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检索条件"任意字段=7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018"

共 475 条记录，以下是451-460 订阅

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Usage of Fully Convolutional Network with Clustering for Traffic Light Detection 7

Usage of Fully Convolutional Network with Clustering for Tra...

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7th Mediterranean conference on Embedded Computing (MECO)

作者： Yudin, Dmitry Slavioglo, Dmitry Belgorod State Technol Univ Dept Tech Cybernet Belgorod Russia

ISBN: (纸本)9781538656839

In this paper we consider a traffic light detector constructed on the basis of a fully convolutional neural network for segmenting traffic lights on image and subsequent clustering, which allows us to obtain bounding boxes for traffic lights. the proposed approach is compared with one of the most effective object detectors - Single Shot Multibox detector (SSD). We implemented algorithms for objects detection on an embedded system based on the NVidia Jetson TX2 platform. Traffic light detection recall for the proposed approach is better than SSD and higher than 0.9 on both the testing and training samples from relatively small data set (500 training images and 107 testing images). Time of traffic lights detection on one frame is about 50 ms. the results of the traffic light detection prove the possibility of applying the approach based on fully convolutional neural network with clustering for embedded autonomous vehicle control systems and driver assistance systems.

关键词： image recognition detection traffic light deep learning fully convolutional network clustering single shot multibox detector embedded system

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Hand Gesture Classification in Transradial Amputees Using the Myo Armband Classifier 7

Hand Gesture Classification in Transradial Amputees Using th...

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7th ieee International conference on Biomedical Robotics and Biomechatronics (BIOROB)

作者： Cognolato, M. Atzori, M. Faccio, D. Tiengo, C. Bassetto, F. Gassert, R. Mueller, H. Univ Appl Sci Western Switzerland HES SO Valais Informat Syst Inst Sierre Switzerland Swiss Fed Inst Technol Dept Hlth Sci & Technol Rehabil Engn Lab Zurich Switzerland Padova Univ Hosp Clin Plast Surg Padua Italy

ISBN: (纸本)9781538681831

Dexterous hand prostheses controlled via surface electromyography represent the most advanced non invasive functional restorative solution for hand amputees. However, control difficulties, comfort problems and high costs are still the main limitations of such commercial devices. the high cost can represent a barrier that is difficult to overcome, especially for pediatric populations and in developing countries. Low-cost technology was successfully used in the hand prosthetics field in recent years. In previous work, a low-cost gesture recognition armband called Myo showed promising results for hand gesture classification tasks in intact subjects. Most of these applications were based on machine learning techniques applied to the Myo raw data. However, the classifier provided with the Myo is able to identify five hand gestures, providing capabilities as a myoelectric control system. No studies have quantitatively investigated its performance in subjects with hand amputation, yet. the aim of this study is to quantitatively evaluate the performance of the Myo hand gesture classifier in hand amputees. three subjects with hand amputation were asked to attempt performing the five pre-set hand gestures. Each gesture was repeated three times with the arm in three different postures. the subjects did not perform any training and did not receive any feedback. Overall classification accuracy for the four hand gestures based on electromyographic data ranged between 50% and 97%. A clear relation between the length of the residual limb and the classification accuracy was observed. the results show that the Myo built-in classifier can provide good performance when tested on hand amputees, supporting its applicability as a low-cost myoelectric control system.

关键词： Prosthetics Gesture recognition Performance evaluation Calibration Electromyography control systems Electrodes

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Dow Jones Trading with Deep learning: the Unreasonable Effectiveness of Recurrent Neural Networks 7

Dow Jones Trading with Deep Learning: The Unreasonable Effec...

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7th International conference on data Science, Technology and Applications, data 2018

作者： Fabbri, Mirco Moro, Gianluca University of Bologna Via Cesare Pavese CesenaI-47522 Italy

ISBN: (纸本)9789897583186

though recurrent neural networks (RNN) outperform traditional machine learning algorithms in the detection of long-term dependencies among the training instances, such as in term sequences in sentences or among values in time series, surprisingly few studies so far have deployed concrete solutions with RNNs for the stock market trading. Presumably the current difficulties of training RNNs have contributed to discourage their wide *** work presents a simple but effective solution, based on a deep RNN, whose gains in trading with Dow Jones Industrial Average (DJIA) outperform the state-of-the-art, moreover the gain is 50% higher than that produced by similar feed forward deep neural networks. the trading actions are driven by the predictions of the price movements of DJIA, using simply its publicly available historical series. To improve the reliability of results with respect to the literature, we have experimented the approach on a long consecutive period of 18 years of historical DJIA series, from 2000 to 2017. In 8 years of trading in the test set period from 2009 to 2017, the solution has quintupled the initial capital, moreover since DJIA has on average an increasing trend, we also tested the approach with a decreasing averagely trend by simply inverting the same historical series of DJIA. In this extreme case, in which hardly any investor would risk money, the approach has more than doubled the initial capital. Copyright © 2018 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.

关键词： Financial markets

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Modeling and Optimizing Building HVAC Energy systems Using Deep Neural Networks 7

Modeling and Optimizing Building HVAC Energy Systems Using D...

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7th International conference on Smart Grid and Clean Energy Technologies (ICSGCE)

作者： Deng, Jiahao Wang, Haoran Depaul Univ Coll Comp & Digital Media Chicago IL 60604 USA Arris Int Sunnyvale CA 94089 USA

ISBN: (纸本)9781538667965

the heating, ventilating and air conditioning (HVAC) systems consumes more than half of the building energy consumption. An efficient HVAC system ensures the comfortable living and working environment. In this research, we proposes a novel approach to maximize the HVAC system efficiency regarding a typical office-type facility. One year energy data is collected from a commercial building in Chicago, IL for this research. the future room temperature and air humidity are predicted by deep neural networks. Deep neural network is also selected to extract the highly non-linear relationship between the control settings and the energy utility. A comparative analysis between the deep neural network and other state-of-arts data driven models is conducted. the deep neural networks outperforms the other algorithms and is applied to model the room temperature and air humidity. the predicted temperature and humidity are integrated into an energy optimization algorithm. through numerical experiments, the systematic efficiency has been optimized to an acceptable level. Significant energy savings have been obtained while the facility's thermal comfort has been maintained.

关键词： data-driven deep neural network HVAC particle swarm optimization

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Real-Time Localized control of Transient Stability by the Localized Transient Stability (LTS) Method with Static Var Compensator (SVC) 7

Real-Time Localized Control of Transient Stability by the Lo...

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7th International conference on Smart Grid and Clean Energy Technologies (ICSGCE)

作者： Miah, Abdul Malek South Carolina State Univ Dept Ind & Elect Engn Technol Orangeburg SC 29115 USA

ISBN: (纸本)9781538667965

Real-time localized control of transient stability by the very recently proposed Localized Transient Stability (LTS) Method with Static Var Compensator (SVC) as the local control means is presented here. However, since the technique is based on the LTS method, transient stability in this technique is viewed as the interaction of each individual generator with its respective remaining generators. therefore, in this technique, the post-fault power system is represented by a two-generator localized power system at the site of each individual generator. Each of these localized power systems is then driven to its respective stable equilibrium by local control actions with local computations using the locally measured data to drive the full (entire) power system to its stable equilibrium. New investigative results are presented here to demonstrate the potential of the real-time localized control of transient stability by the LTS method.

关键词： localized power system localized transient stability real-time localized control transient stability

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ieee International conference on Adaptive Science and Technology, ICAST

IEEE International Conference on Adaptive Science and Techno...

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7th ieee International conference on Adaptive Science and Technology, ICAST 2018

ISBN: (纸本)9781538642337

the proceedings contain 44 papers. the topics discussed include: dvelopment of an embedded obstacle avoidance and path planning autonomous solar grass cutting robot for semistructured outdoor environment;sentiment analysis with word embedding;OFDM chirp radar target detection and adaptive design in clutter environment;low complexity clipped frequency shift keying (FSK) for visible light communications;quality of service assessment on some major mobile network operators in Ghana;affordable modular autonomous vehicle development platform;design of power distribution network fault data collector for fault detection, location and classification using machine learning;efficient modeling of a PCB transmission line for high speed digital systems;and methods of chattering reduction in sliding mode control: a case study of ball and plate system.

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Dynamic Transcriptomic data Analysis by Integrating data-driven and Model-guided Approaches

Dynamic Transcriptomic Data Analysis by Integrating Data-dri...

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7th conference on Foundation of systems Biology in Engineering (FOSBE)

作者： Hilliard, Matthew He, Q. Peter Wang, Jin Auburn Univ Dept Chem Engn Auburn AL 36849 USA

the rapid development in "omics" technologies offer unprecedented opportunities to help understand cellular metabolism at genome-scale. Among different "omics" data, RNAseq based transcriptome profiling has been used to enhance the understanding of the genome-scale response of the organism to different stimuli. While these studies have provided insightful findings, they are most often limited to studying two-steady-state conditions. From a control perspective, as cellular metabolism is a highly complex dynamic system, the transient response could offer significantly more information on the system dynamics, especially the internal control mechanism In this work, we present our initial results on analysing the dynamic transcriptomic profiles of Scheffersomyces stipitis during the transition from aerobic growth to oxygen-limited fermentation. Our results confirm that the transient states indeed offer important information that the steady-states cannot, which can be crucial in understanding the cellular metabolism. (C) 2018, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： transcriptome profiling transcriptomic data analysis steady-state transient response Scheffersomyces stipites principal component analysis genome-scale metabolic model

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How Meta-heuristic Algorithms Contribute to Deep learning in the Hype of Big data Analytics 4th

How Meta-heuristic Algorithms Contribute to Deep Learning in...

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4th International conference on Advanced Computing, Networking and Informatics (ICACNI)

作者： Fong, Simon Deb, Suash Yang, Xin-she Univ Macau Dept Comp Informat Sci Macau Peoples R China INNS India Reg Chapter 7th Floor Jorar Ranchi 834010 Jharkhand India Middlesex Univ Sch Sci & Technol London NW4 4BT England

ISBN: (纸本)9789811033735;9789811033728

Deep learning (DL) is one of the most emerging types of contemporary machine learning techniques that mimic the cognitive patterns of animal visual cortex to learn the new abstract features automatically by deep and hierarchical layers. DL is believed to be a suitable tool so far for extracting insights from very huge volume of so-called big data. Nevertheless, one of the three "V" or big data is velocity that implies the learning has to be incremental as data are accumulating up rapidly. DL must be fast and accurate. By the technical design of DL, it is extended from feed-forward artificial neural network with many multi-hidden layers of neurons called deep neural network (DNN). In the training process of DNN, it has certain inefficiency due to very long training time required. Obtaining the most accurate DNN within a reasonable run-time is a challenge, given there are potentially many parameters in the DNN model configuration and high dimensionality of the feature space in the training dataset. Meta-heuristic has a history of optimizing machine learning models successfully. How well meta-heuristic could be used to optimize DL in the context of big data analytics is a thematic topic which we pondered on in this paper. As a position paper, we review the recent advances of applying meta-heuristics on DL, discuss about their pros and cons and point out some feasible research directions for bridging the gaps between meta-heuristics and DL.

关键词： Deep learning Meta-heuristic algorithm Neural network training Nature-inspired computing algorithms Algorithm design

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DMPC Applied to the Temperature Regulation System of Building under Packet Dropout Communication

DMPC Applied to the Temperature Regulation System of Buildin...

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ieee data driven control and learning systems conference

作者： Qingnan Huang Liujun Xie College of Electrical and Information Engineering Guangxi University Of Science and Technology Liu zhou 545006

ISBN: (纸本)9781538626191

A solution to the interference of control signals and signal loss in the process of signal transmission in the process of decentralized model predictive control (DMPC) is introduced in this paper. Judging whether the data packet is lost at each time sampling signal, then the approximate value of the lost signal at this time is calculated in an alternative way. the results show that: this method is feasible.

关键词： Decentralized model predictive control Packet dropout communication Modeling

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Exponential Stability for Event-driven Impulsive control systems

Exponential Stability for Event-Driven Impulsive Control Sys...

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ieee data driven control and learning systems conference

作者： Zidong Ai College of Automation and Electronic Engineering Qingdao University of Science and Technology

ISBN: (纸本)9781538626191

In this work, we conduct stability analysis for a class of multi-module impulsive control systems via an event-driven scheme. By designing some event-driven conditions and a proper event-driven impulsive control law, we establish some sufficient stability criteria for the considered systems. the proposed event-driven control scheme is advantageous to reduce the utilization of communication and computation resources. Further, we study the impulsive synchronization problem for two continuous-time dynamical systems with different initial values. Finally, an example of Chua's circuit with simulations results are provided to illustrate the validity of the method.

关键词： Stability Analysis Event-driven control Multi-Module Impulse Synchronization

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