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Increasing Flexibility of Time Triggered Ethernet based Systems by Optimal Greedy Scheduling Approach 20

Increasing Flexibility of Time Triggered Ethernet based Syst...

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20th IEEE Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Wisniewski, Lukasz Schumacher, Markus Jasperneite, Juergen Diedrich, Christian Ostwestfalen Lippe Univ Appl Sci inIT Inst Ind IT D-32657 Lemgo Germany Fraunhofer IOSB INA Applicat Ctr Ind Automat D-32657 Lemgo Germany Univ Magdeburg Inst Automat Technol D-39106 Magdeburg Germany

ISBN: (纸本)9781467379298

Many aspects have to be considered while achieving flexibility of production systems. One of them is flexible communication system that on one hand fulfils requirements of a particular industrial application and on the other hand allows integration of new network components optimally in a seamless way. In this paper, an approach to increase, rather static, Time Triggered Ethernet based systems is proposed. It is based on a greedy scheduling approach that can be implemented directly in a processing limited hardware such as IO Controller. This, together with the discovery and auto configuration mechanism proposed in Ill allows to shift the engineering process typically performed offline using engineering tool to the field level. Furthermore, it has been proven that the proposed scheduling approach is not only simple but also powerful. It is able to calculate an optimal schedule for any kind of tree graph and the complexity of the algorithm is defined by the complexity of the used sorting algorithm.

关键词： Tree graphs Ethernet networks greedy method sorting algorithm Engineering tools Network components field level IO Flexibility

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Automatic Design algorithm of a Robotic End-Effector for a set of Sheet-Metal Parts 7

Automatic Design Algorithm of a Robotic End-Effector for a s...

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Proceedings of the 2015 7th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) And Robotics, Automation and Mechatronics (RAM)

作者： Sintov, Avishai Shapiro, Amir Ben Gurion Univ Negev Dept Mech Engn IL-84105 Beer Sheva Israel

ISBN: (纸本)9781467373388

Robotic end-effectors in automated production lines are specially designed and built for a unique task and for a particular part. An end-effector capable of grasping different parts with different geometries will expand its benefit and reduce costs. This work focuses on the grasp of sheet metal parts for the automotive industry. In order to maximize the use of a single end-effector, this paper proposes a search algorithm for a simple common grasp configuration. Such configuration imply for an end-effector design capable of grasping a set of sheet metal parts. The algorithm maps possible grasps which are candidate to be common. We define a novel quality measure estimating the distribution of the contacts across the sheet metal part. Candidate grasps which have a sufficient quality are mapped into high-dimensional feature vectors. These feature vectors parameterize the geometry of the polyhedron formed by the contacts locations and the direction of the surface normals relative to the polyhedron. Thereby, they describe the design of the end-effector compatible to the grasp. A database is generated for all possible grasps for each part in the feature vector space. A similarity join based on nearest-neighbor search and classification algorithm are used for intersecting all possible feature vectors over all sheet metal parts and finding common ones. Simulations of a 3-finger grasp on four meshed sheet metal parts resulted in a common grasp. Results of the simulations validate the feasibility of the proposed algorithm.

关键词： Sheet metal components Grasp Eigenvector end effectors polyhedrons Nearest neighbor query surface normal SEARCHING algorithm Computer-Aided Design Design algorithm metal parts sorting algorithm

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Facebook User's Like Behavior Can Reveal Personality 7

Facebook User's Like Behavior Can Reveal Personality

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2015 7th Conference on Information and Knowledge Technology (IKT)

作者： Ghavami, Seyed Morteza Asadpour, Masoud Hatami, Javad Mahdavi, Mohammad Univ Tehran Sch Elect & Comp Engn Tehran Iran Univ Tehran Dept Psychol Tehran Iran

ISBN: (纸本)9781467374859

Personality is an important factor that affects human's behavior. In recent years attraction towards online social networks has increased tremendously. Due to its unique features social networks are very suitable platforms for psychological analysis. By investigating 65 facebook users which participated in this research, correlation between personality traits and user liking behavior was observed. Classification algorithms were applied to classify personality traits based on user liking activity. Among classifiers, the SVM classifier outperforms other classifiers in most traits.

关键词： Personality personality trait Social Networks Social Media Facebook sorting algorithm Classifiers Affection Consumers

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Entropy Chain Multi-Label Classifiers for Traditional Medicine Diagnosing Parkinson's Disease

Entropy Chain Multi-Label Classifiers for Traditional Medici...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Peng, Yue Fang, Ming Wang, Chongjun Xie, Junyuan Nanjing Univ Dept Comp Sci & Technol Natl Key Lab Novel Software Technol Nanjing 210008 Jiangsu Peoples R China

ISBN: (纸本)9781467367981

Parkinson disease is a chronic, degenerative disease of the central nervous system, which commonly occurs in the elderly. Until now, no treatment has shown efficacy. Traditional Chinese Medicine is a new way for Parkinson, and the data of Chinese Medicine for Parkinson is a multi -label dataset. Classifier Chains(CC) is a popular multi -label classified algorithm, this algorithm considers the relativity between labels, and contains the high efficiency of Binary classification algorithm at the same time. But CC algorithm does not indicate how to obtain the predicted order chain actually, while more emphasizes the randomness or artificially specified. In this paper, we try to apply Multi -label classification technology to build a model of Chinese Medicine for Parkinson, which we hope to improve this field. We propose a new algorithm ETCC based on CC model. This algorithm can optimize the order chain on global perspective and have a better result than the algorithm CC.

关键词： Biology Biomedical imaging Integrated circuits Senior citizens aged person senior citizen Biomedical imaging Parkinson Disease Degenerative Diseases, Central Nervous System Chinese medicine Biological Science Disciplines biology Integrated circuits Electronic circuits Classifiers sorting algorithm

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Multi-sensor and multi-platform data fusion for buried objects detection and localization 9

Multi-sensor and multi-platform data fusion for buried objec...

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9th IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Prado, Jose Marques, Lino Univ Coimbra Inst Syst & Robot Dept Elect & Comp Engn P-3030290 Coimbra Portugal

ISBN: (纸本)9781467369916

This paper describes a multi-sensor data-fusion approach to detect and localize landmines and unexploded ordnances (UXO) in a field using multiple mobile sensor carriers covering the area of interest. A procedure for high-accuracy geo-referencing the field-data acquired by multiple platforms is proposed. Later, the geo-referenced sensor data is fused by a multi-step algorithm consisting on the detection of heterogeneities corresponding to objects followed by the classification of those objects through a decision-level fusion. The proposed approach was tested and validated in a military field containing more than twenty different objects buried on the ground, using two different metal detector (MD) arrays and one ground penetrating radar (GPR) array. Although the preliminary results can be considered encouraging, since it was possible to detect and classify all the buried objects, further tests are necessary in terms of classification, since the classification algorithm was trained with data collected previously from the same soil.

关键词： Bayesian classification Field robotics Multi-sensor data fusion Robotics demining multi-sensor data fusion multi sensor Buried object detection underground sorting algorithm platform-independent Sensor Data ANTITANK MINES Data fusion data integration ground penetrating radar

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Association Rules Mining with Categories and Regular Expressions based Pruning 11

Association Rules Mining with Categories and Regular Express...

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11th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP)

作者： Slavescu, Radu Razvan Persa, Raul Alexandru Slavescu, Kinga Cristina Tech Univ Cluj Napoca Dept Comp Sci Baritiu 28 RO-400027 Cluj Napoca Romania Iuliu Hatieganu Univ Med & Pharm Pediat Clin 2 RO-400027 Cluj Napoca Romania

ISBN: (纸本)9781467382007

We present an extended version of the well known Apriori algorithm used for mining association rules. Its novelty consists in employing regular expressions in order to specify the form of the rules intended to be generated. These are further used for pruning in the rule generation step, thus speeding up the process. We show that the new version of the algorithm was used for generating medical hypotheses for some problems in the field without risking to loose generality.

关键词： data mining Apriori algorithm association rules mining expressions based pruning Association rules Classification algorithms Correlation Diseases Itemsets Yttrium Association rules Itemsets sorting algorithm Rare earths yttrium data mining tree surgery maladjustment mining learning (artificial intelligence) Information Extraction

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Classification with a reject option under Concept Drift: the Droplets algorithm

Classification with a reject option under Concept Drift: the...

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Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA 2015)

作者： Loeffel, Pierre-Xavier Marsala, Christophe Detyniecki, Marcin UPMC Univ Paris 06 Sorbonne Univ CNRS LIP6 UMR 7606 4 Pl Jussieu F-75005 Paris France Polish Acad Sci IBS PAN Warsaw Poland

ISBN: (纸本)9781467382731

In this paper a new on-line algorithm is proposed (the Droplets algorithm) for dealing with concept drifts and to produce reliable predictions. The two main characteristics of this algorithm are that it is able to adapt to different types of drifts without making any assumptions regarding their type or when they occur, and can provide reliable predictions in a non-stationary environment without using a fixed confidence threshold. Experimental results on five datasets based on Random RBF and Rotating Hyperplane generators as well as a new semi-synthetic dataset based weather temperatures show that, by discarding difficult observations, the Droplets algorithm manages to obtain the best average accuracy against ten classifiers. The results also indicate that the algorithm manages to provide reliable prediction by accurately distinguishing which observations are easily classifiable.

关键词： algorithm design and analysis Bagging Classification algorithms Indexes Machine learning algorithms Prediction algorithms Reliability algorithm design and analysis Machine learning algorithms Prediction algorithms sorting algorithm Concept Drift bagging PURSE Confidence Threshold droplets

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Traffic Video Surveillance: Vehicle Detection and Classification

Traffic Video Surveillance: Vehicle Detection and Classifica...

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International Conference on Control Communication & Computing India (ICCC)

作者： Saran, K. B. Sreelekha, G. Coll Engn Vadakara Dept Elect & Commun Engn Calicut Kerala India Natl Inst Technol Calicut Dept Elect & Commun Engn Kozhikode Kerala India

ISBN: (纸本)9781467373494

Vehicle detection and classification is the most important and challenging stage of traffic surveillance using computer vision techniques. The videos captured using the closed circuit television (CCTV) cameras placed in roadsides or driveways are used for the surveillance. The surveillance system includes detection of moving vehicles, counting the number of vehicles and the classification of the detected vehicles. The main challenge of the computer vision technique is the real time applicability of the algorithms used. In this work a vehicle detection and classification algorithm which works in real time is proposed. The detection is carried out by the method of background subtraction where the background is modeled using the mixture of Gaussians and the detected vehicles are classified using the Artificial Neural Network (ANN) with a new set of features, Histograms of Oriented Gradients (HOG) and geometric measures of the vehicles. Experimental results shows that the proposed method with the new combination of features as training parameters for ANN give better result as compared to other popular algorithms.

关键词： Vehicle detection images computer vision vehicle new combination Artificial neural networks Drive ways traffic monitoring Gauss sorting algorithm video monitoring SURVEILLANCE SYSTEMS Roadside

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LANDCOVER CLASSIFICATION WITH SELF-TAUGHT LEARNING ON ARCHETYPAL DICTIONARIES

LANDCOVER CLASSIFICATION WITH SELF-TAUGHT LEARNING ON ARCHET...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Ribana Roscher Christoph Romer Bjorn Waske Lutz Plumer Division of Remote Sensing and Geoinformatics Institute of Geographical Sciences Freie Universitat Berlin Department of Geoinformation Institute of Geodesy and Geoinformation University of Bonn

ISBN: (纸本)9781479979301

This paper introduces archetypal dictionaries for a self-taught learning framework for the application of landcover classification. Self-taught learning, an unsupervised representation learning method, is exploited to learn low-dimensional and discriminative higher-level features, which are used as input into a classification algorithm. Experiments are conducted using a multi-spectral Landsat 5 TM image of a study area in the north of Novo Progresso located in South America. Our results confirm that self-taught learning with archetypal dictionaries provide features, which can be used as input into a linear logistic regression classifier. The obtained classification accuracies are comparable to kernel-based classifier using the original features.

关键词： Self-taught learning Archetypal analysis Landcover classification Dictionaries as Topic Classifiers Learning Logistic Regression South America sorting algorithm Learning methods MULTISPECTRAL

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Improving Medical Diagnosis Reliability Using Boosted C5.0 Decision Tree empowered by Particle Swarm Optimization

Improving Medical Diagnosis Reliability Using Boosted C5.0 D...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Elnaz Pashaei Mustafa Ozen Nizamettin Aydin the Department of Computer Engineering Yildiz Technical University Istanbul Turkey Istanbul University Istanbul Turkey Department of Computer Engineering Yildiz Technical University Istanbul Turkey

ISBN: (纸本)9781424492695

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

关键词： PSO UCI CART parametric subharmonic oscillator Particle swarm optimization Decision Trees Classification Methods MICROCURIES useful area cart sorting algorithm Medical diagnosis Dataset Disease diagnosis

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