engineering, ICCAE 2019

ISBN: (纸本)9781450362870

the proceedings contain 29 papers. the topics discussed include: using classification technique for customer relationship management based on thai social media data;the effect of learning analytics system towards learner’s computational thinking capabilities;recognition algorithm for sleep postures using a smart fabric pad with multiple pressure sensors;comparing local invariant algorithms for dorsal hand vein recognition system;real-time iris recognition system for non-ideal iris images;perception of difficulty using functional magnetic resonance imaging data;automatic detection on appearance changes using local moment invariants;electrocardiogram monitoring using low powered mixed signal analog-to-digital with lossless decompression algorithm;an intrusion detection system based on convolutional neural network;and research and analysis of the front-end frameworks and libraries in e-business development.

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Detection of Myocardial Infarction from ECG Signal through Combining CNN and Bi-LSTM

Detection of Myocardial Infarction from ECG Signal Through C...

引用

international conference on Electrical and Computer engineering (ICECE)

作者： Nuzaer Omar Monisha Dey Muhammad Ahsan Ullah Chittagong University of Engineering & Technology Chattogram Bangladesh

ISBN: (数字)9781665422543

ISBN: (纸本)9781665446617

Myocardia infarction (MI) otherwise known as heart attack, is one of the prime causes of death of individuals worldwide. the electrocardiogram (ECG) signal is clinically used by cardiologists as a screening tool to precisely detect MI. this paper presents a detection approach for MI using 21 time-domain features that are extracted from 12-lead ECG signals. the 12-lead ECG signal facilitates the diversification of features and supports the integrity of machine learning. the features are inserted into a deep learning model based on the combination of a one-dimensional convolutional neural network (1D-CNN) and bidirectional long short-term memory (Bi-LSTM). the 1D-CNN is used for extracting the spatial information of each lead and the Bi-LSTM is used to process the information sequentially in the subsequent leads along with classifying into two distinct classes: healthy and MI. In this paper, the Physikalisch Technische Bundesanstalt (PTB) database is used for evaluation in which 549 patient records are available. the CNN combination with the Bi-LSTM model shows a training accuracy of 99.05% and testing accuracy of 98.50% with 20% data being validated.

关键词： Training Electrocardiography Myocardium Tools Feature extraction Time-domain analysis Testing

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intelligent Cardiovascular Disease Risk Estimation Prediction System 6

Intelligent Cardiovascular Disease Risk Estimation Predictio...

引用

6th IEEE international conference on Advances in Computing, Communication and Control, ICAC3 2019

作者： Mendonca, Froyston Manihar, Riyazuddin Pal, Ashishkumar Prabhu, Sapna U. Fr. Conceicao Rodrigues College of Engineering. University of Mumbai Department of Electronics Mumbai India

ISBN: (数字)9781728123868

ISBN: (纸本)9781728123868

Cardiovascular diseases have been on the rise from the past few decades. In developing countries, cardiovascular diseases (CVDs) have become one of the most causes of death around the world. thus, there is a need to predict CVDs at the earlier stages to minimize treatment costs and avoid critical cases. thus, this work provides a novel method to predict cardiovascular diseases using machine learning. A comparison between various machine learning algorithms is made to analyse the performance on the dataset and the proposed method uses s K Nearest Neighbour which has an accuracy of 92.30%. Additionally, data reduction techniques have been utilised to reduce the number of attributes to 11 which significantly outperforms the related work. © 2019 IEEE.

关键词： Forecasting

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Case Study of an On-premise data Warehouse Configuration

Case Study of an On-premise Data Warehouse Configuration

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international conference on Cognitive Infocommunications (CogInfoCom)

作者： Bence Bogdándy Ádám Kovács Zsolt Tóth Institute of Computational Science Eszterházy Károly University Eszterházy Károly University

ISBN: (数字)9781728182131

ISBN: (纸本)9781728182148

the development of machine learning over the years has facilitated the joint upsurge of complex cognitive infocommunication systems. Machine learning methods are vital elements of modern cognitive infocommunications systems because they can be used in various ways such as behavior modeling or sentiment analysis. Machine learning algorithms requires a reliable infrastructure and vast amount of data. therefore building data warehouse systems is one of the essential steps of of building reliable cognitive infocommunication systems. Finding and preprocessing data streams of different origins are the first steps during the creation of a data warehouse. Unfortunately, online data streams are most often formatted uniquely. therefore, the obtained data sets must be transformed into a unified data model. the modelling and conversion of data sources serves as a key step during the unification of heterogeneous data. Storage should be persistent, and optimized for the analytical processing of data. these requirements raise technological challenges that are not common during the design of data sources. this paper gives an overview of current data warehouse technologies and suggests an infrastructure implementation. Hive is used for accessing, modifying, and running complex analytics on the stored data sets. Economical data can often be unique to the product, or the industry it covers. Different data sources used unique data formats which were tailored for their application area or needs. Moreover, some of these data sources may change their format in time. therefore, a flexible data transformation step is required which can be configured easily. the ETL processes of the data sources are implemented in Python, and Hive. the data is loaded in a Hive data warehouse which stores data in the distributed Hadoop File System.

关键词： Buildings Distributed databases Machine learning Documentation data warehouses data engineering data models

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