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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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Remote Monitoring System for intelligent Slaughter Production Line Based on Internet of things and Cloud Platform

Remote Monitoring System for Intelligent Slaughter Productio...

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

作者： Fang Wu Zhonghua Miao Chuangxin He School of Mechatronics Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9781728151816

ISBN: (纸本)9781728151823

At present, the slaughter production line has problems such as low information level and high cost of downtime. this paper studies the visual status monitoring of sheep slaughter production lines based on industrial Ethernet and Internet of things technology. the remote monitoring system of intelligent slaughter production line based on the Internet of things and cloud platform is proposed and implemented in this paper. PLC (Programmable Logic Controller) at the production site collects real-time status data of the production equipment. the real-time status data are encapsulated in the form of MQTT (Message Queuing Telemetry Transport) messages via the MQTT gateway and sends to the Alibaba Cloud IoT (Internet of things) platform. the Alibaba Cloud IoT platform forwards the status data to the data analysis server of the cloud server to complete the analysis and storage of the data. then realize remote status monitoring through the Web server. the test results show that all modules of the system can be consistent with the design requirements. the system runs stably and job data are monitored clearly which meets the need for industrial remote monitoring systems.

关键词： Production Cloud computing Logic gates Servers Remote monitoring Monitoring Internet of things

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11th international conference on Multimedia and Network Information Systems, MISSI 2018

11th International Conference on Multimedia and Network Info...

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11th international conference on Multimedia and Network Information Systems, MISSI 2018

ISBN: (纸本)9783319986777

the proceedings contain 58 papers. the special focus in this conference is on Multimedia and Network Information Systems. the topics include: Comparative analysis of accuracy of fuzzy clustering methods applied for image processing;an efficient method for sign language recognition from image using convolutional neural network;Audio/speech coding based on the perceptual sparse representation of the signal with DAE neural network quantizer and near-end listening enhancement;the features and functions of an effective multimedia information retrieval system (MMIR);youTube timed metadata enrichment using a collaborative approach;improving the responsiveness of geospatial web applications through client-side processing;isotone galois connections and generalized one-sided concept lattices;Transfer learning in GMDH-type neural networks;modelling of taxi dispatch problem using heuristic algorithms;modern approaches to multi-dimensional visual signals analysis;malware detection using black-box neural method;intrusion detection and risk evaluation in online transactions using partitioning methods;Prediction of autism severity level in Bangladesh using fuzzy logic: FIS and ANFIS;a framework for analyzing academic data;verifying usefulness of algorithms for WordNet based similarity sense disambiguation;comparison of paragram and glove results for similarity benchmarks;calculating optimal queries from the query relevance file;survey on neural machine translation into Polish;Multilingual and intercultural competence for ICT: Accessing and assessing electronic information in the global world;selection of most suitable secondary school alternative by multi-criteria fuzzy analytic hierarchy process;new applications of sound and vision engineering;comparison of the novel classification methods on the reuters-21578 corpus;sparse solution to inverse problem of nonlinear dimensionality reduction.

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13th EAI international conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TridentCom 2018

13th EAI International Conference on Testbeds and Research I...

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13th EAI international conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TridentCom 2018

ISBN: (纸本)9783030129705

the proceedings contain 11 papers. the special focus in this conference is on Testbeds and Research Infrastructures for the Development of Networks and Communities. the topics include: throughput Analytics of data Transfer Infrastructures;exploring intelligent Service Migration in Vehicular Networks;a Balanced Cloudlet Management Method for Wireless Metropolitan Area Networks;uncertainty Analysis of Rainfall Spatial Interpolation in Urban Small Area;automated and Optimized Formal Approach to Verify SDN Access-Control Misconfigurations;energy-Efficient Computation Offloading for Multimedia Workflows in Mobile Cloud Computing;a Secure Contained Testbed for Analyzing IoT Botnets;capturing Domain Knowledge through Extensible Components.

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A Smart Ambulatory Cognitive State Taxonomy System through EEG Signal Analysis

A Smart Ambulatory Cognitive State Taxonomy System Through E...

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international conference on Computing and Networking Technology (ICCNT)

作者： Sumanto Dutta Sumit Hazra Anup Nandy Computer Science & Engineering National Institute of Technology Rourkela India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Human mental state classification using Electroen-cephalogram(EEG) signal is one of the dynamic investigating areas in developing intelligent machine frameworks. In this paper, we propose an ambulatory cognitive state classification system using statistical feature analysis. the aim is to develop a realtime system which captures human cognitive states through computer-mediated help based on physiological signals. the walking cognitive state estimator is developed using a realtime Augmented Cognition (AugCog) framework. Six different normality testing methods, i.e. Kolmogorov-Smirnov, Anderson Darling, Cramer-Von Mises, Shapiro Wilk, Jarque-Bera, D'Ago-stino and Pearson's test are applied to achieve better accuracy. Box-Cox-transformation is also applied to ensure whether the data follows a normal distribution. We apply Fisher Discriminant Ratio (FDR) for selecting the most discriminative feature amongst all the statistical features (i.e. Mean, Median, Mode, Standard Deviation, Variance, Skewness and Kurtosis) obtained from the signal. Various machine learning methods are applied on the extracted features for training, testing and validation. Probabilistic neural network (P-NN) with Kurtosis feature is found to produces a maximum accuracy of 92.61 % as compared to other classifiers.

关键词： Electroencephalography Feature extraction Legged locomotion Headphones Support vector machines Testing Protocols

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Few-shot learning for Rolling Bearing Fault Diagnosis Via Siamese Two-dimensional Convolutional Neural Network

Few-shot Learning for Rolling Bearing Fault Diagnosis Via Si...

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

作者： Yizhuo Yang Huan Wang Zhiliang Liu Zeyu Yang Glasgow Colledge University of Electronic Science and Technology of China Chengdu PR China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu PR China

ISBN: (数字)9781728151816

ISBN: (纸本)9781728151823

data-driven approaches such as deep learning have made great achievements in the field of fault diagnosis. However, most deep learning models acquire a large number of annotated samples to support their training. And for bearing fault detection, it is often difficult or even impossible to collect enough signals for each fault type. In this paper, we demonstrate how a few-shot learning method can be applied in fault diagnosis to lower the amounts of data required to make meaningful predictions. Specifically, we first use a Siamese two-dimensional convolutional neural network to extract the feature vectors of the input fault signal pairs. Second, the absolute difference (L1 distance) between the feature vectors is computed and then input to a fully connected layer with a sigmoid activation function to assess whether the input signal pairs belong to the same class. the Case Western Reserve University bearing data set is used to test the performance of the proposed method. Experimental results show that the proposed few-shot learning approach can obtain good accuracy when training samples are limited.

关键词： Training Feature extraction Fault diagnosis data models Two dimensional displays Deep learning learning systems

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Mitigating data Integrity Attacks in Building Automation Systems using Denoising Autoencoders 11

Mitigating Data Integrity Attacks in Building Automation Sys...

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11th international conference on Ubiquitous and Future Networks (ICUFN)

作者： Calimbahin, Caezarina Marie Pancho-Festin, Susan Pedrasa, Jhoanna Rhodette Univ Philippines Dept Comp Sci Quezon City Philippines Univ Philippines Elect & Elect Engn Inst Quezon City Philippines

ISBN: (纸本)9781728113395

Building automation systems (BAS) are a class of cyber-physical systems that aim to improve the efficiency of buildings through intelligent control. Typically, sensor measurements are used to compute for the optimal control action for equipment that minimizes the energy consumption while maintaining the comfort of occupants. Given the heavy reliance on sensor data, ensuring their validity is an essential concern in BAS. Several work have explored sensor validation, mostly for the purpose of automatic fault detection and diagnostics (AFDD). However, recent studies show BAS vulnerable to adversarial attacks - an area that has lacked consideration in the early design of BAS. In this work, we propose a sensor correction model, based on deep learning framework, for mitigating against data integrity attacks in BAS. We test our approach on real-world data obtained from a retrofitted air conditioning (AC) control testbed, with injected data simulating different attacker types and number of attacked sensors. We show that the model is capable of mitigating attacks by comparing with a baseline where no model is employed.

关键词： sensors data integrity attack building automation system cyber-physical system

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IRES Program in Sensors and Machine learning for Energy Systems

IRES Program in Sensors and Machine Learning for Energy Syst...

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international conference on Information, Intelligence, Systems and Applications (IISA)

作者： K. Jaskie J. Martin S. Rao W. Barnard P. Spanias E. Kyriakides Y. Tofis L. Hadjidemetriou M. Michael T. theocharides S. Hadjistassou A. Spanias SenSIP Center School of ECEE Arizona State University KIOS Center University of Cyprus

ISBN: (数字)9781665422284

ISBN: (纸本)9781665422291

the international research experiences for students (IRES) program addresses multidisciplinary research at the overlap of sustainability, power systems, and signal processing with the aim of improving efficiency in PV power generation. the IRES program engages faculty at the ASU SenSIP Center and at the University of Cyprus' (UCy) KIOS Center to address fault detection and other research problems in solar energy arrays. IRES participants are tasked with studying algorithms and software to monitor and control solar arrays. Research involves using data from programmable sensors embedded in smart monitoring devices (SMDs) that are attached to solar panels. the SMDs have sensors, actuators and radios that enable researchers to work with a solar array where every panel provides data. IRES participants are trained to use machine learning to assess the solar array condition. the program also trains the students to perform research and present results in international settings. In the first year of the project, four students travelled to the University of Cyprus and worked with UCy faculty on fault detection. the program included weekly research presentations by the students at UCy, presentations at a local workshop and continued engagement after the summer experience at ASU. Two of the students were able to present and publish their work in international conferences.

关键词： Training Fault detection conferences Machine learning Monitoring intelligent sensors Sensor arrays

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Effects of activation functions and optimizers on stock price prediction using LSTM recurrent networks 3

Effects of activation functions and optimizers on stock pric...

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3rd international conference on Computer Science and Artificial Intelligence, CSAI 2019 and its Workshop - the 11th international conference on Information and Multimedia Technology, ICIMT 2019

作者： Rana, Masud Uddin, Md. Mohsin Hoque, Md. Mohaimnul Department of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (纸本)9781450376273

In stock exchange market, investors need to decide which shares to buy based on their future market value. Because of the variable market, it is obligatory to have a reliable prediction of the values of the stocks. Now-a-days the machine learning system can forecast well than the contemporary stock prediction methods. Machine learning system provides a scientific demonstration based on sample data to forecast. In this work, Linear Regression (LR), Support Vector Regression (SVR) and, Long Short-Term Memory (LSTM) algorithms are used to predict stock market prediction. Among the several features, the most important feature has been selected by using the feature selection algorithm, which is closing price. the effects of different activation functions and optimizers are experimented on stock price prediction using LSTM networks. © 2019 Association for Computing Machinery.

关键词： Long short-term memory

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Kinect-Based Badminton Motion Analysis Using intelligent Adaptive Range of Movement Index 11

Kinect-Based Badminton Motion Analysis Using Intelligent Ada...

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

作者： Ting, J.T.W. Ting, H.Y. Tan, D.W.Y. Yong, C.Y. School of Computing and Creative Media University College of Technology Sarawak 868 Persiaran Brooke Sarawak Sibu96000 Malaysia School of Engineering and Technology University College of Technology Sarawak 868 Persiaran Brooke Sarawak Sibu96000 Malaysia

Badminton is a type of racquet sport that is played using racquets to strike a shuttlecock within a netted rectangular court, and it is one of the most popular sport in Malaysia. In this paper, the team expanded their previous adaptive novel lossless compact view invariant compression technique, named adaptive range of movement index (RoMI) by embedding an intelligent mapping algorithm. Badminton players are broadly classified into two handedness groups, which are left-handed players and right-handed players. the adaptive RoMI technique in the previous module is capable of identifying the labels of normalized RoMI and perform adaptive mapping for badminton players of different handedness. However, the mechanism requires manual judgement from humans to select the type of handedness. this technique will increase the error rate and effort of importing data properties with incorrect manual handedness input or data stream from different handedness datasets of badminton players. thus, the proposed intelligent adaptive RoMI technique enables the analyzing system to outperform the previous module by detecting the labels of normalized RoMI automatically and by performing intelligent mapping. the motion analysis system will then become much more efficient, intelligent hence simplifying data collection and the handedness invariant benchmarking approach. © Published under licence by IOP Publishing Ltd.

关键词： Sports

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Transfer Reinforcement learning under Unobserved Contextual Information

Transfer Reinforcement Learning under Unobserved Contextual ...

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IEEE/ACM international conference on Cyber-Physical Systems (ICCPS)

作者： Yan Zhang Michael M. Zavlanos Department of Mechanical Engineering and Materials Science Duke University

ISBN: (数字)9781728155012

ISBN: (纸本)9781728155029

In this paper, we study a transfer reinforcement learning problem where the state transitions and rewards are affected by the environmental context. Specifically, we consider a demonstrator agent that has access to a context-aware policy and can generate transition and reward data based on that policy. these data constitute the experience of the demonstrator. then, the goal is to transfer this experience, excluding the underlying contextual information, to a learner agent that does not have access to the environmental context, so that they can learn a control policy using fewer samples. It is well known that, disregarding the causal effect of the contextual information, can introduce bias in the transition and reward models estimated by the learner, resulting in a learned suboptimal policy. To address this challenge, in this paper, we develop a method to obtain causal bounds on the transition and reward functions using the demonstrator's data, which we then use to obtain causal bounds on the value functions. Using these value function bounds, we propose new Q learning and UCB-Q learning algorithms that converge to the true value function without bias. We provide numerical experiments for robot motion planning problems that validate the proposed value function bounds and demonstrate that the proposed algorithms can effectively make use of the data from the demonstrator to accelerate the learning process of the learner.

关键词： Task analysis learning (artificial intelligence) Context modeling Computational modeling Markov processes Robot motion Planning

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