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Sensitivity Analysis for Driver Energy Prediction with Environmental Features and Naturalistic data

Sensitivity Analysis for Driver Energy Prediction with Envir...

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IEEE international conference on intelligent engineering Systems

作者： Johannes Ziegmann Michael Schmid Christian Endisch Institute of Innovative Mobility (lIMo) Technische Hochschule Ingolstadt Ingolstadt Germany

With increasing predictive data in the driving environment it is possible to estimate the driver's energy demand more accurate. Precise energy prediction guarantees optimal range management and efficient operational strategies of electric and hybrid electric vehicles. Naturalistic driving data are analyzed in a study with the impact of high dimensional accumulated environmental features to the specific driver energy demand. Thereby a framework is proposed to collect and transform naturalistic driving data, accumulated with driving environment features. Thus, multidimensional environment data corresponds to a particular driver energy demand, making it challenging for energy prediction algorithms due to the curse of dimensionality. In this paper a sensitivity analysis approach with four filters and two wrapper methods are proposed to select relevant features. Results leading to thirteen final elected environmental features which are sensitive to the driver energy demand.

关键词： Vehicles Engines Sensitivity analysis Mathematical model Roads Vehicle dynamics Energy consumption

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A full-protocol-stack testbed for space network protocol emulation 2nd

A full-protocol-stack testbed for space network protocol emu...

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2nd international conference on Machine learning and intelligent Communications, MLICOM 2017

作者： Ni, Xiaoqin Zhao, Kanglian Li, Wenfeng School of Electronic Science and Engineering Nanjing University 22 Hankou Rd. NanjingJiangsu210093 China

ISBN: (纸本)9783319735634

With the rapid development of space networks, new space communications protocols are emerging, for which emulation is an essential step during design and test. In this paper, we propose a lab-based testbed, in which software and hardware tools are utilized together to emulate full network protocol stack. A software protocol gateway is implemented to preform protocol conversion like IP over CCSDS in data Link Layer. A specified hardware, Cortex CRT-Q is adopted for accurate emulation of space links, which connects upper layers with Physical Layer. Thus, our testbed benefit from both the fidelity provided by hardware and flexibility brought by software. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2018.

关键词： Testbeds

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Comparison of different deep-learning methods for image classification

Comparison of different deep-learning methods for image clas...

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IEEE international conference on intelligent engineering Systems

作者： Kamil Szyc Wroclaw University of Science and Technology

In this paper, we showed how to solve exemplary image classification problem. The goal of image classification problem is to correctly classify the input image within the expected class/label. In our case, we focused on classifying the animal image into one of two categories: mammals or birds. That classification problem is not a trivial task using standard machine learning algorithms. The main reason for that is the fact that the algorithms are based on previously prepared features for classifying object. It is often done by hand by a researcher. Defining specific features for example how a beak looks, wing, tail, fur and etc. is natural for humans, but understanding it by computers is extremely difficult. However, nowadays deep learning algorithms managed to overcome some obstacles and work best for these types of problems. We can use Deep Neural Networks (DNN) in two ways - by developing it from scratch for the specific problem or using method calls transfer learning. The main goal of this paper is comparing the two methods by using them in a real-life example. We showed how to correctly prepare a dataset, create a DNN model from scratch and how to adjust it. We also showed how to use the transfer learning technique. All the steps we made are described in a way which allows for easy adaptation of these algorithms to similar problems.

关键词： Birds Machine learning algorithms databases Biological neural networks Task analysis Classification algorithms

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Label-And-Learn: Visualizing the Likelihood of Machine learning Classifier's Success During data Labeling 17

Label-And-Learn: Visualizing the Likelihood of Machine Learn...

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22nd international conference on intelligent User Interfaces (IUI)

作者： Sun, Yunjia Lank, Edward Terry, Michael Univ Waterloo Cheriton Sch Comp Sci Waterloo ON Canada

ISBN: (纸本)9781450343480

While machine learning is a powerful tool for the analysis and classification of complex real-world datasets, it is still challenging, particularly for developers with limited expertise, to incorporate this technology into their software systems. The first step in machine learning, data labeling, is traditionally thought of as a tedious, unavoidable task in building a machine learning classifier. However, in this paper, we argue that it can also serve as the first opportunity for developers to gain insight into their dataset. Through a Label-and-Learn interface, we explore visualization strategies that leverage the data labeling task to enhance developers' knowledge about their dataset, including the likely success of the classifier and the rationale behind the classifier's decisions. At the same time, we show that the visualizations also improve users' labeling experience by showing them the impact they have made on classifier performance. We assess the visualizations in Label-and-Learn and experimentally demonstrate their value to software developers who seek to assess the utility of machine learning during the data labeling process.

关键词： Machine learning visualization data labeling

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K-means clustering approach for damage evolution monitoring in RCF tests 22

K-means clustering approach for damage evolution monitoring ...

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22nd World Congress of the international Measurement Confederation, IMEKO 2018

作者： Bini Chiesa, M. Bodini, I. Petrogalli, C. Provezza, L. Faccoli, M. Mazzu, A. Solazzi, L. Sansoni, G. Lancini, M. Department of Mechanical and Industrial Engineering University of Brescia Italy

A machine learning technique was used to monitor the state of damage of steel specimens subjected to cyclic contact fatigue tests and to make a continuous measurement without coming into contact with the specimens examined. Six different sets of specimens were considered and the torque and vibration data obtained from their respective tests were analyzed. Different statistical and spectral characteristics were obtained from each dataset, to represent their information content. The correlation existing between the characteristics was studied to select only the essential ones. The global data volume was reduced by a principal component analysis. The data thus obtained were subsequently processed, and classified, developing a K-means algorithm. The results were formulated in probabilistic terms with the aid of membership charts, confusion matrices and probability curves. The results obtained from this first analysis are highly encouraging. The algorithm is able to assess the damage status of the specimens, as well as detect the operating conditions of the machine. A substantial advantage of this method is the ability to estimate quantitatively the damage by calculating the distance of the current data from specific references. The performances of this approach were numerically estimated in terms of sensitivity, specificity and accuracy. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： K-means clustering

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data-Consistent Toolchain for a requirements-based Specification with the Interdisciplinary Modeling Language (IML)

Data-Consistent Toolchain for a requirements-based Specifica...

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IEEE international conference on intelligent engineering Systems

作者： Werner Herfs Jerome Flender Simon Storms Martin Witte WZL - Laboratory for Machine Tools and Production Engineering RWTH Aachen - Aachen University of Technology Aachen Siemens AG Nuremberg

Disruptions within engineering toolehains as well as scattered system specifications still impede the integration of phase-spanning system design approaches for production machines. Aside of the necessary high efforts to gather the essential requirements for the implementation of a component or process, this also inhibits documenting the reasons for a specific choice of a particular machine concept. Though to achieve reasonable system designs, it is necessary to link and track requirements with the implementing machine structures - consistently and synchronized across all design phases. Therefore, this article presents an approach for a requirementsbased toolchain, which enables the data-consistent use of requirements in the system design phase of production machines. Based on the specification language IML (Interdisciplinary Modeling Language) and its central interdisciplinary system model, intuitive operation principles shall support the definition of component-specific requirements sets while round-trip strategies realize synchronized requirement profiles and histories.

关键词： System analysis and design Modeling Production Synchronization Requirements management Stakeholders History

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New learning Strategy for Prototypes in Linear Vector Quantization

New Learning Strategy for Prototypes in Linear Vector Quanti...

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international conference on System Theory, Control, and Computing (ICSTCC)

作者： Ghiorghe Puşcaşu Alexandru Stancu Bogdan Codreş Alexandru odreş Faculty of Automatic Control Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati School of Electrical and Electronic Engineering Faculty of Engineering and Physical Sciences The University of Manchester Sackville Street Building E1d United Kingdom

This paper proposes a new strategy for prototypes initialization in linear vector quantization algorithm (LVQ). Three principles which must be satisfied during the learning stage are shown in the paper. These principles are essential to guarantee appropriate learning for the LVQ algorithm. However, all versions of LVQ algorithms try to answer to one of the principles, but unfortunately contradicting with the other ones. The new strategy proposed in the paper aims to solve this issue and consists of two steps: (1) analyse the a-priori data set and (2) apply a pre-learning algorithm to initialize the prototypes. The pre-learned prototypes resulted from step 2 are used by the LVQ algorithm in the learning process. The examples presented in the case study and the criterion used to assess the training performance of the prototypes reinforce that the training strategy of the prototypes proposed in the paper provides better results in certain situations compared to classical LVQ algorithms.

关键词： Prototypes Signal processing algorithms Training data Training Heuristic algorithms Classification algorithms Approximation algorithms

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Vehicle Avoidance Reaction By Two Step Motion Flow Cluster

Vehicle Avoidance Reaction By Two Step Motion Flow Cluster

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international Computer Science and engineering conference (ICSEC)

作者： Woranon Rassameepaiboon Wudhichai Assawinchaichote Electronics and Telecommunication Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

ISBN: (纸本)9781538681657;9781538681640

Presently we have seen the road accidents in every day. Automobile manufacturers then have created a system that aims to reduce and prevent such accidents. The most popular ones include intelligent collision warning system and intelligent braking system. However, such systems are not able to cover every case and could even cause more accidents. Interactive accident avoidance system with 2 steps movement detection is therefore a recommended method to reduce and prevent accidents with more efficiency. This study focuses on the feature of this system, which is forward movement detection and automatic obstacles avoidance. With this, external environment will be distinguished from object movement before calculating the duration that the vehicle would possibly hit the object. This duration will then be grouped, and the clustering data will be used for vehicle control i.e. to turn left, right, or stop. Finally, to show the effectiveness of our designed approach, we have used the computer simulation to show the results of our proposed method.

关键词： Optical flow Cameras Accidents Automobiles data processing

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Proposal of effective preprocessing techniques of financial data

Proposal of effective preprocessing techniques of financial ...

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IEEE international conference on intelligent engineering Systems

作者： Jela Abasova Jan Janosik Veronika Simoncicova Pavol Tanuska Faculty of Material Sciences and Technology in Trnava Slovak University of Technology in Bratislava Trnava Slovak Republic

The article focuses on describing process of customization data obtained from ERP software. The aim of this customization was ensuring usability of data collection for obtaining knowledge from data via methods of data mining. First part describes importance of preprocessing phase of working with big data sets. It deals with terms commonly used in the topic - methodology CRISP-DM and its phases, with closer look at individual steps of preprocessing. Second part contains description of the chosen dataset, extraction of raw data and their required format (according to their expected future use). The data are financial indications of a real concern. This part also focuses on methods and programs used for preprocessing the data. Third part corresponds with the main task of this article, what is adjusting obtained data to format which would be appropriate to further processing and analyzing purposes with data Mining techniques. It describes five phases of preprocessing, modified in a way needed for manipulating with chosen data. The last part compiles acquired knowledge and presents further use of the data after transformation from raw form to demanded format. The paper concludes with BPMN diagram of the process with focusing on preprocessing (data preparation) stage.

关键词： Companies Software data mining Production Correlation Proposals

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Clinical Practice for Diagnostic Causes for Obstructive Sleep Apnea Using Artificial intelligent Neural Networks 1st

Clinical Practice for Diagnostic Causes for Obstructive Slee...

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1st international conference on Emerging Technologies in Computing (ICETIC)

作者： Alsalamah, Mashail Amin, Saad Palade, Vasile Coventry Univ Priory St Coventry CV1 5FB W Midlands England Qassim Univ Buraydah Al Mulida Saudi Arabia

ISBN: (纸本)9783319954509;9783319954493

Sleep apnea is a serious sleep disorder phenomena which happens when a person's breathing is paused during sleep. The most common diagnostic technique that is used to deal with sleep apnea is Polysomnography (PSG) which is done at special sleeping labs. This technique is expensive and uncomfortable. New automated methods have been developed for sleep apnea detection using artificial intelligence algorithms, which are more convenient and comfortable for patients. This paper proposes a novel scheme based on deep learning for sleep apnea detection and quantification using statistical features of ECG signals. The proposed approach is experimented with three phases: (1) minute-based apnea classification, (2) class identification and minute-by-minute detection for each ECG recording unlike state-of-the-art methods which either identify apnea class or detect its presence at each minute and (3) comparison of the proposed scheme with the well-known methods that have been proposed in the literature, which may have not used the same features and/or the same dataset. The obtained results demonstrate that the proposed approach provides significant performance improvements when compared to state-of-the-art methods. The outcome of this study can be used as an assistant tool by cardiologists to help them make more consistent diagnosis of sleep apnea disorder.

关键词： Obstructive sleep apnea Deep learning Neural networks Sleep disorder Big data

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