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检索条件"任意字段=2014 IEEE Symposium on Computational Intelligence in Big Data, CIBD 2014"

共 247 条记录，以下是151-160 订阅

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Towards Model-Driven Engineering for big data Analytics - An Exploratory Analysis of Domain-Specific Languages for Machine Learning

Towards Model-Driven Engineering for Big Data Analytics - An...

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47th Annual Hawaii International Conference on System Sciences

作者： Breuker, Dominic Univ Muenster ERCIS Munster Germany

ISBN: (纸本)9781479925049

Graphical models and general purpose inference algorithms are powerful tools for moving from imperative towards declarative specification of machine learning problems. Although graphical models define the principle information necessary to adapt inference algorithms to specific probabilistic models, entirely model-driven development is not yet possible. However, generating executable code from graphical models could have several advantages. It could reduce the skills necessary to implement probabilistic models and may speed up development processes. Both advantages address pressing industry needs. They come along with increased supply of data scientist labor, the demand of which cannot be fulfilled at the moment. To explore the opportunities of model-driven big data analytics, I review the main modeling languages used in machine learning as well as inference algorithms and corresponding software implementations. Gaps hampering direct code generation from graphical models are identified and closed by proposing an initial conceptualization of a domain-specific modeling language.

关键词： big data computer graphics data analysis inference mechanisms learning (artificial intelligence) program compilers specification languages big data analytics direct code generation domain-specific languages domain-specific modeling language general purpose inference algorithms graphical models machine learning problems model-driven development model-driven engineering modeling languages probabilistic models Adaptation models computational modeling data models Graphical models Inference algorithms Random variables Unified modeling language Graphical Models Machine Learning Model-driven Engineering

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Adaptive Aggregated Predictions for Renewable Energy Systems

Adaptive Aggregated Predictions for Renewable Energy Systems

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ieee symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

作者： Csaji, Balazs Csanad Kovacs, Andras Vancza, Jozsef Hungarian Acad Sci Inst Comp Sci & Control Fraunhofer Project Ctr Prod Management & Informat H-1051 Budapest Hungary Budapest Univ Technol & Econ Budapest Hungary

ISBN: (纸本)9781479945528

The paper addresses the problem of generating forecasts for energy production and consumption processes in a renewable energy system. The forecasts are made for a prototype public lighting microgrid, which includes photovoltaic panels and LED luminaries that regulate their lighting levels, as inputs for a receding horizon controller. Several stochastic models are fitted to historical times-series data and it is argued that side information, such as clear-sky predictions or the typical system behavior, can be used as exogenous inputs to increase their performance. The predictions can be further improved by combining the forecasts of several models using online learning, the framework of prediction with expert advice. The paper suggests an adaptive aggregation method which also takes side information into account, and makes a state-dependent aggregation. Numerical experiments are presented, as well, showing the efficiency of the estimated time-series models and the proposed aggregation approach.

关键词： LED lamps distributed power generation lighting photovoltaic power systems power generation planning renewable energy sources stochastic processes time series LED luminaries adaptive aggregated predictions adaptive aggregation method clear-sky predictions consumption processes energy production forecast generation exogenous inputs historical times-series data lighting level regulation photovoltaic panels public lighting microgrid renewable energy systems stochastic models Autoregressive processes computational modeling Lighting Noise Predictive models Production Standards Lighting Power generation planning Renewable energy autoregressive processes photovoltaic power systems LED lamps distributed power generation Stochastic models ENERGY SYSTEMS computational modeling Prediction models Markov chain renewable energy sources Solar collectors Noise

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An Improved Evolution-COnstructed (iECO) Features Framework Distribution Statement A: Approved for public release;distribution is unlimited.

An Improved Evolution-COnstructed (iECO) Features Framework ...

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ieee symposium on computational intelligence for Multimedia, Signal and Vision Processing (CIMSIVP)

作者： Price, Stanton R. Anderson, Derek T. Luke, Robert H. Mississippi State Univ Elect & Comp Engn Dept Mississippi State MS 39762 USA US Army RDECOM CERDEC Night Vis & Elect Sensors Directorate Ft Belvoir VA USA

ISBN: (纸本)9781479945030

In image processing and computer vision, significant progress has been made in feature learning for exploiting important cues in data that elude non-learned features. While the field of deep learning has demonstrated state-of-the-art performance, the Evolution-COnstructed (ECO) work of Lillywhite et. al has the advantage of interpretability, and it does not predispose the solution to one of convolution. This paper presents a novel approach for extending the ECO framework. We achieve this through two overarching ideas. First, we address a potential major shortcoming of ECO features- the "features" themselves. The so-called ECO features are simply a transformed image that has been unrolled into a large one dimensional vector. We propose employing feature descriptors to extract pertinent information from the ECO imagery. Furthermore, it is our hypothesis that there exists a unique set of transforms for each feature descriptor used on a given problem domain that leads to the descriptors extracting maximal discriminative information. Second, we introduce constraints on each individual's chromosome to promote population diversity and prevent infeasible solutions. We show through experiments that our proposed iECO framework results in, and benefits from, a unique series of transforms for each descriptor being learned and maintaining population diversity.

关键词： computer vision evolutionary computation feature extraction learning (artificial intelligence) object recognition vectors 1D vector ECO imagery and computer vision feature descriptors feature learning iECO features framework image processing improved evolution-constructed features framework maximal discriminative information extraction pertinent information extraction population diversity promotion public release Biological cells data mining Feature extraction Object recognition Sociology Statistics Transforms

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Fuzzy Algorithm for Intelligent Wireless Sensors with Solar Harvesting

Fuzzy Algorithm for Intelligent Wireless Sensors with Solar ...

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ieee symposium on Intelligent Embedded Systems (IES)

作者： Prauzek, Michal Musilek, Petr Watts, Asher G. Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada Tech Univ Ostrava Fac Elect Engn & Comp Sci VSB Ostrava Czech Republic

ISBN: (纸本)9781479944859

Wireless sensors are sophisticated embedded systems designed for collecting data on systems or processes of interest. In many cases, they are expected to operate in inaccessible locations, without user supervision. As a result, such monitoring systems need to operate autonomously and independently of external sources of energy. To achieve long-lived sustainability, monitoring systems often rely on energy extracted from the environment, e.g. through solar harvesting. Their design is a challenging problem with several conflicting goals and a number of design and implementation possibilities. For obvious reasons, these devices must be designed in an energy efficient way. As a result, they usually have low computational performance and cannot implement complicated control algorithms. At the same time, due to the requirements for autonomy and dependability, they must be endowed with certain degree of adaptability and fault tolerance - properties typically found in intelligent systems. In this contribution, we describe the design flow of an intelligent embedded control system for management of energy use in wireless monitoring systems. The paper also provides a simulation-based analysis of the control system performance.

关键词： computerised monitoring embedded systems energy harvesting energy management systems fuzzy control intelligent control intelligent sensors wireless sensor networks control system performance degree of adaptability energy extraction energy management environmental monitoring systems fault tolerance fuzzy algorithm intelligent embedded control system intelligent wireless sensors simulation-based analysis solar harvesting wireless monitoring systems Artificial intelligence Fuzzy logic Hardware Sensor systems Wireless communication Wireless sensor networks Fuzzy algorithms wireless monitoring system Wireless sensor networks Computerized monitoring Energy management systems Embedded systems Fault tolerance Energy management Sensor systems intelligent sensors Artificial intelligence Intelligent control CARRIER SYSTEM Fuzzy control energy harvesting

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Spatial Prediction of Wireless Links and Its Application to the Path Control of Mobile Robots

Spatial Prediction of Wireless Links and Its Application to ...

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9th ieee International symposium on Industrial Embedded Systems (SIES)

作者： Flushing, Eduardo Feo Kudelski, Michal Gambardella, Luca M. Di Caro, Gianni A. Dalle Molle Inst Artificial Intelligence IDSIA Lugano Switzerland

ISBN: (纸本)9781479940226

We consider the path planning problem of a mobile robot that has to travel towards a given target location. The robot shares the environment with other mobile robots, altogether forming a wireless mobile ad hoc network relaying data in a multi-hop fashion. In this scenario, the robot's path planner has to optimally balance two potentially conflicting goals: keep the traveled distance within an assigned maximum value while letting the robot effectively communicate with the other robots in the network. We propose a solution method relying on the use of a link quality predictor built offline through a supervised learning approach. Together with the information gathered online from the other robots, the predictor allows to adaptively build a spatial map of expected communication quality, for both local and distant areas. In turn, the map is used by the path planner, based on a mixed integer linear formulation and an intelligent strategy for discretizing the environment, to iteratively find the best network-aware path to follow. The proposed approach is evaluated in various realistic simulation scenarios, showing the effectiveness of using the link quality map and the robustness to different restrictions regarding available information and computational resources.

关键词： Mobile ad hoc networks Mobile robots Path planning Planning Robot kinematics Wireless communication Mobile robots Mobile ad hoc networks trajectory control Path planning Robot kinematics CARRIER SYSTEM Robot Radio links communication quality maps

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Takagi-Sugeno-Kang type collaborative fuzzy rule based system

Takagi-Sugeno-Kang type collaborative fuzzy rule based syste...

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ieee symposium on computational intelligence and data Mining (CIDM)

作者： K. P. Chou M. Prasad Y. Y. Lin S. Joshi C. T. Lin J. Y. Chang Dept. of Computer Science Brain Research Center National Chiao Tung University Hsinchu Taiwan School of Technology Doon University Dehradun India Dept. of Electrical Engineering

In this paper, a Takagi-Sugeno-Kang (TSK) type collaborative fuzzy rule based system is proposed with the help of knowledge learning ability of collaborative fuzzy clustering (CFC). The proposed method split a huge dataset into several small datasets and applying collaborative mechanism to interact each other and this process could be helpful to solve the big data issue. The proposed method applies the collective knowledge of CFC as input variables and the consequent part is a linear combination of the input variables. Through the intensive experimental tests on prediction problem, the performance of the proposed method is as higher as other methods. The proposed method only uses one half information of given dataset for training process and provide an accurate modeling platform while other methods use whole information of given dataset for training.

关键词： Training Testing Engines computational modeling Prototypes

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Extracting Train Driver's Perception Strategies Using Interpretive Structural Modeling 7

Extracting Train Driver's Perception Strategies Using Interp...

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Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International symposium on Advanced Intelligent Systems (ISIS)

作者： Horiguchi, Yukio Suzuki, Takayuki Sawaragi, Tetsuo Nakanishi, Hiroaki Nakamura, Tetsuya Muneshige, Michinori Nishimoto, Hidefumi Takimoto, Tomoharu Kyoto Univ Dept Mech Engn & Sci Kyoto 6158540 Japan West Japan Railway Co Safety Res Inst Osaka 5450053 Japan

ISBN: (纸本)9781479959556

In this study, we applied the methodology of Interpretive Structural Modeling (ISM) in order to extract eye-gaze movement patterns characteristic of experienced train drivers in their visual attentive behavior. Application of ISM to eye-gaze data generates multilevel structural models that represent dominant transition patterns hidden in complex eye-gaze movements. The extracted structural models demonstrate that, as a basic perception strategy, the drivers repetitively moved their gaze ahead soon after looking at some other specific area. What differentiates the experienced and younger drivers is that the former group of drivers can consistently follow this basic perception strategy during driving while the latter can not. The experienced drivers can balance their limited attentional resources between vigilance to the external environment and other operational requirements, distributing their attention effectively and efficiently among various areas of interest.

关键词： behavioural sciences computing gaze tracking human factors ISM attention distribution complex eye-gaze movement pattern extraction dominant transition patterns experienced train drivers external environment interpretive structural modeling limited-attentional resources multilevel structural model generation operational requirements perception strategy structural model extraction train driver perception strategy extraction visual attentive behavior younger drivers Analytical models computational modeling Computers data models Matrix converters Schedules Visualization

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Automated inferential measurement system for traffic surveillance: Enhancing situation awareness of UAVs by computational intelligence

Automated inferential measurement system for traffic surveil...

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ieee symposium on computational intelligence in Control and Automation, CICA

作者： Prapa Rattadilok Andrei Petrovski School of Computing Science and Digital Media Robert Gordon University Aberdeen UK

An adaptive inferential measurement framework for control and automation systems has been proposed in the paper and tested on simulated traffic surveillance data. The use of the framework enables making inferences related to the presence of anomalies in the surveillance data with the help of statistical, computational and clustering analysis. Moreover, the performance of the ensemble of these tools can be dynamically tuned by a computational intelligence technique. The experimental results have demonstrated that the framework is generally applicable to various problem domains and reasonable performance is achieved in terms of inferential accuracy. computational intelligence can also be effectively utilised for identifying the main contributing features in detecting anomalous data points within the surveillance data.

关键词： Vehicles Surveillance computational intelligence Statistical analysis Process control Real-time systems Traffic control

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MIC-SVM: Designing A Highly Efficient Support Vector Machine For Advanced Modern Multi-Core and Many-Core Architectures

MIC-SVM: Designing A Highly Efficient Support Vector Machine...

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ieee 28th International Parallel & Distributed Processing symposium (IPDPS)

作者： You, Yang Song, Shuaiwen Leon Fu, Haohuan Marquez, Andres Dehnavi, Maryam Mehri Barker, Kevin Cameron, Kirk W. Randles, Amanda Peters Yang, Guangwen Tsinghua Univ Ctr Earth Sci Minist Educ Key Lab Earth Syst Modeling Beijing 100084 Peoples R China Tsinghua Univ TNList Beijing Peoples R China Pacific NW Natl Lab Richland WA 02139 USA Harvard Univ MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA Virginia Tech Blacksburg VA USA Lawrence Livermore Natl Lab Livermore CA USA

ISBN: (纸本)9780769552071

Support Vector Machine (SVM) has been widely used in data-mining and big data applications as modern commercial databases start to attach an increasing importance to the analytic capabilities. In recent years, SVM was adapted to the field of High Performance Computing for power/performance prediction, auto-tuning, and runtime scheduling. However, even at the risk of losing prediction accuracy due to insufficient runtime information, researchers can only afford to apply offline model training to avoid significant runtime training overhead. Advanced multi-and many-core architectures offer massive parallelism with complex memory hierarchies which can make runtime training possible, but form a barrier to efficient parallel SVM design. To address the challenges above, we designed and implemented MIC-SVM, a highly efficient parallel SVM for x86 based multi-core and many-core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi co-processor (MIC). We propose various novel analysis methods and optimization techniques to fully utilize the multilevel parallelism provided by these architectures and serve as general optimization methods for other machine learning tools. MIC-SVM achieves 4.4-84x and 18-47x speedups against the popular LIBSVM, on MIC and Ivy Bridge CPUs respectively, for several real-world data-mining datasets. Even compared with GPUSVM, run on a top of the line NVIDIA k20x GPU, the performance of our MIC-SVM is competitive. We also conduct a cross-platform performance comparison analysis, focusing on Ivy Bridge CPUs, MIC and GPUs, and provide insights on how to select the most suitable advanced architectures for specific algorithms and input data patterns.

关键词： Support Vector Machine Multi & Many-Core architectures Parallelization Optimization Techniques Performance Analysis

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Combined linguistic and sensor models for machine learning

Combined linguistic and sensor models for machine learning

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ieee symposium on computational intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)

作者： Roman Ilin Sensors Directorate Air Force Research Laboratory Wright Patterson AFB OH USA

This work builds on a cognitive theory called dynamic logic and considers the relationship between language and cognition. We explore the idea of dual models that combine linguistic and sensor features. We demonstrate that simultaneous learning of textual and image data results in formation of meaningful concepts and subsequent improvement in concept recognition.

关键词： data models Pragmatics Mathematical model Acceleration Vectors computational modeling Numerical models

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